Safety inspection method and system applied to intelligent security and protection

Abstract

The invention relates to the field of remote monitoring, and discloses a safety inspection method and system applied to intelligent security, and the method comprises the steps: calculating a potential risk coefficient of a scene element; marking risk nodes of the intelligent security scene, analyzing risk indexes of the risk nodes, defining multi-modal inspection equipment of the risk nodes, and analyzing inspection resistance of the multi-modal inspection equipment to the risk nodes; configuring a mobile inspection device of the risk node, capturing an abnormal behavior of the risk node, locking an abnormal target of the intelligent security scene, analyzing target characteristics of the abnormal target, and constructing a multi-modal tracking path of the multi-modal inspection device and the mobile inspection device; the abnormal development state of the abnormal target is analyzed, when the abnormal development state accords with a preset abnormal development threshold value, the abnormal level of the abnormal target is determined, and abnormal alarm of the intelligent security scene is executed based on the abnormal level. According to the invention, the automatic test efficiency of the X86 tablet interface can be improved.

Description

Technical Field

[0001] The invention relates to a safety inspection method and system applied in intelligent security, belonging to the field of remote monitoring. Background Art

[0002] Security inspection under intelligent security refers to the process of using advanced scientific and technological means and techniques to monitor, detect and evaluate specific areas to ensure the safety of the area and prevent potential security threats. Security inspection under intelligent security aims to improve the efficiency and accuracy of inspections through technical means, reduce human errors, and at the same time reduce dependence on human resources to improve the overall security level.

[0003] Traditional smart security inspections usually rely on manual patrols and fixed camera monitoring, which is often inefficient, has limited coverage, and is easily affected by human factors such as fatigue and negligence. It is unable to accurately locate and continuously track risk nodes, and lacks the ability of multimodal equipment and mobile inspection equipment to work together, resulting in insufficient and timely security protection for security scenarios. Summary of the invention

[0004] The present invention provides a safety inspection method and system for intelligent security, the main purpose of which is to improve the efficiency of automatic testing of X86 flat panel interfaces.

[0005] To achieve the above object, the present invention provides a security inspection method applied to intelligent security, comprising: Acquire a smart security scene and its scene features, mark scene elements of the smart security scene according to the scene features, construct a three-dimensional element framework of the smart security scene through the scene elements, and calculate a potential risk coefficient of the scene element based on the three-dimensional element framework; According to the potential risk coefficient, mark the risk node of the smart security scenario, analyze the risk index of the risk node, define the multimodal inspection device of the risk node based on the risk index, and analyze the inspection resistance of the multimodal inspection device to the risk node; According to the inspection resistance, configure the mobile inspection device of the risk node, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the intelligent security scene based on the inspection collaborative network; Based on the multimodal inspection data, the abnormal behavior of the risk node is captured using a trained anomaly capture model, and based on the abnormal behavior, the abnormal target of the intelligent security scene is locked, the target characteristics of the abnormal target are analyzed, and based on the target characteristics, a preset path tracking algorithm is used to construct a multimodal tracking path of the multimodal inspection device and the mobile inspection device; Based on the multimodal tracking path, the abnormal development state of the abnormal target is analyzed, when the abnormal development state meets the preset abnormal development threshold, the abnormal level of the abnormal target is determined, and the abnormal alarm of the intelligent security scene is executed based on the abnormal level.

[0006] Optionally, constructing the three-dimensional element architecture of the intelligent security scene through the scene elements includes: A scene layout diagram defining the smart security scene; According to the scene layout diagram, construct a three-dimensional foundation of the intelligent security scene; Identifying element attributes of the scene element; Defining element interaction rules of the scene elements; According to the element attributes and the element interaction rules, the three-dimensional foundation is optimized to obtain the three-dimensional element architecture of the intelligent security scene.

[0007] Optionally, the calculating the potential risk coefficient of the scene element based on the three-dimensional element architecture includes: identifying a risk indicator of the scene element in the three-dimensional element framework; Analyzing the risk exposure of the scenario elements according to the risk indicators; Determining the element vulnerability of the scene element through the risk exposure; Based on the risk exposure and the element vulnerability, a potential risk coefficient of the scene element is calculated.

[0008] Optionally, the calculating the potential risk coefficient of the scene element based on the risk exposure and the element vulnerability includes: Define the risk attack value, risk severity coefficient, risk resistance coefficient, correction effectiveness and capability correction factor of the scenario elements; Based on the risk exposure, the element vulnerability, the risk attack value, the risk severity coefficient, the risk resistance coefficient, the correction effectiveness and the capability correction factor, the potential risk coefficient of the scene element is calculated using the following formula: ; in, Indicates the potential risk factor of scene elements, Indicates the risk exposure of scenario elements, Indicates the risk attack value of the scene element, Indicates the risk severity coefficient of the scene element, Indicates the element fragility of the scene element, Indicates the risk resistance coefficient of the scene element, Indicates the modified validity of scene elements, Indicates the ability modifier of scene elements.

[0009] Optionally, the analyzing the inspection resistance of the multimodal inspection device to the risk node includes: Identifying functional parameters of the multimodal inspection device; Collecting node environment data of the risk node, wherein the node environment data includes physical environment data and electromagnetic environment data; Defining inspection resistance indicators of the risk nodes; Based on the inspection resistance index, establishing an inspection resistance analysis model for the risk node; In combination with the functional parameters and the node environment data, the inspection resistance analysis model is used to analyze the inspection resistance of the multimodal inspection equipment to the risk node.

[0010] Optionally, combining the functional parameters and the node environment data and analyzing the inspection resistance of the multimodal inspection device to the risk node using the inspection resistance analysis model includes: Analyzing the functional parameter characteristics and the node environment characteristics of the functional parameter and the node environment data; Constructing a function parameter analysis function and a node environment analysis function of the function parameter characteristics and the node environment characteristics; Based on the functional parameter analysis function, the node environment analysis function, the functional parameter characteristics and the node environment characteristics, the inspection resistance of the multimodal inspection device to the risk node is analyzed using the inspection resistance analysis model corresponding to the inspection resistance analysis algorithm, wherein the inspection resistance analysis algorithm: ; in, Indicates the inspection resistance of multimodal inspection equipment to risk nodes. Indicates the multi-mode inspection equipment Function parameter characteristics, Functional parameter analysis function representing the functional parameter characteristics, represents the number of function parameter characteristics, Indicates the multi-mode inspection equipment The weight of the function parameter feature, Indicates the risk node Node environment characteristics, A node environment analysis function that represents the node environment characteristics, Indicates the risk node The weight of the node environment feature, Represents the number of node environment characteristics.

[0011] Optionally, configuring the mobile inspection device of the risk node according to the inspection resistance includes: Determining invalid inspection points of the risk nodes according to the inspection resistance; Identify inspection point characteristics of the invalid inspection point; Determining the initial mobile inspection equipment of the risk node based on the inspection point characteristics; Analyze the mobile inspection coverage coefficient of the initial mobile inspection equipment on the invalid inspection point; When the mobile inspection coverage coefficient meets the preset mobile inspection coverage threshold, the initial mobile inspection device is used as the mobile inspection device of the risk node.

[0012] Optionally, the capturing the abnormal behavior of the risk node based on the multimodal inspection data using a trained abnormality capturing model includes: Extracting the element body of the multimodal inspection data; Constructing a three-dimensional heat map of the elements of the element body; Based on the three-dimensional heat map of the element, the element key point coordinates of the element key points corresponding to the element body are identified using a preset key point identification function, wherein the key point identification function is: ; in, Represents element key points The coordinates of the key points of the elements, Indicates search The largest value, Represents the horizontal coordinate value of the element's three-dimensional heat map. Represents the vertical coordinate value of the element's three-dimensional heat map. Indicates the index of the row corresponding to the element's three-dimensional heat map. Indicates the index of the column corresponding to the element's three-dimensional heat map. Represents element key points , Indicates the size of the domain, Represents a three-dimensional heat map of elements; Analyzing the main body posture of the element main body based on the coordinates of the key points of the element; Determine the main running track of the element main body through the main body posture; Based on the running trajectory of the subject, the abnormal behavior of the element subject is captured using the abnormal capture model.

[0013] Optionally, based on the target characteristics, constructing a multimodal tracking path of the multimodal inspection device and the mobile inspection device using a preset path tracking algorithm includes: Based on the target characteristics, a preset path tracking algorithm is used to analyze the next position of the abnormal target corresponding to the target characteristics; According to the next position, defining an analysis path of the abnormal target; Defining equipment constraint parameters of the multimodal inspection equipment and the mobile inspection equipment; Determining device tracking parameters of the multimodal inspection device and the mobile inspection device based on the device constraint parameters and the analysis path of the abnormal target; The multimodal tracking paths of the multimodal inspection device and the mobile inspection device are constructed through the device tracking parameters.

[0014] In order to solve the above problems, the present invention also provides a safety inspection system applied to intelligent security, the system comprising: A risk coefficient analysis module is used to obtain the intelligent security scene and its scene characteristics, mark the scene elements of the intelligent security scene according to the scene characteristics, construct a three-dimensional element framework of the intelligent security scene through the scene elements, and calculate the potential risk coefficient of the scene element based on the three-dimensional element framework; An inspection resistance analysis module is used to mark the risk nodes of the smart security scene according to the potential risk coefficient, analyze the risk index of the risk node, define the multimodal inspection equipment of the risk node based on the risk index, and analyze the inspection resistance of the multimodal inspection equipment to the risk node; An inspection data collection module is used to configure the mobile inspection device of the risk node according to the inspection resistance, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the intelligent security scene based on the inspection collaborative network; A tracking path establishment module, which is used to capture the abnormal behavior of the risk node based on the multimodal inspection data using a trained abnormal capture model, lock the abnormal target of the intelligent security scene based on the abnormal behavior, analyze the target characteristics of the abnormal target, and construct a multimodal tracking path of the multimodal inspection device and the mobile inspection device based on the target characteristics using a preset path tracking algorithm; The scene intelligent security module is used to analyze the abnormal development state of the abnormal target based on the multimodal tracking path, determine the abnormal level of the abnormal target when the abnormal development state meets the preset abnormal development threshold, and execute the abnormal alarm of the intelligent security scene based on the abnormal level.

[0015] Compared with the problems described in the background technology, first of all, the construction of the three-dimensional element architecture of the intelligent security scene enables us to fully understand the scene characteristics from macro to micro, accurately mark the scene elements, and calculate their potential risk coefficients. This refined management improves the pertinence and practicality of the security system, ensures the rational allocation of resources, and improves the overall security effectiveness. Secondly, through the precise identification and analysis of risk nodes, we can deploy multimodal inspection equipment in a targeted manner, effectively reduce inspection resistance, and improve inspection efficiency. At the same time, the configuration of mobile inspection equipment and the establishment of an inspection collaborative network have achieved seamless coverage of security scenes, greatly enhancing the continuity and real-time nature of monitoring. Moreover, based on multi Modal inspection data, the abnormal capture model can capture abnormal behavior in real time, quickly lock abnormal targets, and provide strong technical support for tracking abnormal targets by analyzing target characteristics and using the multimodal tracking path constructed by the path tracking algorithm, effectively improving the processing speed and accuracy of abnormal events. Finally, when the abnormal development state reaches the preset threshold, the system can automatically determine the abnormal level and execute abnormal alarm. This mechanism significantly improves the emergency response capability and ensures the safety of personnel and property. Overall, the system not only improves the intelligent level of security work, but also enhances the ability to prevent and deal with various security risks, providing a strong guarantee for building a safe and stable social environment. Therefore, the present invention can improve the stability of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a safety inspection method for intelligent security provided by an embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing the security inspection method applied to intelligent security provided in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] 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.

[0019] The embodiment of the present application provides a security inspection method for intelligent security. The execution subject of the security inspection method for intelligent security includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the security inspection method for intelligent security can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0020] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of a safety inspection method applied to intelligent security provided by an embodiment of the present invention. In this embodiment, the safety inspection method applied to intelligent security includes: S1. Acquire a smart security scene and its scene features, mark the scene elements of the smart security scene according to the scene features, construct a three-dimensional element architecture of the smart security scene through the scene elements, and calculate the potential risk coefficient of the scene elements based on the three-dimensional element architecture.

[0021] It should be explained that the smart security scene refers to a specific environment or area that requires security monitoring and management. It can be a public place, private property, industrial facilities, commercial buildings, transportation hubs, or any other place that needs protection. The scene characteristics refer to a series of unique attributes and features in the smart security scene, such as physical characteristics, environmental factors, and characteristics of human and logistics flows.

[0022] The present invention marks the scene elements of the smart security scene according to the scene characteristics to ensure that the scene elements of the smart security scene are accurately marked, laying a solid foundation for the subsequent three-dimensional element architecture construction and risk assessment. The scene elements refer to various components or objects that can be identified, described and classified in a specific scene, including doors, windows, cameras, sensors, important assets and other elements.

[0023] The present invention constructs a three-dimensional element architecture of the intelligent security scene through the scene elements, and can construct a detailed and practical three-dimensional element architecture of the intelligent security scene, providing strong technical support for security management.

[0024] In detail, the three-dimensional element architecture of the intelligent security scene is constructed through the scene elements, including: A scene layout diagram defining the smart security scene; According to the scene layout diagram, construct a three-dimensional foundation of the intelligent security scene; Identifying element attributes of the scene element; Defining element interaction rules of the scene elements; According to the element attributes and the element interaction rules, the three-dimensional foundation is optimized to obtain the three-dimensional element architecture of the intelligent security scene.

[0025] The scene layout diagram refers to a detailed two-dimensional graphical representation that shows a plan view of the smart security scene, the three-dimensional foundation refers to the base layer of the three-dimensional model of the smart security scene, and the element attributes refer to the parameters that describe the characteristics of each element in the scene. Attributes can be static, such as physical size, color, material type; or dynamic, such as range of motion, function, response time, the element interaction rules refer to the interaction and response between different elements in the scene, and the three-dimensional element architecture refers to the three-dimensional model of the entire smart security scene.

[0026] Optionally, the three-dimensional foundation of the intelligent security scene can be constructed according to the scene layout diagram using 3Dmax software.

[0027] The present invention is based on the three-dimensional element architecture and calculates the potential risk coefficient of the scene elements, which can effectively calculate the potential risk coefficient of each element in the intelligent security scene, thereby providing a scientific basis for risk management.

[0028] In detail, the calculating the potential risk coefficient of the scene element based on the three-dimensional element architecture includes: identifying a risk indicator of the scene element in the three-dimensional element framework; Analyzing the risk exposure of the scenario elements according to the risk indicators; Determining the element vulnerability of the scene element through the risk exposure; Based on the risk exposure and the element vulnerability, a potential risk coefficient of the scene element is calculated.

[0029] Among them, the risk index refers to a specific parameter or feature used to evaluate the security risk of scene elements, the risk exposure refers to the degree to which scene elements may be threatened within a specific period of time, the element vulnerability refers to the sensitivity of scene elements to possible damage when threatened, and the potential risk coefficient refers to a combination of risk exposure and element vulnerability to quantify the risk level of scene elements.

[0030] Furthermore, the calculating the potential risk coefficient of the scene element based on the risk exposure and the element vulnerability includes: Define the risk attack value, risk severity coefficient, risk resistance coefficient, correction effectiveness and capability correction factor of the scenario elements; Based on the risk exposure, the element vulnerability, the risk attack value, the risk severity coefficient, the risk resistance coefficient, the correction effectiveness and the capability correction factor, the potential risk coefficient of the scene element is calculated using the following formula: ; in, Indicates the potential risk factor of scene elements, Indicates the risk exposure of scenario elements, Indicates the risk attack value of the scene element, Indicates the risk severity coefficient of the scene element, Indicates the element fragility of the scene element, Indicates the risk resistance coefficient of the scene element, Indicates the modified validity of scene elements, Indicates the ability modifier of scene elements.

[0031] Among them, the risk attack value refers to the attacker's intention to attack the scene element, the risk severity coefficient refers to the severity of the loss or impact that may be caused if the scene element is attacked, the risk resistance coefficient refers to the ability of the existing protection measures of the scene element to resist the attack, the correction effectiveness refers to the effectiveness of the existing control measures in reducing risks, and the capability correction factor refers to the correction factor of the attacker's capability, which may include factors such as the attacker's skills and resources.

[0032] S2. According to the potential risk coefficient, mark the risk nodes of the smart security scenario, analyze the risk indicators of the risk nodes, define the multimodal inspection equipment of the risk nodes based on the risk indicators, and analyze the inspection resistance of the multimodal inspection equipment to the risk nodes.

[0033] It should be explained that the risk node refers to the part or link in the smart security scenario that is identified as having a higher risk based on the calculation results of the potential risk coefficient. The risk indicator refers to the quantitative indicator used to evaluate the security status of the risk node, such as attack frequency, attack success rate, impact range and other indicators. The multimodal inspection equipment refers to equipment that can use a variety of sensors and detection methods to monitor and analyze risk nodes, including cameras, infrared imaging equipment, drones and other equipment.

[0034] The present invention analyzes the inspection resistance of the multimodal inspection equipment to the risk nodes, can systematically analyze the inspection resistance of the multimodal inspection equipment to the risk nodes, and takes measures to optimize the inspection process to ensure the effectiveness and reliability of the intelligent security system.

[0035] In detail, the analyzing the inspection resistance of the multimodal inspection device to the risk node includes: Identifying functional parameters of the multimodal inspection device; Collecting node environment data of the risk node, wherein the node environment data includes physical environment data and electromagnetic environment data; Defining inspection resistance indicators of the risk nodes; Based on the inspection resistance index, establishing an inspection resistance analysis model for the risk node; In combination with the functional parameters and the node environment data, the inspection resistance analysis model is used to analyze the inspection resistance of the multimodal inspection equipment to the risk node.

[0036] Among them, the functional parameters refer to the technical specifications and performance characteristics that the multimodal inspection equipment relies on when performing inspection tasks, the physical environment data refers to the surrounding environmental conditions that affect the performance of the multimodal inspection equipment, the electromagnetic environment data refers to the electromagnetic conditions that affect the communication and sensor performance of the multimodal inspection equipment, the inspection resistance index refers to a metric used to quantify the difficulties and challenges encountered by the multimodal inspection equipment when performing inspection tasks, the inspection resistance analysis model refers to a mathematical or computational model used to evaluate the inspection performance of the multimodal inspection equipment at specific risk nodes based on the inspection resistance index, and the inspection resistance refers to the difficulties and obstacles encountered by the multimodal inspection equipment when performing inspection tasks.

[0037] Furthermore, combining the functional parameters and the node environment data and using the inspection resistance analysis model to analyze the inspection resistance of the multimodal inspection device to the risk node includes: Analyzing the functional parameter characteristics and the node environment characteristics of the functional parameter and the node environment data; Constructing a function parameter analysis function and a node environment analysis function of the function parameter characteristics and the node environment characteristics; Based on the functional parameter analysis function, the node environment analysis function, the functional parameter characteristics and the node environment characteristics, the inspection resistance of the multimodal inspection device to the risk node is analyzed using the inspection resistance analysis model corresponding to the inspection resistance analysis algorithm, wherein the inspection resistance analysis algorithm: ; in, Indicates the inspection resistance of multimodal inspection equipment to risk nodes. Indicates the multi-mode inspection equipment Function parameter characteristics, Functional parameter analysis function representing the functional parameter characteristics, represents the number of function parameter characteristics, Indicates the multi-mode inspection equipment The weight of the function parameter feature, Indicates the risk node Node environment characteristics, A node environment analysis function that represents the node environment characteristics, Indicates the risk node The weight of the node environment feature, Represents the number of node environment characteristics.

[0038] Among them, the functional parameter characteristics refer to the technical attributes and capabilities exhibited by the multimodal inspection equipment when performing inspection tasks, the node environment characteristics refer to the environmental conditions around the risk nodes, the functional parameter analysis function refers to a mathematical function used to evaluate the impact of each functional parameter characteristic of the multimodal inspection equipment on the inspection resistance, the node environment analysis function refers to a mathematical function used to evaluate the impact of each environmental characteristic of the risk node on the inspection resistance, and the inspection resistance analysis algorithm refers to a calculation algorithm that combines the functional parameter characteristics and the node environment characteristics to evaluate the inspection resistance through an analysis function.

[0039] S3. According to the inspection resistance, configure the mobile inspection device of the risk node, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the smart security scene based on the inspection collaborative network.

[0040] The present invention configures the mobile inspection equipment of the risk node according to the inspection resistance to ensure that the mobile inspection equipment can effectively adapt to the environment of the risk node, reduce the inspection resistance, and improve the overall performance of the intelligent security system.

[0041] In detail, configuring the mobile inspection device of the risk node according to the inspection resistance includes: Determining invalid inspection points of the risk nodes according to the inspection resistance; Identify inspection point characteristics of the invalid inspection point; Determining the initial mobile inspection equipment of the risk node based on the inspection point characteristics; Analyze the mobile inspection coverage coefficient of the initial mobile inspection equipment on the invalid inspection point; When the mobile inspection coverage coefficient meets the preset mobile inspection coverage threshold, the initial mobile inspection device is used as the mobile inspection device of the risk node.

[0042] Among them, the invalid inspection point refers to the point in the risk node where the inspection equipment cannot effectively perform the inspection task due to various reasons (such as environmental obstacles, equipment limitations, etc.), the inspection point feature refers to the specific attribute that describes the invalid inspection point, the initial mobile inspection equipment refers to the equipment preliminarily selected according to the inspection point features and used to perform inspection tasks at the risk node, the mobile patrol coverage coefficient refers to a quantitative indicator used to evaluate the coverage and inspection effect of the mobile patrol equipment on the invalid inspection points, the mobile patrol coverage threshold refers to a pre-set standard used to determine whether the mobile patrol equipment can effectively cover the invalid inspection points, and the mobile patrol equipment refers to the equipment that is finally determined and deployed at the risk node to perform inspection tasks.

[0043] It should be explained that the patrol inspection collaborative network refers to a network system composed of multi-modal patrol inspection equipment and mobile patrol inspection equipment, which cooperate with each other through advanced communication technology and data processing capabilities, and the multi-modal patrol inspection data refers to a data set collected through the patrol inspection collaborative network, which contains information obtained by various sensors and equipment types. These data reflect the real-time status of the intelligent security scene, including information in multiple modes such as vision, thermal imaging, vibration, and sound.

[0044] S4. Based on the multimodal inspection data, the abnormal behavior of the risk node is captured using a trained anomaly capture model. Based on the abnormal behavior, the abnormal target of the smart security scene is locked, the target characteristics of the abnormal target are analyzed, and based on the target characteristics, a preset path tracking algorithm is used to construct a multimodal tracking path for the multimodal inspection device and the mobile inspection device.

[0045] The present invention is based on the multimodal inspection data and uses a trained anomaly capture model to capture the abnormal behavior of the risk node, which can provide a basis for subsequent anomaly tracking.

[0046] In detail, based on the multimodal inspection data, using a trained anomaly capture model to capture the abnormal behavior of the risk node includes: Extracting the element body of the multimodal inspection data; Constructing a three-dimensional heat map of the elements of the element body; Based on the three-dimensional heat map of the element, the element key point coordinates of the element key points corresponding to the element body are identified using a preset key point identification function, wherein the key point identification function is: ; in, Represents element key points The coordinates of the key points of the elements, Indicates search The largest value, Represents the horizontal coordinate value of the element's three-dimensional heat map. Represents the vertical coordinate value of the element's three-dimensional heat map. Indicates the index of the row corresponding to the element's three-dimensional heat map. Indicates the index of the column corresponding to the element's three-dimensional heat map. Represents element key points , Indicates the size of the domain, Represents a three-dimensional heat map of elements; Analyzing the main body posture of the element main body based on the coordinates of the key points of the element; Determine the main running track of the element main body through the main body posture; Based on the running trajectory of the subject, the abnormal behavior of the element subject is captured using the abnormal capture model.

[0047] Among them, the element subject refers to the object that needs to be analyzed and paid attention to in the multimodal inspection data, which can be personnel, vehicles, equipment or other moving objects. The element three-dimensional heat map refers to a data visualization tool, which displays the spatial distribution and activity intensity of the element subject in the form of a three-dimensional graph. The key point identification function refers to a function used to identify and locate the key points of the element subject in the element three-dimensional heat map. The element key point coordinates refer to the key point positions of the element subject in the three-dimensional space. The element key points refer to important points on the element subject used to describe its posture and movement, such as the joints of the human body and the axles of the vehicle. The subject posture refers to the specific position and direction of the element subject in space. The subject movement trajectory refers to the movement path of the element subject in space within a period of time. The anomaly capture model refers to an algorithm for detecting and identifying abnormal behavior of the element subject. The domain size refers to the range of the risk node.

[0048] It should be explained that the abnormal target refers to an object detected by patrol equipment and systems in the intelligent security scene, whose behavior or characteristics do not conform to the normal mode or violate specific security rules, and the target characteristics refer to a set of attributes or characteristics used to describe and distinguish abnormal targets. These characteristics can be static (such as shape, color, size) or dynamic (such as speed, direction, behavior pattern).

[0049] The present invention constructs the multimodal tracking path of the multimodal inspection device and the mobile inspection device based on the target characteristics and utilizes a preset path tracking algorithm to legally and effectively construct and execute the multimodal tracking path.

[0050] In detail, based on the target characteristics, the multimodal tracking path of the multimodal inspection device and the mobile inspection device is constructed using a preset path tracking algorithm, including: Based on the target characteristics, a preset path tracking algorithm is used to analyze the next position of the abnormal target corresponding to the target characteristics; According to the next position, defining an analysis path of the abnormal target; Defining equipment constraint parameters of the multimodal inspection equipment and the mobile inspection equipment; Determining device tracking parameters of the multimodal inspection device and the mobile inspection device based on the device constraint parameters and the analysis path of the abnormal target; The multimodal tracking paths of the multimodal inspection device and the mobile inspection device are constructed through the device tracking parameters.

[0051] Among them, the next position refers to the expected position of the abnormal target in the next time step or observation period predicted based on the target characteristic analysis and the preset path tracking algorithm, the analysis path refers to the expected moving route defined for the abnormal target, the equipment constraint parameters refer to the restrictions imposed on the multimodal inspection equipment and the mobile inspection equipment when performing tracking tasks, the equipment tracking parameters refer to the parameter settings adjusted according to the equipment constraint parameters in order to effectively track the abnormal targets, and the multimodal tracking path refers to a path designed for tracking abnormal targets that combines the capabilities of the multimodal inspection equipment and the mobile inspection equipment.

[0052] S5. Based on the multimodal tracking path, analyze the abnormal development state of the abnormal target, when the abnormal development state meets the preset abnormal development threshold, determine the abnormal level of the abnormal target, and execute the abnormal alarm of the intelligent security scene based on the abnormal level.

[0053] The present invention is based on the multimodal tracking path, and the abnormal development state of the abnormal target is analyzed to achieve the state tracking of the abnormal target. The abnormal development state refers to the abnormal state generated by the abnormal target during the tracking process. In detail, the abnormal development state can be analyzed by the tracking data collected by the multimodal tracking path process. It should be explained that the abnormal level refers to the degree of abnormality of the abnormal target.

[0054] Compared with the problems described in the background technology, first of all, the construction of the three-dimensional element architecture of the intelligent security scene enables us to fully understand the scene characteristics from macro to micro, accurately mark the scene elements, and calculate their potential risk coefficients. This refined management improves the pertinence and practicality of the security system, ensures the rational allocation of resources, and improves the overall security effectiveness. Secondly, through the precise identification and analysis of risk nodes, we can deploy multimodal inspection equipment in a targeted manner, effectively reduce inspection resistance, and improve inspection efficiency. At the same time, the configuration of mobile inspection equipment and the establishment of an inspection collaborative network have achieved seamless coverage of security scenes, greatly enhancing the continuity and real-time nature of monitoring. Moreover, based on multi Modal inspection data, the abnormal capture model can capture abnormal behavior in real time, quickly lock abnormal targets, and provide strong technical support for tracking abnormal targets by analyzing target characteristics and using the multimodal tracking path constructed by the path tracking algorithm, effectively improving the processing speed and accuracy of abnormal events. Finally, when the abnormal development state reaches the preset threshold, the system can automatically determine the abnormal level and execute abnormal alarm. This mechanism significantly improves the emergency response capability and ensures the safety of personnel and property. Overall, the system not only improves the intelligent level of security work, but also enhances the ability to prevent and deal with various security risks, providing a strong guarantee for building a safe and stable social environment. Therefore, the present invention can improve the stability of the network.

[0055] Embodiment 2: like Figure 2 The figure shows a functional module diagram of a safety inspection system applied to intelligent security in the present invention.

[0056] The security inspection system 200 applied to intelligent security in the present invention can be installed in an electronic device. According to the functions implemented, the security inspection system applied to intelligent security can include a risk factor analysis module 201, an inspection resistance analysis module 202, an inspection data acquisition module 203, a tracking path establishment module 204 and a scene intelligent security module 205. The module in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0057] In the embodiment of the present invention, the functions of each module / unit are as follows: The risk coefficient analysis module 201 is used to obtain the smart security scene and its scene characteristics, mark the scene elements of the smart security scene according to the scene characteristics, build a three-dimensional element framework of the smart security scene through the scene elements, and calculate the potential risk coefficient of the scene element based on the three-dimensional element framework; The inspection resistance analysis module 202 is used to mark the risk nodes of the smart security scenario according to the potential risk coefficient, analyze the risk index of the risk node, define the multimodal inspection equipment of the risk node based on the risk index, and analyze the inspection resistance of the multimodal inspection equipment to the risk node; The inspection data collection module 203 is used to configure the mobile inspection device of the risk node according to the inspection resistance, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the intelligent security scene based on the inspection collaborative network; The tracking path establishment module 204 is used to capture the abnormal behavior of the risk node based on the multimodal inspection data using the trained abnormal capture model, lock the abnormal target of the intelligent security scene based on the abnormal behavior, analyze the target characteristics of the abnormal target, and construct the multimodal tracking path of the multimodal inspection device and the mobile inspection device based on the target characteristics using a preset path tracking algorithm; The scene intelligent security module 205 is used to analyze the abnormal development state of the abnormal target based on the multimodal tracking path, determine the abnormal level of the abnormal target when the abnormal development state meets the preset abnormal development threshold, and execute the abnormal alarm of the intelligent security scene based on the abnormal level.

[0058] In detail, each module in the security inspection system 200 applied to intelligent security in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the security inspection method applied to intelligent security described in , and can produce the same technical effects, will not be repeated here.

[0059] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A security inspection method applied to intelligent security, characterized in that: The method comprises: Acquire a smart security scene and its scene features, mark scene elements of the smart security scene according to the scene features, construct a three-dimensional element framework of the smart security scene through the scene elements, and calculate a potential risk coefficient of the scene element based on the three-dimensional element framework; According to the potential risk coefficient, mark the risk node of the smart security scenario, analyze the risk index of the risk node, define the multimodal inspection device of the risk node based on the risk index, and analyze the inspection resistance of the multimodal inspection device to the risk node; According to the inspection resistance, configure the mobile inspection device of the risk node, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the intelligent security scene based on the inspection collaborative network; Based on the multimodal inspection data, the abnormal behavior of the risk node is captured using a trained anomaly capture model, and based on the abnormal behavior, the abnormal target of the intelligent security scene is locked, the target characteristics of the abnormal target are analyzed, and based on the target characteristics, a preset path tracking algorithm is used to construct a multimodal tracking path of the multimodal inspection device and the mobile inspection device; Based on the multimodal tracking path, the abnormal development state of the abnormal target is analyzed, when the abnormal development state meets the preset abnormal development threshold, the abnormal level of the abnormal target is determined, and the abnormal alarm of the intelligent security scene is executed based on the abnormal level.

2. The security inspection method applied to intelligent security as claimed in claim 1, characterized in that: The three-dimensional element architecture of the intelligent security scene is constructed by using the scene elements, including: A scene layout diagram defining the smart security scene; According to the scene layout diagram, construct a three-dimensional foundation of the intelligent security scene; Identifying element attributes of the scene element; Defining element interaction rules of the scene elements; According to the element attributes and the element interaction rules, the three-dimensional foundation is optimized to obtain the three-dimensional element architecture of the intelligent security scene.

3. The security inspection method applied to intelligent security as claimed in claim 2 is characterized in that: The calculating the potential risk coefficient of the scene element based on the three-dimensional element framework includes: identifying a risk indicator of the scene element in the three-dimensional element framework; Analyzing the risk exposure of the scenario elements according to the risk indicators; Determining the element vulnerability of the scene element through the risk exposure; Based on the risk exposure and the element vulnerability, a potential risk coefficient of the scene element is calculated.

4. The security inspection method applied to intelligent security as claimed in claim 3 is characterized in that: The calculating the potential risk coefficient of the scene element based on the risk exposure and the element vulnerability includes: Define the risk attack value, risk severity coefficient, risk resistance coefficient, correction effectiveness and capability correction factor of the scenario elements; Based on the risk exposure, the element vulnerability, the risk attack value, the risk severity coefficient, the risk resistance coefficient, the correction effectiveness and the capability correction factor, the potential risk coefficient of the scene element is calculated using the following formula: ； in, Indicates the potential risk factor of scene elements, Indicates the risk exposure of scenario elements, Indicates the risk attack value of the scene element, Indicates the risk severity coefficient of the scene element, Indicates the element fragility of the scene element, Indicates the risk resistance coefficient of the scene element, Indicates the modified validity of scene elements, Indicates the ability modifier of scene elements.

5. The security inspection method applied to intelligent security as claimed in claim 4 is characterized in that: The analyzing the inspection resistance of the multimodal inspection device to the risk node includes: Identifying functional parameters of the multimodal inspection device; Collecting node environment data of the risk node, wherein the node environment data includes physical environment data and electromagnetic environment data; Defining inspection resistance indicators of the risk nodes; Based on the inspection resistance index, establishing an inspection resistance analysis model for the risk node; In combination with the functional parameters and the node environment data, the inspection resistance analysis model is used to analyze the inspection resistance of the multimodal inspection equipment to the risk node.

6. The security inspection method for intelligent security as claimed in claim 5, characterized in that: The combining the functional parameters and the node environment data and using the inspection resistance analysis model to analyze the inspection resistance of the multimodal inspection equipment to the risk node includes: Analyzing the functional parameter characteristics and the node environment characteristics of the functional parameter and the node environment data; Constructing a function parameter analysis function and a node environment analysis function of the function parameter characteristics and the node environment characteristics; Based on the functional parameter analysis function, the node environment analysis function, the functional parameter characteristics and the node environment characteristics, the inspection resistance of the multimodal inspection device to the risk node is analyzed using the inspection resistance analysis model corresponding to the inspection resistance analysis algorithm, wherein the inspection resistance analysis algorithm: ； in, Indicates the inspection resistance of multimodal inspection equipment to risk nodes. Indicates the multi-mode inspection equipment Function parameter characteristics, Functional parameter analysis function representing the functional parameter characteristics, represents the number of function parameter characteristics, Indicates the multi-mode inspection equipment The weight of the function parameter feature, Indicates the risk node Node environment characteristics, A node environment analysis function that represents the node environment characteristics, Indicates the risk node The weight of the node environment feature, Represents the number of node environment characteristics.

7. The security inspection method for intelligent security as claimed in claim 6, characterized in that: The configuring the mobile inspection device of the risk node according to the inspection resistance includes: Determining invalid inspection points of the risk nodes according to the inspection resistance; Identify inspection point characteristics of the invalid inspection point; Determining the initial mobile inspection equipment of the risk node based on the inspection point characteristics; Analyze the mobile inspection coverage coefficient of the initial mobile inspection equipment on the invalid inspection point; When the mobile inspection coverage coefficient meets the preset mobile inspection coverage threshold, the initial mobile inspection device is used as the mobile inspection device of the risk node.

8. The security inspection method for intelligent security as claimed in claim 7, characterized in that: The method of capturing abnormal behavior of the risk node based on the multimodal inspection data using a trained abnormality capture model includes: Extracting the element body of the multimodal inspection data; Constructing a three-dimensional heat map of the elements of the element body; Based on the three-dimensional heat map of the element, the element key point coordinates of the element key points corresponding to the element body are identified using a preset key point identification function, wherein the key point identification function is: ； in, Represents element key points The coordinates of the key points of the elements, Indicates search The largest value, Represents the horizontal coordinate value of the element's three-dimensional heat map. Represents the vertical coordinate value of the element's three-dimensional heat map. Indicates the index of the row corresponding to the element's three-dimensional heat map. Indicates the index of the column corresponding to the element's three-dimensional heat map. Represents element key points , Indicates the size of the domain, Represents a three-dimensional heat map of elements; Analyzing the main body posture of the element main body based on the coordinates of the key points of the element; Determine the main running track of the element main body through the main body posture; Based on the running trajectory of the subject, the abnormal behavior of the element subject is captured using the abnormal capture model.

9. The security inspection method for intelligent security as claimed in claim 8, characterized in that: The method of constructing a multimodal tracking path of the multimodal inspection device and the mobile inspection device based on the target characteristics using a preset path tracking algorithm includes: Based on the target characteristics, a preset path tracking algorithm is used to analyze the next position of the abnormal target corresponding to the target characteristics; According to the next position, defining an analysis path of the abnormal target; Defining equipment constraint parameters of the multimodal inspection equipment and the mobile inspection equipment; Determining device tracking parameters of the multimodal inspection device and the mobile inspection device based on the device constraint parameters and the analysis path of the abnormal target; The multimodal tracking paths of the multimodal inspection device and the mobile inspection device are constructed through the device tracking parameters.

10. A safety inspection system applied to intelligent security, characterized in that: The system comprises: A risk coefficient analysis module is used to obtain the intelligent security scene and its scene characteristics, mark the scene elements of the intelligent security scene according to the scene characteristics, construct a three-dimensional element framework of the intelligent security scene through the scene elements, and calculate the potential risk coefficient of the scene element based on the three-dimensional element framework; An inspection resistance analysis module is used to mark the risk nodes of the smart security scene according to the potential risk coefficient, analyze the risk index of the risk node, define the multimodal inspection equipment of the risk node based on the risk index, and analyze the inspection resistance of the multimodal inspection equipment to the risk node; An inspection data collection module is used to configure the mobile inspection device of the risk node according to the inspection resistance, configure the inspection collaborative network of the multimodal inspection device and the mobile inspection device, and collect the multimodal inspection data of the intelligent security scene based on the inspection collaborative network; A tracking path establishment module, which is used to capture the abnormal behavior of the risk node based on the multimodal inspection data using a trained abnormal capture model, lock the abnormal target of the intelligent security scene based on the abnormal behavior, analyze the target characteristics of the abnormal target, and construct a multimodal tracking path of the multimodal inspection device and the mobile inspection device based on the target characteristics using a preset path tracking algorithm; The scene intelligent security module is used to analyze the abnormal development state of the abnormal target based on the multimodal tracking path, determine the abnormal level of the abnormal target when the abnormal development state meets the preset abnormal development threshold, and execute the abnormal alarm of the intelligent security scene based on the abnormal level.

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