Three-dimensional visualization design method of 5G smart building based on edge cloud computing

Abstract

The application provides a 5G smart building three-dimensional visualization design method based on edge cloud computing, relates to the technical field of smart building three-dimensional visualization, and comprises the following steps: deploying an edge computing node with data storage, real-time processing and low-delay response capability in a key area of a 5G smart building, loading a 5G communication module on the edge computing node, establishing a communication link between the edge computing node and building intelligent devices and user terminals, and completing data interface adaptation between the edge computing node and a building device management system. The application stores user preference information locally by deploying an edge computing node in the building and avoids uploading data to the cloud, thereby achieving the effect of fast loading of a three-dimensional visualization scene. By configuring permissions through identity authentication and dynamically collecting and updating preference information, the application generates a customized scene in combination with user demand, solves the problem that a unified scene cannot meet the demand of different user groups, and achieves the effect of improving the practicability of three-dimensional visualization services and user stickiness.

Description

Technical Field

[0001] This invention relates to the field of smart building 3D visualization technology, and in particular to a 3D visualization design method for 5G smart buildings based on edge cloud computing. Background Technology

[0002] 5G smart buildings use 5G technology as the core communication carrier, integrating various equipment subsystems such as HVAC, lighting, security, elevators, and energy management within the building. Through sensors and intelligent controllers, they collect real-time equipment operation data and environmental data. Relying on data interaction and collaborative control technologies, they achieve intelligent equipment scheduling, efficient energy utilization, early warning of safety hazards, and precise delivery of user services, creating a modern building form that significantly improves building operational efficiency and user work and living experience. 5G smart building 3D visualization uses BIM, GIS, and other technologies to construct a comprehensive 3D model including building structure, equipment installation locations, and spatial layout. It transforms equipment operating parameters, environmental monitoring data, and personnel flow information transmitted via the 5G network into dynamic visual elements within the 3D model, presenting the building's real-time status in an intuitive 3D scene. Users can view, zoom, rotate, and perform other interactive operations through terminal devices, making it easier to intuitively understand the overall building operation compared to traditional 2D charts.

[0003] Edge computing features localized data processing, low latency, and low bandwidth consumption. In the process of 5G smart building 3D visualization, tasks such as 3D model data processing, user interaction command parsing, and customized scene generation can be deployed on the building's local edge computing nodes. This eliminates the need to upload all data to the remote cloud, avoiding latency issues caused by cloud transmission, ensuring real-time updates and smooth interaction of the 3D scene, and storing personalized user data at the edge to provide data support for subsequent customized services. It is a key technological guarantee for ensuring the efficient and stable implementation of 5G smart building 3D visualization.

[0004] Current 5G smart building 3D visualization mostly adopts the implementation method of "centralized cloud processing + unified scene display". User personalized settings and preferences need to be uploaded to cloud storage. This not only makes it easy for loading delays to occur when users request scenes due to network fluctuations, affecting the user experience, but also poses security risks of theft and tampering during data transmission and cloud storage. At the same time, this method does not design scenarios for the different needs of different user groups such as property management personnel and ordinary tenants. As a result, property management personnel have difficulty quickly obtaining professional data such as equipment operating parameters and maintenance records, and ordinary tenants cannot easily view life-related information such as spatial layout and surrounding facilities. The system has low usability and user stickiness. Therefore, it is of great significance to develop a 5G smart building 3D visualization design method based on edge cloud computing. Summary of the Invention

[0005] To address the technical problems existing in the prior art, the present invention provides the following technical solution:

[0006] On the one hand, a 3D visualization design method for 5G smart buildings based on edge cloud computing is provided. This method is implemented by electronic devices and includes:

[0007] In key areas of 5G smart buildings, deploy edge computing nodes with data storage, real-time processing and low-latency response capabilities, equip the edge computing nodes with 5G communication modules, establish communication links between the edge computing nodes and building smart devices and user terminals, and complete the data interface adaptation between the edge computing nodes and the building equipment management system.

[0008] Users submit identity information through terminal devices. After verifying the identity information, the edge computing nodes configure and store the user's operation permissions according to preset rules.

[0009] Edge computing nodes push preference settings interfaces to user terminals, collect the types of information selected by users, establish independent preference databases according to user identities, and store them locally;

[0010] Edge computing nodes acquire building equipment operation data and spatial status data at preset cycles, and update the 3D basic model after preprocessing.

[0011] When a user initiates a scene access request, the edge computing node retrieves the user's permissions and preferences, filters and matches the data, and combines it with the 3D basic model to generate a customized 3D scene.

[0012] Edge computing nodes push customized 3D scenes to user terminals via 5G links;

[0013] When a user initiates a preference modification request, the edge computing node updates the user preference database in real time and generates a scenario based on the updated preferences the next time the user requests a scenario.

[0014] Furthermore, the deployment of edge computing nodes in key areas of the 5G smart building specifically involves deploying edge computing nodes at building entrances, equipment rooms, and lobbies. The coverage area of ​​each edge computing node is limited to a single floor. Data synchronization channels are established between adjacent edge computing nodes. The number of edge computing nodes deployed is determined by a formula: ,in, The number of edge computing nodes to deploy, Total building floor area (unit: square meters) The density of smart devices in the building (unit: units / square meter). This represents the maximum number of devices that can be connected to a single edge computing node (unit: units). To improve the signal coverage efficiency of edge computing nodes, Obtain actual values ​​from building architectural design drawings. The total number of smart devices is calculated from the building equipment installation list and then divided by the total building area. Determined based on the hardware specifications of the edge computing nodes. The signal coverage data was collected in different areas of the building through on-site signal testing experiments, and the average value was calculated to determine the signal coverage. The symbol indicates rounding up. Because the number of edge computing nodes must be an integer and must fully cover the building equipment access and signal requirements, even if the division result is a decimal, it must be rounded up to the nearest integer.

[0015] Furthermore, the data interface between the edge computing node and the building equipment management system is adapted using a dual-protocol adaptation method of OPCUA and BACnet. For HVAC and lighting equipment in the building, a data interaction channel is established through the BACnet protocol, while for security and elevator equipment, a data interaction channel is established through the OPCUA protocol. A protocol conversion module is set up in the edge computing node.

[0016] Furthermore, the user submits identity information through a terminal device. Specifically, the user terminal provides two identity submission methods: inputting the ID number and associating with the room number. Property management personnel submit identity information by inputting their work ID number, while ordinary tenants submit identity information by inputting their room number and verifying the linked mobile phone number verification code. The edge computing node associates and stores the verified identity information with the user terminal device identifier.

[0017] Furthermore, the edge computing nodes acquire building equipment operation data and spatial status data according to a preset cycle. Specifically, the edge computing nodes are set with multiple acquisition cycles: a 5-minute acquisition cycle for elevator operating speed and fire equipment status, a 15-minute acquisition cycle for lighting switch status and meeting room occupancy, and a 24-hour acquisition cycle for building structure information. The adjustment coefficients for each acquisition cycle are determined by calculation using a formula: ,in, For the first Data acquisition cycle adjustment coefficient For data importance weights, For the first Historical anomaly frequency of the data type Weight the data update frequency. For the first Historical update frequency of class data and The analytic hierarchy process (AHP) was used, combined with the building management's prioritization scoring of different data, to determine the appropriate approach. and The result is calculated by statistically analyzing the number of anomalies and updates of the corresponding data over the past 30 days using historical data stored on edge computing nodes.

[0018] Furthermore, the preprocessing of the acquired data specifically includes two steps: data cleaning and format standardization.

[0019] During data cleaning, the 3σ principle is used to remove abnormal fluctuations in equipment operation data, and duplicate data is deleted using a duplicate data detection algorithm.

[0020] When standardizing the format, all data is converted to JSON format and encapsulated according to the field structure of "device number-data type-collection time-value".

[0021] Furthermore, the edge computing node generates a customized 3D scene. Specifically, the edge computing node has a built-in scene rendering engine. The rendering engine first loads the updated 3D base model, then determines the information layers to be displayed based on user preference information. The preprocessed device data and spatial data are mapped to the corresponding information layers, and the display priority of the information layers is determined by calculation using a formula: ,in, For the first The display priority of each information layer Weighting based on user preferences For the first User selection frequency for each information layer For the first The validity of data updates in each information layer; The degree of reliance on preference information by different user groups is determined through user behavior research.

[0022] Furthermore, the edge computing node pushes the customized 3D scene to the user terminal via the 5G link. Specifically, the edge computing node selects the push method according to the user terminal type. For computer terminals, the WebSocket protocol is used to push complete scene data, while for mobile terminals such as mobile phones and tablets, the HTTP / 2 protocol is used to push lightweight scene data. The lightweight scene data is compressed by reducing the texture resolution and simplifying the number of faces in non-critical areas.

[0023] On the other hand, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, wherein when executed by the processor, the computer-readable instructions implement any of the methods described above for the 3D visualization design of 5G smart buildings based on edge cloud computing.

[0024] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods of the three-dimensional visualization design method for 5G smart buildings based on edge cloud computing.

[0025] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0026] This invention addresses the issue of scene loading delays caused by cloud storage by deploying edge computing nodes within buildings and storing user preference information locally, thus avoiding data uploads to the cloud. This enables rapid loading of 3D visualization scenes. By configuring permissions through identity verification and dynamically collecting and updating preference information, customized scenes are generated based on user needs, solving the problem that uniform scenes cannot meet the needs of different user groups. This improves the usability and user stickiness of 3D visualization services. By processing data locally at the edge, data transmission links are reduced, mitigating security risks associated with cloud data transmission and storage, while also reducing cloud data processing pressure, thereby enhancing data security and the operational stability of 3D visualization services. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a 3D visualization design method for 5G smart buildings based on edge cloud computing, provided in an embodiment of the present invention.

[0029] Figure 2 This is a flowchart illustrating the steps of a 3D visualization design method for 5G smart buildings based on edge cloud computing, as provided in an embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0032] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0033] The 3D visualization design method for 5G smart buildings based on edge cloud computing provided by this invention can be found in [reference needed]. Figure 1 and Figure 2 This method addresses the latency, security, and personalization issues of traditional smart building 3D visualization by integrating edge computing and 5G technology. The specific implementation steps are as follows:

[0034] I. Edge Computing Node Deployment and Communication Adaptation

[0035] In key areas of the 5G smart building, including floor entrances, equipment rooms, and lobbies, edge computing nodes with data storage, real-time processing, and low-latency response capabilities are deployed. The coverage of each node is controlled within a single floor. Data synchronization channels are set up between adjacent edge computing nodes to ensure data transmission continuity. Each edge computing node is equipped with a 5G communication module to establish communication links between the node and various smart devices in the building (such as HVAC, lighting, security, and elevator equipment) and user terminals (computers, mobile phones, tablets, etc.). In terms of data interface adaptation, a dual-protocol adaptation method of OPCUA and BACnet is adopted. Data interaction channels are established through the BACnet protocol for HVAC and lighting equipment, and through the OPCUA protocol for security and elevator equipment. Protocol conversion modules are set up in the edge computing nodes to ensure smooth data interaction between different devices and nodes.

[0036] II. User Authentication and Permission Configuration

[0037] When users submit identity information through their terminal devices, the terminals offer two submission methods: inputting the ID number and associating it with the room number. Property management personnel need to enter their work ID number to submit their identity information, while ordinary tenants need to enter their room number and verify the linked mobile phone number verification code to complete the submission. Edge computing nodes verify the identity information submitted by users. After successful verification, they configure the corresponding operation permissions for users according to preset rules and associate the verified identity information with the user's terminal device identifier to ensure that the permissions for subsequent user operations match.

[0038] III. Collection and Storage of User Preference Information

[0039] After completing user authentication and permission configuration, the edge computing node pushes a preference settings interface to the user terminal. In this interface, the user selects the information type they need. Based on the user's selection, the edge computing node establishes an independent preference database according to the user's identity and stores this preference information locally. This avoids the security risks and latency issues caused by uploading data to the cloud, and at the same time provides data support for the subsequent generation of customized scenarios.

[0040] IV. Building Data Acquisition and 3D Basic Model Update

[0041] Edge computing nodes acquire building equipment operation data and spatial status data at preset cycles, setting multiple acquisition cycles to adapt to different data needs: shorter acquisition cycles are set for critical data such as elevator operating speed and fire equipment status; medium-length acquisition cycles are set for lighting switch status and meeting room occupancy; and longer acquisition cycles are set for data with low update frequency, such as building structure information. The acquired data is preprocessed, first by cleaning to remove abnormal fluctuations and duplicate data, and then by standardizing the format, converting all data into a specific format and encapsulating it according to a fixed field structure. After preprocessing, this data is used to update the 3D basic model to ensure that the model can reflect the real-time status of the building.

[0042] V. Customized 3D Scene Generation and Delivery

[0043] When a user initiates a scene access request, the edge computing node retrieves previously stored user permissions and preference information. Based on this information, it filters and matches the corresponding device data and spatial data, and generates a customized 3D scene by combining it with the updated 3D base model. During the scene generation process, the scene rendering engine built into the edge computing node first loads the updated 3D base model, then determines the information layers to be displayed based on user preferences, and maps the preprocessed data to the corresponding layers. At the same time, it determines the display priority of each information layer. Afterward, the edge computing node selects an appropriate push method based on the user terminal type and pushes the customized 3D scene to the user terminal through the 5G link. For computer terminals, the complete scene data is pushed, while for mobile terminals such as mobile phones and tablets, lightweight scene data with compressed data volume is pushed.

[0044] VI. User Preference Modification and Scene Update

[0045] If a user initiates a preference modification request, the edge computing node updates the user preference database in real time. When the user initiates a scene access request again, the edge computing node will generate a new customized 3D scene according to the updated preference information, ensuring that the scene can continuously meet the user's changing needs.

[0046] Example 1

[0047] This embodiment applies to the smart transformation project of a large-scale commercial complex in a core urban business district. This complex has a large total building area and a high density of smart devices, encompassing retail shops, restaurants, office areas, and public service spaces. It needs to simultaneously meet the equipment operation and maintenance needs of property management personnel, the operational data viewing needs of merchants, and the navigation and service inquiry needs of consumers. (See also...) Figure 1 and Figure 2The present invention adopts the 5G smart building 3D visualization design method based on edge cloud computing. By deploying edge computing nodes in the building and combining 5G communication technology, localized data processing and customized scenario services are realized, thereby improving system operating efficiency and user experience.

[0048] First, a deployment plan for edge computing nodes within the commercial complex is developed, based on the formula for the number of edge computing nodes to be deployed. (in The number of edge computing nodes to deploy, The total floor area of ​​the building. The density of smart devices within the building. This refers to the maximum number of devices that can be connected to a single edge computing node. (For the signal coverage efficiency of edge computing nodes) Obtain the actual total building area from the architectural design drawings of the commercial complex. The total number of smart devices is calculated from the building equipment installation list and then divided by the total building area. Determined based on the hardware specifications of the selected edge computing node. Through on-site signal testing experiments, signal coverage data was collected in different areas of the complex (such as underground parking garage, high-rise office area, atrium, etc.) and the average value was calculated to determine the final decision. It was finally determined that edge computing nodes would be deployed at floor entrances, equipment rooms and lobby areas respectively. Each node covers a single floor, and high-speed data synchronization channels are set up between adjacent nodes to ensure efficient data flow between nodes.

[0049] Through the formula The operating mechanism and technical effects of calculating the number of nodes are explained below:

[0050] The operational mechanism first extracts the building area of ​​each floor of the commercial complex from the building design drawings (e.g., from the basement to the 10th floor above ground, each floor has an area of ​​800 square meters). 2 1200m 2 ...), summing them up yields the total building area. Next, count the total number of smart devices in the building (e.g., 300 HVAC units, 800 lighting units, 200 security units, and 15 elevator units), and divide by the total building area. To obtain the density of smart devices Then determine based on the edge computing node hardware specifications (e.g., the selected XX model node, with hardware parameters indicating a maximum number of connected devices of 500). Finally, signal test points were set up in different areas of the building (underground parking garage, high-rise office area, atrium, elevator lobby). Signal coverage data was continuously collected for 24 hours at each test point (sampling interval of 1 minute). The percentage of effective signal coverage time at each point was calculated, and the average value of all test points was taken as the mean. Substitute the above parameters into the formula. If the calculated result is not an integer (such as 12.3), then use the floor function to round up. Get the number of nodes deployed (That is, 13).

[0051] The data synchronization channel between adjacent edge computing nodes adopts the "master-slave node polling synchronization" algorithm. Each master node (such as the lobby node on each floor) sends a data synchronization request to its subordinate slave nodes (such as the floor entrance and equipment room nodes) at 10-second intervals. After receiving the request, the slave node verifies the integrity of the local data through the CRC32 check algorithm and feeds back the verified incremental data (which only includes the data updated since the last synchronization) to the master node. After receiving the data, the master node merges it with the local data to complete the synchronization.

[0052] Technical benefits: This formula quantifies building area, equipment density, node access capability, and signal coverage efficiency to avoid resource waste caused by deploying too many nodes (e.g., deploying 5 more nodes in the original experience deployment scheme increases hardware costs by 30%), or insufficient device access and signal coverage blind spots caused by deploying too few nodes (when the original scheme deployed 3 fewer nodes, 20% of devices could not access stably, and 15% of the area had weak signal).

[0053] The master-slave node polling synchronization algorithm combined with CRC32 check reduces data transmission volume by more than 85% compared to traditional full data synchronization, and shortens synchronization time to within 0.5 seconds. This ensures data consistency across nodes while reducing 5G communication link bandwidth usage and avoiding scenario loading delays caused by data synchronization.

[0054] Each edge computing node is equipped with a 5G communication module to establish communication links between the node and intelligent devices and user terminals within the building. In the data interface adaptation phase, a dual-protocol adaptation method using OPCUA and BACnet is adopted. For HVAC and lighting systems, a data interaction channel is established via the BACnet protocol to enable real-time transmission of device operating parameters (such as temperature setpoints and lighting brightness). For security monitoring equipment (cameras, access control systems) and elevator equipment, a data interaction channel is established via the OPCUA protocol to ensure stable transmission of device status information (such as camera footage and elevator floor information). Simultaneously, a protocol conversion module is set up within the edge computing node to resolve incompatibility issues between different device protocols, ensuring smooth data interaction between the edge computing node and the building equipment management system.

[0055] When users submit identity information via terminal devices (computers, mobile phones, tablets), the system provides two submission methods: inputting the ID number and associating it with the room number. Property management personnel must enter their work ID number when logging into the system. The edge computing node verifies this by accessing the built-in property personnel identity database. After successful verification, advanced permissions such as modifying device parameters and viewing maintenance records are configured according to preset rules. Ordinary merchants submit their identity by entering their store number and verifying the linked mobile phone verification code. After successful verification by the edge computing node, merchant-specific permissions such as viewing energy consumption data for the store's area and air conditioning usage permissions are configured. Consumers submit information by entering their reserved parking space number or a temporary identity code obtained by scanning a QR code. After successful verification, they gain basic permissions such as navigation to public areas and querying merchant information. The edge computing node associates and stores the verified identity information with the user's terminal device identifier (such as the device MAC address and IMEI code) to ensure rapid permission matching for subsequent user access. The associated storage operations are legally obtained with user consent. For example, legal acquisition and user authorization are achieved through the following methods: During the user's initial registration or login process, the system clearly informs the user of the data collection purpose (quick permission matching), storage scope (identity information and device identification binding relationship) and retention period (account duration + 30-day buffer period) through pop-up windows or agreement pages, and obtains the user's active authorization through methods such as check-in confirmation and SMS verification code secondary confirmation; For institutional users such as property management personnel and merchants, organizational-level data authorization filing must be completed through the enterprise administrator account; All authorization behaviors generate tamper-proof log records.

[0056] After user authentication and permission configuration are completed, the edge computing node pushes a personalized preference settings interface to the user terminal. The property management personnel's preference settings interface includes options such as device operating parameter display type (e.g., real-time data, historical trend charts) and maintenance reminder methods (e.g., pop-ups, SMS); the merchant interface provides options such as store energy consumption data display period (e.g., hourly, daily) and surrounding customer traffic statistics dimensions; the consumer interface includes options such as navigation route preferences (e.g., shortest path, path with few stairs) and types of merchants of interest (e.g., restaurants, clothing). After the user makes their selections, the edge computing node establishes an independent preference database based on the user's identity (property management personnel ID, merchant ID, consumer temporary identity code), storing the preference information locally on the edge computing node without uploading to a remote cloud. This reduces the risk of data leakage and provides data support for subsequent customized scenario generation.

[0057] Edge computing nodes are configured with multi-level data acquisition cycles: a 5-minute acquisition cycle for critical data such as elevator speed and fire equipment status; a 15-minute acquisition cycle for data such as lighting switch status and shop operating status; and a 24-hour acquisition cycle for data with low update frequency, such as building structure information and public area layout. Simultaneously, a formula is used... (in Adjustment coefficient for the acquisition period of the t-th type of data For data importance weights, Let be the historical anomaly frequency of the t-th data type. Weight the data update frequency. Calculate the acquisition cycle adjustment coefficient for each level of data (where the historical update frequency is for the t-th type of data). and The analytic hierarchy process (AHP) was used, combined with the priority scoring of different data by the management of the commercial complex, to determine the appropriate approach. and The calculation is based on historical data stored on edge computing nodes, calculated by statistically analyzing the number of anomalies and updates of corresponding data over the past 30 days. If the frequency of historical anomalies for a certain type of data (such as the status of fire-fighting equipment) increases, an adjustment factor is applied. As the number of records increases, the system automatically shortens the acquisition cycle for this type of data, improving the timeliness of data collection.

[0058] Through the formula The operating mechanism and technical effects are explained below when calculating the adjustment coefficient:

[0059] Operating mechanism: determined using the analytic hierarchy process (AHP). and At that time, a three-layer structure model was constructed: "Target Layer (data acquisition cycle optimization) - Criterion Layer (data importance, update frequency) - Solution Layer (various types of data)". Ten building management experts (including 3 equipment maintenance engineers, 3 system architects, and 4 operation and management personnel) were invited to compare and score the criteria layer indicators pairwise (using the 1-9 scale, e.g., "data importance" is more important than "update frequency" and scores 5 points). After passing the consistency test (CR < 0.1), the results were calculated. , .

[0060] statistics and At that time, the edge computing node calls the local historical database to filter the first [item] within the past 30 days. For records of similar data (e.g., 7200 records of fire equipment status data), count the number of abnormal data entries (e.g., 15 entries). The total number of statistical data updates (e.g., fire equipment status updated every 5 minutes, totaling 8640 times in 30 days) is as follows: .Will , , , Substituting into the formula, we can calculate... If the original cycle is 5 minutes, then the cycle will be adjusted. Minutes, rounded to 4 minutes.

[0061] Technical effect: The analytic hierarchy process (AHP) combines expert experience to determine weights, avoiding parameter bias caused by subjective assumptions, thus... and More aligned with the actual management needs of buildings, such as fire equipment data... The higher frequency of abnormal data collection significantly impacts the period adjustment, ensuring the timeliness of key data collection. Compared to a fixed period, the dynamic adjustment period increases the collection frequency of key data such as elevator operating speed and fire equipment status by 20%-30% when abnormalities are frequent. For example, when the frequency of elevator malfunctions increases, the period is shortened from 5 minutes to 4 minutes, and the malfunction detection time is shortened from an average of 12 minutes to 8 minutes. At the same time, for data with low update frequency (such as building structure information), the period can be extended to 26 hours, reducing invalid data collection and lowering the CPU utilization rate of edge computing nodes (from 35% to 22%).

[0062] When preprocessing the acquired data, the 3σ principle is first used to remove abnormal fluctuations in the equipment operation data (such as temperature values ​​exceeding the normal range and abnormal elevator speed values). Then, a duplicate data detection algorithm is used to delete duplicate data collected due to communication delays. Subsequently, the data is standardized by converting all data into JSON format and encapsulating it according to the field structure of "equipment number-data type-collection time-value". After preprocessing, the edge computing node synchronizes the data to the 3D base model and updates the equipment status, spatial environment and other elements in the model to ensure that the 3D model is consistent with the actual building status.

[0063] When a user initiates a scenario access request, the edge computing node first retrieves the user's permissions and preferences, and then filters and matches the corresponding data. For example, when a property manager requests a scenario, the system filters professional data such as elevator operation and maintenance data and fire equipment status data; when a merchant requests a scenario, it filters operation-related data such as store energy consumption data and surrounding customer traffic data; and when a consumer requests a scenario, it filters service-related data such as navigation data and merchant recommendation data.

[0064] The scene rendering engine built into the edge computing node first loads the updated 3D base model, then determines the information layers to be displayed based on user preferences, using formulas. (in The display priority of the first information layer. Weighting based on user preferences Select the frequency for the user in the first information layer. Calculate the display priority of each information layer (for the data update validity of the first information layer). By conducting user behavior research, we can determine the degree to which different user groups rely on preference information. The percentage of times a user selected this layer in the historical data stored for edge computing nodes. This determines the degree of match between the layer data and the actual status of the equipment. Higher priority layers (such as elevator malfunction warning layers that property managers are concerned about, and navigation layers that consumers are concerned about) will be displayed at the top of the 3D scene.

[0065] Through the formula The operating mechanism and technical effects of priority calculation are explained below:

[0066] Operating mechanism: Determined A user behavior survey questionnaire was designed (including five dimensions of questions such as "Do you frequently adjust the display of information layers?" and "Do you prioritize data in preferred layers?", using a 5-point Likert scale). It was distributed to 500 users (100 property management staff, 200 merchants, and 200 consumers), and 482 valid questionnaires were collected. The dependence scores of each user group on preference information were calculated (e.g., property management staff averaged 4.2, merchants 3.8, and consumers 3.5), and weighted averages were used to obtain... .

[0067] statistics At that time, the edge computing node queries the user's activity regarding the first item within the past month. The layer selection record (e.g., property management personnel selected the "elevator malfunction warning layer" 1200 times, with a total of 3000 layer selections) then... ;calculate At that time, compare the layer data with the actual equipment status (e.g., the "Elevator Fault Warning Layer" shows 10 faults, and 10 faults actually occurred). Substituting into the formula, we get... If the priority of other layers is lower than 0.61, then this layer will be displayed first.

[0068] Technical effectiveness: determined based on user behavior research This makes priority calculations more aligned with the needs of different user groups, such as the professional layers that property management staff are concerned about. Higher priority levels and greater frequency of selection have a greater impact on priority, ensuring rapid acquisition of critical operational data; navigation layers, which are of interest to consumers, are prioritized based on data update effectiveness. This formula typically prioritizes high-demand, high-efficiency layers (such as elevator malfunction warning layers for property staff and navigation layers for consumers) by more than 40%, reducing the average time for users to find target information from 15 seconds to 8 seconds, thus improving the efficiency of 3D scene interaction and user experience.

[0069] After the scene is generated, the edge computing nodes select the push method according to the user terminal type. For the computer terminals used by property management personnel, the WebSocket protocol is used to push complete scene data, supporting users to perform complex interactive operations such as model scaling, rotation, and viewing device parameter details. For mobile terminals such as mobile phones and tablets used by merchants and consumers, the HTTP / 2 protocol is used to push lightweight scene data. Data volume compression is achieved by reducing the scene texture resolution (such as compressing high-definition textures to standard definition) and simplifying the number of faces in non-critical areas (such as simplifying the detailed model inside the bathroom to an outline model), ensuring that the mobile terminal can load the scene quickly.

[0070] If a user needs to modify their preferences (such as property management personnel adjusting the data display cycle of equipment, merchants changing the dimensions for viewing energy consumption data, or consumers updating their navigation route preferences), they can initiate a preference modification request through their terminal device. Upon receiving the request, the edge computing node updates the user's independent preference database in real time, overwriting the original preference information. When the user initiates a scene access request again, the edge computing node will retrieve the updated preference information, re-filter the data, and generate a customized 3D scene that matches the new preferences, ensuring that the scene continuously meets the changing needs of the user.

[0071] In summary, this embodiment deploys edge computing nodes within a commercial complex and combines them with 5G communication technology to achieve localized data processing and storage, avoiding loading delays and security risks caused by uploading data to the cloud. The loading speed of 3D scenes is significantly improved compared to traditional cloud-based models. Through identity verification and permission configuration, and personalized preference collection, customized scenario services are provided for three different user groups: property managers, merchants, and consumers. This solves the problem of low practicality of traditional unified scenarios. Property managers can efficiently carry out equipment maintenance, merchants can conveniently obtain operational data, and consumers can quickly obtain navigation and service information.

[0072] Example 2

[0073] This example is applied to a smart upgrade project for a high-end office building in a city's central business district. The building houses numerous financial and technology companies and includes standard office floors, a cluster of meeting rooms, underground parking, and supporting recreational areas. The project needs to meet the equipment monitoring requirements of the property management team, the office service needs of company employees, and the access guidance needs of visitors. (See also...) Figure 1 and Figure 2 By employing the method of this invention, edge computing nodes are deployed within buildings in combination with 5G communication to achieve localized data processing and customized scenario services, adapting to the differentiated needs of multiple scenarios and user groups in office buildings.

[0074] First, a deployment plan for edge computing nodes in the office building is developed, based on the formula for the number of edge computing nodes to be deployed. (in Determine the number of nodes to deploy. The total floor area of ​​the office building. For smart device density, This represents the maximum number of devices that can be connected to a single node. (for signal coverage efficiency) Obtain accurate values ​​from the as-built drawings of office buildings. The total number of intelligent devices such as HVAC, security, and elevators is calculated by dividing the total building area by the total number of devices in the equipment management list. The selection is determined based on the hardware parameters of the chosen edge computing node (such as processor performance and number of interfaces). Through on-site signal testing, coverage data was collected and averaged in areas with weak signals, such as high-rise buildings, elevator shafts, and underground parking garages. Ultimately, it was determined that edge computing nodes would be deployed in the low-voltage electrical shafts at the entrances of each floor, the underground equipment room, and the lobby duty room on the first floor. Each node would cover a single floor, and adjacent nodes would establish a data synchronization channel via fiber optic cables to ensure real-time sharing of equipment data and user information between nodes.

[0075] Through the formula The operating mechanism and technical effects of calculating the number of nodes are explained below:

[0076] Operating mechanism: Extract the total building area of ​​the office building At that time, based on parameters such as floor elevations and wall thicknesses in the as-built drawings, non-usable areas such as elevator shafts and stairwells are deducted (e.g., in a 20-story building with a floor area of ​​1500m2 per floor, after deducting 200m2 of non-usable area, the actual usable area per floor is 1300m2, totaling...). 2 underground floors When counting the total number of smart devices, m2 categorizes them by device type (HVAC equipment 250 units, lighting equipment 600 units, security equipment 180 units, elevator equipment 12 units, conference equipment 80 units), resulting in a total of 1122 devices. The maximum number of connected devices per unit / m2 is determined based on the selected XX industrial-grade edge computing node hardware parameters. Taiwan; test At that time, 30 test points were set up in areas with weak signals, such as high-rise buildings (18-20 floors), elevator shafts, and underground parking garages. Signal data was collected for 1 hour at each test point (sampling interval of 5 seconds). The percentage of sampling times with signal strength of 2-85dBm was calculated (e.g., an average percentage of 88%). Substituting into the formula, we get... Based on the floor distribution, it was finally determined that 5 nodes would be deployed (1 in the equipment room on the 1st basement floor, 1 in the lobby on floors 1-10, 1 in the lobby on floors 11-20, 1 in the low-voltage electrical shaft on the 5th floor, and 1 in the low-voltage electrical shaft on the 15th floor).

[0077] The data synchronization between adjacent nodes adopts the "Distributed Hash Table (DHT)" algorithm. Each node is assigned a unique hash identifier, and data is hashed and mapped to the corresponding node according to keywords (such as device number). When the data of a node is updated, the node that needs to be synchronized is quickly located through the DHT routing table. Only the hash value of the updated data and the difference content are transmitted. After the synchronization is completed, the ACK confirmation mechanism is used to ensure that the data is successfully received.

[0078] Technical effect: Calculation after deducting non-used area Calculation of the number of classified and statistical equipment This makes the parameters more accurate and avoids miscalculation of the number of nodes due to deviations in area statistics (when non-useful areas were not deducted). (Too large, requiring one more node to be computed, increasing hardware costs by 25%); Encrypt test points in areas with weak signals to make... It better reflects the actual coverage situation and avoids signal blind spots in high-rise buildings and underground parking garages (when there were not enough test points, the signal coverage rate of the 18th-20th floors was only 70%, which was improved to 92% after optimization).

[0079] The DHT algorithm, combined with the ACK confirmation mechanism, reduces data synchronization latency from 2 seconds to 0.3 seconds and increases the synchronization success rate from 95% to 99.9% compared to the traditional star-topology synchronization architecture. It also eliminates the need for central node coordination, reducing the risk of single-point failures and ensuring real-time data consistency among edge computing nodes in various areas of the office building.

[0080] Each edge computing node is equipped with a 5G industrial-grade communication module, establishing a two-way communication link between the node and building intelligent devices and user terminals. The data interface adopts a dual-protocol scheme: for building automation equipment such as HVAC and lighting systems, a data interaction channel is established via the BACnet protocol to collect real-time equipment operating parameters (such as air conditioning return air temperature and lighting circuit current); for security monitoring (facial recognition access control, fire alarms) and elevator control systems, a high-security data channel is established via the OPCUA protocol to transmit equipment status (such as access control switch records and elevator fault codes). The node has an internal protocol conversion module that automatically converts the protocol formats of different devices to a unified standard format, ensuring seamless integration between the edge computing node and the office building equipment management system, enabling bidirectional data flow.

[0081] When users submit their identity information via terminals (office computers, enterprise apps, visitor registration machines), the system provides two methods: Property maintenance personnel need to enter their employee ID and password. The edge computing node then compares their information with the internally stored database of maintenance personnel identities. Upon successful verification, they are granted permissions such as modifying device parameters, handling fault work orders, and viewing historical maintenance data. Enterprise employees can submit their identity by linking their enterprise email account to their office terminal, or by entering their workstation number and verifying a WeChat verification code. Upon successful verification, they gain permissions such as viewing meeting room reservations on their floor, adjusting office air conditioning temperature, and linking their personal attendance data. Visitors need to enter their name and mobile phone number at the lobby registration machine and receive a verification code. After verification, they only gain basic permissions such as navigation to their designated floor and location access to public rest areas. The edge computing node binds and stores the verified identity information with the user's terminal identifier (such as computer IP address or mobile phone IMEI code). Subsequent user accesses do not require repeated verification; the system directly matches the corresponding permissions.

[0082] After identity verification, the edge computing node pushes a preference settings interface to the user terminal: the property maintenance personnel interface provides options for equipment data display formats (such as real-time dashboards and trend graphs) and fault warning notification methods (such as local node pop-ups and SMS reminders); the enterprise employee interface includes quick access to frequently used functions (such as meeting room booking and air conditioning control) and office scene display modes (such as day / night modes); the visitor interface provides options for navigation language (Chinese / English) and route preferences (fastest route / accessible route). After the user completes the selection, the edge computing node establishes an independent preference database based on the user's identity (maintenance worker ID, employee ID, visitor temporary code). All preference data is stored locally on the node and is not transmitted to the remote cloud, which reduces the risk of data leakage and provides a data foundation for the subsequent generation of customized scenarios.

[0083] Edge computing nodes are configured with multi-level data acquisition cycles: a 5-minute acquisition cycle for critical safety data such as elevator speed and fire alarm status; a 15-minute acquisition cycle for high-frequency changing data such as lighting switch status and meeting room occupancy; and a 24-hour acquisition cycle for static data such as office building structure and floor layout. This is further supported by formulas. (in The adjustment factor is for the t-th type of data. For data importance weights, This is a historical anomalous frequency. To update the frequency weights, The update cycle is dynamically adjusted based on the historical update frequency. and The analytic hierarchy process (AHP) was used, combined with the office building management's scoring of data priorities, to determine the appropriate data. and Calculations are performed based on 30 days of historical data stored in the nodes. For example, if the frequency of malfunctions in a certain elevator has increased recently, Enlargement leads to As the elevator ascends, the system automatically shortens the data acquisition cycle, improving the sensitivity of fault monitoring.

[0084] Through the formula The operating mechanism and technical effects are explained below when calculating the adjustment coefficient:

[0085] Operating mechanism: determined using the analytic hierarchy process (AHP). and At that time, eight office building management experts (two property managers, two equipment engineers, two IT maintenance personnel, and two company representatives) were invited to construct a judgment matrix to compare the importance of "data importance" and "update frequency" pairwise (e.g., the importance scale of "data importance" to "update frequency" was 6). After passing the consistency test (CR=0.08<0.1), the following results were calculated. , .

[0086] After dynamically adjusting the cycle, the frequency of elevator fault data collection increased by 20%, and the fault response time was shortened from an average of 10 minutes to 6 minutes. The lighting switch status data was updated at a stable frequency, with the cycle maintained at 15 minutes. The processing time of this type of data by the edge computing node was reduced from 1.2 hours per day to 0.9 hours, freeing up more computing power for scene rendering.

[0087] Technical effect: Introducing enterprise representatives to participate in the analytic hierarchy process (AHP) scoring process makes... and Balancing the needs of property maintenance and corporate employees, such as elevator data, which is important because employees are sensitive to commuting efficiency. The higher setting ensures that the cycle shortens rapidly when the abnormal frequency increases, guaranteeing timely detection of elevator malfunctions; simultaneously Configure settings appropriately to avoid wasting resources due to excessively high update frequency.

[0088] After dynamically adjusting the cycle, the frequency of elevator fault data collection increased by 20%, and the fault response time was shortened from an average of 10 minutes to 6 minutes. The lighting switch status data was updated at a stable frequency, with the cycle maintained at 15 minutes. The processing time of this type of data by the edge computing node was reduced from 1.2 hours per day to 0.9 hours, freeing up more computing power for scene rendering.

[0089] Data preprocessing consists of two steps: first, outliers are removed using the 3σ principle (such as air conditioning temperatures exceeding the normal range or elevator speed exceeding the limit); then, duplicate data is detected and deleted using a hash algorithm (such as duplicate device status data caused by communication delays); subsequently, the data is standardized by converting all data into JSON format and encapsulating it according to the fields "device number-data type-collection time-value". After preprocessing, the edge computing nodes synchronize the data to the 3D base model, updating elements such as device status and space occupancy in the model to ensure that the model is consistent with the actual operating status of the office building.

[0090] When a user initiates a scenario access request, the edge computing node first retrieves the user's permissions and preferences information and filters and matches the data: when maintenance personnel request a scenario, data such as equipment operating parameters and fault records are filtered; when employees request a scenario, office-related data such as workstation location and meeting room status are filtered; when visitors request a scenario, navigation data such as appointment floor routes and public area locations are filtered.

[0091] The node's built-in scene rendering engine first loads the updated 3D base model, then determines the information layer based on user preferences, using formulas. (in For layer priority, Weighting based on user preferences Select a frequency for the layer. (To ensure data update validity) calculate the layer display order. Determined through user behavior research This represents the percentage of times a user selected this layer in historical data. This determines the degree of matching between layer data and the actual status of the devices. High-priority layers (such as faulty device layers that are of interest to maintenance personnel, or meeting room layers that are of interest to employees) are displayed at the top of the scene.

[0092] Through the formula The operating mechanism and technical effects of priority calculation are explained below:

[0093] Operating mechanism: Determined During the survey, questionnaires were designed for three groups: maintenance personnel, company employees, and visitors. The maintenance personnel questionnaire focused on "device data layer dependency," the company employee questionnaire focused on "office service layer dependency," and the visitor questionnaire focused on "navigation layer dependency." A total of 300 questionnaires were distributed (100 per group), and 291 valid questionnaires were collected. Scores for each group were calculated (average dependency score for maintenance personnel: 4.3; for company employees: 3.9; for visitors: 3.6), and weighted by user percentage (maintenance personnel 5%, company employees 75%, visitors 20%). .

[0094] statistics Taking the "Meeting Room Status Layer" that company employees pay attention to as an example, if we query the selection records of this layer by company employees in the past month (a total of 2400 times), and the total number of layer selections is 5000, then... ;calculate At that time, compare the meeting room occupancy status displayed in the comparison layer with the actual booking records (the layer shows 1200 occupancy records, which matches the actual 1188). Substituting into the formula, we get... In the enterprise employee scene, it ranks first in priority among all layers and is displayed first.

[0095] Technical effectiveness: determined by user group classification survey This allows for more accurate priority calculations to match the needs of different users, such as the "device fault layer" that maintenance personnel are concerned about. and All are of high priority, consistently ranking among the top, allowing maintenance personnel to view key data without having to search through pages; the "Meeting Room Status Layer," which is of great interest to enterprise employees, is also highly prioritized. High, even Slightly lower priority, but still maintain high priority to avoid office arrangement errors due to data failure.

[0096] Compared to traditional fixed priorities, this formula increases the display priority of target layers by more than 35% and improves the efficiency of users in obtaining core information by 50%. For example, the time for company employees to find available meeting rooms is reduced from an average of 20 seconds to 9 seconds, and the time for visitors to locate and reserve floors is reduced from an average of 25 seconds to 10 seconds, significantly optimizing the user experience.

[0097] After the scene is generated, the node selects the push method according to the terminal type: for the office computers of maintenance personnel, the WebSocket protocol is used to push the complete scene data, supporting interactive operations such as model rotation and viewing device details; for mobile terminals such as employee mobile phones and visitor tablets, the HTTP / 2 protocol is used to push the lightweight scene, compressing the data volume by reducing the texture resolution and simplifying the number of faces of non-critical areas (such as ceiling and corridor decorations), ensuring that the mobile terminal loads the scene quickly.

[0098] When users need to modify their preferences (such as maintenance personnel adjusting data refresh frequency, employees changing frequently used function entry points, or visitors changing navigation language), they can initiate a modification request through the terminal. The edge computing node updates the corresponding user's preference database in real time, overwriting the original information. The next time the user initiates a scene access, the node retrieves the updated preference information, re-filters the data, and generates a new customized scene, ensuring that the scene continuously adapts to changes in user needs.

[0099] In summary, this embodiment addresses the latency issues of traditional cloud-based models by deploying edge computing nodes in high-end office buildings and combining them with 5G technology to achieve localized data processing. This significantly improves the response speed of device fault warnings and the scene loading speed. Through differentiated identity verification and personalized preference collection, it provides customized services for maintenance personnel, employees, and visitors, avoiding the lack of practicality in uniform scenarios. This improves maintenance efficiency, optimizes the employee work experience, and enhances visitor navigation convenience. Simultaneously, local storage of preference data reduces security risks, and edge-side data processing alleviates cloud pressure, ensuring long-term stable system operation and providing a reliable technical solution for the intelligent operation of high-end office buildings.

[0100] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3 As shown, optionally, electronic device 410 may include a first processor 2001.

[0101] Optionally, the electronic device 410 may also include a memory 2002 and a transceiver 2003.

[0102] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0103] The following is combined with Figure 3 A detailed description of each component of electronic device 410 is provided below:

[0104] The first processor 2001 is the control center of the electronic device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0105] Optionally, the first processor 2001 can perform various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0106] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0107] In a specific implementation, as one example, the electronic device 410 may also include multiple processors, for example... Figure 3 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0108] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0109] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be accessed through the interface circuit of the electronic device 410. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0110] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0111] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0112] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected via the interface circuit of the electronic device 410. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0113] It should be noted that, Figure 3 The structure of the electronic device 410 shown does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] Furthermore, the technical effects of the electronic device 410 can be referenced from the technical effects of the 3D visualization design method for 5G smart buildings based on edge cloud computing described in the above method embodiments, and will not be repeated here.

[0115] It should be understood that the first processor 2001 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0116] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0118] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0119] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0120] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A 3D visualization design method for 5G smart buildings based on edge cloud computing, characterized in that, The method includes: In key areas of 5G smart buildings, edge computing nodes with data storage, real-time processing, and low-latency response capabilities are deployed. These nodes are equipped with 5G communication modules to establish communication links between the edge computing nodes and building smart devices and user terminals. Data interface adaptation between the edge computing nodes and the building equipment management system is also completed. Specifically, edge computing nodes are deployed at building entrances, equipment rooms, and lobbies. The coverage area of ​​each edge computing node is limited to a single floor. Data synchronization channels are established between adjacent edge computing nodes. The number of edge computing nodes deployed is determined by a formula. ,in, The number of edge computing nodes to deploy, The total floor area of ​​the building. The density of smart devices within the building. This refers to the maximum number of devices that can be connected to a single edge computing node. To improve the signal coverage efficiency of edge computing nodes, Indicates the rounding up symbol; Users submit identity information through terminal devices. After verifying the identity information, the edge computing nodes configure and store the user's operation permissions according to preset rules. Edge computing nodes push preference settings interfaces to user terminals, collect the types of information selected by users, establish independent preference databases according to user identities, and store them locally; Edge computing nodes acquire building equipment operation data and spatial status data at preset cycles, and update the 3D basic model after preprocessing. When a user initiates a scene access request, the edge computing node retrieves the user's permissions and preferences, filters and matches the data, and combines it with the 3D basic model to generate a customized 3D scene. Edge computing nodes push customized 3D scenes to user terminals via 5G links; When a user initiates a preference modification request, the edge computing node updates the user preference database in real time and generates a scenario based on the updated preferences the next time the user requests a scenario.

2. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The data interface between the edge computing node and the building equipment management system is adapted by adopting a dual protocol adaptation method of OPCUA and BACnet. For HVAC and lighting equipment in the building, a data interaction channel is established through the BACnet protocol, and for security and elevator equipment, a data interaction channel is established through the OPCUA protocol. A protocol conversion module is set up in the edge computing node.

3. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The user submits identity information through a terminal device. Specifically, the user terminal provides two identity submission methods: inputting the ID number and associating with the room number. Property management personnel submit identity information by inputting their work ID number, while ordinary tenants submit identity information by inputting their room number and verifying the linked mobile phone number verification code. The edge computing node associates and stores the verified identity information with the user terminal device identifier.

4. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The edge computing nodes acquire building equipment operation data and spatial status data according to a preset cycle. Specifically, the edge computing nodes are set with multiple acquisition cycles: a 5-minute acquisition cycle for elevator operating speed and fire equipment status, a 15-minute acquisition cycle for lighting switch status and meeting room occupancy, and a 24-hour acquisition cycle for building structure information. The adjustment coefficients for each acquisition cycle are determined by calculation using a formula: ,in, For the first Data acquisition cycle adjustment coefficient For data importance weights, For the first Historical anomaly frequency of the data type Weight the data update frequency. For the first Historical update frequency of data types.

5. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The preprocessing specifically includes two steps: data cleaning and format standardization. During data cleaning, the 3σ principle is used to remove abnormal fluctuations in equipment operation data, and duplicate data is deleted using a duplicate data detection algorithm. When standardizing the format, all data is converted to JSON format and encapsulated according to the field structure of "device number-data type-collection time-value".

6. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The edge computing node generates a customized 3D scene. Specifically, the edge computing node has a built-in scene rendering engine. The rendering engine first loads the updated 3D base model, then determines the information layers to be displayed based on user preference information, and maps the preprocessed device data and spatial data to the corresponding information layers. The display priority of the information layers is determined by calculation using a formula: ,in, For the first The display priority of each information layer Weighting based on user preferences For the first User selection frequency for each information layer For the first The validity of data updates for each information layer.

7. The 3D visualization design method for 5G smart buildings based on edge cloud computing according to claim 1, characterized in that, The edge computing nodes push customized 3D scenes to user terminals via 5G links. Specifically, the edge computing nodes select the push method according to the user terminal type. For computer terminals, the WebSocket protocol is used to push complete scene data, while for mobile terminals such as mobile phones and tablets, the HTTP / 2 protocol is used to push lightweight scene data. The lightweight scene data is compressed by reducing texture resolution and simplifying the number of faces in non-critical areas.

8. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory stores computer-readable instructions, which, when executed by the processor, implement the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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