Multi-source information fusion safety production risk intelligent early warning and disposal platform

Abstract

The invention discloses a multi-source information fusion safety production risk intelligent early warning and disposal platform, which comprises a multi-source information acquisition module, an information preprocessing module, a multi-source information fusion module, an intelligent risk identification and early warning module, a disposal scheduling module and a data storage and interaction module, heterogeneous data such as sensor monitoring, video monitoring, equipment logs and manual reporting are collected and cleaned and standardized, and then a weighted fusion algorithm and a cross-modal deep learning model are adopted to realize multi-source data fusion; based on a preset risk assessment index system and the trained risk identification model, mapping risk levels by quantizing comprehensive risk scores, generating graded early warning information and pushing the graded early warning information in multiple channels; meanwhile, a disposal scheme is automatically matched, an instruction is issued, a closed loop is tracked, and data security is guaranteed in combination with distributed and block chain storage. Comprehensive perception, accurate identification, intelligent early warning and efficient disposal of safety production risks are achieved, and the intelligent and refined level of safety production management is remarkably improved.

Description

Technical Field

[0001] This invention relates to the technical field of work safety, and more particularly to a multi-source information fusion-based intelligent early warning and handling platform for work safety risks. Background Technology

[0002] Safety in production is the core bottom line in industrial production, park operation, and other scenarios. The timeliness and accuracy of risk warning and response are directly related to the safety of personnel, the protection of equipment and property, and the stability of production order. As safety in production scenarios become more complex, risk sources are becoming more diversified, concealed, and dynamic. Single data sources or traditional monitoring methods are no longer sufficient to meet the needs of risk prevention and control.

[0003] Existing safety production early warning systems have several shortcomings: First, their multi-source data fusion capabilities are weak. Heterogeneous data such as sensor data, video surveillance, equipment logs, and manual reports are fragmented and lack an effective integration mechanism, making it difficult to fully explore the value of the data and comprehensively reflect the risk situation. Second, risk identification and level determination methods are crude, relying heavily on single indicators or fixed thresholds, lacking quantitative calculation and dynamic adjustment mechanisms, which easily leads to false alarms, missed alarms, or level determination deviations, affecting the credibility of early warnings. Third, there is a disconnect between early warning and response processes. Most systems can only provide risk alerts, lacking automatic linkage with the response process, and there is no complete process tracking and result archiving mechanism, making it difficult to form closed-loop management. Fourth, data storage security is insufficient. Key risk data and response records are easily tampered with, affecting the credibility of traceability. Fifth, risk identification models lack continuous optimization capabilities, making it difficult to adapt to changes in different industry scenarios or new risk types, and the accuracy of early warnings decreases after long-term use.

[0004] Therefore, there is an urgent need for an intelligent platform that can integrate multi-source heterogeneous data, achieve accurate risk assessment, coordinate efficient handling, and ensure data security, in order to overcome the shortcomings of existing technologies and improve the overall effectiveness of safety production risk prevention and control. Summary of the Invention

[0005] This invention proposes a multi-source information fusion-based intelligent early warning and handling platform for safety production risks. By integrating multi-source heterogeneous data, it achieves accurate risk assessment, coordinated and efficient handling, and ensures data security, thereby solving the problems existing in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-source information fusion-based intelligent early warning and handling platform for safety production risks is provided, including: The multi-source information acquisition module is used to collect heterogeneous data sources in safe production scenarios. The heterogeneous data sources include real-time sensor monitoring data, video surveillance data, equipment operation log data, manually reported data, environmental parameter data, and industry standard data. The information preprocessing module is communicatively connected to the multi-source information acquisition module and is used to perform data cleaning, format standardization, and outlier removal on the acquired heterogeneous data sources. The multi-source information fusion module is communicatively connected to the information preprocessing module. It uses a combination of weighted fusion algorithm and deep learning fusion model to perform feature extraction and cross-modal data fusion on the preprocessed multi-source data, and outputs fused feature data with a unified dimension. The intelligent risk identification and early warning module is communicatively connected to the multi-source information fusion module. Based on a preset risk assessment index system and a trained risk identification model, it determines the risk level of the fused feature data, generates graded early warning information, and pushes it through multiple channels. The disposal and scheduling module is communicatively connected to the intelligent risk identification and early warning module. It is used to automatically match the preset disposal plan library according to the early warning information, generate disposal instructions and send them to the corresponding execution terminal, and at the same time track the disposal process and record the disposal results. The data storage and interaction module communicates with each of the above modules and is used to store the entire process data and provide a human-computer interaction interface, supporting data query, statistical analysis and system parameter configuration.

[0007] Preferably, the multi-source information acquisition module includes: The sensor data acquisition unit is used to collect data on temperature, humidity, gas concentration, pressure, vibration, and location. The video image acquisition unit is used to acquire live video streams through a network camera, and supports real-time frame extraction and preliminary image feature recognition. The data interface unit is used to obtain equipment operation logs, ERP system data, and industry regulatory platform data through API interfaces and database synchronization. The manual reporting unit provides reporting access via web and mobile devices, supporting the collection of risk and hazard reporting data in the form of text, images, and voice.

[0008] Preferably, in the multi-source information fusion module, the weighted fusion algorithm is used to perform credibility weighted calculation on data of the same type and source, and the deep learning fusion model is a cross-modal fusion model based on Transformer. The encoder performs feature mapping on data of different modalities, and then the attention mechanism is used to realize feature interaction and fusion between modalities.

[0009] Preferably, the intelligent risk identification and early warning module includes: The risk assessment indicator system unit has built-in multi-level risk assessment indicators based on industry standards, safety specifications and historical accident data, and supports dynamic adjustment of indicator weights; The risk identification model unit employs a risk identification model, which is trained and optimized using historical risk data to achieve risk type identification and risk level determination. The early warning push unit supports multiple early warning methods, including SMS, APP push, platform pop-up, and linkage with sound and light alarm devices. The push priority and recipients can be configured according to the risk level. Furthermore, the risk identification model and weighted fusion algorithm are regularly iterated and trained using risk type identification results feedback data and newly added risk data to improve the accuracy of early warning and the adaptability of response.

[0010] More preferably, a comprehensive risk score is calculated and mapped to a risk level, which includes general, significant, major, and extremely major risks. The comprehensive risk score S is calculated as follows: ; in, This represents the quantitative score of the j-th indicator. The fusion weight of the j-th indicator is represented by the relationship between the score range and the risk level: general risk: S∈[0,20); relatively high risk: S∈[20,40); major risk: S∈[40,70); extremely major risk: S∈[70,100].

[0011] Preferably, the disposal scheduling module includes: The disposal plan library unit stores standardized disposal procedures and emergency plans categorized by risk type and risk level, and supports custom editing and version management of the plans; The instruction issuing unit can automatically issue handling instructions to the terminal devices of the corresponding responsible departments and staff, including the risk location, risk description, handling requirements and time limit; The process tracking unit tracks the progress of handling in real time through GPS positioning and execution feedback upload function, and automatically triggers a secondary warning for matters that have not been handled within the time limit. The results archiving unit records data on the handling process, rectification results, and effectiveness evaluation information, forming a closed-loop data for risk management.

[0012] Preferably, the data storage and interaction module adopts a distributed storage architecture, including a real-time database for storing collected real-time data, a relational database for storing structured business data, and a blockchain storage node for storing key risk data and handling records, ensuring that the data is tamper-proof.

[0013] Preferably, the human-computer interaction interface supports visual chart display, including risk distribution heatmaps, risk level statistical charts, and handling progress flowcharts, while also providing a custom report generation function and supporting data export and printing.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Comprehensive data collection: Through multi-source information collection modules, it covers various heterogeneous data sources such as real-time sensor monitoring, video surveillance, equipment logs, and manual reporting, breaking down data silos, ensuring comprehensive risk perception, and providing complete data support for subsequent risk identification.

[0015] High efficiency and high accuracy of multi-source fusion: By combining a weighted fusion algorithm with a Transformer-based cross-modal deep learning model, it not only achieves the credibility-weighted integration of similar data, but also completes the deep interactive fusion of cross-modal data through an attention mechanism, effectively extracting multi-dimensional risk features and improving the accuracy and effectiveness of data fusion.

[0016] Accurate Risk Identification and Level Determination: Based on a multi-level risk assessment indicator system, risk levels are mapped by quantifying comprehensive risk scores. Combined with dynamically adjusted indicator weights and a continuously trained and optimized risk identification model, the crudeness of traditional threshold determination is avoided, significantly reducing false alarm and false negative rates and ensuring the accuracy of risk type and level determination.

[0017] Highly efficient closed-loop early warning and response: It realizes closed-loop management of the entire process from risk early warning to response scheduling, progress tracking and result archiving, automatically matches response plans and issues instructions, and avoids response timeouts through a secondary early warning mechanism, which greatly improves the speed of risk response and response efficiency and reduces the losses caused by risk spread.

[0018] Data storage is secure and reliable: It adopts a distributed storage architecture combined with blockchain storage nodes to adapt to the storage needs of real-time data, structured business data and critical risk data, ensuring both the efficiency and scalability of data storage, and ensuring that critical data is tamper-proof, providing a reliable basis for risk tracing and liability determination.

[0019] The system is highly adaptable and easy to use: it supports dynamic adjustment of risk assessment indicator weights and custom editing of disposal plans. Combined with the model iteration and optimization mechanism, it can be adapted to different industry safety production scenarios. At the same time, it provides a visual interactive interface and custom report function to reduce the operation threshold and improve management convenience. Attached Figure Description

[0020] Figure 1 This is a framework diagram of a multi-source information fusion-based intelligent early warning and handling platform for safety production risks, as described in a specific embodiment of the present invention. Detailed Implementation

[0021] 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.

[0022] Please refer to Figure 1 As shown, this application proposes a multi-source information fusion-based intelligent early warning and handling platform for safety production risks, including: The multi-source information acquisition module is used to collect heterogeneous data sources in safe production scenarios. The heterogeneous data sources include real-time sensor monitoring data, video surveillance data, equipment operation log data, manually reported data, environmental parameter data, and industry standard data. The multi-source information acquisition module includes: The sensor data acquisition unit is used to collect data on temperature, humidity, gas concentration, pressure, vibration, and location. The video image acquisition unit is used to acquire live video streams through a network camera, and supports real-time frame extraction and preliminary image feature recognition. The data interface unit is used to obtain equipment operation logs, ERP system data, and industry regulatory platform data through API interfaces and database synchronization. The manual reporting unit provides reporting access via web and mobile devices, supporting the collection of risk and hazard reporting data in the form of text, images, and voice.

[0023] The multi-source information fusion module achieves full-dimensional data acquisition through sensor data acquisition units, video image acquisition units, data interface units, and manual reporting units. The hardware configuration and data acquisition parameters of each unit are as follows: Sensor data acquisition unit: In this embodiment, a fixed gas concentration sensor, pressure sensor, temperature sensor and vibration sensor are used. The sensors are connected to the edge node through the LoRa wireless communication protocol. The data acquisition frequency is 1 time / 10 seconds. In abnormal situations (such as when the concentration is close to the threshold), it is automatically increased to 1 time / second. The acquired data is transmitted in JSON format and includes the following fields: device ID, acquisition timestamp, physical quantity type, measured value and signal strength.

[0024] Video image acquisition unit: It connects to the edge node of Ethernet through multiple high-definition network cameras, and transmits video streams in real time by default. The edge node extracts key frames at a frequency of 1 frame / second and performs preliminary feature recognition (such as smoke, abnormal objects). Keyframes are stored in JPEG format, along with the camera ID, acquisition timestamp, and preliminary identification tag.

[0025] Data Interface Unit: Connects to the park's ERP system and equipment maintenance management system via RESTful API interface; synchronizes equipment operation logs in the SQL Server database via JDBC protocol; accesses the local safety production supervision platform via HTTPS protocol to obtain updated industry standard data and regulatory requirements; equipment operation logs and ERP data are synchronized once per hour, and industry regulatory data is synchronized once per day, with support for manual triggering of instant synchronization; finally, the data is uniformly converted into JSON format, including data source identifier, associated equipment / region ID, data content, and synchronization timestamp.

[0026] Manual reporting unit: The web-based version is deployed on the park's internal management platform, and the mobile APP supports Android 8.0 and iOS 12.0 and above; Reporting functions: Supports text (≤500 characters), images (JPEG / PNG format, single image ≤5MB), and voice (MP3 format, duration ≤60 seconds) uploads, with a built-in risk type drop-down selection box (including 10 categories such as equipment abnormality, personnel violation, and environmental abnormality); Reported data is automatically associated with the reporting personnel ID, location information, and reporting timestamp, and is transmitted to the cloud after preliminary verification by edge nodes.

[0027] The information preprocessing module is communicatively connected to the multi-source information acquisition module and is used to perform data cleaning, format standardization, and outlier removal on the acquired heterogeneous data sources. The information preprocessing module communicates with the multi-source information acquisition module via TCP / IP protocol. After receiving the acquired data, it processes it according to the following steps: Data cleaning: For sensor data: outliers are removed using the 3σ criterion, that is, when the data exceeds the range of [μ-3σ,μ+3σ], it is identified as an outlier and replaced with the mean of the previous 3 data collections; where μ is the mean and σ is the standard deviation. For video image data: Calculate sharpness and brightness indices using image quality assessment algorithms; filter out blurry or overly dark invalid frames, and retain keyframes with a sharpness ≥ 0.8; For manually reported data: invalid data will be rejected and feedback will be given to the reporting personnel through keyword verification (such as whether it contains risk location and description) and format verification.

[0028] Format standardization: Timestamp standardization: Convert all data timestamps to UTC time format; Standardization of physical quantity units: unify sensor data units (e.g., pressure is standardized to MPa, concentration to ppm); Data structure standardization: All preprocessed data is stored in a fixed field structure of "data type-region ID-device ID-timestamp-value / content".

[0029] Data compression: The JPEG2000 compression algorithm is used for video image data, with a compression ratio of 10:1 to ensure transmission efficiency; the GZIP compression format is used for structured data to reduce storage and transmission bandwidth usage.

[0030] The preprocessed data stream is pushed to the multi-source information fusion module via a message queue.

[0031] The multi-source information fusion module is communicatively connected to the information preprocessing module. It uses a combination of weighted fusion algorithm and deep learning fusion model to perform feature extraction and cross-modal data fusion on the preprocessed multi-source data, and outputs fused feature data with a unified dimension. In the multi-source information fusion module, the weighted fusion algorithm is used to perform credibility weighted calculation on data of the same type and source. The deep learning fusion model is a cross-modal fusion model based on Transformer. It performs feature mapping on data of different modalities through encoder, and then realizes feature interaction and fusion between modalities through attention mechanism.

[0032] The multi-source information fusion module adopts a combination of a weighted fusion algorithm and a Transformer-based cross-modal fusion model. The specific implementation steps are as follows: Weighted fusion of similar data: For data from multiple similar sensors at the same monitoring point (e.g., three gas concentration sensors), a confidence-weighted algorithm is used to calculate the fusion value: ; in , Let w1 be the confidence level of the i-th sensor. For example, if the measured values ​​of three gas concentration sensors at a certain point are 85ppm, 88ppm, and 90ppm, and the confidence levels are 0.95, 0.98, and 0.92, then w1 = 0.33, w2 = 0.34, w3 = 0.33, and the fusion value = 85 × 0.33 + 88 × 0.34 + 90 × 0.33 ≈ 87.6ppm.

[0033] Cross-modal data deep learning fusion: Transformer model configuration: 6 encoder layers, 8 multi-head attention heads, 512 hidden layer dimensions, and ReLU activation function; Feature mapping: Structured data (sensor values, device log parameters) are mapped into 512-dimensional feature vectors through a fully connected layer; video image data is processed by CNN (ResNet50) to extract 2048-dimensional visual features, and then reduced to 512-dimensionality through a linear layer; text data (manually reported text, log descriptions) are encoded into 512-dimensional semantic features through a BERT model. Attention mechanism fusion: The association weights between features of different modalities are calculated through the self-attention mechanism to realize cross-modal feature interaction and output fused feature data with a unified dimension (512 dimensions); Model training: The training set was based on multi-source data of 1,000 historical risk events in the park over the past 3 years. The batch size was 32, the number of iterations was 100, the loss function was cross-entropy loss, and the accuracy of the fused features after model convergence was ≥92%.

[0034] The intelligent risk identification and early warning module is communicatively connected to the multi-source information fusion module. Based on a preset risk assessment index system and a trained risk identification model, it determines the risk level of the fused feature data, generates graded early warning information, and pushes it through multiple channels. The intelligent risk identification and early warning module includes: The risk assessment indicator system unit has built-in multi-level risk assessment indicators based on industry standards, safety specifications and historical accident data, and supports dynamic adjustment of indicator weights; The risk identification model unit employs a risk identification model, which is trained and optimized using historical risk data to achieve risk type identification and risk level determination. The early warning push unit supports multiple early warning methods, including SMS, APP push, platform pop-up, and linkage with sound and light alarm devices. The push priority and recipients can be configured according to the risk level. Furthermore, the risk identification model and weighted fusion algorithm are regularly iterated and trained using risk type identification results feedback data and newly added risk data to improve the accuracy of early warning and the adaptability of response.

[0035] In this invention, a three-level risk assessment indicator system specifically for the chemical industry is built in, with four first-level indicators (scope of impact, degree of hazard, diffusion speed, and difficulty of emergency response), twelve second-level indicators, and thirty-six third-level indicators; The weights of the indicators are determined by a combination of the Analytic Hierarchy Process (AHP) and the entropy weight method. For example, in the scenario of a chemical industrial park, the final weight vector is [0.172, 0.39, 0.206, 0.232] (corresponding to the first-level indicator). The weights can be manually adjusted through the human-computer interaction interface.

[0036] The risk identification model adopts a CNN-LSTM dual-channel hybrid model, with the CNN channel processing image features and the LSTM channel processing time series data; Training data: Contains 5,000 labeled risk samples (covering 10 risk types such as equipment malfunction, environmental exceedance, and personnel violations), divided into training set, validation set, and test set in a 7:2:1 ratio; Model training: The initial learning rate is 0.001, using the Adam optimizer, and training stops when the accuracy on the validation set is stable above 95%, and the accuracy and recall on the test set are ≥93% and ≥92%, respectively. Reasoning process: Input the fused feature data, the model outputs the probability value of each type of risk, and takes the type with the highest probability (≥0.8) as the identification result. If the probability is lower than 0.8, it is marked as "suspected risk" and manual review is triggered.

[0037] Risk level assessment: The measured data is converted into an indicator score of 0-100 using a segmented threshold mapping method. A comprehensive risk score is then calculated and mapped to a risk level, which includes general, significant, major, and extremely major risk levels. The comprehensive risk score S is calculated as follows: ; in, This represents the quantitative score of the j-th indicator. The fusion weight of the j-th indicator is represented by the relationship between the score range and the risk level: general risk: S∈[0,20); relatively high risk: S∈[20,40); major risk: S∈[40,70); extremely major risk: S∈[70,100].

[0038] When multiple risks are identified simultaneously, the total score is calculated using a coupling coefficient matrix (trained based on historical composite accident data). For example, the coupling coefficient between "toxic gas leak" and "fire" is 1.5, so the total score is S1 + 1.5 × S2.

[0039] Indicator quantification: The measured data are converted into indicator scores of 0-100 points using a segmented threshold mapping method (e.g., in the hazard level indicator, propylene concentration > 250 ppm corresponds to 100 points). Push notification channels are configured according to risk levels: general risks are only prompted by platform pop-ups; significant risks are added via APP push notifications; major risks are added via SMS notifications and audible and visual alarms; and extremely major risks are linked to the park's broadcasting system. Push notification content includes risk type, level, location of occurrence (accurate to within 10m), core monitoring data, and warning timestamp.

[0040] Regularly iterate and optimize the risk identification model and information fusion algorithm: Data collection: Quarterly data collection includes feedback on disposal results and newly added risk data (≥100 items) as new training samples; Model training: The risk identification model is updated using incremental training to avoid system downtime caused by retraining; Optimization evaluation: Verify the accuracy and recall of the optimized model using the test set. If the accuracy and recall are improved by ≥5% compared to the original model, replace the original model; otherwise, abandon this optimization. Optimization cycle: once per quarter by default, with the option to manually trigger emergency optimization.

[0041] The disposal and scheduling module is communicatively connected to the intelligent risk identification and early warning module. It is used to automatically match the preset disposal plan library according to the early warning information, generate disposal instructions and send them to the corresponding execution terminal, and at the same time track the disposal process and record the disposal results. The dispatch module includes: The disposal plan library unit stores standardized disposal procedures and emergency plans categorized by risk type and risk level, and supports custom editing and version management of the plans; The instruction issuing unit can automatically issue handling instructions to the terminal devices of the corresponding responsible departments and staff, including the risk location, risk description, handling requirements and time limit; The process tracking unit tracks the progress of handling in real time through GPS positioning and execution feedback upload function, and automatically triggers a secondary warning for matters that have not been handled within the time limit. The results archiving unit records data on the handling process, rectification results, and effectiveness evaluation information, forming a closed-loop data for risk management.

[0042] Construction of a solution library: The system stores standardized handling plans categorized by risk type and risk level, including 40 basic plans across 10 risk types and 4 risk levels. Administrators can customize and edit plans via the web interface, including adding handling steps, responsible departments, and required equipment. Example of a plan: A major risk - toxic gas leak plan includes the following steps: ① Cut off the leak source, responsible department: Equipment Department, time limit: 15 minutes; ② Evacuate personnel from the affected area, responsible department: Security Department, time limit: 10 minutes; ③ Activate explosion-proof ventilation equipment, responsible department: Engineering Department, time limit: 5 minutes, etc.

[0043] After automatically matching the solution, the system issues handling instructions to the responsible department's terminal and the staff's APP via TCP / IP protocol, supporting both text and voice instructions; The instructions include a unique identifier (ID), risk details, handling steps, completion deadline, and contact information for the responsible person.

[0044] The arrival status of personnel is tracked in real time via GPS location through the staff APP, and the progress of the handling is uploaded through the feedback portal, such as not started / in progress / completed; For steps that are not completed within the time limit, such as failing to shut off the leak source within 15 minutes, a secondary warning will be automatically triggered to notify the superior management department. Tracking data update frequency: 1 time / 30 seconds.

[0045] Record data on the handling process, including arrival time, completion time of each handling step, and number of equipment / personnel involved; rectification results and effectiveness evaluation; Archived data is synchronized to the blockchain storage node to generate an immutable disposal record certificate.

[0046] The data storage and interaction module communicates with each of the above modules and is used to store the entire process data and provide a human-computer interaction interface, supporting data query, statistical analysis and system parameter configuration.

[0047] The data storage and interaction module adopts a distributed storage architecture, including a real-time database for storing collected real-time data, a relational database for storing structured business data, and blockchain storage nodes for storing key risk data and handling records, ensuring that the data is tamper-proof.

[0048] The human-computer interaction interface supports visual chart display, including risk distribution heatmaps, risk level statistical charts, and handling progress flowcharts. It also provides a custom report generation function and supports data export and printing.

[0049] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

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The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to a safety production risk early warning system and method. The application relates to 5. The safety production risk intelligent early warning and disposal platform of multi-source information fusion according to claim 4, characterized in that, By calculating the comprehensive risk score, which is mapped to a risk level including general, greater, major and particularly major, the comprehensive risk score S is calculated as follows: ； wherein, represents the quantification score of the jth indicator, represents the fusion weight of the jth indicator, the correspondence relationship between the score interval and the risk level: general risk: S ∈ [0, 20); greater risk: S ∈ [20, 40); major risk: S ∈ [40, 70); particularly major risk: S ∈ [70, 100].

6. The safety production risk intelligent early warning and disposal platform of multi-source information fusion according to claim 1, characterized in that, The treatment scheduling module comprises: A treatment scheme library unit stores standardized treatment procedures and emergency plans classified by risk type and risk level, supports custom editing and version management of the scheme; An instruction issuing unit can automatically issue treatment instructions to the terminal devices of the corresponding responsible departments and staff, including risk location, risk description, treatment requirements and time limit; A process tracking unit tracks the treatment progress in real time through GPS positioning and execution feedback uploading functions, and automatically triggers a second early warning for overdue non-treatment matters; A result archiving unit records treatment process data, rectification results and effect evaluation information to form a risk treatment closed-loop data.

7. The safety production risk intelligent early warning and disposal platform of multi-source information fusion according to claim 1, characterized in that, The data storage and interaction module adopts a distributed storage architecture, including a real-time database for storing collected real-time data, a relational database for storing structured business data, and a blockchain storage node for storing key risk data and treatment records to ensure data tamper resistance.

8. The safety production risk intelligent early warning and disposal platform of multi-source information fusion according to claim 1, characterized in that, The human-computer interaction interface supports visual chart display, including risk distribution heat map, risk level statistical chart, treatment progress flowchart, and provides custom report generation function, supports data export and printing.

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