CN-122022473-A - Workshop equipment safety production real-time monitoring and analyzing method and system based on Internet of things

CN122022473ACN 122022473 ACN122022473 ACN 122022473ACN-122022473-A

Abstract

The invention relates to the technical field of control of the Internet of things and discloses a real-time monitoring and analyzing method and a real-time monitoring and analyzing system for workshop equipment safety production based on the Internet of things, wherein the method comprises the steps of carrying out standardized pretreatment on multi-source sensor data to generate a multi-dimensional safety data stream, extracting an equipment characteristic parameter set, an environment characteristic parameter set and a personnel behavior characteristic parameter set in parallel from the multi-source sensor data, analyzing real-time interaction relation among the three to generate a system coupling relation network, carrying out equipment failure risk prediction to obtain a basic risk prediction value, carrying out dynamic correction to generate an equipment dynamic risk index, carrying out risk assessment to generate a comprehensive safety risk level, and carrying out early warning and visual display based on the level; the invention can improve the efficiency of real-time monitoring and analysis of workshop equipment safety production based on the Internet of things.

Inventors

TONG CHAOZHEN
Tong Bingrui
JIN XIAOLIANG
GUO XINGXING
LI JINCHAO

Assignees

宁波乾业检测技术研究有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. The real-time monitoring and analyzing method for workshop equipment safety production based on the Internet of things is characterized by comprising the following steps: S1, acquiring multi-source sensor data of target equipment, environment and personnel in a workshop in real time, performing space-time synchronization and standardization pretreatment on the multi-source sensor data, and generating a standardized multi-dimensional safety data stream of the workshop equipment; s2, based on the multi-dimensional safety data stream, extracting a device characteristic parameter set representing the health state of target devices, an environment characteristic parameter set representing the environment danger level and a personnel behavior characteristic parameter set representing personnel operation compliance in parallel; S3, analyzing real-time mutual influence relations among the target equipment, the environment and the personnel based on the equipment characteristic parameter set, the environment characteristic parameter set and the personnel behavior characteristic parameter set, and generating a system coupling relation network; S4, predicting equipment failure risk based on the historical sequence of the equipment characteristic parameter set to obtain a base risk predicted value, dynamically correcting the base risk predicted value based on the system coupling relation network, and generating a corrected equipment dynamic risk index; and S5, carrying out risk assessment by integrating the equipment dynamic risk index, the environment characteristic parameter set and the personnel behavior characteristic parameter set, generating an integrated security risk level, and carrying out early warning and visual display based on the integrated security risk level.
2. The real-time monitoring and analyzing method for safety production of workshop equipment based on the internet of things according to claim 1, wherein the performing space-time synchronization and standardization preprocessing on the multi-source sensor data to generate a standardized multi-dimensional safety data stream comprises: performing time stamp alignment processing on each dimension data stream in the multi-source sensor data to generate a unified time sequence; based on a predefined workshop space coordinate system, carrying out space position fusion on each source data stream in the unified time sequence to generate a space-time data cube; and carrying out normalization processing on the original data of each dimension in the space-time data cube to generate a standardized multidimensional safety data stream.
3. The real-time monitoring and analyzing method for workshop appliance safety production based on the internet of things according to claim 1, wherein the parallel extraction of the appliance characteristic parameter set representing the health status of the target appliance, the environment characteristic parameter set representing the environmental risk level and the personnel behavior characteristic parameter set representing the personnel operation compliance based on the multi-dimensional safety data stream comprises: Extracting time domain features, frequency domain features and state change features of the multi-dimensional safety data stream in parallel to generate a device feature parameter set for representing the health state of target devices; Performing composite environment index calculation and risk accumulation evaluation on the multi-dimensional security data stream to generate an environment characteristic parameter set representing an environment risk level; and carrying out dangerous area proximity analysis and operation compliance comparison on the multidimensional safety data stream, and generating a personnel behavior characteristic parameter set representing personnel operation compliance.
4. The method for real-time monitoring and analyzing the production of the workshop equipment based on the internet of things according to claim 1, wherein the analyzing the real-time interaction relationship among the target equipment, the environment and the personnel based on the equipment characteristic parameter set, the environment characteristic parameter set and the personnel behavior characteristic parameter set to generate the system coupling relationship network comprises the following steps: Extracting multidimensional time sequence data of the equipment characteristic parameter set, the environment characteristic parameter set and the personnel behavior characteristic parameter set in a preset time window; Analyzing the pairwise association strength between the key parameters in the equipment characteristic parameter set and the key parameters in the environment characteristic parameter set, and simultaneously analyzing the pairwise association strength between the key parameters in the equipment characteristic parameter set and the key parameters in the personnel behavior characteristic parameter set to generate an initial association strength matrix; And constructing a weighted undirected graph which takes the key safety parameters as nodes and the screened association strength values as side weights based on the significant association relation set, and generating a system coupling relation network.
5. The method for real-time monitoring and analyzing the production of the workshop equipment based on the internet of things according to claim 4, wherein the analyzing the two-by-two correlation strengths between the key parameters in the equipment characteristic parameter set and the key parameters in the environment characteristic parameter set and simultaneously analyzing the two-by-two correlation strengths between the key parameters in the equipment characteristic parameter set and the key parameters in the personnel behavior characteristic parameter set to generate the initial correlation strength matrix comprises: the mathematical expression used to analyze the pairwise correlation intensities is as follows: ; in the formula, MI (X; Y) represents mutual information values between random variables X and Y, X and Y respectively represent discretization time sequences of two key safety parameters of association strength to be calculated in a sliding window, X and Y are respectively specific values of the variables X and Y in discretization value domains thereof, p (X) and p (Y) are respectively edge probability distributions of the variables X and Y, and p (X, Y) is joint probability distribution of the variables X and Y.
6. The real-time monitoring and analyzing method for workshop equipment safety production based on the internet of things according to claim 1, wherein the performing equipment failure risk prediction based on the historical sequence of the equipment characteristic parameter set to obtain a base risk prediction value comprises: Extracting standardized time sequence data of the equipment characteristic parameter set in a preset prediction time span from a time sequence database to generate an equipment characteristic historical sequence; Inputting the equipment characteristic history sequence into a pre-trained long-short-term memory network model, and performing characteristic learning to obtain a hidden state vector sequence corresponding to each history time step; based on an attention mechanism, calculating the attention weight of the current prediction moment to each historical time step in the hidden state vector sequence; and carrying out weighted summation on the hidden state vector sequence based on the attention weight to obtain a context vector fused with key history information, and generating the basic risk prediction value through full-connection layer mapping.
7. The real-time monitoring and analyzing method for workshop appliance safety production based on the internet of things according to claim 1, wherein the dynamically correcting the base risk prediction value based on the system coupling relation network to generate a corrected appliance dynamic risk index comprises the following steps: based on the dynamic system coupling relation network, identifying all environment characteristic parameters and personnel behavior characteristic parameters with significant coupling edges with key characteristic parameters of target equipment; respectively calculating the mutual information average value between the key characteristic parameters of the target equipment and all the identified environmental characteristic parameters and the mutual information average value between the key characteristic parameters of the target equipment and all the identified personnel behavior characteristic parameters to generate the environmental average coupling strength and the personnel average coupling strength; weighting and fusing the environment average coupling strength and the personnel average coupling strength to generate a total coupling strength factor; and dynamically correcting the basic risk predicted value and the total coupling strength factor to generate a corrected equipment dynamic risk index.
8. The real-time monitoring and analyzing method for safety production of workshop equipment based on the internet of things according to claim 1, wherein the step of integrating the equipment dynamic risk index, the environment characteristic parameter set and the personnel behavior characteristic parameter set to perform risk assessment and generate an integrated safety risk level comprises the steps of: performing risk quantization conversion on the environment characteristic parameter set and the personnel behavior characteristic parameter set to generate an environment risk quantization value and a personnel risk quantization value; Fusing the equipment dynamic risk index, the environment risk quantized value and the personnel risk quantized value to generate a comprehensive safety risk value; and mapping the comprehensive security risk value to a corresponding discrete level based on a preset risk threshold interval to generate a comprehensive security risk level.
9. The real-time monitoring and analyzing method for safety production of workshop equipment based on the internet of things according to claim 1, wherein the early warning and visual display based on the comprehensive safety risk level comprises the following steps: Based on a predefined risk level-early warning action mapping table, matching the comprehensive security risk level to generate a corresponding multi-mode early warning strategy; Based on the multi-mode early warning strategy, performing audible and visual alarm driving, monitoring interface message pushing and linkage control instruction issuing of a workshop control system in parallel; Based on the equipment dynamic risk index, the environment characteristic parameter set, the personnel behavior characteristic parameter set and the comprehensive security risk level, performing visual information integration and rendering to generate a workshop security situation billboard view; Pushing the workshop security situation signboard view to a preset monitoring terminal for display.
10. Workshop equipment safety production real-time monitoring analysis system based on thing networking, its characterized in that, the system includes: the data synchronization module is used for acquiring multi-source sensor data of target equipment, environment and personnel in the workshop in real time, carrying out space-time synchronization and standardization pretreatment on the multi-source sensor data, and generating a standardized multidimensional safety data stream of the workshop equipment; The multi-dimensional safety parameter extraction and analysis module is used for extracting a device characteristic parameter set representing the health state of target devices, an environment characteristic parameter set representing the environment risk level and a personnel behavior characteristic parameter set representing personnel operation compliance in parallel based on the multi-dimensional safety data stream; The coupling strength analysis module is used for analyzing the real-time interaction relationship among the target equipment, the environment and the personnel based on the equipment characteristic parameter set, the environment characteristic parameter set and the personnel behavior characteristic parameter set to generate a system coupling relationship network; The dynamic risk prediction correction module is used for predicting equipment failure risk based on the historical sequence of the equipment characteristic parameter set to obtain a basic risk prediction value, dynamically correcting the basic risk prediction value based on the system coupling relation network, and generating a corrected equipment dynamic risk index; And the comprehensive evaluation and intelligent early warning module is used for comprehensively evaluating the risk of the equipment dynamic risk index, the environment characteristic parameter set and the personnel behavior characteristic parameter set, generating a comprehensive safety risk level, and carrying out early warning and visual display based on the comprehensive safety risk level.

Description

Workshop equipment safety production real-time monitoring and analyzing method and system based on Internet of things Technical Field The invention relates to the technical field of control of the Internet of things, in particular to a workshop equipment safety production real-time monitoring and analyzing method and system based on the Internet of things. Background In the field of safety production monitoring of current workshop equipment, most technical schemes only aim at equipment, environment and personnel, data acquisition and risk assessment are carried out on single dimension, systematic consideration of real-time interaction relation among the equipment operation data, environment sensing data and personnel behavior data is lacked, the traditional method often carries out isolation processing on the equipment operation data, the environment sensing data and the personnel behavior data, coupling association between equipment faults, environment abnormality and operation violations is not captured, risk prediction only depends on single dimension historical data, misjudgment or missed judgment is easy to occur, meanwhile, a risk assessment model of a traditional monitoring system is mainly static setting, prediction logic cannot be adjusted according to multi-element dynamic change of a workshop site, and real-time safety management and control requirements under complex production scenes are difficult to adapt. On the data processing and analysis flow, the prior art has the common flow splitting problem, a closed loop mechanism of association analysis-prediction correction is not formed, on one hand, an effective means for quantifying the association strength among multiple safety elements is not available, the mutual influence degree of equipment, environment and personnel cannot be accurately depicted, on the other hand, a risk prediction result is not dynamically optimized by combining with a real-time association relation, static risk values are only output based on a fixed algorithm, the prediction precision is insufficient, potential compound safety hazards are difficult to early warn, the defects enable the response timeliness and decision effectiveness of the existing monitoring system to be limited, comprehensive and accurate technical support cannot be provided for workshop safety production, and therefore, the method is suitable for the real-time safety control requirement under the complex production scene, and provides comprehensive and accurate technical support, so that the problem to be solved is urgent. Disclosure of Invention The invention provides a workshop equipment safety production real-time monitoring and analyzing method and system based on the Internet of things, which are used for solving the problems in the background technology. In order to achieve the above purpose, the method for monitoring and analyzing the safety production of workshop equipment based on the Internet of things in real time comprises the following steps: S1, acquiring multi-source sensor data of target equipment, environment and personnel in a workshop in real time, performing space-time synchronization and standardization pretreatment on the multi-source sensor data, and generating a standardized multi-dimensional safety data stream of the workshop equipment; s2, based on the multi-dimensional safety data stream, extracting a device characteristic parameter set representing the health state of target devices, an environment characteristic parameter set representing the environment danger level and a personnel behavior characteristic parameter set representing personnel operation compliance in parallel; S3, analyzing real-time mutual influence relations among the target equipment, the environment and the personnel based on the equipment characteristic parameter set, the environment characteristic parameter set and the personnel behavior characteristic parameter set, and generating a system coupling relation network; S4, predicting equipment failure risk based on the historical sequence of the equipment characteristic parameter set to obtain a base risk predicted value, dynamically correcting the base risk predicted value based on the system coupling relation network, and generating a corrected equipment dynamic risk index; and S5, carrying out risk assessment by integrating the equipment dynamic risk index, the environment characteristic parameter set and the personnel behavior characteristic parameter set, generating an integrated security risk level, and carrying out early warning and visual display based on the integrated security risk level. In a preferred embodiment, the performing space-time synchronization and normalization preprocessing on the multi-source sensor data to generate a normalized multi-dimensional security data stream includes: performing time stamp alignment processing on each dimension data stream in the multi-source sensor data to generate a unified time sequence; based on a p