Flowmeter detection system and method based on data analysis

Abstract

The invention discloses a flowmeter detection system and method based on data analysis, and the system comprises an appearance detection module which is used for detecting the appearance quality of each flowmeter corresponding to a target region to obtain an appearance quality evaluation coefficient of each flowmeter corresponding to the target region; the performance detection module is used for detecting performance parameters of the flowmeters corresponding to the target area to obtain performance evaluation coefficients of the flowmeters corresponding to the target area; the anomaly analysis module is used for obtaining anomaly type signals according to the gradient evaluation interval based on the appearance quality evaluation coefficient and the performance evaluation coefficient of each flowmeter corresponding to the target area, and the anomaly type signals comprise performance anomaly signals and appearance anomaly signals. According to the method, the abnormal signals can be recognized, corresponding treatment measures are triggered, normal operation of the flowmeter is ensured, the abnormal conditions of the flowmeter can be accurately and rapidly recognized, and a powerful guarantee is provided for stable operation of the flowmeter.

Description

Technical Field

[0001] The present invention relates to the technical field of flow meter detection, and in particular to a flow meter detection system and method based on data analysis. Background Art

[0002] In the modern industrial field, flow meters occupy a core position and are widely used in the precise measurement and control of various fluid flows. However, the performance and quality of this key equipment have a vital impact on the safety, stability and efficiency of the production process. Therefore, timely, efficient and accurate detection of flow meter anomalies has become a vital task;

[0003] The traditional flow meter detection method mainly relies on manual operation and experience judgment. This method is not only inefficient, but also difficult to ensure the accuracy and timeliness of the detection results. When abnormal conditions occur, they are often not detected in time, and by the time the problem is discovered, losses may have already occurred. In addition, different environmental factors have different effects on flow meters, and detection personnel mainly rely on subjective experience to make judgments. It is difficult to make accurate judgments on abnormal conditions of flow meters based on environmental changes. Therefore, there are large errors in the detection results. This method not only consumes a lot of manpower and material resources, but may also bring huge safety hazards and economic losses to enterprises. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing traditional flow meter detection method mainly relies on manual operation and experience judgment, which is not only inefficient but also difficult to ensure the accuracy and timeliness of the detection results, and propose a flow meter detection system and a detection method based on data analysis.

[0005] The technical solution to achieve the purpose of the present invention is:

[0006] A flow meter detection system based on data analysis, comprising:

[0007] An appearance inspection module is used to inspect the appearance quality of each flow meter corresponding to the target area, obtain the appearance quality parameters of each flow meter corresponding to the target area, and calculate the appearance quality evaluation coefficient of each flow meter corresponding to the target area;

[0008] The performance detection module is used to detect the performance parameters of each flow meter corresponding to the target area, obtain the performance parameters of each flow meter corresponding to the target area, extract the output signal value of each detection point of each level of environment of each flow meter from the performance parameters of each flow meter corresponding to the target area, and compare the output signal value of each detection point of each level of environment of each flow meter with the reference output signal value corresponding to the corresponding level of environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to each level of environment, then the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, then the detection point is recorded as a detection point, and the number of a detection points and the number of b detection points of the primary environment of each flow meter are statistically obtained;

[0009] Extract the output signal value of each a checkpoint in each level of environment of each flowmeter from the output signal value of each detection point in each level of environment of each flowmeter, and subtract it from the reference output signal value corresponding to each level of environment to obtain the output signal difference of each a checkpoint in each level of environment of each flowmeter, and then sum up all the output signal differences of the a checkpoint to obtain the total output signal difference of the a checkpoint in each level of environment of each flowmeter, and similarly obtain the total output signal difference of the b checkpoint in each level of environment of each flowmeter; calculate and obtain the output signal evaluation index of each level of environment of each flowmeter in the target area; perform weighted calculation based on the obtained output signal evaluation index of each level of environment of each flowmeter in the target area to obtain the performance evaluation coefficient of each flowmeter corresponding to the target area;

[0010] The abnormality analysis module is used to perform weighted calculation based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area to obtain the comprehensive evaluation coefficient of each flow meter corresponding to the target area, and obtain the abnormal type signal according to the gradient evaluation interval, wherein the abnormal type signal includes a performance abnormality signal and an appearance abnormality signal.

[0011] A flow meter detection method based on data analysis, comprising:

[0012] Appearance inspection: inspect the appearance quality of each flow meter in the target area, obtain the appearance quality parameters of each flow meter in the target area, and calculate the appearance quality evaluation coefficient of each flow meter in the target area;

[0013] Performance testing: Test the performance parameters of each flow meter in the target area to obtain the performance parameters of each flow meter in the target area, extract the output signal value of each detection point in each level of environment of each flow meter from the performance parameters of each flow meter in the target area, and compare the output signal value of each detection point in each level of environment of each flow meter with the reference output signal value corresponding to each level of environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to each level of environment, then the detection point is recorded as a detection point. If the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, then the detection point is recorded as b detection point. The number of a detection points and b detection points in the primary environment of each flow meter is statistically obtained.

[0014] Extract the output signal value of each a checkpoint in each level of environment of each flowmeter from the output signal value of each detection point in each level of environment of each flowmeter, and subtract it from the reference output signal value corresponding to each level of environment to obtain the output signal difference of each a checkpoint in each level of environment of each flowmeter, and then sum up all the output signal differences of the a checkpoint to obtain the total output signal difference of the a checkpoint in each level of environment of each flowmeter, and similarly obtain the total output signal difference of the b checkpoint in each level of environment of each flowmeter; calculate and obtain the output signal evaluation index of each level of environment of each flowmeter in the target area; perform weighted calculation based on the obtained output signal evaluation index of each level of environment of each flowmeter in the target area to obtain the performance evaluation coefficient of each flowmeter corresponding to the target area;

[0015] Abnormal analysis: Based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area, a weighted calculation is performed to obtain the comprehensive evaluation coefficient of each flow meter in the target area, and the abnormal type signal is obtained according to the gradient evaluation interval. The abnormal type signal is sent to the abnormal processing terminal, where the abnormal type signal includes a performance abnormality signal and an appearance abnormality signal.

[0016] Compared with the prior art, the present invention has the following significant advantages:

[0017] The present invention, through advanced image processing and data analysis technology, realizes the detection and analysis of the appearance quality of the flow meter to obtain the appearance quality parameters, and obtains the appearance quality evaluation coefficient based on the analysis and processing of the appearance quality parameters, thereby realizing the accurate, comprehensive, real-time and scientific evaluation of the appearance quality of the flow meter, improving the accuracy and efficiency of the detection, and providing a strong data basis for the subsequent analysis;

[0018] By testing the performance parameters of each flow meter corresponding to the target area, the performance parameters of each flow meter corresponding to the target area are obtained, and a series of scientific methods are used to calculate and analyze the output signal values ​​of each detection point in the corresponding performance parameters of the flow meter in different levels of environment, so as to obtain the performance evaluation coefficient of each flow meter corresponding to the target area, thereby evaluating the performance status of the flow meter, thereby helping to improve the accuracy and reliability of the evaluation results, providing a strong guarantee for the stable and efficient operation of industrial production, and comprehensively evaluating the performance of the flow meter in different environments. This comprehensive evaluation can more accurately reflect the actual performance of the flow meter and avoid evaluation errors in a single environment;

[0019] The appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area are analyzed and processed by calculation to obtain a comprehensive evaluation coefficient. At the same time, two evaluation gradient comparison intervals of the comprehensive evaluation coefficient are set. When the comprehensive evaluation coefficient is in the first gradient evaluation interval, a performance abnormality signal is generated, indicating that the flow meter has a performance abnormality or fault. When the comprehensive evaluation coefficient is in the second gradient evaluation interval, an appearance abnormality signal is generated, indicating that the appearance quality of the flow meter has problems or does not meet the standards. This enables accurate and rapid identification of abnormal conditions of the flow meter and provides a basis for subsequent maintenance and overhaul. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall block diagram of the system of the present invention;

[0021] Figure 2 The figure is a general block diagram of the method of the present invention. DETAILED DESCRIPTION

[0022] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, the flow meter detection system based on data analysis includes: an appearance detection module, a performance detection module, an abnormality analysis module and an abnormality processing terminal;

[0024] The appearance inspection module is used to inspect the appearance quality of each flow meter corresponding to the target area, and obtain the appearance quality parameters of each flow meter corresponding to the target area. The specific inspection process is as follows:

[0025] The appearance images of the target area corresponding to the current detection time period of each flow meter are collected by a high-definition camera to obtain the appearance images of the target area corresponding to the current detection time period of each flow meter, and the damaged areas of each flow meter in the current detection time period are extracted from the appearance images of the target area corresponding to the current detection time period of each flow meter, and the damaged areas of each flow meter in the current detection time period are accumulated to obtain the total damaged area of ​​each flow meter in the current detection time period as the damage value of each flow meter in the current detection time period;

[0026] Extract the display identification features of each flow meter in the current detection time period from the appearance image of the target area corresponding to the current detection time period of each flow meter, the display identification features include arrow symbols, digital symbols and nameplate parameters, etc., each display identification feature corresponds to a preset display clarity value, and all the identified display identification features are matched to the corresponding preset display clarity values ​​and summed to obtain the display identification value;

[0027] It should be noted that the arrow symbol is used to indicate the direction of water flow, the digital symbol is used to display the real-time fluid flow value, and the nameplate parameters indicate the basic parameters of the flow meter including model, range, number, etc.

[0028] The vibration frequencies of the target area corresponding to each flow meter at each detection time point in the current detection time period are obtained, and the vibration frequencies of the target area corresponding to each flow meter at each detection time point in the current detection time period are obtained. A line graph of the change amplitude of the vibration frequency with respect to each detection time point is drawn with the vibration frequency as the vertical axis and each detection time point as the horizontal axis. The slope of each broken line in the change amplitude broken line graph is obtained, and the slope ratio between adjacent broken lines is calculated to obtain the slope ratio between adjacent broken lines. When the slope ratio is zero or a negative number, the point is recorded as a breaking point, and the point where the slope ratio does not exist is recorded as a breaking point. It can be ignored. A comparison reference line parallel to the horizontal axis is made in the variation amplitude line graph. The setting of the comparison reference line is made by the staff according to the specific implementation. The distance between each adjacent inflection point above the comparison reference line is obtained and recorded as the amplitude variation distance value. The amplitude variation distance threshold is set. The amplitude variation distance value is compared and analyzed with the amplitude variation distance threshold. If the amplitude variation distance value is greater than or equal to the amplitude variation distance threshold, it is judged as an abnormal state. The number of times it is judged as an abnormal state is counted, and the number of times it is judged as an abnormal state and the total number of times are extracted for proportion analysis to obtain the vibration abnormality value.

[0029] The appearance quality evaluation coefficients of the flow meters in the target area are analyzed to obtain the appearance quality evaluation coefficients of the flow meters in the target area. The specific analysis steps are as follows:

[0030] Extract the damage value, display mark value and vibration variation value of the appearance quality parameters of each flow meter in the target area corresponding to the current detection time period, and calibrate them as psz j 、xszj and zdz j , j represents the number of each flow meter corresponding to the target area, j = 1, 2, 3...n, n represents the total number of flow meter numbers corresponding to the target area, according to the formula: The appearance quality evaluation coefficient ZPZ is obtained, where e represents a natural constant, psz j * 、xsz j * and zdz j * They represent the reference damage value, reference display mark value and reference vibration abnormality value respectively. η1, η2 and η3 represent the weight coefficients of the damage value degree, display mark degree and vibration abnormality degree respectively. The weight coefficients are used to balance the weight of each data in the formula calculation, so as to promote the accuracy of the calculation results.

[0031] The performance detection module is used to detect the performance parameters of each flow meter corresponding to the target area, and obtain the performance parameters of each flow meter corresponding to the target area. The specific detection process is as follows:

[0032] The environment where each flow meter is located corresponding to the target area is set. The specific environment is set as a primary environment, a secondary environment, and a tertiary environment. The primary environment is set as a normal environment, the secondary environment is set as an abnormal environment, and the tertiary environment is set as an extreme environment. It should be noted that the normal environment refers to the flow meter in a normal working environment, and the normal working environment is normal temperature, pressure, and flow. The abnormal environment refers to the flow meter in an abnormal working environment (exceeding a certain range of the set value), and the abnormal working environment is abnormal temperature, pressure, and flow. The extreme environment refers to the flow meter in an extreme working environment (exceeding the range of the abnormal environment), and the extreme working environment is extremely high temperature, extremely low pressure, or extremely high flow.

[0033] The output signal wave images of each detection point of the primary environment of each flow meter corresponding to the target area are acquired to obtain the output signal wave images of each detection point of the primary environment of each flow meter corresponding to the target area, and the output signal wave images are compared and analyzed for coincidence with the primary environment reference output signal wave images stored in the cloud database to obtain the coincidence value as the output signal value, thereby obtaining the output signal values ​​of each detection point of the primary environment of each flow meter corresponding to the target area;

[0034] The output signal wave images of each detection point of the secondary environment of each flow meter corresponding to the target area are acquired to obtain the output signal wave images of each detection point of the secondary environment of each flow meter corresponding to the target area, and the output signal wave images are compared and analyzed for coincidence with the reference output signal wave images of the secondary environment stored in the cloud database to obtain the coincidence value as the output signal value, thereby obtaining the output signal values ​​of each detection point of the secondary environment of each flow meter corresponding to the target area;

[0035] The output signal wave images of each detection point of the three-level environment of each flow meter in the target area are acquired to obtain the output signal wave images of each detection point of the three-level environment of each flow meter in the target area, and the output signal wave images are compared and analyzed for coincidence with the three-level environment reference output signal wave images stored in the cloud database, and the coincidence value is recorded as the output signal value, thereby obtaining the output signal values ​​of each detection point of the three-level environment of each flow meter in the target area;

[0036] The performance parameters of each flow meter corresponding to the target area are constituted by the output signal values ​​of each detection point of the primary environment, the output signal values ​​of each detection point of the secondary environment and the output signal values ​​of each detection point of the tertiary environment of each flow meter corresponding to the target area;

[0037] The performance evaluation coefficients of the flow meters corresponding to the target area are analyzed to obtain the performance evaluation coefficients of the flow meters corresponding to the target area. The specific analysis steps are as follows:

[0038] The output signal value of each detection point in the primary environment of each flow meter is extracted from the performance parameters of each flow meter corresponding to the target area, and the reference output signal value corresponding to the set primary environment is obtained at the same time, and the output signal value of each detection point in the primary environment of each flow meter is compared with the reference output signal value corresponding to the primary environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to the primary environment, the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, the detection point is recorded as b detection point. The number of a detection points and b detection points in the primary environment of each flow meter is obtained by counting and recorded as M respectively. j 1 and N j 1 ;

[0039] The output signal value of each a-check point in the primary environment of each flowmeter is extracted from the output signal value of each detection point in the primary environment of each flowmeter, and the output signal value is subtracted from the reference output signal value corresponding to the primary environment to obtain the output signal difference of each a-check point in the primary environment of each flowmeter, and then all the output signal differences of the a-check point are summed up to obtain the total output signal difference of the a-check point in the primary environment of each flowmeter, which is recorded as T j 1Similarly, the total output signal difference Y of the b check point in the primary environment of each flow meter is obtained j 1 ;

[0040] According to the formula: The output signal evaluation index yjc1 of the primary environment of each flowmeter in the target area is obtained, where μ1 1 、μ2 1 、μ3 1 and μ4 1 Expressed as a set correction factor;

[0041] The output signal value of each detection point of the secondary environment of each flow meter is extracted from the performance parameters of each flow meter corresponding to the target area, and the reference output signal value corresponding to the set secondary environment is obtained at the same time, and the output signal value of each detection point of the secondary environment of each flow meter is compared with the reference output signal value corresponding to the secondary environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to the secondary environment, the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the secondary environment, the detection point is recorded as b detection point, and the number of a detection points and b detection points of the secondary environment of each flow meter are statistically obtained and recorded as M respectively. j 2 and N j 2 ;

[0042] The output signal value of each a-check point in the secondary environment of each flowmeter is extracted from the output signal value of each detection point in the secondary environment of each flowmeter, and the output signal value is subtracted from the reference output signal value corresponding to the secondary environment to obtain the output signal difference of each a-check point in the secondary environment of each flowmeter, and then all the output signal differences of the a-check point are summed up to obtain the total output signal difference of the a-check point in the secondary environment of each flowmeter, which is recorded as T j 2 Similarly, the total output signal difference Y of the b check point in the secondary environment of each flow meter is obtained j 2 ;

[0043] According to the formula: The output signal evaluation index yjc2 of the secondary environment of each flowmeter in the target area is obtained, where μ1 2 、μ2 2 、μ3 2 and μ4 2 Expressed as a set correction factor;

[0044] The output signal value of each detection point of the three-level environment of each flow meter is extracted from the performance parameters of each flow meter corresponding to the target area, and the reference output signal value corresponding to the set three-level environment is obtained at the same time, and the output signal value of each detection point of the three-level environment of each flow meter is compared with the reference output signal value corresponding to the three-level environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to the three-level environment, the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the three-level environment, the detection point is recorded as b detection point, and the number of a detection points and b detection points of the three-level environment of each flow meter are statistically obtained and recorded as M respectively. j 3 and N j 3 ;

[0045] The output signal value of each a-check point in the three-level environment of each flowmeter is extracted from the output signal value of each detection point in the three-level environment of each flowmeter, and the output signal value is subtracted from the reference output signal value corresponding to the three-level environment to obtain the output signal difference of each a-check point in the three-level environment of each flowmeter, and then all the output signal differences of the a-check point are summed up to obtain the total output signal difference of the a-check point in the three-level environment of each flowmeter, which is recorded as T j 3 Similarly, the total output signal difference Y of the b check point in the three-level environment of each flow meter is obtained j 3 ;

[0046] According to the formula: The output signal evaluation index yjc3 of the three-level environment of each flowmeter in the target area is obtained, where μ1 3 、μ2 3 、μ3 3 and μ4 3 Expressed as a set correction factor;

[0047] The output signal evaluation index of the primary environment, the output signal evaluation index of the secondary environment, and the output signal evaluation index of the tertiary environment of each flowmeter in the target area are extracted and normalized. According to the formula: XNP=yjc1×δ1+yjc2×δ2+yjc3×δ3, the performance evaluation coefficient XNP of each flowmeter in the target area is obtained, where δ1, δ2, and δ3 are represented as the set weight factors;

[0048] The abnormality analysis module is used to perform abnormality analysis on each flow meter in the target area based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area. The specific analysis process is as follows:

[0049] The values ​​of the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area are extracted and normalized. According to the formula: ZHG = ZPZ × λ1 + XNP × λ2, the comprehensive evaluation coefficient ZHG of each flow meter in the target area is obtained, where λ1 and λ2 represent the weight coefficients of the appearance quality evaluation coefficient and the performance evaluation coefficient, respectively. The weight coefficient is used to balance the weight of each data in the formula calculation, thereby promoting the accuracy of the calculation result;

[0050] Two evaluation gradient comparison intervals are set, namely, a first gradient evaluation interval znqs1 and a second gradient evaluation interval znqs2, and znqs1=A*znqs2, wherein znqs1>znqs2, A represents the multiple of the gradient, and the specific value of A is set by those skilled in the art in a specific flow meter detection example;

[0051] If the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold, and the value of the performance evaluation coefficient is in the first gradient evaluation interval znqs1, a performance abnormality signal is generated; if the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold, and the appearance quality evaluation coefficient is in the second gradient evaluation interval znqs2, an appearance abnormality signal is generated;

[0052] Sending the generated abnormality type signal to the abnormality processing terminal, wherein the abnormality type signal includes a performance abnormality signal and an appearance abnormality signal;

[0053] The exception handling terminal performs corresponding operation processing according to the received exception type signal. The specific operation processing is as follows:

[0054] If a performance abnormality signal is captured, a red light flashing command will be triggered. According to the triggered red light flashing command, relevant technicians will be assigned to perform maintenance operations within the Q1 time period;

[0055] If an abnormal appearance signal is detected, the yellow light flashing command will be triggered. According to the triggered yellow light flashing command, relevant technicians will be assigned to perform maintenance operations within the Q2 time period;

[0056] It should be noted that Q1<Q2;

[0057] like Figure 2 As shown, the flow meter detection method based on data analysis includes the following steps:

[0058] Step 1: Appearance inspection: used to inspect and analyze the damaged area, display mark clarity and vibration frequency of the appearance quality of each flow meter in the target area, thereby obtaining the appearance quality parameters of each flow meter in the target area. The appearance quality parameters include damage value, display mark value and vibration abnormality value, and thereby analyze the appearance quality evaluation coefficient of each flow meter in the target area, thereby obtaining the appearance quality evaluation coefficient of each flow meter in the target area;

[0059] Step 2: Performance detection: used to detect the performance parameters of each flow meter corresponding to the target area, obtain the performance parameters of each flow meter corresponding to the target area, extract the output signal value of each detection point in the primary environment of each flow meter from the performance parameters of each flow meter corresponding to the target area, and simultaneously obtain the reference output signal value corresponding to the set primary environment, and compare the output signal value of each detection point in the primary environment of each flow meter with the reference output signal value corresponding to the primary environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to the primary environment, then the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, then the detection point is recorded as b detection point, and the number of a detection points and b detection points in the primary environment of each flow meter are statistically obtained;

[0060] Extract the output signal value of each a-check point in the primary environment of each flowmeter from the output signal value of each detection point in the primary environment of each flowmeter, and subtract it from the reference output signal value corresponding to the primary environment to obtain the output signal difference of each a-check point in the primary environment of each flowmeter, and then sum them up to obtain the total output signal difference of the a-check point in the primary environment of each flowmeter, and similarly analyze to obtain the total output signal difference of the b-check point in the primary environment of each flowmeter; calculate the output signal evaluation index of the primary environment of each flowmeter in the target area according to the formula;

[0061] Similarly, the output signal evaluation index of the secondary environment of each flow meter in the target area and the output signal evaluation index of the tertiary environment of each flow meter in the target area are analyzed and obtained;

[0062] Calculate the output signal evaluation index of the primary environment, the output signal evaluation index of the secondary environment, and the output signal evaluation index of the tertiary environment of each flow meter in the target area to obtain the performance evaluation coefficient of each flow meter in the target area;

[0063] Step 3: Abnormal analysis: Based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area, abnormal analysis is performed on each flow meter in the target area. The specific analysis process is as follows:

[0064] Extract the values ​​of the appearance quality evaluation coefficient and the performance evaluation coefficient of each flow meter corresponding to the target area, perform normalization calculation and processing, and obtain the comprehensive evaluation coefficient of each flow meter corresponding to the target area;

[0065] Two evaluation gradient comparison intervals are set, namely, a first gradient evaluation interval znqs1 and a second gradient evaluation interval znqs2, and znqs1=Aznqs2, wherein znqs1>znqs2, A represents the multiple of the gradient, and the specific value of A is set by those skilled in the art in a specific flow meter detection example;

[0066] If the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold, and the value of the performance evaluation coefficient is in the first gradient evaluation interval znqs1, a performance abnormality signal is generated; if the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold, and the appearance quality evaluation coefficient is in the second gradient evaluation interval znqs2, an appearance abnormality signal is generated; and the abnormality type signal is sent to the abnormality processing terminal, wherein the abnormality type signal includes a performance abnormality signal and an appearance abnormality signal;

[0067] Step 4: Exception handling terminal: Based on the received exception type signal, a red light flashing command or a yellow light flashing command is triggered, and based on the triggered command, relevant technical personnel are assigned to perform maintenance operations within the set time point.

[0068] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A flow meter detection system based on data analysis, characterized in that: include: An appearance inspection module is used to inspect the appearance quality of each flow meter corresponding to the target area, obtain the appearance quality parameters of each flow meter corresponding to the target area, and calculate the appearance quality evaluation coefficient of each flow meter corresponding to the target area; The performance detection module is used to detect the performance parameters of each flow meter corresponding to the target area, obtain the performance parameters of each flow meter corresponding to the target area, extract the output signal value of each detection point of each level of environment of each flow meter from the performance parameters of each flow meter corresponding to the target area, and compare the output signal value of each detection point of each level of environment of each flow meter with the reference output signal value corresponding to the corresponding level of environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to each level of environment, then the detection point is recorded as a detection point; if the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, then the detection point is recorded as a detection point, and the number of a detection points and the number of b detection points of the primary environment of each flow meter are statistically obtained; Extract the output signal value of each a checkpoint in each level of environment of each flowmeter from the output signal value of each detection point in each level of environment of each flowmeter, and subtract it from the reference output signal value corresponding to each level of environment to obtain the output signal difference of each a checkpoint in each level of environment of each flowmeter, and then sum up all the output signal differences of the a checkpoint to obtain the total output signal difference of the a checkpoint in each level of environment of each flowmeter, and similarly obtain the total output signal difference of the b checkpoint in each level of environment of each flowmeter; calculate and obtain the output signal evaluation index of each level of environment of each flowmeter in the target area; perform weighted calculation based on the obtained output signal evaluation index of each level of environment of each flowmeter in the target area to obtain the performance evaluation coefficient of each flowmeter corresponding to the target area; The abnormality analysis module is used to perform weighted calculation based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area to obtain the comprehensive evaluation coefficient of each flow meter corresponding to the target area, and obtain the abnormal type signal according to the gradient evaluation interval, wherein the abnormal type signal includes a performance abnormality signal and an appearance abnormality signal.

2. The flow meter detection system based on data analysis according to claim 1, characterized in that: The appearance quality evaluation coefficient is calculated by the following formula: Wherein, ZPZ is the appearance quality assessment coefficient, pszj, xszj and zdzj represent the numerical values ​​of the breakage value, display mark value and vibration abnormality value in the appearance quality parameters respectively, e represents a natural constant, pszj*, xszj* and zdzj* represent the reference breakage value, reference display mark value and reference vibration abnormality value respectively, η1, η2 and η3 represent the weight coefficients of the breakage value degree, display mark degree and vibration abnormality degree respectively.

3. The flow meter detection system based on data analysis according to claim 2 is characterized in that: The process of obtaining the damage value, display identification value and vibration abnormality value is as follows: The appearance images of the target area corresponding to the current detection time period of each flow meter are collected to obtain the total damaged area of ​​each flow meter in the current detection time period as the damage value of each flow meter in the current detection time period; Extract the display identification features of each flow meter in the current detection time period from the appearance image of the target area corresponding to the current detection time period of each flow meter, match the display identification features to the corresponding preset display clarity values ​​and sum them to obtain the display identification value; The vibration frequency of each detection time point in the current detection time period of each flow meter in the target area is obtained, and a line graph of the change amplitude of the vibration frequency at each detection time point is drawn with the vibration frequency as the vertical axis and the detection time point as the horizontal axis. The slope of each broken line in the change amplitude line graph is obtained, and the slope ratio between adjacent broken lines is calculated to obtain the slope ratio between adjacent broken lines. When the slope ratio is zero or a negative number, the point is recorded as a break point. A comparison reference line parallel to the horizontal axis is made in the change amplitude line graph, and the distance between each adjacent break point above the comparison reference line is obtained and recorded as the amplitude change distance value. The amplitude change distance value is compared and analyzed with the amplitude change distance threshold. If the amplitude change distance value is greater than or equal to the amplitude change distance threshold, it is judged as an abnormal state, and the number of times it is judged as an abnormal state is counted. The number of times it is judged as an abnormal state and the value of the total number of judgments are extracted for proportion analysis to obtain the vibration abnormality value.

4. The flow meter detection system based on data analysis according to claim 1, characterized in that: The performance evaluation coefficient is calculated by the following formula: XNP=yjc1×δ1+yjc2×δ2+yjc3×δ3 Where XNP is the performance evaluation coefficient, yjc1, yjc2, yjc3 represent the output signal evaluation index of the primary environment, the secondary environment and the tertiary environment respectively; δ1, δ2 and δ3 are the set weight factors; in k = 1, 2, 3; M j k 、N j k 、T j k , Y j k They are the number of a checkpoints, the number of b checkpoints, the total output signal difference of a checkpoints, and the total output signal difference of b checkpoints in the k-level environment, μ1 k 、μ2 k 、μ3 k 、μ4 k Correction factors for k-level environment settings respectively.

5. The flow meter detection system based on data analysis according to claim 1, characterized in that: The process of extracting the output signal value of each flow meter at each level of environment and each detection point is as follows: The output signal wave images of each detection point of each level of environment of each flow meter in the target area are acquired, and the output signal wave images of each detection point of each level of environment of each flow meter in the target area are obtained. The output signal wave images are compared with the reference output signal wave images of each level of environment stored in the cloud database for coincidence analysis, and the coincidence values ​​are recorded as output signal values, thereby obtaining the output signal values ​​of each detection point of each level of environment of each flow meter in the target area.

6. The flow meter detection system based on data analysis according to claim 1, characterized in that: The comprehensive evaluation coefficient is calculated by the following formula: ZHG=ZPZ×λ1+XNP×λ2 Where ZHG is the comprehensive evaluation coefficient, ZPZ is the appearance quality evaluation coefficient, XNP is the performance evaluation coefficient, λ1 and λ2 represent the weight coefficients of the appearance quality evaluation coefficient and the performance evaluation coefficient respectively; Set two evaluation gradient comparison intervals, namely the first gradient evaluation interval and the second gradient evaluation interval; If the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold and the value of the performance evaluation coefficient is in the first gradient evaluation interval, a performance abnormality signal is generated; if the comprehensive evaluation coefficient of each flow meter corresponding to the target area is greater than the set threshold and the appearance quality evaluation coefficient is in the second gradient evaluation interval, an appearance abnormality signal is generated.

7. The flow meter detection system based on data analysis according to claim 1, characterized in that: Also includes: The exception handling terminal is used to receive the exception type signal obtained by the exception analysis module and send different alarm signals to indicate that corresponding operations need to be performed.

8. A flow meter detection method based on data analysis, characterized in that: include: Appearance inspection: inspect the appearance quality of each flow meter in the target area, obtain the appearance quality parameters of each flow meter in the target area, and calculate the appearance quality evaluation coefficient of each flow meter in the target area; Performance testing: Test the performance parameters of each flow meter in the target area to obtain the performance parameters of each flow meter in the target area, extract the output signal value of each detection point in each level of environment of each flow meter from the performance parameters of each flow meter in the target area, and compare the output signal value of each detection point in each level of environment of each flow meter with the reference output signal value corresponding to each level of environment. If the output signal value of a detection point is greater than the reference output signal value corresponding to each level of environment, then the detection point is recorded as a detection point. If the output signal value of a detection point is less than the reference output signal value corresponding to the primary environment, then the detection point is recorded as b detection point. The number of a detection points and b detection points in the primary environment of each flow meter is statistically obtained. Extract the output signal value of each a checkpoint in each level of environment of each flowmeter from the output signal value of each detection point in each level of environment of each flowmeter, and subtract it from the reference output signal value corresponding to each level of environment to obtain the output signal difference of each a checkpoint in each level of environment of each flowmeter, and then sum up all the output signal differences of the a checkpoint to obtain the total output signal difference of the a checkpoint in each level of environment of each flowmeter, and similarly obtain the total output signal difference of the b checkpoint in each level of environment of each flowmeter; calculate and obtain the output signal evaluation index of each level of environment of each flowmeter in the target area; perform weighted calculation based on the obtained output signal evaluation index of each level of environment of each flowmeter in the target area to obtain the performance evaluation coefficient of each flowmeter corresponding to the target area; Abnormal analysis: Based on the appearance quality evaluation coefficient and performance evaluation coefficient of each flow meter in the target area, a weighted calculation is performed to obtain the comprehensive evaluation coefficient of each flow meter in the target area, and the abnormal type signal is obtained according to the gradient evaluation interval. The abnormal type signal is sent to the abnormal processing terminal, where the abnormal type signal includes a performance abnormality signal and an appearance abnormality signal.

9. The flow meter detection method based on data analysis according to claim 8, characterized in that: The appearance quality evaluation coefficient is calculated by the following formula: Wherein, ZPZ is the appearance quality assessment coefficient, pszj, xszj and zdzj represent the numerical values ​​of the breakage value, display mark value and vibration abnormality value in the appearance quality parameters respectively, e represents a natural constant, pszj*, xszj* and zdzj* represent the reference breakage value, reference display mark value and reference vibration abnormality value respectively, η1, η2 and η3 represent the weight coefficients of the breakage value degree, display mark degree and vibration abnormality degree respectively.

10. The flow meter detection method based on data analysis according to claim 8, characterized in that: The performance evaluation coefficient is calculated by the following formula: XNP=yjc1×δ1+yjc2×δ2+yjc3×δ3 Where XNP is the performance evaluation coefficient, yjc1, yjc2, yjc3 represent the output signal evaluation index of the primary environment, the secondary environment and the tertiary environment respectively; δ1, δ2 and δ3 are the set weight factors; in k = 1, 2, 3; M j k 、N j k 、T j k , Y j k They are the number of a checkpoints, the number of b checkpoints, the total output signal difference of a checkpoints, and the total output signal difference of b checkpoints in the k-level environment, μ1 k 、μ2 k 、μ3 k 、μ4 k Correction factors for k-level environment settings respectively.

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