Data correction method of temperature, pressure, flow and humidity integrated monitor

Abstract

The invention provides a data correction method of a temperature, pressure, flow and humidity integrated monitor, and relates to the technical field of data processing, and the method comprises the steps: executing the layout of the temperature, pressure, flow and humidity integrated monitor according to regional layout features, and obtaining L temperature, pressure, flow and humidity monitoring modules; monitoring the target area in real time to obtain L groups of temperature, pressure, flow and humidity monitoring data; performing collection process punishment correction to obtain a temperature, pressure, flow and humidity initial correction data set; performing monitoring operation characteristic punishment correction according to the temperature, pressure, flow and humidity initial correction data set to obtain a temperature, pressure, flow and humidity enhanced correction data set; and drawing a global node temperature, pressure, flow and humidity monitoring cloud picture. The technical problem that temperature, pressure, flow and humidity monitoring data errors are large due to the fact that a data correction method is single, the intelligent degree is low, and data deviation cannot be accurately recognized and corrected can be solved, the accuracy and comprehensiveness of monitoring data correction can be improved, the data monitoring quality is improved, and the user experience is improved. And the authenticity and reliability of temperature, pressure, flow and humidity monitoring data are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument. Background Art

[0002] The temperature, pressure, flow and humidity integrated monitor is a device specially used to monitor flue gas emissions. It can measure the temperature, pressure, flow rate and humidity of flue gas in real time, and provide data of these parameters through a specific signal output method. The instrument is usually composed of a temperature sensor, a pressure sensor, a flow sensor and a humidity sensor. These sensors measure the target parameters through their respective working principles.

[0003] However, due to interference from factors such as the external environment and the accuracy of the equipment itself, any measuring device may have monitoring errors. Therefore, the temperature, pressure, flow and humidity integrated monitoring instrument also needs to perform data correction when monitoring data to ensure the accuracy and reliability of the monitoring data.

[0004] At present, the data correction method of the existing integrated temperature, pressure, flow and humidity monitoring instrument is unable to accurately identify and correct data deviations due to its single data correction method and low degree of intelligence, resulting in a technical problem of large errors in the temperature, pressure, flow and humidity monitoring data. Summary of the invention

[0005] The purpose of this application is to provide a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument, so as to solve the technical problem that the existing data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument is single and has a low degree of intelligence, and cannot accurately identify and correct data deviations, resulting in large errors in the temperature-pressure-flow-humidity monitoring data.

[0006] In view of the above problems, the present application provides a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument, the method comprising: obtaining regional layout characteristic information of a target area; deploying a temperature-pressure-flow-humidity integrated monitoring instrument in the target area according to the regional layout characteristic information, and obtaining L temperature-pressure-flow-humidity integrated monitoring modules corresponding to L regional monitoring nodes, wherein L is a positive integer greater than 1; performing real-time monitoring of the target area according to the L temperature-pressure-flow-humidity integrated monitoring modules, and obtaining L groups of temperature-pressure-flow-humidity monitoring data; performing collection process penalty correction according to the L groups of temperature-pressure-flow-humidity monitoring data, and obtaining an initial temperature-pressure-flow-humidity correction data set; performing monitoring operation characteristic penalty correction according to the initial temperature-pressure-flow-humidity correction data set, and obtaining an enhanced temperature-pressure-flow-humidity correction data set; and drawing a global node temperature-pressure-flow-humidity monitoring cloud map according to the enhanced temperature-pressure-flow-humidity correction data set.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: By obtaining regional layout characteristic information of the target area; deploying the temperature-pressure-flow-humidity integrated monitoring instrument in the target area according to the regional layout characteristic information, obtaining L temperature-pressure-flow-humidity integrated monitoring modules corresponding to L regional monitoring nodes, wherein L is a positive integer greater than 1; performing real-time monitoring of the target area according to the L temperature-pressure-flow-humidity integrated monitoring modules, obtaining L groups of temperature-pressure-flow-humidity monitoring data; performing collection process penalty correction according to the L groups of temperature-pressure-flow-humidity monitoring data, obtaining an initial temperature-pressure-flow-humidity correction data set; performing monitoring operation characteristic penalty correction according to the initial temperature-pressure-flow-humidity correction data set, obtaining an enhanced temperature-pressure-flow-humidity correction data set; and drawing a global node temperature-pressure-flow-humidity monitoring cloud map according to the enhanced temperature-pressure-flow-humidity correction data set. That is to say, by performing an offset analysis of the acquisition process, determining the penalty factor of the acquisition process, and correcting the monitoring data according to the penalty factor of the acquisition process, the initial correction data set of temperature, pressure, flow and humidity is obtained, which can reduce or avoid data errors caused by improper operation during the acquisition process; then the monitoring operation characteristic offset analysis is performed, and the monitoring operation penalty factor is determined to correct the initial correction data set of temperature, pressure, flow and humidity, and obtain the enhanced correction data set of temperature, pressure, flow and humidity, which can reduce or avoid data errors caused by equipment status and environmental interference; finally, the global node temperature, pressure, flow and humidity monitoring cloud map is drawn according to the enhanced correction data set of temperature, pressure, flow and humidity. The accuracy and comprehensiveness of the correction of temperature, pressure, flow and humidity monitoring data can be improved, thereby improving the quality of data monitoring and ensuring the authenticity and reliability of temperature, pressure, flow and humidity monitoring data.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument of the present application; Figure 2 This is a schematic diagram of the process of obtaining an initial temperature-pressure-flow-humidity calibration data set in a data calibration method for a temperature-pressure-flow-humidity integrated monitoring instrument of the present application. DETAILED DESCRIPTION

[0011] This application provides a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument, which solves the technical problem that the existing data correction method for the temperature-pressure-flow-humidity integrated monitoring instrument cannot accurately identify and correct data deviations due to the single data correction method and low intelligence level, resulting in large errors in the temperature-pressure-flow-humidity monitoring data. The accuracy and comprehensiveness of the temperature-pressure-flow-humidity monitoring data correction can be improved, thereby improving the quality of data monitoring and ensuring the authenticity and reliability of the temperature-pressure-flow-humidity monitoring data.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0013] Example Please see attached Figure 1 The present application provides a data correction method for a temperature-pressure-flow-humidity integrated monitoring instrument, the method specifically comprising the following steps: Step 1: Obtain regional layout feature information of the target area; Specifically, the temperature, pressure, flow and humidity integrated monitor is a new generation of online monitoring instrument, which measures temperature, flue pressure, flow rate and humidity. The flow rate is designed based on the differential pressure measurement principle of the traditional Pitot tube; humidity is measured using the humidity-sensitive capacitor principle (to achieve temperature control and heating of the humidity probe, prevent condensation, and protect the sensor). It can perform online monitoring of gas flow rate, dynamic pressure, flue pressure, temperature and humidity of various boilers, furnace flues and mine exhaust ducts, and is mainly used in CEMS and VOC systems.

[0014] First, determine the target area, where the target area refers to the area where temperature, pressure, flow and humidity data monitoring is to be carried out, such as: furnace flue, mine exhaust duct, refinery, turbine, lime kiln, power boiler, etc., which can be set according to the actual situation; then analyze the regional layout characteristics of the target area to obtain the regional layout characteristic information of the target area, where the regional layout characteristic information includes the spatial planning and component structure in the region, such as: the regional layout characteristic information of the furnace flue mainly involves the design, structure, function of the flue and its positional relationship in the furnace system. By obtaining the regional layout characteristic information, it provides a basis for the subsequent precise deployment of the temperature, pressure, flow and humidity integrated monitoring instrument.

[0015] Step 2: deploying the temperature-pressure-flow-humidity integrated monitoring instrument in the target area according to the regional layout characteristic information, and obtaining L temperature-pressure-flow-humidity integrated monitoring modules corresponding to L regional monitoring nodes, where L is a positive integer greater than 1; Specifically, based on the regional layout characteristic information, the temperature-pressure-flow-humidity integrated monitoring instrument is deployed in the target area, that is, based on the regional characteristic information, representative monitoring points are selected, which should be able to reflect the environmental conditions of different functional areas and try to avoid being affected by local factors; then, according to the number and location of the monitoring points, the layout of the monitoring network is planned to ensure that the monitoring network can cover the entire target area, and consider the mutual influence and complementarity between the monitoring points to reduce repeated monitoring and blind spots; obtain L temperature-pressure-flow-humidity integrated monitoring modules corresponding to L regional monitoring nodes, wherein each temperature-pressure-flow-humidity integrated monitoring module includes a temperature-pressure-flow-humidity integrated monitoring instrument, wherein L is a positive integer greater than 1, and the specific value of L can be set according to the actual situation of the target area. By deploying the temperature-pressure-flow-humidity integrated monitoring instrument according to the regional layout characteristic information, the accuracy of the deployment of the temperature-pressure-flow-humidity integrated monitoring instrument can be improved, thereby improving the accuracy of the temperature-pressure-flow-humidity monitoring data.

[0016] Step 3: Perform real-time monitoring of the target area according to the L temperature-pressure-flow-humidity integrated monitoring modules to obtain L groups of temperature-pressure-flow-humidity monitoring data; Specifically, the target area is monitored in real time through the L integrated temperature, pressure, flow and humidity monitoring modules to obtain L groups of temperature, pressure, flow and humidity monitoring data, wherein each group of temperature, pressure, flow and humidity monitoring data includes temperature monitoring data, pressure monitoring data, flow rate monitoring data and humidity monitoring data.

[0017] Step 4: Performing collection process penalty correction based on the L groups of temperature, pressure, flow and humidity monitoring data to obtain an initial correction data set of temperature, pressure, flow and humidity; Specifically, the monitoring data of the collection process of the L groups of temperature, pressure, flow and humidity monitoring data are obtained, and then the offset analysis of the data collection process is performed according to the monitoring data of the collection process to determine the penalty factor of the collection process. Further, the L groups of temperature, pressure, flow and humidity monitoring data are subjected to collection process penalty correction according to the penalty factor of the collection process to obtain the initial correction data set of temperature, pressure, flow and humidity. By performing the data collection process offset analysis to determine the penalty factor of the collection process, and correcting the temperature, pressure, flow and humidity monitoring data according to the penalty factor of the collection process, data errors caused by improper operation during the collection process can be reduced or avoided, and the accuracy of data correction can be improved.

[0018] Step 5: Performing a penalty correction on monitoring operation characteristics according to the initial correction data set of temperature, pressure, flow and humidity to obtain a temperature, pressure, flow and humidity enhanced correction data set; Specifically, the monitoring operation characteristic offset analysis is then performed based on the temperature, pressure, flow and humidity initial correction data set to obtain the parameter monitoring operation penalty factor; the temperature, pressure, flow and humidity initial correction data set is further corrected based on the parameter monitoring operation penalty factor to obtain the temperature, pressure, flow and humidity enhanced correction data set. By performing monitoring operation characteristic offset analysis to determine the parameter monitoring operation penalty factor, and performing data correction on the temperature, pressure, flow and humidity initial correction data set based on the parameter monitoring operation penalty factor, data errors caused by equipment status and environmental interference can be reduced or avoided, thereby improving the accuracy of data correction.

[0019] Step 6: Draw a global node temperature, pressure, flow and humidity monitoring cloud map based on the temperature, pressure, flow and humidity enhanced correction data set.

[0020] Specifically, finally, based on the three-dimensional visualization technology, a monitoring cloud map of the target area is drawn according to the temperature, pressure, flow and humidity enhancement correction data set to obtain a global node temperature, pressure, flow and humidity monitoring cloud map.

[0021] The above method can solve the data correction method of the existing temperature, pressure, flow and humidity integrated monitoring instrument. Due to the single data correction method and low intelligence level, it is impossible to accurately identify and correct data deviations, resulting in large errors in temperature, pressure, flow and humidity monitoring data. Technical problems. First, obtain the regional layout feature information of the target area; then, according to the regional layout feature information, the temperature, pressure, flow and humidity integrated monitoring instrument is deployed in the target area to obtain L temperature, pressure, flow and humidity integrated monitoring modules corresponding to L regional monitoring nodes, where L is a positive integer greater than 1; then, according to the L temperature, pressure, flow and humidity integrated monitoring modules, the target area is monitored in real time to obtain L groups of temperature, pressure, flow and humidity monitoring data; then, according to the L groups of temperature, pressure, flow and humidity monitoring data, the collection process is penalized and corrected to obtain the initial correction data set of temperature, pressure, flow and humidity; further, according to the initial correction data set of temperature, pressure, flow and humidity, the monitoring operation characteristics are penalized and corrected to obtain the enhanced correction data set of temperature, pressure, flow and humidity; finally, according to the enhanced correction data set of temperature, pressure, flow and humidity, a global node temperature, pressure, flow and humidity monitoring cloud map is drawn. By performing an acquisition process offset analysis, determining the acquisition process penalty factor, and correcting the monitoring data according to the acquisition process penalty factor, the initial correction data set of temperature, pressure, flow and humidity is obtained, which can reduce or avoid data errors caused by the acquisition process; then the monitoring operation characteristic offset analysis is performed, and the monitoring operation penalty factor is determined to correct the initial correction data set of temperature, pressure, flow and humidity, and obtain the enhanced correction data set of temperature, pressure, flow and humidity, which can reduce or avoid data errors caused by equipment status and environmental interference; finally, the global node temperature, pressure, flow and humidity monitoring cloud map is drawn according to the enhanced correction data set of temperature, pressure, flow and humidity. The accuracy and comprehensiveness of the correction of temperature, pressure, flow and humidity monitoring data can be improved, thereby improving the quality of data monitoring and ensuring the authenticity and reliability of temperature, pressure, flow and humidity monitoring data.

[0022] Further, as attached Figure 2 As shown, step 4 of this application includes: Performing standardization processing on the L groups of temperature, pressure, flow and humidity monitoring data to obtain L temperature, pressure, flow and humidity monitoring standard groups; Extracting a first temperature-pressure-flow-humidity monitoring standard group according to the L temperature-pressure-flow-humidity monitoring standard groups, wherein the first temperature-pressure-flow-humidity monitoring standard group is any one of the L temperature-pressure-flow-humidity monitoring standard groups; Retrieve the collection process monitoring data corresponding to the first temperature, pressure, flow and humidity monitoring standard group; Performing an acquisition process deviation analysis based on the acquisition process monitoring data to determine an acquisition process penalty factor; Specifically, first, the L groups of temperature, pressure, flow and humidity monitoring data are standardized based on a predetermined data cleaning strategy, and the predetermined data cleaning strategy includes missing value supplementation, redundant anomaly elimination, etc., which can be set according to actual needs to obtain L temperature, pressure, flow and humidity monitoring standard groups after data cleaning. Then, in the L temperature, pressure, flow and humidity monitoring standard groups, a first temperature, pressure, flow and humidity monitoring standard group is selected, wherein the first temperature, pressure, flow and humidity monitoring standard group is any one of the L temperature, pressure, flow and humidity monitoring standard groups. Further retrieve the acquisition process monitoring data corresponding to the first temperature, pressure, flow and humidity monitoring standard group, and the acquisition process monitoring data includes acquisition process monitoring images and acquisition transmission processing monitoring data. Then, according to the acquisition process monitoring data, an acquisition process offset analysis is performed, wherein the acquisition process offset analysis refers to the deviation analysis of improper operation steps or states in the acquisition process, such as: improper placement of the sensor probe, the sensor is not initialized and calibrated before use, etc., to determine the acquisition process penalty factor.

[0023] Furthermore, the present application also includes the following steps: The acquisition process monitoring data includes acquisition process monitoring images and acquisition transmission processing monitoring data; Focusing on the abnormality of the acquisition process according to the acquisition process monitoring image to obtain abnormal feature data of the acquisition process; According to the abnormal characteristic data of the acquisition process, the temperature, pressure, flow and humidity influence correlation is performed to obtain the influence correlation characteristic information of the acquisition process; Performing anomaly detection based on the collected, transmitted, processed and monitored data to obtain collected and processed abnormal feature data; Perform temperature, pressure, flow and humidity impact analysis based on the abnormal feature data collected and processed to obtain abnormal feature information of the impact; The collection process impact associated feature information and the collection processing abnormality impact feature information are integrated to obtain the collection process penalty factor.

[0024] Specifically, the method of performing the acquisition process offset analysis and determining the acquisition process penalty factor according to the acquisition process monitoring data is as follows: first, the acquisition process monitoring data is obtained, and the acquisition process monitoring data includes the acquisition process monitoring image and the acquisition transmission processing monitoring data; then, the acquisition process abnormal focus is performed according to the acquisition process monitoring image, wherein the acquisition process abnormal focus refers to the analysis of abnormal operation states that may exist in the acquisition process, such as: improper placement of the sensor probe, unreasonable position of the air flow port, and failure to initialize and calibrate the sensor before use, etc., to obtain the acquisition process abnormal feature data. Further, the temperature, pressure, flow and humidity influence association is performed according to the acquisition process abnormal feature data, that is, the deviation of the temperature, pressure, flow and humidity monitoring data is analyzed according to the acquisition process abnormal feature data. Existing statistical methods such as correlation analysis, regression analysis or time series analysis can be used to quantify the degree of association between the acquisition process abnormal feature data and the monitoring data deviation to obtain the acquisition process influence association feature information.

[0025] Anomaly detection is performed based on the collected, transmitted, processed and monitored data, wherein anomaly detection refers to the analysis of errors during data transmission and data processing, for example: the data collected from the monitoring instrument may also produce errors during the transmission and processing process, such as packet loss during data transmission or inaccurate processing algorithms, etc., to obtain the collection and processing abnormal feature data. Then, the temperature, pressure, flow and humidity impact analysis is performed based on the collection and processing abnormal feature data, that is, using existing statistical methods such as correlation analysis, regression analysis or time series analysis, the degree of correlation between the collection and processing abnormal feature data and the monitoring data deviation is quantified to obtain the collection and processing abnormal impact feature information, and finally the collection process impact association feature information and the collection and processing abnormal impact feature information are fused, that is, if the collection process impact association feature information and the collection and processing abnormal impact feature information have the same or similar information, only one is retained to obtain the collection process penalty factor.

[0026] The first temperature-pressure-flow-humidity monitoring standard group is corrected according to the acquisition process penalty factor to generate the temperature-pressure-flow-humidity initial correction data set.

[0027] Specifically, based on the acquisition process penalty factor, data correction is performed on the first temperature-pressure-flow-humidity monitoring standard group to obtain the temperature-pressure-flow-humidity initial correction data set.

[0028] Furthermore, the present application also includes the following steps: Retrieve a sample collection process penalty factor set and a sample collection process penalty compensation data set; Based on the preset compensator establishment conditions, according to the sample acquisition process penalty factor set and the sample acquisition process penalty compensation data set, a collection process penalty compensator is built, wherein the preset compensator establishment conditions include a predetermined data partition weight and a predetermined training convergence condition; Inputting the acquisition process penalty factor into the acquisition process penalty compensator to obtain acquisition process penalty compensation data; According to the penalty compensation data of the acquisition process, compensation optimization is performed on the first temperature-pressure-flow-humidity monitoring standard group to obtain a first temperature-pressure-flow-humidity monitoring correction group; The first temperature-pressure-fluid-humidity monitoring correction group is added to the temperature-pressure-fluid-humidity initial correction data set.

[0029] Specifically, first, retrieve the sample collection process penalty factor set and the sample collection process penalty compensation data set, where the sample collection process penalty factor and the sample collection process penalty compensation data correspond one to one, and can be retrieved based on big data or by retrieving historical monitoring logs for information extraction.

[0030] Based on the BP neural network and the preset compensator establishment conditions, the acquisition process penalty compensator is a neural network model that can be iteratively optimized in machine learning, which is obtained through supervised training of the data set. The acquisition process penalty compensator includes an input layer, multiple hidden layers and an output layer, wherein the input data of the input layer is the acquisition process penalty factor, and the output data is the acquisition process penalty compensation data. The preset compensator establishment conditions include predetermined data partition weights and predetermined training convergence conditions. The predetermined data partition weights include training data weights and verification data weights, which can be set according to actual conditions, such as: training data weight 0.8, verification data weight 0.2, that is, the training data accounts for 80%, and the verification data accounts for 20%; the predetermined training convergence condition is the expected output accuracy, which can be set according to actual needs, such as setting the expected output accuracy to 93%. The sample acquisition process penalty factor set and the sample acquisition process penalty compensation data set are used as sample data sets; and the sample data set is divided according to the predetermined data partitioning weight to obtain a sample training set and a sample verification set; then the acquisition process penalty compensator is supervised and trained based on the sample training set, and when the acquisition process penalty compensator tends to converge, the acquisition process penalty compensator is then verified and trained using the sample verification set until the output accuracy is greater than or equal to the predetermined training convergence condition, thereby obtaining a trained acquisition process penalty compensator.

[0031] Then, the acquisition process penalty factor is input into the acquisition process penalty compensator to obtain acquisition process penalty compensation data; and further, according to the acquisition process penalty compensation data, the first temperature-pressure-flow-humidity monitoring standard group is optimized for data compensation to obtain the first temperature-pressure-flow-humidity monitoring correction group; and then the first temperature-pressure-flow-humidity monitoring correction group is added to the temperature-pressure-flow-humidity initial correction data set to obtain the temperature-pressure-flow-humidity initial correction data set. By constructing an acquisition process penalty compensator based on the BP neural network to analyze the acquisition process penalty compensation data, the efficiency and accuracy of obtaining the acquisition process penalty compensation data can be improved, thereby improving the accuracy and efficiency of the temperature-pressure-flow-humidity monitoring data correction.

[0032] Furthermore, step five of this application includes: Based on the initial correction data set of temperature, pressure, flow and humidity, the characteristics of each parameter monitoring operation are traced back to obtain the characteristic data set of each parameter monitoring operation; Perform monitoring operation characteristic deviation analysis according to the characteristic data sets of each parameter monitoring operation to generate a penalty factor for each parameter monitoring operation; Specifically, first, the monitoring operation characteristics of each parameter are traced back based on the initial correction data set of temperature, pressure, flow and humidity, that is, the monitoring instrument status data and monitoring instrument environment data corresponding to each parameter in the initial correction data set of temperature, pressure, flow and humidity are collected, where each parameter includes temperature parameter, pressure parameter, flow rate parameter and humidity parameter, and each parameter corresponds to a different monitoring instrument, including temperature sensor, pressure sensor, flow meter and humidity sensor; obtain the characteristic data set of each parameter monitoring operation. Then, the monitoring operation characteristic offset analysis is performed based on the characteristic data set of each parameter monitoring operation, and the penalty factor of each parameter monitoring operation is obtained according to the offset analysis result.

[0033] Furthermore, the present application also includes the following steps: Conducting a multi-parameter monitoring operation characteristic benchmark analysis based on the temperature, pressure, flow and humidity integrated monitoring instrument to construct a multi-dimensional monitoring operation characteristic benchmark matrix; Specifically, a method for generating penalty factors for each parameter monitoring operation by performing monitoring operation characteristic offset analysis based on the characteristic data sets of each parameter monitoring operation is as follows: first, a multi-parameter monitoring operation characteristic benchmark analysis is performed based on the temperature, pressure, flow and humidity integrated monitoring instrument, wherein the parameter monitoring operation characteristic benchmark analysis refers to analyzing the normal operating status corresponding to the parameter, and then constructing a multi-dimensional monitoring operation characteristic benchmark matrix based on the benchmark analysis results.

[0034] Furthermore, the present application also includes the following steps: Extracting a first characteristic monitoring component according to the temperature-pressure-flow-humidity integrated monitoring instrument, wherein the temperature-pressure-flow-humidity integrated monitoring instrument includes a plurality of characteristic monitoring components corresponding to a plurality of characteristic monitoring indicators, and the first characteristic monitoring component is any one of the plurality of characteristic monitoring components; Perform global backtracking of normal samples of monitoring operation characteristics according to the first characteristic monitoring component to obtain a normal operation characteristic sample set of the first component; Performing a central tendency analysis on the first component normal operation feature sample set to obtain a first component normal operation feature concentrated interval set; According to the concentrated interval set of the normal operation characteristics of the first component, a first component monitoring operation characteristic benchmark matrix is ​​constructed, and the first component monitoring operation characteristic benchmark matrix is ​​added to the multi-dimensional monitoring operation characteristic benchmark matrix.

[0035] Specifically, first, a first characteristic monitoring component is extracted in the temperature-pressure-flow-humidity integrated monitor, wherein the temperature-pressure-flow-humidity integrated monitor includes multiple characteristic monitoring components corresponding to multiple characteristic monitoring indicators, the first characteristic monitoring component is any one of the multiple characteristic monitoring components, the multiple characteristic monitoring indicators include temperature parameters, pressure parameters, flow rate parameters and humidity parameters, and the multiple characteristic monitoring components include temperature sensors, pressure sensors, flow meters and humidity sensors.

[0036] Then, a global backtracking of normal samples of monitoring operation characteristics is performed based on the first characteristic monitoring component, that is, the historical monitoring log is called to collect the first characteristic monitoring component, and the corresponding historical monitoring component status data and historical monitoring component environment data when the same model component of the first characteristic monitoring component is in the historical normal state, so as to obtain the normal operation characteristic sample set of the first component. Further, a central tendency analysis is performed based on the normal operation characteristic sample set of the first component, wherein the central tendency analysis refers to determining the equilibrium form of the normal operation characteristic sample set of the first component to be concentrated in the central position. Commonly used central tendency analysis methods include the mean method, the median method, the mode method, etc., and the adapted method can be selected according to the actual scenario to obtain the first component normal operation characteristic concentrated interval set, wherein the first component normal operation characteristic concentrated interval refers to the normal fluctuation range of the operation characteristic. Then, based on the first component normal operation characteristic concentrated interval set, a first component monitoring operation characteristic benchmark matrix is ​​constructed, and the first component monitoring operation characteristic benchmark matrix is ​​added to the multidimensional monitoring operation characteristic benchmark matrix to obtain the multidimensional monitoring operation characteristic benchmark matrix.

[0037] Performing standardization processing on the characteristic data sets of each parameter monitoring operation to construct a characteristic matrix of each parameter monitoring operation; Based on the characteristic matrix of each parameter monitoring operation, the multi-dimensional monitoring operation characteristic reference matrix is ​​associated and mapped to determine the reference matrix corresponding to each parameter; Based on the reference matrix corresponding to each parameter, the deviation of the characteristic matrix of the monitoring operation of each parameter is calculated to obtain the characteristic deviation matrix of the monitoring operation of each parameter; The influence of each parameter is identified according to the characteristic deviation matrix of each parameter monitoring operation to obtain the penalty factor of each parameter monitoring operation.

[0038] Specifically, first, the characteristic data sets of each parameter monitoring operation are standardized based on a predetermined data cleaning strategy, and the predetermined data cleaning strategy includes missing value supplementation, redundant anomaly elimination, etc., which can be set according to actual needs; then, each parameter monitoring operation characteristic matrix is ​​constructed based on each standard parameter monitoring operation characteristic data set.

[0039] Then, based on the characteristic matrix of each parameter monitoring operation, the multi-dimensional monitoring operation characteristic reference matrix is ​​associated and mapped, that is, the parameter monitoring operation characteristic is matched with the normal operation characteristic concentration interval one by one, and the corresponding reference matrix of each parameter is determined. Further, based on the corresponding reference matrix of each parameter, the deviation of the characteristic matrix of each parameter monitoring operation is calculated, that is, if the parameter monitoring operation characteristic is not in the normal operation characteristic concentration interval, the characteristic deviation between the normal operation characteristic concentration interval and the parameter monitoring operation characteristic is calculated, wherein the characteristic deviation includes the deviation greater than the upper limit of the interval and the deviation less than the lower limit of the interval, and the characteristic deviation matrix of each parameter monitoring operation is obtained. Finally, the influence of each parameter is identified according to the characteristic deviation matrix of each parameter monitoring operation, that is, the influence of the deviation of each parameter monitoring operation characteristic on the accuracy of each parameter data monitoring is quantified, and the parameter monitoring operation characteristic with an influence greater than the predetermined influence threshold is extracted and set as the parameter monitoring operation penalty factor, and the penalty factor of each parameter monitoring operation is obtained.

[0040] Build a monitoring operation penalty compensator; Based on the penalty factors of the monitoring operations of each parameter, penalty compensation analysis is performed according to the monitoring operation penalty compensator to obtain penalty compensation data of the monitoring operations of each parameter; The temperature, pressure, fluid and humidity initial correction data set is subjected to parameter mapping compensation according to the penalty compensation data of the parameter monitoring operations to obtain the temperature, pressure, fluid and humidity enhanced correction data set.

[0041] Specifically, a monitoring operation penalty compensator is constructed based on a BP neural network, and the monitoring operation penalty compensator is a neural network model that can be iteratively optimized in machine learning, wherein the input data of the monitoring operation penalty compensator is a parameter monitoring operation penalty factor, and the output data is a parameter monitoring operation penalty compensation data. Then, a sample data set is obtained, and the sample data set is used to perform supervised training and verification training on the monitoring operation penalty compensator until the predetermined training constraints are met, and a trained monitoring operation penalty compensator is obtained, wherein the construction and training process of the monitoring operation penalty compensator is the same as that of the acquisition process penalty compensator, and in order to reflect the brevity of the specification, it will not be described in detail here.

[0042] Then, the penalty factors of each parameter monitoring operation are input into the monitoring operation penalty compensator for penalty compensation analysis, and the penalty compensation data of each parameter monitoring operation are output; further, the temperature, pressure, flow and humidity initial correction data set is subjected to parameter mapping compensation according to the penalty compensation data of each parameter monitoring operation, so as to obtain the temperature, pressure, flow and humidity enhanced correction data set. By constructing a monitoring operation penalty compensator based on the BP neural network to analyze the monitoring operation penalty compensation data, the efficiency and accuracy of obtaining the monitoring operation penalty compensation data can be improved, thereby improving the accuracy and efficiency of the correction of the temperature, pressure, flow and humidity initial correction data set.

[0043] Furthermore, the present application also includes the following steps: According to the temperature, pressure, flow and humidity enhancement correction data set, a set of penalty factors for each parameter is retrieved; Performing global cross analysis on each parameter penalty factor set respectively to obtain global cross analysis results of each parameter penalty; The cross-interference effect of penalty factors is corrected for the temperature-pressure-flow-humidity enhancement correction data set according to the global cross-analysis results of the penalty for each parameter.

[0044] Specifically, based on the temperature, pressure, flow and humidity enhancement correction data set, the penalty factors of each parameter are determined to obtain the penalty factor sets of each parameter. Then, the penalty factor sets of each parameter are subjected to global cross-analysis respectively, and the global cross-analysis refers to the analysis of the mutual interference between the penalty factors of each parameter and the quantification of the interference degree, wherein the global cross-analysis is a statistical method used to study the interaction effects between multiple parameters or variables to obtain the global cross-analysis results of the penalties of each parameter. Finally, according to the global cross-analysis results of the penalties of each parameter, the penalty factor cross-interference effect correction is performed on the temperature, pressure, flow and humidity enhancement correction data set. By performing a global cross-analysis on the penalty factor sets of each parameter, and correcting the penalty factor cross-interference effect on the temperature, pressure, flow and humidity enhancement correction data set according to the global cross-analysis results of the penalties of each parameter, the influence of the mutual interference of the penalty factors on the monitoring data can be avoided, and the accuracy of the temperature, pressure, flow and humidity monitoring data obtained can be further improved.

[0045] In summary, the data correction method of the temperature, pressure, flow and humidity integrated monitoring instrument provided by the present application has the following technical effects: By performing an offset analysis of the acquisition process, determining the penalty factor of the acquisition process, and correcting the monitoring data according to the penalty factor of the acquisition process, the initial correction data set of temperature, pressure, flow and humidity is obtained, which can reduce or avoid data errors caused by improper operation during the acquisition process; then the monitoring operation characteristic offset analysis is performed to determine the penalty factor of the monitoring operation to correct the initial correction data set of temperature, pressure, flow and humidity, and obtain the enhanced correction data set of temperature, pressure, flow and humidity, which can reduce or avoid data errors caused by equipment status and environmental interference; finally, the global node temperature, pressure, flow and humidity monitoring cloud map is drawn according to the enhanced correction data set of temperature, pressure, flow and humidity. The accuracy and comprehensiveness of the correction of the temperature, pressure, flow and humidity monitoring data can be improved, thereby improving the quality of data monitoring and ensuring the authenticity and reliability of the temperature, pressure, flow and humidity monitoring data.

[0046] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A data correction method for a temperature, pressure, flow and humidity integrated monitoring instrument, characterized in that: The method comprises: Obtaining regional layout feature information of the target area; Deploy the temperature-pressure-flow-humidity integrated monitoring instrument in the target area according to the regional layout characteristic information, and obtain L temperature-pressure-flow-humidity integrated monitoring modules corresponding to L regional monitoring nodes, where L is a positive integer greater than 1; Performing real-time monitoring of the target area according to the L temperature-pressure-flow-humidity integrated monitoring modules to obtain L groups of temperature-pressure-flow-humidity monitoring data; Performing collection process penalty correction based on the L groups of temperature, pressure, flow and humidity monitoring data to obtain an initial correction data set of temperature, pressure, flow and humidity; Performing a monitoring operation characteristic penalty correction according to the temperature, pressure, fluidity and humidity initial correction data set to obtain a temperature, pressure, fluidity and humidity enhanced correction data set; A global node temperature, pressure, flow and humidity monitoring cloud map is drawn based on the temperature, pressure, flow and humidity enhanced correction data set.

2. The method according to claim 1, characterized in that Performing collection process penalty correction according to the L groups of temperature, pressure, flow and humidity monitoring data to obtain an initial correction data set of temperature, pressure, flow and humidity, including: Performing standardization processing on the L groups of temperature, pressure, flow and humidity monitoring data to obtain L temperature, pressure, flow and humidity monitoring standard groups; Extracting a first temperature-pressure-flow-humidity monitoring standard group according to the L temperature-pressure-flow-humidity monitoring standard groups, wherein the first temperature-pressure-flow-humidity monitoring standard group is any one of the L temperature-pressure-flow-humidity monitoring standard groups; Retrieve the collection process monitoring data corresponding to the first temperature, pressure, flow and humidity monitoring standard group; Performing an acquisition process deviation analysis based on the acquisition process monitoring data to determine an acquisition process penalty factor; The first temperature-pressure-flow-humidity monitoring standard group is corrected according to the acquisition process penalty factor to generate the temperature-pressure-flow-humidity initial correction data set.

3. The method according to claim 2, characterized in that Performing an acquisition process deviation analysis based on the acquisition process monitoring data to determine an acquisition process penalty factor includes: The acquisition process monitoring data includes acquisition process monitoring images and acquisition transmission processing monitoring data; Focusing on the abnormality of the acquisition process according to the acquisition process monitoring image to obtain abnormal feature data of the acquisition process; According to the abnormal characteristic data of the acquisition process, the temperature, pressure, flow and humidity influence correlation is performed to obtain the influence correlation characteristic information of the acquisition process; Performing anomaly detection based on the collected, transmitted, processed and monitored data to obtain collected and processed abnormal feature data; Perform temperature, pressure, flow and humidity impact analysis based on the abnormal feature data collected and processed to obtain abnormal feature information of the impact; The collection process impact associated feature information and the collection processing abnormality impact feature information are integrated to obtain the collection process penalty factor.

4. The method according to claim 2, characterized in that According to the acquisition process penalty factor, the first temperature, pressure, flow and humidity monitoring standard group is corrected to generate the temperature, pressure, flow and humidity initial correction data set, including: Retrieve a sample collection process penalty factor set and a sample collection process penalty compensation data set; Based on the preset compensator establishment conditions, according to the sample acquisition process penalty factor set and the sample acquisition process penalty compensation data set, a collection process penalty compensator is built, wherein the preset compensator establishment conditions include a predetermined data partition weight and a predetermined training convergence condition; Inputting the acquisition process penalty factor into the acquisition process penalty compensator to obtain acquisition process penalty compensation data; According to the penalty compensation data of the acquisition process, compensation optimization is performed on the first temperature-pressure-flow-humidity monitoring standard group to obtain a first temperature-pressure-flow-humidity monitoring correction group; The first temperature-pressure-fluid-humidity monitoring correction group is added to the temperature-pressure-fluid-humidity initial correction data set.

5. The method according to claim 1, characterized in that The monitoring operation characteristic penalty correction is performed according to the temperature, pressure, flow and humidity initial correction data set to obtain the temperature, pressure, flow and humidity enhanced correction data set, including: Based on the temperature, pressure, flow and humidity initial correction data set, the characteristics of each parameter monitoring operation are traced back to obtain the characteristic data set of each parameter monitoring operation; Perform monitoring operation characteristic deviation analysis according to the characteristic data sets of each parameter monitoring operation to generate a penalty factor for each parameter monitoring operation; Build a monitoring operation penalty compensator; Based on the penalty factors of the monitoring operations of each parameter, penalty compensation analysis is performed according to the monitoring operation penalty compensator to obtain penalty compensation data of the monitoring operations of each parameter; The temperature, pressure, fluid and humidity initial correction data set is subjected to parameter mapping compensation according to the penalty compensation data of the parameter monitoring operations to obtain the temperature, pressure, fluid and humidity enhanced correction data set.

6. The method according to claim 5, characterized in that Performing monitoring operation feature deviation analysis according to the parameter monitoring operation feature data set to generate each parameter monitoring operation penalty factor includes: Conducting a multi-parameter monitoring operation characteristic benchmark analysis based on the temperature, pressure, flow and humidity integrated monitoring instrument to construct a multi-dimensional monitoring operation characteristic benchmark matrix; Performing standardization processing on the characteristic data sets of each parameter monitoring operation to construct a characteristic matrix of each parameter monitoring operation; Based on the characteristic matrix of each parameter monitoring operation, the multi-dimensional monitoring operation characteristic reference matrix is ​​associated and mapped to determine the reference matrix corresponding to each parameter; Based on the reference matrix corresponding to each parameter, the deviation of the characteristic matrix of the monitoring operation of each parameter is calculated to obtain the characteristic deviation matrix of the monitoring operation of each parameter; The influence of each parameter is identified according to the characteristic deviation matrix of each parameter monitoring operation to obtain the penalty factor of each parameter monitoring operation.

7. The method according to claim 6, characterized in that A multi-parameter monitoring operation characteristic benchmark analysis is performed based on the temperature, pressure, flow and humidity integrated monitoring instrument to construct a multi-dimensional monitoring operation characteristic benchmark matrix, including: Extracting a first characteristic monitoring component according to the temperature-pressure-flow-humidity integrated monitoring instrument, wherein the temperature-pressure-flow-humidity integrated monitoring instrument includes a plurality of characteristic monitoring components corresponding to a plurality of characteristic monitoring indicators, and the first characteristic monitoring component is any one of the plurality of characteristic monitoring components; Perform global backtracking of normal samples of monitoring operation characteristics according to the first characteristic monitoring component to obtain a normal operation characteristic sample set of the first component; Performing a central tendency analysis on the first component normal operation feature sample set to obtain a first component normal operation feature concentrated interval set; According to the concentrated interval set of the normal operation characteristics of the first component, a first component monitoring operation characteristic benchmark matrix is ​​constructed, and the first component monitoring operation characteristic benchmark matrix is ​​added to the multi-dimensional monitoring operation characteristic benchmark matrix.

8. The method according to claim 1, characterized in that Obtain temperature, pressure, flow and humidity enhancement correction data set, including: According to the temperature, pressure, flow and humidity enhancement correction data set, a set of penalty factors for each parameter is retrieved; Performing global cross analysis on each parameter penalty factor set respectively to obtain global cross analysis results of each parameter penalty; The cross-interference effect of penalty factors is corrected for the temperature-pressure-flow-humidity enhancement correction data set according to the global cross-analysis results of the penalty for each parameter.

Images