A multi-source heterogeneous data fusion industrial safety real-time monitoring and early warning method

Abstract

The application belongs to the technical field of industrial safety monitoring, and particularly relates to a multi-source heterogeneous data fusion industrial safety real-time monitoring and early warning method, which has the following specific steps: multi-source heterogeneous data global collection, multi-source heterogeneous data preprocessing, data fusion, construction of a comprehensive safety risk map, safety risk real-time monitoring and hierarchical early warning, data review and model optimization; through multi-source heterogeneous data global collection, deep fusion, multi-factor correlation modeling and global safety risk map construction, the method realizes systematic analysis of multi-dimensional factors such as equipment, environment, process, personnel and safety protection, breaks through the limitation of traditional methods that can only monitor single type of risk, can effectively identify and early warn composite safety risks caused by multi-factor cooperation, and comprehensively improves the integrity and correlation of safety monitoring in complex industrial scenes.

Description

Technical Field

[0001] This invention relates to the field of industrial safety monitoring technology, specifically to a method for real-time monitoring and early warning of industrial safety through multi-source heterogeneous data fusion. Background Technology

[0002] With the deepening of industrial development, industrial production is rapidly developing towards intelligence, large scale, and complexity. The factors involved in the production process, such as equipment, technology, environment, and personnel, are becoming increasingly diverse, and the complexity and concealment of safety risks are also increasing significantly. Industrial safety monitoring has become a core link in ensuring the stable operation of industrial production, and the transformation of safety management from "passive response" to "active defense" has become an urgent need for the industry.

[0003] Currently, various monitoring methods have been gradually introduced into industrial production processes, enabling the collection of multi-type and multi-dimensional safety-related data, forming a multi-source heterogeneous data system. This multi-source data mainly includes equipment operation data, environmental monitoring data, process parameter data, personnel operation data, and safety protection data; while heterogeneity is manifested in different data sources, varying data formats, uneven data granularity, and significant differences in data reliability. However, existing industrial safety monitoring and early warning methods still suffer from many technical bottlenecks, making it difficult to adapt to the application needs of modern industrial multi-source heterogeneous data. They are unable to achieve accurate, real-time monitoring and effective early warning of safety risks. The specific shortcomings are as follows: At present, safety accidents are mostly caused by the combined effect of multiple factors. Existing methods mostly monitor and warn of single types of risks, such as only monitoring equipment failures or environmental exceedances. They lack systematic analysis of multiple factors and multiple links, cannot build a comprehensive safety risk map, and are difficult to deal with complex safety risks in complex industrial scenarios. To address these issues, a real-time monitoring and early warning method for industrial safety based on multi-source heterogeneous data fusion is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for real-time monitoring and early warning of industrial safety based on the fusion of multi-source heterogeneous data, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion, the specific steps of which are as follows: Step 1: Comprehensive Collection of Multi-Source Heterogeneous Data: Build a multi-dimensional data collection terminal to comprehensively collect multi-source heterogeneous data related to safety throughout the entire industrial production process; Step 2: Multi-source heterogeneous data preprocessing: Targeted preprocessing is performed on the collected multi-source heterogeneous data to ensure data quality and provide reliable data for multi-factor collaborative analysis; Step 3: Data Fusion: Adopt a layered fusion strategy to achieve deep fusion of multi-source data and establish the correlation between multiple factors such as equipment, environment, process, personnel, and safety protection. Step 4: Construct a comprehensive safety risk map: Based on a multi-factor correlation model, construct a safety risk map covering the entire industrial production process, multiple stages, and multiple factors, to comprehensively present the distribution, correlation, and evolution patterns of various risks; Step 5: Real-time monitoring and graded early warning of safety risks: Based on the safety risk map, realize real-time monitoring and graded early warning of complex safety risks caused by the synergy of multiple factors; Step Six: Data Review and Model Optimization: Regularly review monitoring data, early warning records, and accident handling, analyze the causes of false alarms and missed alarms, and optimize multi-source data fusion algorithms, multi-factor correlation models, and safety risk maps.

[0006] Preferably, the data collected in step one includes: equipment operation data, environmental monitoring data, process parameter data, personnel operation data, and safety protection data.

[0007] Preferably, in step one, during the process of collecting multi-source heterogeneous data across the entire domain, the collection time, collection location, data source, and reliability level of each data are recorded simultaneously to ensure data traceability.

[0008] Preferably, the detailed steps of step two are as follows: S1. Data format standardization: Transform semi-structured and unstructured data into a unified structured format; parse operation logs, extract key operation parameters, and unify data fields and formats; S2. Data Denoising and Completion: Filtering algorithms are used to eliminate random noise in equipment vibration and environmental monitoring data, and interpolation methods are used to complete missing data, avoiding the impact of noise and missing data on the accuracy of multi-factor analysis. S3. Unified data granularity: Based on the safety monitoring cycle of industrial production, data of different granularities are uniformly adjusted to the preset granularity. Through data interpolation and sampling, it is ensured that multi-source data can be analyzed collaboratively. S4. Data credibility assessment: Based on the data source, each type of data is scored for credibility. Subsequent fusion analysis is performed according to the credibility weight to improve the rationality of multi-factor analysis.

[0009] Preferably, in step three, the data fusion consists of three layers, as follows: The first layer, data-level fusion: feature extraction is performed on various types of preprocessed structured data, concentration thresholds and fluctuation amplitude features are extracted from environmental data, and unstructured data is transformed into feature vectors to achieve the initial fusion of multi-source data; The second layer, feature-level fusion: adopts a feature fusion algorithm, combines the credibility weights of each data point, and fuses the features of different types of data to generate a multi-dimensional comprehensive feature vector, capturing the potential correlation between multiple factors; The third layer, decision-level fusion: Based on the feature-level fusion results and combined with the safety rules of industrial production, a multi-factor correlation model is established to quantify the correlation strength between different factors, providing core support for constructing a safety risk map.

[0010] Preferably, the specific steps of step four are as follows: S1. Risk Node Definition: Each single risk, such as equipment failure, environmental exceedance, process abnormality, personnel violation, and safety protection failure, is defined as a risk node. Each node is associated with corresponding multi-source data features. S2. Relationship Construction: Based on the quantitative results of the multi-factor correlation model, construct the correlation edges between each risk node, label the correlation strength, and clarify the logic of the synergistic effect of multiple factors; S3. Risk Level Classification: In conjunction with industrial safety standards, classify the risk level of each risk node and the associated complex risks, and clarify the judgment criteria for different risk levels; S4. Dynamic Update of Risk Map: Receives the latest collected data in real time, processes it, and dynamically updates the node status and correlation strength of the risk map to ensure that the risk map can reflect the safety status of industrial production in real time and realize dynamic and systematic analysis of multiple factors.

[0011] Preferably, the specific steps of step five are as follows: S1. Real-time risk monitoring: Real-time comparison of the fused multi-source data with the risk node judgment criteria in the risk map, monitoring the status of a single risk node, analyzing the correlation changes between risk nodes, and identifying complex risks formed by the synergy of multiple factors. S2. Risk Trend Prediction: Based on historical data and real-time monitoring data, a trend prediction algorithm is used to predict the evolution trend of various risk nodes and the probability of occurrence of compound risks, so as to capture risk signs in advance. S3. Tiered early warning trigger: Based on the risk level and probability of occurrence, trigger the corresponding level of early warning signal, clearly define the early warning information, and push it synchronously through multiple channels such as industrial control terminal, mobile APP, and on-site sound and light alarm. S4. Early Warning and Response Linkage: After an early warning is triggered, the corresponding risk handling process is automatically linked, and a targeted emergency response plan is pushed to achieve linkage between early warning and handling, thereby improving the efficiency of emergency response.

[0012] Preferably, step six specifically includes data review, model optimization, and adaptive update; Data review involves statistical analysis of early warning accuracy, underreporting rate, and false alarm rate; analysis of the multi-factor correlation logic corresponding to false alarms and underreporting; and optimization of data credibility weights and risk assessment criteria. Model optimization involves adjusting the parameters of the multi-source data fusion algorithm and the quantification standard of the correlation strength of the multi-factor correlation model based on the review results, and improving the nodes and correlation relationships of the security risk map. Adaptive updates dynamically adjust the data collection scope, risk node definitions, and early warning thresholds based on changes in industrial production processes, equipment, and environment. This ensures that the method can adapt to the dynamic changes in complex industrial scenarios and continuously address various complex safety risks.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This application achieves a systematic analysis of multiple dimensions of factors, including equipment, environment, process, personnel, and safety protection, through multi-source heterogeneous data collection, deep fusion, multi-factor correlation modeling, and global safety risk map construction. It breaks through the limitations of traditional methods that can only monitor single types of risks, and can effectively identify and warn of complex safety risks caused by the synergy of multiple factors, thus comprehensively improving the integrity and correlation of safety monitoring in complex industrial scenarios. Attached Figure Description

[0014] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a flowchart of step two; Figure 3 This is a diagram composed of data fusion from step three; Figure 4 Here is a flowchart detailing the steps in step four; Figure 5 This is a flowchart showing the specific steps in step five. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: Please see Figure 1-5 The present invention provides a technical solution: A method for real-time monitoring and early warning of industrial safety through multi-source heterogeneous data fusion, the specific steps of which are as follows: Step 1: Comprehensive Collection of Multi-Source Heterogeneous Data: Establish a multi-dimensional data collection terminal to comprehensively collect multi-source heterogeneous data related to safety throughout the entire industrial production process; establish a multi-dimensional collection terminal to comprehensively cover safety-related data in all dimensions such as equipment, environment, process, personnel, and safety protection, breaking the limitations of single data monitoring, providing a complete data foundation for subsequent multi-factor and complex risk analysis, and solving the problems of incomplete data and single perspective of traditional methods from the source.

[0017] Step 2: Multi-source heterogeneous data preprocessing: Targeted preprocessing is performed on the collected multi-source heterogeneous data to ensure data quality and provide reliable data for multi-factor collaborative analysis. The collected heterogeneous data is standardized, denoised, completed, and normalized to remove noise, fill in missing data, and unify the format, ensuring reliable, comparable, and collaboratively analyzeable data quality. This avoids risk misjudgment due to data disorder and provides high-quality data support for multi-factor correlation analysis.

[0018] Step 3, Data Fusion: A layered fusion strategy is adopted to achieve deep fusion of multi-source data and establish the correlation between multiple factors such as equipment, environment, process, personnel, and safety protection. Through layered fusion, multi-source data is deeply integrated, and quantitative correlations between equipment, environment, process, personnel, and safety protection are established. Various parameters are no longer viewed in isolation. It can capture the coupling, superposition, and chain effects of multiple factors, providing a core model foundation for identifying complex safety risks.

[0019] Step 4: Construct a comprehensive safety risk map: Based on a multi-factor correlation model, construct a safety risk map covering the entire industrial production process, multiple stages, and multiple factors, comprehensively presenting the distribution, correlation, and evolution patterns of various risks; based on a multi-factor correlation model, construct a safety risk map covering the entire process, multiple stages, and multiple factors, clearly presenting the risk distribution, correlation, transmission path, and evolution patterns, achieving systematic and global risk perception, and solving the shortcomings of traditional methods in constructing an overall risk situation.

[0020] Step 5: Real-time monitoring and graded early warning of safety risks: Based on the safety risk map, realize real-time monitoring and graded early warning of complex safety risks caused by the synergy of multiple factors; based on the dynamic risk map, simultaneously monitor single risks and complex risks formed by the synergy of multiple factors, and perform trend prediction and graded early warning, push through multiple channels and coordinate response plans to achieve accurate early warning and rapid response to chain and superimposed risks in complex industrial scenarios.

[0021] Step Six: Data Review and Model Optimization: Regularly review monitoring data, early warning records, and incident handling procedures to analyze the causes of false alarms and missed alarms, and optimize multi-source data fusion algorithms, multi-factor correlation models, and safety risk maps. By reviewing early warning effectiveness and analyzing the causes of false alarms and missed alarms, continuously optimize fusion algorithms, correlation models, risk maps, and early warning rules to enable the system to adapt to changes in industrial scenarios, continuously improve the accuracy of identifying complex risks, and ensure long-term stable and reliable operation.

[0022] In step one, the collected data includes: equipment operation data, environmental monitoring data, process parameter data, personnel operation data, and safety protection data. Clearly defining the collected data as including equipment operation data, environmental monitoring data, process parameter data, personnel operation data, and safety protection data ensures coverage of all key factors that could lead to safety accidents, guaranteeing a complete and comprehensive multi-factor analysis and avoiding the omission of important risk factors.

[0023] In step one, during the full-domain collection of multi-source heterogeneous data, the collection time, collection location, data source, and credibility level of each data point are recorded simultaneously to ensure data traceability. Recording the collection time, location, source, and credibility level simultaneously during collection enables full-process data traceability, facilitating subsequent anomaly identification and risk source investigation. It also provides a basis for data weight allocation and fusion analysis, improving the accuracy and reliability of multi-factor correlation judgments.

[0024] The detailed steps of step two are as follows: S1. Data Format Standardization: Transform semi-structured and unstructured data into a unified structured format. For example, convert the violations of personnel in videos into structured violation type and violation time data through image recognition; parse operation logs, extract key operation parameters, and unify data fields and formats. S2. Data Denoising and Completion: Filtering algorithms are used to eliminate random noise in equipment vibration and environmental monitoring data, and interpolation methods are used to complete missing data, such as local data loss caused by sensor failure, to avoid noise and missing data affecting the accuracy of multi-factor analysis. S3. Unified data granularity: Based on the safety monitoring cycle of industrial production, data of different granularities (millisecond-level equipment data, minute-level environmental data, hour-level process data) are uniformly adjusted to a preset granularity (e.g., 10 seconds / time). Through data interpolation and sampling, it is ensured that multi-source data can be analyzed collaboratively. S4. Data credibility rating: Based on the data source, such as high-precision sensors, ordinary sensors, and manual records, each type of data is scored with credibility (0-10 points). In subsequent fusion analysis, the credibility is weighted according to the data to improve the rationality of multi-factor analysis.

[0025] In step three, the data fusion consists of three layers, as detailed below: The first layer, data-level fusion, involves feature extraction from various pre-processed structured data, such as extracting peak values ​​and frequencies from equipment vibration data, extracting concentration thresholds and fluctuation amplitudes from environmental data, and converting unstructured data (videos, images) into feature vectors to achieve preliminary fusion of multi-source data. The second layer, feature-level fusion: adopts feature fusion algorithms, such as weighted fusion algorithms and neural network fusion algorithms, and combines the confidence weights of each data to fuse the features of different types of data, generate multi-dimensional comprehensive feature vectors, and capture the potential correlation between multiple factors, such as the correlation between abnormal equipment temperature and ambient temperature and process pressure. The third layer, decision-level fusion: Based on the feature-level fusion results, combined with the safety rules of industrial production, such as the collaborative risk rules of equipment failure and process parameter exceedance, and personnel violations, a multi-factor correlation model is established to quantify the correlation strength between different factors, providing core support for constructing a safety risk map.

[0026] The specific steps of step four are as follows: S1. Risk Node Definition: Define each type of single risk, such as equipment failure, environmental exceedance, process abnormality, personnel violation, and safety protection failure, as a risk node. Each node is associated with corresponding multi-source data features. For example, the equipment failure node is associated with data such as equipment vibration, temperature, and fault codes. Standardize various abnormalities into monitorable and quantifiable risk nodes. S2. Relationship Construction: Based on the quantitative results of the multi-factor correlation model, construct the correlation edges between each risk node, and mark the correlation strength, such as the correlation strength between "equipment temperature exceeding the standard" and "process pressure being too high", and the correlation strength between "personnel violation operation" and "equipment failure". Clarify the logic of multi-factor synergy, clarify the correlation strength and transmission logic between nodes, and reflect the multi-factor synergy. S3. Risk Level Classification: In conjunction with industrial safety standards, classify each risk node and the associated complex risks into risk levels (general, major, serious, and extremely serious), clarify the judgment criteria for different risk levels, establish unified judgment criteria, and provide a basis for graded early warning; S4. Dynamic Risk Map Update: Receives the latest collected data in real time, processes it, and dynamically updates the node status and correlation strength of the risk map to ensure that the risk map can reflect the safety status of industrial production in real time, enabling dynamic and systematic analysis of multiple factors, reflecting the production safety status in real time, and achieving dynamic, global, and systematic risk analysis.

[0027] The specific steps of step five are as follows: S1. Real-time risk monitoring: Real-time comparison of the fused multi-source data with the risk node judgment criteria in the risk map, monitoring the status of a single risk node, analyzing the correlation changes between each risk node, identifying composite risks formed by the synergy of multiple factors, such as composite safety risks formed by "excessive process pressure, excessive equipment temperature, and personnel violation of operating procedures", and identifying single risks and multi-factor composite risks. S2. Risk Trend Prediction: Based on historical data and real-time monitoring data, a trend prediction algorithm is used to predict the evolution trend of various risk nodes and the probability of occurrence of compound risks, capture risk signs in advance, predict risk evolution in advance, and achieve early warning. S3. Tiered Early Warning Trigger: Based on the risk level and probability of occurrence, trigger the corresponding level of early warning signal, clearly define the early warning information, including the risk type, the multiple influencing factors involved, the risk location, the early warning level, and the handling suggestions. The information is pushed simultaneously through multiple channels such as industrial control terminals, mobile APP, and on-site audible and visual alarms, with clear prompts and distinct levels. S4. Early Warning and Response Linkage: After an early warning is triggered, the corresponding risk handling process is automatically linked, and targeted emergency response plans are pushed, such as equipment shutdown, personnel evacuation, and process parameter adjustment. This achieves linkage between early warning and handling, improves emergency response efficiency, and automatically links handling plans to improve emergency efficiency and reduce accident losses.

[0028] Step six specifically includes data review, model optimization, and adaptive updating; Data review involves statistical analysis of early warning accuracy, underreporting rate, and false alarm rate; analysis of the multi-factor correlation logic corresponding to false alarms and underreporting; and optimization of data credibility weights and risk assessment criteria. Model optimization involves adjusting the parameters of the multi-source data fusion algorithm and the quantification standard of the correlation strength of the multi-factor correlation model based on the review results, and improving the nodes and correlation relationships of the security risk map. Adaptive updates dynamically adjust the data collection scope, risk node definitions, and early warning thresholds based on changes in industrial production processes, equipment, and environment. This ensures that the method can adapt to the dynamic changes in complex industrial scenarios and continuously address various complex safety risks.

[0029] Statistical early warning indicators are used to pinpoint the root causes of false alarms and missed alarms. Algorithms, weights, and correlation strength are adjusted to improve recognition accuracy, adapt to changes in processes, equipment, and environment, and continuously address new and complex risks.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time monitoring and early warning of industrial safety through multi-source heterogeneous data fusion, characterized in that, The specific steps of this method are as follows: Step 1: Comprehensive Collection of Multi-Source Heterogeneous Data: Build a multi-dimensional data collection terminal to comprehensively collect multi-source heterogeneous data related to safety throughout the entire industrial production process; Step 2: Multi-source heterogeneous data preprocessing: Targeted preprocessing is performed on the collected multi-source heterogeneous data to ensure data quality and provide reliable data for multi-factor collaborative analysis; Step 3: Data Fusion: Adopt a layered fusion strategy to achieve deep fusion of multi-source data and establish the correlation between multiple factors such as equipment, environment, process, personnel, and safety protection. Step 4: Construct a comprehensive safety risk map: Based on a multi-factor correlation model, construct a safety risk map covering the entire industrial production process, multiple stages, and multiple factors, to comprehensively present the distribution, correlation, and evolution patterns of various risks; Step 5: Real-time monitoring and graded early warning of safety risks: Based on the safety risk map, realize real-time monitoring and graded early warning of complex safety risks caused by the synergy of multiple factors; Step Six: Data Review and Model Optimization: Regularly review monitoring data, early warning records, and accident handling, analyze the causes of false alarms and missed alarms, and optimize multi-source data fusion algorithms, multi-factor correlation models, and safety risk maps.

2. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: In step one, the collected data includes: equipment operation data, environmental monitoring data, process parameter data, personnel operation data, and safety protection data.

3. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: In step one, during the process of collecting multi-source heterogeneous data across the entire domain, the collection time, collection location, data source, and credibility level of each data are recorded simultaneously to ensure data traceability.

4. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: The detailed steps of step two are as follows: S1. Data format standardization: Transform semi-structured and unstructured data into a unified structured format; parse operation logs, extract key operation parameters, and unify data fields and formats; S2. Data Denoising and Completion: Filtering algorithms are used to eliminate random noise in equipment vibration and environmental monitoring data, and interpolation methods are used to complete missing data, avoiding the impact of noise and missing data on the accuracy of multi-factor analysis. S3. Unified data granularity: Based on the safety monitoring cycle of industrial production, data of different granularities are uniformly adjusted to the preset granularity. Through data interpolation and sampling, it is ensured that multi-source data can be analyzed collaboratively. S4. Data credibility assessment: Based on the data source, each type of data is scored for credibility. Subsequent fusion analysis is performed according to the credibility weight to improve the rationality of multi-factor analysis.

5. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: In step three, the data fusion consists of three layers, as detailed below: The first layer, data-level fusion: feature extraction is performed on various types of preprocessed structured data, concentration thresholds and fluctuation amplitude features are extracted from environmental data, and unstructured data is transformed into feature vectors to achieve the initial fusion of multi-source data; The second layer, feature-level fusion: adopts a feature fusion algorithm, combines the credibility weights of each data point, and fuses the features of different types of data to generate a multi-dimensional comprehensive feature vector, capturing the potential correlation between multiple factors; The third layer, decision-level fusion: Based on the feature-level fusion results and combined with the safety rules of industrial production, a multi-factor correlation model is established to quantify the correlation strength between different factors, providing core support for constructing a safety risk map.

6. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: The specific steps of step four are as follows: S1. Risk Node Definition: Each single risk, such as equipment failure, environmental exceedance, process abnormality, personnel violation, and safety protection failure, is defined as a risk node. Each node is associated with corresponding multi-source data features. S2. Relationship Construction: Based on the quantitative results of the multi-factor correlation model, construct the correlation edges between each risk node, label the correlation strength, and clarify the logic of the synergistic effect of multiple factors; S3. Risk Level Classification: In conjunction with industrial safety standards, classify the risk level of each risk node and the associated complex risks, and clarify the judgment criteria for different risk levels. S4. Dynamic Update of Risk Map: Receives the latest collected data in real time, processes it, and dynamically updates the node status and correlation strength of the risk map to ensure that the risk map can reflect the safety status of industrial production in real time and realize dynamic and systematic analysis of multiple factors.

7. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: The specific steps of step five are as follows: S1. Real-time risk monitoring: Real-time comparison of the fused multi-source data with the risk node judgment criteria in the risk map, monitoring the status of a single risk node, analyzing the correlation changes between risk nodes, and identifying complex risks formed by the synergy of multiple factors. S2. Risk Trend Prediction: Based on historical data and real-time monitoring data, a trend prediction algorithm is used to predict the evolution trend of various risk nodes and the probability of occurrence of compound risks, so as to capture risk signs in advance. S3. Tiered early warning trigger: Based on the risk level and probability of occurrence, trigger the corresponding level of early warning signal, clearly define the early warning information, and push it synchronously through multiple channels such as industrial control terminal, mobile APP, and on-site sound and light alarm. S4. Early Warning and Response Linkage: After an early warning is triggered, the corresponding risk handling process is automatically linked, and a targeted emergency response plan is pushed to achieve linkage between early warning and handling, thereby improving the efficiency of emergency response.

8. The method for real-time monitoring and early warning of industrial safety based on multi-source heterogeneous data fusion according to claim 1, characterized in that: Step six specifically includes data review, model optimization, and adaptive updating; Data review involves statistical analysis of early warning accuracy, underreporting rate, and false alarm rate; analysis of the multi-factor correlation logic corresponding to false alarms and underreporting; and optimization of data credibility weights and risk assessment criteria. Model optimization involves adjusting the parameters of the multi-source data fusion algorithm and the quantification standard of the correlation strength of the multi-factor correlation model based on the review results, and improving the nodes and correlation relationships of the security risk map. Adaptive updates dynamically adjust the data collection scope, risk node definitions, and early warning thresholds based on changes in industrial production processes, equipment, and environment. This ensures that the method can adapt to the dynamic changes in complex industrial scenarios and continuously address various complex safety risks.

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