CN116204927A - Intracardiac sign data processing system and method - Google Patents

Intracardiac sign data processing system and method Download PDF

Info

Publication number
CN116204927A
CN116204927A CN202310484780.0A CN202310484780A CN116204927A CN 116204927 A CN116204927 A CN 116204927A CN 202310484780 A CN202310484780 A CN 202310484780A CN 116204927 A CN116204927 A CN 116204927A
Authority
CN
China
Prior art keywords
data
sign
medical
data set
coupling
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
CN202310484780.0A
Other languages
Chinese (zh)
Inventor
黄启臣
玄继昌
张静
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zoucheng People's Hospital
Original Assignee
Zoucheng People's Hospital
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zoucheng People's Hospital filed Critical Zoucheng People's Hospital
Priority to CN202310484780.0A priority Critical patent/CN116204927A/en
Publication of CN116204927A publication Critical patent/CN116204927A/en
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2141—Access rights, e.g. capability lists, access control lists, access tables, access matrices
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Bioethics (AREA)
  • Medical Informatics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Security & Cryptography (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

The invention relates to the technical field of data processing, in particular to a system and a method for processing heart internal physical sign data. The method comprises the following steps: obtaining a heart internal medicine sign data set, wherein the heart internal medicine sign data set comprises heart internal medicine main sign data, heart internal medicine auxiliary sign data, medical file data and personal medical authority data; performing primary and secondary sign interference analysis based on the cardiac and secondary sign data to generate a cardiovascular biomarker dataset; encrypting by a single key algorithm based on the cardiovascular biomarker data set to generate a terminal coupling ciphertext data set; acquiring a medical data set of the department of cardiology, performing the labeling feedback operation of the department of cardiology based on the medical data set of the department of cardiology and the terminal coupling ciphertext data, and generating coupling labeling feedback data; according to the invention, the data processing is carried out on the intracardiac physical sign data set, so that the intracardiac physical sign data processing efficiency is improved.

Description

Intracardiac sign data processing system and method
Technical Field
The invention relates to the technical field of data processing, in particular to a system and a method for processing heart internal physical sign data.
Background
The technology for processing the heart internal physical sign data is one of the core applications of the medical big data technology, at present, the technology for processing the heart internal physical sign data has been advanced to a certain extent, but some problems and challenges still exist, most of the technology for processing the heart internal physical sign data still stays on the traditional data processing method, such as manual recording and processing, manual interference analysis, traditional algorithm processing and the like, and the problems of low data precision, low processing efficiency, large workload, low data privacy, weak safety protection, low doctor-patient data exchange trust degree and the like exist, and how to develop a system and a method for processing the heart internal physical sign data are called as the problems to be solved urgently in the data processing and sharing process.
Disclosure of Invention
The invention provides a system and a method for processing intracardiac sign data, which aim to solve at least one technical problem.
To achieve the above object, the present invention provides a system and a method for processing cardiac sign data, the method comprising the steps of:
step S1: obtaining a heart internal medicine sign data set, wherein the heart internal medicine sign data set comprises heart internal medicine main sign data, heart internal medicine auxiliary sign data, medical file data and personal medical authority data; performing primary and secondary sign interference analysis based on the cardiac and secondary sign data to generate a cardiovascular biomarker dataset;
Step S2: encrypting by a single key algorithm based on the cardiovascular biomarker data set to generate a terminal coupling ciphertext data set;
step S3: acquiring a medical data set of the department of cardiology, performing the labeling feedback operation of the department of cardiology based on the medical data set of the department of cardiology and the terminal coupling ciphertext data, and generating coupling labeling feedback data;
step S4: and performing multi-metadata analysis based on the coupling labeling feedback data, so as to realize the processing of the intracardiac vital sign data.
The invention provides a method for processing cardiac sign data, which is characterized in that a cardiac sign data set is obtained and main and auxiliary sign interference analysis is carried out, a more accurate cardiovascular biomarker data set is generated, a more reliable basis is provided for prediction and diagnosis of cardiovascular diseases, a terminal coupling ciphertext data set generated based on single key algorithm encryption is used for effectively protecting privacy data and preventing sensitive information from leakage, meanwhile, a system can be improved and optimized through coupling annotation feedback data generated by cardiac annotation feedback operation, the accuracy and practicability of the system are improved, the re-encryption operation is carried out on the cardiac medical data set based on a single key algorithm, the exchange trust degree of medical data is improved, the processing efficiency of cardiac sign data is improved, and finally, comprehensive processing and analysis of cardiac sign data can be realized based on multi-element data analysis carried out by coupling annotation feedback data, the utilization efficiency of medical data is improved, and the digital transformation of the medical industry is promoted.
Preferably, step S1 comprises the steps of:
step S11: using a heart rate sensor to monitor and record electrocardiosignals to generate an electrocardiogram data set;
step S12: an eye movement data collection is carried out by using an eye movement instrument, and an eye movement data set is generated;
step S13: measuring skin microvascular change by utilizing a photoelectric sensor to generate light reflection map data;
step S14: collecting frequency depth data by using a respiration sensor to generate a respiration data set;
step S15: performing signal enhancement processing by using an amplifier based on the electrocardiographic data to generate enhanced electrocardiographic data;
step S16: denoising processing is carried out by using a filter based on the enhanced electrocardiogram data and preset standard frequency band data, so as to generate filter enhanced electrocardiogram data;
step S17: digitally binding based on the filter enhanced electrocardiogram data and the medical archive data to generate main intracardiac sign data;
step S18: performing data fusion processing based on the eye movement data set and the light reflection map data to generate auxiliary physical sign data of the heart department;
step S19: and performing primary and secondary sign monitoring and evaluation flow operation processing based on the intracardiac branch of academic or vocational study sign data to generate a cardiovascular biomarker data set.
The method comprises the steps of collecting biological signal data such as heart rate, eye movement, skin microvascular change and respiration, generating main and auxiliary heart medical sign data through the steps of signal enhancement, denoising, digital binding, data fusion processing and the like, further performing a main and auxiliary heart medical sign monitoring and evaluating process, and generating a cardiovascular biomarker data set.
Preferably, the specific steps of step S19 are:
step S191: acquiring a physical sign weight database, extracting weight data based on the main physical sign data of the department of cardiology, the auxiliary physical sign data of the department of cardiology and the physical sign weight database, and generating associated weight data;
step S192: performing primary and secondary sign data interference calculation by using a primary sign interference formula based on the associated weight data and the heart internal secondary sign data to generate primary interference data;
step S193: performing prediction difference comparison processing by using the primary interference data and the main intracardiac sign data to generate an interference prediction difference data set;
Step S194: comparing the interference prediction difference data set with a preset interference balance difference value, when the interference prediction difference data set is larger than the preset interference balance difference value, performing self-adaptive adjustment on the primary physical sign interference formula by using an LMS algorithm, and returning to the step S192, and when the interference prediction difference data set is smaller than the preset interference balance difference value, selecting the primary physical sign interference formula as a secondary physical sign interference formula;
step S195: and performing intelligent signature binding processing based on the main cardiac sign data, the auxiliary cardiac sign data and the secondary sign interference formula to generate a cardiovascular biomarker data set.
According to the invention, through acquiring the physical sign weight database, carrying out weight data extraction based on the main physical sign data of the heart department, the auxiliary physical sign data of the heart department and the physical sign weight database, generating associated weight data, extracting the weight data by utilizing a weight data extraction technology, accurately monitoring and predicting the cardiovascular biomarker, improving the diagnosis and treatment accuracy of cardiovascular diseases, carrying out interference calculation on the main and auxiliary physical sign data by utilizing a primary physical sign interference formula based on the associated weight data and the auxiliary physical sign data of the heart department, generating primary interference data, improving the accuracy and stability of the cardiovascular biomarker data in terms of feature representation by utilizing an interference calculation technology, reducing the occurrence rate of false positives and false negatives to a certain extent, providing data support for labeling analysis of subsequent medical staff, carrying out more accurate verification on the prediction result of the cardiovascular biomarker by comparing and processing the interference prediction difference value, improving the reliability and accuracy of the prediction result, improving the processing capacity of the interference data by adaptively adjusting the interference formula, simultaneously avoiding the problem of excessive interference, ensuring the reliability and the accuracy of the prediction result, and ensuring the safety of the cardiovascular biomarker by intelligent signature binding processing.
In one embodiment of the present disclosure, the primary sign-on-interference formula in step S192 is specifically:
Figure SMS_1
；
wherein ,
Figure SMS_13
is->
Figure SMS_5
Intracardiac subject sign data and +.>
Figure SMS_10
Primary interference data between individual endocardial side-sign data,/->
Figure SMS_7
Data amount of auxiliary sign data of heart surgery, < +.>
Figure SMS_14
Is->
Figure SMS_16
Auxiliary physical sign data of individual endocardial departmentThe%>
Figure SMS_17
Eye movement frequency data->
Figure SMS_8
Is->
Figure SMS_11
The cardiac by-product of the subject is +.>
Figure SMS_2
Pupil center horizontal offset in individual light reflectance data,/->
Figure SMS_4
For eye movement parameters>
Figure SMS_6
For the light reflection map data parameter, < >>
Figure SMS_9
Is->
Figure SMS_12
The cardiac by-product of the subject is +.>
Figure SMS_15
Pupil center vertical offset in individual light reflectance data,/->
Figure SMS_3
Is an interference sign parameter.
The primary physical sign interference formula starts from the eye movement data collection and the light reflection graph collection, deep digs the association relation between the eye movement data and the light reflection data, and according to the eye movement frequency data
Figure SMS_19
Pupil center horizontal offset
Figure SMS_25
Pupil center vertical offset>
Figure SMS_27
A primary disturbance data is calculated on the basis of the secondary sign data, wherein first the linear model is used to base the eye movement frequency data +.>
Figure SMS_20
Pupil center horizontal offset->
Figure SMS_23
Data amount of auxiliary physical sign data of endocardial department +. >
Figure SMS_26
Form a functional relationship->
Figure SMS_30
Next, a quadratic model is applied based on the frequency data of the other eye movement>
Figure SMS_18
Pupil center vertical offset->
Figure SMS_22
The square of the relationship formed->
Figure SMS_28
Use of eye movement parameters->
Figure SMS_31
Light reflection map data parameter +.>
Figure SMS_21
Calculating to form a functional relationship by considering the nonlinear relationship based on the derivative thought>
Figure SMS_24
And utilize the interference sign parameter +>
Figure SMS_29
Data conditioning is performed to form a functional relationship:
Figure SMS_32
；
thus enabling the calculation of primary interference data. Preferably, step S2 comprises the steps of:
step S21: carrying out formula data parameter extraction based on the cardiovascular biomarker data set to generate a formula parameter data set;
step S22: calculating by using a coupling parameter calculation formula based on the formula parameter data set to generate a main coupling parameter and a secondary coupling parameter;
step S23: extracting physical sign detection time by using the main and auxiliary coupling parameters to generate a physical sign detection time stamp;
step S24: performing parameter encryption by using a coupling symmetric encryption algorithm based on the primary and secondary coupling parameters to generate primary and secondary coupling ciphertext;
step S25: and outputting the time sequence based on the primary and secondary coupling ciphertext and the sign detection time stamp, and generating a terminal coupling ciphertext data set.
According to the invention, the formula data parameter extraction is carried out on the cardiovascular biomarker data set, the extracted formula parameter is utilized to provide basic data for the subsequent coupling parameter calculation and physical sign detection time extraction, the formula parameter data set is utilized to carry out the coupling parameter calculation, the main and auxiliary coupling parameters are generated, the data support is provided for the physical sign detection time extraction and parameter encryption in the subsequent steps, and the physical sign detection time extraction is utilized to carry out the physical sign detection time extraction, so that the physical sign detection time stamp can be accurately acquired, the time sequence and consistency of the subsequent data can be ensured, the privacy and the safety of the data can be protected, the data leakage and the tampering can be prevented, the integrity and the reliability of the data can be ensured, the encrypted data can be conveniently transmitted to a remote terminal through the time sequence output terminal, the remote access and the transmission of the data can be realized, and the safety and the confidentiality of the data can be ensured.
Preferably, the coupling parameter calculation formula in step S22 is specifically:
Figure SMS_33
；
wherein ,
Figure SMS_34
is the main and auxiliary coupling parameter->
Figure SMS_41
For the number of samples in the formula parameter dataset, +.>
Figure SMS_45
For formula parameter data set +.>
Figure SMS_37
Eye gaze point data in the individual samples, +.>
Figure SMS_40
For formula parameter data set +.>
Figure SMS_43
Gaze duration data in individual samples, +.>
Figure SMS_46
For formula parameter data set +.>
Figure SMS_36
Eye gaze point parameter data in the individual samples, < >>
Figure SMS_38
For formula parameter data set +.>
Figure SMS_39
Gaze duration parameter data in each sample, +.>
Figure SMS_42
Is +.>
Figure SMS_35
Interference sign parameter in individual samples, +.>
Figure SMS_44
Is coupled with main and auxiliary physical signsCoefficients.
The invention provides a coupling parameter calculation formula, which obtains a main and auxiliary sign coupling coefficient which represents the coupling relation of main and auxiliary signs as far as possible by processing formula parameters after self-adaptive adjustment, and the formula comprises a formula parameter data set
Figure SMS_49
Eye gaze point data in the individual samples +.>
Figure SMS_52
Formula parameter data set +.>
Figure SMS_55
Gaze duration data, formula parameter data set +.>
Figure SMS_48
Gaze duration parameter data in individual samples +.>
Figure SMS_51
Formula parameter data set +.>
Figure SMS_56
Eye gaze point parameter data in the individual samples +.>
Figure SMS_58
By functional relation- >
Figure SMS_47
Gaze attention data formed by gaze duration and eye gaze point data is calculated and by means of this data the disturbance sign parameter +.>
Figure SMS_54
Major and minor sign coupling coefficient->
Figure SMS_57
Obtaining data product to form single-case coupling parameter data, and utilizing sample number in the formula parameter data set to obtain the single-case coupling parameter data>
Figure SMS_59
Summing the data to form a functional relationship->
Figure SMS_50
Realize the main and auxiliary coupling parameters>
Figure SMS_53
Thereby obtaining the primary and secondary coupling parameters.
Preferably, step S3 comprises the steps of:
step S31: acquiring a medical data set of a department of cardiology, wherein the medical data set comprises a medical staff data set, a medical service data set, a medical commission data set and medical facility geographic information;
step S32: based on a coupling symmetric encryption algorithm, a medical staff data set and a medical service data set, performing medical data re-encryption operation by utilizing a medical data re-encryption calculation formula, and generating encrypted medical data;
step S33: performing data access control operation based on the personal medical authority data, the terminal coupling ciphertext data set and the encrypted medical data to generate encrypted authorization coupling data;
step S34: performing coupling data authority labeling operation based on the encryption authorized coupling data to generate a security coupling labeling data set;
Step S35: and carrying out terminal feedback operation based on the safe coupling annotation data set to generate coupling annotation feedback data.
According to the invention, the medical data re-encryption operation is carried out by acquiring the medical data set of the department of cardiology and utilizing comprehensive medical information resources, a coupling symmetric encryption algorithm and a medical data re-encryption calculation formula, so that confidentiality and privacy of medical data can be effectively protected, the medical data is prevented from being illegally acquired and utilized in the transmission and storage processes, the data access control operation is carried out based on personal medical authority data, a terminal coupling ciphertext data set and the encrypted medical data, the medical data can be ensured to be only accessed by authorized visitors, unauthorized access is prevented, the safety of the medical data is ensured, the coupling data authority marking operation is carried out based on the encrypted authorization coupling data, the authority of the medical data can be marked, the management and control of the access of the medical data are facilitated, and the statistics collection and the database of the safety coupling marking data set are required to be carried out, the related data sample can be provided for the association research of main and auxiliary signs, the safety and the controllability of the medical data are ensured, the access condition of the medical data can be fed back in time based on the terminal feedback operation of the safety coupling marking data set, the access condition of the medical data is convenient to manage and regulate the access of the medical data, and the safety and the controllability of the medical data are ensured. In a word, the beneficial effect of above-mentioned step is that the security, controllability and privacy of improvement medical data ensure the safe access and the utilization of medical data.
In one embodiment of the present specification, the medical data re-encryption calculation formula in step S32 is specifically:
Figure SMS_60
；
wherein ,
Figure SMS_63
for encrypting medical data->
Figure SMS_64
Representing the total number of samples of the medical staff data set, for example>
Figure SMS_68
For coupling functional relations in symmetric encryption algorithm +.>
Figure SMS_61
As a natural exponential function>
Figure SMS_66
Is->
Figure SMS_69
Working time data in personal medical staff data set, < +.>
Figure SMS_70
Is->
Figure SMS_62
Medical age data in the personal medical personnel data set, < >>
Figure SMS_65
Is->
Figure SMS_67
And the number data of the physiological detection devices of the department of cardiology in the medical service data set corresponds to the personal medical personnel data set.
The invention provides a medical data re-encryption calculation formula, which is based on carrying out formula re-encryption on a coupling symmetric encryption algorithm and medical data by applying a functional relation to form ciphertext data, thereby realizing the rights and interests protection of doctor-patient privacy data exchange logic, wherein the formula firstly carries out the functional relation in the coupling symmetric encryption algorithm
Figure SMS_71
Logic processing is performed to form data->
Figure SMS_72
Second, by +_for the duration data>
Figure SMS_73
Medical age data->
Figure SMS_74
Intracardiac branch of academic or vocational study physiology check out test set quantity data +.>
Figure SMS_75
Using natural exponential functions
Figure SMS_76
Forming a functional relation on the basis of a Gaussian function principle:
Figure SMS_77
;
by convolving the functional relationship and utilizing the total number of samples of the medical personnel data set
Figure SMS_78
Building a summation formula:
Figure SMS_79
;
thus, a summation calculation of the convolution results is achieved, thereby enabling a calculation of the encrypted medical data.
Preferably, the specific steps of step S4 are:
step S41: performing storage time analysis operation based on the data re-encryption calculation formula to generate formula time sequence data;
step S42: performing time coding calculation by using a hash algorithm based on the formula time sequence data to generate formula coding data;
step S43: performing feedback result processing based on the coupling standard feedback data to generate a positive feedback processing result and a negative feedback processing result, and performing medical sequence data generating operation based on the medical facility geographic information to generate medical sequence data for the positive feedback processing result;
step S44: performing instruction set acquisition operation based on medical sequence data, generating a first instruction set and a second instruction set, and performing single-key algorithm decryption based on a terminal coupling ciphertext data set aiming at the first instruction set, so as to generate a decrypted cardiovascular marker data set;
step S45: and (3) carrying out feedback data collection processing based on the decrypted cardiovascular marker data set, thereby realizing intracardiac physical sign data processing.
According to the invention, the formula time sequence data is generated by carrying out the storage time analysis operation through the data re-encryption calculation formula, the efficiency and the accuracy of data storage and management are improved, the time coding calculation is carried out by utilizing the hash algorithm based on the formula time sequence data to generate the formula coding data, and the rapid processing and the accurate extraction of the data time information can be realized, so that the speed and the efficiency of data processing are improved, the feedback result processing is carried out based on the coupling standard feedback data, and the positive feedback processing result and the negative feedback processing result are generated. And for the forward feedback processing result, performing medical sequence data generation operation based on the medical facility geographic information to generate medical sequence data. These operations may help medical personnel to better understand and analyze the data, provide support for formulating a more efficient medical regimen, perform instruction set acquisition operations based on medical sequence data, and generate a first instruction set and a second instruction set. And for the first instruction set, performing single-key algorithm decryption based on the terminal coupling ciphertext data set to generate a decrypted cardiovascular marker data set. The operations can help medical staff better acquire and process data, support is provided for making a more accurate and personalized medical scheme, and feedback data collection processing is performed based on the decrypted cardiovascular marker data set, so that intracardiac vital sign data processing is realized. These operations may help medical personnel to better understand and analyze the data, thereby formulating more effective medical solutions, improving the quality and efficiency of medical services.
In one embodiment of the present specification, there is provided a medical sign data processing system comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of endocardial vital sign data processing of any of the above.
The invention provides a system for processing the physical sign data of the heart, which can realize any physical sign data method of the heart, realize the acquisition, operation and generation of data, acquire a physical sign data set of the heart, operate the graphic sign information in the physical sign data set according to a designed instruction sequence to generate preprocessed graphic information, then perform main and auxiliary physical sign interference analysis through the preprocessed graphic information to generate a cardiovascular biomarker data set, and perform encryption multi-element data operation according to the cardiovascular biomarker data set to realize the processing of the physical sign data of the heart, and the system internally follows a set instruction set to finish the operation steps of the method and promote the completion of the physical sign data processing method of the heart.
The invention provides a method for processing intracardiac sign data by combining a multidisciplinary and multidisciplinary model, which solves the problems of low data precision, low processing efficiency, large workload, low data privacy, weak safety protection and low doctor-patient data exchange trust degree in the process of processing the intracardiac sign data and realizes the intracardiac data processing method with high efficiency, high precision and multidimensional data application capability.
Drawings
FIG. 1 is a flow chart illustrating steps of a system and method for processing cardiac physical sign data according to the present invention;
FIG. 2 is a detailed implementation step flow diagram of step S1;
fig. 3 is a detailed implementation step flow diagram of step S2.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a system and a method for processing intracardiac sign data. The main implementation body of the intracardiac vital sign data system and the method includes, but is not limited to, the implementation of the system: mechanical devices, data processing platforms, cloud server nodes, network transmission devices, etc. may be considered general purpose computing nodes of the present application. The data processing platform includes, but is not limited to: at least one of an audio management system, an image management system and an information management system.
Referring to fig. 1 to 3, the present invention provides a method for processing cardiac sign data, the method comprising the following steps:
step S1: obtaining a heart internal medicine sign data set, wherein the heart internal medicine sign data set comprises heart internal medicine main sign data, heart internal medicine auxiliary sign data, medical file data and personal medical authority data; performing primary and secondary sign interference analysis based on the cardiac and secondary sign data to generate a cardiovascular biomarker dataset;
step S2: encrypting by a single key algorithm based on the cardiovascular biomarker data set to generate a terminal coupling ciphertext data set;
step S3: acquiring a medical data set of the department of cardiology, performing the labeling feedback operation of the department of cardiology based on the medical data set of the department of cardiology and the terminal coupling ciphertext data, and generating coupling labeling feedback data;
step S4: and performing multi-metadata analysis based on the coupling labeling feedback data, so as to realize the processing of the intracardiac vital sign data.
The invention provides a method for processing cardiac sign data, which is characterized in that a cardiac sign data set is obtained and main and auxiliary sign interference analysis is carried out, a more accurate cardiovascular biomarker data set is generated, a more reliable basis is provided for prediction and diagnosis of cardiovascular diseases, a terminal coupling ciphertext data set generated based on single key algorithm encryption is used for effectively protecting privacy data and preventing sensitive information from leakage, meanwhile, a system can be improved and optimized through coupling annotation feedback data generated by cardiac annotation feedback operation, the accuracy and practicability of the system are improved, the re-encryption operation is carried out on the cardiac medical data set based on a single key algorithm, the exchange trust degree of medical data is improved, the processing efficiency of cardiac sign data is improved, and finally, comprehensive processing and analysis of cardiac sign data can be realized based on multi-element data analysis carried out by coupling annotation feedback data, the utilization efficiency of medical data is improved, and the digital transformation of the medical industry is promoted.
In the embodiment of the present invention, as described with reference to fig. 1, a flow chart of steps of a system and a method for processing cardiac sign data according to the present invention is shown, where in this example, the steps of the method for processing cardiac sign data include:
step S1: obtaining a heart internal medicine sign data set, wherein the heart internal medicine sign data set comprises heart internal medicine main sign data, heart internal medicine auxiliary sign data, medical file data and personal medical authority data; performing primary and secondary sign interference analysis based on the cardiac and secondary sign data to generate a cardiovascular biomarker dataset;
in the embodiment of the invention, for example, the heart rate is calculated by measuring the blood flow speed by using an LED lamp and an optical sensor by using optical heart rate sensing, an electrocardiogram data set is generated, eye movement data collection is performed by using an eye movement instrument, an eye movement data set is generated, skin microvascular change measurement is performed by using an optical sensor, light reflection map data is generated, frequency depth data collection is performed by using a respiratory sensor, a respiratory data set is generated, signal enhancement processing is performed by using an amplifier on the basis of the electrocardiogram data, enhanced electrocardiogram data is generated, denoising processing is performed by using a filter on the basis of the enhanced electrocardiogram data and preset standard frequency band data, filtering enhanced electrocardiogram data is generated, digital binding is performed on the basis of the filtering enhanced electrocardiogram data and medical archive data, data fusion processing is performed by using data tag binding on the basis of the eye movement data set and the light reflection map data, interference analysis is performed on the basis of the intracardiac heart diagnosis data and the intracardiac diagnosis data, and the intracardiac diagnosis data set is generated, and the cardiovascular diagnosis data set is generated.
Step S2: encrypting by a single key algorithm based on the cardiovascular biomarker data set to generate a terminal coupling ciphertext data set;
in the embodiment of the invention, for example, data fitting is performed on the secondary sign interference formula bound in the previous step according to the cardiovascular biomarker data set and the cardiovascular biomarker data set to obtain a parameter value in the formula, the parameter value is stored as a formula parameter data set, the formula parameter data set is input into a coupling parameter calculation formula to obtain a value of a main coupling parameter and a secondary coupling parameter, the starting time and the ending time of sign detection are calculated according to the main coupling parameter and the secondary coupling parameter detection time in the main coupling parameter and the secondary coupling parameter, the starting time and the ending time are expressed as time stamps, and the main coupling parameter and the secondary coupling parameter are subjected to sequential uploading encryption operation based on the time stamps to generate the terminal coupling ciphertext data set.
Step S3: acquiring a medical data set of the department of cardiology, performing the labeling feedback operation of the department of cardiology based on the medical data set of the department of cardiology and the terminal coupling ciphertext data, and generating coupling labeling feedback data;
in the embodiment of the invention, for example, a medical data set of a cardiology department is obtained, wherein the medical data set comprises a medical staff data set, a medical service data set, a medical commission data set and geographical information of medical facilities, and the coupling labeling feedback data is generated by performing the labeling feedback operation of the cardiology department in the specific implementation step based on the medical data set of the cardiology department and the terminal coupling ciphertext data.
Step S4: and performing multi-metadata analysis based on the coupling labeling feedback data, so as to realize the processing of the intracardiac vital sign data.
In the embodiment of the invention, for example, a coupling labeling feedback data set is obtained, wherein the coupling labeling feedback data set comprises positive feedback processing results, negative feedback processing results, medical sequence data and other data, and the multivariate data analysis operation in the specific implementation steps is performed to generate formula time sequence data, formula coding data, medical sequence data and decrypted cardiovascular marker data set, so that the intracardiac vital sign data processing is realized.
In the embodiment of the present invention, as described with reference to fig. 2, a detailed implementation step flow diagram of the step S1 is shown, and in one embodiment of the present specification, the detailed implementation step of the step S1 includes:
step S11: using a heart rate sensor to monitor and record electrocardiosignals to generate an electrocardiogram data set;
step S12: an eye movement data collection is carried out by using an eye movement instrument, and an eye movement data set is generated;
step S13: measuring skin microvascular change by utilizing a photoelectric sensor to generate light reflection map data;
step S14: collecting frequency depth data by using a respiration sensor to generate a respiration data set;
step S15: performing signal enhancement processing by using an amplifier based on the electrocardiographic data to generate enhanced electrocardiographic data;
Step S16: denoising processing is carried out by using a filter based on the enhanced electrocardiogram data and preset standard frequency band data, so as to generate filter enhanced electrocardiogram data;
step S17: digitally binding based on the filter enhanced electrocardiogram data and the medical archive data to generate main intracardiac sign data;
step S18: performing data fusion processing based on the eye movement data set and the light reflection map data to generate auxiliary physical sign data of the heart department;
step S19: and performing primary and secondary sign monitoring and evaluation flow operation processing based on the intracardiac branch of academic or vocational study sign data to generate a cardiovascular biomarker data set.
The method comprises the steps of collecting biological signal data such as heart rate, eye movement, skin microvascular change and respiration, generating main and auxiliary heart medical sign data through the steps of signal enhancement, denoising, digital binding, data fusion processing and the like, further performing a main and auxiliary heart medical sign monitoring and evaluating process, and generating a cardiovascular biomarker data set.
In the embodiment of the invention, a plurality of biological sensors such as a heart rate sensor, an eye movement meter, a photoelectric sensor, a respiration sensor and the like are included in the system to collect and monitor biological parameters such as electrocardiosignals, eye movement data, skin microvascular changes, respiration data and the like of a tested person to generate corresponding data sets, for example, the heart rate is calculated by measuring the blood flow speed by using an LED lamp and a light sensor through optical heart rate sensing to generate an electrocardiogram data set, the eye movement data collection is carried out by using the eye movement meter to generate the eye movement data set, the skin microvascular changes are measured by using the photoelectric sensor to generate light reflection map data, the frequency depth data collection is carried out by using the respiration sensor to generate the respiration data set, the signal enhancement processing is carried out by using an amplifier based on the electrocardiogram data, generating enhanced electrocardiogram data, denoising the enhanced electrocardiogram data and preset standard frequency band data by using a filter, generating the filtered enhanced electrocardiogram data, digitally binding the enhanced electrocardiogram data and medical file data, generating main cardiac feature data, performing data fusion processing by data tag binding on the eye movement data set and the light reflection map data, generating auxiliary cardiac feature data, performing weight data extraction in subsequent steps on the basis of the main cardiac feature data and the auxiliary cardiac feature data, performing interference calculation of the main and auxiliary cardiac feature data, performing prediction difference comparison processing, performing equalization difference comparison processing, performing self-adaptive adjustment processing and performing intelligent signature binding processing, and generating a cardiovascular biomarker data set.
In one embodiment of the present specification, the specific steps of step S19 are as follows:
step S191: acquiring a physical sign weight database, extracting weight data based on the main physical sign data of the department of cardiology, the auxiliary physical sign data of the department of cardiology and the physical sign weight database, and generating associated weight data;
step S192: performing primary and secondary sign data interference calculation by using a primary sign interference formula based on the associated weight data and the heart internal secondary sign data to generate primary interference data;
step S193: performing prediction difference comparison processing by using the primary interference data and the main intracardiac sign data to generate an interference prediction difference data set;
step S194: comparing the interference prediction difference data set with a preset interference balance difference value, when the interference prediction difference data set is larger than the preset interference balance difference value, performing self-adaptive adjustment on the primary physical sign interference formula by using an LMS algorithm, and returning to the step S192, and when the interference prediction difference data set is smaller than the preset interference balance difference value, selecting the primary physical sign interference formula as a secondary physical sign interference formula;
step S195: and performing intelligent signature binding processing based on the main cardiac sign data, the auxiliary cardiac sign data and the secondary sign interference formula to generate a cardiovascular biomarker data set.
According to the invention, through acquiring the physical sign weight database, carrying out weight data extraction based on the main physical sign data of the heart department, the auxiliary physical sign data of the heart department and the physical sign weight database, generating associated weight data, extracting the weight data by utilizing a weight data extraction technology, accurately monitoring and predicting the cardiovascular biomarker, improving the diagnosis and treatment accuracy of cardiovascular diseases, carrying out interference calculation on the main and auxiliary physical sign data by utilizing a primary physical sign interference formula based on the associated weight data and the auxiliary physical sign data of the heart department, generating primary interference data, improving the accuracy and stability of the cardiovascular biomarker data in terms of feature representation by utilizing an interference calculation technology, reducing the occurrence rate of false positives and false negatives to a certain extent, providing data support for labeling analysis of subsequent medical staff, carrying out more accurate verification on the prediction result of the cardiovascular biomarker by comparing and processing the interference prediction difference value, improving the reliability and accuracy of the prediction result, improving the processing capacity of the interference data by adaptively adjusting the interference formula, simultaneously avoiding the problem of excessive interference, ensuring the reliability and the accuracy of the prediction result, and ensuring the safety of the cardiovascular biomarker by intelligent signature binding processing.
In the embodiment of the invention, for example, a physical sign weight database is obtained, which comprises main physical sign data of a heart, auxiliary physical sign data of a heart and physical sign weight data, data retrieval and extraction are carried out by using database retrieval language, associated weight data is generated, main and auxiliary physical sign data interference calculation is carried out by using a primary physical sign interference formula based on the associated weight data and the auxiliary physical sign data of the heart, primary interference data is generated, predictive difference value comparison processing is carried out by using the primary interference data and the main physical sign data of the heart, an interference prediction difference value data set is generated, whether the primary physical sign interference formula needs to be adaptively adjusted is judged by comparing the interference prediction difference value data set with a preset interference balance difference value, when the interference prediction difference value data set is larger than the preset interference balance difference value, the primary physical sign interference formula is adaptively adjusted by using an LMS algorithm, main and auxiliary physical sign data interference calculation, predictive difference value comparison processing and self-adaptive judgment adjustment processing are carried out again in step S192, when the interference prediction difference value data is smaller than the preset interference balance difference value, the primary physical sign interference is selected as the secondary physical sign interference data, the main physical sign is carried out by using the auxiliary physical sign interference data, the intelligent sign is bound by using the internal sign data, and the intelligent signature key is realized by adopting a digital signature algorithm, and the digital signature key is converted to realize.
In one embodiment of the present disclosure, the primary sign-on-interference formula in step S192 is specifically:
Figure SMS_80
；
wherein ,
Figure SMS_89
is->
Figure SMS_82
Intracardiac subject sign data and +.>
Figure SMS_86
Primary interference data between individual endocardial side-sign data,/->
Figure SMS_83
Data amount of auxiliary sign data of heart surgery, < +.>
Figure SMS_88
Is->
Figure SMS_92
Third in the cardiac by-sign data>
Figure SMS_94
Eye movement frequency data->
Figure SMS_90
Is->
Figure SMS_93
The cardiac by-product of the subject is +.>
Figure SMS_81
Pupil center horizontal offset in individual light reflectance data,/->
Figure SMS_85
For eye movement parameters>
Figure SMS_87
For the light reflection map data parameter, < >>
Figure SMS_91
Is->
Figure SMS_95
The cardiac by-product of the subject is +.>
Figure SMS_96
Pupil center vertical offset in individual light reflectance data,/->
Figure SMS_84
Is an interference sign parameter.
The primary physical sign interference formula starts from the eye movement data collection and the light reflection graph collection, deep digs the association relation between the eye movement data and the light reflection data, and according to the eye movement frequency data
Figure SMS_98
Pupil center horizontal offset
Figure SMS_103
Pupil center vertical offset>
Figure SMS_106
A primary disturbance data is calculated on the basis of the secondary sign data, wherein first the linear model is used to base the eye movement frequency data +.>
Figure SMS_100
Pupil center horizontal offset->
Figure SMS_102
Data amount of auxiliary physical sign data of endocardial department +. >
Figure SMS_105
Form a functional relationship->
Figure SMS_108
Next, a quadratic model is applied based on the frequency data of the other eye movement>
Figure SMS_97
Pupil center vertical offset->
Figure SMS_101
The square of the relationship formed->
Figure SMS_109
Use of eye movement parameters->
Figure SMS_110
Light reflection map data parameter +.>
Figure SMS_99
Calculating to form a functional relationship by considering the nonlinear relationship based on the derivative thought>
Figure SMS_104
And utilize the interference sign parameter +>
Figure SMS_107
Data conditioning is performed to form a functional relationship:
Figure SMS_111
;
thus, the calculation of the primary interference data is implemented, in this embodiment of the present invention, as described with reference to fig. 3, which is a detailed implementation step flow diagram of step S2, in one embodiment of the present specification, the detailed implementation step of step S2 includes:
step S21: carrying out formula data parameter extraction based on the cardiovascular biomarker data set to generate a formula parameter data set;
step S22: calculating by using a coupling parameter calculation formula based on the formula parameter data set to generate a main coupling parameter and a secondary coupling parameter;
step S23: extracting physical sign detection time by using the main and auxiliary coupling parameters to generate a physical sign detection time stamp;
step S24: performing parameter encryption by using a coupling symmetric encryption algorithm based on the primary and secondary coupling parameters to generate primary and secondary coupling ciphertext;
Step S25: and outputting the time sequence based on the primary and secondary coupling ciphertext and the sign detection time stamp, and generating a terminal coupling ciphertext data set.
According to the invention, the formula data parameter extraction is carried out on the cardiovascular biomarker data set, the extracted formula parameter is utilized to provide basic data for the subsequent coupling parameter calculation and physical sign detection time extraction, the formula parameter data set is utilized to carry out the coupling parameter calculation, the main and auxiliary coupling parameters are generated, the data support is provided for the physical sign detection time extraction and parameter encryption in the subsequent steps, and the physical sign detection time extraction is utilized to carry out the physical sign detection time extraction, so that the physical sign detection time stamp can be accurately acquired, the time sequence and consistency of the subsequent data can be ensured, the privacy and the safety of the data can be protected, the data leakage and the tampering can be prevented, the integrity and the reliability of the data can be ensured, the encrypted data can be conveniently transmitted to a remote terminal through the time sequence output terminal, the remote access and the transmission of the data can be realized, and the safety and the confidentiality of the data can be ensured.
In the embodiment of the invention, for example, a secondary sign interference formula bound in the previous step according to a cardiovascular biomarker data set is subjected to data fitting with the cardiovascular biomarker data set to obtain a parameter value in the formula, the parameter value is stored as a formula parameter data set, the formula parameter data set is input into a coupling parameter calculation formula to obtain a value of a main and auxiliary coupling parameter, the starting time and the ending time of sign detection are calculated according to the main and auxiliary coupling parameter detection time in the main and auxiliary coupling parameter and are expressed as time stamps, a coupling symmetric encryption algorithm, such as an AES algorithm, is used for encrypting the main and auxiliary coupling parameter to generate a main and auxiliary coupling ciphertext, the main and auxiliary coupling ciphertext and the sign detection time stamp are combined together to form time sequence data, and the time sequence data is stored as a terminal coupling ciphertext data set.
In the embodiment of the present invention, the coupling parameter calculation formula in step S22 specifically includes:
Figure SMS_112
；
wherein ,
Figure SMS_114
is the main and auxiliary coupling parameter->
Figure SMS_117
For the number of samples in the formula parameter dataset, +.>
Figure SMS_121
For formula parameter data set +.>
Figure SMS_116
Eye gaze point data in the individual samples, +.>
Figure SMS_118
For formula parameter data set +.>
Figure SMS_122
Gaze duration data in individual samples, +. >
Figure SMS_124
For formula parameter data set +.>
Figure SMS_113
Eye gaze point parameter data in the individual samples, < >>
Figure SMS_120
For formula parameter data set +.>
Figure SMS_123
Gaze duration parameter data in each sample, +.>
Figure SMS_125
Is +.>
Figure SMS_115
Interference sign parameter in individual samples, +.>
Figure SMS_119
Is the coupling coefficient of the main and auxiliary physical signs.
The invention provides a coupling parameter calculation formula, which is generalProcessing the adaptively adjusted formula parameters to obtain a main and auxiliary sign coupling coefficient which represents the coupling relation of the main and auxiliary signs as much as possible, wherein the formula is obtained by the first step of the formula parameter data set
Figure SMS_128
Eye gaze point data in the individual samples +.>
Figure SMS_132
Formula parameter data set +.>
Figure SMS_135
Gaze duration data, formula parameter data set +.>
Figure SMS_127
Gaze duration parameter data in individual samples +.>
Figure SMS_133
Formula parameter data set +.>
Figure SMS_136
Eye gaze point parameter data in the individual samples +.>
Figure SMS_138
By functional relation->
Figure SMS_126
Gaze attention data formed by gaze duration and eye gaze point data is calculated and by means of this data the disturbance sign parameter +.>
Figure SMS_131
Major and minor sign coupling coefficient->
Figure SMS_134
Obtaining data product to form single-case coupling parameter data, and utilizing sample number in the formula parameter data set to obtain the single-case coupling parameter data >
Figure SMS_137
The summation of the data is performed and,form a functional relationship->
Figure SMS_129
Realize the main and auxiliary coupling parameters>
Figure SMS_130
Thereby obtaining the primary and secondary coupling parameters.
In one embodiment of the present specification, step S3 includes the steps of:
step S31: acquiring a medical data set of a department of cardiology, wherein the medical data set comprises a medical staff data set, a medical service data set, a medical commission data set and medical facility geographic information;
step S32: based on a coupling symmetric encryption algorithm, a medical staff data set and a medical service data set, performing medical data re-encryption operation by utilizing a medical data re-encryption calculation formula, and generating encrypted medical data;
step S33: performing data access control operation based on the personal medical authority data, the terminal coupling ciphertext data set and the encrypted medical data to generate encrypted authorization coupling data;
step S34: performing coupling data authority labeling operation based on the encryption authorized coupling data to generate a security coupling labeling data set;
step S35: and carrying out terminal feedback operation based on the safe coupling annotation data set to generate coupling annotation feedback data.
According to the invention, the medical data re-encryption operation is carried out by acquiring the medical data set of the department of cardiology and utilizing comprehensive medical information resources, a coupling symmetric encryption algorithm and a medical data re-encryption calculation formula, so that confidentiality and privacy of medical data can be effectively protected, the medical data is prevented from being illegally acquired and utilized in the transmission and storage processes, the data access control operation is carried out based on personal medical right data, a terminal coupling ciphertext data set and encrypted medical data, the medical data can be ensured to be only accessed by authorized visitors, unauthorized access is prevented, the safety of the medical data is ensured, the coupling data right marking operation is carried out based on encrypted authorization coupling data, the rights of the medical data can be marked, the management and control of the access of the medical data are facilitated, and the statistics collection and the database of the safety coupling marking data set can be provided for the association research of main and auxiliary physical signs, the safety and the controllability of the medical data are ensured, the access condition of the medical data can be fed back in time based on the safety coupling marking data set, the access condition of the medical data is convenient to manage and adjust, the safety and the safety of the medical data and the controllability of the medical data are improved, and the safety of the medical data and the safety access right and the safety of the medical data can be guaranteed.
In the embodiment of the invention, for example, a medical data set of a department of cardiology is obtained from a data source, wherein the medical data set comprises a medical staff data set, a medical service data set, a medical commission data set and medical facility geographic information, medical data re-encryption operation is carried out on the medical staff data set and the medical service data set by utilizing a medical data re-encryption calculation formula based on a coupling symmetric encryption algorithm, the medical staff data set and the medical service data set, encrypted medical data is generated, personal medical authority data is encrypted so as to ensure the safety of access authority, the encrypted medical data and a terminal coupling ciphertext data set are decrypted so as to obtain original medical data, the original medical staff data is filtered according to an access control rule in the personal medical authority data, only authorized data is reserved for the individual, the filtered data and the terminal coupling ciphertext data set are re-encrypted so as to generate encrypted authorized coupling data, the encrypted authorized coupling data is decrypted so as to obtain the original data, the authorized medical staff marks the original data according to the safety coupling annotation rule so as to indicate the sensitivity degree and the safety level of the data, the marked data and the terminal coupling ciphertext data set are re-encrypted, the safety coupling annotation data set is generated, the safety coupling annotation data set is sent to a terminal equipment and the terminal equipment is subjected to the feedback processing and the service data is processed according to the result of the coupling and the feedback processing data.
In one embodiment of the present specification, the medical data re-encryption calculation formula in step S32 is specifically:
Figure SMS_139
；
wherein ,
Figure SMS_141
for encrypting medical data->
Figure SMS_144
Representing the total number of samples of the medical staff data set, for example>
Figure SMS_148
For coupling functional relations in symmetric encryption algorithm +.>
Figure SMS_140
As a natural exponential function>
Figure SMS_145
Is->
Figure SMS_146
Working time data in personal medical staff data set, < +.>
Figure SMS_149
Is->
Figure SMS_142
Medical age data in the personal medical personnel data set, < >>
Figure SMS_143
Is->
Figure SMS_147
And the number data of the physiological detection devices of the department of cardiology in the medical service data set corresponds to the personal medical personnel data set.
The invention provides a medical data re-encryption calculation formula, which is based on carrying out formula re-encryption on a coupling symmetric encryption algorithm and medical data by applying a functional relation to form ciphertext data, thereby realizing the rights and interests protection of privacy data exchange logic of doctors and patients, wherein the formula firstly relates to functions in the coupling symmetric encryption algorithmIs tied up with
Figure SMS_150
Logic processing is performed to form data->
Figure SMS_151
Second, by +_for the duration data>
Figure SMS_152
Medical age data->
Figure SMS_153
Intracardiac branch of academic or vocational study physiology check out test set quantity data +.>
Figure SMS_154
Using natural exponential functions
Figure SMS_155
Forming a functional relation on the basis of a Gaussian function principle:
Figure SMS_156
;
by convolving the functional relationship and utilizing the total number of samples of the medical personnel data set
Figure SMS_157
Building a summation formula:
Figure SMS_158
;
thus, a summation calculation of the convolution results is achieved, thereby enabling a calculation of the encrypted medical data.
In one embodiment of the present disclosure, the specific steps of step S4 are:
step S41: performing storage time analysis operation based on the data re-encryption calculation formula to generate formula time sequence data;
step S42: performing time coding calculation by using a hash algorithm based on the formula time sequence data to generate formula coding data;
step S43: performing feedback result processing based on the coupling standard feedback data to generate a positive feedback processing result and a negative feedback processing result, and performing medical sequence data generating operation based on the medical facility geographic information to generate medical sequence data for the positive feedback processing result;
step S44: performing instruction set acquisition operation based on medical sequence data, generating a first instruction set and a second instruction set, and performing single-key algorithm decryption based on a terminal coupling ciphertext data set aiming at the first instruction set, so as to generate a decrypted cardiovascular marker data set;
step S45: and (3) carrying out feedback data collection processing based on the decrypted cardiovascular marker data set, thereby realizing intracardiac physical sign data processing.
According to the invention, the formula time sequence data is generated by carrying out the storage time analysis operation through the data re-encryption calculation formula, the efficiency and the accuracy of data storage and management are improved, the time coding calculation is carried out by utilizing the hash algorithm based on the formula time sequence data to generate the formula coding data, and the rapid processing and the accurate extraction of the data time information can be realized, so that the speed and the efficiency of data processing are improved, the feedback result processing is carried out based on the coupling standard feedback data, and the positive feedback processing result and the negative feedback processing result are generated. And for the forward feedback processing result, performing medical sequence data generation operation based on the medical facility geographic information to generate medical sequence data. These operations may help medical personnel to better understand and analyze the data, provide support for formulating a more efficient medical regimen, perform instruction set acquisition operations based on medical sequence data, and generate a first instruction set and a second instruction set. And for the first instruction set, performing single-key algorithm decryption based on the terminal coupling ciphertext data set to generate a decrypted cardiovascular marker data set. The operations can help medical staff better acquire and process data, support is provided for making a more accurate and personalized medical scheme, and feedback data collection processing is performed based on the decrypted cardiovascular marker data set, so that intracardiac vital sign data processing is realized. These operations may help medical personnel to better understand and analyze the data, thereby formulating more effective medical solutions, improving the quality and efficiency of medical services.
In the embodiment of the invention, the stored cardiovascular biomarker data is analyzed based on the data re-encryption calculation formula, so that formula time sequence data is generated, the formula time sequence data can be calculated through the hash algorithm, so that formula coding data is generated, the result is processed based on the coupling standard feedback data, and the positive feedback processing result and the negative feedback processing result are generated. For the forward feedback processing result, medical sequence data generation operation can be performed based on the medical facility geographic information so as to generate medical sequence data, a first instruction set and a second instruction set are acquired based on the medical sequence data, wherein the first instruction set indicates that the client receives the marked main and auxiliary sign coupling data, and the second instruction set indicates that the client denies the marked main and auxiliary sign coupling data, and single-key algorithm decryption is performed based on the terminal coupling ciphertext data set so as to generate a decrypted cardiovascular marker data set, so that the intracardiac sign data processing is realized.
There is also provided in one embodiment of the present specification a system for processing cardiac vital signs data, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of endocardial vital sign data processing as described.
The invention provides a system for processing the physical sign data of the heart, which can realize any physical sign data method of the heart, realize the acquisition, operation and generation of data, acquire a physical sign data set of the heart, operate the graphic sign information in the physical sign data set according to a designed instruction sequence to generate preprocessed graphic information, then perform main and auxiliary physical sign interference analysis through the preprocessed graphic information to generate a cardiovascular biomarker data set, and perform encryption multi-element data operation according to the cardiovascular biomarker data set to realize the processing of the physical sign data of the heart, and the system internally follows a set instruction set to finish the operation steps of the method and promote the completion of the physical sign data processing method of the heart.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method for processing cardiac sign data, comprising the steps of:
step S1: obtaining a heart internal medicine sign data set, wherein the heart internal medicine sign data set comprises heart internal medicine main sign data, heart internal medicine auxiliary sign data, medical file data and personal medical authority data; performing primary and secondary sign interference analysis based on the cardiac and secondary sign data to generate a cardiovascular biomarker dataset;
step S2: encrypting by a single key algorithm based on the cardiovascular biomarker data set to generate a terminal coupling ciphertext data set;
step S3: acquiring a medical data set of the department of cardiology, performing the labeling feedback operation of the department of cardiology based on the medical data set of the department of cardiology and the terminal coupling ciphertext data, and generating coupling labeling feedback data;
step S4: and performing multi-metadata analysis based on the coupling labeling feedback data, so as to realize the processing of the intracardiac vital sign data.
2. The method according to claim 1, wherein the specific steps of step S1 are:
step S11: using a heart rate sensor to monitor and record electrocardiosignals to generate an electrocardiogram data set;
step S12: an eye movement data collection is carried out by using an eye movement instrument, and an eye movement data set is generated;
Step S13: measuring skin microvascular change by utilizing a photoelectric sensor to generate light reflection map data;
step S14: collecting frequency depth data by using a respiration sensor to generate a respiration data set;
step S15: performing signal enhancement processing by using an amplifier based on the electrocardiographic data to generate enhanced electrocardiographic data;
step S16: denoising processing is carried out by using a filter based on the enhanced electrocardiogram data and preset standard frequency band data, so as to generate filter enhanced electrocardiogram data;
step S17: digitally binding based on the filter enhanced electrocardiogram data and the medical archive data to generate main intracardiac sign data;
step S18: performing data fusion processing based on the eye movement data set and the light reflection map data to generate auxiliary physical sign data of the heart department;
step S19: and performing primary and secondary sign monitoring and evaluation flow operation processing based on the intracardiac branch of academic or vocational study sign data to generate a cardiovascular biomarker data set.
3. The method according to claim 2, wherein the specific steps of step S19 are:
step S191: acquiring a physical sign weight database, extracting weight data based on the main physical sign data of the department of cardiology, the auxiliary physical sign data of the department of cardiology and the physical sign weight database, and generating associated weight data;
Step S192: performing primary and secondary sign data interference calculation by using a primary sign interference formula based on the associated weight data and the heart internal secondary sign data to generate primary interference data;
step S193: performing prediction difference comparison processing by using the primary interference data and the main intracardiac sign data to generate an interference prediction difference data set;
step S194: comparing the interference prediction difference data set with a preset interference balance difference value, when the interference prediction difference data set is larger than the preset interference balance difference value, performing self-adaptive adjustment on the primary physical sign interference formula by using an LMS algorithm, and returning to the step S192, and when the interference prediction difference data set is smaller than the preset interference balance difference value, selecting the primary physical sign interference formula as a secondary physical sign interference formula;
step S195: and performing intelligent signature binding processing based on the main cardiac sign data, the auxiliary cardiac sign data and the secondary sign interference formula to generate a cardiovascular biomarker data set.
4. The method of claim 3, wherein the primary sign-on-interference formula in step S192 is specifically:
Figure QLYQS_1
；
wherein ,
Figure QLYQS_14
is->
Figure QLYQS_3
Intracardiac subject sign data and +.>
Figure QLYQS_9
Primary interference data between individual endocardial side-sign data,/- >
Figure QLYQS_11
Data amount of auxiliary sign data of heart surgery, < +.>
Figure QLYQS_15
Is->
Figure QLYQS_16
Third in the cardiac by-sign data>
Figure QLYQS_17
Eye movement frequency data->
Figure QLYQS_10
Is->
Figure QLYQS_12
The cardiac by-product of the subject is +.>
Figure QLYQS_2
Pupil center horizontal offset in individual light reflectance data,/->
Figure QLYQS_6
For eye movement parameters>
Figure QLYQS_5
For the light reflection map data parameter, < >>
Figure QLYQS_8
Is->
Figure QLYQS_7
The cardiac by-product of the subject is +.>
Figure QLYQS_13
Pupil center vertical offset in individual light reflectance data,/->
Figure QLYQS_4
Is an interference sign parameter.
5. The method according to claim 2, wherein the specific steps of step S2 are:
step S21: carrying out formula data parameter extraction based on the cardiovascular biomarker data set to generate a formula parameter data set;
step S22: calculating by using a coupling parameter calculation formula based on the formula parameter data set to generate a main coupling parameter and a secondary coupling parameter;
step S23: extracting physical sign detection time by using the main and auxiliary coupling parameters to generate a physical sign detection time stamp;
step S24: performing parameter encryption by using a coupling symmetric encryption algorithm based on the primary and secondary coupling parameters to generate primary and secondary coupling ciphertext;
step S25: and outputting the time sequence based on the primary and secondary coupling ciphertext and the sign detection time stamp, and generating a terminal coupling ciphertext data set.
6. The method according to claim 5, wherein the coupling parameter calculation formula in step S22 is specifically:
Figure QLYQS_18
；
wherein ,
Figure QLYQS_20
is the main and auxiliary coupling parameter->
Figure QLYQS_24
For the number of samples in the formula parameter dataset, +.>
Figure QLYQS_26
Is the formula parameter data set
Figure QLYQS_21
Eye gaze point data in the individual samples, +.>
Figure QLYQS_25
For formula parameter data set +.>
Figure QLYQS_29
Gaze duration data in individual samples, +.>
Figure QLYQS_31
For formula parameter data set +.>
Figure QLYQS_19
Eye gaze point parameter data in the individual samples, < >>
Figure QLYQS_23
For formula parameter data set +.>
Figure QLYQS_28
Gaze duration parameter data in each sample, +.>
Figure QLYQS_30
Is +.>
Figure QLYQS_22
Interference sign parameter in individual samples, +.>
Figure QLYQS_27
Is the coupling coefficient of the main and auxiliary physical signs.
7. The method according to claim 1, wherein the specific step of step S3 is:
step S31: acquiring a medical data set of a department of cardiology, wherein the medical data set comprises a medical staff data set, a medical service data set, a medical commission data set and medical facility geographic information;
step S32: based on a coupling symmetric encryption algorithm, a medical staff data set and a medical service data set, performing medical data re-encryption operation by utilizing a medical data re-encryption calculation formula, and generating encrypted medical data;
Step S33: performing data access control operation based on the personal medical authority data, the terminal coupling ciphertext data set and the encrypted medical data to generate encrypted authorization coupling data;
step S34: performing coupling data authority labeling operation based on the encryption authorized coupling data to generate a security coupling labeling data set;
step S35: and carrying out terminal feedback operation based on the safe coupling annotation data set to generate coupling annotation feedback data.
8. The method according to claim 7, wherein the medical data re-encryption calculation formula in step S32 is specifically:
Figure QLYQS_32
；
wherein ,
Figure QLYQS_33
for encrypting medical data->
Figure QLYQS_40
Representing the total number of samples of the medical staff data set, for example>
Figure QLYQS_42
Symmetric encryption for couplingFunctional relation in algorithm->
Figure QLYQS_34
As a natural exponential function>
Figure QLYQS_36
Is->
Figure QLYQS_39
Working time data in personal medical staff data set, < +.>
Figure QLYQS_41
Is->
Figure QLYQS_35
Medical age data in the personal medical personnel data set, < >>
Figure QLYQS_37
Is->
Figure QLYQS_38
And the number data of the physiological detection devices of the department of cardiology in the medical service data set corresponds to the personal medical personnel data set.
9. The method according to claim 1, wherein the specific step of step S4 is:
step S41: performing storage time analysis operation based on the data re-encryption calculation formula to generate formula time sequence data;
Step S42: performing time coding calculation by using a hash algorithm based on the formula time sequence data to generate formula coding data;
step S43: performing feedback result processing based on the coupling standard feedback data to generate a positive feedback processing result and a negative feedback processing result, and performing medical sequence data generating operation based on the medical facility geographic information to generate medical sequence data for the positive feedback processing result;
step S44: performing instruction set acquisition operation based on medical sequence data, generating a first instruction set and a second instruction set, and performing single-key algorithm decryption based on a terminal coupling ciphertext data set aiming at the first instruction set, so as to generate a decrypted cardiovascular marker data set;
step S45: and (3) carrying out feedback data collection processing based on the decrypted cardiovascular marker data set, thereby realizing intracardiac physical sign data processing.
10. A system for processing cardiac vital sign data, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the intracardiac vital signs data processing method according to any one of claims 1 to 9.
CN202310484780.0A 2023-05-04 2023-05-04 Intracardiac sign data processing system and method Withdrawn CN116204927A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310484780.0A CN116204927A (en) 2023-05-04 2023-05-04 Intracardiac sign data processing system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310484780.0A CN116204927A (en) 2023-05-04 2023-05-04 Intracardiac sign data processing system and method

Publications (1)

Publication Number Publication Date
CN116204927A true CN116204927A (en) 2023-06-02

Family

ID=86515014

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310484780.0A Withdrawn CN116204927A (en) 2023-05-04 2023-05-04 Intracardiac sign data processing system and method

Country Status (1)

Country Link
CN (1) CN116204927A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116453670A (en) * 2023-06-16 2023-07-18 高密市人民医院 Storage system and method for blood sample test data
CN116612899A (en) * 2023-07-19 2023-08-18 潍坊医学院附属医院 Cardiovascular surgery data processing method and service platform based on Internet

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116453670A (en) * 2023-06-16 2023-07-18 高密市人民医院 Storage system and method for blood sample test data
CN116612899A (en) * 2023-07-19 2023-08-18 潍坊医学院附属医院 Cardiovascular surgery data processing method and service platform based on Internet
CN116612899B (en) * 2023-07-19 2023-10-10 潍坊医学院附属医院 Cardiovascular surgery data processing method and service platform based on Internet

Similar Documents

Publication Publication Date Title
CN117238458A (en) Critical care cross-mechanism collaboration platform system based on cloud computing
CN116204927A (en) Intracardiac sign data processing system and method
US10937160B1 (en) Dental images processed with artificial intelligence
Christianson et al. Automated size‐specific CT dose monitoring program: assessing variability in CT dose
KR20180127506A (en) System and method for validating and performing operations on homogeneously encrypted data
CN109478418A (en) System and method for making health data anonymization and across geographic area transmission health data is analyzed
CN112509655B (en) Intelligent medical data acquisition system based on blockchain
CN113254967A (en) Standardization method of medical digital image cloud system
CN117238518A (en) Liver disease pathology consultation case data collection system based on small program
Deepa et al. A novel electronic medical record design using cryptography and steganography techniques
US12008749B2 (en) Dental images correlated to the human genome with artificial intelligence
US11783072B1 (en) Filter for sensitive data
US10235537B2 (en) Computer system of computer servers and dedicated computer clients specially programmed to generate synthetic non-reversible electronic data records based on real-time electronic querying and methods of use thereof
CN116805536A (en) Data processing method and system based on tumor case follow-up
Watkins et al. Artificial intelligence for the practical assessment of nutritional status in emergencies
Donga et al. Internet of Healthcare Things (IoHT): Towards a Digital Chain of Custody
CN117438086A (en) Health file system and method of cloud platform
CN114783557A (en) Method and device for processing tumor patient data, storage medium and processor
CN116612899B (en) Cardiovascular surgery data processing method and service platform based on Internet
Koren et al. Personal wireless data in formal electronic health records: future potential of Internet of Medical Things data
Nagaraj et al. Design of Intelligent Healthcare Information System Using Data Analytics
Patil et al. Smart Healthcare Monitoring and Patient Report Generation System Using IOT
CN113642669A (en) Fraud prevention detection method, device and equipment based on feature analysis and storage medium
CN116682544B (en) Artificial intelligence medical automation test integrated system
CN112133392A (en) Electronic medical record sharing method based on block chain

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20230602