CN-122001925-A - Factory electrical equipment explosion-proof safety intelligent monitoring system and method based on Internet of things

CN122001925ACN 122001925 ACN122001925 ACN 122001925ACN-122001925-A

Abstract

The invention relates to the technical field of electric equipment monitoring and discloses an explosion-proof safety intelligent monitoring system and method for factory electric equipment based on the Internet of things. By implementing the invention, multi-layer technical data such as equipment qualification, environmental corrosion, operation loss, digital twin deduction and the like are integrated by utilizing a multi-source data collaborative analysis technology, the data characteristics are converted into clear safety state results, the generated evaluation results have complete technical deduction links, and accurate technical decision basis is provided for factory explosion-proof safety management, so that the problem that the current factory electric equipment explosion-proof safety intelligent monitoring system based on the Internet of things is difficult to achieve full-flow data linkage, dynamic accurate evaluation and multi-system collaborative management and control and cannot fully meet the high-level safety management and control requirements of dangerous factories is solved.

Inventors

ZHANG LULIN
JIANG YING
FU CHUNXIAO
WANG YANCHAO
WANG LINJIE
Zhang Diewen

Assignees

中质(天津)科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260123

Claims (10)

1. An explosion-proof safety intelligent monitoring system of factory electrical equipment based on thing networking, its characterized in that, the system includes: The intelligent certificate management module is used for acquiring explosion-proof qualified certificate data of the electrical equipment, analyzing the explosion-proof qualified certificate data to obtain certificate information, performing two-dimensional verification based on the certificate information to obtain equipment explosion-proof qualification state data, and outputting the certificate information and the equipment explosion-proof qualification state data as first-layer monitoring data; The environment sensing and corrosion analysis module is used for acquiring an environment parameter set by utilizing the intrinsically safe distributed sensing network, determining corrosion degree data and residual life data of the electrical equipment based on a preset coupling simulation model and the environment parameter set, and outputting the corrosion degree data and the residual life data as second-layer monitoring data; the equipment loss internet of things monitoring module is used for acquiring the operation parameters of the electrical equipment by using a monitoring terminal, determining loss characteristics and fault location information of the electrical equipment based on the operation parameters, and outputting the loss characteristics and the fault location information as third-layer monitoring data; the edge computing gateway is used for determining target monitoring data and hierarchical abnormality early warning trigger information based on the first layer monitoring data, the second layer monitoring data and the third layer monitoring data; The cloud platform is used for constructing a digital twin model of the equipment based on the target monitoring data, the hierarchical anomaly early warning trigger information and the historical monitoring data; and the explosion-proof judging module is used for carrying out explosion-proof safety state assessment based on the target monitoring data, the grading abnormal early warning trigger information, the historical monitoring data and the digital twin model to obtain an assessment result.
2. The system of claim 1, wherein the certificate intelligent management module, the environment awareness and corrosion analysis module, and the equipment loss internet of things monitoring module are respectively in communication connection with the edge computing gateway, the edge computing gateway is in communication connection with the cloud platform, the cloud platform is in communication connection with the explosion proof determination module, and the certificate intelligent management module comprises: The certificate input unit is used for receiving paper scanning data, electronic file data and manual input data of the explosion-proof qualified certificate to obtain explosion-proof qualified certificate data; The analysis unit is used for carrying out structural analysis on the explosion-proof qualified certificate data and extracting certificate information, wherein the certificate information comprises certificate number information, equipment model information, equipment three-dimensional model information, certification authority information, validity period information, explosion-proof grade information, material characteristic information, equipment explosion-proof structure information and applicable environment parameter information; The first verification unit is used for generating an expiration early warning signal based on the system time and the validity period information; The second verification unit is used for verifying the certificate number information, the equipment model information and the certification authority information based on an official database and a certification authority traceability platform to determine a qualification judgment result; The qualification judging unit is used for generating equipment explosion-proof qualification state data based on the expiration early warning signal and the qualification judging result; The certificate association unit is used for establishing a unique association relation between the explosion-proof qualified certificate and the electrical equipment to obtain association mapping data, and integrating the association mapping data into the certificate information; and the first layer data output unit is used for integrating the certificate information and the equipment explosion-proof qualification state data into first layer monitoring data and outputting the first layer monitoring data.
3. The system of claim 2, wherein the environmental awareness and corrosion analysis module comprises: The intrinsic safety type sensing unit is used for acquiring environmental parameters of a corresponding factory area of the electrical equipment by utilizing the intrinsic safety type distributed sensing network to obtain an environmental parameter set, wherein the environmental parameter set comprises a salt spray parameter, a humidity parameter, a temperature parameter, a harmful gas concentration parameter, a dust concentration parameter and an ultraviolet intensity parameter; the environment data preprocessing unit is used for carrying out wavelet transformation denoising processing, standardization processing and time sequence alignment processing on the environment parameter set to obtain an environment parameter time sequence data set; The coupling simulation unit is used for calculating corrosion degree data and residual corrosion life data of each part of the electrical equipment based on the environmental parameter time sequence data set and the material characteristic information by using a preset coupling simulation model, wherein the material characteristic information comprises a material heat conductivity coefficient, an electrochemical corrosion constant and an insulating medium parameter; The environment risk level classification unit is used for judging equipment aging corrosion risk levels based on a preset risk judging rule, the corrosion degree data and the residual corrosion life data, generating risk level identification data, and integrating the risk level identification data and the environment parameter time sequence data set into the corrosion degree data; and the second layer data output unit is used for integrating the corrosion degree data and the residual life data into second layer monitoring data and outputting the second layer monitoring data.
4. The system of claim 3, wherein the coupling simulation unit comprises: The parameter cooperation subunit is used for decomposing the environmental parameter time sequence data set into a plurality of groups of time sequence fragments according to time dimension, carrying out parameter normalization processing and cooperation mapping processing on each group of time sequence fragments based on the material heat conductivity coefficient, the electrochemical corrosion constant and the insulating medium parameter, and generating a multi-dimensional time sequence parameter set; The collaborative computing subunit is used for constructing a multi-field coupling control equation based on the multi-dimensional time sequence parameter set by utilizing a preset coupling simulation model, and computing the multi-field coupling control equation to obtain dynamic corrosion rate, corrosion depth distribution data and insulation performance degradation curves of all parts of the electrical equipment, wherein the preset coupling simulation model is used for simulating the coupling effect of a salt spray humidity temperature collaborative corrosion field and an equipment operation electric field temperature field; The residual life dynamic prediction subunit is used for extracting characteristic inflection points of the dynamic corrosion rate, the corrosion depth distribution data and the insulation performance degradation curve, constructing a corrosion life mapping model based on the characteristic inflection points and the equipment explosion-proof structure information, and obtaining a prediction result based on the corrosion life mapping model and the environmental parameter time sequence data set; The result correction subunit is used for correcting the prediction result by utilizing a preset environmental parameter fluctuation coefficient to obtain residual corrosion life data and life decay trend prediction curves of all parts of the electrical equipment; And the data determining subunit is used for determining corrosion degree data based on the corrosion depth distribution data, integrating the life decay trend prediction curve into the residual corrosion life data and outputting the corrosion degree data and the residual corrosion life data.
5. The system of claim 4, wherein the equipment loss internet of things monitoring module comprises: The intrinsic safety type monitoring terminal unit adopts a three-level energy isolation intrinsic safety circuit architecture, wherein the three-level energy isolation intrinsic safety circuit architecture comprises a power supply isolation subunit, a signal isolation subunit and an energy storage isolation subunit; The operation parameter acquisition unit is used for acquiring operation parameters of the electrical equipment, wherein the operation parameters comprise a mechanical vibration parameter, an electrical discharge parameter, a temperature parameter, a current parameter, an insulation resistance parameter and a rotating speed parameter; the operation parameter preprocessing unit is used for carrying out denoising treatment, format standardization treatment and outlier preliminary screening treatment on the operation parameters and outputting a standardized operation parameter data set; A loss feature extraction unit for extracting device loss features based on the standardized operating parameter data set; the fault positioning unit is used for determining three-dimensional position information of a fault point and generating fault positioning information by adopting a local discharge ripple positioning algorithm and an ultrasonic cooperative positioning algorithm based on the electrical discharge parameters and the electrical loss characteristics; The positioning transmission unit is used for transmitting the equipment loss characteristics and the fault positioning information to an edge computing gateway, and is configured with a national cipher SM4 encryption protocol; And the third layer data output unit is used for integrating the equipment loss characteristics and the fault locating information into third layer monitoring data and outputting the third layer monitoring data.
6. The system of claim 5, wherein the normalized operating parameter dataset includes normalized mechanical vibration parameters, normalized electrical discharge parameters, normalized temperature parameters, normalized current parameters, normalized insulation resistance parameters, and normalized rotational speed parameters, the loss feature extraction unit comprising: The mechanical loss feature extraction subunit is used for processing the standardized mechanical vibration parameters by using an order analysis algorithm to obtain mechanical vibration features, and synchronously calibrating the mechanical vibration features based on the standardized rotating speed parameters to obtain mechanical loss features; The electrical loss characteristic extraction subunit is used for processing the standardized electrical discharge parameters by utilizing a frequency spectrum analysis algorithm to obtain discharge parameter characteristics, and verifying the insulation degradation degree of the discharge parameter characteristics based on the standardized insulation resistance parameters to obtain electrical loss characteristics; And the operation loss feature extraction subunit is used for calculating a temperature current correlation coefficient based on the standardized temperature parameter and the standardized current parameter, determining load heating matching relation data based on the temperature current correlation coefficient, and obtaining operation loss features based on the load heating matching relation data.
7. The system of claim 6, wherein the edge computing gateway comprises: The hierarchical preprocessing unit is used for extracting certificate text data, environment time sequence data and equipment operation parameter data in the first layer of monitoring data, the second layer of monitoring data and the third layer of monitoring data, carrying out semantic alignment and format standardization processing on the certificate text data, carrying out wavelet threshold denoising processing and timestamp calibration processing on the environment time sequence data, carrying out Kalman filtering denoising processing and outlier primary screening processing on the equipment operation parameters, and outputting a standardized multi-source data set; the edge side real-time computing unit is used for executing cross-dimension data association matching on the standardized multi-source data set based on a preset characteristic weight matrix and a weighted Euclidean distance, screening to obtain target monitoring data, and performing trend fitting on the target monitoring data by utilizing a sliding window mechanism to generate a data change slope and a fluctuation coefficient; The early warning trigger unit is used for generating hierarchical abnormal early warning trigger information based on a preset dynamic threshold model, the target monitoring data, the data change slope and the fluctuation coefficient by using an absolute threshold and trend threshold double-judgment mechanism.
8. The system of claim 7, wherein the historical monitoring data comprises historical first tier monitoring data, historical second tier monitoring data, historical third tier monitoring data, historical target monitoring data, and historical hierarchical anomaly pre-warning trigger information, the cloud platform comprising: The multi-source data storage unit is used for storing the target monitoring data, the hierarchical abnormal early warning trigger information and the historical monitoring data in a classified mode by adopting a distributed time sequence database, and constructing a data index based on the unique identification of the equipment; The digital twin model construction unit is used for constructing a digital twin model based on the equipment three-dimensional model information, the target monitoring data, the historical monitoring data, the corrosion degree data, the residual life data and the equipment loss characteristics and outputting digital twin mapping data, wherein the digital twin mapping data comprises an equipment full-state association relation and an equipment state deduction result; and the dynamic threshold training unit is used for training and updating the preset dynamic threshold model based on the historical monitoring data and transmitting the updated preset dynamic threshold model to an edge computing gateway.
9. The system of claim 8, wherein the explosion proof determination module comprises: The index system matching unit is used for matching a corresponding explosion-proof safety evaluation index system based on the equipment model information, and the evaluation index system is constructed based on a analytic hierarchy process and comprises a certificate qualification compliance index, an environment corrosion risk index, an equipment loss degradation index and an early warning trigger level index, wherein the certificate qualification compliance index, the environment corrosion risk index, the equipment loss degradation index and the early warning trigger level index are respectively associated with corresponding data items in target monitoring data; the dynamic weight configuration unit is used for adjusting weight coefficients for the certificate qualification compliance index, the environment corrosion risk index, the equipment loss degradation index and the early warning trigger level index based on fault case data in the historical monitoring data to obtain updated weight coefficients; The evidence fusion unit is used for carrying out fusion processing on the target monitoring data and the hierarchical abnormality early warning trigger information based on the updated weight coefficient by adopting an evidence theory and the equipment full-state association relation to generate a credibility distribution result; The security level judging unit is used for mapping the credibility distribution result to the corresponding explosion-proof security level based on a preset level judging rule and the state deduction result and outputting a security level judging result.
10. An intelligent monitoring method for explosion-proof safety of factory electrical equipment based on the Internet of things is characterized by comprising the following steps: acquiring explosion-proof qualified certificate data of electrical equipment, analyzing the explosion-proof qualified certificate data to obtain certificate information, performing two-dimensional verification based on the certificate information to obtain equipment explosion-proof qualification state data, and outputting the certificate information and the equipment explosion-proof qualification state data as first-layer monitoring data; Acquiring an environment parameter set by utilizing an intrinsically safe distributed sensing network, determining corrosion degree data and residual life data of the electrical equipment based on a preset coupling simulation model and the environment parameter set, and outputting the corrosion degree data and the residual life data as second-layer monitoring data; Acquiring operation parameters of the electrical equipment by using a monitoring terminal, determining loss characteristics and fault positioning information of the electrical equipment based on the operation parameters, and outputting the loss characteristics and the fault positioning information as third-layer monitoring data; Determining target monitoring data and hierarchical abnormality early warning trigger information based on the first layer monitoring data, the second layer monitoring data and the third layer monitoring data; Constructing a digital twin model of the equipment based on the target monitoring data, the hierarchical anomaly early warning trigger information and the historical monitoring data; And carrying out explosion-proof safety state assessment based on the target monitoring data, the grading abnormal early warning triggering information, the historical monitoring data and the digital twin model to obtain an assessment result.

Description

Factory electrical equipment explosion-proof safety intelligent monitoring system and method based on Internet of things Technical Field The invention relates to the technical field of electric equipment monitoring, in particular to an explosion-proof safety intelligent monitoring system and method for factory electric equipment based on the Internet of things. Background In flammable and explosive dangerous factories such as petrochemical industry, coal mining, metallurgy and the like, electric equipment is a core power support for production operation, and the explosion-proof safety state of the electric equipment is directly related to the production safety, personnel and life and property safety and ecological environment safety of the factories. With the increasing strictness of the advanced advancement of industry 4.0 and the safety production standard, the factory above most scales at present has gradually eliminated the traditional pure manual inspection mode, the technical means of sensor monitoring, preliminary data networking and the like are introduced, the explosion-proof monitoring level of electrical equipment is obviously improved, but the development trend of equipment enlargement and complicacy and the high-level safety management and control requirement are faced, and a plurality of short boards to be perfected still exist in the existing monitoring system. The current main stream factory electric equipment explosion-proof monitoring mode mainly comprises fixed-point sensor networking and periodic manual review. The sensor network can collect core operation parameters such as temperature, current, insulation resistance and the like in real time, so that the defects of long period and high risk of operation in dangerous areas of the traditional manual inspection are overcome to a certain extent, and the scene that the sensors such as the appearance integrity of the focusing equipment and the explosion-proof sealing state are difficult to cover is manually inspected, so that complementation is formed. However, the mode has inherent limitations that on one hand, the sensor monitors local parameters of multi-focus single equipment, data of different monitoring dimensions (such as environmental corrosion, equipment loss and qualification compliance) are mutually independent and are not subjected to linkage analysis, and on the other hand, the manual rechecking is greatly influenced by experience differences, has limited identification capability on problems of hidden equipment aging, insulation degradation and the like, and is difficult to realize advanced prejudgment of hidden danger. In order to further improve the monitoring efficiency, multi-sensor fusion and preliminary edge calculation technology are introduced in a plurality of factories, so that the concentrated acquisition of multiple parameters and the pretreatment of basic data are realized, and the early warning response speed is obviously improved in an early mode. From the practical application effect, the key technical bottlenecks still exist, namely, firstly, the multi-source data integration is insufficient, the centralized storage of data can be realized by the existing system, but the deep association analysis of equipment qualification data, environment corrosion data, operation parameter data and historical fault data is lacking, the core association characteristics of the explosion-proof safety of the equipment are difficult to extract from mass data, the data value is insufficient, the dynamic adaptation capability is insufficient, the existing early warning threshold is mostly based on a fixed value set by an equipment manual, and although part of the system can be simply and finely adjusted, the self-adaption adjustment cannot be realized according to the fluctuation of plant environment parameters such as salt fog, humidity change, equipment operation age, historical fault law and the like, so that the problem of insufficient early warning precision is easy to occur under complex working conditions, and the operation and maintenance decision is difficult to accurately support. In recent years, technologies such as the Internet of things, digital twin, artificial intelligence and the like are gradually applied to test points in the field of explosion-proof monitoring, and partial advanced factories try to construct a digital twin model of equipment so as to realize visual display of equipment states. However, the existing test point scheme has the problems of heavy display and light application, for example, the existing digital twin model multi-focus equipment physical form restoration can be related to partial real-time operation data, but key information such as equipment corrosion aging data, historical fault cases and the like are not fused deeply, dynamic deduction and prediction of the equipment explosion-proof safety state cannot be realized, in a safety evaluation link, although the existing scheme is tried to