Intelligent launching pad management system based on AI visual auxiliary supervision

Abstract

The invention provides an intelligent launching pad management system based on AI visual auxiliary supervision. The system carries out target identification through a YOLO model, carries out behavior sequence analysis in combination with an LSTM model, and carries out cross-modal dynamic feature fusion through spatio-temporal feature alignment, time sequence and spatial feature enhancement and a graph convolution network and graph attention mechanism to generate globally optimized feature representation. And further utilizing a knowledge reasoning engine to combine with the case database and the equipment knowledge graph to generate an intelligent emergency processing scheme. And finally, generating a preliminary potential safety hazard analysis report based on a Transform technology, and performing semantic calibration in combination with the central control module and the domain knowledge base to generate an accurate and normalized alarm analysis report. The system also has the functions of adaptive algorithm optimization, multi-modal data fusion processing and real-time alarm and feedback, realizes the intelligent and automatic management of the launching pad, improves the safety supervision efficiency and fault processing capability, and effectively improves the intelligent level of the safety management of the launching pad.

Description

Technical field:

[0001] The present invention relates to artificial intelligence technology, and in particular to an intelligent transmitter management system based on AI vision-assisted supervision, which applies vision-assisted supervision and multimodal data analysis technology to the safety management of radio and television transmitters. Background technology:

[0002] As the core infrastructure for broadcast and television signal transmission, the safe operation of transmitters is directly related to the stability and reliability of broadcast and television systems. Traditional security monitoring methods rely primarily on manual inspections and basic video surveillance systems. The former cannot provide 24 / 7 real-time monitoring, while the latter, while capable of recording on-site conditions, lacks intelligent analysis capabilities and often requires manual playback and troubleshooting. This is inefficient and prone to missing important information. Furthermore, existing systems struggle to provide timely warnings and rapid responses to abnormal behavior, posing significant security risks.

[0003] In recent years, with the rapid development of artificial intelligence technology, especially breakthroughs in computer vision and deep learning technology, new possibilities have been provided for the development of intelligent monitoring systems. However, the application of AI technology to launch pad safety management still faces many challenges: (1) The launch pad environment is complex, with a large number of equipment interferences and special lighting conditions, which affect the accuracy of image recognition; (2) It is necessary to monitor multiple targets (such as personnel, equipment, objects, etc.) at the same time, and the characteristics of various targets vary greatly, which increases the difficulty of recognition; (3) The judgment of security events often requires the combination of multi-source information, and single visual information is difficult to accurately judge; (4) The system needs to have real-time processing capabilities to meet the requirements of rapid response. Summary of the invention:

[0004] In response to the shortcomings of the existing technology, the present invention proposes an intelligent transmitter management system based on AI visual assisted supervision.

[0005] To achieve the above objectives, an intelligent transmitter management system based on AI visually assisted supervision is provided, characterized in that the system includes the following modules:

[0006] Data acquisition module, which collects video, image data and environmental data inside and outside the launch station through acquisition equipment, and displays the data in real time on the system web page;

[0007] A data analysis module, wherein the data analysis module uses the YOLOv5 model to extract visual features from the collected video and image data to obtain visual information, uses the LSTM model to analyze the extracted visual information, performs target recognition and behavior recognition, obtains target recognition results and behavior recognition results, performs spatiotemporal feature alignment and time series analysis on the target recognition results and behavior recognition results, extracts time-varying features, optimizes features using adaptive enhancement technology to enhance the representation of key information, and obtains enhanced target recognition results and enhanced behavior recognition results;

[0008] An optimization module, which uses a graph convolutional network to fuse the enhanced target recognition results with the enhanced behavior recognition results to construct a multimodal relationship graph of the device state, and uses a graph attention mechanism to assign weights to key nodes and edges to generate a globally optimized feature representation;

[0009] An emergency response module, which uses a knowledge reasoning engine to call a safety hazard case database and equipment knowledge graph, combines current environmental data to generate an intelligent emergency response plan, and uses a Bayesian network for probabilistic reasoning to generate an optimized emergency response strategy;

[0010] The alarm feedback module analyzes the globally optimized feature representation based on the Transformer architecture, generates a preliminary safety hazard analysis report, and performs structural optimization on the report content through the central control module. It also performs semantic calibration on the report in combination with the domain knowledge base to generate an alarm analysis report.

[0011] In the data acquisition module, the acquisition devices are cameras and sensors.

[0012] In the data acquisition module, the environmental data include temperature and humidity, gas concentration and noise data.

[0013] The data acquisition module includes:

[0014] a multi-source data synchronization unit configured to align video data, image data, and environmental data through time stamps;

[0015] The abnormal data filtering unit is configured to perform real-time verification of temperature and humidity, gas concentration data and noise data according to preset thresholds, and discard abnormal values ​​that exceed the sensor range.

[0016] In the data analysis module, the input of the LSTM model is a sequence of visual features of N consecutive frames, and the output is a behavior classification label;

[0017] The target identification is the identification of people and objects, and the behavior identification includes the detection of people crossing the boundary, illegal operation of equipment, and the leaving of dangerous objects.

[0018] In the data analysis module, the adaptive enhancement technology includes a residual feature pyramid and a gated recurrent unit, and adaptive adjustment of the feature space is achieved through deformable convolution.

[0019] In the optimization module, the nodes of the multimodal relationship graph of the equipment status include three types of objects: equipment entities, operators, and environmental factors. The edge attribute definitions of the multimodal relationship graph of the equipment status include three topological connections: spatial proximity, operation correlation, and energy transfer relationship.

[0020] In the optimization module, the graph attention mechanism adopts a multi-head graph attention layer, calculates the attention coefficient between nodes through a learnable parameter matrix, establishes a dynamic update mechanism, and adjusts the graph structure weight distribution strategy according to real-time sensor data.

[0021] In the emergency response module, the safety hazard case database adopts ontology modeling to construct a semantic network including failure modes, treatment plans, and impact links;

[0022] The equipment knowledge graph integrates equipment three-dimensional models, maintenance logs, and multi-dimensional data on material properties;

[0023] In the alarm feedback module, the Transformer architecture uses a hierarchical encoder to process text data and structured logs;

[0024] The structured optimization includes three processing stages: event element extraction, causal chain construction, and impact degree quantification. It is equipped with an alarm strategy optimizer to dynamically adjust the alarm threshold and response level according to historical disposal effects.

[0025] Furthermore, the present invention comprises the following steps:

[0026] S1. Use the YOLO model to identify targets in camera images, and the LSTM model to identify device behavior sequences. Align the spatiotemporal features of the target and behavior recognition results.

[0027] S2. Use the LSTM model to perform time series analysis on the target recognition and behavior recognition results, extracting temporal variation features. Adaptive enhancement technology is then used to optimize the features of the target recognition and behavior recognition results to enhance the representation of key information.

[0028] S3. Graph convolutional networks (GCNs) are used to fuse the target recognition results with the behavior recognition results to construct a multimodal relationship graph of the device state. The graph attention mechanism (GAT) is then used to assign weights to key nodes and edges, generating a globally optimized feature representation.

[0029] S4. The knowledge inference engine uses the safety hazard case database and equipment knowledge graph to generate an intelligent emergency response plan based on current equipment sensor data. The Bayesian network is then used for probabilistic reasoning to generate the optimal emergency response strategy.

[0030] S5. Generate a preliminary safety hazard analysis report based on Transformer technology, and use the central control module to structurally optimize the report content. Combined with the domain knowledge base, the report is semantically calibrated to generate an accurate and standardized alarm analysis report.

[0031] Furthermore, the spatiotemporal feature alignment includes the following steps: aligning the timestamp of the target recognition result with the timestamp of the behavior recognition result, and mapping the spatial position of the target recognition with the device position information in the behavior recognition, and realizing the spatiotemporal context matching of the visual features and the behavior features through the cross-modal attention mechanism.

[0032] Furthermore, the time series processing uses an LSTM model to analyze the target recognition sequence and the behavior recognition sequence to extract the trend characteristics of the target state evolving over time.

[0033] Furthermore, the adaptive enhancement technology includes the following steps: dividing the feature space based on the target recognition results and the behavior recognition results, calculating the association weights between different areas and the behavior recognition keywords, focusing on key areas through adaptive enhancement technology, and suppressing irrelevant background interference.

[0034] Furthermore, the graph convolutional network (GCN) includes the following steps: constructing the target recognition results and the behavior recognition results into a graph structure, where the nodes represent the target states and the edges represent the association relationships between states, and extracting the global features of the graph structure through graph convolution operations.

[0035] Furthermore, the graph attention mechanism (GAT) includes the following steps: calculating attention weights for nodes and edges in the graph, focusing on key nodes and edges, and generating optimized graph feature representations.

[0036] Furthermore, the knowledge reasoning engine includes the following steps: retrieving similar cases from the safety hazard case database, combining the topological relationships in the equipment knowledge graph, using the Bayesian network for probabilistic reasoning, and generating the optimal emergency response strategy.

[0037] Furthermore, the generation and optimization of safety hazard analysis reports based on Transformer technology combined with a central control module is to generate a preliminary analysis report by pre-training the Transformer model, introduce the central control module to perform structured optimization on the report content, and calibrate the technical terms and logical relationships in combination with the domain knowledge base to generate an accurate and standardized alarm analysis report.

[0038] Through the above steps, the present invention can realize intelligent and automated management of the transmitter station, reduce the burden of manual monitoring, and improve safety management capabilities and fault handling efficiency.

[0039] Beneficial effects of the present invention:

[0040] 1. Improved monitoring efficiency and accuracy: By combining the YOLO and LSTM models, the system can identify targets in the monitoring image and analyze device behavior sequences in real time, significantly improving the accuracy of target recognition and behavior analysis. Furthermore, spatiotemporal feature alignment technology ensures precise matching of visual and behavioral features in time and space, further enhancing the system's monitoring capabilities.

[0041] 2. Enhanced multimodal data analysis capabilities: Utilizing a graph convolutional network (GCN) and a graph attention mechanism (GAT), the system can fuse the graph structure features of target recognition and behavior recognition results to construct a multimodal relationship graph of device states. This multimodal data analysis method enables a more comprehensive understanding of device states and their interrelationships, thereby improving the ability to analyze complex scenarios.

[0042] 3. Intelligent Emergency Response: By leveraging a knowledge-based reasoning engine, incorporating a database of potential safety hazards and a device knowledge graph, and combining it with a Bayesian network for probabilistic reasoning, the system generates an optimized emergency response strategy. This intelligent emergency response mechanism enables rapid response to abnormal situations, reduces potential safety hazards, and improves the safety and reliability of the launch pad.

[0043] 4. Real-time Processing and Rapid Response: The system possesses real-time processing capabilities, enabling rapid analysis of monitoring data and generation of emergency response plans, meeting the transmitter's requirements for rapid response. This real-time processing capability ensures the system can immediately identify and address potential safety hazards, effectively reducing the risk of accidents.

[0044] 5. Generate accurate alarm analysis reports: Based on Transformer technology and a central control module, the system generates preliminary safety hazard analysis reports. Through structural optimization and semantic calibration, it produces accurate and standardized alarm analysis reports. This automated report generation mechanism not only improves report quality but also reduces the need for manual intervention, thereby improving management efficiency.

[0045] In summary, the present invention realizes intelligent and automated management of the transmitter station by introducing advanced AI technology and multimodal data analysis methods, significantly improves safety monitoring capabilities and fault handling efficiency, reduces the burden of manual monitoring, and has important practical application value. Description of the drawings:

[0046] Figure 1 This is the architecture diagram of the intelligent transmitter management system;

[0047] Figure 2 Flowchart for visual semantic understanding and multimodal data analysis;

[0048] Figure 3 This is a schematic diagram of the structure of the visual information feature extraction module;

[0049] Figure 4 This is a schematic diagram of the structure of the visual information feature extraction module;

[0050] Figure 5 This is a structural diagram of the behavior recognition module;

[0051] Figure 6 Schematic diagram of the graph convolutional network structure;

[0052] Figure 7 Alarm infographic for robots. Specific implementation method:

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0054] 1. System structure:

[0055] The overall structural design of the system is as follows Figure 1 As shown, it is divided into five modules: data collection, data analysis, optimization, emergency processing, and alarm and feedback. Each module works closely together to achieve comprehensive monitoring and intelligent processing of the internal and external environment of the launch pad.

[0056] Data Acquisition Module: This module is the foundation of the system, responsible for collecting various data from the launch pad and its surrounding environment in real time. It includes multiple high-definition cameras, environmental sensors, and other detection equipment to comprehensively capture images, videos, and environmental data from both inside and outside the launch pad. Cameras are primarily deployed in key areas of the launch pad and on mobile robots to monitor the status of personnel, objects, and equipment in real time, ensuring internal safety. Environmental sensors monitor parameters such as temperature, humidity, air pressure, and gas concentration, providing essential environmental variables for subsequent optimization modules.

[0057] Data Analysis Module: Leveraging artificial intelligence algorithms, particularly computer vision and deep learning algorithms, the system performs real-time analysis of collected image and video data to identify people, objects, and their behaviors. To improve recognition accuracy and robustness, the system automatically adjusts its analysis algorithms based on factors such as ambient lighting and object occlusion. For example, when ambient lighting is low, the system automatically adjusts image enhancement and recognition algorithms to ensure accurate target identification even in low-light conditions.

[0058] Optimization Module: Based on real-time environmental changes, the optimization module dynamically adjusts recognition accuracy through an adaptive algorithm. Environmental factors such as varying distances, lighting conditions, and weather changes can affect recognition performance. The optimization module intelligently adjusts the system's recognition strategy. For example, in complex environments, the system enhances image processing accuracy to accommodate long-distance or low-light scenarios, thereby ensuring system stability and accuracy. The core of the optimization module is an adaptive algorithm based on deep learning. Through continuous feedback and training, it gradually enhances the system's intelligence, enabling it to adapt to a variety of real-world scenarios.

[0059] Emergency Response Module: The core of this module is an emergency response mechanism based on intelligent reasoning and knowledge graphs. Through the knowledge reasoning engine, the system can access a library of safety hazard cases and a device knowledge graph to generate intelligent emergency response plans when encountering abnormal situations. Furthermore, combined with real-time environmental data, the system can develop targeted and optimal emergency response strategies. During emergency response, Bayesian networks are used to perform probabilistic reasoning on various potential risks, helping the system determine the likelihood of an event and make optimal decisions based on this information. This module intelligently recommends relevant remediation measures based on different safety hazards, thereby improving safety and reducing human error.

[0060] Alarm and feedback module: When the system detects an abnormal situation, such as a safety hazard, a violation, or a missing person, the alarm and feedback module will immediately trigger an alarm. Alarm methods include voice alarms, screen flashing, short message reminders, and large-screen text prompts, ensuring that relevant personnel can detect and take necessary countermeasures in a timely manner. The system can also automatically generate detailed alarm reports for on-duty personnel to review and handle. Alarm information includes types such as excessive gas concentration, excessive noise, and abnormal equipment temperature. Alarm information will be displayed in real time on the system's web page, and the latest alarm data will be displayed through graphs and pop-up windows to ensure that staff can respond quickly and accurately. At the same time, the alarm module supports real-time data refresh and historical data query, facilitating staff to conduct subsequent analysis and problem tracking.

[0061] 2. Multi-dimensional feature extraction and spatiotemporal feature alignment

[0062] like Figure 2 As shown, multi-dimensional feature extraction includes visual feature extraction and behavioral feature extraction.

[0063] Visual feature extraction: Use the YOLO model to identify targets in camera images and extract key target features in device images. Figure 3 and Figure 4 As shown in the figure, YOLO divides the image into grids through a single-stage detection framework, and predicts the bounding box and category probability of the target for each grid to generate a feature representation of the target.

[0064] Behavior feature extraction: Use LSTM model to identify the target behavior sequence. Figure 5 As shown in the figure, LSTM captures the temporal dependencies of target behaviors through its gating mechanism (input gate, forget gate, output gate) and extracts the characteristics of the target state evolving over time.

[0065] Spatiotemporal feature alignment: The timestamps of the object recognition results are aligned with those of the action recognition results to ensure consistency between the image acquisition time and the action recording time. Furthermore, the spatial location of the object recognition is mapped to the device location in action recognition, enabling spatiotemporal contextual matching of visual and action features through a cross-modal attention mechanism.

[0066] 3. Adaptive Feature Enhancement

[0067] Temporal feature enhancement: Use the LSTM model to analyze target recognition sequences and behavior recognition sequences to extract trend features of target state evolution over time. LSTM captures long-term dependencies through its memory cells and generates time-dependent representations:

[0068] h t =LSTM(x t ,h t-1 )

[0069] Among them, x t represents the input feature at time (t), h t represents the hidden state at time (t) after LSTM processing, h t-1 It represents the hidden state at the (t-1)th moment after LSTM processing, and LSTM represents the processing of the LSTM model.

[0070] Spatial feature enhancement: Adaptive enhancement technology is used to optimize the features of target recognition and behavior recognition results. Adaptive enhancement technology uses weighted training samples to focus on key areas and suppress irrelevant background interference. Specifically, for each spatial region, its association weight with the behavior recognition keyword is calculated:

[0071]

[0072] Among them, w i Represents the spatial feature f i The weight of this spatial region determines the importance of this feature in the spatial enhancement process, f i represents the features in the spatial region, k j Represents the keyword features related to behavior recognition, cos(f i ,k j ) represents the spatial feature f i With the behavior keyword k j The cosine similarity between Indicates all f from j=1 to n (the total number of behavior keywords) i With k j The exponential sum of the cosine similarities between .

[0073] 4. Graph Structure Feature Fusion

[0074] Graph Convolutional Network (GCN) fusion: The target recognition results and behavior recognition results are constructed into a graph structure, such as Figure 6 As shown in Figure 2, nodes represent target states and edges represent relationships between states. Global features of the graph structure are extracted through graph convolution operations:

[0075]

[0076] Among them, H l+1 Represents the feature of the l+1th layer node, which is the feature H of the previous layer l The output after graph convolution operation, is the activation function used to introduce nonlinear transformation, D is the degree matrix, A is the adjacency matrix of the graph, H l Represents the node features of the (l)th layer, that is, the representation or feature of each node in the graph at the (l)th layer, W l is the learning parameter of the (l)th layer, that is, the weight matrix.

[0077] Graph Attention Mechanism (GAT) optimization: Calculate attention weights for nodes and edges in the graph, focus on key nodes and edges, and generate optimized graph feature representations:

[0078]

[0079] Among them, γ (i,j) represents the attention weight between node i and node j. It reflects the degree of attention of node i to node j. i and h jRepresents the feature vectors of node i and node j respectively. The feature of each node is passed from the previous layer of the graph, representing the state of the node. W is the learned weight matrix, which is used to linearly transform the node features. a is the weight vector in the attention mechanism, which is used to calculate the relationship between nodes. It is usually obtained through learning. || represents the splicing operation, which is to concatenate the two feature vectors. and (or ) are spliced ​​together to capture the relationship between them. ReLU is an activation function used to introduce nonlinear changes. ∑k represents the summation of all nodes k and is used to normalize the attention weights between nodes.

[0080] 5. Inference engine generates processing solutions

[0081] Knowledge Reasoning Engine: Calls on the safety hazard case database and equipment knowledge graph, combines the current target status and environmental sensor data to generate intelligent emergency response plans. Probabilistic reasoning through Bayesian networks:

[0082]

[0083] Here, P(F|E) represents the conditional probability of the potential event F occurring given the current target state E. This is the outcome of interest: the likelihood of a potential event occurring given a target state. P(E|F) represents the conditional probability of the current target state E given the occurrence of the potential event F. In other words, if a potential event occurs, then the given target state will occur. P(F) represents the prior probability of the potential event F. This is the probability of the potential event occurring without considering any target state E. P(E) represents the total probability of the target state E. This is the sum of the probabilities of all possible combinations of potential events and target states.

[0084] 6. Alarm report generation and optimization

[0085] Transformer report generation: Generate a preliminary alarm analysis report using a pre-trained Transformer model. Transformer captures the global dependencies of text through a self-attention mechanism:

[0086]

[0087] Among them, Q is the query vector, which represents the specific information you want to obtain from the information. K is the key vector, which represents the identifier of the information and is usually paired with the query vector to determine relevance. V is the value vector, which represents the actual data information and is the part returned in the final calculation. kThe dimension of the key vector is used to prevent the dot product from being too large. Softmax is an activation function that converts the dot product result into a probability distribution. It normalizes each element so that the sum of all elements is 1.

[0088] Structural optimization: Structural optimization of report content, calibration of technical terms and logical relationships based on domain knowledge base, and generation of accurate and standardized alarm analysis reports. Figure 7 As shown, the robot's alarm information is displayed.

[0089] 7. Overall Loss Function and Optimization

[0090] In order to ensure the optimization effect of the model and the quality of the generated report, we need to introduce an overall loss function to constrain the training process at each stage. The loss function is defined as follows:

[0091] The overall loss function (L rep ):

[0092] L rep =λ1·L img +λ2·L act +λ3·L gbl +λ4·L lcl

[0093] Among them, L rep : represents the total loss function, which is the final optimization objective and is used to measure the overall performance of the model. λ1, λ2, λ3, and λ4 are the weights of each loss term.

[0094] L img Visual feature loss: used to measure the difference between the image features extracted by YOLO and the true label, using the Huber loss function:

[0095] L img =Huber(F img ,F ground_truth )

[0096] Among them, L img represents the visual feature loss, F img represents the image features extracted by the YOLO model, F ground_truth represents the actual annotation feature, and Huber is the Huber loss function, which is used to measure the difference between the model prediction value and the true value.

[0097] L act Behavioral feature loss: It is used to measure the difference between the behavioral features extracted by LSTM and the true label, using the cross entropy loss function:

[0098] L act =Cross-Entropy(Fact ,F ground_truth )

[0099] Among them, L act represents the behavioral feature loss, F act represents the behavioral features extracted by the LSTM model, F ground_truth represents the actual annotation features, and Cross-Entropy is the cross entropy loss function, which is used to measure the difference between two probability distributions.

[0100] L gbl Graph structure loss: helps optimize the representation of nodes or edges of the graph, l i For the node vector in graph E, the graph smoothing loss function is used:

[0101] L gbl =∑ i,j in E (l i -l j ) 2

[0102] Among them, L gbl Represents the graph structure loss, E represents the edge set in the graph, including all edges connecting nodes. i and l j Represent the feature representation of node i and node j in the graph respectively, (l i -l j ) 2 It represents the difference between the feature representations of node i and node j, and the square error is used to measure the gap.

[0103] L lcl Local consistency loss: To ensure the consistency between adjacent spatial or temporal points, a local consistency loss function with a regularization term is used:

[0104]

[0105] Among them, L lcl Represents the local consistency loss. E represents the edge set in the graph, including all edges connecting nodes. i and l j Represent the feature representation of node i and node j in the graph respectively. (l i -l j ) 2 It represents the difference between the feature representations of node i and node j, and the square error is used to measure the gap. It is a regularization term that prevents the eigenvalue from being too large. It constrains the size of node features by summing the squares of all node features and weighting them (by λ).

[0106] The model parameters are optimized by RMSprop optimizer, with an initial learning rate of 2×10-4 , the network weight parameters are optimized through continuous iteration of adaptive learning rate:

[0107]

[0108] Among them, θ t+1 Represents the parameters of the model at step t+1. t Represents the parameters of the model at step t. α is the learning rate, which controls the step size of each parameter update. The initial learning rate given in the figure is 2×10 -4 . g t Represents the gradient at step t (i.e., the derivative of the loss function with respect to the model parameters). E[g 2 ] t Represents the exponential moving average of the square of the gradient. ∈ is a small constant used to prevent division by zero errors, here it is 1×10 -8 .

[0109] The embodiments of the present invention described above do not limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. An intelligent transmitter management system based on AI visually assisted supervision, characterized in that: The system includes the following modules: Data acquisition module, which collects video, image data and environmental data inside and outside the launch station through acquisition equipment, and displays the data in real time on the system web page; A data analysis module, wherein the data analysis module uses the YOLOv5 model to extract visual features from the collected video and image data to obtain visual information, uses the LSTM model to analyze the extracted visual information, performs target recognition and behavior recognition, obtains target recognition results and behavior recognition results, performs spatiotemporal feature alignment and time series analysis on the target recognition results and behavior recognition results, extracts time-varying features, optimizes features using adaptive enhancement technology to enhance the representation of key information, and obtains enhanced target recognition results and enhanced behavior recognition results; An optimization module, which uses a graph convolutional network to fuse the enhanced target recognition results with the enhanced behavior recognition results to construct a multimodal relationship graph of the device state, and uses a graph attention mechanism to assign weights to key nodes and edges to generate a globally optimized feature representation; An emergency response module, which uses a knowledge reasoning engine to call a safety hazard case database and equipment knowledge graph, combines current environmental data to generate an intelligent emergency response plan, and uses a Bayesian network for probabilistic reasoning to generate an optimized emergency response strategy; The alarm feedback module analyzes the globally optimized feature representation based on the Transformer architecture, generates a preliminary safety hazard analysis report, and structurally optimizes the report content through the central control module, performs semantic calibration on the report in combination with the domain knowledge base, and generates an alarm analysis report.

2. The system according to claim 1, wherein: In the data acquisition module, the acquisition devices are cameras and sensors.

3. The system according to claim 1, wherein: In the data acquisition module, the environmental data include temperature and humidity, gas concentration and noise data.

4. The system according to claim 1, wherein: The data acquisition module includes: a multi-source data synchronization unit configured to align video data, image data, and environmental data through time stamps; The abnormal data filtering unit is configured to perform real-time verification of temperature and humidity, gas concentration data and noise data according to preset thresholds, and discard abnormal values ​​that exceed the sensor range.

5. The system according to claim 1, wherein: In the data analysis module, the input of the LSTM model is a sequence of visual features of consecutive N frames, and the output is a behavior classification label; The target identification is the identification of people and objects, and the behavior identification includes the detection of people crossing the boundary, illegal operation of equipment, and the leaving of dangerous objects.

6. The system according to claim 1, wherein: In the data analysis module, the adaptive enhancement technology includes a residual feature pyramid and a gated recurrent unit, and adaptive adjustment of the feature space is achieved through deformable convolution.

7. The system according to claim 1, wherein: In the optimization module, the nodes of the multimodal relationship graph of the equipment status include three types of objects: equipment entities, operators, and environmental factors. The edge attribute definitions of the multimodal relationship graph of the equipment status include three topological connections: spatial proximity, operation correlation, and energy transfer relationship.

8. The system according to claim 1, wherein: In the optimization module, the graph attention mechanism adopts a multi-head graph attention layer, calculates the attention coefficient between nodes through a learnable parameter matrix, establishes a dynamic update mechanism, and adjusts the graph structure weight distribution strategy according to real-time sensor data.

9. The system according to claim 1, wherein: In the emergency response module, the safety hazard case database adopts ontology modeling to construct a semantic network including failure modes, treatment plans, and impact links; The equipment knowledge graph integrates equipment three-dimensional models, maintenance logs, and multi-dimensional data of material properties.

10. The system according to claim 1, wherein: In the alarm feedback module, the Transformer architecture uses a hierarchical encoder to process text data and structured logs; The structured optimization includes three processing stages: event element extraction, causal chain construction, and impact degree quantification. It is equipped with an alarm strategy optimizer to dynamically adjust the alarm threshold and response level according to historical disposal effects.

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