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CN118863870B - Special pressure gauge performance data monitoring management system and method based on Internet of things - Google Patents

Special pressure gauge performance data monitoring management system and method based on Internet of things Download PDF

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CN118863870B
CN118863870B CN202411350666.XA CN202411350666A CN118863870B CN 118863870 B CN118863870 B CN 118863870B CN 202411350666 A CN202411350666 A CN 202411350666A CN 118863870 B CN118863870 B CN 118863870B
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CN118863870A (en
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俞婧
王�琦
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Wuxi City Special Pressure Gauge Co ltd
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Abstract

The invention discloses a special pressure gauge performance data monitoring management system and method based on the Internet of things, and relates to the technical field of pressure gauge performance data monitoring, wherein the special pressure gauge performance data monitoring management system comprises a data acquisition module, a data storage module, a data analysis module, a pressure gauge performance test module and a control module; the system comprises a data acquisition module, a data analysis module, a data storage module, a control module, a simulation annealing algorithm and a multi-linear regression model, wherein the data acquisition module is used for acquiring running environment data of the pressure gauge, the data analysis module is used for establishing a loss degree evaluation model of the pressure gauge, the data storage module is used for running environment data, pressure gauge performance test is used for detecting performance of the pressure gauge, the control module is used for informing a manager to monitor the pressure gauge, the loss degree is adjusted by the simulation annealing algorithm, the loss degree is calculated by the multi-linear regression model, the weight of the multi-linear regression model is adjusted once in the internal circulation of the simulation annealing algorithm, and a global optimal solution or a better local optimal solution of the weight of the multi-linear regression model is obtained when the simulation annealing algorithm is finished.

Description

Special pressure gauge performance data monitoring management system and method based on Internet of things
Technical Field
The invention relates to the technical field of pressure gauge performance data monitoring, in particular to a special pressure gauge performance data monitoring management system and method based on the Internet of things.
Background
The pressure gauge is a pressure gauge for measuring pressure of fluid or gas, the pressure gauge can be used for measuring the pressure through a pressure sensor or a pressure transmitter and converting pressure signals into electric signals so as to be convenient for measurement and recording, the pressure gauge is generally used in the field of industrial production and is used for monitoring the pressure of the fluid, the verification period of the pressure gauge is generally one year or half year, depending on the use environment and requirements, the pressure gauge needs to be calibrated and tested regularly in the verification period to ensure that the performance of the pressure gauge meets the requirements, the pressure gauge is influenced by various environmental factors such as high temperature, high humidity, strong vibration and the like in the long-term use process of the pressure gauge, and in addition, some acidic substances and alkaline substances can influence the pressure gauge, so that the problem of judging whether the pressure gauge needs to be verified or not is solved according to different environmental factors to which the pressure gauge is subjected to.
Disclosure of Invention
The invention aims to provide a special pressure gauge performance data monitoring management system and method based on the Internet of things, so as to solve the problems in the background technology.
The special pressure meter performance data monitoring management system based on the Internet of things comprises a data acquisition module, a data storage module, a data analysis module, a pressure meter performance test module and a control module, wherein the output end of the data acquisition module is connected with the input ends of the data analysis module and the data storage module and used for acquiring operation environment data of the pressure meter, the output end of the data analysis module is connected with the input end of the control module, a loss degree evaluation model of the pressure meter is built based on the operation environment data of the pressure meter and used for judging the loss condition of the pressure meter, the data storage module is connected with the data analysis module and used for storing historical performance test data, operation environment data of the pressure meter and production information of products, the output end of the pressure meter performance test module is connected with the input end of the data storage module and used for detecting the performance of the pressure meter, and when the control module determines that the performance of the pressure meter does not meet the requirement of production specifications, a manager is informed to monitor the pressure meter, and a parallel maintenance person is informed to check and maintain the pressure meter.
The method comprises the steps of obtaining loss characteristics of a pressure gauge by integrating operation environment data of the pressure gauge, training a multiple linear regression model by taking the loss characteristics of the pressure gauge as input and loss as output, taking the loss as a loss evaluation model of the pressure gauge, evaluating the performance of the pressure gauge, adjusting the loss by using a simulated annealing algorithm, adjusting weights of the multiple linear regression model, and optimizing the effect of the performance evaluation model of the pressure gauge.
The test system comprises a force meter performance test module, a pressure source unit, a test data acquisition unit, a test data processing unit and a display unit, wherein the controller unit is used for setting test parameters and controlling a test flow, the pressure source unit is used for testing pressure signals, the test data acquisition unit is used for acquiring test data of the pressure meter, the test data processing unit is used for analyzing and processing the acquired test data of the pressure meter and calculating performance indexes of the pressure meter, and the display unit is used for displaying the processed test results of the pressure meter.
The data acquisition module comprises a direct measurement unit and an indirect measurement unit, wherein the direct measurement unit directly acquires environmental data influencing the pressure gauge through a sensor;
For example, in the industrial production process, when the pressure of the fluid is measured by using the pressure gauge, the corrosion degree cannot be directly measured, for industrial production, the analysis of the concentration of the main product is a necessary process, and the concentration of the main product has a correlation with the corrosion degree of the fluid, at this time, the concentration of the main product can be used to replace the corrosion degree of the fluid, the change of the performance of the spring wire is one of the main sources of errors of the pressure gauge, the fluctuation of the pressure of the fluid can influence the deformation of the spring wire, the performance of the pressure gauge is further influenced, the deformation data of the spring wire can be indirectly obtained by measuring the fluctuation of the pressure of the fluid, and the fluctuation of the pressure of the fluid is the purpose of installing the pressure gauge, so that extra resource consumption is not brought.
In another aspect of the invention, a special pressure gauge performance data monitoring and management method based on the internet of things is provided, which comprises the following steps:
S5-1, acquiring use data of the pressure gauge, and extracting loss degree characteristics from the use data of the pressure gauge;
s5-2, establishing a pressure gauge loss degree evaluation model based on the use data of the pressure gauge;
s5-3, judging whether the measurement error of the current pressure gauge meets the requirement of the production specification by using a pressure gauge loss evaluation model, if so, repeating the step S5-3, and if not, identifying and maintaining the pressure gauge.
In step S5-2, the method establishes a loss degree evaluation model of the pressure gauge based on the loss degree characteristics of the pressure gauge, and further includes the following steps:
The loss degree characteristic of the pressure gauge is recorded as X 1、X2、…、Xn, n is the number of the loss degree characteristics, the loss degree characteristic of the pressure gauge is taken as input, the loss degree of the pressure gauge is taken as output, an n-element linear regression model is trained as a pressure gauge loss degree evaluation model, Wherein the value range of i is a positive integer between intervals [1, m ], m is the number of historical data used by the pressure gauge, k is a natural number and represents the iteration times;、、...、 Weights representing the loss characteristics after the kth iteration; 、、...、 Loss characteristics representing historical usage data of an ith pressure gauge; After the kth iteration, expressing the regression value of the loss degree of the ith pressure gauge, and optimizing the weight of the n-element linear regression model through a simulated annealing algorithm;
The method comprises the steps of obtaining the integral loss degree of a pressure gauge by connecting different environmental factors through a multiple linear regression model, wherein the measurement error of the pressure gauge and the loss degree are in positive correlation, the loss degree of the pressure gauge needs to be adjusted according to the measurement error of the pressure gauge so as to optimize the weight of the multiple linear regression model, the measurement error of the pressure gauge is determined through testing, the pressure gauge is usually verified with a period of one year or half year before the method provided by the invention is used, and the measurement error of the pressure gauge can be obtained from historical verification results.
The optimization of the weight of the n-ary linear regression model by the simulated annealing algorithm specifically comprises the following steps:
s7-1, setting an initial temperature T 0 and a loss degree characteristic weight 、、...、Is 1, will wear the degree characteristic、、...、Substituting into formula to calculate initial value of loss degree,The set consumption degree characteristic weight value、、...、Initial value of lossThe method comprises the steps of determining a loss value loss 0 corresponding to an initial solution as the initial solution, taking the initial solution as a current solution, taking an initial temperature as a current temperature, and setting an initial value of iteration times k to be 0;
In the pressure gauge history use data, the pressure gauge history use data with the measurement error closest to the threshold value is found, and the loss degree initial value of the pressure gauge history use data is obtained As a boundary for the decision-making,Comparing the initial value of the loss degree of the historical use data of other pressure gauges with a decision boundaryThe method comprises the steps of determining whether a loss degree initial value is larger than or equal to a decision boundary, judging that the pressure gauge needs to be overhauled, otherwise, determining the obtained decision accuracy as a loss value, wherein the correct decision comprises two conditions that the loss degree regression value of the pressure gauge is larger than or equal to the decision boundary and the measurement error of the pressure gauge is larger than or equal to the production specification, and the loss degree regression value of the pressure gauge is smaller than the decision boundary and the measurement error of the pressure gauge is smaller than the production specification;
s7-2, repeating steps S7-3 to S7-4;L for the number of internal cycles for the count unit b=1, 2, & gt, L;
S7-3, adding 1 to the value of the iteration number k, generating disturbance change loss characteristic weight and loss on the basis of the current solution, taking the disturbance-selected loss characteristic weight and loss as a new solution, and determining decision accuracy p corresponding to the new solution;
S7-4, calculating an increment delta p of a response time expected value brought by the new solution, if the increment is smaller than 0, taking the new solution as the new current solution with probability 1, and if the increment is larger than or equal to 0, taking the new solution with probability Accepting the new solution as a new current solution;
if the new solution is not accepted, the value of k is subtracted by 1, and the parameters of the n-ary linear regression model are not changed;
S7-5, selecting a cooling scheme, reducing the current temperature according to the cooling scheme, entering a step S7-2 if the current temperature is greater than or equal to a set threshold value, and determining the characteristic weight of the loss degree and the loss degree according to the current solution if the current temperature is less than the set threshold value.
In particular, loss characteristics、、...、The calculation is performed by the following formula:
,,..., In the middle of 、、...、The measured value of the loss degree characteristic of the historical use data of the ith pressure gauge at the moment t is used as an integral variable;
The integration interval is the time period between two times of verification of the pressure gauge, the measurement of the loss degree characteristic is discrete due to the limitation of equipment conditions, and the loss degree characteristic can be approximately calculated: In the middle of For the time point between the time periods between two assays performed for the pressure gauge,Approximating the area of the curved trapezoid with the sum of a plurality of rectangular areas for the measurement period of the measurement device;
in step S7-3, the generating a disturbance based on the current solution, and changing the loss characteristic weight and the loss further includes the following steps:
S9-1, at the present solution 、、...、AndOn the basis of (1), the loss degree of all pressure gauges is adjusted according to probability to obtain;
S9-2, loss degree characteristics、、...、Loss of pressure gauge as inputTraining an n-ary linear regression model as an output to obtain a loss degree characteristic weight、、...、;
The input of the n-ary linear regression model is kept unchanged, the output loss degree is adjusted along with iteration, after the loss degree is adjusted, the weight of the n-ary linear regression model is required to be adjusted, the regression value of the historical use data of the pressure gauge is calculated by using the adjusted weight, the regression value is compared with a decision boundary, and the decision accuracy is obtained and is used for judging whether the loss degree after iteration adjustment is required to be received or not;
S9-4, finding the loss characteristic of the historical usage data of the pressure gauge, which has the closest measurement error to the threshold value, in the historical usage data of the pressure gauge 、、...、,According to the characteristic weight of the loss degree、、...、Calculating the loss degree of the historical usage data of the pressure gaugeCalculating the loss degree regression value of other pressure gauge historical use data to obtain;
The invention judges whether the current pressure gauge needs to be checked or not by using a linear regression model, inputs the loss degree characteristic of the current pressure gauge into the linear regression model to obtain a regression value of the loss degree, and compares the regression value of the loss degree with a decision boundary to judge whether the current pressure gauge needs to be checked or not.
The decision accuracy is determined by:
Comparing loss regression values of pressure gauge historical usage data And decision boundaryCounting the number NUM1 of history use data of the pressure gauge for correct decision, taking the ratio NUM1/m of NUM1 to m as the decision accuracy, wherein the history use data do not participate in calculation;
the loss degree of all the pressure gauges is adjusted according to the probability to obtain The method comprises the following steps:
S10-1, using mis 1、mis2、…、mism to represent the historical measurement error of the pressure gauge, and using mis max as the measurement error closest to the threshold value;
the measurement error is positive, no matter the measured value is larger or smaller than the actual value, the absolute value is taken as the measurement error;
s10-2, for i=1, 2,..m, executing step S10-3;
S10-3, probability-based through random numbers Will beIs adjusted to,,As a symbolic variable ifGreater than or equal to a threshold value, andLess than the decision boundary, then1, IfGreater than or equal to a threshold value, andGreater than the decision boundary, then0, IfIs less than a threshold value, andLess than the decision boundary, then0, IfIs less than a threshold value, andGreater than the decision boundary, then1 Is shown in the specification; And (3) with Is positive correlation;
algebraic difference With signs, whenWhen the loss is smaller than 0, the performance of the pressure gauge meets the requirement of production rules, the loss degree is adjusted towards the direction of reduction, whenWhen the loss is larger than 0, the performance of the pressure gauge is not in accordance with the requirement of the production rule, and the loss degree is adjusted towards the increasing direction;
For the pressure gauge usage data which is already correctly decided, the adjustment of the loss degree is unnecessary, the decision accuracy cannot be improved under the current decision boundary, and therefore, the symbol variable is set to 0, and for the pressure gauge usage data which is not correctly decided, the adjustment is not optimal, and an optimal adjustment mode needs to be found, so, the loss degree of the usage data is iteratively adjusted according to probability through a simulated annealing algorithm.
Compared with the prior art, the method has the advantages that the loss degree is adjusted by means of the simulated annealing algorithm, poor loss degree combination is accepted with a certain probability, the method is beneficial to avoiding sinking into a local optimal solution and finding out a global optimal solution, the loss degree is calculated by using the multiple linear regression model, the weight of the multiple linear regression model is adjusted once every time one step is executed in the internal circulation of the simulated annealing algorithm, the global optimal solution or the better local optimal solution of the weight of the multiple linear regression model is obtained after the simulated annealing algorithm is finished, environmental factors influencing the pressure gauge are measured in an indirect mode, the resource cost is reduced, and the profit rate of enterprises is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a special pressure gauge performance data monitoring and management system based on the internet of things according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the embodiment of the invention, referring to fig. 1, a special pressure meter performance data monitoring management system based on the Internet of things is provided, and comprises a data acquisition module, a data storage module, a data analysis module, a pressure meter performance test module and a control module, wherein the output end of the data acquisition module is connected with the input ends of the data analysis module and the data storage module and used for acquiring operation environment data of a pressure meter, the output end of the data analysis module is connected with the input end of the control module, a loss degree evaluation model of the pressure meter is built based on the operation environment data of the pressure meter and used for judging the loss condition of the pressure meter, the data storage module is connected with the data analysis module and used for storing historical performance test data, operation environment data of the pressure meter and production information of products, the output end of the pressure meter performance test module is connected with the input end of the data storage module and used for detecting the performance of the pressure meter, and when the performance of the pressure meter is determined to be inconsistent with the requirements of production specifications, a manager is informed to monitor the pressure meter, and a maintainer is in parallel system to verify and maintain the pressure meter.
The data analysis module obtains the loss degree characteristic of the pressure gauge by integrating the operation environment data of the pressure gauge, takes the loss degree characteristic of the pressure gauge as input, takes the loss degree as output, trains a multiple linear regression model as a loss degree evaluation model of the pressure gauge to evaluate the performance of the pressure gauge, and simultaneously adjusts the loss degree by using a simulated annealing algorithm, further adjusts the weight of the multiple linear regression model and optimizes the effect of the performance evaluation model of the pressure gauge.
The pressure meter performance test module further comprises a controller unit, a pressure source unit, a test data acquisition unit, a test data processing unit and a display unit, wherein the controller unit is used for setting test parameters and controlling a test flow, the pressure source unit is used for testing pressure signals, the test data acquisition unit is used for acquiring test data of the pressure meter, the test data processing unit is used for analyzing and processing the acquired test data of the pressure meter and calculating performance indexes of the pressure meter, and the display unit is used for displaying the processed test results of the pressure meter.
The data acquisition module comprises a direct measurement unit and an indirect measurement unit, wherein the direct measurement unit is used for directly acquiring environmental data influencing the pressure gauge through a sensor, and the indirect measurement unit is used for extracting the environmental data which cannot be measured through the sensor directly from the production information of a product.
In the embodiment of the invention, a special pressure gauge performance data monitoring and management method based on the Internet of things is provided, which comprises the following steps:
S5-1, acquiring use data of the pressure gauge, and extracting loss degree characteristics from the use data of the pressure gauge;
The loss degree characteristic of the pressure gauge is X 1、X2、X3、X4, which respectively represents relative humidity, temperature, corrosiveness and pressure change rate, wherein corrosiveness is indirectly represented by detecting the concentration of a main product in the production process;
s5-2, establishing a pressure gauge loss degree evaluation model based on the use data of the pressure gauge;
taking the loss degree characteristic of the pressure gauge as input and the loss degree of the pressure gauge as output, training a 4-element linear regression model as a loss degree evaluation model of the pressure gauge, Wherein the value range of i is a positive integer between intervals [1, m ], m is the number of historical data used by the pressure gauge, k is a natural number and represents the iteration times;、、、 Weights representing the loss characteristics after the kth iteration; 、、、 Loss characteristics representing historical usage data of an ith pressure gauge; A regression value representing the loss of the ith pressure gauge after the kth iteration;
Loss degree characteristics 、、、The calculation is performed by the following formula:
,,..., In the middle of 、、...、The measured value of the loss degree characteristic of the historical use data of the ith pressure gauge at the moment t is used as an integral variable;
Optimizing the weight of the 4-membered linear regression model through a simulated annealing algorithm, comprising the following steps S7-1 to S7-5:
s7-1, setting the initial temperature T 0 as 100, and setting the loss degree characteristic weight 、、、Is 1, will wear the degree characteristic、、、Substituting into formula to calculate initial value of loss degree,The set consumption degree characteristic weight value、、、Initial value of lossThe method comprises the steps of determining a loss value loss 0 corresponding to an initial solution as the initial solution, taking the initial solution as a current solution, taking an initial temperature as a current temperature, and setting an initial value of iteration times k to be 0;
In the pressure gauge history use data, the pressure gauge history use data with the measurement error closest to the threshold value is found, and the loss degree initial value of the pressure gauge history use data is obtained As a boundary for the decision-making,Comparing the initial value of the loss degree of the historical use data of other pressure gauges with a decision boundaryIf the loss degree initial value is larger than or equal to the decision boundary, judging that the pressure gauge needs to be overhauled, otherwise, judging that the pressure gauge does not need to be overhauled, taking the obtained decision accuracy as a loss value, wherein the correct decision comprises two conditions that the loss degree regression value of the pressure gauge is larger than or equal to the decision boundary and the measurement error of the pressure gauge is larger than or equal to the requirement of production specification, and the loss degree regression value of the pressure gauge is smaller than the decision boundary and the measurement error of the pressure gauge is smaller than the requirement of the production specification;
S7-2, repeating steps S7-3 to S7-4 for count units b=1, 2, & gt, 20;
S7-3, adding 1 to the value of the iteration number k, generating disturbance change loss characteristic weight and loss on the basis of the current solution, using the disturbance-selected loss characteristic weight and loss as a new solution, determining decision accuracy p corresponding to the new solution, comparing loss regression values of historical use data of the pressure gauge And decision boundaryCounting the number NUM1 of the history use data of the pressure gauge for correct decision, taking the ratio NUM1/m of NUM1 to m as the decision accuracy, wherein the history use data of the pressure gauge does not participate in calculation;
generating a perturbation on the basis of the current solution comprises the following steps S9-1 to S9-4:
S9-1, at the present solution 、、、AndOn the basis of (1), the loss degree of all pressure gauges is adjusted according to probability to obtainComprising steps S10-1 to S10-3,
S10-1, using mis 1、mis2、…、mism to represent the historical measurement error of the pressure gauge, and using mis max as the measurement error closest to the threshold value;
s10-2, for i=1, 2,..m, executing step S10-3;
S10-3, probability-based through random numbers Will beIs adjusted to,,As a symbolic variable ifGreater than or equal to a threshold value, andLess than the decision boundary, then1, IfGreater than or equal to a threshold value, andGreater than the decision boundary, then0, IfIs less than a threshold value, andLess than the decision boundary, then0, IfIs less than a threshold value, andGreater than the decision boundary, then1 Is shown in the specification; And (3) with Is positive correlation; Can be taken as G G is a positive constant;
S9-2, loss degree characteristics 、、、Loss of pressure gauge as inputTraining a 4-element linear regression model as output to obtain loss degree characteristic weight、、、;
S9-4, finding the loss characteristic of the historical usage data of the pressure gauge, which has the closest measurement error to the threshold value, in the historical usage data of the pressure gauge、、、,According to the characteristic weight of the loss degree、、、Calculating the loss degree of the historical usage data of the pressure gaugeCalculating the loss degree regression value of other pressure gauge historical use data to obtain;
S7-4, calculating an increment delta p of a response time expected value brought by the new solution, if the increment is smaller than 0, taking the new solution as the new current solution with probability 1, and if the increment is larger than or equal to 0, taking the new solution with probabilityAccepting the new solution as a new current solution;
if the new solution is not accepted, the value of k minus the parameters of the 1, 4-element linear regression model are not changed;
S7-5, selecting a cooling scheme, reducing the current temperature according to the cooling scheme, if the current temperature is greater than or equal to a set threshold, entering a step S7-2, if the current temperature is less than the set threshold, determining a loss degree characteristic weight and a loss degree according to the current solution, after cooling by adopting a linear cooling curve with a cooling coefficient of 0.9, changing the temperature from 100 ℃ to 100 multiplied by 0.9 ℃ after the first iteration, setting the threshold to a smaller value, wherein the threshold can be 1 DEG C
The specific flow of updating the weight of the 4-membered linear regression model is as follows:
first, calculate m pressure gauges historical usage data 、、、Calculating the loss degree of each data according to a formula,At this time, the loss degree and the loss degree regression valueSimilarly, find out the loss degree of the pressure gauge history usage data with the measurement error closest to the threshold valueAs a decision boundary, the measurement error is determined by national regulations or the requirements of the generation specification, and the decision accuracy is calculated according to the decision boundary and the loss degree regression value;
Performing first iteration to Is adjusted toTo、、、In order to be able to input the input,For output, obtain weight of 4-element linear regression model、、、In accordance with the weight、、、And input、、、Obtaining a loss degree regression valueAt this time, the new decision boundary is changed toAccording to step S10-3,And (3) withThe same will not change, and will be manually operatedIs also changed toCalculating the decision accuracy according to the decision boundary and the loss degree regression value, and determining whether to accept according to the probability、、、AndIf so, adding 1 to the value of the iteration number k,、、、、Will be at、、、、On the basis of (a), if not accepted, k remains unchanged, continuing on、、、、Is based on (1)、、、、Continuously repeating the steps to adjust the weight of the 4-membered linear regression model until the simulated annealing algorithm is finished;
s5-3, judging whether the measurement error of the current pressure gauge meets the requirement of the production specification by using a pressure gauge loss evaluation model, if so, repeating the step S5-3, and if not, identifying and maintaining the pressure gauge.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features thereof by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1.一种基于物联网的特种压力表性能数据监测管理系统，其特征在于，包括数据采集模块、数据存储模块、数据分析模块、压力表性能测试模块和控制模块；所述数据采集模块的输出端与所述数据分析模块和数据存储模块的输入端相互连接，用于获取压力表的运行环境数据；所述数据分析模块的输出端与所述控制模块的输入端相互连接，基于压力表的运行环境数据，建立压力表的损耗度评估模型，判断压力表的损耗情况；所述数据存储模块与所述数据分析模块相互连接，用于存储压力表的历史性能测试数据、运行环境数据和产品的生产信息；所述压力表性能测试模块的输出端与所述数据存储模块的输入端相互连接，用于对压力表的性能进行检测；所述控制模块，当确定压力表性能不符合生产规格的要求时，通知管理人员对压力表进行监视，并联系维修人员对压力表进行检定和维护保养；1. A special pressure gauge performance data monitoring and management system based on the Internet of Things, characterized in that it includes a data acquisition module, a data storage module, a data analysis module, a pressure gauge performance test module and a control module; the output end of the data acquisition module is interconnected with the input end of the data analysis module and the data storage module to obtain the operating environment data of the pressure gauge; the output end of the data analysis module is interconnected with the input end of the control module, and based on the operating environment data of the pressure gauge, a loss evaluation model of the pressure gauge is established to judge the loss of the pressure gauge; the data storage module is interconnected with the data analysis module to store the historical performance test data, operating environment data and production information of the pressure gauge; the output end of the pressure gauge performance test module is interconnected with the input end of the data storage module to detect the performance of the pressure gauge; when the control module determines that the performance of the pressure gauge does not meet the requirements of the production specifications, it notifies the management personnel to monitor the pressure gauge and contacts the maintenance personnel to calibrate and maintain the pressure gauge; 所述数据分析模块通过对压力表的运行环境数据进行积分，得到压力表的损耗度特征；将压力表的损耗度特征作为输入，损耗度作为输出，训练多元线性回归模型，作为压力表的损耗度评估模型，对压力表的性能进行评估；同时，利用模拟退火算法对损耗度进行调整，进而对多元线性回归模型的权值进行调整，优化压力表性能评估模型的效果。The data analysis module integrates the operating environment data of the pressure gauge to obtain the loss characteristics of the pressure gauge; uses the loss characteristics of the pressure gauge as input and the loss as output to train a multivariate linear regression model as a loss evaluation model for the pressure gauge to evaluate the performance of the pressure gauge; at the same time, uses a simulated annealing algorithm to adjust the loss, and then adjusts the weights of the multivariate linear regression model to optimize the effect of the pressure gauge performance evaluation model. 2.根据权利要求1所述的一种基于物联网的特种压力表性能数据监测管理系统，其特征在于，压力表性能测试模块还包括控制器单元、压力源单元、测试数据采集单元、测试数据处理单元和显示单元；所述控制器单元用于设置测试参数和控制测试流程；所述压力源单元用于测试的压力信号；所述测试数据采集单元用于采集压力表的测试数据；所述测试数据处理单元用于对采集到的压力表测试数据进行分析和处理，并计算压力表的性能指标；所述显示单元，用于显示处理后的压力表测试结果。2. According to a special pressure gauge performance data monitoring and management system based on the Internet of Things as described in claim 1, it is characterized in that the pressure gauge performance test module also includes a controller unit, a pressure source unit, a test data acquisition unit, a test data processing unit and a display unit; the controller unit is used to set test parameters and control the test process; the pressure source unit is used for the test pressure signal; the test data acquisition unit is used to collect the test data of the pressure gauge; the test data processing unit is used to analyze and process the collected pressure gauge test data, and calculate the performance indicators of the pressure gauge; the display unit is used to display the processed pressure gauge test results. 3.根据权利要求1所述的一种基于物联网的特种压力表性能数据监测管理系统，其特征在于，所述数据采集模块包括直接测量单元和间接测量单元，所述直接测量单元通过传感器直接获取对压力表产生影响的环境数据；所述间接测量单元用于从产品的生产信息中提取出无法直接通过传感器进行测量的环境数据。3. According to a special pressure gauge performance data monitoring and management system based on the Internet of Things as described in claim 1, it is characterized in that the data acquisition module includes a direct measurement unit and an indirect measurement unit, the direct measurement unit directly obtains the environmental data affecting the pressure gauge through a sensor; the indirect measurement unit is used to extract environmental data that cannot be directly measured by sensors from the production information of the product. 4.一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，包括以下步骤：4. A special pressure gauge performance data monitoring and management method based on the Internet of Things, characterized in that it includes the following steps: S5-1，获取压力表的使用数据，从压力表的使用数据中提取出损耗度特征；S5-1, obtaining usage data of the pressure gauge, and extracting a loss characteristic from the usage data of the pressure gauge; S5-2，基于压力表的使用数据，建立压力表损耗度评估模型；获取压力表的损耗度特征，以压力表的损耗度特征为输入，压力表的损耗度为输出，训练线性回归模型作为压力表损耗度评估模型；并通过模拟退火算法优化线性回归模型的权值；S5-2, based on the usage data of the pressure gauge, establish a pressure gauge loss evaluation model; obtain the loss characteristics of the pressure gauge, take the loss characteristics of the pressure gauge as input and the loss of the pressure gauge as output, train a linear regression model as the pressure gauge loss evaluation model; and optimize the weights of the linear regression model through a simulated annealing algorithm; S5-3，使用压力表损耗度评估模型判断当前压力表的测量误差是否满足生产规格的要求，若满足，则重复执行步骤S5-3；若不满足，则对压力表进行鉴定和维修保养。S5-3, using the pressure gauge loss assessment model to determine whether the measurement error of the current pressure gauge meets the requirements of the production specifications, if so, repeat step S5-3; if not, identify and repair the pressure gauge. 5.根据权利要求4所述的一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，在步骤S5-2中，所述基于压力表的损耗度特征，建立压力表损耗度评估模型，还包括以下步骤：5. A method for monitoring and managing performance data of special pressure gauges based on the Internet of Things according to claim 4, characterized in that in step S5-2, the pressure gauge loss evaluation model is established based on the loss characteristics of the pressure gauge, and further comprising the following steps: 记压力表的损耗度特征为X1、X2、…、Xn，n为损耗度特征的数量；以压力表的损耗度特征为输入，压力表的损耗度为输出，训练n元线性回归模型作为压力表损耗度评估模型，，式中i的取值范围是区间[1,m]之间的正整数，m是压力表历史使用数据的数量；k为自然数，表示迭代次数；、、…、表示第k次迭代后，损耗度特征的权值；、、…、表示第i个压力表历史使用数据的损耗度特征；表示第k次迭代后，第i个压力表的损耗度的回归值；通过模拟退火算法优化n元线性回归模型的权值。The loss characteristics of the pressure gauge are recorded as X 1 , X 2 , …, X n , where n is the number of loss characteristics. With the loss characteristics of the pressure gauge as input and the loss of the pressure gauge as output, an n-dimensional linear regression model is trained as a pressure gauge loss evaluation model. , where i is a positive integer between [1, m], m is the number of historical usage data of the pressure gauge; k is a natural number, indicating the number of iterations; , , …, Represents the weight of the loss feature after the kth iteration; , , …, Represents the loss characteristics of the historical usage data of the i-th pressure gauge; Represents the regression value of the loss degree of the i-th pressure gauge after the k-th iteration; the weights of the n-variable linear regression model are optimized by the simulated annealing algorithm. 6.根据权利要求5所述的一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，所述通过模拟退火算法优化n元线性回归模型的权值具体包括以下步骤：6. A method for monitoring and managing performance data of special pressure gauges based on the Internet of Things according to claim 5, characterized in that the optimization of the weights of the n-variable linear regression model by the simulated annealing algorithm specifically comprises the following steps: S7-1，设置初始温度T0，设定损耗度特征权值、、…、为1，将损耗度特征、、…、代入到公式中计算损耗度的初始值，，将设定的耗度特征权值、、…、和损耗度的初始值作为初始解，确定初始解对应的损失值loss0；将初始解作为当前解，将初始温度作为当前温度，将迭代次数k的初始值设置为0；S7-1, set the initial temperature T 0 and set the loss characteristic weight , , …, =1, the loss feature , , …, Substitute into the formula to calculate the initial value of the loss , , set the consumption feature weight , , …, and the initial value of the loss As the initial solution, determine the loss value loss 0 corresponding to the initial solution; take the initial solution as the current solution, take the initial temperature as the current temperature, and set the initial value of the number of iterations k to 0; 在压力表历史使用数据中，找到测量误差与阈值最接近的压力表历史使用数据data，将压力表历史使用数据data的损耗度初始值作为决策边界，，比较其他压力表历史使用数据的损耗度初始值与决策边界；若损耗度初始值大于或等于决策边界，则判断压力表需要进行检修；反之压力表不需要进行检修；将得到的决策正确率作为损失值；正确决策包括两种情况：压力表损耗度回归值大于等于决策边界且压力表的测量误差大于或等于生产规格的要求，以及压力表损耗度回归值小于决策边界且压力表的测量误差小于生产规格的要求；In the historical usage data of the pressure gauge, find the historical usage data of the pressure gauge whose measurement error is closest to the threshold, and set the initial loss value of the historical usage data of the pressure gauge to As the decision boundary, , compare the initial loss value of other pressure gauge historical usage data with the decision boundary ; If the initial value of the loss degree is greater than or equal to the decision boundary, it is judged that the pressure gauge needs to be repaired; otherwise, the pressure gauge does not need to be repaired; the obtained decision accuracy is used as the loss value; the correct decision includes two situations: the regression value of the pressure gauge loss degree is greater than or equal to the decision boundary and the measurement error of the pressure gauge is greater than or equal to the requirements of the production specifications, and the regression value of the pressure gauge loss degree is less than the decision boundary and the measurement error of the pressure gauge is less than the requirements of the production specifications; S7-2，对计数单位b=1、2、…、L，重复步骤S7-3至S7-4；L为内循环的次数；S7-2, for counting units b=1, 2, ..., L, repeat steps S7-3 to S7-4; L is the number of inner loops; S7-3，将迭代次数k的值加1，通过在当前解的基础上产生扰动改变损耗度特征权值和损耗度，将扰动后选择的损耗度特征权值和损耗度作为新解，确定新解对应的决策正确率p；S7-3, adding 1 to the value of the iteration number k, changing the loss feature weight and loss by generating a disturbance based on the current solution, taking the loss feature weight and loss selected after the disturbance as a new solution, and determining the decision accuracy p corresponding to the new solution; S7-4，计算新解带来的响应时间期望值的增量Δp，若增量小于0，则以概率1接受新解作为新的当前解，若增量大于或等于0，则以概率接受新解作为新的当前解；S7-4, calculate the increment Δp of the expected value of the response time brought by the new solution. If the increment is less than 0, the new solution is accepted as the new current solution with probability 1. If the increment is greater than or equal to 0, the new solution is accepted as the new current solution with probability Accept the new solution as the new current solution; 若新解被接受，则k保持不变；若新解不被接受，则k的值减1，n元线性回归模型的参数没有发生变化；If the new solution is accepted, k remains unchanged; if the new solution is not accepted, the value of k decreases by 1, and the parameters of the n-variable linear regression model do not change; S7-5，选择降温方案，按照降温方案降低当前温度，若当前温度大于或等于设定阈值，则进入步骤S7-2；若当前温度小于设定阈值，则根据当前解，确定损耗度特征权值和损耗度。S7-5, select a cooling plan, and lower the current temperature according to the cooling plan. If the current temperature is greater than or equal to the set threshold, enter step S7-2; if the current temperature is less than the set threshold, determine the loss feature weight and loss degree based on the current solution. 7.根据权利要求5所述的一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，损耗度特征、、…、通过以下公式进行计算：7. According to the method for monitoring and managing the performance data of a special pressure gauge based on the Internet of Things in claim 5, it is characterized in that the loss characteristic , , …, The calculation is done by the following formula: ，，…，；式中、、…、为第i个压力表历史使用数据的损耗度特征在t时刻的测量值，t为积分变量。 , , …, ; In the formula , , …, is the measured value of the loss characteristic of the historical usage data of the ith pressure gauge at time t, and t is the integral variable. 8.根据权利要求7所述的一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，在步骤S7-3中，所述在当前解的基础上产生扰动，改变损耗度特征权值和损耗度还包括以下步骤：8. A method for monitoring and managing performance data of special pressure gauges based on the Internet of Things according to claim 7, characterized in that in step S7-3, generating disturbances based on the current solution, changing the loss characteristic weight and the loss further comprises the following steps: S9-1，在当前解、、…、和的基础上，依概率对所有压力表的损耗度进行调整得到；S9-1, in the current solution , , …, and Based on the probability, the loss of all pressure gauges is adjusted to obtain ; S9-2，将损耗度特征、、…、作为输入，压力表的损耗度作为输出，训练n元线性回归模型，得到损耗度特征权值、、…、；S9-2, the loss characteristic , , …, As input, the loss of the pressure gauge As output, train the n-ary linear regression model to obtain the loss feature weights , , …, ; S9-4，在压力表历史使用数据中，找到测量误差与阈值最接近的压力表历史使用数据data的损耗度特征、、…、，，根据损耗度特征权值、、…、计算该压力表历史使用数据的损耗度，作为新的决策边界；计算其他压力表历史使用数据的损耗度回归值得到。S9-4, in the historical usage data of the pressure gauge, find the loss characteristic of the historical usage data of the pressure gauge whose measurement error is closest to the threshold , , …, , , according to the loss feature weight , , …, Calculate the loss of the pressure gauge based on historical usage data , as the new decision boundary; calculate the loss regression value of the historical usage data of other pressure gauges to obtain . 9.根据权利要求8所述的一种基于物联网的特种压力表性能数据监测管理方法，其特征在于，决策正确率通过以下方式进行确定：9. The method for monitoring and managing performance data of special pressure gauges based on the Internet of Things according to claim 8 is characterized in that the decision accuracy is determined by the following method: 比较压力表历史使用数据的损耗度回归值与决策边界，统计正确决策的压力表历史使用数据的数量NUM1，取NUM1与m的比值NUM1/m作为决策正确率，压力表历史使用数据data不参与进行计算；Compare the loss regression values of the pressure gauge's historical usage data With decision boundary , count the number of historical usage data of the pressure gauge with correct decisions NUM1, take the ratio of NUM1 to m NUM1/m as the decision accuracy rate, and the historical usage data of the pressure gauge data does not participate in the calculation; 依概率对所有压力表的损耗度进行调整得到包括以下步骤：Adjust the loss of all pressure gauges according to probability to obtain The following steps are involved: S10-1，以mis1、mis2、…、mism表示压力表的历史测量误差，则mismax为最接近阈值的测量误差；计算mis1、mis2、…、mism与mismax的代数差，得到Δmis1、Δmis2、…、Δmism；S10-1, mis 1 , mis 2 , …, mis m represent the historical measurement errors of the pressure gauge, and mis max is the measurement error closest to the threshold; calculate the algebraic difference between mis 1 , mis 2 , …, mis m and mis max to obtain Δmis 1 , Δmis 2 , …, Δmis m ; S10-2，对i=1、2、…m，执行步骤S10-3；S10-2, for i=1, 2, ...m, execute step S10-3; S10-3，通过随机数依概率将调整为，，为符号变量，若大于等于阈值，且小于决策边界，则为1；若大于等于阈值，且大于决策边界，则为0；若小于阈值，且小于决策边界，则为0；若小于阈值，且大于决策边界，则为1；与的绝对值成正相关。S10-3, by random number according to probability Will Adjust to , , is a symbolic variable, if is greater than or equal to the threshold, and is less than the decision boundary, then is 1; if is greater than or equal to the threshold, and is greater than the decision boundary, then is 0; if is less than the threshold, and is less than the decision boundary, then is 0; if is less than the threshold, and is greater than the decision boundary, then is 1; and The absolute value of is positively correlated.
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