A method for visualizing the state of a video monitoring device in a smart park digital twin scenario

Abstract

The application provides a video monitoring device state visualization implementation method in a smart park digital twin scene, which accurately solves the pain point of traditional park device identification confusion by assigning a unique identifier of embedded type and area information to the camera, sensor and edge device, presetting a sensor multi-protocol analysis rule, laying a foundation for subsequent binding and analysis, establishing a three-binding relationship and a double-synchronization mechanism, forming a hierarchical management and control architecture, solving the problem of loose device association, ensuring data collection accuracy, resolving multi-source data into a standard format at the edge, relieving cloud pressure, solving the pain points of centralized data processing, high transmission delay and slow abnormal response, building a lightweight digital twin scene at the edge, building an overall 3D scene at the cloud and realizing an abnormal identification and alarm closed loop, respectively solving the problems of single traditional visualization dimension, static disconnection of twins, cumbersome on-site operation and lack of overall management and control, and comprehensively supporting efficient management and control of smart parks.

Description

Technical Field

[0001] This invention relates to the intersection of digital twin and smart park technologies, and in particular to a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. Background Technology

[0002] A smart park integrates technologies such as the Internet of Things, big data, and artificial intelligence to intelligently manage and optimize all aspects of the park, including security, energy consumption, operation and maintenance, office work, and services. This results in a modern park model characterized by efficient operation, low carbon emissions, and convenient services. Currently, smart park video surveillance systems face the following core problems:

[0003] Limited visualization dimensions: Traditional monitoring systems often display device status using 2D lists or flat maps, which cannot intuitively present the physical location of cameras, the coverage area of ​​the viewpoint, and the linkage between devices. Maintenance personnel need to repeatedly compare the physical scene with the data interface, which is inefficient.

[0004] Severe data silos: Cameras, temperature and humidity sensors, human body sensors, and edge devices in the park often come from different manufacturers, and the data transmission protocols include RTSP, MQTT, and HTTP. The inconsistent formats make it impossible for data to be shared, making it difficult to achieve a comprehensive assessment of the device status.

[0005] Insufficient real-time performance and stability: If the existing system relies entirely on cloud processing for data, network latency will cause delays in device status updates, such as camera angle adjustments and offline alarms; if it relies only on the edge, it cannot achieve unified monitoring and historical data backtracking at the park level.

[0006] Poor adaptability of digital twin scenarios: Some 3D monitoring solutions can only statically display device models and cannot dynamically bind the real-time status of the device, such as online / offline, recording status, and lens rotation angle, with the 3D model, resulting in a disconnect between the digital twin scenario and the actual device status. Summary of the Invention

[0007] This invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, in order to solve the problems mentioned in the background art.

[0008] A method for visualizing the status of video surveillance equipment in a smart park digital twin scenario includes:

[0009] S1: Assign unique camera identifiers to cameras in the smart park, assign unique sensor identifiers and resolution rules to sensors, and assign unique device identifiers and configure access information to edge devices;

[0010] S2: Establish the binding relationship between edge device, camera, and sensor, and set up a dual synchronization mechanism between the edge and the cloud based on the binding relationship;

[0011] S3: Acquire camera data and sensor data based on a dual synchronization mechanism, and parse the camera data and sensor data into the same format at the edge device to obtain standard data;

[0012] S4: Based on standard data, establish the mapping position of cameras in edge devices and digital twin scenarios, determine the three-dimensional coordinates and viewpoint parameters, and establish a lightweight digital twin scenario at the edge of the device;

[0013] S5: Build a complete digital twin 3D scene of the smart park in the cloud, and perform anomaly identification and alarm processing based on standard data.

[0014] Preferably, in step S1, assigning a unique camera identifier to the cameras within the smart park includes:

[0015] Define the core attributes of the camera, including device type, installation location, supported protocols, and maximum rotation angle, to obtain definition information;

[0016] The defined information is encoded according to the coding rules of park code-area code-device type code-serial number to obtain the unique identifier of the camera.

[0017] Preferably, in step S1, assigning a unique sensor identifier and resolution rules to the sensor includes:

[0018] The core attributes of the sensor, including sensor type, acquisition frequency, transmission protocol, and normal threshold range, are defined to obtain sensor definition information;

[0019] The sensor definition information is parsed according to the preset parsing template to obtain the parsed information;

[0020] The parsed information is encoded according to the coding rules of park code-region code-sensor type code-serial number to obtain the unique identifier of the sensor.

[0021] Preferably, in step S1, assigning a unique device identifier and configuration access information to the edge device includes:

[0022] Define the core attributes of edge devices, including device model, management area, maximum number of connected devices, and data cache capacity, to obtain edge device definition information;

[0023] The edge device definition information is encoded according to the coding rule of park code-edge device type code-serial number to obtain a unique device identifier;

[0024] Information about edge devices is recorded and stored in a cloud database.

[0025] Preferably, in step S2, establishing the binding relationship between the edge device, camera, and sensor includes:

[0026] Edge devices, cameras, and sensors undergo triple authentication using hardware fingerprints, logical identity, and location fingerprints. Based on the authentication results, device identity identifiers are generated and stored in the cloud.

[0027] The similarity of edge devices, cameras, and sensors is calculated based on the cosine similarity algorithm. Devices with similarity greater than the preset similarity are added to the binding candidate pool, while devices with similarity less than or equal to the preset similarity are removed.

[0028] The smart park is divided into regional grids, and a unique code is configured for each grid. The unique code is then matched with the devices in the candidate pool. Based on the matching results, the basic binding relationship is obtained.

[0029] The smart park is divided into security zone, office zone and production zone, and binding rules are configured for each functional zone. The binding rules are deployed on the cloud alliance node based on smart contracts. When the characteristics of the binding candidate pool meet the binding rules, the basic binding relationship is automatically upgraded, and functional requirement information is added to obtain a strengthened binding relationship.

[0030] The primary edge device is obtained from the enhanced binding relationship, and other edge devices are used as backup edge devices. The primary edge device and backup edge devices are clustered using the K-means clustering algorithm with the host location and performance parameters as the cluster centers. The distance between the backup edge devices and the primary edge devices is determined based on the clustering results. The backup edge devices are prioritized according to the rule that the smaller the distance, the higher the priority. Each primary edge device is bound to two backup edge devices according to the priority. Based on the device binding relationship, the enhanced binding relationship is updated to obtain the final binding relationship.

[0031] Preferably, in step S2, the dual synchronization mechanism between the edge and the cloud based on the binding relationship includes:

[0032] A core decision matrix is ​​constructed based on six dimensions: network quality, device status, data importance, edge cache utilization, cloud load rate, and binding relationship change type.

[0033] The goal layer is to maximize synchronization efficiency and data consistency; the criterion layer is to allocate network overhead, synchronization latency, data integrity, system complexity, and fault tolerance according to a preset ratio; and the solution layer includes real-time synchronization, near real-time synchronization, batch synchronization, asynchronous synchronization, and breakpoint resume synchronization.

[0034] Based on the current data, determine the matrix value of the core decision matrix. Based on the matrix value, combined with the target layer and the criterion layer, determine the weight value of each synchronization scheme in the scheme layer, and select the synchronization scheme with the largest weight value as the target synchronization scheme.

[0035] After data synchronization based on the target synchronization scheme, the hash value of the binding relationship is calculated at the edge and the cloud respectively, a hash consistency check is established, and a vector clock is introduced to assign a vector to each binding relationship change event. The monotonicity of the vector is checked in the cloud to establish a timing consistency check. Pre-set business rules are configured in the smart contract and automatically checked after synchronization to establish a business rule check.

[0036] After data synchronization fails, a fault tolerance mechanism is established, consisting of a first-level fault tolerance mechanism with local caching and exponential backoff retries, a second-level fault tolerance mechanism with backup edge devices for synchronization, and a third-level fault tolerance mechanism with cloud consortium blockchain as a backup for recovery.

[0037] By integrating the synchronization scheme with the largest weight value as the target synchronization scheme, hash consistency verification, time sequence consistency verification, business rule verification, and fault tolerance mechanism, a dual synchronization mechanism for the edge and cloud is obtained.

[0038] Preferably, in step S3, camera data and sensor data are acquired based on a dual synchronization mechanism, and the camera data and sensor data are parsed into the same format at the edge device to obtain standard data, including:

[0039] The target parsing template is obtained by matching the protocol type based on camera data and sensor data from the parsing template library;

[0040] The camera data and sensor data are parsed based on the target parsing template to obtain standard data.

[0041] Preferably, in step S4, based on standard data, the mapping position of the camera in the edge device and the digital twin scene is established, the three-dimensional coordinates and viewpoint parameters are determined, and a lightweight digital twin scene is established at the edge of the device, including:

[0042] The edge device code, camera code, 3D coordinates, smart park coordinate system, and viewing angle parameters are obtained and matched. Based on the matching results, the 3D coordinates and viewing angle parameters of the camera are determined.

[0043] Write the camera data, 3D coordinates, and viewpoint parameters into a database in the cloud;

[0044] Relevant data is extracted from cloud databases to create lightweight digital twin scenarios at the device edge.

[0045] Preferably, in step S5, establishing a complete digital twin 3D scene of the smart park in the cloud includes:

[0046] Acquire standard data, smart park photography models, and third-party system data, perform coordinate alignment and standardization, and obtain target data;

[0047] Lightweight dynamic twin rendering, device model loading and assembly, and dynamic state binding are performed on the target data to obtain the overall digital twin 3D scene.

[0048] Preferably, in step S5, anomaly identification and alarm processing based on standard data includes:

[0049] Extract anomalous data from standard data and determine the scene data of the anomalous data within the overall digital twin 3D scene;

[0050] Alarm data is obtained by integrating the aforementioned abnormal data and scenario data;

[0051] Alarms are pushed and processed in the cloud based on the alarm data.

[0052] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0053] By assigning unique identifiers to cameras, sensors, and edge devices, pre-setting parsing rules for sensors, and configuring access information for edge devices, this system fundamentally addresses the pain points of traditional campus equipment identification problems such as chaotic data formats and difficulties in cross-device interoperability. The unique identifier embeds key information such as device type and region, enabling rapid location of the device's physical location without additional database queries. This solves the problems of meaningless identifiers and low identification efficiency in traditional systems, providing a precise foundation for subsequent binding and data parsing. Pre-setting multi-protocol parsing rules for sensors and defining data conversion logic in advance avoids data incompatibility issues caused by protocol and format differences between devices from different manufacturers, laying the foundation for unified parsing and reducing redundant development costs. Configuring fixed IP addresses, communication ports, and other access information for edge devices and registering them in the cloud database ensures that the cloud can actively access edge devices, solving the problems of lack of standardization in edge device access and difficulty in cloud management in traditional systems, and supporting the implementation of an edge-cloud collaborative architecture. By establishing a binding relationship between edge devices, cameras, and sensors, and a dual synchronization mechanism, the core solution addresses the pain points of traditional systems, such as loose device connections, insufficient stability of single-end architecture, and easy configuration failures. It clarifies the binding relationship between edge devices and terminal devices, forming a hierarchical architecture of edge device coordination and terminal device data collection, avoiding chaotic device management and ensuring the accuracy of data collection. Based on the dual synchronization mechanism, data is acquired and parsed into a standard format at the edge, solving the pain points of traditional systems, such as concentrated data processing pressure, high transmission latency, and slow anomaly response. It establishes device mapping positions, determines 3D coordinates and viewpoint parameters, and builds a lightweight digital twin scene at the edge, addressing the pain points of traditional single visualization dimensions, static disconnect between digital twins and traditional systems, and cumbersome on-site operation and maintenance. It builds an overall digital twin 3D scene in the cloud, realizing anomaly identification and alarm handling, addressing the pain points of traditional systems, such as lack of global control, inability to trace historical data, and lack of alarm closure.

[0054] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a flowchart illustrating a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, as described in an embodiment of the present invention.

[0058] Figure 2 This is a flowchart illustrating the process of assigning unique camera identifiers to cameras within a smart park, as described in this embodiment of the invention.

[0059] Figure 3 This is a flowchart illustrating the creation of a smart park's overall digital twin 3D scene in the cloud, as described in an embodiment of the present invention. Detailed Implementation

[0060] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0061] Example 1: This embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, such as... Figure 1 As shown, it includes:

[0062] S1: Assign unique camera identifiers to cameras in the smart park, assign unique sensor identifiers and resolution rules to sensors, and assign unique device identifiers and configure access information to edge devices;

[0063] S2: Establish the binding relationship between edge device, camera, and sensor, and set up a dual synchronization mechanism between the edge and the cloud based on the binding relationship;

[0064] S3: Acquire camera data and sensor data based on a dual synchronization mechanism, and parse the camera data and sensor data into the same format at the edge device to obtain standard data;

[0065] S4: Based on standard data, establish the mapping position of cameras in edge devices and digital twin scenarios, determine the three-dimensional coordinates and viewpoint parameters, and establish a lightweight digital twin scenario at the edge of the device;

[0066] S5: Build a complete digital twin 3D scene of the smart park in the cloud, and perform anomaly identification and alarm processing based on standard data.

[0067] In this embodiment, the sensors include temperature and humidity sensors, human body sensors, etc.

[0068] In this embodiment, a single edge device can support up to 32 cameras and 64 sensors.

[0069] In this embodiment, a dual synchronization mechanism is implemented at the edge and in the cloud: a cloud-based primary and edge-based secondary synchronization mechanism is adopted to solve the configuration failure problem when the network is interrupted and to ensure the stability of the device binding relationship.

[0070] The beneficial effects of the above design scheme are as follows: By assigning unique identifiers to cameras, sensors, and edge devices, pre-setting parsing rules for sensors, and configuring access information for edge devices, the core solution addresses the pain points of traditional campus equipment identification chaos, heterogeneous data formats, and difficulties in cross-device interoperability. The unique identifier embeds key information such as device type and region, enabling rapid location of the device's physical location without additional database queries, solving the problems of meaningless and inefficient traditional identifiers. This provides a precise foundation for subsequent binding and data parsing. Pre-setting multi-protocol parsing rules for sensors and defining data conversion logic in advance avoids data incompatibility issues caused by protocol and format differences between devices from different manufacturers, laying the foundation for unified parsing and reducing redundant development costs. Configuring fixed IP addresses, communication ports, and other access information for edge devices and registering them in the cloud database ensures that the cloud can actively access edge devices, solving the problems of lack of standard access for traditional edge devices and difficulties in cloud management, thus supporting edge-to-cloud communication. The collaborative architecture is implemented by establishing a binding relationship between edge devices, cameras, and sensors, and a dual synchronization mechanism. This addresses the pain points of traditional systems, such as loose device connections, insufficient stability of single-end architectures, and easy configuration failures. It clarifies the binding relationship between edge devices and terminal devices, forming a hierarchical architecture of edge device coordination and terminal device data collection, avoiding chaotic device management and ensuring the accuracy of data collection. Based on the dual synchronization mechanism, data is acquired and parsed into a standard format at the edge, solving the pain points of traditional systems, such as concentrated data processing pressure, high transmission latency, and slow anomaly response. It establishes device mapping positions, determines 3D coordinates and viewpoint parameters, and builds a lightweight digital twin scene at the edge, addressing the pain points of traditional single visualization dimensions, static disconnect between digital twins and traditional systems, and cumbersome on-site operation and maintenance. It builds an overall digital twin 3D scene in the cloud, enabling anomaly identification and alarm handling, addressing the pain points of traditional systems, such as lack of global control, inability to trace historical data, and lack of alarm closure.

[0071] Example 2: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, such as... Figure 2 As shown, in step S1, a unique identifier is assigned to each camera within the smart park, including:

[0072] Define the core attributes of the camera, including device type, installation location, supported protocols, and maximum rotation angle, to obtain definition information;

[0073] The defined information is encoded according to the coding rules of park code-area code-device type code-serial number to obtain the unique identifier of the camera.

[0074] In this embodiment, the device type is any one of bullet camera, PTZ camera, hemispherical camera, etc., the installation location is, for example, East Gate of the park -1F-001, the supported protocol is, for example, RTSP or ONVIF, and the maximum rotation angle is, for example, 0-350° horizontally and -30°-90° vertically.

[0075] In this embodiment, the unique identifier of the camera is, for example, PY01-DM01-QJ01-001, where PY01 = Industrial Park 01, DM01 = East Gate Area, and QJ01 = Camera. The encoding length is fixed at 12 bits to ensure uniqueness and readability.

[0076] The beneficial effects of the above design scheme are: by embedding device type and location information in the code, the physical location of the device can be directly located through the code without additional database queries, solving the problems of meaningless and low recognition efficiency of traditional identifiers, and providing an accurate foundation for subsequent binding and data parsing.

[0077] Example 3: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S1, a unique sensor identifier and resolution rule are assigned to the sensor, including:

[0078] The core attributes of the sensor, including sensor type, acquisition frequency, transmission protocol, and normal threshold range, are defined to obtain sensor definition information;

[0079] The sensor definition information is parsed according to the preset parsing template to obtain the parsed information;

[0080] The parsed information is encoded according to the coding rules of park code-region code-sensor type code-serial number to obtain the unique identifier of the sensor.

[0081] In this embodiment, the sensor type is, for example, a temperature and humidity sensor, a human body sensor, a smoke sensor, etc., the sampling frequency is, for example, 1 time / second or 1 time / 5 minutes, the transmission protocol is, for example, MQTT or HTTP, and the normal threshold range is, for example, a temperature of 10-30℃ and a humidity of 30%-70%.

[0082] In this embodiment, the sensor's unique identifier is, for example, PY01-DM01-WS01-001, where WS01 = temperature and humidity sensor.

[0083] In this embodiment, a standardized parsing template is preset, and the corresponding template is automatically matched through sensor coding, avoiding repeated development and improving parsing efficiency.

[0084] The benefits of the above design scheme are: it pre-defines the multi-protocol parsing rules for sensors and pre-defined data conversion logic, avoiding data incompatibility caused by differences in protocols and formats between devices from different manufacturers, laying the foundation for unified parsing in the future, and reducing the cost of repeated development.

[0085] Example 4: A method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, wherein in step S1, a unique device identifier and configuration access information are assigned to the edge device, including:

[0086] Define the core attributes of edge devices, including device model, management area, maximum number of connected devices, and data cache capacity, to obtain edge device definition information;

[0087] The edge device definition information is encoded according to the coding rule of park code-edge device type code-serial number to obtain a unique device identifier;

[0088] Information about edge devices is recorded and stored in a cloud database.

[0089] In this embodiment, the device model is, for example, NVIDIA Jetson Xavier, the management area is, for example, the East Gate area and the North Gate area, the maximum number of connected devices is, for example, ≤32 cameras and ≤64 sensors, and the data cache capacity is ≥10GB for temporary storage when the network is disconnected.

[0090] In this embodiment, edge device definition information is, for example, PY01-ED01-001, where ED01 = general edge computing node.

[0091] In this embodiment, the edge device information includes IP address (fixed internal network IP: 192.168.1.101) and communication port (MQTT port 1883 / HTTP port 8080) to ensure that the cloud can actively access it.

[0092] The beneficial effects of the above design scheme are: configuring fixed IP addresses, communication ports and other access information for edge devices and registering them in the cloud database, ensuring that the cloud can actively access edge devices, solving the problems of lack of standards for traditional edge device access and difficulty in cloud management, and supporting the implementation of edge-cloud collaborative architecture.

[0093] Example 5: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S2, establishing the binding relationship between edge devices, cameras, and sensors includes:

[0094] Edge devices, cameras, and sensors undergo triple authentication using hardware fingerprints, logical identity, and location fingerprints. Based on the authentication results, device identity identifiers are generated and stored in the cloud.

[0095] The similarity of edge devices, cameras, and sensors is calculated based on the cosine similarity algorithm. Devices with similarity greater than the preset similarity are added to the binding candidate pool, while devices with similarity less than or equal to the preset similarity are removed.

[0096] The smart park is divided into regional grids, and a unique code is configured for each grid. The unique code is then matched with the devices in the candidate pool. Based on the matching results, the basic binding relationship is obtained.

[0097] The smart park is divided into security zone, office zone and production zone, and binding rules are configured for each functional zone. The binding rules are deployed on the cloud alliance node based on smart contracts. When the characteristics of the binding candidate pool meet the binding rules, the basic binding relationship is automatically upgraded, and functional requirement information is added to obtain a strengthened binding relationship.

[0098] The primary edge device is obtained from the enhanced binding relationship, and other edge devices are used as backup edge devices. The primary edge device and backup edge devices are clustered using the K-means clustering algorithm with the host location and performance parameters as the cluster centers. The distance between the backup edge devices and the primary edge devices is determined based on the clustering results. The backup edge devices are prioritized according to the rule that the smaller the distance, the higher the priority. Each primary edge device is bound to two backup edge devices according to the priority. Based on the device binding relationship, the enhanced binding relationship is updated to obtain the final binding relationship.

[0099] In this embodiment, hardware fingerprint authentication specifically involves extracting the underlying hardware features of the device, including the camera and sensor acquisition addresses, firmware hash values, and chip serial numbers. Edge devices additionally acquire CPU / GPU serial numbers and hard disk physical IDs, and generate a hardware fingerprint using the national cryptographic SM3 algorithm.

[0100] In this embodiment, logical identity authentication is specifically based on an assigned unique identifier, superimposed with the permission level (core device / ordinary device / redundant device) and the grid code of the affiliated park (10m×10m fine grid) to generate a logical identity identifier.

[0101] In this embodiment, location fingerprint authentication collects the three-dimensional coordinates of the terminal device through the park's high-precision positioning system, compares them with the location range managed by the edge device, and generates a location fingerprint (coordinates and grid affiliation).

[0102] In this embodiment, generating a device identity identifier based on the authentication result specifically involves concatenating the three authentication results and then hashing them again.

[0103] In this embodiment, the purpose of generating a device identity identifier based on the authentication result and storing it in the cloud is to ensure that only authenticated devices can enter the binding candidate pool.

[0104] In this embodiment, the dimensional characteristics of the camera include, for example, device type, protocol type, acquisition frequency, maximum rotation angle, fault history, power consumption, and data encryption level; the dimensional characteristics of the sensor include, for example, type, transmission protocol, acquisition threshold, battery level, and data reporting stability; and the dimensional characteristics of the edge device include, for example, access capability, cache capacity, computing power, network type (5G / WiFi6 / wired), functional area, and synchronization history success rate.

[0105] In this embodiment, the binding rules for the security area are, for example, edge device cache capacity ≥ 20GB, synchronization latency ≤ 50ms, number of backup devices ≥ 2, and camera acquisition frequency ≤ 1 frame / second; the binding rules for the office area are, for example, edge device cache capacity ≥ 10GB, synchronization latency ≤ 100ms, number of backup devices ≥ 1, and sensor acquisition frequency ≤ 5 times / minute; the binding rules for the production area are, for example, edge devices supporting explosion-proof protocols, cache disaster recovery level ≥ 3, synchronization encryption level of national cryptographic SM4, and cameras supporting infrared mode.

[0106] In this embodiment, functional requirement information includes fields such as supplementary functional priority and data encryption requirements.

[0107] The beneficial effects of the above design scheme are as follows: By constructing device identity identifiers through triple authentication, unauthorized device access is prevented, ensuring uniqueness and eliminating binding conflicts. This lays a reliable foundation for subsequent similarity screening and grid matching. High-dimensional feature matching is achieved through cosine similarity, avoiding data parsing failures due to protocol incompatibility and edge overload caused by performance mismatches from the source. This reduces the operational costs of readjusting after traditional binding due to adaptation issues. By dividing the smart park into regional grids and configuring a unique code for each grid, and matching this unique code with devices in the binding candidate pool, a basic binding relationship is obtained based on the matching results. This solves the inefficiency of relying solely on device codes for location and requiring comparison with paper maps, providing a spatial basis for subsequent functional area binding. By dividing the smart park into security, office, and production areas, and configuring binding rules for each functional area, and deploying these binding rules on cloud-based alliance nodes based on smart contracts, the basic binding relationship is automatically upgraded when the features of the binding candidate pool meet the binding rules, supplementing functional requirement information to obtain a strengthened binding relationship, thus achieving binding... By defining relationships to adapt to business needs and enhancing scenario-based management capabilities, smart contracts automatically upgrade basic binding relationships to enhanced binding relationships, while simultaneously recording upgrade logs to the consortium blockchain. This avoids errors caused by traditional manual rule adjustments and binding failures due to malicious rule tampering, ensuring the seriousness and consistency of functional area binding rules. It solves the problem of needing to rebind all devices when adjusting traditional functions, adapting to the business expansion needs of smart parks. By obtaining the primary edge device from the enhanced binding relationship and using other edge devices as backup edge devices, the primary and backup edge devices are clustered using the K-means clustering algorithm, with host location and performance parameters as cluster centers. The distance between the backup edge devices and the primary edge devices is determined based on the clustering results, and the backup edge devices are prioritized according to the rule that the smaller the distance, the higher the priority. Each primary edge device is bound to two backup edge devices according to the priority ranking. Based on the device binding relationship, the enhanced binding relationship is updated to obtain the final binding relationship, realizing rapid primary-backup switching, avoiding data interruption, matching the performance of the backup machine with the primary machine to ensure that the processing capacity is not degraded, and providing dual backup redundancy to enhance the system's risk resistance.

[0108] Example 6: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S2, a dual synchronization mechanism is set up between the edge and the cloud based on the binding relationship, including:

[0109] A core decision matrix is ​​constructed based on six dimensions: network quality, device status, data importance, edge cache utilization, cloud load rate, and binding relationship change type.

[0110] The goal layer is to maximize synchronization efficiency and data consistency; the criterion layer is to allocate network overhead, synchronization latency, data integrity, system complexity, and fault tolerance according to a preset ratio; and the solution layer includes real-time synchronization, near real-time synchronization, batch synchronization, asynchronous synchronization, and breakpoint resume synchronization.

[0111] Based on the current data, determine the matrix value of the core decision matrix. Based on the matrix value, combined with the target layer and the criterion layer, determine the weight value of each synchronization scheme in the scheme layer, and select the synchronization scheme with the largest weight value as the target synchronization scheme.

[0112] After data synchronization based on the target synchronization scheme, the hash value of the binding relationship is calculated at the edge and the cloud respectively, a hash consistency check is established, and a vector clock is introduced to assign a vector to each binding relationship change event. The monotonicity of the vector is checked in the cloud to establish a timing consistency check. Pre-set business rules are configured in the smart contract and automatically checked after synchronization to establish a business rule check.

[0113] After data synchronization fails, a fault tolerance mechanism is established, consisting of a first-level fault tolerance mechanism with local caching and exponential backoff retries, a second-level fault tolerance mechanism with backup edge devices for synchronization, and a third-level fault tolerance mechanism with cloud consortium blockchain as a backup for recovery.

[0114] By integrating the synchronization scheme with the largest weight value as the target synchronization scheme, hash consistency verification, time sequence consistency verification, business rule verification, and fault tolerance mechanism, a dual synchronization mechanism for the edge and cloud is obtained.

[0115] In this embodiment, network quality is determined based on packet loss rate and latency, and device status includes normal online, abnormal online, offline, and faulty; data importance is determined according to device classification, and binding relationship change types include primary / backup switchover, adding devices, parameter adjustment, and log update.

[0116] In this embodiment, the preset ratio allocation of network overhead, synchronization latency, data integrity, system complexity, and fault tolerance is set based on the actual situation, for example, 0.3:0.25:0.20:0.15:0.10.

[0117] In this embodiment, hash consistency verification specifically involves comparing the hash values ​​for consistency. If the comparison is consistent, the verification passes; otherwise, it is marked as a synchronization anomaly, triggering a fault tolerance mechanism.

[0118] In this embodiment, the cloud verifies the monotonicity of the vector clock (new event timestamp > old event) to prevent old data from overwriting new data.

[0119] In this embodiment, the preset business rules include, for example, ≤32 cameras connected to a single edge device and ≤100ms standby switching latency.

[0120] In this embodiment, the business rule verification specifically involves the smart contract pre-setting business rules, which are automatically verified after synchronization. If the limits are exceeded, synchronization is refused and an alarm is triggered, while the system rolls back to the most recent valid binding relationship.

[0121] In this embodiment, when the synchronization of the first-level fault-tolerant edge device fails, an exponential backoff retry is automatically triggered. For the second-level fault-tolerant device, when the synchronization of the main edge device fails and there are ≥10 retries, the cloud smart contract automatically triggers the synchronization request of the backup device. For the third-level fault-tolerant device, if the synchronization of all edge devices fails, the cloud reads the latest binding relationship hash value from the consortium blockchain, reverse-parses the on-chain index, restores the binding relationship from the cloud encrypted database, and marks the device as abnormal, notifying the operation and maintenance personnel to intervene.

[0122] The beneficial effects of the above design scheme are as follows: By constructing a decision matrix based on six dimensions—network quality, device status, data importance, edge cache utilization, cloud load rate, and binding relationship change type—it solves the problem of mismatch between synchronization strategies and actual scenarios caused by traditional single-dimensional decision-making. All six dimensions are real-time collectable quantifiable indicators, and the matrix values ​​can be dynamically updated based on real-time data to ensure that the synchronization strategy adjusts with changes in the scenario, rather than remaining fixed. This adapts to the dynamic fluctuations in device status and network load in smart parks. The scheme is structured in three layers: a target layer, a criterion layer, and a solution layer. With maximizing synchronization efficiency and data consistency as dual objectives, it avoids the extreme problems of traditional single-objective decision-making. The criterion layer allocates weights according to a preset ratio, transforming abstract synchronization requirements into calculable quantifiable indicators, thus solving the subjective errors of traditional experience-based solution selection. The solution layer includes five synchronization modes. It covers all scenarios, including high-priority core data, low-priority ordinary data, unstable networks, and limited bandwidth. Compared with traditional solutions that only support 1-2 synchronization modes, it can meet the differentiated needs of different devices and scenarios in smart parks. By calculating weights to select the target synchronization scheme, it can dynamically adapt to changes in scenarios, achieve optimal synchronization efficiency, and achieve precise resource allocation to avoid waste. By constructing a triple consistency check of hash, time sequence, and business rules, it solves the security vulnerability of data transmission being tampered with, ensures the authenticity of binding relationships, and addresses the time sequence conflict problem of multiple devices changing concurrently, which cannot be distinguished by timestamps alone. It also prevents binding failure caused by illegal data entering the system, ensuring the legality and compliance of synchronized data. Through the design of a three-level fault tolerance mechanism, it can solve instantaneous failures, provide backup synchronization, ensure the continuity of critical businesses, and provide backup recovery for consortium blockchains, achieving a backup in extreme scenarios.

[0123] Example 7: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S3, camera data and sensor data are acquired based on a dual synchronization mechanism, and the camera data and sensor data are parsed into the same format at the edge device to obtain standard data, including:

[0124] The target parsing template is obtained by matching the protocol type based on camera data and sensor data from the parsing template library;

[0125] The camera data and sensor data are parsed based on the target parsing template to obtain standard data.

[0126] The beneficial effects of the above design scheme are: acquiring data based on a dual synchronization mechanism and parsing it into a standard format at the edge, thus solving the pain points of traditional systems such as concentrated data processing pressure, high transmission latency, and slow response to anomalies.

[0127] Example 8: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S4, based on standard data, the mapping positions of edge devices and cameras in the digital twin scenario are established, the three-dimensional coordinates and viewing angle parameters are determined, and a lightweight digital twin scenario is established at the edge of the device, including:

[0128] The edge device code, camera code, 3D coordinates, smart park coordinate system, and viewing angle parameters are obtained and matched. Based on the matching results, the 3D coordinates and viewing angle parameters of the camera are determined.

[0129] Write the camera data, 3D coordinates, and viewpoint parameters into a database in the cloud;

[0130] Relevant data is extracted from cloud databases to create lightweight digital twin scenarios at the device edge.

[0131] The beneficial effects of the above design scheme are: by establishing the device mapping position, determining the three-dimensional coordinates and view parameters, and building a lightweight digital twin scene at the edge, the core solution is to address the pain points of traditional visualization being single in dimension, digital twin being statically disconnected, and on-site operation and maintenance being cumbersome.

[0132] Example 9: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, such as... Figure 3 As shown, in step S5, establishing a complete digital twin 3D scene of the smart park in the cloud includes:

[0133] Acquire standard data, smart park photography models, and third-party system data, perform coordinate alignment and standardization, and obtain target data;

[0134] Lightweight dynamic twin rendering, device model loading and assembly, and dynamic state binding are performed on the target data to obtain the overall digital twin 3D scene.

[0135] In this embodiment, lightweight dynamic twin rendering includes view configuration, lighting configuration, and rendering configuration.

[0136] In this embodiment, the third-party system data includes fire protection and access control data, etc.

[0137] In this embodiment, the device model loading and assembly involves splitting the camera model into three independently movable components: a base, a bracket, and a lens, and binding corresponding control parameters to each component; then calling EdgeScene.add(base, bracket, lens) to set the initial position of each component.

[0138] The beneficial effects of the above design scheme are: to build an overall digital twin 3D scene in the cloud, realize anomaly identification and alarm handling, and solve the pain points of traditional systems such as lack of global control, inability to trace historical data, and lack of alarm closure.

[0139] Example 10: Based on Example 1, this embodiment of the invention provides a method for visualizing the status of video surveillance equipment in a smart park digital twin scenario. In step S5, anomaly identification and alarm processing are performed based on standard data, including:

[0140] Extract anomalous data from standard data and determine the scene data of the anomalous data within the overall digital twin 3D scene;

[0141] Alarm data is obtained by integrating the aforementioned abnormal data and scenario data;

[0142] Alarms are pushed and processed in the cloud based on the alarm data.

[0143] In this embodiment, alarm push notifications are sent to operations and maintenance personnel via SMS and email, and alarm pop-ups appear in the digital twin scenario; alarm processing allows operations and maintenance personnel to mark alarm status in the cloud, and processing results are synchronized to the edge.

[0144] The beneficial effects of the above design scheme are: to build an overall digital twin 3D scene in the cloud, realize anomaly identification and alarm handling, and solve the pain points of traditional systems such as lack of global control, inability to trace historical data, and lack of alarm closure.

[0145] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this application and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for visualizing the status of video surveillance equipment in a smart park digital twin scenario, characterized in that, include: S1: Assign unique camera identifiers to cameras in the smart park, assign unique sensor identifiers and resolution rules to sensors, and assign unique device identifiers and configure access information to edge devices; S2: Establish the binding relationship between edge device, camera, and sensor, and set up a dual synchronization mechanism between the edge and cloud based on the binding relationship, including: A core decision matrix is ​​constructed based on six dimensions: network quality, device status, data importance, edge cache utilization, cloud load rate, and binding relationship change type. The goal layer is to maximize synchronization efficiency and data consistency; the criterion layer is to allocate network overhead, synchronization latency, data integrity, system complexity, and fault tolerance according to a preset ratio; and the solution layer includes real-time synchronization, near real-time synchronization, batch synchronization, asynchronous synchronization, and breakpoint resume synchronization. Based on the current data, determine the matrix value of the core decision matrix. Based on the matrix value, combined with the target layer and the criterion layer, determine the weight value of each synchronization scheme in the scheme layer, and select the synchronization scheme with the largest weight value as the target synchronization scheme. After data synchronization based on the target synchronization scheme, the hash value of the binding relationship is calculated at the edge and the cloud respectively, a hash consistency check is established, and a vector clock is introduced to assign a vector to each binding relationship change event. The monotonicity of the vector is checked in the cloud to establish a timing consistency check. Pre-set business rules are configured in the smart contract and automatically checked after synchronization to establish a business rule check. After data synchronization fails, a fault tolerance mechanism is established, consisting of a first-level fault tolerance mechanism with local caching and exponential backoff retries, a second-level fault tolerance mechanism with backup edge devices for synchronization, and a third-level fault tolerance mechanism with cloud consortium blockchain as a backup for recovery. By integrating the synchronization scheme with the largest weight value as the target synchronization scheme, hash consistency verification, time sequence consistency verification, business rule verification, and fault tolerance mechanism, a dual synchronization mechanism for the edge and cloud is obtained. S3: Acquire camera data and sensor data based on a dual synchronization mechanism, and parse the camera data and sensor data into the same format at the edge device to obtain standard data; S4: Based on standard data, establish the mapping position of cameras in edge devices and digital twin scenarios, determine the three-dimensional coordinates and viewpoint parameters, and establish a lightweight digital twin scenario at the edge of the device; S5: Build a complete digital twin 3D scene of the smart park in the cloud, and perform anomaly identification and alarm processing based on standard data.

2. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S1, a unique identifier is assigned to each camera within the smart park, including: Define the core attributes of the camera, including device type, installation location, supported protocols, and maximum rotation angle, to obtain definition information; The defined information is encoded according to the coding rules of park code-area code-device type code-serial number to obtain the unique identifier of the camera.

3. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S1, a unique sensor identifier and resolution rules are assigned to the sensor, including: The core attributes of the sensor, including sensor type, acquisition frequency, transmission protocol, and normal threshold range, are defined to obtain sensor definition information; The sensor definition information is parsed according to the preset parsing template to obtain the parsed information; The parsed information is encoded according to the coding rules of park code-region code-sensor type code-serial number to obtain the unique identifier of the sensor.

4. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S1, a unique device identifier and configuration access information are assigned to the edge device, including: Define the core attributes of edge devices, including device model, management area, maximum number of connected devices, and data cache capacity, to obtain edge device definition information; The edge device definition information is encoded according to the coding rule of park code-edge device type code-serial number to obtain a unique device identifier; Information about edge devices is recorded and stored in a cloud database.

5. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S2, establishing the binding relationship between the edge device, camera, and sensor includes: Edge devices, cameras, and sensors undergo triple authentication using hardware fingerprints, logical identity, and location fingerprints. Based on the authentication results, device identity identifiers are generated and stored in the cloud. The similarity of edge devices, cameras, and sensors is calculated based on the cosine similarity algorithm. Devices with similarity greater than the preset similarity are added to the binding candidate pool, while devices with similarity less than or equal to the preset similarity are removed. The smart park is divided into regional grids, and a unique code is configured for each grid. The unique code is then matched with the devices in the candidate pool. Based on the matching results, the basic binding relationship is obtained. The smart park is divided into security zone, office zone and production zone, and binding rules are configured for each functional zone. The binding rules are deployed on the cloud alliance node based on smart contracts. When the characteristics of the binding candidate pool meet the binding rules, the basic binding relationship is automatically upgraded, and functional requirement information is added to obtain a strengthened binding relationship. The primary edge device is obtained from the enhanced binding relationship, and other edge devices are used as backup edge devices. The primary edge device and backup edge devices are clustered using the K-means clustering algorithm with the host location and performance parameters as the cluster centers. The distance between the backup edge devices and the primary edge devices is determined based on the clustering results. The backup edge devices are prioritized according to the rule that the smaller the distance, the higher the priority. Each primary edge device is bound to two backup edge devices according to the priority. Based on the device binding relationship, the enhanced binding relationship is updated to obtain the final binding relationship.

6. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S3, camera data and sensor data are acquired based on a dual synchronization mechanism, and the camera data and sensor data are parsed into the same format at the edge device to obtain standard data, including: The target parsing template is obtained by matching the protocol type based on camera data and sensor data from the parsing template library; The camera data and sensor data are parsed based on the target parsing template to obtain standard data.

7. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S4, based on standard data, the mapping positions of cameras in the edge device and digital twin scene are established, the three-dimensional coordinates and viewpoint parameters are determined, and a lightweight digital twin scene is established at the device edge, including: The edge device code, camera code, 3D coordinates, smart park coordinate system, and viewing angle parameters are obtained and matched. Based on the matching results, the 3D coordinates and viewing angle parameters of the camera are determined. Write the camera data, 3D coordinates, and viewpoint parameters into a database in the cloud; Relevant data is extracted from cloud databases to create lightweight digital twin scenarios at the device edge.

8. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In S5, the overall digital twin 3D scene of the smart park is established in the cloud, including: Acquire standard data, smart park photography models, and third-party system data, perform coordinate alignment and standardization, and obtain target data; Lightweight dynamic twin rendering, device model loading and assembly, and dynamic state binding are performed on the target data to obtain the overall digital twin 3D scene.

9. The method for visualizing the status of video surveillance equipment in a smart park digital twin scenario according to claim 1, characterized in that, In step S5, anomaly identification and alarm processing based on standard data include: Extract anomalous data from standard data and determine the scene data of the anomalous data within the overall digital twin 3D scene; Alarm data is obtained by integrating the aforementioned abnormal data and scenario data; Alarms are pushed and processed in the cloud based on the alarm data.

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