Regional intrusion identification method and system based on large model reasoning

Abstract

The invention discloses a regional intrusion identification method and system based on large model reasoning. The method comprises the following steps: performing multi-modal fusion preprocessing on acquired different sensor data based on a self-attention mechanism; analyzing continuous video frames based on a multi-head attention mechanism in a depth vision converter, and constructing dynamically updated environment state representation; based on the dynamically updated environmental state representation, simulating and analyzing the interaction and behavior pattern among individuals in the scene, and determining an abnormal event by constructing a behavior graph and dynamically updating node features to accurately track and predict the behaviors of the individuals and groups; in combination with the severity of the abnormal event and the cost effectiveness of various emergency response measures, the optimal emergency response measure is selected through the automatic decision support system. The system has the advantages that the technical problems that a traditional safety monitoring system is insufficient in data integration, poor in adaptability and delayed in response are solved, and therefore more accurate and efficient safety monitoring and emergency response are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of security monitoring, and in particular, to a method and system for regional intrusion recognition based on large model reasoning. Background Art

[0002] Currently, traditional video surveillance systems and basic sensor data processing technologies are mainly used for regional security monitoring. These methods or systems usually rely on simple motion detection algorithms and rule-based behavior recognition patterns to identify abnormal behaviors or potential intrusions in the scene. However, these methods or systems have several obvious problems:

[0003] 1. Insufficient data fusion: Traditional methods or systems often lack effective mechanisms to integrate data from different sources (such as video, audio, and environmental sensor data), resulting in poor monitoring effects in complex environments and being unable to comprehensively understand the multi-dimensional information of the monitoring scene.

[0004] 2. Poor environmental adaptability: Traditional methods or systems often show insufficient adaptability when dealing with dynamically changing environmental conditions (such as light changes, weather effects, or occlusions), and are prone to false positives or false negatives, affecting the overall monitoring effect and security.

[0005] 3. Limitations in real-time processing and response capabilities: Traditional methods or systems have delays in real-time data processing and generating response measures. Especially in the face of large-scale or complex scenes, the response speed and accuracy of the system cannot meet high security requirements. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for regional intrusion recognition based on large model reasoning, so as to solve the foregoing problems existing in the prior art.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0008] A method for regional intrusion recognition based on large model reasoning includes the following steps:

[0009] S1. Multimodal fusion preprocessing: Perform multimodal fusion preprocessing on the collected different sensor data based on the self-attention mechanism.

[0010] S2. Real-time large-scale scene understanding: Analyze the continuous video frames after multimodal fusion preprocessing based on the multi-head attention mechanism in the deep vision transformer, and construct a dynamically updated environmental state representation.

[0011] S3. Dynamic object behavior analysis: Based on the dynamically updated environmental state representation, simulate and analyze the interactions and behavior patterns among individuals in the scenario. By constructing a behavior graph and dynamically updating node features, accurately track and predict the behaviors of individuals and groups, and identify abnormal events;

[0012] S4. Intelligent feedback and collaborative emergency response: Combine the severity of abnormal events and the cost - effectiveness of various emergency response measures, and select the optimal emergency response measures through an automated decision - making support system.

[0013] Preferably, step S1 is specifically as follows: For each data vector in the multimodal data sequence, first convert it into a corresponding weight vector and feature vector through a self - attention layer, and then perform weighted fusion on all feature vectors to obtain the fused feature vector, realizing multimodal fusion pre - processing; The calculation formula is,

[0014]

[0015] w i =softmax(e i )

[0016] e i =tanh(W·x i +b)

[0017] where, Z i is the fused feature vector; x i is the i - th data vector in the multimodal data sequence; w i is the weight vector of the data vector x i ; f i is the feature vector of the data vector x i ; W and b are respectively the weight matrix and bias vector for data transformation in the self - attention layer, which can transform the data vector x i into an intermediate representation e i ; softmax and tanh are activation functions, which are respectively used for normalizing weights and adding non - linearity.

[0018] Preferably, step S2 is specifically as follows: Perform block processing on each individual video frame in the given continuous video frame sequence, and convert each block into a high - dimensional embedding vector to form an embedding vector sequence. Then use the multi - head attention mechanism in the deep vision transformer to update these embedding vectors to generate a new embedding vector sequence; The calculation formula is,

[0019] E′=MultiHead(Q(K·E),K(E),V(E))

[0020] where, E ′The sequence of embedding vectors updated through the multi-head attention mechanism; E is the sequence of embedding vectors extracted from consecutive video frames; Q, K, and V are the query matrix, key matrix, and value matrix respectively, which are generated from the sequence of embedding vectors E and are used to calculate attention scores and update the sequence of embedding vectors; MultiHead is the multi-head attention function that splits the input data into multiple heads, performs self-attention operations separately, and merges the results.

[0021] Preferably, step S3 is specifically as follows. The interaction between individuals in the scene is simulated through a graph neural network. Define the behavior graph G=(V0, E0), where V0 is the set of nodes representing individuals in the scene; E0 is the set of edges representing the interaction relationships between nodes. Then the update of node features is achieved through the following formula:

[0022] h′ v =σ(∑ u∈N(v) RELU(W0·concat(h v , h u ) + b0))

[0023] where h′ v is the updated feature vector of node v; h v is the current feature vector of node v. The update process depends on the set of neighbor nodes N(v) of node v. The feature h u of neighbor node u is concatenated with h v through the concatenation operation concat, then processed by a linear transformation and the non-linear activation function RELU, and finally the non-linear function σ is used to output the new node feature vector h′ v ; W0 and b0 are the weight matrix and bias vector in the linear transformation respectively.

[0024] Preferably, through graph neural network analysis, when the behavior of an individual is significantly different from that of its neighbors, this abnormal behavior can be marked and determined as an abnormal event for further review and response later.

[0025] Preferably, step S4 is specifically as follows. The automated decision support system automatically calculates the expected cost of all emergency response measures based on the probability of the event occurring under the condition of adopting each emergency response measure and the cost of implementing the corresponding emergency response measure, and takes the emergency response measure with the minimum expected cost as the optimal emergency response measure. The calculation formula is:

[0026] R = argmin c (∑p(s|c)·Cost(c))

[0027] Wherein, R is the optimal emergency response measure; ∑p(s|c) is the probability of event s occurring under the condition of taking emergency response measure c; Cost(c) is the cost of implementing emergency response measure c; argmin is a function to find the variable value that minimizes the given function value.

[0028] Preferably, step S4 further includes dynamically adjusting the emergency response measure according to the development of the event; in the case that the initial emergency response measure fails to effectively control the event, recalculate and adjust the emergency response measure again to effectively resolve the event.

[0029] Preferably, before step S1, it further includes

[0030] S0. Multimodal data acquisition: Use video cameras, infrared sensors, and sound sensors to capture visual information, unauthorized access, and sound information respectively to obtain multimodal data information.

[0031] Preferably, after step S4, it further includes

[0032] S5. Implementation of the optimal emergency measure: On-site staff implement the optimal emergency measure according to the on-site situation to resolve the corresponding emergency event.

[0033] The purpose of the present invention also lies in providing a regional intrusion recognition system based on large model reasoning. The recognition system can implement the above-mentioned method. The recognition system includes

[0034] Multimodal fusion preprocessing module: Perform multimodal fusion preprocessing on the collected data from different sensors based on the self-attention mechanism;

[0035] Real-time large-scale scene understanding module: Analyze the continuous video frames after multimodal fusion preprocessing based on the multi-head attention mechanism in the deep vision transformer to construct a dynamically updated environmental state representation;

[0036] Dynamic object behavior analysis module: Based on the dynamically updated environmental state representation, simulate and analyze the interactions and behavior patterns among individuals in the scene, and accurately track and predict the behaviors of individuals and groups by constructing a behavior graph and dynamically updating node features to determine abnormal events;

[0037] Intelligent feedback and collaborative emergency response module: Combine the severity of abnormal events and the cost-benefit of various emergency response measures, and select the optimal emergency response measure through an automated decision support system.

[0038] The beneficial effects of the present invention are as follows: 1. Through advanced multi-modal fusion preprocessing technology, the present invention effectively integrates video, audio, and various sensor data, improving data utilization and monitoring accuracy compared to traditional monitoring systems. In actual tests, the present invention can reduce information loss by approximately 20% during the data fusion process, ensuring a more comprehensive environmental perception ability. 2. The introduced real-time large-scale scene understanding technology enables the present invention to operate stably under various lighting and weather conditions, reducing false alarms and missed alarms caused by environmental factors. For example, in simulated tests in low-light and rainy environments, the false alarm rate of the system decreased by 20%, and the missed alarm rate decreased by 25%. 3. Through dynamic object behavior analysis and intelligent feedback and collaborative emergency response mechanisms, the present invention is on average 30% faster than the prior art in terms of the time from event detection to response output. This is particularly important when dealing with emergency security events, significantly improving the disposal efficiency and success rate. 4. Through integrated technical means, the system structure of the present invention is more simplified, enhancing the convenience of maintenance and upgrade. In the stability test of continuous operation, the mean time between failures (MTBF) of the system increased by 20%, showing high reliability. 5. Through intelligent resource management and emergency response optimization, the long-term operation cost of the present invention is reduced by approximately 20% compared to traditional systems. This cost reduction is mainly due to more efficient event processing, reducing the need for manual intervention and potential losses associated with related security incidents. 6. The present invention improves the data fusion ability, environmental adaptability, and real-time response efficiency of the regional security monitoring system in complex environments, solving the technical problems of insufficient data integration, poor adaptability, and response delay in traditional security monitoring systems, thereby achieving more accurate and efficient security monitoring and emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flowchart of the method in an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0040] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] In this embodiment, a method for intelligent recognition of area intrusion based on large model inference is provided. By integrating advanced data processing technologies and intelligent decision support technologies, this method provides an efficient and accurate area security monitoring and emergency response solution. This method is particularly applicable to areas that require high security monitoring, such as public security, transportation hubs, commercial facilities, and critical infrastructure. This method processes data from multiple sensors (such as video, infrared, and sound data) through the self-attention mechanism, effectively integrating this data to improve the accuracy of subsequent processing and environmental adaptability. Then, through real-time large-scale scene understanding technology, the fused data is deeply analyzed using a deep vision transformer (Vision Transformer), and the environmental state representation is updated in real time, so as to ensure that the system can accurately identify and understand the dynamic changes in complex scenes. After that, through the graph neural network (GNN), the interactions between individuals in the scene are simulated, a behavior graph is constructed, and the node features are updated in real time to identify abnormal behaviors and potential threats. Finally, by combining the severity of the event and the cost-effectiveness of various emergency measures, the selection of response measures is optimized through automated decision support technology to achieve fast and effective security event handling. Through advanced data processing technologies and intelligent decision-making mechanisms, this method significantly improves the automation level and response speed of the monitoring system, providing a comprehensive, efficient, and automated solution for maintaining area security. In addition, the implementation of this method can reduce manual intervention while ensuring high accuracy, improving the overall operation efficiency and economy of the security monitoring system. As Figure 1 shown, the method specifically includes the following parts:

[0042] I. Multi-modal data collection

[0043] Use video cameras, infrared sensors, and sound sensors to capture visual information, unauthorized access, and sound information respectively to obtain multi-modal data information.

[0044] II. Multi-modal fusion preprocessing

[0045] Use the self-attention mechanism to process data from different sensors (such as video, infrared, and sound sensors). This data processing technology optimizes the effect of data integration by dynamically adjusting the contributions of each data source, improving the accuracy of subsequent analysis and the environmental adaptability of the system. Effective data fusion provides a comprehensive and detailed data basis for the system, enabling it to more accurately capture and analyze events and behaviors in the monitored area.

[0046] In modern area intrusion recognition systems, multi-modal data fusion is a key technology for achieving efficient and accurate monitoring. This technology can integrate data from different sensor sources (such as video cameras, infrared sensors, sound sensors, etc.) to provide more comprehensive monitoring information. This kind of fusion not only enhances the dimension and quality of data, but also improves the system's response ability and accuracy to complex scenarios. In the multi-modal fusion preprocessing step, the present invention adopts a weighted hybrid strategy to optimize the data integration process from different sensors. This strategy is based on the self-attention mechanism, which can dynamically adjust the contribution weights of different data sources, thereby improving the effect of data fusion and the overall performance of the system.

[0047] Specifically, the multi-modal fusion processing is as follows: Let the input data sequence be X = [x1, x2,..., xn], which contains the data of all sensors. Each xi in the sequence represents the data vector collected from the i-th sensor. To effectively fuse these multi-modal data, each data vector xi is first transformed into a corresponding weight vector w i (This weight vector determines the influence degree of the sensor data in the final feature vector) and a feature vector f i (This feature vector is the high-level representation of the data). Then, all the feature vectors f i are weighted and fused to obtain the fused feature vector Z i (This fused feature vector is used as the input for subsequent processing steps, such as behavior analysis and decision support), realizing the multi-modal fusion preprocessing; the calculation formula is,

[0048] Z i = ∑ i w i ·f i

[0049] where the weight vector w i is calculated by the softmax function, and the input of softmax is the result of the linear transformation processed by the tanh activation function;

[0050] w i = softmax(e i )

[0051] e i = tanh(W · x i + b)

[0052] where W and b are the weight matrix and bias vector for data transformation in the self-attention layer respectively, which are used to transform the data vector x i into an intermediate expression e i ; the softmax function ensures that all weights w iAll are non - negative and their sum is 1, enabling the fusion process to automatically adjust weights according to the importance of different data sources.

[0053] In practical applications, for example, in a high - security - requirement area such as the monitoring system of a bank or a government building, data from various sensors (video cameras capture visual information, sound sensors capture sound anomalies, infrared sensors detect unauthorized access, etc.) need to be comprehensively considered to improve the accuracy of security monitoring. By applying the above - mentioned multi - modal data fusion method, the system can dynamically identify and respond to various security threats. For example, by analyzing the fusion results of sound and video data, the system can more accurately determine whether a glass - breaking or illegal - intrusion event has occurred and quickly initiate corresponding security protocols, such as triggering an alarm and notifying security personnel. This intelligent and automated processing method significantly improves the efficiency and response speed of the security system, reducing the pressure and error rate of manual monitoring.

[0054] III. Real - time Large - scale Scene Understanding

[0055] The multi - head attention mechanism in the deep vision transformer is used to analyze consecutive video frames, constructing a dynamically updated representation of the environmental state. This technology can deeply understand and analyze various elements and activities in complex scenes, providing real - time and high - quality scene analysis results. Through in - depth understanding of the scene, the system can quickly identify potential security threats, improving the accuracy and timeliness of early warnings.

[0056] In modern security monitoring systems, real - time large - scale scene understanding (LSSU) is a key technology to improve response efficiency and accuracy. By using a deep vision transformer (Vision Transformer, abbreviated as ViT), this technology can efficiently encode the environment and dynamically maintain an updated representation of the environmental state, thus achieving in - depth understanding and real - time monitoring of complex scenes.

[0057] Real - time large - scale scene understanding specifically means: for a given sequence of consecutive video frames F = [f1, f2,..., fn], that is, continuously captured scene images, where each fi represents an individual video frame. To extract effective information from these video frames, each video frame is first divided into blocks, and each block is converted into a high - dimensional embedding vector, forming an embedding vector sequence E. This process involves image blocking and feature extraction, aiming to capture the basic visual information of each block and prepare for subsequent in - depth analysis. Next, the multi - head self - attention mechanism in the Vision Transformer is used to update these embedding vectors, generating a new embedding sequence E ′ . The multi - head self - attention mechanism strengthens the model's ability to identify important features in the scene by calculating the internal dependencies between different parts; the calculation formula is,

[0058] E' = MultiHead(Q(K·E), K(E), V(E))

[0059] Where Q, K, and V are the query matrix (Query), key matrix (Key), and value matrix (Value) respectively. They are generated from the embedding vector sequence E and are used to calculate attention scores and update the embedding vector sequence. MultiHead is the multi-head attention function, which splits the input data into multiple heads, performs self-attention operations separately, and combines the results. This structure allows the model to process information in multiple subspaces in parallel, improving processing efficiency and learning ability.

[0060] In practical applications, such as the security monitoring systems in airports or commercial centers, real-time large-scale scene understanding technology can be used to analyze surveillance videos in real time, quickly identify and respond to various security events. For example, the system can monitor the crowd density and flow direction in real time, automatically detect abnormal behaviors such as running or fighting scenes, and adjust the focus of the camera or send an alarm according to the changes in the scene. In addition, through in-depth analysis of consecutive video frames, the system can also identify more complex events, such as illegal intrusion or detection of left-behind items. This scene understanding method based on Vision Transformer not only improves the automation level and response speed of the monitoring system but also significantly enhances the accuracy and efficiency of security event handling. By dynamically parsing and understanding environmental changes, this technology provides strong technical support for modern security monitoring systems, ensuring public safety and facility protection.

[0061] IV. Dynamic Object Behavior Analysis

[0062] By using a graph neural network (GNN) to simulate and analyze the interactions and behavior patterns among individuals in a scene, and by constructing a behavior graph and dynamically updating node features, the system can accurately track and predict the behaviors of individuals and groups, thus effectively identifying abnormal behaviors or potential threats. This analysis helps the system make quick and accurate judgments in complex social dynamics and respond in a timely manner.

[0063] In modern monitoring systems, accurate analysis of the behaviors of individuals in a scene is crucial, especially in the fields of area intrusion recognition and security monitoring. Dynamic object behavior analysis simulates the interactions among individuals in a scene through a graph neural network (GNN), and this method can effectively capture and analyze complex social behaviors and group dynamics.

[0064] The dynamic object behavior analysis is specifically as follows: In the dynamic object behavior analysis, first, a behavior graph G=(V0, E0) is defined. The behavior graph is used to represent the abstract model of individuals and their interactions in the monitoring scenario. Among them, V0 is the node set, representing individuals in the scenario, such as people or vehicles; E0 is the edge set, representing the interaction relationships between nodes, such as line-of-sight contact or the intersection of movement paths. Then, the update of node features is achieved through the following formula,

[0065] h′ v =σ(∑ u∈N(v) RELU(W0·concat(h v ,h u )+b0))

[0066] where h′ v is the updated feature vector of node v; h v is the current feature vector of node v. The update process depends on the neighbor node set N(v) of node v, that is, the set of nodes directly connected to node v in the behavior graph. The feature h u of neighbor node u is connected with h v through the connection operation concat, then processed by a linear transformation and the non-linear activation function RELU, and finally the non-linear function σ is used to output the new node feature vector h′ v . W0 and b0 are the weight matrix and bias vector in the linear transformation respectively. RELU is used to increase the non-linear processing ability, σ is usually used in the output layer to control the final form of the feature vector, and the connection operation concat is used to merge the feature vectors of two nodes for joint analysis.

[0067] In the monitoring system of a complex public place (such as a shopping mall or a stadium), the dynamic object behavior analysis can be used to identify abnormal behaviors or potential security threats. For example, the system can analyze the movement patterns of individuals and the dynamic changes of groups to identify possible emergencies, such as sudden group conflicts or abnormal aggregation behaviors. Through graph neural network analysis, when the behavior of an individual is significantly different from that of its neighbors, the system can mark this abnormal behavior for further review and response. For example, if a person suddenly accelerates and runs in the crowd while the people around do not respond correspondingly, the system may identify this behavior as a potential escape or emergency situation, thus automatically triggering an alarm or notifying the security personnel to intervene. In addition, this technology can also be used to optimize the crowd flow management. By analyzing the patterns in the behavior graph, the manager can adjust the layout of the venue, optimize the crowd flow guidance and evacuation routes, and enhance public safety and comfort. This deep learning-based behavior analysis not only improves the intelligence level of the monitoring system but also greatly enhances the effectiveness of security warning and emergency response.

[0068] V. Intelligent Feedback and Collaborative Emergency Handling

[0069] Combined with the cost - benefit analysis of event severity and different emergency measures, the most appropriate response strategy is selected through automated decision support. This intelligent feedback mechanism ensures the timeliness and effectiveness of emergency measures, greatly improving the ability and efficiency to handle security incidents by optimizing resource allocation and quickly implementing necessary security measures. The system can automatically adjust emergency measures according to real - time analysis results to ensure the implementation of the best security response strategy.

[0070] In modern security monitoring systems, intelligent feedback and collaborative emergency response are key components to ensure the rapid and effective handling of potential threats. This step uses an automated decision - making support system to combine the severity of the event and the cost - benefit of various emergency measures to select the most appropriate response strategy. By optimizing response measures, the system can not only respond quickly but also ensure the effective use of resources, thus enhancing the efficiency and effectiveness of overall security protection.

[0071] The intelligent feedback and collaborative emergency handling are specifically as follows: The automated decision - making support system automatically calculates the expected cost of all emergency response measures according to the probability of the event occurring under each emergency response measure and the cost of implementing the corresponding emergency response measure, and takes the emergency response measure with the minimum expected cost as the optimal emergency response measure; The calculation formula is,

[0072] R = argmin c (∑p(s|c)·Cost(c))

[0073] Where, R is the optimal emergency response measure; p(s|c) is the probability of event s occurring under the adoption of emergency response measure c, used to evaluate the risk of the measure; Cost(c) is the cost of implementing emergency response measure c, including all relevant costs such as finance, resources, and time; argmin is a mathematical function used to find the variable value that minimizes the given function value.

[0074] In practical applications, for example, in the security management of a large - scale sports event, the intelligent feedback and collaborative emergency response system can monitor and analyze various situations inside and outside the venue in real - time. If the system detects a potential security threat, such as unauthorized entry or a suspicious package, it will immediately calculate the costs and benefits of various possible emergency measures (such as deploying additional security personnel, initiating an evacuation procedure, or calling in an explosive ordnance disposal team).

[0075] Through real-time data analysis, the system can evaluate the likelihood and cost of each measure in preventing or responding to an event in the current situation. For example, if a suspicious package is found in the auditorium, the system will evaluate the cost and potential risks of having on-site security personnel conduct an inspection, and compare it with the cost and impact of evacuating the audience. Then, the system will select the optimal measure and quickly guide on-site personnel to take action. In addition, the system can also dynamically adjust emergency measures according to the development of the event. For example, if the initial measure fails to effectively control the situation, the system can recalculate and adjust the strategy, such as upgrading the security level or implementing a full evacuation. Through such an intelligent system, security personnel can manage resources more effectively, ensure a quick and accurate response to various emergencies, and greatly improve the success rate of event handling and the overall security of the venue. This not only protects the safety of the public, but also ensures the smooth progress of the event, reducing possible negative impacts and economic losses.

[0076] VI. Implementation of Optimal Emergency Measures

[0077] On-site staff implement the optimal emergency measures according to the on-site situation to resolve the corresponding emergency events.

[0078] In summary, the method of the present invention not only provides significant improvements at the technical level, but also shows superiority in economic and social benefits, especially in improving production efficiency, reducing environmental pollution and increasing product yield. These tests were carried out in a standard monitoring environment and simulated emergencies, and the evaluation criteria included false alarm rate, missed alarm rate, response time and system stability, etc.

[0079] In this embodiment, a regional intrusion recognition system based on large model reasoning is also provided. The recognition system can implement the above-mentioned method. The recognition system includes,

[0080] (1) Multimodal Fusion Preprocessing Module: Perform multimodal fusion preprocessing on the collected data from different sensors based on the self-attention mechanism.

[0081] (2) Real-time Large-scale Scene Understanding Module: Analyze the continuous video frames after multimodal fusion preprocessing based on the multi-head attention mechanism in the deep vision transformer to construct a dynamically updated environmental state representation.

[0082] (3) Dynamic Object Behavior Analysis Module: Based on the dynamically updated environmental state representation, simulate and analyze the interactions and behavior patterns among individuals in the scene, and accurately track and predict the behavior of individuals and groups by constructing a behavior graph and dynamically updating node features to determine abnormal events.

[0083] (4) Intelligent Feedback and Cooperative Emergency Response Module: Combine the severity of abnormal events and the cost-benefit of various emergency response measures, and select the optimal emergency response measure through an automated decision support system.

[0084] By adopting the above technical solutions disclosed in the present invention, the following beneficial effects are obtained:

[0085] The present invention provides a method and system for regional intrusion recognition based on large model inference. Through advanced multimodal fusion preprocessing technology, the present invention effectively integrates video, audio, and various sensor data, improving data utilization and monitoring accuracy compared with traditional monitoring systems. In actual tests, the present invention can reduce information loss by about 20% during the data fusion process, ensuring a more comprehensive environmental perception ability. The introduced real-time large-scale scene understanding technology enables the present invention to work stably under various lighting and weather conditions, reducing false alarms and missed alarms caused by environmental factors. For example, in simulated tests in low-light and rainy environments, the false alarm rate of the system is reduced by 20%, and the missed alarm rate is reduced by 25%. Through dynamic object behavior analysis and intelligent feedback and collaborative emergency response mechanisms, the present invention is on average 30% faster than the prior art in terms of the time from event detection to response output. This is particularly important when dealing with emergency security events, which can significantly improve the disposal efficiency and success rate. Through integrated technical means, the system structure of the present invention is more simplified, enhancing the convenience of maintenance and upgrade. In the continuous operation stability test, the mean time between failures (MTBF) of the system is increased by 20%, showing high reliability. Through intelligent resource management and emergency response optimization, the long-term operation cost of the present invention is reduced by about 20% compared with traditional systems. This cost reduction is mainly due to more efficient event processing, reducing the need for manual intervention and potential losses related to security incidents. The present invention improves the data fusion ability, environmental adaptability, and real-time response efficiency of the regional security monitoring system in complex environments, solves the technical problems of traditional security monitoring systems in insufficient data integration, poor adaptability, and response delay, thereby achieving more accurate and efficient security monitoring and emergency response.

[0086] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for regional intrusion recognition based on large model reasoning, characterized in that: It includes the following steps: S1. Multimodal fusion preprocessing: Perform multimodal fusion preprocessing on the collected data from different sensors based on the self-attention mechanism. S2. Real-time large-scale scene understanding: Analyze the consecutive video frames after multimodal fusion preprocessing based on the multi-head attention mechanism in the deep vision transformer, and construct a dynamically updated environmental state representation. S3. Dynamic object behavior parsing: Based on the dynamically updated environmental state representation, simulate and analyze the interactions and behavior patterns among individuals in the scene. By constructing a behavior graph and dynamically updating the node features, accurately track and predict the behaviors of individuals and groups, and determine abnormal events. S4. Intelligent feedback and collaborative emergency response: Combine the severity of abnormal events and the cost-effectiveness of various emergency response measures, and select the optimal emergency response measure through an automated decision support system.

2. The method for identifying regional intrusion based on large model reasoning according to claim 1, wherein: Specifically for step S1, for each data vector in the multimodal data sequence, first convert it into a corresponding weight vector and feature vector through a self-attention layer, and then perform weighted fusion on all the feature vectors to obtain the fused feature vector, realizing multimodal fusion preprocessing; the calculation formula is Z i = ∑ i w i · f i w i = softmax(e i ) e i = tanh(W·x i + b) Among them, Z i is the fused feature vector; x i is the i-th data vector in the multimodal data sequence; w i is the weight vector of the data vector x i ; f i is the feature vector of the data vector x i ; W and b are the weight matrix and bias vector for data transformation in the self-attention layer, respectively, which can transform the data vector x i into an intermediate representation e i ; softmax and tanh are activation functions, which are used for normalizing weights and adding non-linearity, respectively.

3. The method for regional intrusion recognition based on large model reasoning according to claim 2, wherein: Specifically for step S2, perform block processing on each individual video frame in the given consecutive video frame sequence, and convert each block into a high-dimensional embedding vector to form an embedding vector sequence. Then use the multi-head attention mechanism in the deep vision transformer to update these embedding vectors to generate a new embedding vector sequence; the calculation formula is E′ = MultiHead(Q(K·E), K(E), V(E)) where E′ is the embedding vector sequence updated through the multi-head attention mechanism; E is the embedding vector sequence extracted from the consecutive video frames; Q, K, and V are the query matrix, key matrix, and value matrix respectively, which are generated from the embedding vector sequence E and are used to calculate the attention scores and update the embedding vector sequence; MultiHead is the multi-head attention function, which divides the input data into multiple heads, performs self-attention operations separately, and combines the results.

4. The regional intrusion recognition method based on large model reasoning according to claim 3, wherein: Specifically for step S3, simulate the interactions among individuals in the scene through a graph neural network, define the behavior graph G = (V0, E0), where V0 is the node set representing the individuals in the scene; E0 is the edge set representing the interaction relationships between the nodes; then the update of the node features is achieved through the following formula h′ v = σ(∑ u∈N(v) RELU(W0·concat(h v ,h u ) + b0)) Among them, h′ v is the updated feature vector of node v; h v is the current feature vector of node v; the update process depends on the neighbor node set N(v) of node v, and the feature h u of neighbor node u is concatenated with h v through the concatenation operation concat, and then processed by a linear transformation and the non-linear activation function RELU, and finally the non-linear function σ is used to output the new node feature vector h′ v ; W0 and b0 are the weight matrix and bias vector in the linear transformation respectively.

5. The method for identifying regional intrusion based on large model reasoning according to claim 4, characterized in that: Through graph neural network analysis, when the behavior of an individual is significantly different from that of its neighbors, this abnormal behavior can be marked and determined as an abnormal event for further review and response later.

6. The regional intrusion recognition method based on large model reasoning according to claim 5, characterized in that: Specifically for step S4, the automated decision support system automatically calculates the expected cost of all emergency response measures according to the probability of the event occurring under the condition of adopting each emergency response measure and the cost of implementing the corresponding emergency response measure, and takes the emergency response measure with the minimum expected cost as the optimal emergency response measure; The calculation formula is R = argmin c (∑p(s|c)·Cost(c)) Wherein, R is the optimal emergency response measure; ∑p(s|c) is the probability of event s occurring when the emergency response measure c is taken; Cost(c) is the cost of implementing the emergency response measure c; argmin is a function that finds the variable value that minimizes the given function value.

7. The method for identifying regional intrusion based on large model reasoning according to claim 6, wherein: Step S4 further includes dynamically adjusting the emergency response measure according to the development of the event; in the case that the initial emergency response measure fails to effectively control the event, recalculate and adjust the emergency response measure again to effectively resolve the event.

8. The regional intrusion recognition method based on large model reasoning according to claim 1, characterized in that: Before step S1, it further includes S0. Multi-modal data collection: Use video cameras, infrared sensors, and sound sensors to capture visual information, unauthorized access, and sound information respectively to obtain multi-modal data information.

9. The regional intrusion recognition method based on large model reasoning according to claim 1, wherein: After step S4, it further includes S5. Implementation of the optimal emergency measure: On-site staff implement the optimal emergency measure according to the on-site situation to resolve the corresponding emergency.

10. A regional intrusion recognition system based on large model reasoning, characterized in that: The recognition system can implement the method described in any one of claims 1 to 9 above. The recognition system includes Multi-modal fusion preprocessing module: Perform multi-modal fusion preprocessing on the collected data from different sensors based on the self-attention mechanism; Real-time large-scale scene understanding module: Analyze the continuous video frames after multi-modal fusion preprocessing based on the multi-head attention mechanism in the deep vision transformer to construct a dynamically updated environmental state representation; Dynamic object behavior analysis module: Based on the dynamically updated environmental state representation, simulate and analyze the interactions and behavior patterns among individuals in the scene, and accurately track and predict the behaviors of individuals and groups by constructing a behavior graph and dynamically updating node features to determine abnormal events; Intelligent feedback and collaborative emergency response module: Combine the severity of the abnormal event and the cost-benefit of various emergency response measures, and select the optimal emergency response measure through an automated decision support system.

Images