A Method for Industrial Environment Monitoring and Accident Prediction Integrating Multimodal Data

Abstract

This invention provides a method for industrial environment monitoring and accident prediction that integrates multimodal data, relating to the field of data processing technology. It constructs a dynamic industrial knowledge graph to perform semantic acquisition and causal correlation preprocessing on real-time collected multimodal heterogeneous data; employs a customized deep learning model to extract deep abstract features of each modality, accurately representing weak signals and potential risks and quantifying uncertainty; utilizes a high-fidelity digital twin model to drive a deep reinforcement learning algorithm, dynamically optimizing and validating the generation of multi-level, multi-objective preventative intervention strategy combinations; and executes intervention strategies through an edge-end-cloud three-layer collaborative intelligent architecture, utilizing a closed-loop data feedback mechanism to achieve online learning and continuous system evolution. This invention can significantly improve the perception and early warning capabilities of early, weak, and complex abnormal states in the industrial environment, accurately predict accident evolution paths, and provide reliable explanations.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method for industrial environment monitoring and accident prediction that integrates multimodal data. Background Technology

[0002] Traditional industrial environmental monitoring and accident prevention methods mainly rely on the following levels: First, manual inspection and judgment based on operator experience. This method is highly subjective, labor-intensive, and has a slow response time, making it difficult to cope with the massive amounts of information and rapid changes in complex industrial systems. Second, automated monitoring and alarms based on distributed control systems and safety instrumented systems. These systems mainly monitor single or a few key process parameters by setting fixed thresholds. Although they can achieve basic process control and safety interlocking, they have significant shortcomings in predictability and comprehensiveness, are prone to generating a large number of false alarms or missed alarms, and are difficult to identify. The gradual process of early weak anomalies and complex faults; third, rule-based expert systems, which solidify the experience and knowledge of domain experts into IF-THEN rule bases for reasoning and judgment, but such systems are difficult to acquire knowledge, have high update and maintenance costs, and have limited rule coverage, making them difficult to adapt to changing operating conditions and unknown risk scenarios; fourth, traditional statistical process control and single physical quantity monitoring technologies, these methods have limited capabilities in processing multi-source heterogeneous data fusion, capturing complex nonlinear correlations, and understanding deep causal mechanisms, making it difficult to form a comprehensive, dynamic, and accurate understanding of the overall operating status and potential risks of industrial systems. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for industrial environment monitoring and accident prediction that integrates multimodal data, thus solving the problems of existing technologies.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an industrial environment monitoring and accident prediction method integrating multimodal data, wherein the industrial environment monitoring and accident prediction method includes the following:

[0005] Sp1. Semantic Acquisition and Causal Association Preprocessing of Multimodal Heterogeneous Data Based on Dynamic Knowledge Graph: Multimodal heterogeneous data in the industrial environment is acquired in real time to obtain a dynamic knowledge graph. The dynamically updated industrial domain knowledge graph is used to perform semantic annotation, spatiotemporal alignment, and preliminary mining and labeling of potential causal relationships on the data, generating a structured dataset rich in semantic information and causal hypotheses.

[0006] Sp2, Deep Feature Extraction and Multimodal Cognitive Fusion for Weak Signals and Potential Risks: Deep learning models are used to extract deep abstract features representing early weak equipment faults, subtle environmental changes, and atypical human behaviors from different modal data. These multimodal deep abstract features are then fused at the cognitive level using a fusion strategy to generate a unified feature representation of the current industrial system's operating status, potential risk factors, and their interactions. The uncertainty of the fusion results is then quantitatively assessed.

[0007] Sp3. Accident Chain Evolution Path Probability Prediction and Interpretability Analysis Based on Hybrid Intelligence and Evolutionary Learning: Construct a hybrid intelligent prediction engine that integrates physical mechanism models, statistical models, and deep learning models to obtain accident chain evolution path probability prediction results. Based on the unified feature representation and historical accident data, predict the occurrence probability, triggering conditions, spatiotemporal evolution paths, and secondary / derived accident chains of various potential accidents within a specific future time window. Use interpretable artificial intelligence technology to provide key influencing factor analysis and accident evolution logic explanation for the prediction results.

[0008] Sp4. Dynamic optimization and verification of preventive intervention strategies driven by digital twins: Construct a digital twin model that maps in real time to the physical industrial environment with high fidelity. Input the probability prediction results of the accident chain evolution path into the digital twin model. Use deep reinforcement learning algorithms to simulate in the digital twin environment. With the goal of minimizing expected risks and intervention costs, dynamically optimize and generate multi-level and multi-objective preventive intervention strategy combinations. Verify and iteratively optimize the generated intervention strategy combinations in the digital twin environment.

[0009] Sp5, edge-end-cloud collaborative adaptive monitoring, closed-loop feedback and continuous system evolution: Monitoring and prediction tasks are executed collaboratively at the device, edge and cloud, collecting data on the execution effect of intervention strategies in the physical environment and new data, which are used as feedback information to update the knowledge graph, feature extraction and fusion model, prediction engine and reinforcement learning policy network online, realizing closed-loop feedback and adaptive evolution of the entire monitoring and prediction system.

[0010] Preferably, the construction and updating of the dynamic knowledge graph in Sp1 further includes: using natural language processing technology to automatically identify and extract entity concepts, attribute information, failure modes, causal relationships, and operational constraint rules related to industrial safety, equipment health, and process flow from unstructured and semi-structured text data, including maintenance records, operation manuals, safety procedures, and accident investigation reports; and after verifying and standardizing these extracted information, to structurally integrate them into and dynamically update the dynamic knowledge graph, thereby significantly enhancing the breadth of knowledge coverage, richness of detail, real-time information, and accuracy of subsequent reasoning and decision-making.

[0011] Preferably, the deep feature extraction of weak signals in Sp2 further includes the following steps:

[0012] Sp2.1. For signals (including vibration signals, acoustic signals, or high-speed visual data) containing high-frequency noise and non-stationary components in the acquired raw multimodal data, advanced signal processing techniques, including adaptive wavelet transform, empirical mode decomposition, or Hilbert-Huang transform, are used for time-frequency analysis, feature enhancement, and extraction of target frequency components to initially separate and highlight sensitive features related to early faults or minor anomalies.

[0013] Sp2.2. The features or raw data processed in Sp2.1 are input into a pre-trained or task-specific fine-tuned deep learning network (including ResNet residual network with attention mechanism, DenseNet dense connection network, or GRU network gated recurrent unit for time-series data). Through multi-layer nonlinear transformation, deep, high-dimensional, and more discriminative abstract feature representations that indicate early fault initiation, material micro-damage accumulation, or minor deviations in the operation process are learned and captured, thereby effectively improving the sensitivity, specificity, and robustness of detecting weak signals of potential risks.

[0014] Preferably, when faced with challenges such as sparse historical data, frequent changes in operating conditions, or the existence of unknown new accident modes, the evolutionary learning capability of the hybrid intelligent prediction engine in Sp3 is further achieved through at least one of the following methods:

[0015] The rapid adaptation module based on meta-learning is launched. By learning "what to learn" on a large number of historical related tasks or simulation tasks, the prediction model can quickly adjust its parameters to adapt to new working conditions or accident types using a very small number of new target domain samples.

[0016] The application utilizes a knowledge transfer module based on transfer learning. This module measures and reduces the differences in data distribution or feature space between the source domain (including similar industrial scenarios with sufficient data and high-fidelity simulation environments) and the target domain (the current specific industrial environment). It selectively transfers and adapts the accident mode knowledge, fault feature representations, or model parameters learned in the source domain to the prediction model in the target domain. This effectively improves the cross-domain generalization ability of the prediction engine and the early warning accuracy for low-probability, high-risk events when there is insufficient data in the target domain or when there are unseen accident modes.

[0017] Preferably, the digital twin model in Sp4 further includes: an integrated first-principles-based multiphysics coupling simulation engine capable of high-precision simulation of the nonlinear interactions between multiple physical quantities involved in industrial processes, including thermodynamics, fluid mechanics, electromagnetics, structural mechanics, and chemical reaction kinetics, and their dynamic evolution under fault or accident scenarios; providing a configurable parameterized scenario construction interface, allowing users to flexibly define complex accident scenarios, including initial equipment state, material properties, environmental parameters, operation sequences, potential fault injection points, and propagation paths, according to actual needs or historical cases; supporting interactive manual review, parameter fine-tuning, logic correction, or rejection of the preventive intervention strategies automatically generated by the system through a graphical interface or programming interface, and quickly verifying the manually adjusted strategies by re-integrating them into the digital twin environment, thereby achieving an effective combination of human and machine intelligence and ensuring the actual feasibility and acceptability of the final intervention strategy.

[0018] Preferably, the adaptive monitoring, closed-loop feedback, and continuous system evolution of the edge-end-cloud collaboration in Sp5 are specifically implemented as follows: A lightweight edge AI model is deployed on the device or embedded system close to the data source to perform data quality verification, basic feature extraction, and low-latency initial screening and rapid response to simple anomalies; edge computing nodes are deployed at the industrial site or workshop level to perform more complex multimodal data fusion, real-time risk assessment, short-term accident prediction, and execute localized preventative control commands on the aggregated end-device data and local sensor data; large-scale, highly complex global knowledge graph management, deep learning model training and updating, long-term trend analysis, complex accident chain deduction, global resource optimization scheduling, and cross-plant / cross-enterprise federated learning coordination are run on the cloud computing platform; the closed-loop feedback mechanism ensures that the physical world's operational data, intervention effects, and newly discovered risk patterns can be captured in a timely manner and used to continuously optimize the models, algorithms, and knowledge bases deployed at each level, forming a dynamically adaptive, continuously learning, and performance-improving intelligent monitoring and prediction system.

[0019] Preferably, the industrial environment monitoring and accident prediction system that integrates multimodal data includes:

[0020] A multimodal data semantic acquisition and preprocessing unit is used to execute Sp1;

[0021] The deep feature extraction and cognitive fusion unit is used to perform Sp2;

[0022] The accident evolution prediction and interpretability analysis unit is used to perform Sp3;

[0023] The digital twin-driven strategy optimization and verification unit is used to execute Sp4;

[0024] The Cooperative Control and System Evolution Unit is used to execute Sp5;

[0025] And the sensor network, industrial control system interface, distributed edge computing device, cloud computing platform and human-computer interaction and visualization interface connected to the above units.

[0026] Preferably, the multimodal data semantic acquisition and preprocessing unit further includes:

[0027] The multi-source heterogeneous data access module is used to collect multi-source heterogeneous data in real time and in batches from sensor networks, PLC / DCS systems, MES systems, video surveillance systems, and manual record databases through standard industrial protocols or customized interfaces.

[0028] The Natural Language Processing and Knowledge Extraction module is configured to use machine learning models and semantic analysis algorithms to automatically extract key entities, attributes, relationships, events and rules from maintenance logs, operating procedures, safety standards and accident report text data to assist in building and updating knowledge graphs in the industrial field.

[0029] The knowledge graph management and reasoning engine module is used to store and manage dynamically updated knowledge graphs, and to perform semantic queries, logical reasoning, and data association analysis based on the graphs, providing rich background knowledge and contextual information for subsequent data fusion and prediction.

[0030] Preferably, the cooperative control and system evolution unit further includes:

[0031] The distributed model deployment and management module is used to securely and efficiently deploy trained or continuously updated monitoring models, prediction models, and control strategy models to corresponding devices, edge computing nodes, and cloud platforms, and to manage the model's version, configuration, and running status.

[0032] The Federated Learning Coordination and Secure Aggregation Module is responsible for coordinating the local model training process of each participant (edge ​​node or facility) when the system adopts the federated learning mechanism, securely collecting and aggregating the model updates contributed by each party, updating the globally shared model, and distributing the updated model back to each participant, while ensuring data privacy and communication security throughout the process.

[0033] The closed-loop feedback and continuous learning engine is used to collect system operation data, model prediction results, human intervention records, and policy execution effects. Through online learning, incremental learning, or reinforcement learning mechanisms, it continuously optimizes model parameters, knowledge base content, and control strategies at each level to improve the overall performance of the system and its adaptability to dynamic environments.

[0034] This invention provides a method for industrial environment monitoring and accident prediction that integrates multimodal data. It has the following beneficial effects:

[0035] 1. This invention constructs a dynamically updated industrial domain knowledge graph and combines targeted natural language processing techniques with multimodal data semantic acquisition, high-precision spatiotemporal alignment, and preliminary causal correlation mining to achieve a deep understanding of massive, heterogeneous industrial data and accurate construction of contextual scenarios. This significantly improves the system's sensitivity, accuracy, and comprehensive judgment capabilities in perceiving early, subtle, and complex anomalies in the industrial environment. It enables the accurate capture and characterization of potential risks that were previously difficult to detect or easily overlooked at the nascent stage, providing a data foundation and feature support for subsequent accurate prediction and proactive intervention.

[0036] 2. This invention employs deep reinforcement learning driven by high-fidelity digital twins for dynamic optimization and closed-loop verification of preventive intervention strategies. In a digital twin environment that maps to the physical world in real time and integrates multi-physics coupling simulation capabilities, it can automatically explore and learn multi-level, multi-objective combinations of preventive intervention strategies while minimizing expected risks and intervention costs. The closed-loop decision-making mechanism of "prediction-deduction-optimization-verification-execution" ensures that the preventive intervention measures taken not only possess theoretical optimality but also robustness, safety, and acceptability in practical operating environments. Attached Figure Description

[0037] Figure 1 This is a system composition diagram of the present invention;

[0038] Figure 2 This is a flowchart illustrating the operation of the present invention;

[0039] Figure 3 This is an illustration of the core interaction of knowledge graph-driven multimodal data fusion and prediction in this invention. Detailed Implementation

[0040] 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. Specific Implementation Example 1:

[0042] like Figures 1 to 3 As shown, an industrial environment monitoring and accident prediction method integrating multimodal data is proposed. The industrial environment monitoring and accident prediction method includes the following:

[0043] Sp1. Semantic Acquisition and Causal Association Preprocessing of Multimodal Heterogeneous Data Based on Dynamic Knowledge Graph: This step first uses a multi-modal heterogeneous sensor network and data interfaces deployed in the industrial site to collect comprehensive, high-fidelity multimodal heterogeneous data from the industrial environment in real time. Specifically, this includes: Visual data: using industrial cameras (including RGB, infrared thermal imaging, and hyperspectral camera arrays) with a resolution of at least 2K and a frame rate of at least 60fps to capture image sequences of equipment surface conditions, personnel behavior, and environmental smoke and flames; Acoustic data: using a high-precision microphone array (with beamforming capability) covering the 20Hz-100kHz frequency band to synchronously collect acoustic signature information of equipment operation and environmental abnormalities; Vibration data: collected by attaching data to key equipment core components (including bearing housings, motor housings, gears, etc.). Vibration signals of the equipment are acquired using a triaxial accelerometer (range ±50g, frequency response 0.2Hz-20kHz) on the wheel well. Equipment operating parameters are collected in real-time from DCS, PLC, and SCADA systems using standard industrial protocols such as OPCUA, ModbusTCP, and EtherNet / IP, covering at least 5000 key process parameters (including temperature, pressure, flow rate, liquid level, speed, current, voltage, and valve opening, with a sampling period of no more than 100ms). Environmental parameters include temperature and humidity, flammable / toxic gas concentration (using electrochemical or infrared sensors), and dust concentration (using laser scattering principle sensors). Personnel data is obtained through name tag positioning and video behavior analysis, capturing the position, posture, action sequence, and important operation records of workers. The collected raw data undergoes rigorous quality verification and cleaning, including outlier removal, missing value filling (using a multivariable temporal interpolation-based algorithm), and signal denoising (adaptive Wiener filtering and wavelet thresholding for vibration and acoustic signals). Subsequently, the data was subjected to high-precision semantic annotation, sub-second spatiotemporal alignment (based on NTP network time synchronization and unified spatial coordinate system transformation) and preliminary mining and labeling of potential causal relationships using a dynamically updated industrial domain knowledge graph. This was achieved by using association rule mining based on the Apriori algorithm and causal structure learning based on the PC algorithm to initially screen candidate causal pairs. Finally, a structured dataset rich in accurate spatiotemporal stamps and deep semantic information was generated, and potential causal links were preliminarily labeled, providing a high-quality data foundation for subsequent analysis.

[0044] The construction and updating of the dynamic knowledge graph in Sp1 further includes: the knowledge graph construction adopts a combined top-down and bottom-up strategy. The basic ontology framework is based on the ISO15926 and ISA-95 international standards, combined with industry-specific ontology (OntoCAPE for the chemical industry, CIM model for the power industry) and the enterprise's internal expert knowledge base (organized through structured interviews and knowledge acquisition tools). Ontology modeling is performed using Protégé 5.5.0, defining no less than 500 core entity categories (pumps, valves, compressors, reactors, sensors, alarm events, failure modes, operating procedures, safety measures), 1000 entity attributes (equipment ID, rated power, material, installation location, operating status, fault description, maintenance history), and 200 semantic relationships ("is-a", "part-of", "connected-to", "causes", "prevents", "requires"). Natural language processing technology is used to automatically identify and extract structured information from massive amounts of unstructured and semi-structured text data (including but not limited to equipment maintenance records in PDF format, operation manuals and process cards in Word documents, safety checklists in Excel spreadsheets, and accident investigation reports and industry standard specifications in HTML or plain text format). Specifically, a domain-pre-trained model based on BERT (Bidirectional Encoder Representations from Transformers) is used (secondary pre-training on over 1TB of industrial domain text corpus for Masked Language Model and NextSentencePrediction tasks, with a model parameter count of no less than 340M, BERT-Large). This is combined with a downstream BiLSTM-CRF model for Named Entity Recognition (NER), achieving an entity recognition F1 score of no less than 95%. A Relation Extraction (RE) model based on R-BERT or SpanBERT is used to identify predefined semantic relationships between entities, achieving a relationship extraction accuracy of no less than 90%. Event Extraction (EE) technology based on dependency parsing and semantic role labeling is used to extract key events ("Equipment A malfunctioned X", "Operator B performed operation Y") and their arguments.All extracted information undergoes multiple verifications (including consistency verification with existing knowledge graphs, rule base verification, and manual sampling when necessary) and strict standardization processing (physical quantity units are uniformly adopted using the International System of Units, professional terms are standardized according to standard dictionaries, and time formats are uniformly standardized to ISO8601). Finally, it is structured and dynamically updated into the Neo4j4.x enterprise graph database in the form of RDF triples or attribute graphs (cluster deployment ensures high availability and scalability, and the graph scale is designed to support tens of billions of nodes and relationships). This dynamic knowledge graph ensures its breadth of knowledge coverage (from a single component to the entire factory's complete hierarchical structure), richness of detail (multi-dimensional attributes and complex relationship networks for each entity), real-time information (through API interfaces with real-time data streams, the equipment status and parameter information in the knowledge graph are kept in near real-time synchronization with the physical world, with a delay of no more than 5 seconds), and the accuracy and reliability of subsequent graph-based complex reasoning (using the Cypher query language for multi-hop association queries and pattern matching, or combining with the rule engine Drools for logical reasoning) and intelligent decision-making through regular (hourly) incremental updates and full reconstruction when necessary.

[0045] Sp2. Deep Feature Extraction and Multimodal Cognitive Fusion for Weak Signals and Potential Risks: For different modal data, a customized deep learning model is used for deep abstract feature extraction: Vibration signal: A multi-scale residual convolutional temporal network (MSRCTN) is used. This network first extracts the multi-scale local patterns of the original vibration time domain signal through parallel one-dimensional convolutional kernel groups with different receptive field sizes (3,5,7,9) (each group contains 64 convolutional kernels, ReLU activation, BatchNormalization). Then, it learns its long-term temporal dependencies through a stacked temporal convolutional network (TCN) module with residual connections (containing dilated convolutional layers, with dilation factor sequences of 1,2,4,8 to ensure large-scale dependency capture). Finally, it outputs a high-dimensional feature vector (dimension 512) containing early weak fault features of the equipment (bearing pitting, gear tooth breakage, periodic impact pulses or specific frequency modulation phenomena caused by rotor imbalance). Acoustic signals: First, a log-Mel spectrogram (25ms frame length, 10ms frame shift, 128 Mel filters) is extracted and then input into a 2D convolutional neural network based on the EfficientNet-B3 architecture and incorporating channel and spatial attention mechanisms (CBAM). This network efficiently extracts the fine temporal-spectral structure features (512 dimensions) of acoustic events (gas leak sound, mechanical friction noise, discharge sound) through depthwise separable convolutions and a Squeeze-and-Excitation module. Visual data: For equipment status monitoring, an image encoder based on SwinTransformerV2-Large is used to extract visual representations of key areas (cracks on equipment surface, abnormal oil color, instrument readings). For personnel behavior analysis, a human pose estimation algorithm based on HRNet is used to obtain the key point coordinate sequence, and then a spatiotemporal graph convolutional network (ST-GCN) is used to identify atypical or illegal behaviors (not wearing a safety helmet, entering a restricted area, abnormal fall), outputting the behavior classification probability and related visual features (512 dimensions). The deep abstract features extracted from each of the above modalities are cognitively fused using a Transformer Fusion Network (MCTFN) based on a multi-modal cross-attention mechanism. This network contains multiple encoder layers, each with a self-attention module for each modality and a cross-attention module across all modal pairs. It can dynamically learn complex nonlinear dependencies within and between modalities and adaptively assign different fusion weights to features from different modalities and at different time steps.The output of MCTFN is a unified feature representation vector with a dimension of 1024. This vector comprehensively represents the overall operating state of the current industrial system at a specific moment, the potential risk factors (equipment degradation level, environmental hazard level, and unsafe personnel conditions), and the combined effect of their interactions. Simultaneously, to quantitatively assess the uncertainty of the fusion results, this scheme employs a deep ensemble learning method. This involves independently training multiple (5) MCTFN models with different random initialization seeds or slightly different hyperparameters. During inference, their output results (feature vectors or predicted probabilities of downstream tasks) are averaged or weighted averaged, and the variance or standard deviation of the prediction results is calculated as a measure of uncertainty. This uncertainty measure will be used to guide subsequent risk decision-making and active learning.

[0046] The deep feature extraction for weak signals in Sp2 further includes the following steps:

[0047] Sp2.1. For signals containing high-frequency noise and non-stationary components in the acquired raw multimodal data, especially vibration signals from high-speed rotating machinery (steam turbines, compressors, large fans) (which typically contain rich fault harmonics and sideband information, but are easily drowned out by background noise), acoustic signals generated by high-pressure fluid (steam, natural gas) leakage or cavitation phenomena (whose characteristic frequencies are as high as tens of kHz and exhibit suddenness and non-stationarity), and industrial visual data captured on high-speed production lines (steel rolling, papermaking, printing) (which suffer from motion blur and uneven illumination), an advanced signal processing technique based on Synchrosqueezed Wavelet Transform (SSWT) is first adopted. SSWT can redistribute and concentrate signal energy in the time-frequency plane, improving time-frequency resolution, and is particularly suitable for mode separation and instantaneous frequency estimation of multi-component non-stationary signals. By performing time-frequency analysis on the signal using SSWT and combining it with an adaptive threshold denoising algorithm (based on wavelet coefficients SUREshrink or BayesShrink) to enhance the signal-to-noise ratio, the target frequency components or instantaneous features with a signal-to-noise ratio improvement of at least 10dB are extracted. This allows for the initial separation and highlighting of sensitive features related to early faults (micro-peeling of bearing outer ring with a diameter of less than 0.1mm, initial pitting on gear tooth surfaces, and micro-cracks in rotor blades) or minor anomalies (loosening of equipment foundation bolts within 0.5mm, and slight changes in oil film stiffness due to trace amounts of metal shavings in the lubricating oil).

[0048] Sp2.2, the sensitive features extracted after Sp2.1 preprocessing (the instantaneous energy values ​​and kurtosis of specific fault frequency bands in the time-spectrum obtained by SSWT, or the waveform data of a single IMF component after modal reconstruction) or the raw data directly in an end-to-end learning scenario, are input into a deep learning network designed for a specific industrial monitoring task and rigorously fine-tuned. This network adopts a Hybrid Attention Residual Temporal Convolutional Network (HARTCN). The input layer of HARTCN receives the preprocessed feature sequence, and its core structure consists of stacked residual temporal convolutional modules. Each module contains multiple layers of dilation factor convolutions (the dilation factor sequence is 1, 2, 4, 8, 16, ensuring exponential growth of the receptive field), and introduces a dual mechanism of channel attention and temporal attention between the convolutional layers: channel attention (SE module) is used to adaptively learn the importance of different feature channels and enhance the weight of key fault indication features; temporal attention (the self-attention mechanism in Transformer) is used to capture the long-term dependencies and key time points within the temporal features. The activation function is uniformly GeLU, the optimizer is AdamW, and the learning rate employs a cosine annealing strategy with preheating. This network learns and captures deep, high-dimensional (output feature vector dimension 512) and highly discriminative abstract feature representations from the input, indicating early fault initiation states (the first 10% of the lifespan of material fatigue cracks during initiation and propagation), material micro-damage accumulation (the initial stage of material creep or corrosion under high temperature and pressure), or minor deviations in the operating process (persistent small fluctuations of process parameters within 0.5% of the set value). Its goal is to minimize the intra-class distance and maximize the inter-class distance of samples with different health states or risk levels in the feature space during multi-class classification or regression tasks. This step effectively improves the sensitivity (aiming for an early detection rate of over 98% for known fault modes), specificity (aiming for a false alarm rate of less than 0.5% under normal conditions), and robustness (detection performance degradation of no more than 5% within ±20% of operating condition fluctuations).

[0049] Sp3. Accident Chain Evolution Path Probability Prediction and Interpretability Analysis Based on Hybrid Intelligence and Evolutionary Learning: The hybrid intelligent prediction engine constructed in this solution has a core architecture of a three-layer decision fusion system: the bottom layer is a cluster of physical mechanism models based on first principles, including finite element models for key equipment (using ANSYS Mechanical for rotor dynamics analysis and fatigue life prediction), computational fluid dynamics models (using Fluent to simulate multiphase flow and heat transfer processes in reactors), and system dynamic models based on state-space equations. These models can provide theoretical behavior baselines, stress distributions, and physical predictions of failure probabilities for specific equipment or subsystems based on real-time input operating parameters. The middle layer comprises a multivariate statistical analysis and machine learning model library, including a multivariate time-series prediction model based on Vector Autoregulation (VAR) (used to capture dynamic correlations and short-term trends among key parameters), a device remaining life prediction model based on Survival Analysis (using Weibull distribution or Cox proportional hazards model, combined with historical failure data and real-time state parameters), and a state transition probability estimation model based on Hidden Markov Model (HMM) or Dynamic Bayesian Network (DBN) (used to predict the characteristics of the system migrating from the current healthy state to different failure states). The top layer is a deep learning-based complex accident pattern recognition and evolution prediction module, with its core employing a Spatio-Temporal Dynamic Graph Attention Network (STDGAT). STDGAT takes time-series data of unified feature representation vectors generated by Sp2 and a knowledge graph constructed by Sp1 (representing physical connections, logical dependencies, and causal relationships between devices and systems) as input. The nodes in the graph represent various monitored objects (equipment, sensors, areas) in the industrial system, and the node features are the outputs of Sp2. The edges of the graph are dynamically constructed based on the knowledge graph and assigned time-varying weights. STDGAT learns and captures the propagation patterns and dependencies of incidents in space (between equipment, between areas) and time by alternating stacks of multiple graph attention layers (GATv2) and temporal convolutional layers (TCN or GRU).Based on the unified feature representation generated by Sp2 and a historical accident database (containing detailed accident records for at least the past 5 years, each record including accident ID, occurrence time, duration, equipment involved, triggering factors, direct cause, indirect cause, development process, casualties, economic losses, and structured information on measures taken), this engine accurately predicts the probability of occurrence (a continuous value between 0 and 1), the largest combination of triggering conditions (identified by tracing important paths and node features in STDGAT), the spatiotemporal evolution path (displaying the new sequence and expected time of the accident spreading to other nodes in time and space from the source node in the form of a probability graph), and the secondary / derived accident chain (predicting fires and explosions caused by equipment failures, leading to structural damage and the spread of toxic substances, forming a domino effect) of various potential accidents (A-type compressor surge, B-type storage tank overpressure leakage, C-zone electrical fire, and D-line unplanned shutdown due to critical equipment failure) within a specific future time window (which can be set to the next 15 minutes, 1 hour, 8 hours, or 24 hours). The target accuracy (AUC) for predicting the probability of accident occurrence is no less than 0.90, and the target average precision (MAP) for predicting the evolution path is no less than 0.85. Simultaneously, interpretable artificial intelligence (XAI) techniques combining integrated gradients and graph counterfactual explanations (GCE) are employed. Integrated gradients are used to quantify the contribution of input features (each dimension in the unified feature representation of Sp2) to the STDGAT prediction result (probability of accident occurrence), identifying key influencing factors. GCE, by finding the minimum subgraph structure or edge perturbation on the knowledge graph that can change the prediction result, generates counterfactual explanations in the form of "if X does not occur, then the probability of accident Y will decrease by Z%", thus providing deep insights into the logic of accident evolution and a basis for decision-making.

[0050] When faced with challenges such as sparse historical data, frequent changes in operating conditions, or the existence of unknown new accident modes, the hybrid intelligent prediction engine in Sp3 further enhances its evolutionary learning capabilities through at least one of the following methods:

[0051] Launch a rapid adaptation module based on the Model-Agnostic Meta-Learning (MAML) algorithm. MAML learns a general set of model initialization parameters or a meta-network capable of rapidly generating task-specific parameters by meta-training on a large number (hundreds) of historically relevant tasks or high-fidelity simulation tasks (fault prediction of different models of centrifugal pumps, reactor runaway simulation under different catalyst ratios). This meta-knowledge enables the overall prediction model to quickly adjust its internal parameters to adapt to new data distributions and task requirements when encountering new operating conditions (production load increases from 70% to 95%), new equipment models (replacing sensors or actuators with new models), or historically unrecorded accident types (a rare failure mode caused by material corrosion), using only a very small number (5-10) of new target domain samples and a few steps of gradient descent. This achieves rapid learning and high-accuracy prediction of new scenarios, with a learning convergence speed on new tasks that is at least 30% faster than traditional transfer learning.

[0052] This application utilizes a knowledge transfer module based on Adversarial Domain Adaptation. The core idea of ​​this module is to introduce a domain discriminator and a feature extractor (typically the low-level graph convolutional and temporal convolutional layers of STDG AT) that are adversarially trained between the source domain (a baseline factory A with sufficient labeled data and complete operational records, or a large-scale simulation dataset containing multiple failure modes generated in a digital twin environment) and the target domain (a specific industrial environment B currently facing sparse data or unknown patterns). The feature extractor aims to learn domain-independent feature representations that can both accomplish the main prediction task (accident probability prediction) and confuse the domain discriminator; the domain discriminator aims to distinguish whether features originate from the source or target domain. Through this adversarial game, the feature extractor is forced to learn transferable, invariant feature representations between the two domains. The model parameter transfer employs a partial transfer strategy: first, the entire STDG AT model is pre-trained in the source domain; then, the parameters of its feature extraction part are fixed or fine-tuned with a small learning rate, while the top-level prediction layer and the domain discriminator are trained on data from the target domain. This method selectively transfers and adapts learned accident pattern knowledge (manifested as feature combinations sensitive to specific faults), fault feature representations (i.e., parameters of the feature extractor), or model parameters (attention weights) from the source domain to the prediction model in the target domain. This effectively improves the cross-domain generalization ability of the prediction engine and the early warning accuracy for low-probability, high-risk events (chain failures caused by extreme weather, raw material quality problems caused by supply chain changes) when there is insufficient data in the target domain (in the early stages of new equipment production, fault samples are scarce) or there are unseen accident patterns. The goal is to improve the prediction accuracy of the target domain by at least 15% (compared to not using transfer learning).

[0053] Sp4. Dynamic Optimization and Verification of Preventive Intervention Strategies Driven by Digital Twins: The digital twin model constructed in this solution uses the NVIDIA Omniverse platform as the core visualization and collaboration foundation, integrating multi-source data and multi-physics simulation capabilities. The 3D geometric model is constructed by importing CAD / BIM (SolidWorks, Revit, Plant3D) design data and performing lightweight processing, achieving millimeter-level accuracy. The physical property database includes equipment materials (mechanical, thermal, and electrical properties corresponding to ASTM standard grades), fluid media (viscosity, density, specific heat capacity), and environmental parameters (atmospheric pressure, soil properties). Behavioral logic is imported into the control system logic (FMU compiled from the Simulink model) and operating procedures (represented in the form of state machines or behavior trees) through the Functional Model Unit (FMU) / Functional Prototype Interface (FMI) standard. This digital twin model receives real-time sensor data collected by Sp1 (with a latency of less than 100ms) and the unified feature representation generated by Sp2 via MQTT and Kafka message queues. Through a built-in Kalman filter and data assimilation algorithm, its internal state maintains high-fidelity synchronization with the physical entity (state deviation less than 2%). The probability prediction results of the accident chain evolution path predicted by Sp3 are overlaid in the 3D scene of the digital twin model as a dynamic risk heatmap and accident evolution sequence. Dynamic optimization of preventative intervention strategies employs a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO). The state space of the PPO agent consists of the system-wide state vector provided by the digital twin model (including key equipment health indices, process parameters, and environmental parameters, with approximately 2000 dimensions), the unified feature representation of Sp2 (1024 dimensions), and the predicted risk vector of Sp3 (including the probability of occurrence of each potential accident and the probability of key nodes in the evolution path, with approximately 500 dimensions). The action space is a discrete-continuous hybrid, including: discrete actions: selecting the intervention object (specific equipment, specific area) and selecting the intervention type (adjusting parameters, activating standby, performing maintenance, issuing alarms, triggering ESD); continuous actions: adjusting the specific values ​​of parameters (setting target values ​​for temperature, pressure, and flow rate, which fluctuate within the allowable range).The reward function R is designed as follows: R = w1(ΔSafety) - w2(Cost_Intervention) - w3(Cost_Downtime) - w4(Cost_Energy) + w5(ΔEfficiency), where ΔSafety is the reduction in accident probability or risk level, Cost_Intervention is the direct cost of the intervention, Cost_Downtime is the production downtime loss caused by the intervention or accident, Cost_Energy is the energy consumption change brought about by the intervention, ΔSafety is the set value of the safety level, which is defined as (0-10) where 0 is the lowest safety requirement and 10 is the highest safety requirement. The initial standard setting is 6, ΔEfficiency is the improvement in production efficiency, and w1-w5 are adjustable weight coefficients set according to the company's risk appetite and operational goals. The PPO agent undergoes simulation training for at least 10^7 time steps in a digital twin environment. Through interaction with the environment, it learns a policy network (Actor network) and a value network (Critic network) that maximizes the cumulative expected reward. Both networks employ a neural network structure containing multiple fully connected layers and residual connections, with Tanh activation function. After training, the policy network dynamically optimizes and generates multi-level (equipment-level parameter fine-tuning, system-level process reengineering, and factory-level emergency response initiation) multi-objective preventive intervention strategy combinations based on real-time input states. The generated strategy combinations are first validated in the digital twin environment through at least 1000 Monte Carlo simulations under different initial conditions and random perturbations. Their effectiveness (99% accident avoidance success rate), robustness (strategy effectiveness decreases by no more than 5% under ±10% perturbation of key parameters), and potential negative impacts (perturbations on other related systems) are evaluated. Based on the validation results, the parameters or reward function of the policy network are iteratively optimized until the preset performance indicators are achieved.

[0054] The digital twin model in SP4 further includes an integrated first-principles-based multiphysics coupling simulation engine. Specifically, this is achieved by integrating professional simulation software such as COMSOL Multiphysics (for electromagnetic-thermal-fluid-structural coupling analysis), ANSYS Fluent (for complex fluid dynamics and combustion simulation), and Siemens SimCenter Amesim (for one-dimensional system-level multi-domain dynamic simulation) as callable simulation solvers into the digital twin platform via the FMI / FMU interface. These solvers can accurately simulate the nonlinear interactions between multiple physical quantities involved in industrial processes, including thermodynamics (heat exchanger efficiency, reaction thermal runaway), fluid mechanics (pipeline pressure loss, pump cavitation), electromagnetics (motor electromagnetic torque, transformer core loss), structural mechanics (pressure vessel stress concentration, pipeline vibration fatigue), and chemical reaction kinetics (catalyst activity changes, product yield), and their dynamic evolution under fault (equipment overheating, pipeline rupture) or accident (fire spread, explosion shock wave propagation) scenarios. The simulation results show a consistency of no less than 90% with experimental or historical data. It provides a web-based, configurable, parameterized scenario building graphical user interface (GUI), allowing authorized users (process engineers, safety engineers) to flexibly define complex accident scenarios based on actual needs, historical accident cases (imported from the accident database), or HAZOP analysis results. These scenarios include initial equipment state (setting equipment uptime, initial wear coefficient, material defect size), material properties (inputting the chemical composition, viscosity, and ignition point of different batches of raw materials), environmental parameters (setting extreme temperatures, humidity, wind speed, and external vibration sources), operation sequences (simulating misoperation, emergency shutdown, and maintenance procedures), potential fault injection points (virtually applying crack propagation, valve jamming, and sensor failure to specific components in the digital twin model), and accident propagation path constraints (simulating severe conditions such as firewall collapse, fire protection system failure, and ventilation system reverse operation). These scenarios enable targeted simulation and emergency plan verification.Users can interactively review, fine-tune parameters (e.g., adjusting the suggested alarm threshold from 80% to 75%, limiting the control valve opening adjustment rate to within 5% / second), and modify logic (e.g., adding a manual confirmation step before automatic shutdown, or adjusting the execution priority of multiple parallel intervention measures) or completely reject the automatically generated preventive intervention strategies by the PPO agent through a graphical interface (integrated in the cockpit of the digital twin platform) or Python API. The manually adjusted strategy (or the selected manual contingency plan) is then reintegrated into the digital twin environment for rapid verification (a single simulation evaluation is completed within 1 minute) and effect comparison. This enables deep collaboration and optimization between human and machine intelligence, ensuring the actual feasibility, operational compliance, regulatory compliance, and acceptability of the final intervention strategy for on-site personnel, and mitigating the ethical risks and experience blind spots inherent in purely AI decision-making.

[0055] Sp5, Edge-End-Cloud Collaborative Adaptive Monitoring, Closed-Loop Feedback, and Continuous System Evolution: This solution adopts a rigorous three-layer intelligent collaborative architecture. On the device side: Ultra-lightweight AI models are deployed at key sensor nodes (intelligent vibration sensors, intelligent cameras), actuators (intelligent valve positioners), or small embedded controllers. These models are extremely optimized (8-bit integer quantization, weight pruning rate not less than 70%), primarily performing: online data quality verification (sensor self-diagnosis, data packet CRC verification, physical range rationality judgment); basic feature extraction (RMS, peak-to-peak value, kurtosis, zero-crossing rate time-domain statistics, or simple frequency-domain energy proportion); and extremely low latency (response time less than 10ms) initial screening of simple abnormal events and instinctive rapid response (immediately triggering local audible and visual alarms upon detecting vibration intensity exceeding the secondary threshold, immediately cutting off heating power upon detecting temperature exceeding limits). Edge Computing (“Edge Intelligence”): Deploy edge computing nodes (equipped with at least 32GB RAM, 256GB NVMe SSD, and supporting GPU acceleration) at the industrial site or workshop level, connecting to end devices and upper-level cloud platforms via 5G URLLC or TSN industrial Ethernet. Edge nodes are responsible for: performing more complex multimodal data fusion (a lightweight version of the MCTFN model of Sp2) on aggregated end device data and local high-value sensor data (high-frequency vibration, multispectral vision); real-time risk assessment (calculating equipment health index and regional safety score based on fused features, with a refresh rate of at least 1Hz); short-term accident prediction (probability of specific equipment failure or small-scale accident risk within the next 1-60 minutes, using GRU or a small Transformer model); and executing localized, somewhat autonomous preventative control commands (fine-tuning compressor guide vane opening in advance based on predicted surge risk; automatically isolating relevant pipe sections and initiating exhaust ventilation based on predicted leakage risk). Cloud Intelligence: Deploy a central intelligent platform on a private cloud (built on OpenStack and Kubernetes) or hybrid cloud architecture that has massive parallel computing (distributed training using MPI and Horovod), massive storage (HDFS, Ceph) and complex analysis capabilities.Cloud platform operation includes: construction, management, reasoning, and continuous updating of the global knowledge graph (Sp1); periodic (daily or weekly) retraining and fine-tuning of ultra-large-scale deep learning models (Transformer models with over 1 billion parameters and DRL models requiring thousands of GPU hours of training) involved in Sp2, Sp3, and Sp4; long-term trend analysis based on plant-wide historical data and real-time situation (prediction of equipment group lifespan, in-depth mining of accident occurrence patterns, and global optimization of maintenance strategies); complex accident chain simulation and domino effect analysis (Sp3, simulating major accident scenarios across systems and regions); collaborative optimization scheduling of global production operations and safety risks (dynamically adjusting production plans to maximize overall benefits while ensuring safety); and serving as a central coordination server and model aggregation center for federated learning (if adopted). The closed-loop feedback mechanism is the core of the system's continuous evolution: real-time operational data from the physical world (including baseline data under normal operating conditions, early warning event data, and detailed process data of failed events), the effectiveness of preventative intervention strategies (quantitatively evaluated by comparing changes in risk level, accident rate, and production indicators before and after intervention), feedback information from manual input (confirmation and handling records of alarms by maintenance personnel, and expert evaluations and scores of prediction accuracy and intervention suggestions), and newly discovered risk patterns or failure mechanisms through the continuous learning module are all structurally collected and securely transmitted to the corresponding model update process. Specifically, the online update module uses streaming learning algorithms based on HoeffdingTree or OnlineRandomForest to process real-time small-batch data and quickly fine-tune the parameters of edge and some cloud models; periodic model retraining utilizes all accumulated historical and feedback data to comprehensively optimize the core complex models in the cloud; and the policy network of the reinforcement learning agent is iteratively improved through continuous online interaction or offline policy evaluation. The entire system forms a virtuous cycle of data-driven, model-iterative, and knowledge-enhanced approaches, enabling it to dynamically adapt to the ever-changing industrial environment, equipment aging, process adjustments, and the emergence of new risks, while continuously improving monitoring accuracy, prediction accuracy, and intervention effectiveness (with the goal of improving key performance indicators by no less than 5% annually).

[0056] Sp5's edge-end-cloud collaborative adaptive monitoring, closed-loop feedback, and continuous system evolution are specifically implemented as follows: At the device end or embedded system (using a RISC-V-based SoC chip with integrated NPU, power consumption less than 5W), a deeply pruned (removing over 80% of redundant connections), extremely quantized (INT8 or INT4), and dedicated instruction set optimized ultra-lightweight edge AI model is deployed near the data source. This model performs data quality verification, basic feature extraction, and low-latency initial screening and rapid response for simple anomalies. At the industrial site or workshop level, an edge computing gateway or server with at least 128 TOPSAI computing power and supporting multi-channel high-speed concurrent data processing is deployed. This gateway is responsible for performing more complex multimodal data fusion, real-time risk assessment, short-term accident prediction, and executing localized preventative control commands on the aggregated end-device data and local sensor data. On the cloud computing platform (which adopts a microservice architecture, uses Kubernetes for container orchestration and resource scheduling, supports GPU / TPU heterogeneous computing clusters, and integrates the MLOps toolchains Kubeflow and MLflow), large-scale, highly complex global knowledge graph management, periodic training and updating of deep learning models (especially ultra-large-scale models that utilize distributed parameter server architecture or AllReduce algorithm for efficient parallel training), long-term trend analysis, complex incident chain deduction, global resource optimization and scheduling, and serves as a coordination server for federated learning are all performed. The closed-loop feedback mechanism ensures that the operational data of the physical world, the effects of intervention, and newly discovered risk patterns can be captured in a timely manner and used to continuously optimize the models, algorithms, and knowledge bases deployed at all levels, forming a dynamically adaptive, continuously learning, and performance-improving intelligent monitoring and prediction system. Specific Implementation Example 2:

[0058] like Figures 1 to 3 As shown, an industrial environment monitoring and accident prediction system that integrates multimodal data includes:

[0059] The multimodal data semantic acquisition and preprocessing unit is primarily composed of one or more high-performance data servers (CPU: Intel Xeon Scalable series, RAM: at least 512GB, storage: NVMe SSD RAID10 array at least 20TB) running specially developed distributed data acquisition and processing software. This software integrates a multi-heterogeneous data access module for SP1 (built-in OPCUA Clivent / ServerSDK, Modbus Master / SlaveLibrary, MQTTBroker / Client, Kafka Producer / Consumer, and JDBC / ODBC drivers and file parsing engines for mainstream databases), a data cleaning and verification module (employing outlier detection and repair algorithms based on statistical rules and machine learning), a high-precision spatiotemporal alignment module (integrating NTP client and coordinate transformation service), a knowledge graph construction and management module (backend using Neo4j Enterprise Edition cluster, frontend providing knowledge editing and visualization tools), and a natural language processing module based on a domain-pretrained Transformer model (used to extract entities, relationships, and events from text). This unit strictly follows the technical solution set in Sp1 to perform data collection, semantic annotation, spatiotemporal alignment, causal preprocessing, and knowledge graph construction and dynamic updating tasks.

[0060] The deep feature extraction and cognitive fusion unit is deployed on edge computing nodes and cloud platforms. Edge nodes (such as the aforementioned NVIDIA Jetson AGX Orin or equivalent platforms) are responsible for real-time or near-real-time feature extraction and initial fusion, while the cloud platform (equipped with a large-scale GPU cluster, NVIDIA A100 / H100) is responsible for training complex models and deeper fusion. Internally, this unit integrates customized deep learning feature extractors for various modalities of Sp2 (MSRCTN, EfficientNet-B3+CBAM, SwinTransformer V2-Large+ST-GCN, with rigorously tuned and validated model parameters), a Transformer fusion network (MCTFN) module based on a multi-head cross-modal attention mechanism, and an uncertainty quantification module based on deep ensemble learning. This unit strictly adheres to the technical solutions defined in Sp2 to perform deep feature extraction for weak signals and potential risks, multimodal cognitive fusion, and uncertainty assessment of the fusion results.

[0061] The accident evolution prediction and interpretability analysis unit is primarily deployed on a cloud platform, with some lightweight prediction models available for deployment to edge nodes. Internally, this unit integrates Sp3's hybrid intelligent prediction engine (including a callable physical mechanism model library (FMU wrapper), a statistical model library (implemented in R / Python scripts), and the core STDGAT deep learning model), an accident chain inference module (based on STDGAT graph traversal and probabilistic inference), a probabilistic prediction output module (outputting structured accident risk reports), and the XAI toolset, which integrates gradient and graph counterfactual interpretation. This unit strictly adheres to the technical scheme defined by Sp3 to perform probabilistic prediction of the accident chain evolution path and provides interpretable analysis results.

[0062] The digital twin-driven strategy optimization and verification unit, deployed on a cloud platform, has a visual interactive interface accessible via the web. Internally, this unit integrates a digital twin construction, rendering, and synchronization module based on NVIDIA Omniverse (real-time bidirectional communication with the physical world data interface, latency less than 200ms), a multiphysics simulation engine (integrated with COMSOL, Fluent, and Amesim via the FMI / FMU standard), a PPO-based deep reinforcement learning training and inference framework (using RayRLlib or Acme distributed RL library), and a human-computer interaction interface supporting parameterized scene construction and manual policy intervention. This unit strictly adheres to the technical scheme defined in SP4 to dynamically optimize and verify preventative intervention strategies.

[0063] The Collaborative Control and System Evolution Unit is a distributed unit spanning the edge, cloud, and endpoint layers. Its core logic and coordination management functions are deployed on the cloud platform, while the specific execution modules are distributed across each layer. Internally, this unit integrates a resource monitoring and task scheduling module based on the edge-end-cloud three-layer computing architecture; a distributed model deployment and version management module based on containerization (Docker) and orchestration (Kubernetes) technologies (supporting blue-green deployment and canary release); a structured closed-loop data feedback collection and preprocessing module; an online learning and continuous evolution engine (including a streaming learning algorithm library and regularization methods for catastrophic forgetting); and (if enabled) a federated learning coordination and model aggregation module based on secure multi-party computation and differential privacy. This unit strictly adheres to the technical solutions defined in SP5 to perform adaptive monitoring, closed-loop feedback, and continuous system evolution.

[0064] And those connected to the above units, forming a complete physical information system:

[0065] (1) Sensor network: includes at least 10,000 smart and traditional sensors of various types, which are connected to the data acquisition gateway via wired (industrial Ethernet, fieldbus) and wireless (5G, LoRaWAN, Wi-SUN) methods;

[0066] (2) Industrial control system interface: Ensure seamless and secure (using one-way gateway or secure data channel) two-way data interaction with existing DCS, PLC and SIS systems;

[0067] (3) Distributed edge computing devices: Deploy at least one edge computing node in each major production unit or key area (as described above);

[0068] (4) Cloud computing platform: It has an FP32 computing power of no less than 1000 TFLOPS and a storage capacity of 10 PB;

[0069] (5) Human-computer interaction and visualization interface: A comprehensive monitoring, early warning and decision support platform developed based on Web technology (React / Vue+Three.js / Babylon.js), providing multi-dimensional data visualization, risk heat map, digital twin interaction, accident evolution animation, early warning information push, intervention strategy recommendation and execution confirmation functions, and supporting desktop and mobile access.

[0070] The multimodal data semantic acquisition and preprocessing unit further includes:

[0071] The multi-dimensional heterogeneous data access module's core is a configurable data acquisition engine. It incorporates drivers and parsing libraries for mainstream industrial protocols (OPCDA / UA, Modbus TCP / IP & RTU, EtherNet / IP, ProofinetIO, IEC61850, MQTT, CoAP, DNP3), enabling stable and efficient data connections with field devices and systems in master-slave or publish-subscribe modes. For database systems (Oracle, SQL Server, MySQL, PostgreSQL, and time-series databases InfluxDB, Prometheus), it extracts data through a high-performance JDBC / ODBC connection pool and optimized SQL queries. For video streams, it supports RTSP, RTMP, and GB / T28181 protocols and performs H.264 / H.265 stream parsing. For file data, it provides batch import and incremental synchronization functions for CSV, Excel (XLSX), JSON, XML, Parquet, and HDF5 formats. All data undergoes timestamp alignment (based on the central NTP server time) and source authentication upon access.

[0072] The Natural Language Processing and Knowledge Extraction module is configured with a cascaded model architecture based on a "pre-training-fine-tuning-distillation" paradigm: First, a Transformer model (BERT-Large or RoBERTa-Large, with approximately 340 million parameters) pre-trained on over 10TB of industrial text corpus (including patents, standards, journal articles, technical manuals, and internal enterprise documents) serves as a general semantic understanding encoder; second, for the Named Entity Recognition (NER) task, a BiLSTM-CRF layer is built on top of this pre-trained model and fine-tuned on domain-specific labeled data (at least 200,000 entity annotations) to achieve accurate identification of key entities such as equipment, faults, processes, and safety measures (F1 score not lower than 0.96); for the Relation Extraction (RE) task, an attention-based... The mechanism uses a Graph Convolutional Network (GCN) or a path-dependent LSTM model, trained on entity-labeled data (at least 100,000 relation labels), to extract hierarchical, causal, and relational relationships between entities (F1 score not lower than 0.92); for the event extraction (EE) task, a model based on Dynamic Multi-hop Graph Attention Network (DMHAN) is used to identify key event trigger words and their argument roles (F1 score not lower than 0.90); all extracted knowledge triples or event structures are used to assist in the construction and dynamic updating of the industrial domain knowledge graph.

[0073] The knowledge graph management and inference engine module employs a distributed graph database system (a combination of Janus Graph and HBase / Cassandra backends, or a TigerGraph MPP architecture) to store and manage petabyte-scale dynamically updated industrial knowledge graphs. It provides high-performance graph traversal, indexing (including full-text indexing, geospatial indexing, and time-series indexing), and query interfaces (supporting Gremlin and SPAR QL1.1). It includes a built-in descriptive logic inference engine based on RDFS and OWL2DL (an integrated version of Pellet or FaCT++), and a customizable rule engine based on SWRL or Drools. This engine enables automated logical reasoning based on ontology definitions and user rules (attribute inheritance, relation propagation, consistency checks, and new knowledge discovery). It also supports representation learning based on graph embeddings (RotatE, ComplEx, GraphSAGE), mapping entities and relations to a low-dimensional vector space for advanced graph analysis tasks such as similarity calculation, link prediction, node classification, and community discovery. This provides deep, multi-dimensional background knowledge and contextual information for subsequent data fusion and prediction.

[0074] The collaborative control and system evolution unit further includes:

[0075] The distributed model deployment and management module, built on Kubernetes and KubeflowPipeline, enables automated and strategic deployment of AI models across multiple environments (devices, edge nodes, and the cloud) from training, validation, packaging (Docker containerization, including model files, dependencies, and runtime environment), registration (version control and metadata management in the model repository MLflowModelRegistry), to selecting deployment nodes based on resource utilization, latency requirements, and data limitations. It supports blue-green deployment, canary releases, and A / B testing to allow for model updates and performance evaluation without service interruption. A unified API gateway and monitoring dashboard provide real-time monitoring and alerts for the operational status of deployed models (QPS, inference latency, error rate, resource consumption), input / output data distribution, and prediction drift.

[0076] The Federated Learning Coordination and Security Aggregation module is responsible for the following when the system uses a federated learning mechanism to collaboratively train a global model while protecting the data privacy of multiple parties (different subsidiaries, different production lines, or different suppliers):

[0077] Client selection and task distribution: Based on factors such as availability, data volume, and computing power of the clients (edge ​​nodes or facilities), a subset of clients are selected to participate in this round of training, and the global model and training tasks are distributed.

[0078] Secure parameter aggregation: Receives model updates (gradients, weights, or parameters processed with local differential privacy) uploaded by each client after training on local data via a secure communication channel (TLS 1.3); employs robust aggregation algorithms (FedAvg combined with Krum or Median's anti-poisoning mechanism, or FedPro x to address client heterogeneity, or SCAFFOLD to address client drift) to perform weighted averaging or other forms of aggregation on the model updates, generating a new global model; during the aggregation process, homomorphic encryption or secure multi-party computation (SMC) techniques can be selectively applied to enhance privacy protection levels;

[0079] Global model update and validation: The aggregated global model parameters are distributed to each client for the next round of local training or inference; at the same time, the performance of the updated global model is evaluated on a reserved global validation set or through a simulation environment; the whole process follows the strict principles of data minimization and purpose restriction, ensuring that the original data does not leave the local machine and only the necessary model information is transmitted.

[0080] The closed-loop feedback and continuous learning engine, at its core, is an event-driven, configurable automated workflow engine used for:

[0081] Multi-source feedback data acquisition and alignment: Real-time collection of system operation data (performance indicators, resource utilization), model prediction results (and their confidence and explanatory reports), human intervention records (operator confirmation of warnings, handling measures, and effect evaluation), as well as new truth labels or error samples labeled by digital twin environment or domain experts; precise alignment of these feedback data with the original input data, model version, and timestamps;

[0082] Performance drift detection and attribution analysis: Continuously monitor the prediction accuracy, recall, and F1 score performance metrics of key models, as well as covariate drift and concept drift in data distribution; when a significant performance degradation or change in data distribution is detected, the attribution analysis process is automatically triggered to attempt to locate the root cause of the problem (sensor aging, change in operating conditions, model obsolescence);

[0083] Automated model retraining and validation: Based on preset rules (performance below a threshold, new data accumulation to a certain amount) or manual instructions, the model retraining process (including data preparation, feature engineering, model training, and hyperparameter optimization) is automatically initiated; after retraining is completed, a rigorous evaluation is performed on a new validation set, and A / B testing or shadow deployment is conducted with the current online model. The new model is only replaced when it significantly outperforms the old model.

[0084] Continuous learning and knowledge increment: By adopting continuous learning techniques based on experience replay, elastic weight consolidation, knowledge distillation or progressive network, the model can effectively alleviate the problem of catastrophic forgetting when learning new tasks or adapting to new data, retain and accumulate historically learned knowledge and capabilities, thereby continuously optimizing model parameters, knowledge base content and control strategies at each level, so as to improve the overall intelligence level of the system and its long-term adaptability to dynamically changing industrial environments. Specific Implementation Example 3:

[0086] Based on the technical solutions of Specific Embodiment 1 and Specific Embodiment 2, further explanations and descriptions are provided in light of actual circumstances:

[0087] In large-scale integrated petrochemical bases, the core cluster of units—a million-ton-level ethylene cracking unit and dozens of large-scale continuous production units for polyolefins and aromatics—constitutes a massive industrial system characterized by high complexity, interconnected materials, and coupled risks. Leaks, fires, and explosions in such clusters can easily trigger a domino effect, leading to catastrophic consequences. Traditional models based on single-parameter threshold alarms and decentralized emergency response plans are no longer sufficient to meet the overall safety management requirements of such complex mega-systems. The application of this solution in this scenario first involves comprehensively deploying the multimodal data acquisition system described in Sp1 in key areas and equipment such as cracking furnaces, compressor units, separation towers, tank areas, and pipe corridors. This allows for real-time acquisition of data including high-definition / infrared video (covering key flanges, pump bodies, valve groups, and densely piped areas for leak and flame identification), open-path gas detector arrays (FTIR / TDLAS technology for detecting trace leaks of hydrocarbons and toxic gases), distributed fiber optic vibration / temperature sensors (DVS / DTS, laid along pipe corridors and key pipelines for monitoring abnormal vibration and temperature changes), equipment operating parameters (such as cracking furnace outlet temperature and pressure, compressor vibration and shaft displacement, tower bottom liquid level and temperature), and dynamic distribution data of emergency resources (fire stations, rescue teams, material warehouses, evacuation routes, and medical points) combined with a three-dimensional geographic information system (3DGIS). The dynamic knowledge graph module of Sp1 constructs a dynamic safety knowledge network covering the entire plant group, which includes material balance relationships between units, process dependencies, protection layer logic of safety instrumented systems (SIS), command hierarchy and resource scheduling rules in emergency plans, and potential accident scenarios and risk levels obtained from HAZOP / LOPA analysis. When the weak signal feature extraction and fusion module of Sp2 detects local overheating in the radiation section of a cracking furnace (indicating that the furnace tube is about to experience creep or coking blockage), or weak energy at a specific fault frequency in the vibration spectrum of a large centrifugal compressor bearing (indicating early wear), or a gas detector in the open optical path of the pipe gallery detects a persistent concentration of micro-hydrocarbons below the alarm threshold, the hybrid intelligent prediction engine of Sp3 will combine the device association information in the knowledge graph and the accident propagation model (based on the dynamic risk propagation model of STDGAT) to quickly predict the probability of this initial abnormal state evolving into a major accident (such as furnace tube rupture, compressor damage, pipeline leakage leading to fire and explosion), the final evolution path (furnace tube rupture leading to leakage of high-temperature hydrocarbon materials, forming a combustible cloud upon contact with air, exploding upon encountering an ignition source, and the shock wave causing damage to nearby pipelines or equipment, leading to secondary accidents), and the expected critical time window (predicting that the risk of furnace tube rupture will reach an unacceptable level within 30 minutes).At the same time, the Sp4 digital twin-driven module will dynamically simulate and predict the entire process of accident evolution in real time in a high-fidelity 3D digital twin environment that is mapped 1:1 with the petrochemical base, and automatically optimize and generate a multi-level collaborative emergency response strategy covering the entire affected area based on deep reinforcement learning (PPO algorithm). This strategy not only includes emergency response measures for the source of the fault (such as automatically triggering the emergency shutdown procedure of the pyrolysis furnace and starting the compressor standby machine), but more importantly, it can intelligently generate a coordinated emergency command plan for the entire area based on the real-time wind direction, wind speed, leakage diffusion simulation results and the dynamic availability of emergency resources (such as the coverage and effective range of fire monitors, the arrival time of rescue teams, and the safety of different evacuation routes). This plan includes: (1) accurately delineating the warning area and evacuation range, and sending personalized evacuation instructions to personnel in the area through emergency broadcasts and personnel positioning systems; (2) automatically dispatching the nearest and most capable fire rescue forces and planning the optimal route of travel; (3) intelligently controlling the emergency shut-off valves, vent valves, and steam curtain safety facilities of the accident area and upstream and downstream related devices to prevent the spread of the accident and reduce material leakage to the greatest extent possible; and (4) dynamically adjusting the production load of the surrounding unaffected devices to ensure the supply safety of important intermediate products or to achieve orderly shutdown. Sp5's edge-end-cloud collaborative architecture ensures efficient closed-loop operation throughout the entire process, from weak signal detection to regional collaborative emergency command: edge nodes are responsible for real-time processing and rapid response of on-site data, while the cloud platform performs complex accident evolution prediction, digital twin simulation, and global emergency strategy optimization, and distributes instructions to on-site execution units and the mobile terminals of command personnel at all levels via the industrial 5G network. The implementation of this solution will elevate the accident prevention capabilities of the petrochemical base from "post-accident response" and "localized handling" to "pre-accident prediction" and "regional collaboration," and is expected to reduce the incidence of major accidents by at least 80%, shorten emergency response time by more than 50%, and significantly reduce direct economic losses and indirect impacts (such as environmental pollution and social panic) caused by accidents, comprehensively improving the base's inherent safety level and emergency management effectiveness.

[0088] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0089] 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 likenesses.

Claims

1. A method for fusing industrial environment monitoring and incident prediction with multi-modal data, the method comprising: The industrial environment monitoring and accident prediction method comprises The following: Sp1, multi-modal heterogeneous data semantic collection and causal correlation preprocessing based on dynamic knowledge graph: real-time collection of multi-modal heterogeneous data in the industrial environment to obtain a dynamic knowledge graph; Sp2, deep feature extraction and multi-modal cognitive fusion for weak signals and potential risks: deep learning models are used for different modal data to extract deep abstract features representing early weak faults, subtle changes in the environment, and atypical behaviors of personnel; Sp3, probabilistic prediction and explainability analysis of accident chain evolution path based on hybrid intelligence and evolutionary learning: a hybrid intelligent prediction engine is constructed by combining physical mechanism models, statistical models, and deep learning models to obtain probabilistic prediction results of the accident chain evolution path; Sp4, dynamic optimization and verification of preventive intervention strategies based on digital twin driving: a digital twin model is constructed that maps the physical industrial environment in real time with high fidelity, and the probabilistic prediction results of the accident chain evolution path are input into the digital twin model; Sp5, edge-end-cloud collaborative adaptive monitoring, closed-loop feedback, and system continuous evolution: monitoring and prediction tasks are performed collaboratively at the device end, edge end, and cloud end, and the execution effect data and new data of the intervention strategies in the physical environment are collected as feedback information for online updating of the knowledge graph, feature extraction and fusion model, prediction engine, and reinforcement learning strategy network.

2. The method of claim 1, wherein, The construction and updating of the dynamic knowledge graph in Sp1 further comprises: using natural language processing technology, automatically identifying and extracting entity concepts, attribute information, fault modes, causal relationships, and operation constraint rules related to industrial safety, device health, and process flow from unstructured and semi-structured text data including maintenance records, operation manuals, safety procedures, and accident investigation reports, and after verification and standardization processing, these extracted information is structured and dynamically updated into the dynamic knowledge graph, thereby significantly enhancing the knowledge coverage, detail richness, information real-time, and accuracy of subsequent reasoning and decision-making of the knowledge graph.

3. The method of claim 1, wherein, The deep feature extraction for weak signals in Sp2 further comprises the following steps: Sp2.1, the original multi-modal data collected contains high-frequency noise and non-stationary components; Sp2.2, input the features or original data processed in Sp2.1 into a pre-trained or fine-tuned deep learning network to learn and capture deep, high-dimensional, and more discriminative abstract feature representations indicating early fault initiation, material micro-damage accumulation, or minor deviations in the operation process.

4. The method of claim 1, wherein, The evolutionary learning capability of the hybrid intelligent prediction engine in Sp3 is further realized by at least one of the following ways when facing challenges such as sparse historical data, frequent changes in working conditions, or unknown new accident patterns: Start a rapid adaptation module based on meta-learning, and the prediction model adjusts parameters quickly to adapt to new working conditions or accident types using new target domain samples; The knowledge transfer module based on transfer learning is applied, and the knowledge transfer module selectively transfers and adapts the accident pattern knowledge, fault feature representation or model parameters learned by the source domain into the prediction model of the target domain by measuring and reducing the data distribution difference or feature space difference between the source domain and the target domain.

5. The method of claim 1, wherein, The Sp4 digital twin model further comprises a first-principle-based multi-physical field coupling simulation engine integrated with a configurable parameterized scenario construction interface.

6. The method of claim 1, wherein, The Sp5 edge-end-cloud collaborative adaptive monitoring, closed-loop feedback and system continuous evolution is implemented in the following manner: a lightweight edge AI model is deployed on a device close to a data source or an embedded system to perform data quality verification, basic feature extraction and simple abnormal event preliminary screening and rapid response with extremely low delay; an edge computing node is deployed at an industrial site or workshop level to perform more complex multi-modal data fusion, real-time risk assessment, short-term accident prediction and localized preventive control instructions on the converged end device data and local sensor data; and a large-scale, high-complexity global knowledge graph management, deep learning model training and updating, long-term trend analysis, complex accident chain deduction, global resource optimization scheduling and cross-factory / cross-enterprise federated learning coordination are performed on a cloud computing platform.

7. A system corresponding to the method of fusing multi-modal data for industrial environment monitoring and incident prediction according to any one of claims 1-6, characterized in that, The industrial environment monitoring and accident prediction system based on fusion of multi-modal data comprises: a multi-modal data semantic acquisition and preprocessing unit for performing Sp1; a deep feature extraction and cognitive fusion unit for performing Sp2; an accident evolution prediction and explainability analysis unit for performing Sp3; a digital twin driven strategy optimization and verification unit for performing Sp4; a collaborative control and system evolution unit for performing Sp5; a sensor network, an industrial control system interface, a distributed edge computing device, a cloud computing platform and a human-computer interaction and visualization interface connected with the above units.

8. The system corresponding to the method of claim 7, wherein, The multi-modal data semantic acquisition and preprocessing unit further comprises: a multi-element heterogeneous data access module for real-time and batch acquisition of multi-source heterogeneous data from a sensor network, a PLC / DCS system, a MES system, a video monitoring system and a manual record database through a standard industrial protocol or a customized interface; a natural language processing and knowledge extraction module configured to automatically extract key entities, attributes, relationships, events and rules from maintenance logs, operation procedures, safety standards and accident report text data using machine learning models and semantic analysis algorithms to assist in building and updating an industrial domain knowledge graph; a knowledge graph management and reasoning engine module for storing and managing a dynamically updated knowledge graph, and performing semantic query, logical reasoning and data correlation analysis based on the graph to provide rich background knowledge and context information for subsequent data fusion and prediction.

9. A system corresponding to the method of claim 7, wherein, The collaborative control and system evolution unit further comprises: A distributed model deployment and management module for deploying trained or continuously updated monitoring models, prediction models and control policy models to corresponding device ends, edge computing nodes and cloud platforms securely and efficiently, and managing the versions, configurations and running states of the models; A federated learning coordination and secure aggregation module responsible for coordinating the local model training processes of each participant, securely collecting and aggregating the model updates contributed by each party, updating the global shared model, and distributing the updated model back to each participant when the system adopts a federated learning mechanism; A closed-loop feedback and continuous learning engine for collecting system operation data, model prediction results, manual intervention records and policy execution effects, and implementing online learning, incremental learning or reinforcement learning mechanisms.

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