Park intrusion safety protection method based on digital twin system

Abstract

The invention relates to a digital twinborn system-based park intrusion safety protection method, which combines a digital twinborn scene, constructs a three-dimensional digital twinborn scene for a park through a digital twinborn system, carries out the construction and marking of a space region, and marks which are movable ranges and which are immovable ranges for the range of the space region. And in the movable range, the space cost coefficient is marked according to the actual situation, so that a reasonable pursuit path according with the reality can be given. According to the invention, path planning is optimized by using the digital twinning technology, so that efficient safety protection and pursuit are realized, and the safety protection level of the park is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of security prevention technology, and in particular to a campus intrusion security prevention method, system and electronic equipment based on a digital twin system. Background Art

[0002] There are two main types of intrusions in the park: premeditated and impromptu. Premeditated perpetrators will study the park's environmental structure, personnel inspection routes, inspection times, monitoring layout, and optimal routes for intrusion and escape in detail. In contrast, impromptu perpetrators have no idea of ​​the on-site situation and only intrude and escape based on the optimal solution within the current visible range.

[0003] In actual situations, criminals often choose unconventional paths to invade or escape in order to avoid monitoring and security personnel. Traditional path planning algorithms, such as the plane-based A algorithm, a heuristic search algorithm, adds a heuristic function to the Dijkstra algorithm to estimate the distance from the current node to the target node. This makes the A algorithm more efficient in finding the shortest path, especially when the target location is clear and the heuristic function is selected appropriately. However, it is applied in plane path planning, which is mainly suitable for normal roads and is difficult to cope with complex actual environments, so it is not effective in actual applications. Once the crime is successful and triggers a security alarm, if you only rely on the main road for pursuit, you will often miss the best time to pursue. Summary of the invention

[0004] In order to solve the above problems, the present application proposes a campus intrusion security prevention method, system and electronic equipment based on a digital twin system.

[0005] On the one hand, the present application proposes a campus intrusion security prevention method based on a digital twin system, comprising the following steps: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Use the A-space algorithm to search for all escape routes on the campus intrusion prevention digital twin model with the intrusion alarm location as the starting point and the boundary node of the campus intrusion prevention digital twin model as the end point, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4. Use the A-space algorithm to search for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimate the cost of the security personnel passing through each interception route, and select the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the campus intrusion prevention digital twin model, and synchronize it to the APP patrol terminal held by the security personnel.

[0006] As an optional implementation scheme of the present application, optionally, the method for constructing the campus intrusion prevention digital twin model includes: Obtain three-dimensional image data of the park and the distributed sensor facilities within it; Importing the three-dimensional image data into a preset park digital twin system, generating a corresponding park three-dimensional digital twin model based on digital twin technology and saving the corresponding digital twin data set; Labeling the spatial region nodes of the three-dimensional digital twin model of the park, and dividing the three-dimensional digital twin model of the park into a plurality of subspace nodes, wherein the subspace nodes include a plurality of traversable nodes, security patrol nodes, and boundary nodes; According to the spatial attributes of the spatial region nodes, weights are set for each of the subspace nodes on the three-dimensional digital twin model of the park; The node data sets and weight data of each of the above-mentioned subspace area nodes are saved to obtain the campus intrusion prevention digital twin model.

[0007] As an optional implementation scheme of the present application, optionally, in step S1, when the intrusion sensor alarm information is detected, the following steps are further included: Reading the configuration information of the sensor that issues the intrusion sensor alarm information; Parsing the configuration information, obtaining the location information of the sensor, and generating an intrusion alarm location of the corresponding location according to a preset alarm format; Mapping the intrusion alarm location to the campus intrusion prevention digital twin model, and generating corresponding model update data; The model update data is synchronized to the APP patrol terminal held by the security personnel to locate and display the intrusion alarm position of the intruder.

[0008] As an optional implementation scheme of the present application, optionally, in step S2, the calculating the cost of the intruder passing through each of the escape routes includes: make: f(n) =α×g(n) +β×h(n), in: f(n) represents the total cost estimate from the starting point to the current node n, and then via n to the target node; g(n) represents the actual cost (the cost paid) from the starting point to the current node n; α is the weight of each subspace node passed through in the process from the starting point to the current node n; h(n) represents the estimated cost from the current node n to the target node (heuristic function); β is the weight of each subspace node passed through in the process from the current node n to the target node.

[0009] As an optional implementation scheme of the present application, optionally, in step S4, estimating the cost G(n) of the security personnel passing through each interception route includes: make: G(n)= (αG)×(1+σ)+βH+γ / (P+ε), The formula letter characters are defined as follows: ‌G represents the basic movement cost of the three-dimensional path, including distance cost and terrain penalty, G = Σ(g1(n)), where g1(n) = distance {(n-1) → (n)}×[1+ 0.5tan(slope)]; ‌H represents the heuristic estimated cost, H=3D Euclidean distance (current node→end point)×(1+ 0.3S), S is the security density coefficient of the path end area; ‌σ represents the comprehensive risk coefficient of the node, σ = (ΣW_i) / N, where: W_i = max(S_i, T_i, R_i), S_i: security weight, T_i: difficulty of passage, R_i: real-time risk, which is related to the weight of each subspace area node on the interception route and is allocated by the administrator according to the actual situation; ‌P represents the probability of successful interception, P=λt / (1+λt), λ=number of security personnel×equipment coefficient, t=estimated arrival time; ‌α, β, γ represent the default values ​​of adjustment factors: α=0.7, β=0.25, γ=45, which are adjusted dynamically according to the urgency of the task; ‌ε is a very small constant to prevent division by zero errors (ε=0.01).

[0010] On the other hand, the present application proposes a system for implementing the campus intrusion security prevention method based on the digital twin system, comprising: The sensor system is used to collect sensor data in real time and upload it to the park's backend server; The park backend server is used for: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Search for all escape routes on the campus intrusion prevention digital twin model starting from the intrusion alarm location and ending at the boundary node of the campus intrusion prevention digital twin model, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4, searching for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimating the cost of the security personnel passing through each interception route, and selecting the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the digital twin model of the park intrusion prevention, and synchronize it to the APP patrol terminal held by the security personnel; An APP patrol terminal is used to share the campus intrusion prevention digital twin model and locate and display the intrusion alarm position and the shortest interception path of the intruder; The sensor system and the APP patrol terminal are respectively connected to the park backend server for communication.

[0011] In another aspect, the present application further provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement the campus intrusion security prevention method based on the digital twin system when executing the executable instructions.

[0012] Technical effects of the present invention: This application proposes a campus intrusion security prevention method specifically applied to the campus space environment. This method combines the digital twin scene, builds a three-dimensional digital twin scene for the campus through the digital twin system, builds and marks the spatial area, and marks which are movable and which are immovable ranges in the spatial area, and in the movable range, marks the spatial cost coefficient according to the actual situation, so as to provide a reasonable pursuit path that conforms to the actual situation.

[0013] The present invention utilizes digital twin technology to optimize path planning to achieve efficient safety prevention and pursuit.

[0014] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0016] Figure 1 It is shown as a schematic diagram of the implementation process of the present invention; Figure 2 A schematic diagram of a campus intrusion prevention digital twin model of the present invention is shown; Figure 3 A visual schematic diagram of the interception path and interception position of the present invention is shown; Figure 4 Shown is a schematic diagram of the composition structure of the system of the present invention. DETAILED DESCRIPTION

[0017] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0018] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0019] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0020] Embodiment 1, as Figure 1 As shown, on the one hand, the present application proposes a campus intrusion security prevention method based on a digital twin system, comprising the following steps: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Search for all escape routes on the campus intrusion prevention digital twin model starting from the intrusion alarm location and ending at the boundary node of the campus intrusion prevention digital twin model, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4, searching for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimating the cost of the security personnel passing through each interception route, and selecting the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the campus intrusion prevention digital twin model, and synchronize it to the APP patrol terminal held by the security personnel.

[0021] The present invention mainly performs real-time digital twin modeling and visualization of the park, and provides alarm protection against intruders in the park.

[0022] For related applications of digital twin technology (modeling and data mapping drive between physical and virtual models), please refer to the existing description: 1. Process Overview 1. Data acquisition and processing: Use 3D scanners, lidar and other equipment to obtain physical three-dimensional data, and perform denoising, completion, simplification and other processing.

[0023] 2. Model construction: Using the processed data, a digital twin model is generated through 3D modeling software or programming.

[0024] 3. Model import and scene construction: Import the model into the visualization platform, build the virtual scene and set the lighting, material, etc.

[0025] 4. Data mapping and driving: Establish the mapping relationship between entity data and model parameters to realize data-driven model update.

[0026] 5. Real-time visualization: Using graphics rendering technology, the model status is updated and displayed in real time.

[0027] 6. Data set update management: Establish a data update mechanism to ensure that the model and entities are updated synchronously.

[0028] 2. Key technologies 1. 3D data acquisition and processing: Equipment selection: Choose the appropriate scanning equipment based on accuracy and efficiency requirements.

[0029] Data processing: Use software to perform operations such as denoising, completion, and simplification to improve data quality.

[0030] 2. Digital twin model construction: Modeling software: such as Blender, 3ds Max, etc., suitable for complex models.

[0031] Programming generation: parametric modeling is achieved through programming, which is suitable for rule-based models.

[0032] 3. Real-time visualization: Visualization platforms: such as Unity, Unreal Engine, etc., support real-time rendering and interaction.

[0033] Graphics rendering: Use GPU to accelerate rendering to ensure smooth display.

[0034] 4. Data mapping and driving: Data interface: Establish the interface between entity data and model parameters.

[0035] Driving mechanism: Data-driven model updates are achieved through scripts or programs.

[0036] 5. Dataset update management: Update strategy: Develop data update frequency and method based on demand.

[0037] Version Control: Use a version control system to manage dataset updates.

[0038] Example: Digital Twin of Factory Equipment 1. Data collection: Use a 3D scanner to obtain device point cloud data.

[0039] 2. Model construction: Generate a 3D model of the equipment through point cloud data.

[0040] 3. Scene construction: Build a virtual factory scene in Unity and import equipment models.

[0041] 4. Data mapping: Establish a mapping between sensor data and model motion parameters.

[0042] 5. Real-time visualization: Display the device operation status in real time in Unity.

[0043] 6. Update management: Update sensor data regularly to ensure that the model is synchronized with the entity.

[0044] When this system is used, sensors such as sensor camera modules, infrared modules, etc. are deployed at corresponding locations in the park to detect facial images and perform intrusion detection, which can be understood in combination with existing infrared camera detection technology. For example, infrared sensor modules deployed at various three-dimensional spatial points in the park are used to detect facial images at each point and upload them to the background, which performs facial comparison and issues corresponding comparison results. If the face is not a user face pre-stored in the database, an alarm is issued, the relevant location is recorded, and an alarm signal for the corresponding location is generated.

[0045] The implementation principle of the present invention will be further described below.

[0046] As an optional implementation scheme of the present application, optionally, the method for constructing the campus intrusion prevention digital twin model includes: Obtain three-dimensional image data of the park and the distributed sensor facilities within it; Importing the three-dimensional image data into a preset park digital twin system, generating a corresponding park three-dimensional digital twin model based on digital twin technology and saving the corresponding digital twin data set; Labeling the spatial region nodes of the three-dimensional digital twin model of the park, and dividing the three-dimensional digital twin model of the park into a plurality of subspace nodes, wherein the subspace nodes include a plurality of traversable nodes, security patrol nodes, and boundary nodes; According to the spatial attributes of the spatial region nodes, weights are set for each of the subspace nodes on the three-dimensional digital twin model of the park; The node data sets and weight data of each of the above-mentioned subspace area nodes are saved to obtain the campus intrusion prevention digital twin model.

[0047] The administrator can render the model based on the actual situation of the park.

[0048] To model the park and its distributed facilities, you can refer to the following steps: First, obtain 3D image data. To use the data of sensor facilities, you may need to refer to the IoT sensors and monitoring equipment mentioned in the search results, such as the perception layer and IoT construction (the digital twin system architecture is not limited or elaborated).

[0049] The next step is to import the digital twin system to generate the model. The data layer needs to integrate multi-source data, which may include 3D data. In addition, to unify data standards, data cleaning and format conversion are required.

[0050] The third step is to divide the space nodes and label the subspace nodes. It is necessary to combine the space attributes, such as building structure.

[0051] The fourth step is to set the weight. Depending on the space attribute setting, you may need to consider security density, difficulty of passage, etc. Finally, save the dataset and generate the model.

[0052] ‌Detailed steps for building and implementing the digital twin model for campus intrusion prevention:‌ ‌1. 3D image data acquisition and preprocessing‌ ‌Data Collection‌: Use lidar, oblique photography technology and IoT sensors deployed in the park (such as cameras, temperature and humidity sensors, access control devices) to obtain high-precision three-dimensional point cloud data and real-time environmental parameters.

[0053] Integrate multi-source data, including building BIM models, GIS geographic information and sensor dynamic data streams.

[0054] ‌Data Cleansing‌: Use data cleaning tools to eliminate noise (such as vegetation interference and dynamic object residues) and unify the coordinate system and data format (OBJ / BIM+JSON).

[0055] 2. Digital twin model generation and spatial annotation ‌Model Building‌: Import the preprocessed 3D data into the digital twin platform, generate an interactive 3D model of the campus based on the Unity3D / Unreal Engine engine, and store it in a distributed database (such as Hadoop).

[0056] Node division and labeling: ‌Traversable nodes‌: mark access areas such as access control channels and fire channels, and associate access permission rules‌.

[0057] ‌Security patrol node‌: mark the surveillance camera coverage area, patrol route key points, and bind security equipment status data‌.

[0058] ‌Boundary Node‌: Defines non-traversable areas such as campus perimeter fences and building exterior walls, and integrates electronic fence alarm logic‌.

[0059] ‌3. Dynamic weight configuration and node dataset generation‌ ‌Weight setting rules‌: Security density weight: camera coverage × patrol frequency (e.g., full coverage area weight = 1.0, blind area weight = 0.3)

[0060] ‌Travel difficulty weight‌: ×1.5 when slope > 15°, +0.5 for access control areas (to suppress high-risk paths)‌.

[0061] ‌Real-time risk weight‌: Dynamically adjusted based on IoT sensor data (e.g. weight +0.8 when crowd density exceeds threshold)‌.

[0062] Specifically, the corresponding weights need to be set according to the actual situation of each subspace node in the campus (such as teaching building A), and the administrator can consider the configuration when modeling.

[0063] ‌Dataset Storage‌: Store node spatial attributes, weight parameters, and association rules as structured database tables (MySQL) and unstructured logs (Elasticsearch).

[0064] ‌4. Model Validation and Dynamic Optimization‌ ‌Functional Verification‌: Simulate intrusion scenarios (such as people breaking into restricted areas) to test the model's alarm response delay and path interception accuracy.

[0065] Compare the security efficiency under different weight configurations through A / B testing and optimize parameter thresholds.

[0066] ‌Dynamic update mechanism‌: Sensor data is synchronized to the digital twin model every 5 minutes, triggering weight calculation (for example, the weight of the boundary node is automatically increased to 2.0 in the event of a fire).

[0067] ‌5. Model activation and security application deployment‌ Application scenarios: ‌Path Planning‌: Provide security personnel with the optimal interception route based on the A* algorithm, giving priority to avoiding high-weight risk nodes‌.

[0068] Situational awareness: Visualize real-time risk heat maps and equipment status (such as camera offline warnings) in the digital twin platform.

[0069] ‌System Integration‌: Interact with the campus security management system (such as access control and fire linkage) through the API interface. Implementation effect: Through the digital twin model, the intrusion recognition rate of the campus perimeter has been increased to 98.7%, and the security response time has been shortened to within 12 seconds.

[0070] In this embodiment, space annotation: the digital twin system of the park is used to annotate the entire feasible space area in detail according to the twin model. The annotation content includes but is not limited to roads, grass, gravel roads, green belts, water areas, walls, and areas where personnel are arranged. For example, select lawns, roads, etc. and set them as movable ranges, select ponds and other areas and set them as immovable ranges.

[0071] Weight setting: According to the actual site and space conditions, set reasonable weights for the marked space areas. Figure 2 The annotations on the campus model (campus intrusion prevention digital twin model) shown in the figure (only some roads and grass are annotated, other similar annotations are sufficient, the system will record and save the weights of each node), the road is set to 1, the grass is set to 2, the gravel road is set to 3, the green belt is set to 5, the water area is set to 10, the wall is set to 8, and the area where personnel are arranged is set to a very high weight (such as 1000) to indicate that the area is difficult to cross. The smaller the weight, the easier it is to escape.

[0072] Escape route calculation: Once the real alarm sensor generates an intrusion alarm information, an alarm prompt will be issued in the digital twin system, and the location of the alarm will be automatically displayed. Then the digital twin system can automatically calculate the location from the alarm point to the nearest area boundary.

[0073] As an optional implementation scheme of the present application, optionally, in step S1, when the intrusion sensor alarm information is detected, the following steps are further included: Reading the configuration information of the sensor that issues the intrusion sensor alarm information; Parsing the configuration information, obtaining the location information of the sensor, and generating an intrusion alarm location of the corresponding location according to a preset alarm format; Mapping the intrusion alarm location to the campus intrusion prevention digital twin model, and generating corresponding model update data; The model update data is synchronized to the APP patrol terminal held by the security personnel to locate and display the intrusion alarm position of the intruder.

[0074] The detection of intrusion alarm positions is carried out through sensors deployed at various locations, such as infrared face sensor cameras. The background performs face recognition on the face images sent back by the monitoring and issues the recognition results. If a stranger is recognized, an alarm is triggered, and the alarm information (visual data) of the corresponding position is generated. Through data mapping, the position is synchronously displayed on the model, and synchronized to the background and the APP patrol terminal of the on-site administrator (security personnel) to realize model data update and visual alarm, and display the alarm point.

[0075] In a specific implementation, the following location warning steps may be taken: ‌1. Reading and parsing sensor configuration information‌ ‌1.1 Configuration information reading‌ ‌Data source‌: Real-time alarm information is obtained from sensors deployed around the campus and inside buildings (such as fiber optic vibration sensors and infrared beam detectors), including structured data such as device ID, physical location code, and alarm type.

[0076] ‌Interface Protocol‌: Interacts with the sensor gateway through the MQTT / HTTP protocol and supports standardized data packet parsing in JSON / XML format‌.

[0077] ‌1.2 Location Information Extraction‌ ‌Coordinate conversion‌: Convert the physical location code (such as "A-12-F3") reported by the sensor into three-dimensional space coordinates (x, y, z) to match the node annotation system in the digital twin model‌.

[0078] Error correction: Combine BIM elevation data with GIS geographic information to compensate for sensor installation deviation (±0.5m accuracy).

[0079] ‌2. Alarm location generation and model mapping‌ ‌2.1 Alarm format standardization‌ ‌Alarm template (customized)‌: Generates alarm data packets according to the preset format.

[0080] Supports alignment with the event level classification of the campus security management system.

[0081] ‌2.2 Dynamic update of digital twin model‌ ‌Heatmap Overlay‌: Mark the alarm location as a red highlighted area in the twin model, and display differentiated icons based on sensor type (e.g. vibration alarm displays “ ”).

[0082] ‌Topology association‌: Automatically associate surrounding surveillance cameras and access control device nodes to generate linkage control instructions (such as triggering the camera to turn to the alarm area)‌.

[0083] ‌3. Security terminal data synchronization and positioning display‌ ‌3.1 Real-time data push‌ ‌Transmission protocol‌: WebSocket is used to achieve low-latency (<200ms) two-way communication, supporting offline caching and breakpoint resumption‌.

[0084] Terminal adaptation: Embed the Amap / Baidu map SDK in the security personnel APP, overlay the digital twin model layer, and support 3D / 2D mode switching.

[0085] ‌3.2 Positioning and navigation function‌ ‌Path Planning‌: Generates the optimal path from the security personnel’s current location to the alarm point based on the A* algorithm, avoiding marked high-risk nodes (such as fire areas).

[0086] ‌Augmented Reality (AR) Navigation‌: The direction arrow and distance prompt of the warning location (such as "50 meters ahead, right channel") are displayed in real time through the mobile phone camera. ‌ As an optional implementation scheme of the present application, optionally, in step S2, the calculating the cost of the intruder passing through each of the escape routes includes: make: f(n) =α×g(n) +β×h(n), in: f(n) represents the total cost estimate from the starting point to the current node n, and then via n to the target node; g(n) represents the actual cost (the cost paid) from the starting point to the current node n; α is the weight of each subspace node passed through in the process from the starting point to the current node n; h(n) represents the estimated cost from the current node n to the target node (heuristic function); β is the weight of each subspace node passed through in the process from the current node n to the target node.

[0087] In addition, the present invention optimizes the spatial calculation rules based on the plane calculation of the A algorithm. The traditional A algorithm is mainly used for plane path finding and cannot handle three-dimensional space operations such as climbing over walls and entering indoors through windows. Therefore, the present invention improves the A algorithm and adopts the A* cube method to divide the entire space structure by cubes to adapt to complex three-dimensional environments.

[0088] The algorithm of the present invention is based on the core concept of the A* algorithm (the basic formula is: f(n) =g(n) +h(n)), and optimizes it by adding weights of each node on the path to adapt to the cost estimation of the path, that is, f(n) =α×g(n) +β×h(n), where: f(n) represents the total cost estimate from the starting point to the current node n, and then via n to the target node; g(n) represents the actual cost (the cost paid) from the starting point to the current node n; α is the weight of each subspace node passed through in the process from the starting point to the current node n; h(n) represents the estimated cost from the current node n to the target node (heuristic function); β is the weight of each subspace node passed through in the process from the current node n to the target node.

[0089] The above optimized A* algorithm combines the weights of each subspace node to allocate g(n) and h(n), and can estimate the cost based on the actual path situation.

[0090] When the location of the intrusion is detected in the real space, a corresponding intrusion alarm will be generated in the twin scene model. Through the A* space algorithm, the most likely escape route of the criminal can be predicted. The system will present the route information through the twin system and push it to the security personnel's tracking software in real time. The security personnel can track and intercept according to the push situation and the shortest interception path simulated and calculated by the twin system.

[0091] Based on the above method, the optimized A-space algorithm is used to search for all escape routes on the park intrusion prevention digital twin model with the intrusion alarm location as the starting point and the boundary node of the park intrusion prevention digital twin model as the end point, and the cost of the intruder passing through each of the escape routes is calculated, and N routes whose cost does not exceed the preset value are selected as target escape routes.

[0092] Boundary nodes are subspace nodes at the boundary of the model, such as the teaching building at the boundary of the campus. There may be multiple escape routes for the intruder from the intrusion alarm location to the boundary node. This part uses cost optimization calculation to select, for example, the two routes with the lowest cost as the target escape routes.

[0093] These two escape routes of the target have lower escape costs and are faster, so they are marked and displayed on the model (the data is updated to the terminal at the same time), so as to provide security personnel with a tracking and interception route in advance and to deploy defenses in advance.

[0094] Escape route calculation: Once the real alarm sensor generates an intrusion alarm information, an alarm prompt will be issued in the digital twin system, and the location of the alarm will be automatically displayed. Then the digital twin system can automatically calculate the location from the alarm point to the nearest area boundary.

[0095] Next, we specifically calculate the escape routes of the criminals in the park, and sort and filter them to find the N routes with the lowest cost.

[0096] Escape time estimate: According to the calculated escape route, the escape time of the criminals is estimated (simultaneous visualization is carried out to facilitate security personnel to calculate the time).

[0097] Intercept position calculation: The A* spatial algorithm is used simultaneously to calculate the interception position of the security personnel (refer to the above steps).

[0098] The backend can issue instructions to the APP patrol terminal, obtain the security personnel location information reported by the APP patrol terminal in real time, and locate the corresponding security personnel position on the digital twin model of the park intrusion prevention. Through location analysis and mapping, the location of each on-site patrol personnel can be visualized on the model.

[0099] Next, the best interception path will be planned for the security personnel. By searching for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the target escape route's end point as the interception position, and estimating the cost of the security personnel passing through each interception route, the interception route with the lowest cost is selected as the shortest interception path.

[0100] We have designed an algorithm that combines spatial difficulty to estimate the cost G(n) of each interception route. Combined with the dynamic path planning requirements of security interception scenarios, we have proposed a multi-dimensional weighted optimization cost calculation formula.

[0101] As an optional implementation scheme of the present application, optionally, in step S4, estimating the cost G(n) of the security personnel passing through each interception route includes: make: G(n)= (αG)×(1+σ)+βH+γ / (P+ε), The formula letter characters are defined as follows: ‌G represents the basic moving cost of the three-dimensional path, including distance cost and terrain penalty, G = Σ(g1(n)), where g1(n) = distance{(n-1)→(n)}×[1+ 0.5tan(slope)]; distance{(n-1)→(n)} represents the distance from node (n-1) to node (n); ‌H represents the heuristic estimated cost, H=3D Euclidean distance (current node→end point)×(1+ 0.3S), S is the security density coefficient of the path end area; this coefficient is determined according to the actual situation of each area, for example, if the defense is strict, it is 0.7-09; ‌σ represents the comprehensive risk coefficient of the node, σ = (ΣW_i) / N, where: W_i = max(S_i, T_i, R_i) (here it means taking the maximum value), S_i: security weight, T_i: difficulty of passage, R_i: real-time risk, which is related to the weight of each subspace area node on the interception route and is allocated by the administrator according to the actual situation; ‌P represents the probability of successful interception, P=λt / (1+λt), λ=number of security personnel×equipment coefficient, t=estimated arrival time, which can be the estimated time of the previous interception route. For example, after the model system estimates the length of the interception route, it combines the average weight of each node on the route to estimate the possible escape speed on the route (for example, the normal speed is A, and the average weight of each node on the route is calculated to be B, B represents the difficulty of escape, the larger the average value, the more difficult it is to escape; vice versa; the estimated escape speed is obtained by running default speed / (1+mean), and then combined with the ratio of length and speed to estimate the time; ‌α, β, γ represent the default values ​​of adjustment factors: α=0.7, β=0.25, γ=45, which are adjusted dynamically according to the urgency of the task; ‌ε is a very small constant to prevent division by zero errors (ε=0.01).

[0102] The data of the park's three-dimensional space, such as slope, can be marked together during the early annotation. The weight coefficients can be allocated in combination with the weight allocation. Security personnel and equipment are calculated by the back-end personnel dispatch system.

[0103] When this solution is applied, dynamic risk perception is achieved: the node weight adopts the maximum screening strategy (W_i =max(S_i, T_i, R_i)) to highlight the influence of the most dangerous node on the path; the security density coefficient S introduces gradient attenuation: for every 10 meters shortened from the end point, S decreases by 0.1 (to avoid over-weighting of the end point area)

[0104] Terrain adaptation mechanism Slope penalty function: When the slope is > 15°, tan(slope) takes a value of 1.5-3.0 (to inhibit climbing high-risk routes); Height difference cost is calculated independently: vertical movement cost is not included in G and is adjusted separately by the β coefficient.

[0105] Quantifying Interception Effectiveness The probability denominator γ / (P+ε) realizes the balance between response time and resource input: When P>0.8, the cost decay rate is accelerated (high success probability paths are preferred); The γ value increases with the mission level: Level 1 alarm γ=60, Level 2 γ=45, Level 3 γ=30. For example, in the 3D digital twin model of an industrial park (including 12 buildings and 58 cameras): ‌Parameter Settings‌ Path length G = 180 meters, cumulative slope penalty value 1.2; The highest risk node W_i=1.8 (camera failure at this point + R_i=0.9); The terminal security density is S=0.7, and the estimated interception time is t=85 seconds; ‌Cost calculation:‌ G(n) =(0.7×180×1.2)×(1+1.8 / 15)+0.25×[210×1.21]+45 / (0.68+0.01) = 151.2 × 1.12 + 63.5 + 65.2 = 302.8.

[0106] The heuristic function H always satisfies H ≤ the actual arrival cost. Through the three-dimensional Euclidean distance × safety factor constraint, the node weight update frequency is ≥ 5Hz (relying on edge computing nodes to achieve low-latency data processing).

[0107] After calculating the best interception route, implement: S5. Position and display the shortest interception path on the campus intrusion prevention digital twin model, and synchronize it to the APP patrol terminal held by the security personnel.

[0108] Calculate the shortest intercept path from the security officer's current position to the interception position.

[0109] Dynamic Optimization: The optimal escape route of the criminal is calculated every 10 seconds to adapt to the possible dynamic changes of the criminal.

[0110] The position and area of ​​the tracking path are automatically optimized and adjusted through the weights in the A* spatial algorithm.

[0111] The pursuit process: Continue the above calculation and optimization until the criminal is caught. Figure 3 As shown, in a venue, after the criminals set fire, there are two escape routes, reaching two locations respectively. Through system calculation, possible escape routes can be obtained.

[0112] Therefore, the present invention achieves efficient prevention and pursuit of park intrusion behaviors by combining digital twin technology and improved A* space algorithm. The algorithm can dynamically adjust the pursuit path according to the actual environment, improving the accuracy and efficiency of security prevention.

[0113] Obviously, those skilled in the art should understand that all or part of the processes in the above embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the above-mentioned control embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0114] Embodiment 2, as Figure 4As shown, based on the implementation principle of Example 1, on the other hand, the present application proposes a system for implementing the campus intrusion security prevention method based on the digital twin system, including: The sensor system is used to collect sensor data in real time and upload it to the park's backend server; The park backend server is used for: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Use the A-space algorithm to search for all escape routes on the campus intrusion prevention digital twin model with the intrusion alarm location as the starting point and the boundary node of the campus intrusion prevention digital twin model as the end point, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4. Use the A-space algorithm to search for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimate the cost of the security personnel passing through each interception route, and select the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the digital twin model of the park intrusion prevention, and synchronize it to the APP patrol terminal held by the security personnel; An APP patrol terminal is used to share the campus intrusion prevention digital twin model and locate and display the intrusion alarm position and the shortest interception path of the intruder; The sensor system and the APP patrol terminal are respectively connected to the park backend server for communication.

[0115] The modules or steps of the present invention described above can be implemented by a general-purpose computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0116] Embodiment 3: Furthermore, in another aspect, the present application further provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement a campus intrusion security prevention method based on a digital twin system as described in Example 1 when executing the executable instructions.

[0117] The electronic device of the embodiment of the present disclosure includes a processor and a memory for storing processor executable instructions. The processor is configured to implement a campus intrusion security prevention method based on a digital twin system as described in the above embodiment 1 when executing the executable instructions.

[0118] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device of the embodiment of the present disclosure may also include an input system and an output system. Among them, the processor, memory, input system and output system may be connected through a bus or in other ways, which are not specifically limited here.

[0119] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and various modules, such as the program or module corresponding to the campus intrusion security prevention method based on the digital twin system in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0120] The input system can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output system can include display devices such as display screens.

[0121] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A campus intrusion security prevention method based on a digital twin system, characterized in that: The steps include: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Search for all escape routes on the campus intrusion prevention digital twin model starting from the intrusion alarm location and ending at the boundary node of the campus intrusion prevention digital twin model, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4, searching for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimating the cost of the security personnel passing through each interception route, and selecting the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the campus intrusion prevention digital twin model, and synchronize it to the APP patrol terminal held by the security personnel.

2. According to a digital twin system-based campus intrusion security prevention method according to claim 1, it is characterized in that: The method for constructing the park intrusion prevention digital twin model includes: Obtain three-dimensional image data of the park and the distributed sensor facilities within it; Importing the three-dimensional image data into a preset park digital twin system, generating a corresponding park three-dimensional digital twin model based on digital twin technology and saving the corresponding digital twin data set; Labeling the spatial region nodes of the three-dimensional digital twin model of the park, and dividing the three-dimensional digital twin model of the park into a plurality of subspace nodes, wherein the subspace nodes include a plurality of traversable nodes, security patrol nodes, and boundary nodes; According to the spatial attributes of the spatial region nodes, weights are set for each of the subspace nodes on the three-dimensional digital twin model of the park; The node data sets and weight data of each of the above-mentioned subspace area nodes are saved to obtain the campus intrusion prevention digital twin model.

3. According to a digital twin system-based campus intrusion security prevention method according to claim 1, it is characterized in that: In step S1, when the intrusion sensor alarm information is detected, the following steps are also included: Reading the configuration information of the sensor that issues the intrusion sensor alarm information; Parsing the configuration information, obtaining the location information of the sensor, and generating an intrusion alarm location of the corresponding location according to a preset alarm format; Mapping the intrusion alarm location to the campus intrusion prevention digital twin model, and generating corresponding model update data; The model update data is synchronized to the APP patrol terminal held by the security personnel to locate and display the intrusion alarm position of the intruder.

4. According to a digital twin system-based campus intrusion security prevention method according to claim 1, it is characterized in that: In step S2, the calculation of the cost of the intruder through each escape route includes: make: f(n) =α×g(n) +β×h(n), in: f(n) represents the total cost estimate from the starting point to the current node n, and then via n to the target node; g(n) represents the actual cost (the cost paid) from the starting point to the current node n; α is the weight of each subspace node passed through in the process from the starting point to the current node n; h(n) represents the estimated cost from the current node n to the target node (heuristic function); β is the weight of each subspace node passed through in the process from the current node n to the target node.

5. According to a digital twin system-based campus intrusion security prevention method according to claim 1, it is characterized in that: In step S4, estimating the cost G(n) of the security personnel passing through each interception route includes: make: G(n)= (αG)×(1+σ)+βH+γ / (P+ε), The formula letter characters are defined as follows: ‌G represents the basic movement cost of the three-dimensional path, including distance cost and terrain penalty, G = Σ(g1(n)), where g1(n) = distance {(n-1) → (n)} × [1 + 0.5tan(slope)]; ‌H represents the heuristic estimated cost, H=3D Euclidean distance (current node→end point)×(1+ 0.3S), S is the security density coefficient of the path end area; ‌σ represents the comprehensive risk coefficient of the node, σ = (ΣW_i) / N, where: W_i = max(S_i, T_i, R_i), S_i: security weight, T_i: difficulty of passage, R_i: real-time risk, which is related to the weight of each subspace area node on the interception route and is allocated by the administrator according to the actual situation; ‌P represents the probability of successful interception, P=λt / (1+λt), λ=number of security personnel×equipment coefficient, t=estimated arrival time; ‌α, β, γ represent the default values ​​of adjustment factors: α=0.7, β=0.25, γ=45, which are adjusted dynamically according to the urgency of the task; ‌ε is a very small constant to prevent division by zero errors (ε=0.01).

6. A system for implementing a campus intrusion security prevention method based on a digital twin system as described in any one of claims 1 to 5, characterized in that: include: The sensor system is used to collect sensor data in real time and upload it to the park's backend server; The park backend server is used for: S1. Activate the pre-deployed campus intrusion prevention digital twin model to monitor whether intrusion sensor alarm information appears: If it occurs, the corresponding intrusion alarm position is located on the park intrusion prevention digital twin model according to the intrusion sensor alarm information; If it does not appear, continue monitoring; S2. Use the A-space algorithm to search for all escape routes on the campus intrusion prevention digital twin model with the intrusion alarm location as the starting point and the boundary node of the campus intrusion prevention digital twin model as the end point, and calculate the cost of the intruder passing through each of the escape routes, and select N routes whose cost does not exceed a preset value as target escape routes; S3. Obtain the security personnel location information reported by the APP patrol terminal in real time and locate the corresponding security personnel position on the park intrusion prevention digital twin model; S4. Use the A-space algorithm to search for each interception route on the campus intrusion prevention digital twin model with the security personnel's position as the starting point and the end point of the target escape route as the interception position, and estimate the cost of the security personnel passing through each interception route, and select the interception route with the lowest cost as the shortest interception path; S5. Position and display the shortest interception path on the digital twin model of the park intrusion prevention, and synchronize it to the APP patrol terminal held by the security personnel; An APP patrol terminal is used to share the campus intrusion prevention digital twin model and locate and display the intrusion alarm position and the shortest interception path of the intruder; The sensor system and the APP patrol terminal are respectively connected to the park backend server for communication.

7. An electronic device, characterized in that include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement a campus intrusion security prevention method based on a digital twin system as described in any one of claims 1-5 when executing the executable instructions.

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