CN114459574B - Automatic evaluation method and device for high-speed fluid flow measurement accuracy and storage medium - Google Patents
Automatic evaluation method and device for high-speed fluid flow measurement accuracy and storage medium Download PDFInfo
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Abstract
The application discloses a high-speed fluid flow measurement accuracy automatic evaluation method, a device and a storage medium, wherein the method comprises the following steps: acquiring data of fluid flow in a pipeline; adopting a data cleaning algorithm of an isolated forest to clean the data; performing uncertainty calculation on the cleaned data to obtain total synthetic standard uncertainty; and outputting an accuracy evaluation result according to the uncertainty of the total synthesis standard. The application provides a whole process scheme from the collection of measurement data to the output of an accuracy result, reduces the intervention degree of a user in the aspect of evaluating the accuracy of high-speed fluid flow measurement, relieves the workload of related personnel to a certain extent, and improves the working efficiency by only paying attention to the final result.
Description
Technical Field
The application relates to the field of industrial production monitoring, in particular to an automatic evaluation method, an automatic evaluation device and a storage medium for high-speed fluid flow measurement accuracy.
Background
In industrial production and scientific research experiments, there are many requirements for high-speed fluid flow measurement in closed spaces (such as pipelines and the like). At present, fluid flow measurement often adopts indirect methods such as differential pressure measurement, flow velocity measurement and the like to acquire the state of the current space fluid, so as to calculate the flow of the fluid. In addition, electronic measuring devices such as air flow meters and the like are used for directly acquiring the flow condition. The flowmeter is used as a main tool for measuring the flow rate of fluid at present, and plays an important role in measuring the flow rates of various fluids in a closed pipeline. In view of the large number of flow measurement methods, different calculation modes, different use scenes and the like, the flow meters are various in variety, and different application conditions are provided, so that the special purpose and the limitation of the flow meter are reflected. In terms of high-speed fluid flow measurement, there is currently a lack of widely accepted universal flow measurement tools, and professional or custom-made measurement tools that meet their own needs are often used in the industry. The confidence level of measured data under certain severe standards often needs quantitative values to represent the accuracy, and the accuracy can be trusted when reaching standards.
One of the reasons for affecting the accuracy of high-speed fluid flow measurements is that measurement errors, the extent to which they affect the accuracy of the flow measurements, have been largely known in the industry and often evaluated and quantified using the concept of uncertainty. There is an error in the measurement, which is the difference between the current measurement and the actual value, which is unavoidable. As an important branch of instrumentation and testing disciplines, error theory has been developed for centuries, and uncertainty theory is an upgrade and extension of error theory. At present, the national rule of fluid flow accuracy evaluation is clear, when related personnel calculate the flow measurement accuracy, manual calculation is often needed under the assistance of a calculation tool, time and labor are wasted, and even whether the final calculation result is correct or not cannot be guaranteed. In the process of calculating the accuracy, because of the limitation of manual calculation, some complicated and difficult steps are often omitted, so that an 'error of calculation error' is formed.
With the development of technology and the advancement of industry, flow measurement is currently taken charge of collection by a sensor, and the time interval for collection is short, the measurement position is large, so that the final data volume is too large. If manual calculation or semi-automatic calculation is needed, the workload is extremely huge. If the flow measurement of the high-speed fluid is performed, the acquisition frequency of the sensor is more frequent, and the automatic calculation of the flow measurement accuracy of the high-speed fluid is realized by using a computer software technology. At present, software tools for calculating measurement accuracy are lacking in China, and accuracy calculators for measuring numerical values or some automatic calculation methods exist abroad, but the methods still cannot migrate to a high-speed fluid flow measurement scene, and all calculation flows are not actually opened, so that the automation of accuracy evaluation is not really realized.
Therefore, an automatic evaluation method, an automatic evaluation device and a storage medium for high-speed fluid flow measurement accuracy are provided, and the problems to be solved in the field are urgent.
Disclosure of Invention
The application aims to overcome the defects of the prior art and provides an automatic evaluation method, an automatic evaluation device and a storage medium for the accuracy of high-speed fluid flow measurement.
The aim of the application is realized by the following technical scheme:
in a first aspect of the present application, there is provided a method for automated evaluation of accuracy of high-speed fluid flow measurement, comprising the steps of:
acquiring data of fluid flow in a pipeline;
adopting a data cleaning algorithm of an isolated forest to clean the data;
performing uncertainty calculation on the cleaned data to obtain total synthetic standard uncertainty;
and outputting an accuracy evaluation result according to the uncertainty of the total synthesis standard.
Further, the data of the fluid flow in the acquisition pipeline is data of the fluid flow in the pipeline transmitted by the server;
the data sources of the fluid flow of the server are that a flowmeter or a flow sensor arranged in a pipeline is preliminarily acquired, the data are integrated by being respectively transmitted to a data acquisition card through a universal serial bus, and the data are uploaded through a local area network and adopting RTP and SRTP protocols after being summarized by the data acquisition card;
the data of the fluid flow of the server is stored in a database.
Further, the data cleaning algorithm using the isolated forest cleans the data, including:
s1: firstly, randomly selecting n points from data X as subsamples, and putting the subsamples into a root node of an isolated tree, wherein X is a group of measured data arrays, the maximum value is max, and the minimum value is min;
s2: randomly obtaining one data x in the subsamples, defining:
where i is each data sequence number, x i And x i+1 Representing different adjacent measurement data in the subsamples, the function f (x) represents the constraint result of the sequence number i, and T (u) is the constraint equation currently calculated and is defined as:
definition of the definitions＝∑F i (x) The method comprises the steps of carrying out a first treatment on the surface of the Introducing the second power of the length of the k sections, namely:
g i (x)＝|x i+1 -x i | k f i (x)
so far, calculating a multiplication coefficient c;
s3: randomly generating a value in the current sub-sample range, and enabling the value to be located between the maximum value and the minimum value in the current sub-sample space; multiplying the value by c to form a cut point p;
s4: the cutting point p generates a hyperplane, and the space data is divided into two subspaces; sequentially comparing the sizes of the data in the data segments, wherein subspaces which are smaller than p and are arranged on the left side, namely left branches of the current node; a subspace which is more than or equal to p and is arranged on the right side, namely a right branch of the current node;
s5: continuously repeating the steps S3 and S4, continuously constructing new leaf nodes until only one data exists on the leaf nodes, and then cutting cannot be continued or the tree grows to the set height l;
s6: and the subsequent processing process gradually screens out abnormal values according to the basic algorithm of the isolated forest, and deletes the abnormal values.
Further, the calculating the uncertainty of the cleaned data to obtain the total synthetic standard uncertainty includes:
calculating class A uncertainty;
calculating class B uncertainty;
and synthesizing the results of the class A uncertainty and the class B uncertainty, and calculating the total synthesis standard uncertainty.
Further, the calculating of the class a uncertainty includes:
let x be i For the instantaneous flow value measured by the flowmeter/flow sensor in each time period, u (a) is the class a uncertainty analysis result of the flowmeter/flow sensor, and then:
respectively calculating A-class uncertainty analysis results, and storing the results as u i(A) Where i=1, 2,3, …, n is the number of data.
Further, the calculating of the class B uncertainty includes:
the measurement of each flow meter/flow sensor has no correlation, and accordingly class B uncertainty is defined as follows:
wherein m is 1 ,m 2 ,…,m n For the accuracy factor, v 1 ,v 2 ,…,v n Analyzing equations for flow measurement uncertainty of different types of flow meters/flow sensors; the calculation mode of the accuracy coefficient is as follows:
wherein z is j Different parameters for the flow meter or flow sensor;
and v 1 ,v 2 ,…,v n Evaluating the result for uncertainty of the parameter; v 1 ,v 2 ,…,v n Is suitable for: v=k p ×U p U in p For the extended uncertainty of the flowmeter/flow sensor, k is obtained by query p Is a coverage factor; when the value inquiry of the expanded uncertainty is not available, v 1 ,v 2 ,…,v n Is suitable for:where delta is the estimated error of the parameter.
Further, the step of synthesizing the calculated results of the class a uncertainty and the class B uncertainty, and calculating the total synthesis standard uncertainty includes:
and synthesizing the class A and class B uncertainty analysis results of each flowmeter/flow sensor.
u i Measurement standard uncertainty for each flowmeter/flow sensor;
taking into account the flow measurement standard uncertainty of the high-speed fluid, the average of all flow meter/flow sensor standard uncertainties should be taken, namely:
u all final standard uncertainty for fluid flow in the pipeline;
to avoid the influence caused by individual measurement faults, inWhen the u is discarded i Value, establish new standard uncertainty list u i 'i=1, 2,3, …, n', and taking the minimum value umin and the maximum value umax in the standard uncertainty list, and finally calculating the total synthetic standard uncertainty by the following formula:
u′ all to total synthetic standard uncertainty, q i For the average flow value measured by each flowmeter/flow sensor, 2.34 is an empirical constant that may be replaced with other values.
Further, the outputting the accuracy evaluation result according to the uncertainty of the total synthesis standard includes:
calculating the unified relative standard uncertainty of all the current measured data through the standard uncertainty; the relative standard uncertainty is calculated by the following steps:
ur＝u′ all /Q
in the formula, u' all Q is the average value of the fluid flow in the pipeline for the total synthetic standard uncertainty;
accuracy in percent number of relative standard uncertainty conversion:
R＝(1-10 3 ×ur)×100％
wherein R represents the accuracy in% form.
In a second aspect of the present application, there is provided an automated high-speed fluid flow measurement accuracy assessment device comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, the processor executing the steps of the automated high-speed fluid flow measurement accuracy assessment method when executing the computer instructions.
In a third aspect of the present application, there is provided a storage medium having stored thereon computer instructions which, when executed, perform the steps of the method for automated evaluation of high-speed fluid flow measurement accuracy.
The beneficial effects of the application are as follows:
(1) In an exemplary embodiment of the application, the spool of fluid flow measurement data to be processed is first implemented (preferably from a server to a calculator); adopting simple statistical analysis and a data cleaning algorithm based on an isolated forest to detect abnormal values; the step of uncertainty evaluation starts to read in data, and the accuracy of flow measurement is calculated; subsequently, the uncertainty assessment tool calculation is completed, translating into accuracy by the relative standard uncertainty. Therefore, the whole process scheme from the collection of measurement data to the output of the accuracy result is provided, the intervention degree of a user is reduced in the aspect of evaluating the accuracy of the measurement of the high-speed fluid flow, the workload of related personnel is relieved to a certain extent, and the user only needs to pay attention to the final result, so that the working efficiency is improved.
(2) In an exemplary embodiment of the application, an isolated forest data cleaning algorithm is adopted, and specific steps of the algorithm are specifically disclosed, the algorithm is an improved algorithm of an isolated forest original algorithm, the idea of probability density is applied, the time complexity of a data cleaning link is reduced, and the data processing efficiency is improved. Meanwhile, compared with the original algorithm, the algorithm has a certain improvement in the accuracy of the calculation result, and the intuitiveness of the original algorithm and the advantage of rapid random construction of the isolated forest are maintained.
(3) In an exemplary embodiment of the application, a new uncertainty synthesis calculation method aiming at a specific service scene is provided by calculating the class A uncertainty and the class B uncertainty and synthesizing the same, so that the uncertainty evaluation precision is improved, and finally, an uncertainty result is obtained, so that the data analysis is more accurate.
(4) In an exemplary embodiment of the present application, the calculation of the class B uncertainty is specifically defined for a specific service scenario of high-speed fluid flow measurement, and the calculation mode is extension and optimization on the national standard, and the calculation result meets the requirements, is close to a specific situation and is more accurate.
Drawings
FIG. 1 is a flow chart of an automated evaluation method for measuring accuracy of high-speed fluid flow according to an exemplary embodiment of the present application;
fig. 2 is a schematic diagram of a system topology structure of an overall method and a deployment description of related hardware according to an exemplary embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully understood from the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, it should be noted that directions or positional relationships indicated as being "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are directions or positional relationships described based on the drawings are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
Referring to fig. 1, fig. 1 shows a method for automatically evaluating accuracy of high-speed fluid flow measurement according to an exemplary embodiment of the present application, including the following steps:
s01: acquiring data of fluid flow in a pipeline;
s03: adopting a data cleaning algorithm of an isolated forest to clean the data;
s05: performing uncertainty calculation on the cleaned data to obtain total synthetic standard uncertainty;
s07: and outputting an accuracy evaluation result according to the uncertainty of the total synthesis standard.
Specifically, in this exemplary embodiment, the spool of fluid flow measurement data to be processed (preferably from a server to a calculator) is first implemented; adopting simple statistical analysis and a data cleaning algorithm based on an isolated forest to detect abnormal values; the step of uncertainty evaluation starts to read in data, and the accuracy of flow measurement is calculated; subsequently, the uncertainty assessment tool calculation is completed, translating into accuracy by the relative standard uncertainty.
Therefore, the present exemplary embodiment provides an overall process scheme from measurement data collection to output of accuracy results, which reduces the intervention degree of the user in evaluating the accuracy of high-speed fluid flow measurement, relieves the workload of related personnel to a certain extent, and improves the working efficiency by only paying attention to the final results.
More preferably, in an exemplary embodiment, the data of the fluid flow in the obtaining pipeline in step S01 is data of the fluid flow in the pipeline transmitted by the server;
the data sources of the fluid flow of the server are that a flowmeter or a flow sensor arranged in a pipeline is preliminarily acquired, the data are integrated by being respectively transmitted to a data acquisition card through a universal serial bus, and the data are uploaded through a local area network and adopting RTP and SRTP protocols after being summarized by the data acquisition card;
the data of the fluid flow of the server is stored in a database.
Specifically, in the exemplary embodiment, several flow meters (including differential pressure flow meters, electromagnetic flow meters, vortex shedding flow meters, etc. that support data acquisition and transmission) or other flow sensors mounted on a straight pipe perform preliminary measurements of high-speed fluid flow, i.e., flow information acquisition. The data collected by each flowmeter/flow sensor are respectively transmitted to a data collection card through a universal serial bus to integrate the data.
After the data acquisition card gathers the data, the data is uploaded to the server through a local area network by adopting RTP (Real-time transport protocol ) and SRTP (Secure Real-time transport protocol, secure Real-time Transport Protocol) protocols. SRTP is a secure transmission based on RTP to ensure data integrity, authenticity and reliability, and uses the SRTP protocol to require a server to perform a preliminary check on the received measurement data in accordance with SRTP related standards. The server stores the received measurement data into a database, wherein the type of the database is a time sequence type database, and the embodiment adopts an IoTDB database scheme.
And then, the computer acquires the measurement data from the database of the server through the local area network by adopting a TCP/IP protocol. The acquired data are temporarily stored on a local disk of a computer, and are automatically released and deleted after all the processes of the method are finished. This step requires user approval to be performed.
FIG. 2 provides a deployment illustration of the hardware associated with the present application and the system topology architecture of the overall method. The key facilities needed by the application are a high-speed fluid pipeline, a plurality of flow meters/flow sensors, a data acquisition card, a server and a computer. Further, as a transmission medium, a universal serial bus, a network cable, or the like is required, and other accessories such as accessories necessary for the above-described critical facilities or other unnecessary constituent parts are not described here. As can be seen from fig. 2, a plurality of flow meters/flow sensors may be mounted on a pipe, which flow meters/flow sensors are operated independently. Each flowmeter/flow sensor transmits the independently measured flow data to the data acquisition card through the universal serial bus, and all the data are integrated by the data acquisition card. The data acquisition card, the server and the computer are connected to the same local area network, and the three devices can communicate information according to different communication protocols. The communication protocols of the data acquisition card and the server are RTP and SRTP, and the communication protocol between the server and the computer is TCP/IP. The server can be added and expanded according to actual conditions. The server is internally built with a database which is responsible for storing massive real-time flow measurement data. The user can operate the computer, control the computer to start downloading data and automatically start the data processing and flow accuracy evaluation flow, and finally learn the final accuracy evaluation result from the computer.
In yet another exemplary embodiment, the data processing steps of the method may be performed in a server.
More preferably, in an exemplary embodiment, the data cleaning algorithm using isolated forests in step S03 includes:
the main principle of the isolated forest algorithm is already public technology and will not be described in detail here. The main idea of an isolated forest is to detect outliers in a set of data by creating a randomly constructed isolated tree forest that recursively partitions the subspaces until one data child node is isolated or reaches the tree's height limit. In the original algorithm, there are the following formula definitions:
wherein s (x, n) is an outlier of the data x; e (h (x)) is the expected path length h (x) of data point x in the orphan tree; c (n) generates an average of path lengths for n data points and normalizes h (x); h (n) is a harmonic number and can be estimated to be exactly a certain value, such as ln (n) +0.5772156649.
The application redefines the establishment process of the isolated tree in the isolated forest. The inputs of this process are: x-a group of measurement data sequences, e-the height of the current tree, l-the height limit of the tree, k-the power of the length of the k segments; the output is the orphan tree itrage.
First, n points are randomly selected from the data X as subsamples, and put into the root node of an isolated tree.
Next, the number of sub-samples is known as n, the maximum value is max, and the minimum value is min. Randomly obtaining one data x in the subsamples, defining:
where i is each data sequence number, x i And x i+1 Representing different adjacent measurement data in the subsamples, the function f (x) represents the constraint result of the sequence number i, and T (u) is the constraint equation currently calculated and is defined as:
definition of the definitions＝∑F i (x) The method comprises the steps of carrying out a first treatment on the surface of the Introduction of the fourth power of the length of the k segments (generally empirically assigned), i.e.
g i (x)＝|x i+1 -x i | k f i (x)
The multiplication coefficient c is calculated up to this point and participates in the subsequent process. The algorithm uses the probability density idea, the separation process of the subsequent algorithm can isolate abnormal values earlier through the multiplication coefficient c, and the path of the isolated tree is shorter, so that the circulation is reduced, and the efficiency is improved; meanwhile, higher probability density is distributed in an abnormal range, so that the abnormal value screening of the algorithm can be more accurate.
Third, a value is randomly generated within the current sub-sample range and is located between the maximum and minimum values in the current sub-sample space. This value is multiplied by c, resulting in a cut point p.
Fourth, the cut point creates a hyperplane dividing the spatial data into two subspaces. Sequentially comparing the sizes of the data in the data segments, wherein subspaces which are smaller than p and are arranged on the left side, namely left branches of the current node; the subspace to the right of p or more, i.e. the right branch of the current node.
Finally, the third and fourth steps are repeated continuously, and new leaf nodes are constructed continuously until there is only one data on the leaf nodes (no longer cut) or the tree has grown to the set height l.
The subsequent processing step screens out abnormal values step by step according to the basic algorithm of the isolated forest. The present application deletes these abnormal data.
Specifically, in this exemplary embodiment, the computer performs data processing, i.e., data cleansing or conversion, on the temporary data, to eliminate obvious malfunction data, abnormal data, and unreliable data, avoiding the influence thereof on the accuracy judgment after that. The data cleaning in this step is necessary, and the data conversion is determined according to whether the computer has a relevant requirement, if yes, the user needs to make a conversion rule table, and the program reads the conversion rule table to guide the computer to complete the data conversion. The main work of data cleaning is to detect abnormal values in measured data and delete the abnormal values, and the adopted method is to determine abnormal data items. The main work of data conversion is to convert basic data forms such as formats, dimensions and the like of measurement data according to a conversion rule table.
In the data cleaning algorithm adopting the isolated forest in the present exemplary embodiment, the idea of probability density is applied in the second step, and the selected value of the cutting point is adjusted through the multiplication coefficient, so that the third step and the fourth step of the algorithm cycle times are reduced, the time complexity of the data cleaning link is reduced, and the data processing efficiency is improved; meanwhile, the algorithm redistributes probability densities of normal data and outlier data, improves distinguishing capability of abnormal data, and improves accuracy of calculation results to a certain extent; the algorithm is an optimization improvement of the original isolated tree building algorithm, and the intuitiveness of the original isolated forest algorithm and the advantage of rapid random building of the isolated forest are reserved to a great extent.
More preferably, in an exemplary embodiment, the calculating of uncertainty of the cleaned data in step S05, to obtain the total synthetic standard uncertainty includes:
s0501: calculating class A uncertainty;
s0503: calculating class B uncertainty;
s0505: and synthesizing the results of the class A uncertainty and the class B uncertainty, and calculating the total synthesis standard uncertainty.
Specifically, in this exemplary embodiment, the data analysis is made more accurate by calculating the class a uncertainty and the class B uncertainty, respectively, and synthesizing them, to finally obtain the result of the uncertainty.
More preferably, in an exemplary embodiment, the calculating of the class a uncertainty in step S0501 includes:
let x be i For the instantaneous flow value measured by the flowmeter/flow sensor in each time period, u (a) is the class a uncertainty analysis result of the flowmeter/flow sensor, and then:
respectively calculating A-class uncertainty analysis results, and storing the results as u i(A) Where i=1, 2,3, …, n is the number of data.
More preferably, in an exemplary embodiment, the calculation of the class B uncertainty is performed in step S0503, and the method is improved on the national standard JJF1059.1-2012 for the present scenario, and the calculation result meets the requirements, which are extension and deepening on the basis of the standard, and the accuracy of calculating the class B uncertainty of the high-speed fluid flow measurement is higher by including the internal parameters of different flow meters/flow sensors into the calculation process instead of directly calculating the class B uncertainty of the measured flow data, so that the influence of each factor on the measurement is considered greatly. Comprising the following steps:
the measurement of each flow meter/flow sensor has no correlation, and accordingly class B uncertainty is defined as follows:
wherein m is 1 ,m 2 ,…,m n For the accuracy factor, v 1 ,v 2 ,…,v n Analyzing equations for flow measurement uncertainty of different types of flow meters/flow sensors; the calculation mode of the accuracy coefficient is as follows:
wherein z is j Different parameters for the flow meter or flow sensor;
and v 1 ,v 2 ,…,v n Evaluating the result for uncertainty of the parameter; v 1 ,v 2 ,…,v n Is suitable for: v=k p XU P U in p For the extended uncertainty of the flowmeter/flow sensor, k is obtained by query p Is a coverage factor; when the value inquiry of the expanded uncertainty is not available, v 1 ,v 2 ,…,v n Is suitable for:where delta is the estimated error of the parameter.
For example, if a differential pressure flowmeter is used, by measuring pressure and combining different parameters such as orifice diameter d, flow data is finally obtained, and a class B uncertainty analysis equation is as follows:
where C is the flow coefficient of the flowmeter, D is the orifice diameter of the flowmeter, D is the length of the internal orifice tube of the flowmeter, and p is the instantaneous fluid pressure measured by the flowmeter. The partial derivative calculation process for each parameter is related to the flowmeter product itself, and the different product calculation processes may be different and are not described here. In addition, for the coverage factor k p The present application provides that at a confidence level of 95%, this value is 1.92.
More preferably, in an exemplary embodiment, the step S0505 includes synthesizing the result of the calculated class a uncertainty and class B uncertainty, and calculating the total synthesis standard uncertainty, where the step S0505 includes:
and synthesizing the class A and class B uncertainty analysis results of each flowmeter/flow sensor.
u i Measurement standard uncertainty for each flowmeter/flow sensor;
considering that each flowmeter/flow sensor operates independently, it is substantially consistent with a relative independence on a probabilistic model. In the ordinary case, the flow measurement standard uncertainty of the high-speed fluid can be considered to be the average of all flow meter/flow sensor standard uncertainties, namely:
u all final standard uncertainty for fluid flow in the pipeline;
to avoid individual measurement faultsThe resulting influence is thatWhen (2.34 is an empirical constant), discard the u i Value, establish new standard uncertainty list u i 'i=1, 2,3, …, n', and taking the minimum value umin and the maximum value umax in the standard uncertainty list, and finally calculating the total synthetic standard uncertainty by the following formula:
u′ all to total synthetic standard uncertainty, q i An average flow value measured for each flow meter/flow sensor.
More preferably, in an exemplary embodiment, outputting the accuracy evaluation result according to the uncertainty of the total synthesis criterion in step S07 includes:
calculating the unified relative standard uncertainty of all the current measured data through the standard uncertainty; the relative standard uncertainty is calculated by the following steps:
ur＝u′ all /Q
in the formula, u' all Q is the average value of the fluid flow in the pipeline for the total synthetic standard uncertainty;
accuracy in percent number of relative standard uncertainty conversion:
R＝(1-10 3 ×ur)×100％
wherein R represents the accuracy in% form.
And finally, displaying the result through a display screen.
A further exemplary embodiment of the present application provides a high-speed fluid flow measurement accuracy automated evaluation device having the same inventive concept as the above exemplary embodiment, including a memory and a processor, the memory storing thereon computer instructions executable on the processor, the processor executing the steps of the high-speed fluid flow measurement accuracy automated evaluation method when the computer instructions are executed.
The electronic device is in the form of a general purpose computing device. Components of an electronic device may include, but are not limited to: the at least one processing unit, the at least one memory unit, and a bus connecting the different system components (including the memory unit and the processing unit).
Wherein the storage unit stores program code executable by the processing unit such that the processing unit performs steps according to various exemplary embodiments of the present application described in the above section of the exemplary method of the present specification. For example, the processing unit may perform the method as shown in fig. 1.
The memory unit may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 3201 and/or cache memory units, and may further include Read Only Memory (ROM).
The storage unit may also include a program/utility having a set (at least one) of program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The bus may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any device (e.g., router, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. And, the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be appreciated that other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
As will be readily appreciated by those skilled in the art from the foregoing description, the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Accordingly, the technical solution according to the present exemplary embodiment may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the present exemplary embodiment.
A further exemplary embodiment of the present application provides a storage medium having stored thereon computer instructions which, when executed, perform the steps of the method for automated evaluation of high-speed fluid flow measurement accuracy.
Based on this understanding, the technical solution of the present embodiment may be essentially or, what contributes to the prior art, or part of the technical solution may be embodied in the form of a software product (program product) stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It is apparent that the above examples are given by way of illustration only and not by way of limitation, and that other variations or modifications may be made in the various forms based on the above description by those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present application.
Claims (4)
1. An automatic evaluation method for the accuracy of high-speed fluid flow measurement is characterized by comprising the following steps: the method comprises the following steps:
acquiring data of fluid flow in a pipeline;
adopting a data cleaning algorithm of an isolated forest to clean the data;
performing uncertainty calculation on the cleaned data to obtain total synthetic standard uncertainty;
outputting an accuracy evaluation result according to the uncertainty of the total synthesis standard;
the data cleaning algorithm adopting the isolated forest cleans the data, and the method comprises the following steps:
s1: firstly, randomly selecting n points from data X as subsamples, and putting the subsamples into a root node of an isolated tree, wherein X is a group of measured data arrays, the maximum value is max, and the minimum value is min;
s2: randomly obtaining one data x in the subsamples, defining:
where i is each data sequence number, x i And x i+1 Representing different adjacent measurement data in the subsamples, the function f (x) represents the constraint result of the sequence number i, and T (u) is the constraint equation currently calculated and is defined as:
definition of the definitions＝∑F i (x) The method comprises the steps of carrying out a first treatment on the surface of the Introducing the second power of the length of the k sections, namely:
g i (x)＝|x i+1 -x i | k f i (x)
so far, calculating a multiplication coefficient c;
s3: randomly generating a value in the current sub-sample range, and enabling the value to be located between the maximum value and the minimum value in the current sub-sample space; multiplying the value by c to form a cut point p;
s4: the cutting point p generates a hyperplane, and the space data is divided into two subspaces; sequentially comparing the sizes of the data in the data segments, wherein subspaces which are smaller than p and are arranged on the left side, namely left branches of the current node; a subspace which is more than or equal to p and is arranged on the right side, namely a right branch of the current node;
s5: continuously repeating the steps S3 and S4, continuously constructing new leaf nodes until only one data exists on the leaf nodes, and then cutting cannot be continued or the tree grows to the set height l;
s6: the subsequent processing process gradually screens out abnormal values according to the basic algorithm of the isolated forest, and deletes the abnormal values;
the step of calculating uncertainty of the cleaned data to obtain the total synthetic standard uncertainty comprises the following steps:
calculating class A uncertainty;
calculating class B uncertainty;
synthesizing the results of the class A uncertainty and the class B uncertainty obtained by calculation, and calculating the total synthesis standard uncertainty;
the calculating of the class A uncertainty comprises the following steps:
let x be i Measured for each time period of the flowmeter/flow sensorThe instantaneous flow value, u (a), is the class a uncertainty analysis of the flowmeter/flow sensor, and there are:
respectively calculating A-class uncertainty analysis results, and storing the results as u i(A) Where i=1, 2,3, …, n is the number of data;
the calculating of the class B uncertainty comprises the following steps:
the measurement of each flow meter/flow sensor has no correlation, and accordingly class B uncertainty is defined as follows:
wherein m is 1 ,m 2 ,…,m n For the accuracy factor, v 1 ,v 2 ,…,v n Analyzing equations for flow measurement uncertainty of different types of flow meters/flow sensors; the calculation mode of the accuracy coefficient is as follows:
wherein z is j Different parameters for the flow meter or flow sensor;
and v 1 ,v 2 ,…,v n Evaluating the result for uncertainty of the parameter; v 1 ,v 2 ,…,v n Is suitable for: v=k p ×U p U in p For the extended uncertainty of the flowmeter/flow sensor, k is obtained by query p Is a coverage factor; when the value inquiry of the expanded uncertainty is not available, v 1 ,v 2 ,…,v n Is suitable for:wherein delta is the estimated error of the parameter;
and synthesizing the results of the class A uncertainty and the class B uncertainty, and calculating the total synthesis standard uncertainty, wherein the method comprises the following steps of:
synthesizing the class A and class B uncertainty analysis results of each flowmeter/flow sensor;
u i measurement standard uncertainty for each flowmeter/flow sensor;
taking into account the flow measurement standard uncertainty of the high-speed fluid, the average of all flow meter/flow sensor standard uncertainties should be taken, namely:
u all final standard uncertainty for fluid flow in the pipeline;
to avoid the influence caused by individual measurement faults, inWhen the u is discarded i Value, establish new standard uncertainty list u i 'i=1, 2,3, …, n', and taking the minimum value umin and the maximum value umax in the standard uncertainty list, and finally calculating the total synthetic standard uncertainty by the following formula:
u′ all to total synthetic standard uncertainty, q i For each meter/flow sensor measured average flow value, 2.34 is an empirical constant that can be replaced with other values;
the step of outputting the accuracy evaluation result according to the uncertainty of the total synthesis standard comprises the following steps:
calculating the unified relative standard uncertainty of all the current measured data through the standard uncertainty; the relative standard uncertainty is calculated by the following steps:
ur＝u′ all /Q
in the formula, u' all Q is the average value of the fluid flow in the pipeline for the total synthetic standard uncertainty;
accuracy in percent number of relative standard uncertainty conversion:
R＝(1-10 3 ×ur)×100％
wherein R represents the accuracy in% form.
2. The automated high-speed fluid flow measurement accuracy assessment method of claim 1, wherein: the data for acquiring the fluid flow in the pipeline is data for receiving the fluid flow in the pipeline transmitted by the server;
the data sources of the fluid flow of the server are that a flowmeter or a flow sensor arranged in a pipeline is preliminarily acquired, the data are integrated by being respectively transmitted to a data acquisition card through a universal serial bus, and the data are uploaded through a local area network and adopting RTP and SRTP protocols after being summarized by the data acquisition card;
the data of the fluid flow of the server is stored in a database.
3. An automated high-speed fluid flow measurement accuracy assessment device comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, characterized by: the processor, when executing the computer instructions, performs the steps of a high-speed fluid flow measurement accuracy automated assessment method according to claim 1 or 2.
4. A storage medium having stored thereon computer instructions, characterized by: the computer instructions, when executed, perform the steps of a high-speed fluid flow measurement accuracy automated assessment method according to claim 1 or 2.
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