CN118445738B - Atomizer temperature data analysis method and system - Google Patents
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Abstract
The invention relates to the technical field of data analysis, in particular to a method and a system for analyzing temperature data of an atomizer, which comprise the following steps: based on an internal temperature sensor of the atomizer, real-time data acquisition is carried out, the temperature of each temperature measuring point and a corresponding time stamp are recorded, data stream integration is carried out, time sequence analysis is carried out on the temperature data of the atomizer according to an integration result, abnormal temperature fluctuation and temperature peak values are identified, and a key temperature event list is obtained. According to the invention, abnormal fluctuation and temperature peak values can be immediately identified through real-time data capture and time sequence analysis, early fault prevention of the atomizer is performed, key characteristics can be extracted when a large amount of temperature data is decomposed through wavelet transformation application, the performance of the atomizer is revealed, the transmission path of the temperature in the atomizer is carefully tracked through topology analysis of a temperature distribution network diagram, the accuracy of adjustment and maintenance is improved, the energy efficiency is optimized, and the operation safety and the cost efficiency of the atomizer are improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a system for analyzing temperature data of an atomizer.
Background
Data analysis is a technological field in which statistics, algorithm development and technical means are applied to analyze and understand data. In contemporary various industries and scientific research, data analysis helps decision makers discover patterns and trends from a large amount of data, make predictions, and optimize business and technical decisions, including sub-fields of data mining, machine learning, big data technology, and data visualization, not only involving collection and cleaning of data, but also involving complex data modeling and interpretation work, aiming at improving the practicality and operability of data value.
The atomizer temperature data analysis method focuses on analyzing the data record and change condition of the temperature in the atomizer equipment, and can be used for monitoring and improving the performance of the atomizer to ensure that the equipment operates in a safe and preset temperature range. Through analysis of temperature data, the fault of atomizer equipment can be predicted, the energy use efficiency of the atomizer is optimized, the quality and consistency of the atomization process are improved, and further the long-acting stable use of the atomizer is ensured.
The prior art, while capable of processing large amounts of data, often suffers from deficiencies in the comprehensive processing of real-time data streams and in the deep parsing of complex data structures. Especially the weak ability to capture abnormal changes in a short time may lead to an untimely response to potential faults. Meanwhile, analysis on multi-scale data is usually insufficient, and key information of a temperature fluctuation mode is easy to ignore. The lack of support for network diagrams and topology analysis also makes it difficult for existing methods to describe the dynamic transfer path of temperature inside the device in detail, limiting the effective tracking of abnormal hot spots. Limitations of these techniques not only affect the prediction of equipment failure and optimization of energy usage, but also result in increased operating costs and reduced atomizer reliability.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method and a system for analyzing temperature data of an atomizer.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the atomizer temperature data analysis method comprises the following steps:
s1: based on an internal temperature sensor of the atomizer, real-time data acquisition is carried out, the temperature of each temperature measuring point and a corresponding time stamp are recorded, data stream integration cleaning is carried out, time sequence analysis is carried out on the temperature data of the atomizer according to an integration result, abnormal temperature fluctuation and temperature peak values are identified, and a key temperature event list is obtained;
S2: based on the key temperature event list, carrying out multi-scale decomposition on data in the list by adopting wavelet transformation, revealing a temperature change mode of the atomizer under a time scale, and carrying out node connection to construct an atomizer temperature distribution network diagram;
S3: performing network topology analysis through the temperature distribution network diagram, detecting a key temperature transmission path by identifying key nodes and edges, performing temperature distribution non-uniformity analysis according to a detection result, and identifying a potential path of abnormal temperature transmission to obtain an abnormal transmission path analysis result;
S4: based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, monitoring the adjusted real-time temperature response, converting the monitoring data into a geometric figure, analyzing the temperature dynamic characteristic of the atomizer through the change of the geometric form, and establishing a atomizer temperature dynamic response characteristic diagram.
As a further aspect of the present invention, the step of integrating and cleaning the data stream includes:
s111: collecting real-time temperature by a temperature sensor of an atomizer And corresponding time stampBy the formula, the method comprises the following steps,
，
Forming a set of time-temperature data pairsWherein, the method comprises the steps of, wherein,Indicating the number of measurements to be made,Representing a time stamp of the time of day,Representing a corresponding temperature value;
S112: integrating the time-temperature data pair sets, by a formula,
Generating an integrated time-temperature data streamWherein, the method comprises the steps of, wherein,A time-temperature data pair is represented,Representing the number of pairs of data,Is the coefficient of the weight of the time,Is the sinusoidal modulation factor which is used to modulate the signal,Is the modulation frequency;
S113: data cleaning is carried out on the integrated time-temperature data stream, abnormal temperature data points are removed, a formula is used,
，
Generating a cleaned data streamWherein, the method comprises the steps of, wherein,AndRespectively represent temperature valuesIs defined as the mean value and standard deviation of (c),Is a tolerance threshold, is a standard deviation multiple,Representing the temperature value.
As a further scheme of the present invention, the step of obtaining the key temperature event list includes:
S121: based on the cleaned data stream, time series analysis is performed, temperature fluctuations at time points are calculated, using a formula,
，
A temperature fluctuation dataset of the time point is generated, wherein,Temperature fluctuation data indicating a point in time,Is the point in timeIs used for the temperature value of (a),Is the value of the average temperature and,Is the number of data points;
S122: identifying that a threshold is exceeded based on the temperature fluctuation dataset of the time point Is determined by applying a formula,
，
A list of temperature peaks is obtained, wherein,Indicating the peak value of the temperature,Indicating the temperature fluctuation at a single point in time,Is a threshold multiple of the atomizer temperature;
S123: screening for duration exceeding a time threshold based on the temperature peak list Using a formula,
，
A list of critical temperature events is generated, wherein,Indicating a critical temperature event is to be performed,Indicating a temperature peak event,Indicating the event duration.
As a further scheme of the present invention, the step of obtaining the atomizer temperature distribution network map includes:
s211: extracting the time point of each event from the key temperature event list And corresponding temperatureCombining the data pair sets, adopting a formula,
，
Generating a set of data pairsWherein, the method comprises the steps of, wherein,The point in time is indicated as being the time point,The corresponding temperature is indicated as such,Representing the total number of time points;
S212: wavelet transformation is applied to the data pair set, multi-scale decomposition is performed, a formula is adopted,
，
A wavelet transform result is generated, wherein,The results of the multi-scale decomposition are shown,Is the amplitude adjustment coefficient of the amplitude of the signal,Is a frequency adjustment coefficient, representing a periodically varying frequency,Is a phase adjustment factor for, initial phase offset,Is an attenuation coefficient for adjusting the attenuation speed;
S213: analyzing the wavelet transformation result, screening for significant temperature change patterns, using a formula,
，
A significant set of temperature change patterns is obtained, wherein,A pattern of significant changes is indicated and,A single mode of change is indicated and,Is a threshold;
s214: based on the set of significant temperature change modes, a temperature distribution network diagram is constructed, a formula is adopted,
，
Outputting a temperature distribution network diagram of the atomizer, wherein,Representing the intensity of each significant pattern of change,Representing the strength of the connection between the nodes.
As a further aspect of the present invention, the detecting step of the critical temperature transfer path includes:
S311: analyzing the atomizer temperature distribution network diagram, and calculating the degree centrality of each node By adopting the formula,
Generating a node degree centrality analysis result, wherein,Representing nodesIs characterized by a degree of centrality,The number of connections of the node is indicated,Representing the total number of nodes in the network,Is an adjustment factor for centering;
S312: based on the node degree centrality analysis result, using the betweenness centrality to identify key nodes and edges, adopting a formula,
，
A set of key nodes and edges is generated, wherein,Representing nodesIs characterized by the medium number centrality of (2),The number of total paths is indicated and,Representing passing nodesIs provided with a number of paths of (a),For adjusting the influence of the number of paths;
s313: using the set of key nodes and edges, using a path analysis algorithm,
，
A set of critical temperature transfer paths is generated, wherein,Indicating a critical temperature transfer path and,Represents a key node in a set of key nodes and edges,Representing key nodesThe distance to the other node(s),Representing the number of critical nodes.
As a further aspect of the present invention, the step of obtaining the analysis result of the abnormal propagation path includes:
S321: based on the set of critical temperature transfer paths, non-uniformity analysis is performed, using a formula,
，
A result of the temperature distribution non-uniformity analysis is generated,Representing the variance of the temperature values over the path,Representing a pathThe temperature value of the water at the upper part,Representing the average temperature value in the set of critical temperature delivery paths,Representing a weight factor based on the temperature difference,Representing the number of paths;
S322: identifying an abnormal variance path according to the temperature distribution non-uniformity analysis result, applying a threshold formula, ，
Generating a set of abnormal temperature propagation paths, wherein,Represents the propagation path of the abnormal temperature,Is a single pathIs a function of the variance of (a),Is a preset threshold value, and the threshold value is set,Is a factor that adjusts the threshold;
S323: performing depth analysis on the abnormal temperature propagation path set, adopting a formula,
，
Evaluating the degree of abnormality of each path, generating an abnormal propagation path analysis result, wherein,Representing a pathIs set in the temperature variance of (a),Represents the probability of abnormality of the abnormal propagation path,Representing an abnormal path.
As a further scheme of the present invention, the step of obtaining the atomizer temperature dynamic response characteristic map includes:
s411: based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, adopting a formula,
，
A new set of temperature control parameters is generated, wherein,Representing the new control parameters of the control system,The original parameters are represented by a set of values,Representing the expected temperature change deduced based on the abnormal path,AndRepresenting adjustment parameters for adjusting sensitivity and attenuation;
S412: applying the new set of temperature control parameters, and monitoring the atomizer temperature response, using the formula,
Generating real-time temperature monitoring data, wherein,Time of presentationIs used for the temperature control of the liquid crystal display device,Is the initial temperature of the material to be heated,Is the time decay constant of the time-series,Is the amplitude of the wave,Is the frequency of the vibration and,AndControlling the periodic temperature variation;
S413: mapping to a geometric figure through a trigonometric function according to the real-time temperature monitoring data, using a formula,
，
A geometry of the temperature response is generated, wherein,Time of presentationThe corresponding geometric figure value is used to determine,Is the phase of the light and the phase of the light,Is a parameter for adjusting the amplitude response;
S414: analyzing the geometric figure of the temperature response, identifying the dynamic key characteristics of the temperature of the atomizer, analyzing the change of geometric form, adopting Fourier transformation, ，
Generating a atomizer temperature dynamic response characteristic map, wherein,Is a variable in the frequency domain and,Expressed in frequencyThe following dynamic characteristics highlight the periodicity and amplitude characteristics of the atomizer temperature response.
An atomizer temperature data analysis system, comprising:
The temperature data collection module collects real-time temperature and corresponding time stamps through a temperature sensor to form a time-temperature pair set, and performs time-temperature pair set integration cleaning to generate a cleaned data stream;
the time sequence analysis module performs time sequence analysis based on the cleaned data stream, calculates temperature fluctuation of a time point, identifies a temperature peak value, screens events with duration exceeding a time threshold value, and generates a key temperature event list;
The wavelet transformation and pattern recognition module extracts the time point and the corresponding temperature of each event based on the key temperature event list to form a data pair set, applies wavelet transformation to carry out multi-scale decomposition, screens a significant temperature change pattern and constructs an output atomizer temperature distribution network diagram;
The centrality and path analysis module calculates the centrality of each node based on the atomizer temperature distribution network diagram, identifies key nodes and edges, and generates a key temperature transmission path set by adopting a path analysis algorithm;
The abnormal propagation path analysis module is used for carrying out non-uniformity analysis, identifying abnormal variance paths, carrying out deep analysis on the identification results, and evaluating the degree of abnormality of each path to obtain an abnormal propagation path analysis result;
and the temperature control and dynamic response module adjusts the temperature control parameters of the atomizer based on the analysis result of the abnormal propagation path, monitors the temperature response of the atomizer, maps the monitoring data to a geometric figure through a trigonometric function, identifies the dynamic key characteristics of the temperature of the atomizer and generates an atomizer temperature dynamic response characteristic diagram.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, by combining the real-time monitoring and advanced data processing technology, the accuracy of the temperature data integration and analysis of the atomizer is obviously improved, abnormal fluctuation and temperature peaks can be immediately identified by the real-time data capturing and time sequence analysis, the early failure prevention of the atomizer is carried out, key characteristics can be extracted when a large amount of temperature data is decomposed by the application of wavelet transformation, the performance of the atomizer is revealed, the transmission path of the temperature in the atomizer is carefully tracked through the topology analysis of a temperature distribution network diagram, the accuracy of adjustment and maintenance is improved, the energy efficiency is optimized, and the operation safety and the cost efficiency of the atomizer are improved.
Drawings
FIG. 1 is a schematic workflow diagram of the present invention;
FIG. 2 is a flow chart of an integrated purge acquisition of the data stream of the present invention;
FIG. 3 is a flow chart of the acquisition of a list of key temperature events according to the present invention;
FIG. 4 is a flow chart of the acquisition of a temperature distribution network diagram of the atomizer of the present invention;
FIG. 5 is a flow chart of the detection of the critical temperature path of the present invention;
FIG. 6 is a flowchart for obtaining the analysis result of the abnormal propagation path according to the present invention;
Fig. 7 is a flowchart for acquiring a temperature dynamic response characteristic diagram of the atomizer according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. 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.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, the present invention provides a technical solution: the atomizer temperature data analysis method comprises the following steps:
s1: based on an internal temperature sensor of the atomizer, real-time data acquisition is carried out, the temperature of each temperature measuring point and a corresponding time stamp are recorded, data stream integration cleaning is carried out, time sequence analysis is carried out on the temperature data of the atomizer according to an integration result, abnormal temperature fluctuation and temperature peak values are identified, and a key temperature event list is obtained;
S2: based on a key temperature event list, carrying out multi-scale decomposition on data in the list by adopting wavelet transformation, revealing a temperature change mode of the atomizer under a time scale, and carrying out node connection to construct an atomizer temperature distribution network diagram;
s3: carrying out network topology analysis through a temperature distribution network diagram, detecting a key temperature transmission path by identifying key nodes and edges, carrying out temperature distribution non-uniformity analysis according to a detection result, and identifying a potential path of abnormal temperature transmission to obtain an abnormal transmission path analysis result;
s4: based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, monitoring the adjusted real-time temperature response, converting the monitoring data into a geometric figure, analyzing the temperature dynamic characteristic of the atomizer through the change of the geometric form, and establishing a temperature dynamic response characteristic diagram of the atomizer.
The key temperature event list comprises an abnormal temperature index, a peak time stamp and fluctuation frequency, the atomizer temperature distribution network diagram comprises a node connection structure record, network density and temperature distribution patterns, the abnormal propagation path analysis result comprises key transmission nodes, potential risk areas and influence intensity, and the atomizer temperature dynamic response characteristic diagram comprises a geometric response form, a temperature change trend and response sensitivity.
Referring to fig. 2, the integrated cleaning steps of the data stream are as follows:
s111: collecting real-time temperature by a temperature sensor of an atomizer And corresponding time stampBy the formula, the method comprises the following steps,
，
Forming a set of time-temperature data pairsWherein, the method comprises the steps of, wherein,Indicating the number of measurements to be made,Representing a time stamp of the time of day,Representing a corresponding temperature value;
S112: the time-temperature data pair sets are integrated, and by the formula,
Generating an integrated time-temperature data streamWherein, the method comprises the steps of, wherein,A time-temperature data pair is represented,Representing the number of pairs of data,Is the coefficient of the weight of the time,Is the sinusoidal modulation factor which is used to modulate the signal,Is the modulation frequency;
s113: data cleaning is carried out on the integrated time-temperature data stream, abnormal temperature data points are removed, a formula is used,
，
Generating a cleaned data streamWherein, the method comprises the steps of, wherein,AndRespectively represent temperature valuesIs defined as the mean value and standard deviation of (c),Is a tolerance threshold, is a standard deviation multiple,Representing the temperature value.
Suppose at timeMeasuring the temperature25 Degrees celsius, forming a time-temperature pair
，
Indicating that at 12 pm the internal temperature of the atomizer is 25 degrees celsius.
: The time weight coefficient is assumed to be 0.5.
: The sinusoidal modulation factor, assumed to be 0.3.
: The modulation frequency, assumed to be 1Hz.
: Time, in hours, is assumed to be。
Assume that there are three time-temperature pairs:
Integration is performed using the formula:
for the first time point ：
；
；
Similarly, other points are calculated, and after all points are integrated:
；
data flow Indicating that the integrated time-temperature data stream is an adjusted temperature value, the weight and sinusoidal modulation affects the raw data according to the formula.
Continuing to use the data stream, assuming an average temperatureIs 12.5 degree centigrade, standard deviationIs 1 degree celsius, tolerance thresholdAssume 2 times the standard deviation, i.e., 2 degrees celsius.
Each data point was checked:
：
This data point remains.
：
This data point remains.
：
This data point remains.
Data after washing:
indicating that all data points fit within the range of fluctuation within the standard deviation. This process ensures that the data is within acceptable error limits and suitable for further analysis.
Referring to fig. 3, the key temperature event list is obtained by the steps of:
s121: based on the cleaned data stream, time series analysis is performed, temperature fluctuations at time points are calculated, using a formula,
，
A temperature fluctuation dataset of the time point is generated, wherein,Temperature fluctuation data indicating a point in time,Is the point in timeIs used for the temperature value of (a),Is the value of the average temperature and,Is the number of data points;
S122: identifying that the threshold is exceeded based on the temperature fluctuation dataset at the time point Is determined by applying a formula,
A list of temperature peaks is obtained, wherein,Indicating the peak value of the temperature,Indicating the temperature fluctuation at a single point in time,Is a threshold multiple of the atomizer temperature;
S123: screening duration exceeds a time threshold based on a list of temperature peaks Using a formula,
A list of critical temperature events is generated, wherein,Indicating a critical temperature event is to be performed,Indicating a temperature peak event,Indicating the event duration.
: Assuming 13 degrees celsius (consistent from the previous step).
: The number of data points, assumed to be 3 (consistent with the previous step).
Using the cleaned data
；
Time series analysis is carried out, and temperature fluctuation is calculated by substituting each data point:
The method comprises The degrees celsius represent the average temperature fluctuation rate, providing a statistical measure of the temperature fluctuation, indicating a higher stability of the atomizer temperature value over a given time.
: The custom threshold multiple is assumed to be 0.1.
: The average temperature was 13 degrees celsius.
Assume that the threshold is set to 10% (0.1 times) of the average temperature:
Due to The temperature peak exceeding the threshold is not identified since the degree celsius is below 1.3 degrees celsius:
It is stated that under the current setting, there is no significant temperature peak, and the data shows that the temperature fluctuation is within the normal range, suggesting that the atomizer is running stably.
: Event duration, assuming that more than 30 minutes is required.
: The set time threshold was 30 minutes.
Since in the previous step, no temperature peak exceeding the threshold is identifiedThen:
Indicating that no temperature event has been sustained for more than 30 minutes during the current monitoring period, indicating that the atomizer temperature fluctuations remain stable throughout the observation period with no critical temperature events occurring.
Referring to fig. 4, the steps for obtaining the atomizer temperature distribution network chart are as follows:
S211: extracting the time point of each event from the list of critical temperature events And corresponding temperatureCombining the data pair sets, adopting a formula,
Generating a set of data pairsWherein, the method comprises the steps of, wherein,The point in time is indicated as being the time point,The corresponding temperature is indicated as such,Representing the total number of time points;
s212: wavelet transformation is applied to the data pair sets, multi-scale decomposition is performed, a formula is adopted,
A wavelet transform result is generated, wherein,The results of the multi-scale decomposition are shown,Is the amplitude adjustment coefficient of the amplitude of the signal,Is a frequency adjustment coefficient, representing a periodically varying frequency,Is a phase adjustment factor for, initial phase offset,Is an attenuation coefficient for adjusting the attenuation speed;
s213: analyzing the wavelet transformation result, screening the obvious temperature change mode, using a formula,
A significant set of temperature change patterns is obtained, wherein,A pattern of significant changes is indicated and,A single mode of change is indicated and,Is a threshold;
s214: based on the set of significant temperature change modes, a temperature distribution network diagram is constructed, a formula is adopted,
Outputting a temperature distribution network diagram of the atomizer, wherein,Representing the intensity of each significant pattern of change,Representing the strength of the connection between the nodes.
Assume three time points: Temperatures of 12:00, 12:05, 12:1022 Degrees celsius, 23 degrees celsius, 24 degrees celsius, respectively, then:
Assume that: 。
Three data points are given:
(assuming 12:00 is time 0).
Calculate the contribution of each point:
Final end The value 153.5, 13.8,0.007 reflects the integrated information after the wavelet transform process, which is used to further analyze the temperature change pattern.
Assume that: A significance threshold for determining which results are considered significant.
Assume thatThe results were {153.5, 13.8,0.007}, judging the significance of each result:
Indicating that all results are significant, will be used in the construction of the network map.
Obtained by combiningIs {153.5, 13.8,0.007}
Final endThe values represent the strength of the connection between the nodes, and the lower values represent strong connections, helping to understand the prevailing temperature distribution pattern in the network.
Referring to fig. 5, the detection steps of the critical temperature transmission path are:
S311: analyzing a atomizer temperature distribution network diagram, and calculating the centrality of each node By adopting the formula,
Generating a node degree centrality analysis result, wherein,Representing nodesIs characterized by a degree of centrality,The number of connections of the node is indicated,Representing the total number of nodes in the network,Is an adjustment factor for centering;
S312: based on the node degree centrality analysis result, the key nodes and edges are identified by using the betweenness centrality, a formula is adopted,
A set of key nodes and edges is generated, wherein,Representing nodesIs characterized by the medium number centrality of (2),The number of total paths is indicated and,Representing passing nodesIs provided with a number of paths of (a),For adjusting the influence of the number of paths;
s313: with a set of key nodes and edges, a path analysis algorithm is employed,
A set of critical temperature transfer paths is generated, wherein,Indicating a critical temperature transfer path and,Represents a key node in a set of key nodes and edges,Representing key nodesThe distance to the other node(s),Representing the number of critical nodes.
Assume that 10 nodes exist in a atomizer temperature distribution network diagram, and the nodesThere are 4 direct connections, namely:
Substituting the formula, and calculating:
indicating node After the centering adjustment, it is 0.44, reflecting its relative importance in the atomizer temperature distribution network.
Suppose that at a nodeTo the point ofOf all paths of (a), there are 5 paths, 3 of which pass through the nodeAnd (2) andThen:
Substituting the formula, and calculating:
display node The median center of (c) was adjusted to 0.19, indicating the importance in connecting the different atomizer temperature distribution network portions.
Assuming 3 key nodes, the average distance from each node to the hub is 2,3 and 4 units, namely:
Substituting the formula, and calculating:
Results Representing the average attenuation of the path effects in the set of critical temperature delivery paths, lower values indicate that the link between critical paths may be weaker, helping to determine the network area where enhanced monitoring or control is required.
Referring to fig. 6, the step of obtaining the analysis result of the abnormal propagation path is:
s321: based on the set of critical temperature transfer paths, non-uniformity analysis is performed, using a formula,
A result of the temperature distribution non-uniformity analysis is generated,Representing the variance of the temperature values over the path,Representing a pathThe temperature value of the water at the upper part,Representing the average temperature value in the set of critical temperature delivery paths,Representing a weight factor based on the temperature difference,Representing the number of paths;
s322: identifying an abnormal variance path according to the temperature distribution non-uniformity analysis result, applying a threshold formula,
Generating a set of abnormal temperature propagation paths, wherein,Represents the propagation path of the abnormal temperature,Is a single pathIs a function of the variance of (a),Is a preset threshold value, and the threshold value is set,Is a factor that adjusts the threshold;
s323: performing depth analysis on the abnormal temperature propagation path set, adopting a formula,
Evaluating the degree of abnormality of each path, generating an abnormal propagation path analysis result, wherein,Representing a pathIs set in the temperature variance of (a),Represents the probability of abnormality of the abnormal propagation path,Representing an abnormal path.
Assume that three paths are provided, and the temperature value of each path30 Degrees celsius, 35 degrees celsius and 40 degrees celsius, respectively, the average temperatureAt a temperature of 35 degrees celsius, the temperature of the material is,Assuming 5 degrees celsius, substituting the formula:
The calculation is as follows:
30 degrees celsius:
35 degrees celsius:
40 degrees celsius:
The variance is calculated as follows:
Results Indicating non-uniformity of the temperature distribution, higher values indicate larger temperature fluctuations, requiring attention to these paths.
Is a preset threshold, say 5 degrees celsius. To be used forAndAs an example.
The adjusted threshold is:
Assume that The values of (1) are calculated as before: path onlyThe variance of the upper temperature value of 40 degrees celsius and 30 degrees celsius (both 9.25) exceeds 7.15, so both paths are identified as abnormal paths.
Calculating a final analysis result:
Results Indicating that the identified abnormal path has a high probability of abnormality, such a path should be prioritized for intervention in the atomizer temperature management measures.
Referring to fig. 7, the steps for obtaining the atomizer temperature dynamic response characteristic map are as follows:
S411: based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, adopting a formula,
A new set of temperature control parameters is generated, wherein,Representing the new control parameters of the control system,The original parameters are represented by a set of values,Representing the expected temperature change deduced based on the abnormal path,AndRepresenting adjustment parameters for adjusting sensitivity and attenuation;
S412: a new set of temperature control parameters is applied, and the atomizer temperature response is monitored, using the formula,
Generating real-time temperature monitoring data, wherein,Time of presentationIs used for the temperature control of the liquid crystal display device,Is the initial temperature of the material to be heated,Is the time decay constant of the time-series,Is the amplitude of the wave,Is the frequency of the vibration and,AndControlling the periodic temperature variation;
S413: mapping to geometry by trigonometric function according to real-time temperature monitoring data, using formula,
A geometry of the temperature response is generated, wherein,Time of presentationThe corresponding geometric figure value is used to determine,Is the phase of the light and the phase of the light,Is a parameter for adjusting the amplitude response;
s414: analyzing the geometric figure of the temperature response, identifying the dynamic key feature of the temperature of the atomizer, analyzing the change of geometric form, adopting Fourier transformation,
Generating a atomizer temperature dynamic response characteristic map, wherein,Is a variable in the frequency domain and,Expressed in frequencyThe following dynamic characteristics highlight the periodicity and amplitude characteristics of the atomizer temperature response.
Assume thatThe content of the acid in the solution is 0.05,0.01.
Assuming original control parametersExpected temperature change：
ResultsRepresenting the adjusted atomizer temperature control parameters, is optimized based on the expected temperature change and attenuation coefficient.
Is the initial temperature, assumed to be 100 degrees celsius,Is a time decay constant, assumed to be 0.05,Is the amplitude, assumed to be 5 degrees celsius,Is the vibration frequency, assumed to be 1rad/s.
Assume that the time is monitoredSecond, then:
Results Degrees celsius, which represents the temperature response at 10 seconds, takes into account the effects of exponential decay and periodic vibration.
: Is the phase, assumed to be O.
: Is a parameter for adjusting the amplitude response, and is assumed to be 0.01.
Then:
Results Time of presentationConverted geometry values in seconds.
Hypothesis pairFourier transforming with time interval [0, 10], we sample this time point。
Assume thatIs the sampled value of (1)。
Hypothesis selectionThen:
Results The performance of the atomizer temperature response characteristic in the frequency domain is represented, and the periodicity and amplitude characteristics of the atomizer temperature dynamics are displayed through Fourier transformation.
An atomizer temperature data analysis system, comprising:
The temperature data collection module collects real-time temperature and corresponding time stamps through a temperature sensor to form a time-temperature pair set, and performs time-temperature pair set integration cleaning to generate a cleaned data stream;
The time sequence analysis module performs time sequence analysis based on the cleaned data stream, calculates temperature fluctuation at a time point, identifies a temperature peak value, screens events with duration exceeding a time threshold value, and generates a key temperature event list;
the wavelet transformation and pattern recognition module extracts the time point and the corresponding temperature of each event based on the key temperature event list to form a data pair set, applies wavelet transformation to carry out multi-scale decomposition, screens a significant temperature change pattern and constructs an output atomizer temperature distribution network diagram;
The centrality and path analysis module calculates the centrality of each node based on the atomizer temperature distribution network diagram, identifies key nodes and edges, and generates a key temperature transmission path set by adopting a path analysis algorithm;
The abnormal propagation path analysis module is used for carrying out non-uniformity analysis based on the key temperature transmission path set, identifying abnormal variance paths, carrying out deep analysis on the identification results, and evaluating the degree of abnormality of each path to obtain an abnormal propagation path analysis result;
the temperature control and dynamic response module adjusts the temperature control parameters of the atomizer based on the analysis result of the abnormal propagation path, monitors the temperature response of the atomizer, maps the temperature control parameters to the geometric figure through a trigonometric function according to the monitoring data, identifies the dynamic key characteristics of the temperature of the atomizer, and generates a dynamic response characteristic diagram of the temperature of the atomizer.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (6)
1. The atomizer temperature data analysis method is characterized by comprising the following steps of:
based on an internal temperature sensor of the atomizer, real-time data acquisition is carried out, the temperature of each temperature measuring point and a corresponding time stamp are recorded, data stream integration cleaning is carried out, time sequence analysis is carried out on the temperature data of the atomizer according to an integration result, abnormal temperature fluctuation and temperature peak values are identified, and a key temperature event list is obtained;
Based on the key temperature event list, carrying out multi-scale decomposition on data in the list by adopting wavelet transformation, revealing a temperature change mode of the atomizer under a time scale, and carrying out node connection to construct an atomizer temperature distribution network diagram;
Performing network topology analysis through the temperature distribution network diagram, detecting a key temperature transmission path by identifying key nodes and edges, performing temperature distribution non-uniformity analysis according to a detection result, and identifying a potential path of abnormal temperature transmission to obtain an abnormal transmission path analysis result;
Based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, monitoring the adjusted real-time temperature response, converting the monitoring data into a geometric figure, analyzing the temperature dynamic characteristic of the atomizer through the change of the geometric form, and establishing an atomizer temperature dynamic response characteristic diagram;
the detection steps of the key temperature transmission path are as follows:
analyzing the atomizer temperature distribution network diagram, and calculating the degree centrality of each node By adopting the formula,
，
Generating a node degree centrality analysis result, wherein,Representing nodesIs characterized by a degree of centrality,The number of connections of the node is indicated,Representing the total number of nodes in the network,Is an adjustment factor for centering;
Based on the node degree centrality analysis result, using the betweenness centrality to identify key nodes and edges, adopting a formula,
，
A set of key nodes and edges is generated, wherein,Representing nodesIs characterized by the medium number centrality of (2),The number of total paths is indicated and,Representing passing nodesIs provided with a number of paths of (a),For adjusting the influence of the number of paths;
using the set of key nodes and edges, using a path analysis algorithm,
，
A set of critical temperature transfer paths is generated, wherein,Indicating a critical temperature transfer path and,Represents a key node in a set of key nodes and edges,Representing key nodesThe distance to the other node(s),Representing the number of key nodes;
the step of obtaining the analysis result of the abnormal propagation path comprises the following steps:
Based on the set of critical temperature transfer paths, non-uniformity analysis is performed, using a formula,
，
A result of the temperature distribution non-uniformity analysis is generated,Representing the variance of the temperature values over the path,Representing a pathThe temperature value of the water at the upper part,Representing the average temperature value in the set of critical temperature delivery paths,Representing a weight factor based on the temperature difference,Representing the number of paths;
identifying an abnormal variance path according to the temperature distribution non-uniformity analysis result, applying a threshold formula,
，
Generating a set of abnormal temperature propagation paths, wherein,Represents the propagation path of the abnormal temperature,Is a single pathIs a function of the variance of (a),Is a preset threshold value, and the threshold value is set,Is a factor that adjusts the threshold;
performing depth analysis on the abnormal temperature propagation path set, adopting a formula,
，
Evaluating the degree of abnormality of each path, generating an abnormal propagation path analysis result, wherein,Representing a pathIs set in the temperature variance of (a),Represents the probability of abnormality of the abnormal propagation path,Representing an abnormal path.
2. The nebulizer temperature data analysis method according to claim 1, wherein the integrated purge step of the data stream is:
Collecting real-time temperature by a temperature sensor of an atomizer And corresponding time stampBy the formula, the method comprises the following steps,
，
Forming a set of time-temperature data pairsWherein, the method comprises the steps of, wherein,Indicating the number of measurements to be made,Representing a time stamp of the time of day,Representing a corresponding temperature value;
integrating the time-temperature data pair sets, by a formula,
，
Generating an integrated time-temperature data streamWherein, the method comprises the steps of, wherein,A time-temperature data pair is represented,Representing the number of pairs of data,Is the coefficient of the weight of the time,Is the sinusoidal modulation factor which is used to modulate the signal,Is the modulation frequency;
Data cleaning is carried out on the integrated time-temperature data stream, abnormal temperature data points are removed, a formula is used,
，
Generating a cleaned data streamWherein, the method comprises the steps of, wherein,AndRespectively represent temperature valuesIs defined as the mean value and standard deviation of (c),Is a tolerance threshold, is a standard deviation multiple,Representing the temperature value.
3. The nebulizer temperature data analysis method according to claim 2, wherein the step of obtaining the list of key temperature events is:
based on the cleaned data stream, time series analysis is performed, temperature fluctuations at time points are calculated, using a formula,
，
A temperature fluctuation dataset of the time point is generated, wherein,Temperature fluctuation data indicating a point in time,Is the point in timeIs used for the temperature value of (a),Is the value of the average temperature and,Is the number of data points;
identifying that a threshold is exceeded based on the temperature fluctuation dataset of the time point Is determined by applying a formula,
，
A list of temperature peaks is obtained, wherein,Indicating the peak value of the temperature,Indicating the temperature fluctuation at a single point in time,Is a threshold multiple of the atomizer temperature;
Screening for duration exceeding a time threshold based on the temperature peak list Using a formula,
，
A list of critical temperature events is generated, wherein,Indicating a critical temperature event is to be performed,Indicating a temperature peak event,Indicating the event duration.
4. A nebulizer temperature data analysis method as claimed in claim 3, wherein the nebulizer temperature distribution network diagram obtaining step is:
extracting the time point of each event from the key temperature event list And corresponding temperatureCombining the data pair sets, adopting a formula,
，
Generating a set of data pairsWherein, the method comprises the steps of, wherein,The point in time is indicated as being the time point,The corresponding temperature is indicated as such,Representing the total number of time points;
wavelet transformation is applied to the data pair set, multi-scale decomposition is performed, a formula is adopted,
，
A wavelet transform result is generated, wherein,The results of the multi-scale decomposition are shown,Is the amplitude adjustment coefficient of the amplitude of the signal,Is a frequency adjustment coefficient, representing a periodically varying frequency,Is a phase adjustment factor for, initial phase offset,Is an attenuation coefficient for adjusting the attenuation speed;
Analyzing the wavelet transformation result, screening for significant temperature change patterns, using a formula,
，
A significant set of temperature change patterns is obtained, wherein,A pattern of significant changes is indicated and,A single mode of change is indicated and,Is a threshold;
based on the set of significant temperature change modes, a temperature distribution network diagram is constructed, a formula is adopted,
，
Outputting a temperature distribution network diagram of the atomizer, wherein,Representing the intensity of each significant pattern of change,Representing the strength of the connection between the nodes.
5. The method for analyzing temperature data of a nebulizer of claim 1, wherein the step of obtaining the dynamic response map of the nebulizer temperature is:
based on the analysis result of the abnormal propagation path, adjusting the temperature control parameter of the atomizer, adopting a formula,
，
A new set of temperature control parameters is generated, wherein,Representing the new control parameters of the control system,The original parameters are represented by a set of values,Representing the expected temperature change deduced based on the abnormal path,AndRepresenting adjustment parameters for adjusting sensitivity and attenuation;
applying the new set of temperature control parameters, and monitoring the atomizer temperature response, using the formula,
，
Generating real-time temperature monitoring data, wherein,Time of presentationIs used for the temperature control of the liquid crystal display device,Is the initial temperature of the material to be heated,Is the time decay constant of the time-series,Is the amplitude of the wave,Is the frequency of the vibration and,AndControlling the periodic temperature variation;
mapping to a geometric figure through a trigonometric function according to the real-time temperature monitoring data, using a formula,
，
A geometry of the temperature response is generated, wherein,Time of presentationThe corresponding geometric figure value is used to determine,Is the phase of the light and the phase of the light,Is a parameter for adjusting the amplitude response;
Analyzing the geometric figure of the temperature response, identifying the dynamic key characteristics of the temperature of the atomizer, analyzing the change of geometric form, adopting Fourier transformation,
，
Generating a atomizer temperature dynamic response characteristic map, wherein,Is a variable in the frequency domain and,Expressed in frequencyThe following dynamic characteristics highlight the periodicity and amplitude characteristics of the atomizer temperature response.
6. A nebulizer temperature data analysis system, characterized in that the nebulizer temperature data analysis method according to any one of claims 1 to 5, the system comprising:
The temperature data collection module collects real-time temperature and corresponding time stamps through a temperature sensor to form a time-temperature pair set, and performs time-temperature pair set integration cleaning to generate a cleaned data stream;
the time sequence analysis module performs time sequence analysis based on the cleaned data stream, calculates temperature fluctuation of a time point, identifies a temperature peak value, screens events with duration exceeding a time threshold value, and generates a key temperature event list;
The wavelet transformation and pattern recognition module extracts the time point and the corresponding temperature of each event based on the key temperature event list to form a data pair set, applies wavelet transformation to carry out multi-scale decomposition, screens a significant temperature change pattern and constructs an output atomizer temperature distribution network diagram;
The centrality and path analysis module calculates the centrality of each node based on the atomizer temperature distribution network diagram, identifies key nodes and edges, and generates a key temperature transmission path set by adopting a path analysis algorithm;
The abnormal propagation path analysis module is used for carrying out non-uniformity analysis, identifying abnormal variance paths, carrying out deep analysis on the identification results, and evaluating the degree of abnormality of each path to obtain an abnormal propagation path analysis result;
and the temperature control and dynamic response module adjusts the temperature control parameters of the atomizer based on the analysis result of the abnormal propagation path, monitors the temperature response of the atomizer, maps the monitoring data to a geometric figure through a trigonometric function, identifies the dynamic key characteristics of the temperature of the atomizer and generates an atomizer temperature dynamic response characteristic diagram.
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