Security risk intelligent response method and system

Abstract

The invention discloses a security risk intelligent response method and system. The response method comprises the following steps: acquiring video streams, equipment states and environment sensor data in real time through a multi-source interface; processing through a feature fusion engine to generate a threat matrix containing threat type judgment and confidence; matching an emergency plan library based on a dynamic weighting algorithm; an equipment control instruction queue is generated according to a matching result, conflict detection is executed, and multiple instructions of the same equipment are sequenced according to fire control, access control locking and video tracking; and finally, acquiring an equipment execution state in real time and feeding back the state to a plan library to form closed-loop control, thereby solving the problems of high false alarm rate and response lag in traditional security and protection. Historical and real-time data are fused through a dynamic decision model, the limitation of a static plan is broken through, and the disposal efficiency is improved; instruction conflicts are eliminated through a conflict resolution mechanism; and a self-healing system and model correction are combined to form double-closed-loop optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of security risk assessment and intelligent response, in particular to a security risk intelligent response method and system. BACKGROUND

[0002] Current security systems generally use single-source data detection methods, relying only on video monitoring or environmental sensors and other single data sources for threat judgment. This technical solution has obvious limitations: video analysis is easily disturbed by factors such as light changes and obstructions, and environmental sensors are difficult to accurately distinguish between real threats and false positives, resulting in high false positive rates for the system, seriously affecting the reliability of the security system.

[0003] In terms of emergency response, traditional systems use static pre-plan mechanisms and cannot dynamically adjust response strategies according to real-time threats. When complex security incidents occur, the system needs human intervention to make decisions, resulting in serious lag in emergency response and often missing the best disposal opportunity. In particular, in multi-device collaborative scenarios, due to the lack of intelligent priority scheduling mechanisms, conflicts often occur between key instructions such as fire control and access control locking.

[0004] In addition, the device maintenance of existing systems completely relies on manual inspection, making it difficult to discover and repair device faults in a timely manner. The system lacks self-learning ability and cannot continuously optimize based on historical data, resulting in a gradual decline in system performance over time. These technical defects have seriously restricted the actual application effect of security systems, SUMMARY The purpose of the present application is to provide a security risk intelligent response method and system to solve the problem of conflicts between key instructions such as fire control and access control locking in multi-device collaborative scenarios due to the lack of intelligent priority scheduling mechanisms.

[0005] To achieve the above purpose, the present application adopts the following technical solution: a security risk intelligent response method, comprising the following steps: Step 1: Real-time acquisition of video streams, device states and environmental sensor data through a multi-source data acquisition interface; Step 2: Using a feature fusion engine to process the data to generate a threat matrix containing threat type judgment and confidence; Step 3: Matching an emergency plan library based on a dynamic weighting algorithm, the algorithm satisfying: Where: C is the threat confidence, S is the threat diffusion speed, D is the impact range, R1 to R3 are the device online rate, network bandwidth, and computing resources, ; Step 4, generate device control instruction queue according to matching results, execute conflict detection mechanism, when the same device receives multiple instructions, prioritize fire control > access control > video tracking; Step 5, real-time collection of device execution state feedback to pre-plan library, forming a closed-loop control.

[0006] Further, the threat matrix generation process includes: extracting biological features in the video stream through a face recognition algorithm and matching with a pre-stored database to complete identity recognition, using a behavior analysis algorithm to calculate the degree of deviation of the motion trajectory from the safe path to determine the type of abnormal behavior, combining environmental sensor data to analyze the combined abnormal pattern of temperature, smoke concentration, and vibration intensity to determine the threat type, polling device fault signals in real time through a device state monitoring algorithm, and outputting device health status based on real-time performance data, and fusing the determined threat type and device health status to form threat type coding and real-time threat confidence C X .

[0007] Further, the dynamic weighting algorithm implementation process includes: the device state monitoring algorithm polls device online rate R1 through SNMP protocol, network probe captures transmission bandwidth in real time as R2, system performance counter collects CPU and memory utilization as R3, trajectory tracking algorithm calculates target moving speed as diffusion speed S, and regional influence algorithm calculates influence range D based on geographic information system data; machine learning algorithm analyzes event rules in historical event database, identifies key risk factors and trigger conditions, and outputs historical risk probability value, which is weighted and fused with real-time threat confidence C X to generate the final threat confidence C.

[0008] Further, the conflict detection mechanism implementation process includes: device asset management algorithm classifies devices into three categories: fire fighting devices, access control devices, and monitoring devices; when the device search algorithm locates the target device, the priority scheduling algorithm assigns fire control instructions 9 priority, access control instructions 7 priority, and video tracking instructions 5 priority according to preset rules; when multiple instruction conflicts are detected, the system automatically reorders the instruction queue in descending order of priority, and the performance degradation warning algorithm monitors network transmission quality in real time, and the timeout detection algorithm automatically switches to UDP low delay protocol when the predicted delay approaches 400ms.

[0009] Further, the security risk intelligent response method further comprises a device failure self-recovery mechanism: a device health assessment algorithm continuously analyzes temperature sensor, memory occupancy rate and CPU fluctuation rate data, and when the health score is lower than 60, a three-level maintenance process is automatically triggered: first, try to restart the target device through the device control API, if the restart fails, automatically switch to the standby device, and generate a maintenance work order and push it to the on-duty terminal; when the health degree is in the range of 60-80, secondary maintenance is started, the key functions are degraded, and a detailed diagnosis report is generated.

[0010] Further, it further comprises a pre-plan self-optimization mechanism: a parameter correction engine continuously monitors the deviation δ of the actual response effect and the expectation, and when , the decision weight parameter is automatically adjusted; when negative deviation occurs for 5 consecutive times, the pre-plan reconstruction module starts the model optimization process: first, extract recent historical event data as training samples, then retrain the machine learning model, and finally verify the new model effect and update the pre-plan library, complete the closed-loop optimization.

[0011] Further, a security risk intelligent response system, characterized in that it is used to realize the security risk intelligent response method of claims 1-7, the system comprises a multi-source acquisition module, a decision center module, an instruction arbitration module and a closed-loop feedback module. The multi-source acquisition module comprises a face recognition algorithm and a behavior analysis algorithm, and a device fault diagnosis; The decision center module comprises a threat diffusion speed S and an influence range D, and a device online rate R1 and a network bandwidth R2; The priority mapping of fire 9 levels, access control 7 levels, monitoring 5 levels and the transmission protocol switching when the prediction delay is >400ms; The closed-loop feedback module is used to define the adjustment coefficient δ and to monitor trigger model reconstruction.

[0012] Compared with the prior art, the beneficial effects of the present application are: This invention integrates video biometric recognition, equipment health monitoring, and environmental anomaly pattern analysis in real time through a multi-source data fusion engine to generate a dynamic threat matrix. This effectively solves the problems of high false alarm rates and delayed response caused by single-source data analysis in traditional security systems. Based on a dynamic decision-making model, it fuses historical risk probabilities with real-time threat data, overcoming the limitations of static contingency plans in adapting to complex scenarios and significantly improving the efficiency of handling high-risk events. Through a conflict resolution mechanism, it adopts a three-level priority mapping of fire control, access control, and video tracking, along with dynamic switching of transmission protocols, to completely eliminate the risk of equipment command conflicts. Combined with a self-healing system driven by equipment health and model deviation correction, it forms a dual closed-loop optimization, achieving automatic fault handling and continuous, accurate system evolution. Verified in industrial scenarios, this solution significantly shortens the response time to major incidents, significantly improves equipment utilization, and achieves a breakthrough in threat identification accuracy, reaching a new level of industry efficiency. Attached Figure Description

[0013] The present invention will be further described below with reference to the accompanying drawings: Fig. 1 This is a schematic diagram of the data relationships in this invention; Fig. 2 This is a system architecture diagram of the intelligent response system for security risks of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0015] The technical solution of the present invention will be described in detail below with reference to specific embodiments. The following specific embodiments may be combined or substituted with each other according to the actual situation, and the same or similar concepts or processes may not be described again in some embodiments.

[0016] like Figs. 1-2 As shown, the present invention provides a security risk intelligent response method and system, including... Its implementation process can be divided into the following five steps: S1, First, the system acquires three types of key data in real time through a multi-source data acquisition interface: S2 consists of video stream data, device operating status data, and environmental sensor data. This data forms the foundational information source for the system's risk assessment. Next, the system utilizes a feature fusion engine to comprehensively process the collected data. This engine extracts features and performs correlation analysis on data from different sources, ultimately generating a threat matrix that includes threat type assessments and their confidence levels. This matrix comprehensively reflects the current security risk situation. S3, the system matches the most suitable response plan in the emergency plan library based on a dynamic weighting algorithm. The calculation model of this algorithm is: Where C represents the threat confidence, S indicates the threat spread speed, D is the impact range, R1 to R3 represent the system state parameters such as device online rate, network bandwidth, and computing resources, and This algorithm considers both risk characteristics and system resource conditions; S4, the system generates a device control instruction queue based on the matching results. To ensure the reliability of instruction execution, the system will perform a conflict detection mechanism. When the same device receives multiple instructions, the system will execute them in the order of preset priority: fire control instructions have the highest priority (>), followed by access control locking instructions, and finally video tracking instructions; S5, the system will collect real-time feedback information on device execution status and return these information to the plan library. By analyzing the differences between actual execution effect and expectation, the system can continuously optimize the response strategy, forming a complete closed-loop control system. This closed-loop mechanism enables the system to continuously improve its response capability.

[0017] The generation process of the threat matrix is as follows: The system first processes video stream data through a face recognition algorithm, extracts facial biometric features (such as facial feature contours, texture features, etc.), and compares these features with a pre-established database to complete personnel identity recognition. This step can quickly identify known suspicious personnel or blacklisted personnel.

[0018] The system uses a behavior analysis algorithm to analyze personnel activities in the monitored area. The algorithm calculates the target's motion trajectory and compares it with the preset safety path to assess the degree of deviation. If behaviors such as loitering, entering restricted areas, and abnormal gathering are detected, the system will determine the specific abnormal behavior type (such as suspicious loitering, illegal intrusion, etc.) based on the degree of deviation.

[0019] The system will analyze various environmental sensor data (such as temperature, smoke concentration, vibration intensity, etc.) and detect abnormal combinations of these parameters through pattern recognition algorithms. For example, simultaneous detection of a sudden temperature rise and excessive smoke concentration may indicate a fire risk, while abnormal vibration may suggest violent destruction behavior. The system will determine the specific threat type based on different abnormal combination patterns.

[0020] Through a device state monitoring algorithm, the system will poll the running state data of various security devices (such as cameras, sensors, etc.) in real time. The algorithm will analyze device fault signals (such as signal loss, image blur, etc.) and performance data (such as response delay, storage space, etc.), outputting a health status score for the device.

[0021] Finally, the system fuses all the above analysis results: combines the identified threat types (such as intrusion, fire, etc.) with the device health status data, assigns a type code to each detected threat, and calculates the real-time threat confidence C of the threat based on the confidence of each analysis x . These information together constitute a complete threat matrix, providing data support for subsequent emergency response decisions.

[0022] The dynamic weighting algorithm implementation process is as follows: Adaptive risk response is achieved through multi-dimensional data fusion. The system first polls the online security devices in real time through the SNMP protocol to obtain the device online rate (R1), a key parameter, to ensure that the system decision is based on reliable device status. At the same time, the network probe deployed continuously monitors the transmission bandwidth (R2), and the system performance counter collects CPU and memory utilization data (R3) in real time, which together constitute the evaluation system of system resource status. In terms of threat feature analysis, the system uses trajectory tracking algorithm to accurately calculate the moving speed of the target as the threat spread speed (S), and combines with geographic information system data to quantitatively evaluate the influence range (D) in space.

[0023] In order to improve the accuracy of threat judgment, machine learning algorithm is introduced to deeply analyze the historical event database, to generate historical risk probability value with predictive value by identifying key risk factors and event rules. This probability value and real-time threat confidence (C x ) are fused through a specific weighting algorithm to output the optimized threat confidence (C). All these parameters are integrated into the dynamic weighting calculation formula; Where The constraint condition ensures the balance between real-time threat features and system resource status.

[0024] For example, when a high-confidence threat is detected but the device online rate is low, the system will automatically reduce the dependence on offline devices and increase the monitoring weight of other available sensors; or when the network bandwidth is limited, the transmission of critical instructions is prioritized and the video stream quality is temporarily reduced. Through continuous parameter dynamic adjustment, the system realizes the optimal risk response under resource constraints, avoiding system overload due to insufficient resources, and ensuring that major threats can be disposed of in a timely and effective manner. This intelligent decision-making mechanism that combines real-time monitoring and historical data analysis significantly improves the adaptability and reliability of the security system.

[0025] Device fault self-healing mechanism; through intelligent fault diagnosis and hierarchical response strategy to realize the automatic operation and maintenance management of security equipment. The mechanism first continuously collects key performance indicators such as temperature, memory occupancy and CPU fluctuation rate through the multi-dimensional sensor network deployed on the device, and analyzes these data in real time and calculates the comprehensive health score of the device by a special health evaluation algorithm. When the score is lower than the preset threshold, the system will trigger a hierarchical response process: for the serious fault state with a health score lower than 60, the system automatically starts a three-level maintenance process, first tries to restart the target device through the device control API, if the restart fails, immediately switches to the pre-configured backup device, and at the same time generates a detailed maintenance work order and pushes it to the terminal device of the relevant technical personnel; for the sub-health state with a health score in the range of 60-80, the system executes a two-level maintenance strategy, automatically degrades non-critical functions while maintaining core function operation, and generates an analysis report containing detailed diagnostic data for the reference of operation and maintenance personnel. This hierarchical response mechanism significantly shortens the fault handling time through the preset automatic process, and the intelligent resource allocation ensures the continuous and reliable operation of the system under abnormal device conditions. The entire self-healing process forms a complete closed-loop control, and the system will continuously monitor the execution effect of the maintenance measures, and use the feedback data to optimize the health evaluation model, realizing the continuous evolution of the system maintenance capability.

[0026] Pre-plan self-optimization mechanism: the parameter correction engine continuously monitors the deviation δ of the actual response effect and the expected value, and automatically adjusts the decision weight parameter when |δ|>0.05; when negative deviation occurs for 5 consecutive times, the pre-plan reconstruction module starts the model optimization process: first extract recent historical event data as training samples, then retrain the machine learning model, and finally verify the new model effect and update the pre-plan library, complete the closed-loop optimization.

[0027] In addition to the preferred embodiments described above, the present application has other embodiments, and all other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of the present application.

Claims

1. A security risk intelligent response method, characterized in that, The method comprises the following steps: Step 1, real-time acquisition of video stream, device status and environmental sensor data through multi-source data acquisition interface; Step 2, processing the data using a feature fusion engine to generate a threat matrix containing threat type judgment and confidence level; Step 3, matching the emergency plan library based on a dynamic weighting algorithm, which satisfies wherein: C is threat confidence, S is threat spreading speed, D is impact range, R1 to R3 are device online rate, network bandwidth, computing resource, ; Step 4, generating a device control instruction queue according to the matching results, and implementing a conflict detection mechanism. When the same device receives multiple instructions, the priority is sorted in the order of fire control > access control locking > video tracking; Step 5, real-time collection of device execution state feedback to the plan library to form a closed-loop control.

2. The security risk intelligent response method of claim 1, wherein, The threat matrix generation process includes: matching the biological features in the video stream with the pre-stored database through a face recognition algorithm to complete identity recognition, using a behavior analysis algorithm to calculate the degree of deviation of the motion trajectory from the safe path to determine the abnormal behavior type, combining environmental sensor data to analyze the combined abnormal mode of temperature, smoke concentration and vibration intensity to determine the threat type, polling device failure signals in real time through a device state monitoring algorithm, and outputting the device health status based on real-time performance data, and fusing the determined threat type and device health status to form a threat type code and real-time threat confidence C X .

3. The security risk intelligent response method of claim 2, wherein, The dynamic weighting algorithm implementation process includes: the device state monitoring algorithm obtains the device online rate R1 through SNMP protocol polling, the network probe captures the transmission bandwidth in real time as R2, the system performance counter collects the CPU and memory utilization as R3, the trajectory tracking algorithm calculates the target moving speed as the diffusion speed S, and the area influence algorithm calculates the influence range D based on geographic information system data; the machine learning algorithm analyzes the event rules in the historical event database, identifies the key risk factors and trigger conditions, outputs the historical risk probability value, and compares the historical risk probability value with the real-time threat confidence C X Weighted fusion is performed to generate the final threat confidence C.

4. The security risk intelligence response method of claim 1, wherein, The conflict detection mechanism implementation process includes: the device asset management algorithm classifies devices into three categories: fire-fighting devices, access control devices, and monitoring devices according to function type; when the device search algorithm locates the target device, the priority scheduling algorithm assigns the fire control instruction a priority of 9, the access control locking instruction a priority of 7, and the video tracking instruction a priority of 5 according to the preset rules; when multiple instruction conflicts are detected, the system automatically reorders the instruction queue in descending order of priority, and the performance degradation warning algorithm monitors network transmission quality in real time, and the timeout detection algorithm automatically switches to the UDP low-latency protocol when the predicted delay approaches 400ms.

5. The security risk intelligence response method of claim 1, wherein, It also includes a device fault self-recovery mechanism: the device health assessment algorithm continuously analyzes temperature sensor, memory occupancy rate, and CPU fluctuation rate data, and automatically triggers a three-level maintenance process when the health score is below 60: first, try to restart the target device through the device control API, if the restart fails, automatically switch to the backup device, and generate a maintenance work order and push it to the on-duty terminal; when the health score is in the range of 60-80, start the second-level maintenance, degrade the running of key functions and generate a detailed diagnosis report.

6. The security risk intelligence response method of claim 1, wherein, The method also includes a contingency self-optimization mechanism: the parameter correction engine continuously monitors the deviation between the actual response and the expected response. ,when The decision weight parameters are automatically adjusted in real time. When there are 5 consecutive negative deviations, the contingency plan reconstruction module starts the model optimization process: first, extract recent historical event data as training samples, then retrain the machine learning model, and finally verify the effect of the new model and update the contingency plan library to complete the closed-loop optimization.

7. A security risk intelligent response system characterized by, The system for implementing the security risk intelligent response method of claims 1-6 comprises a multi-source acquisition module, a decision hub module, an instruction arbitration module, and a closed-loop feedback module; The multi-source acquisition module includes an algorithm for performing face recognition and behavior analysis and an algorithm for device fault diagnosis; The decision hub module includes an algorithm for calculating threat spread speed S and influence range D, and an algorithm for obtaining device online rate R1 and network bandwidth R2; The instruction arbitration module includes a priority mapping algorithm for implementing fire control 9, access control 7, and monitoring 5 in hardware, and a transmission protocol switching algorithm when the predicted delay > 400ms; The closed loop feedback module is used to define an adjustment coefficient δ and to monitor triggered model reconstruction.

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