A method, system, and media for drag-and-drop 3D digital space modeling

Abstract

This application provides a drag-and-drop 3D digital space modeling method, system, and medium. The method includes: constructing a base station model element library; dragging and dropping model elements into a 3D digital space and adjusting model spacing; collecting historical user operation data; processing the historical operation data to generate a related component demand prediction model; obtaining recommended related components and their corresponding correlation scores; processing the historical operation data to generate a candidate component time-series prediction model; obtaining time-series candidate components and their corresponding time-series candidate component scores; and generating a component recommendation list based on the correlation scores and time-series candidate component scores, and pushing it to the 3D digital space interface for display. This application enables intelligent and visualized end-to-end processes from modeling to coverage analysis, providing an efficient digital tool for base station planning.

Description

Technical Field

[0001] This application relates to the field of 3D digital space modeling technology, and more specifically, to a drag-and-drop 3D digital space modeling method, system, and medium. Background Technology

[0002] With the popularization of 5G communication technology and the deepening application of smart cities, industrial internet and other scenarios, the demand for 3D visualization modeling in base station planning and network coverage optimization is increasing. Traditional base station modeling methods mainly rely on 2D drawings or static 3D models. The model element library (such as antennas, cabinets, feeders, etc.) is difficult to dynamically combine and quickly deploy. Moreover, traditional modeling systems usually separate geometric modeling and signal simulation into independent modules, lacking real-time data linkage. In the modeling process, the selection of equipment components often relies on the experience of engineers, lacking in-depth utilization of users' historical operation data. It fails to deeply integrate interactive operation, equipment parameters, environmental characteristics and historical experience, resulting in a linear process of "geometric modeling → manual parameter configuration → offline simulation analysis", which makes it difficult to achieve a real-time closed loop of "drag and drop modeling, modeling simulation, and simulation optimization".

[0003] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a drag-and-drop 3D digital space modeling method, system, and medium that integrates interactive drag-and-drop modeling, intelligent component recommendation, and real-time signal simulation. By constructing a parameterized base station model element library, it generates a component recommendation model using historical user operation data to achieve high-precision component recommendation. It also deeply correlates the spatial location of the equipment, signal transmission parameters, and environmental obstacle attributes to achieve real-time analysis of the signal reception strength at each location point. Furthermore, it visually presents the three-dimensional coverage effect of the signal strength through a heat map, realizing intelligent and visualized full-process from modeling to coverage analysis, and providing an efficient digital tool for base station planning.

[0005] This application also provides a drag-and-drop 3D digital space modeling method, including the following steps:

[0006] Build a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing;

[0007] Collect users' historical operation data, process the historical operation data to generate a related component demand prediction model, and process it to obtain recommended related components and their corresponding relatedness scores.

[0008] Based on historical operational data, a candidate component time series prediction model is generated, and the time series candidate components and their corresponding time series candidate component scores are obtained.

[0009] Based on the correlation score and the time-series candidate component score, a component recommendation list is generated and pushed to the 3D digital space interface for display.

[0010] Optionally, in the drag-and-drop 3D digital space modeling method described in this application, the step of constructing a base station model element library, dragging and dropping model elements into the 3D digital space, and adjusting the model spacing includes:

[0011] Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements;

[0012] Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing;

[0013] The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

[0014] Optionally, in the drag-and-drop 3D digital space modeling method described in this application, the step of collecting user historical operation data, processing the historical operation data to generate a related component demand prediction model, and processing to obtain recommended related components and their corresponding relatedness scores includes:

[0015] Collect historical operation data of users in the process of 3D digital space modeling, including model element data, model associated component attribute data, component co-occurrence frequency and drag operation timing data;

[0016] The preset collaborative filtering model is trained using the model element data, model associated component attribute data, and component co-occurrence frequency to generate an associated component demand prediction model;

[0017] Obtain the selected model element data, input the selected model element data into the related component demand prediction model for processing to obtain recommended related components and their corresponding relatedness scores.

[0018] Optionally, in the drag-and-drop 3D digital space modeling method described in this application, the step of generating a candidate component time-series prediction model based on historical operation data and obtaining the time-series candidate components and their corresponding time-series candidate component scores includes:

[0019] The drag-and-drop operation timing data includes a component ID sequence, a timestamp sequence, and operation type identifier data;

[0020] The preset temporal convolutional network model is trained based on the component ID sequence, timestamp sequence, and operation type identifier data to obtain the candidate component temporal prediction model;

[0021] Obtain the timing data of the current drag-and-drop operation, and input the timing data of the current drag-and-drop operation into the candidate component timing prediction model to obtain the timing candidate component and its corresponding timing candidate component score.

[0022] Optionally, in the drag-and-drop 3D digital space modeling method described in this application, the step of generating a component recommendation list based on the correlation score and the temporal candidate component score and pushing it to the 3D digital space interface for display includes:

[0023] Select the intersection component from the recommended related components and the time-series candidate components, and calculate the weighted average of the correlation score corresponding to the intersection component and the score of the time-series candidate component to obtain the fusion score;

[0024] The intersecting components are sorted from highest to lowest according to their fusion scores, and a recommended list of components is generated and pushed to the 3D digital space interface for display.

[0025] Optionally, the drag-and-drop 3D digital space modeling method described in this application further includes:

[0026] Obtain the spatial coordinates of basic equipment model elements in 3D digital space, as well as the spatial coordinates, material data, and size data of obstacles;

[0027] The spatial distance is calculated based on the spatial coordinates of the basic equipment model elements and the spatial coordinates of the obstacles.

[0028] Obtain the signal transmission frequency and signal transmission intensity of the basic equipment model elements, and calculate the free space loss value based on the signal transmission frequency and spatial distance, combined with the preset free space propagation loss formula;

[0029] The signal penetration loss value is determined based on the material and size data of the obstacle;

[0030] The sum of the free space loss and the penetration loss is used as the signal attenuation value;

[0031] The signal attenuation value is compared with a preset signal attenuation threshold. If the signal attenuation value is greater than the preset signal attenuation threshold, a prompt is made to change the placement position of the model element.

[0032] Optionally, the drag-and-drop 3D digital space modeling method described in this application further includes:

[0033] The free space loss value at each location point in an unobstructed environment is calculated based on the preset free space propagation loss formula, and the signal strength at each location point in an unobstructed environment is obtained based on the signal transmission strength.

[0034] The difference between the signal strength and the penetration loss value in an unobstructed environment is used as the received signal strength at each location point in the 3D digital space.

[0035] The received signal strength at each location point is divided into different level intervals, and each level interval is marked with a different color.

[0036] Based on the level range and the corresponding color, a signal coverage heatmap is generated in 3D digital space.

[0037] Secondly, this application provides a drag-and-drop 3D digital space modeling system, which includes: a memory and a processor. The memory stores a program for a drag-and-drop 3D digital space modeling method. When the program for the drag-and-drop 3D digital space modeling method is executed by the processor, it performs the following steps:

[0038] Build a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing;

[0039] Collect users' historical operation data, process the historical operation data to generate a related component demand prediction model, and process it to obtain recommended related components and their corresponding relatedness scores.

[0040] Based on historical operational data, a candidate component time series prediction model is generated, and the time series candidate components and their corresponding time series candidate component scores are obtained.

[0041] Based on the correlation score and the time-series candidate component score, a component recommendation list is generated and pushed to the 3D digital space interface for display.

[0042] Optionally, in the drag-and-drop 3D digital space modeling system described in this application, the step of constructing a base station model element library, dragging and dropping model elements into the 3D digital space, and adjusting the model spacing includes:

[0043] Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements;

[0044] Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing;

[0045] The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

[0046] Thirdly, this application also provides a computer-readable storage medium storing a drag-and-drop 3D digital space modeling method program, which, when executed by a processor, implements the steps of the drag-and-drop 3D digital space modeling method as described in any of the preceding claims.

[0047] As can be seen from the above, the drag-and-drop 3D digital space modeling method, system, and medium provided in this application integrate interactive drag-and-drop modeling, intelligent component recommendation, and real-time signal simulation. By constructing a parameterized base station model element library and generating a component recommendation model using user historical operation data, it achieves the goal of high-precision component recommendation. It also deeply correlates the spatial location of the equipment, signal transmission parameters, and environmental obstacle attributes to achieve real-time analysis of the signal reception strength at each location point. Furthermore, it intuitively presents the three-dimensional coverage effect of the signal strength through a heat map, realizing intelligent and visualized full-process from modeling to coverage analysis, and providing an efficient digital tool for base station planning.

[0048] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating the drag-and-drop 3D digital space modeling method provided in this application embodiment;

[0051] Figure 2 A flowchart illustrating the process of obtaining recommended related components and their corresponding relatedness scores in the drag-and-drop 3D digital space modeling method provided in this application embodiment;

[0052] Figure 3 A flowchart illustrating the process of obtaining temporal candidate components and their corresponding temporal candidate component scores in the drag-and-drop 3D digital space modeling method provided in this application embodiment;

[0053] Figure 4 This is a flowchart illustrating the generation of a recommended list of components for the drag-and-drop 3D digital space modeling method provided in this application embodiment, and its push to the 3D digital space interface for display. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0055] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] Please refer to Figure 1 , Figure 1 This is a flowchart of a drag-and-drop 3D digital space modeling method according to some embodiments of this application. This drag-and-drop 3D digital space modeling method is used in terminal devices, such as computers and mobile terminals. The drag-and-drop 3D digital space modeling method includes the following steps:

[0057] S11. Construct a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing;

[0058] S12. Collect users' historical operation data, process the historical operation data to generate a related component demand prediction model, and process to obtain recommended related components and their corresponding relatedness scores.

[0059] S13. Generate a candidate component time series prediction model based on historical operation data, and process it to obtain the time series candidate components and their corresponding time series candidate component scores.

[0060] S14. Based on the correlation score and the time-series candidate component score, generate a component recommendation list and push it to the 3D digital space interface for display.

[0061] It should be noted that this application integrates interactive drag-and-drop modeling, intelligent component recommendation, and real-time signal simulation. By constructing a parameterized base station model element library, model elements can be dragged and dropped into a 3D digital space, and the model spacing can be adjusted. Historical operation data of users during the 3D digital space modeling process is collected. Collaborative filtering recommendation technology and time-series recommendation model are used to generate recommended components and their corresponding recommendation scores. After weighted fusion, a component recommendation list is output and pushed to the 3D digital space interface for display, achieving the goal of high-precision component recommendation. Furthermore, the spatial location of the equipment, signal transmission parameters, and environmental obstacle attributes are deeply correlated to achieve real-time analysis of the signal reception strength at each location point. The three-dimensional coverage effect of signal strength is presented intuitively through heat maps, realizing intelligent and visualized full-process from modeling to coverage analysis, providing an efficient digital tool for base station planning.

[0062] According to an embodiment of the present invention, the step of constructing a base station model element library, dragging and dropping model elements into a 3D digital space, and adjusting the model spacing includes:

[0063] Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements;

[0064] Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing;

[0065] The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

[0066] It should be noted that the basic equipment model elements cover various types of equipment commonly found in China Unicom base stations, such as communication antennas (including antennas of different frequency bands and types), transceivers, power supply equipment, and cabinets. The combined equipment model elements combine multiple basic equipment model elements according to the actual equipment combination rules of China Unicom base stations. For example, antennas, feeders, and mounting brackets are combined into a complete antenna installation assembly, and cabinets are combined with various internal circuit boards into a cabinet equipment assembly. The environmental model elements include the terrain and topography of the base station (such as mountains, plains, and urban buildings) and the internal environment of the equipment room (such as walls, floors, doors, and windows). Each model element has built-in geometric attribute parameters and technical parameters. Simultaneously, the system automatically detects the spatial relationship between model elements and other surrounding elements to avoid overlaps that do not conform to actual installation specifications.

[0067] Please refer to Figure 2 , Figure 2This is a flowchart illustrating the process of obtaining recommended related components and their corresponding relatedness scores in a drag-and-drop 3D digital space modeling method according to some embodiments of this application. According to embodiments of the present invention, the step of collecting user historical operation data, processing the historical operation data to generate a related component demand prediction model, and processing to obtain recommended related components and their corresponding relatedness scores includes:

[0068] S21. Collect historical operation data of users in the process of 3D digital space modeling, including model element data, model associated component attribute data, component co-occurrence frequency and drag operation timing data;

[0069] S22. The preset collaborative filtering model is trained using the model element data, model associated component attribute data and component co-occurrence frequency to generate an associated component demand prediction model.

[0070] S23. Obtain the selected model element data, input the selected model element data into the related component demand prediction model for processing to obtain recommended related components and their corresponding relatedness scores.

[0071] It should be noted that model element data includes model element type data and model element function data; model-related component attribute data includes component type data and component function data; and drag-and-drop operation time sequence data includes component ID sequence, timestamp sequence, and operation type identifier data. The collaborative filtering model recommends components with similar modeling patterns based on the user's historical component pairings.

[0072] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the process of obtaining temporal candidate components and their corresponding scores in a drag-and-drop 3D digital space modeling method according to some embodiments of this application. According to an embodiment of the present invention, the step of generating a candidate component temporal prediction model based on historical operation data and obtaining the temporal candidate components and their corresponding scores includes:

[0073] S31, The drag-and-drop operation timing data includes a component ID sequence, a timestamp sequence, and operation type identifier data;

[0074] S32. Train the preset temporal convolutional network model according to the component ID sequence, timestamp sequence, and operation type identifier data to obtain the candidate component temporal prediction model.

[0075] S33. Obtain the current drag-and-drop operation timing data, and input the current drag-and-drop operation timing data into the candidate component timing prediction model to obtain the timing candidate component and its corresponding timing candidate component score.

[0076] It should be noted that the temporal convolutional network model is used to capture the sequential dependencies of user behavior and predict the components that the user may need next.

[0077] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the generation of a component recommendation list and its push to a 3D digital space interface for display, as described in some embodiments of this application for drag-and-drop 3D digital space modeling. According to an embodiment of the present invention, the step of generating a component recommendation list based on the correlation score and the temporal candidate component score and then pushing it to the 3D digital space interface for display includes:

[0078] S41. Select the intersection component from the recommended related components and the time-series candidate components, and calculate the weighted average of the correlation score and the time-series candidate component score corresponding to the intersection component to obtain the fusion score.

[0079] S42. Sort the intersection components in descending order of fusion score, generate a component recommendation list and push it to the 3D digital space interface for display.

[0080] It should be noted that the fusion score is obtained by weighted averaging the correlation scores of the intersection components and the scores of the time-series candidate components. The weight values ​​can be determined through historical experience of collaborative filtering and the real-time accuracy of sequence models. This application significantly improves the accuracy and scene adaptability of component recommendations in 3D modeling scenarios by adopting a hybrid recommendation strategy that combines historical collaborative relationships and real-time behavioral sequences. The recommendation list is displayed in a visual form on the 3D digital space interface for users to quickly select, realizing intelligent assistance in the modeling process.

[0081] According to an embodiment of the present invention, it further includes:

[0082] Obtain the spatial coordinates of basic equipment model elements in 3D digital space, as well as the spatial coordinates, material data, and size data of obstacles;

[0083] The spatial distance is calculated based on the spatial coordinates of the basic equipment model elements and the spatial coordinates of the obstacles.

[0084] Obtain the signal transmission frequency and signal transmission intensity of the basic equipment model elements, and calculate the free space loss value based on the signal transmission frequency and spatial distance, combined with the preset free space propagation loss formula;

[0085] The signal penetration loss value is determined based on the material and size data of the obstacle;

[0086] The sum of the free space loss and the penetration loss is used as the signal attenuation value;

[0087] The signal attenuation value is compared with a preset signal attenuation threshold. If the signal attenuation value is greater than the preset signal attenuation threshold, a prompt is made to change the placement position of the model element.

[0088] It should be noted that a correspondence table between different materials and penetration loss coefficients is established in advance. The corresponding penetration loss coefficient is obtained from the correspondence table according to the material of the obstacle, and the penetration thickness of the obstacle is obtained according to the size of the obstacle. The product of the penetration loss coefficient and the penetration thickness of the obstacle is used as the penetration loss value of the signal.

[0089] According to an embodiment of the present invention, it further includes:

[0090] The free space loss value at each location point in an unobstructed environment is calculated based on the preset free space propagation loss formula, and the signal strength at each location point in an unobstructed environment is obtained based on the signal transmission strength.

[0091] The difference between the signal strength and the penetration loss value in an unobstructed environment is used as the received signal strength at each location point in the 3D digital space.

[0092] The received signal strength at each location point is divided into different level intervals, and each level interval is marked with a different color.

[0093] Based on the level range and the corresponding color, a signal coverage heatmap is generated in 3D digital space.

[0094] It should be noted that, based on the signal transmission frequency and the spatial distance between each location point in the 3D digital space and the basic equipment model elements, the free space loss value of each location point in the unobstructed environment is calculated using a preset free space propagation loss formula. Then, the difference between the signal transmission strength and the free space loss value is taken as the signal strength of each location point in the unobstructed environment.

[0095] According to an embodiment of the present invention, it further includes:

[0096] The system obtains the remaining battery power of the terminal device and the network latency of the communication link in real time;

[0097] Compare the remaining battery power with the preset battery power threshold, and compare the network latency with the preset latency threshold;

[0098] If the device's remaining battery power is lower than the preset battery power threshold or the network latency is greater than the preset latency threshold, it will automatically switch to wireframe mode.

[0099] It should be noted that the system monitors the device's remaining battery power and network latency in real time, and adjusts the display method of 3D digital space model elements accordingly to improve modeling efficiency.

[0100] This invention also discloses a drag-and-drop 3D digital space modeling system, including a memory and a processor. The memory stores a drag-and-drop 3D digital space modeling method program, which, when executed by the processor, performs the following steps:

[0101] Build a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing;

[0102] Collect users' historical operation data, process the historical operation data to generate a related component demand prediction model, and process it to obtain recommended related components and their corresponding relatedness scores.

[0103] Based on historical operational data, a candidate component time series prediction model is generated, and the time series candidate components and their corresponding time series candidate component scores are obtained.

[0104] Based on the correlation score and the time-series candidate component score, a component recommendation list is generated and pushed to the 3D digital space interface for display.

[0105] It should be noted that this application integrates interactive drag-and-drop modeling, intelligent component recommendation, and real-time signal simulation. By constructing a parameterized base station model element library, model elements can be dragged and dropped into a 3D digital space, and the model spacing can be adjusted. Historical operation data of users during the 3D digital space modeling process is collected. Collaborative filtering recommendation technology and time-series recommendation model are used to generate recommended components and their corresponding recommendation scores. After weighted fusion, a component recommendation list is output and pushed to the 3D digital space interface for display, achieving the goal of high-precision component recommendation. Furthermore, the spatial location of the equipment, signal transmission parameters, and environmental obstacle attributes are deeply correlated to achieve real-time analysis of the signal reception strength at each location point. The three-dimensional coverage effect of signal strength is presented intuitively through heat maps, realizing intelligent and visualized full-process from modeling to coverage analysis, providing an efficient digital tool for base station planning.

[0106] According to an embodiment of the present invention, the step of constructing a base station model element library, dragging and dropping model elements into a 3D digital space, and adjusting the model spacing includes:

[0107] Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements;

[0108] Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing;

[0109] The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

[0110] It should be noted that the basic equipment model elements cover various types of equipment commonly found in China Unicom base stations, such as communication antennas (including antennas of different frequency bands and types), transceivers, power supply equipment, and cabinets. The combined equipment model elements combine multiple basic equipment model elements according to the actual equipment combination rules of China Unicom base stations. For example, antennas, feeders, and mounting brackets are combined into a complete antenna installation assembly, and cabinets are combined with various internal circuit boards into a cabinet equipment assembly. The environmental model elements include the terrain and topography of the base station (such as mountains, plains, and urban buildings) and the internal environment of the equipment room (such as walls, floors, doors, and windows). Each model element has built-in geometric attribute parameters and technical parameters. Simultaneously, the system automatically detects the spatial relationship between model elements and other surrounding elements to avoid overlaps that do not conform to actual installation specifications.

[0111] According to an embodiment of the present invention, the step of collecting user historical operation data, processing the historical operation data to generate a related component demand prediction model, and processing to obtain recommended related components and their corresponding relatedness scores includes:

[0112] Collect historical operation data of users in the process of 3D digital space modeling, including model element data, model associated component attribute data, component co-occurrence frequency and drag operation timing data;

[0113] The preset collaborative filtering model is trained using the model element data, model associated component attribute data, and component co-occurrence frequency to generate an associated component demand prediction model;

[0114] Obtain the selected model element data, input the selected model element data into the related component demand prediction model for processing to obtain recommended related components and their corresponding relatedness scores.

[0115] It should be noted that model element data includes model element type data and model element function data; model-related component attribute data includes component type data and component function data; and drag-and-drop operation time sequence data includes component ID sequence, timestamp sequence, and operation type identifier data. The collaborative filtering model recommends components with similar modeling patterns based on the user's historical component pairings.

[0116] According to an embodiment of the present invention, the step of generating a candidate component time-series prediction model based on historical operation data and processing it to obtain the time-series candidate components and their corresponding time-series candidate component scores includes:

[0117] The drag-and-drop operation timing data includes a component ID sequence, a timestamp sequence, and operation type identifier data;

[0118] The preset temporal convolutional network model is trained based on the component ID sequence, timestamp sequence, and operation type identifier data to obtain the candidate component temporal prediction model;

[0119] Obtain the timing data of the current drag-and-drop operation, and input the timing data of the current drag-and-drop operation into the candidate component timing prediction model to obtain the timing candidate component and its corresponding timing candidate component score.

[0120] It should be noted that the temporal convolutional network model is used to capture the sequential dependencies of user behavior and predict the components that the user may need next.

[0121] According to an embodiment of the present invention, the step of generating a component recommendation list based on the correlation score and the temporal candidate component score and pushing it to the 3D digital space interface for display includes:

[0122] Select the intersection component from the recommended related components and the time-series candidate components, and calculate the weighted average of the correlation score corresponding to the intersection component and the score of the time-series candidate component to obtain the fusion score;

[0123] The intersecting components are sorted from highest to lowest according to their fusion scores, and a recommended list of components is generated and pushed to the 3D digital space interface for display.

[0124] It should be noted that the fusion score is obtained by weighted averaging the correlation scores of the intersection components and the scores of the time-series candidate components. The weight values ​​can be determined through historical experience of collaborative filtering and the real-time accuracy of sequence models. This application significantly improves the accuracy and scene adaptability of component recommendations in 3D modeling scenarios by adopting a hybrid recommendation strategy that combines historical collaborative relationships and real-time behavioral sequences. The recommendation list is displayed in a visual form on the 3D digital space interface for users to quickly select, realizing intelligent assistance in the modeling process.

[0125] According to an embodiment of the present invention, it further includes:

[0126] Obtain the spatial coordinates of basic equipment model elements in 3D digital space, as well as the spatial coordinates, material data, and size data of obstacles;

[0127] The spatial distance is calculated based on the spatial coordinates of the basic equipment model elements and the spatial coordinates of the obstacles.

[0128] Obtain the signal transmission frequency and signal transmission intensity of the basic equipment model elements, and calculate the free space loss value based on the signal transmission frequency and spatial distance, combined with the preset free space propagation loss formula;

[0129] The signal penetration loss value is determined based on the material and size data of the obstacle;

[0130] The sum of the free space loss and the penetration loss is used as the signal attenuation value;

[0131] The signal attenuation value is compared with a preset signal attenuation threshold. If the signal attenuation value is greater than the preset signal attenuation threshold, a prompt is made to change the placement position of the model element.

[0132] It should be noted that a correspondence table between different materials and penetration loss coefficients is established in advance. The corresponding penetration loss coefficient is obtained from the correspondence table according to the material of the obstacle, and the penetration thickness of the obstacle is obtained according to the size of the obstacle. The product of the penetration loss coefficient and the penetration thickness of the obstacle is used as the penetration loss value of the signal.

[0133] According to an embodiment of the present invention, it further includes:

[0134] The free space loss value at each location point in an unobstructed environment is calculated based on the preset free space propagation loss formula, and the signal strength at each location point in an unobstructed environment is obtained based on the signal transmission strength.

[0135] The difference between the signal strength and the penetration loss value in an unobstructed environment is used as the received signal strength at each location point in the 3D digital space.

[0136] The received signal strength at each location point is divided into different level intervals, and each level interval is marked with a different color.

[0137] Based on the level range and the corresponding color, a signal coverage heatmap is generated in 3D digital space.

[0138] It should be noted that, based on the signal transmission frequency and the spatial distance between each location point in the 3D digital space and the basic equipment model elements, the free space loss value of each location point in the unobstructed environment is calculated using a preset free space propagation loss formula. Then, the difference between the signal transmission strength and the free space loss value is taken as the signal strength of each location point in the unobstructed environment.

[0139] According to an embodiment of the present invention, it further includes:

[0140] The system obtains the remaining battery power of the terminal device and the network latency of the communication link in real time;

[0141] Compare the remaining battery power with the preset battery power threshold, and compare the network latency with the preset latency threshold;

[0142] If the device's remaining battery power is lower than the preset battery power threshold or the network latency is greater than the preset latency threshold, it will automatically switch to wireframe mode.

[0143] It should be noted that the system monitors the device's remaining battery power and network latency in real time, and adjusts the display method of 3D digital space model elements accordingly to improve modeling efficiency.

[0144] A third aspect of the present invention provides a readable storage medium storing a drag-and-drop 3D digital space modeling method program, wherein when the drag-and-drop 3D digital space modeling method program is executed by a processor, it implements the steps of the drag-and-drop 3D digital space modeling method as described in any of the preceding claims.

[0145] This invention discloses a drag-and-drop 3D digital space modeling method, system, and medium that integrates interactive drag-and-drop modeling, intelligent component recommendation, and real-time signal simulation. By constructing a parameterized base station model element library and generating a component recommendation model using historical user operation data, it achieves high-precision component recommendation. Furthermore, it deeply correlates equipment spatial location, signal transmission parameters, and environmental obstacle attributes to achieve real-time analysis of signal reception strength at each location point. It also intuitively presents the three-dimensional coverage effect of signal strength through heat maps, realizing intelligent and visualized end-to-end processes from modeling to coverage analysis, and providing an efficient digital tool for base station planning.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0147] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0149] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This 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 methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A drag-and-drop 3D digital space modeling method, characterized in that, Includes the following steps: Build a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing; Collect historical operation data of users in the process of 3D digital space modeling, including model element data, model associated component attribute data, component co-occurrence frequency and drag operation timing data; The preset collaborative filtering model is trained using the model element data, model associated component attribute data, and component co-occurrence frequency to generate an associated component demand prediction model; Obtain the selected model element data, input the selected model element data into the related component demand prediction model for processing to obtain recommended related components and their corresponding relatedness scores; The drag-and-drop operation timing data includes a component ID sequence, a timestamp sequence, and operation type identifier data; The preset temporal convolutional network model is trained based on the component ID sequence, timestamp sequence, and operation type identifier data to obtain the candidate component temporal prediction model; Obtain the current drag-and-drop operation timing data, input the current drag-and-drop operation timing data into the candidate component timing prediction model for processing to obtain the timing candidate component and its corresponding timing candidate component score; Select the intersection component from the recommended related components and the time-series candidate components, and calculate the weighted average of the correlation score corresponding to the intersection component and the score of the time-series candidate component to obtain the fusion score; The intersecting components are sorted from highest to lowest according to their fusion scores, and a recommended list of components is generated and pushed to the 3D digital space interface for display.

2. The drag-and-drop 3D digital space modeling method according to claim 1, characterized in that, The process of constructing a base station model element library, dragging and dropping model elements into the 3D digital space, and adjusting the model spacing includes: Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements; Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing; The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

3. The drag-and-drop 3D digital space modeling method according to claim 2, characterized in that, Also includes: Obtain the spatial coordinates of basic equipment model elements in 3D digital space, as well as the spatial coordinates, material data, and size data of obstacles; The spatial distance is calculated based on the spatial coordinates of the basic equipment model elements and the spatial coordinates of the obstacles. Obtain the signal transmission frequency and signal transmission intensity of the basic equipment model elements, and calculate the free space loss value based on the signal transmission frequency and spatial distance, combined with the preset free space propagation loss formula; The signal penetration loss value is determined based on the material and size data of the obstacle; The sum of the free space loss and the penetration loss is used as the signal attenuation value; The signal attenuation value is compared with a preset signal attenuation threshold. If the signal attenuation value is greater than the preset signal attenuation threshold, a prompt is made to change the placement position of the model element.

4. The drag-and-drop 3D digital space modeling method according to claim 3, characterized in that, Also includes: The free space loss value at each location point in an unobstructed environment is calculated based on the preset free space propagation loss formula, and the signal strength at each location point in an unobstructed environment is obtained based on the signal transmission strength. The difference between the signal strength and the penetration loss value in an unobstructed environment is used as the received signal strength at each location point in the 3D digital space. The received signal strength at each location point is divided into different level intervals, and each level interval is marked with a different color. Based on the level range and the corresponding color, a signal coverage heatmap is generated in 3D digital space.

5. A drag-and-drop 3D digital space modeling system, characterized in that, It includes a memory and a processor. The memory stores a program for a drag-and-drop 3D digital space modeling method. When the program for the drag-and-drop 3D digital space modeling method is executed by the processor, it performs the following steps: Build a base station model element library, drag and drop model elements into the 3D digital space, and adjust the model spacing; Collect historical operation data of users in the process of 3D digital space modeling, including model element data, model associated component attribute data, component co-occurrence frequency and drag operation timing data; The preset collaborative filtering model is trained using the model element data, model associated component attribute data, and component co-occurrence frequency to generate an associated component demand prediction model; Obtain the selected model element data, input the selected model element data into the related component demand prediction model for processing to obtain recommended related components and their corresponding relatedness scores; The drag-and-drop operation timing data includes a component ID sequence, a timestamp sequence, and operation type identifier data; The preset temporal convolutional network model is trained based on the component ID sequence, timestamp sequence, and operation type identifier data to obtain the candidate component temporal prediction model; Obtain the current drag-and-drop operation timing data, input the current drag-and-drop operation timing data into the candidate component timing prediction model for processing to obtain the timing candidate component and its corresponding timing candidate component score; Select the intersection component from the recommended related components and the time-series candidate components, and calculate the weighted average of the correlation score corresponding to the intersection component and the score of the time-series candidate component to obtain the fusion score; The intersecting components are sorted from highest to lowest according to their fusion scores, and a recommended list of components is generated and pushed to the 3D digital space interface for display.

6. The drag-and-drop 3D digital space modeling system according to claim 5, characterized in that, The process of constructing a base station model element library, dragging and dropping model elements into the 3D digital space, and adjusting the model spacing includes: Construct a base station model element library, including basic equipment model elements, combined equipment model elements, and environmental model elements; Select model elements from the base station model element library and drag them into the 3D digital space, and identify the spacing between the model elements to obtain the model spacing; The system queries the minimum safe spacing from the preset industry standard database and compares the model spacing with the minimum safe spacing. If the model spacing is less than the minimum safe spacing, a spacing adjustment prompt is given.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a drag-and-drop 3D digital space modeling program, which, when executed by a processor, implements the steps of the drag-and-drop 3D digital space modeling method as described in any one of claims 1 to 4.

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