Pressure switch fatigue test system and method based on data analysis

Abstract

The invention relates to the technical field of pressure switches, in particular to a pressure switch fatigue test system and method based on data analysis. When the system receives the abnormal data, the pressure sensors transmitting the abnormal data can be marked, a coverage range is set, the pressure sensors with the correlation reaching the set value in the coverage range are extracted, and the pressure sensors with the correlation reaching the set value are analyzed according to the change of the collected data of the pressure sensors. Whether the pressure sensor transmitting the abnormal data has other reasons or not is verified, so that the transmitted abnormal data can be verified, the credibility of the abnormal data is improved, meanwhile, the maintenance time period of the marked pressure sensor can be predicted, the pressure sensor transmitting the abnormal data is further judged to be caused by the own reason, and the reliability of the pressure sensor is improved. Therefore, resource loss caused by calculation of unreliable data in the testing process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure switches, and in particular to a pressure switch fatigue testing system and method based on data analysis. Background Art

[0002] The pressure switch includes a pressure sensor and an electrical switch. During use, the pressure switch needs to be fatigue tested to ensure its quality. During use, multiple environmental data from multiple pressure sensors are collected and then transmitted to the console for fatigue test analysis.

[0003] After setting up multiple pressure sensors and uploading the data collected by each pressure sensor to the cloud platform, data for judging the status is obtained. When a pressure sensor uploads abnormal data, the system will automatically issue corresponding instructions and alarm information according to the settings. However, the abnormal data transmitted by the pressure sensor may be abnormal data collected due to abnormalities in the monitored environment. At this time, the abnormal data is correct data; however, there may also be situations where the collected data is abnormal due to problems with the pressure sensor itself, or data abnormalities due to other reasons. At this time, the "abnormal data" is incorrect. Since the system cannot screen the authenticity of the "abnormal data", the transmission of incorrect "abnormal data" will cause the system to issue wrong instructions and make wrong responses, resulting in reduced accuracy of the test system and greatly wasting the operating resources of the test system. Summary of the invention

[0004] The object of the present invention is to provide a pressure switch fatigue testing system and method based on data analysis to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a pressure switch fatigue testing method based on data analysis, characterized in that the method comprises the following specific steps:

[0006] S1. Acquire monitoring data of the perception layer and extract the monitored abnormal data; the perception layer includes a plurality of pressure sensors, the pressure sensors upload the monitored data information, mark the pressure sensors that transmit the abnormal data, extract the marked pressure sensor information, and set the time node of the abnormal data transmission as the abnormal time point;

[0007] S2. Taking the marked pressure sensor as a reference point, set a coverage range, obtain information of all pressure sensors within the coverage range, calculate the correlation between each pressure sensor within the coverage range and the marked pressure sensor; obtain information of pressure sensors whose correlation reaches a set value, and extract data transmitted by the pressure sensor whose correlation reaches the set value at the abnormal time point, and use the data as the actual collected value;

[0008] S3, establish a training model, use the historical collected data of the marked pressure sensor and the historical collected data of the pressure sensor whose correlation reaches the set value as the training set, train the training model, calculate the calculated collected data of the pressure sensor whose correlation reaches the set value at the abnormal time point through the training model, and use the data as the output calculated value; compare whether the actual collected data is the same as the output calculated value to obtain a matching result; calculate the matching rate according to the matching result, and judge the trust result of the abnormal data according to the matching rate;

[0009] S4. If S3 outputs a trust result of "high credibility", the system automatically issues a system instruction corresponding to the abnormal data according to the settings; if S3 outputs a trust result of "average credibility", an expansion range is set, and the expansion range is different from the coverage range, and S2 is repeated; if S3 outputs a trust result of "low credibility", the historical maintenance information of the marked pressure sensor is extracted, the maintenance time period of the marked pressure sensor is predicted, and it is determined whether the marked pressure sensor is in the maintenance time period, and the information is sent to the console;

[0010] S5. The test analysis module analyzes the data processed by the data processing module, and then evaluates and analyzes the test performance of the pressure switch; the optimization analysis module analyzes the process optimization direction of the tested pressure switch and obtains the time influence coefficient and the pressure influence coefficient, compares the time influence coefficient with the pressure influence coefficient and generates a corresponding process optimization signal through the comparison result.

[0011] The trust results in S300 include "high credibility", "average credibility" and "low credibility".

[0012] The S1 includes:

[0013] S101, when abnormal data is detected in the perception layer, the abnormal data is captured, and the pressure sensor that transmits the abnormal data is marked, and the pressure sensor that transmits the abnormal data is marked as M pressure sensor, and a three-dimensional coordinate system is established with the marked M pressure sensor as the origin;

[0014] S102 , obtaining the pressure sensor type of the M pressure sensor, obtaining the data type collected by the M pressure sensor, and when the M pressure sensor transmits abnormal data, setting the time node of transmitting the abnormal data as the abnormal time point Tx.

[0015] The S2 includes:

[0016] S201, extract all pressure sensors to form a set B, where set B = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor X};

[0017] S202, extract the names of pressure sensor M and pressure sensor X in set B, assume that the name of pressure sensor M is W1, and the name of pressure sensor X in set B is W2, and use the following formula to calculate the similarity between the text names of each pressure sensor in set B and pressure sensor M

[0018]

[0019] in, represents the similarity in name between pressure sensor M and pressure sensor X in set B; m represents the total number of characters in W1; n represents the total number of characters in W2; c represents the number of matching characters in W1 and W2; L1(i) and L2(i) are the matching order of matching character i in W1 and W2 respectively; α and β are the weights of matching degree and matching order respectively, which are usually taken as 0.6 and 0.4 according to the golden ratio;

[0020] S203, according to the use standard of the pressure sensor M, obtain the monitoring range of the pressure sensor M, extract all the coordinate points within the monitoring range, and form a set A, where the set A = {(x1, y1, z1), (x2, y2, z2) ... (x n ,y n , z n )}; According to the use standard of the pressure sensor X, the monitoring range of the pressure sensor X is obtained, and all the coordinate points within the monitoring range are extracted to form a set C, where the set C = {(a1, b1, c1), (a2, b2, c2)...(a n , b n , c n )}, extract the coincident coordinate points between set C and set A to form set D, and calculate the coincidence rate between set A and set C according to the following formula

[0021]

[0022] S204: Set the correlation between the pressure sensor X and the pressure sensor M to R X, calculate the correlation R between pressure sensor M and pressure sensor X in set B according to the following formula X :

[0023]

[0024] The pressure sensors X in set B are sorted according to the correlation R X Arrange in descending order, set the number of verification values ​​to Z, extract Z pressure sensors X according to the descending order of correlation, and use the data transmitted by the pressure sensor X at the abnormal time point Tx as the actual collected value.

[0025] The S3 includes:

[0026] S301: Establish a historical time set T, extract time node t in the historical time set T n The corresponding transmission data of the pressure sensor M, and the n When the transmission data of the pressure sensor X extracted in S204 is used to establish a training model, the transmission data transmitted by the pressure sensor M is used as input, and the transmission data transmitted by the pressure sensor X is used as output to train the model;

[0027] S302: Calculate the calculated collected data of the pressure sensor X at the abnormal time node Tx according to the training model in S301, and use the data as the output calculated value;

[0028] S303, setting the error range of the output calculation value, comparing the actual collected value with the output calculation value to see if they are within the error range; if the data of both the actual collected value and the output calculation value are within the error range, outputting a "match" result; if the data of both the actual collected value and the output calculation value are not within the error range, outputting a "mismatch" result;

[0029] S304: Calculate the matching rate based on the matching results of the Z pressure sensors X, and output a trust result based on the matching rate, wherein the trust result is "high credibility", "average credibility" or "low credibility". The matching rate is calculated as follows:

[0030]

[0031] Wherein Z represents the total number of pressure sensors X extracted in S204.

[0032] The S4 includes:

[0033] S401. If S304 outputs a trust result of "high credibility", it means that the abnormal data transmitted this time is highly accurate, and the system automatically issues the set instructions;

[0034] S402, if S304 outputs a trust result of "general credibility", then set a new expanded range S2 again, extract all pressure sensors within the expanded range to form a set F, where set F = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor Y}, repeat S2, and output the trust result of judging the abnormal data again;

[0035] S403. If S304 outputs a trust result of "low credibility", the historical maintenance information of the pressure sensor M is extracted, the maintenance time period of the pressure sensor M is predicted, and it is determined whether the abnormal time point Tx is within the maintenance time period of the pressure sensor M; if the abnormal time point Tx is within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is greater than the probability of man-made damage; if the abnormal time point Tx is not within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is less than the probability of man-made damage; the prediction information is sent to the console.

[0036] A pressure switch fatigue test system based on data analysis, the system comprises a monitoring module, a transmission module, a data processing module, a data extraction module, a data verification module, a test analysis module and an optimization analysis module connected to each other;

[0037] The monitoring module is used to collect environmental data of the pressure sensor in the monitored pressure switch, and transmit the collected environmental data to the data processing module through the transmission module. The data processing module screens the monitored data. When abnormal data appears, the data extraction module is used to extract the abnormal data, mark the pressure sensor that transmits the abnormal data, and then mark the pressure switch corresponding to the pressure sensor. The data verification module verifies the credibility of the abnormal data and outputs the corresponding instruction according to the verification result.

[0038] The monitoring module includes a pressure sensor unit, which is used to monitor the data in the environment in real time; the transmission module includes a data transmission unit and an instruction output unit, the data transmission unit is used to transmit the monitored data in real time, and the instruction output unit is used to send monitoring instructions to the pressure sensor unit according to the instructions set by the system.

[0039] The data processing module includes a data preprocessing unit and a classification storage unit. The data preprocessing unit is used to perform denoising on the monitored data, remove redundant data, and then use the classification storage unit to classify and store the monitored data.

[0040] The data extraction module includes an extraction unit, a marking unit and a modeling unit; the extraction unit is used to extract the transmitted abnormal data, and use the marking unit to mark the pressure sensor that transmits the abnormal data. At the same time, through the modeling unit, a three-dimensional coordinate system is established with the marked pressure sensor as a reference point, a coverage range is set, and the position of the pressure sensor within the coverage range is marked.

[0041] The data verification module includes a correlation calculation unit, a model building unit, a comparison unit, and a verification information output unit. The correlation calculation unit is used to calculate the correlation between the marked pressure sensor and the pressure sensors within the coverage range, and arrange the pressure sensors within the coverage range in descending order according to the correlation, and extract the pressure sensors whose correlation reaches a set threshold; the model building unit is used to establish a training module, and use the historical collection data of the marked pressure sensor as an input value, and the historical collection data of the pressure sensor whose correlation reaches the threshold as an output value, to train the model, and finally calculate the output calculation value of the sensor whose correlation reaches the threshold at the abnormal time point Tx; the comparison unit is used to compare the output calculation value with the actual collection value, output the matching result, and calculate the matching rate, and judge the trust result of the abnormal data according to the matching rate; the verification information output unit issues the next step operation corresponding to the trust result according to the output information result.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0043] The present invention can mark the pressure sensor that transmits the abnormal data when the system receives the abnormal data, set a coverage range, extract the pressure sensors whose correlation reaches the set value within the coverage range, and verify whether there are other reasons for the pressure sensor that transmits the abnormal data by analyzing the changes in the collected data of the pressure sensor whose correlation reaches the set value, so as to verify the transmitted abnormal data, improve the credibility of the abnormal data, reduce the number of times the system issues erroneous instructions, and save resources; at the same time, the maintenance time period of the marked pressure sensor can be predicted, and it can be further judged whether the pressure sensor that transmits the abnormal data is caused by its own reasons or is damaged by others, thereby reducing the resource loss caused by calculating unreliable data during the test process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a schematic diagram of the flow chart of the testing method in the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0047] Embodiment 1:

[0048] See also Figure 1 The present invention provides a technical solution: a pressure switch fatigue testing method based on data analysis, the method comprising the following specific steps:

[0049] S1. Acquire monitoring data of the perception layer and extract the monitored abnormal data; the perception layer includes a plurality of pressure sensors, the pressure sensors upload the monitored data information, mark the pressure sensors that transmit the abnormal data, extract the marked pressure sensor information, and set the time node of the abnormal data transmission as the abnormal time point;

[0050] S1 includes:

[0051] S101, when abnormal data is detected in the perception layer, the abnormal data is captured, and the pressure sensor that transmits the abnormal data is marked, and the pressure sensor that transmits the abnormal data is marked as M pressure sensor, and a three-dimensional coordinate system is established with the marked M pressure sensor as the origin;

[0052] S102 , obtaining the pressure sensor type of the M pressure sensor, obtaining the data type collected by the M pressure sensor, and when the M pressure sensor transmits abnormal data, setting the time node of transmitting the abnormal data as the abnormal time point Tx.

[0053] S2. Taking the marked pressure sensor as a reference point, set a coverage range, obtain information of all pressure sensors within the coverage range, calculate the correlation between each pressure sensor within the coverage range and the marked pressure sensor; obtain information of pressure sensors whose correlation reaches a set value, and extract data transmitted by the pressure sensor whose correlation reaches the set value at the abnormal time point, and use the data as the actual collected value;

[0054] S2 includes:

[0055] S201, extract all pressure sensors to form a set B, where set B = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor X}; wherein pressure sensor M is located within a set coverage range;

[0056] S202, extract the names of pressure sensor M and pressure sensor X in set B, assume that the name of pressure sensor M is W1, and the name of pressure sensor X in set B is W2, and use the following formula to calculate the similarity between the text names of each pressure sensor in set B and pressure sensor M

[0057]

[0058] in, represents the similarity in name between pressure sensor M and pressure sensor X in set B; m represents the total number of characters in W1; n represents the total number of characters in W2; c represents the number of matching characters in W1 and W2; L1(i) and L2(i) are the matching order of matching character i in W1 and W2 respectively; α and β are the weights of matching degree and matching order respectively, which are usually taken as 0.6 and 0.4 according to the golden ratio;

[0059] For example, the pressure sensor M; then the matching characters of W1 and W2 are "pressure", "sensor", "sensor", "device"; the matching sequences in W1 and W2 are 2 (pressure), 3 (sensor), 4 (sensor), 5 (device) and 2 (pressure), 3 (sensor), 4 (sensor), 5 (device) respectively; then The calculation process is:

[0060]

[0061] S203, according to the use standard of the pressure sensor M, obtain the monitoring range of the pressure sensor M, extract all the coordinate points within the monitoring range, and form a set A, where the set A = {(x1, y1, z1), (x2, y2, z2) ... (x n ,y n , z n )}; According to the use standard of the pressure sensor X, the monitoring range of the pressure sensor X is obtained, and all the coordinate points within the monitoring range are extracted to form a set C, where the set C = {(a1, b1, c1), (a2, b2, c2)...(a n , b n , c n )}, extract the coincident coordinate points between set C and set A to form set D, and calculate the coincidence rate between set A and set C according to the following formula

[0062]

[0063] For example, there are 10 coordinates in set A and 8 coordinates in set C. There are 5 overlapping coordinates in the two sets, and the overlapping coordinates form set D. Then the calculation process of the overlap rate is:

[0064] S204: Set the correlation between the pressure sensor X and the pressure sensor M to R X , calculate the correlation R between pressure sensor M and pressure sensor X in set B according to the following formula X :

[0065]

[0066] The pressure sensors X in set B are sorted according to the correlation R X Arrange in descending order, set the number of verification values ​​to Z, extract Z pressure sensors X according to the descending order of correlation, and use the data transmitted by the pressure sensor X at the abnormal time point Tx as the actual collected value.

[0067] S3, establish a training model, use the historical collected data of the marked pressure sensor and the historical collected data of the pressure sensor whose correlation reaches the set value as the training set, train the training model, calculate the calculated collected data of the pressure sensor whose correlation reaches the set value at the abnormal time point through the training model, and use the data as the output calculated value; compare whether the actual collected data is the same as the output calculated value to obtain a matching result; calculate the matching rate according to the matching result, and judge the trust result of the abnormal data according to the matching rate, and the trust result in S300 includes "high credibility", "average credibility" and "low credibility";

[0068] S3 includes:

[0069] S301: Establish a historical time set T and extract time node t from the historical time set T n The corresponding transmission data of the pressure sensor M, and the n When the transmission data of the pressure sensor X extracted in S204 is used to establish a training model, the transmission data transmitted by the pressure sensor M is used as input, and the transmission data transmitted by the pressure sensor X is used as output to train the model;

[0070] S302, according to the training model in S301, calculate the calculated collected data of the pressure sensor X at the abnormal time node Tx, and use the data as the output calculated value;

[0071] S303, setting the error range of the output calculation value, comparing the actual collected value with the output calculation value to see if they are within the error range; if the data of both the actual collected value and the output calculation value are within the error range, outputting a "match" result; if the data of both the actual collected value and the output calculation value are not within the error range, outputting a "mismatch" result;

[0072] S304. Calculate the matching rate based on the matching results of the Z pressure sensors X, and set the matching rate to P. If P ≥ 70%, the trust result is output as "high credibility"; if 70% > P ≥ 30%, the trust result is output as "average credibility"; if P < 30%, the trust result is output as "low credibility". The matching rate is calculated as follows:

[0073]

[0074] Wherein Z represents the total number of pressure sensors X extracted in S204.

[0075] S4. If S3 outputs a trust result of "high credibility", the system automatically issues a system instruction corresponding to the abnormal data according to the settings; if S3 outputs a trust result of "average credibility", the expansion range is set, and the expansion range is different from the coverage range, and S2 is repeated; if S3 outputs a trust result of "low credibility", the historical maintenance information of the marked pressure sensor is extracted, the maintenance time period of the marked pressure sensor is predicted, and it is determined whether the marked pressure sensor is in the maintenance time period, and the information is sent to the console;

[0076] S4 includes:

[0077] S401. If S304 outputs a trust result of "high credibility", it means that the abnormal data transmitted this time is highly accurate, and the system automatically issues the set instructions;

[0078] S402, if S304 outputs a trust result of "general credibility", then set a new expanded range S2 again, extract all pressure sensors within the expanded range to form a set F, where set F = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor Y}, repeat S2, and output the trust result of judging the abnormal data again;

[0079] S403. If S304 outputs a trust result of "low credibility", the historical maintenance information of the pressure sensor M is extracted, the maintenance time period of the pressure sensor M is predicted, and it is determined whether the abnormal time point Tx is within the maintenance time period of the pressure sensor M; if the abnormal time point Tx is within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is greater than the probability of man-made damage; if the abnormal time point Tx is not within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is less than the probability of man-made damage; the prediction information is sent to the console.

[0080] S5. The test analysis module analyzes the data processed by the data processing module, and then evaluates and analyzes the test performance of the pressure switch; the optimization analysis module analyzes the process optimization direction of the tested pressure switch and obtains the time influence coefficient and the pressure influence coefficient, compares the time influence coefficient with the pressure influence coefficient and generates a corresponding process optimization signal through the comparison result.

[0081] The specific implementation method is as follows: when abnormal data is transmitted to the system, the pressure sensor M that transmits the abnormal data is marked, a three-dimensional coordinate system is established with the pressure sensor M as the origin, and a coverage range is set. For example, the coverage range is set to a circle with the pressure sensor M as the center and a radius of 10m, the coordinates of all pressure sensors X in the coverage range are extracted, and the correlation between the pressure sensor X and the pressure sensor M is calculated, and the pressure sensor X whose correlation reaches the set value is extracted, and the data collected by the pressure sensor X at the abnormal time node Tx is compared to see whether it matches the abnormal data. That is, when the pressure sensor M collects abnormal data, the collected data of the pressure sensor X should also change. The training model is used to calculate the output calculation value of the pressure sensor X that should be changed, and the output calculation value is compared with the actual collection value actually collected by the pressure sensor X to verify the credibility of the abnormal data collected by the pressure sensor M. According to the matching results of the extracted multiple pressure sensors X, a trust result for the abnormal data is finally output, and the system can make a corresponding next step operation according to the trust result; by verifying the abnormal data, the number of times the system issues erroneous instructions can be reduced, saving resources.

[0082] Embodiment 2:

[0083] A pressure switch fatigue test system based on data analysis, the system comprises a monitoring module, a transmission module, a data processing module, a data extraction module, a data verification module, a test analysis module and an optimization analysis module connected to each other;

[0084] The monitoring module is used to collect environmental data of the pressure sensor in the monitored pressure switch, and transmit the collected environmental data to the data processing module through the transmission module. The data processing module screens the monitored data. When abnormal data appears, the data extraction module is used to extract the abnormal data, mark the pressure sensor that transmits the abnormal data, and then mark the pressure switch corresponding to the pressure sensor. The data verification module verifies the credibility of the abnormal data and outputs the corresponding instruction according to the verification result.

[0085] The monitoring module includes a pressure sensor unit, which is used to monitor the data in the environment in real time; the transmission module includes a data transmission unit and an instruction output unit, the data transmission unit is used to transmit the monitored data in real time, and the instruction output unit is used to send monitoring instructions to the pressure sensor unit according to the instructions set by the system.

[0086] The data processing module includes a data preprocessing unit and a classification storage unit. The data preprocessing unit is used to perform denoising on the monitored data, remove redundant data, and then use the classification storage unit to classify and store the monitored data.

[0087] The data extraction module includes an extraction unit, a marking unit and a modeling unit; the extraction unit is used to extract the transmitted abnormal data, and use the marking unit to mark the pressure sensor that transmits the abnormal data. At the same time, through the modeling unit, a three-dimensional coordinate system is established with the marked pressure sensor as a reference point, a coverage range is set, and the position of the pressure sensor within the coverage range is marked.

[0088] The data verification module includes a correlation calculation unit, a model building unit, a comparison unit, and a verification information output unit. The correlation calculation unit is used to calculate the correlation between the marked pressure sensor and the pressure sensors within the coverage range, and arrange the pressure sensors within the coverage range in descending order according to the correlation, and extract the pressure sensors whose correlation reaches a set threshold; the model building unit is used to establish a training module, and use the historical collection data of the marked pressure sensor as an input value, and the historical collection data of the pressure sensor whose correlation reaches the threshold as an output value, to train the model, and finally calculate the output calculation value of the sensor whose correlation reaches the threshold at the abnormal time point Tx; the comparison unit is used to compare the output calculation value with the actual collection value, output the matching result, and calculate the matching rate, and judge the trust result of the abnormal data according to the matching rate; the verification information output unit issues the next step operation corresponding to the trust result according to the output information result.

[0089] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pressure switch fatigue testing method based on data analysis, characterized in that: The method comprises the following specific steps: S1. Acquire monitoring data of the perception layer and extract the monitored abnormal data; the perception layer includes a plurality of pressure sensors, the pressure sensors upload the monitored data information, mark the pressure sensors that transmit the abnormal data, extract the marked pressure sensor information, and set the time node of the abnormal data transmission as the abnormal time point; S2. Taking the marked pressure sensor as a reference point, set a coverage range, obtain information of all pressure sensors within the coverage range, calculate the correlation between each pressure sensor within the coverage range and the marked pressure sensor; obtain information of pressure sensors whose correlation reaches a set value, and extract data transmitted by the pressure sensor whose correlation reaches the set value at the abnormal time point, and use the data as the actual collected value; S3, establish a training model, use the historical data of the marked pressure sensor and the historical data of the pressure sensor whose correlation reaches the set value as the training set, train the training model, calculate the calculated data of the pressure sensor whose correlation reaches the set value at the abnormal time point through the training model, and use the data as the output calculation value; Compare the actual collected data with the output calculated value to see if they are the same, and obtain a matching result; calculate a matching rate based on the matching result, and determine the trust result of the abnormal data based on the matching rate; S4. If S3 outputs a trust result of "high credibility", the system automatically issues a system instruction corresponding to the abnormal data according to the settings; if S3 outputs a trust result of "average credibility", an expansion range is set, and the expansion range is different from the coverage range, and S2 is repeated; if S3 outputs a trust result of "low credibility", the historical maintenance information of the marked pressure sensor is extracted, the maintenance time period of the marked pressure sensor is predicted, and it is determined whether the marked pressure sensor is in the maintenance time period, and the information is sent to the console; S5. The test analysis module analyzes the data processed by the data processing module, and then evaluates and analyzes the test performance of the pressure switch; the optimization analysis module analyzes the process optimization direction of the tested pressure switch and obtains the time influence coefficient and the pressure influence coefficient, compares the time influence coefficient with the pressure influence coefficient and generates a corresponding process optimization signal through the comparison result.

2. A pressure switch fatigue testing method based on data analysis according to claim 1, characterized in that: The S1 includes: S101, when abnormal data is detected in the perception layer, the abnormal data is captured, and the pressure sensor that transmits the abnormal data is marked, and the pressure sensor that transmits the abnormal data is marked as M pressure sensor, and a three-dimensional coordinate system is established with the marked M pressure sensor as the origin; S102 , obtaining the pressure sensor type of the M pressure sensor, obtaining the data type collected by the M pressure sensor, and when the M pressure sensor transmits abnormal data, setting the time node of transmitting the abnormal data as the abnormal time point Tx.

3. The method for fatigue testing a pressure switch based on data analysis according to claim 2, characterized in that: The S2 includes: S201, extract all pressure sensors to form a set B, where set B = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor X}; S202, extract the names of pressure sensor M and pressure sensor X in set B, assume that the name of pressure sensor M is W1, and the name of pressure sensor X in set B is W2, and use the following formula to calculate the similarity between the text names of each pressure sensor in set B and pressure sensor M in, represents the similarity between the names of pressure sensor M and pressure sensor X in set B; m represents the total number of characters in W1; n represents the total number of characters in W2; c represents the number of matching characters in W1 and W2; L1(i) and L2(i) are the matching sequences of matching character i in W1 and W2 respectively; α and β are the weights of matching degree and matching sequence respectively, which are taken as 0.6 and 0.4 respectively according to the golden ratio; S203, according to the use standard of the pressure sensor M, obtain the monitoring range of the pressure sensor M, extract all the coordinate points within the monitoring range, and form a set A, where the set A = {(x1, y1, z1), (x2, y2, z2) ... (x n ,y n , z n )}; According to the use standard of the pressure sensor X, the monitoring range of the pressure sensor X is obtained, and all the coordinate points within the monitoring range are extracted to form a set C, where the set C = {(a1, b1, c1), (a2, b2, c2)...(a n , b n , c n )}, extract the coincident coordinate points between set C and set A to form set D, and calculate the coincidence rate between set A and set C according to the following formula S204: Set the correlation between the pressure sensor X and the pressure sensor M to R X , calculate the correlation R between pressure sensor M and pressure sensor X in set B according to the following formula X : The pressure sensors X in set B are sorted according to the correlation R X Arrange in descending order, set the number of verification values ​​to Z, extract Z pressure sensors X according to the descending order of correlation, and use the data transmitted by the pressure sensor X at the abnormal time point Tx as the actual collected value.

4. The method for fatigue testing a pressure switch based on data analysis according to claim 3, characterized in that: The S3 includes: S301: Establish a historical time set T, extract time node t in the historical time set T n The corresponding transmission data of the pressure sensor M, and the n When the transmission data of the pressure sensor X extracted in S204 is used to establish a training model, the transmission data transmitted by the pressure sensor M is used as input, and the transmission data transmitted by the pressure sensor X is used as output to train the model; S302: Calculate the calculated collected data of the pressure sensor X at the abnormal time node Tx according to the training model in S301, and use the data as the output calculated value; S303, setting the error range of the output calculation value, comparing the actual collected value with the output calculation value to see if they are within the error range; if the data of both the actual collected value and the output calculation value are within the error range, outputting a "match" result; if the data of both the actual collected value and the output calculation value are not within the error range, outputting a "mismatch" result; S304: Calculate the matching rate based on the matching results of the Z pressure sensors X, and output a trust result based on the matching rate, wherein the trust result is "high credibility", "average credibility" or "low credibility". The matching rate is calculated as follows: Wherein Z represents the total number of pressure sensors X extracted in S204.

5. A pressure switch fatigue testing method based on data analysis according to claim 4, characterized in that: The S4 includes: S401. If S304 outputs a trust result of "high credibility", it means that the abnormal data transmitted this time is highly accurate, and the system automatically issues the set instructions; S402, if S304 outputs a trust result of "general credibility", then set a new expanded range S2 again, extract all pressure sensors within the expanded range to form a set F, where set F = {pressure sensor 1, pressure sensor 2, pressure sensor 3, pressure sensor Y}, repeat S2, and output the trust result of judging the abnormal data again; S403. If S304 outputs a trust result of "low credibility", the historical maintenance information of the pressure sensor M is extracted, the maintenance time period of the pressure sensor M is predicted, and it is determined whether the abnormal time point Tx is within the maintenance time period of the pressure sensor M; if the abnormal time point Tx is within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is greater than the probability of man-made damage; if the abnormal time point Tx is not within the maintenance time period of the pressure sensor M, the output probability of natural failure of the pressure sensor M is less than the probability of man-made damage; the prediction information is sent to the console.

6. A test system for the pressure switch fatigue test method based on data analysis according to any one of claims 1 to 5, characterized in that: The system includes a monitoring module, a transmission module, a data processing module, a data extraction module, a data verification module, a test analysis module and an optimization analysis module which are connected to each other; The monitoring module is used to collect environmental data of the pressure sensor in the monitored pressure switch, and transmit the collected environmental data to the data processing module through the transmission module. The data processing module screens the monitored data. When abnormal data appears, the data extraction module is used to extract the abnormal data, mark the pressure sensor that transmits the abnormal data, and then mark the pressure switch corresponding to the pressure sensor. The data verification module verifies the credibility of the abnormal data and outputs the corresponding instruction according to the verification result. The monitoring module includes a pressure sensor unit, which is used to monitor the data in the environment in real time; the transmission module includes a data transmission unit and an instruction output unit, the data transmission unit is used to transmit the monitored data in real time, and the instruction output unit is used to send monitoring instructions to the pressure sensor unit according to the instructions set by the system.

7. The test system according to claim 6, characterized in that: The data processing module includes a data preprocessing unit and a classification storage unit. The data preprocessing unit is used to perform denoising on the monitored data, remove redundant data, and then use the classification storage unit to classify and store the monitored data.

8. The test system according to claim 6, characterized in that: The data extraction module includes an extraction unit, a marking unit and a modeling unit; the extraction unit is used to extract the transmitted abnormal data, and use the marking unit to mark the pressure sensor that transmits the abnormal data. At the same time, through the modeling unit, a three-dimensional coordinate system is established with the marked pressure sensor as a reference point, a coverage range is set, and the position of the pressure sensor within the coverage range is marked.

9. The test system according to claim 6, characterized in that: The data verification module includes a correlation calculation unit, a model building unit, a comparison unit, and a verification information output unit. The correlation calculation unit is used to calculate the correlation between the marked pressure sensor and the pressure sensors within the coverage range, and to arrange the pressure sensors within the coverage range in descending order according to the correlation, and to extract the pressure sensors whose correlation reaches a set threshold; the model building unit is used to establish a training module, and use the historical collection data of the marked pressure sensor as an input value, and the historical collection data of the pressure sensor whose correlation reaches the threshold as an output value, to train the model, and finally calculate the output calculation value of the sensor whose correlation reaches the threshold at the abnormal time point Tx; The comparison unit is used to compare the output calculated value with the actual collected value, output a matching result, and calculate a matching rate, and determine the trust result of the abnormal data according to the matching rate; The verification information output unit issues a next step operation corresponding to the trust result according to the output information result.

Images