A method and system for determining base station layout configuration scheme in an intelligent building

Abstract

This application discloses a method and system for determining a base station layout configuration scheme within an intelligent building. The method comprises: collecting first / second building data, historical first / second operating condition data, and historical first / second base station layout configuration data; using the acquired first / second building data, historical first / second operating condition data, and historical first / second base station layout configuration data as training data, and using a deep learning algorithm to generate a building overall layout configuration model or a building single-story layout configuration model for corresponding training; collecting the building data and operating condition requirements of the target building, inputting them into the building overall layout configuration model and the building single-story layout configuration model, respectively, to comprehensively obtain the target building base station layout configuration; and monitoring the signal quality at each location within the target building in real time, and using an AI model to dynamically adjust the configuration of base stations around locations where the real-time signal quality is lower than a preset quality. This application can solve the problem of poor high-frequency communication quality in complex building environments.

Description

Technical Field

[0001] The present application relates to the technical field of base station layout configuration in intelligent buildings, and in particular to a method and system for determining a base station layout configuration scheme in an intelligent building. Background Art

[0002] With the rise of smart buildings, high-frequency data exchange has become crucial for ensuring the efficient operation of various intelligent facilities within them. Modern buildings require not only efficient power supply, temperature control systems, and safety measures, but also robust wireless communication capabilities to support the interconnection of various IoT devices. However, achieving high-quality wireless network coverage in existing buildings, especially in high-frequency bands, presents numerous challenges. These challenges primarily include severe signal attenuation, frequent multipath interference, and the overall impact of complex indoor environments on communication efficiency.

[0003] Traditional solutions typically rely on pre-defined models and empirical data, determining the number and location of base stations through theoretical calculations or limited field testing. Common approaches include: 1) estimating the required number of base stations using radio wave propagation models; 2) selecting optimal installation locations based on historical data; 3) adding additional antennas in key areas to enhance local signal strength; and 4) using high-performance materials to reduce signal loss. While these approaches have improved wireless network quality to some extent, they still have significant limitations.

[0004] These methods often fail to accurately assess the dynamic and changing interference factors in complex building environments, resulting in significantly lower-than-expected network performance after deployment. This is particularly evident in scenarios with high-frequency data transmission requirements. Therefore, a method for determining base station layout and configuration that can adapt to complex building environments is needed, improving both network performance and user experience satisfaction. Summary of the Invention

[0005] In order to solve the problem of poor high-frequency communication quality in complex building environments, the present application provides a method and system for determining a base station layout configuration solution in an intelligent building.

[0006] In a first aspect, the present application provides a method for determining a base station layout configuration scheme in an intelligent building, comprising:

[0007] Collect construction data, historical operating data, and historical base station layout configuration data of several buildings;

[0008] Statistically obtaining first building data, historical first operating condition data, and historical first base station layout configuration data belonging to the entire building, and second building data, historical second operating condition data, and historical second base station layout configuration data belonging to a single floor of the building;

[0009] The first building data, the historical first operating condition data and the historical first base station layout configuration data are used as training data, and a deep learning algorithm is used to train and construct a building overall layout configuration model. The input of the building overall layout configuration model is the first building data and the first operating condition data, and the output is the number, configuration and vertical distribution of building base stations that meet the first preset requirement; the first preset requirement includes reaching the maximum value of a reward function with signal coverage and base station installation cost as reward factors; the second building data, the historical second operating condition data and the historical second base station layout configuration data are used as training data, and a deep learning algorithm is used to train and construct a building single-layer layout configuration model. The input of the building single-layer overall layer layout configuration model is the second building data and the second operating condition data, and the output is the number, configuration and horizontal distribution of building single-layer base stations that meet the second preset requirement; the second preset requirement includes reaching the optimal solution of a multi-objective optimization function with signal coverage, capacity and base station installation cost;

[0010] Collect the building data and working condition requirements of the target building and input them into the building overall layout configuration model and the building single-layer layout configuration model respectively, and comprehensively obtain the layout configuration of the base station of the target building;

[0011] The sensor array deployed in the target building is used to monitor the signal quality of each location in the target building in real time. The AI ​​model is used to dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality, so that the location where the real-time signal quality is lower than the preset quality reaches the preset signal quality at the next moment. The configuration includes carrier frequency occupancy, antenna transmission power and beam direction.

[0012] By adopting the above solution, a base station layout configuration model with strong environmental adaptability is constructed using a deep learning algorithm. Layout configuration models for the entire building and a single floor are trained and generated respectively. This fully ensures that the model takes into account factors affecting data exchange such as building structure and working conditions, and automatically adjusts the number and optimal layout of base stations. On this basis, signal quality is monitored in real time, and the base station configuration is dynamically adjusted using an AI model, further improving the system stability and user experience.

[0013] Preferably, the method of using a deep learning algorithm to train and construct a building overall layout configuration model specifically includes:

[0014] Performing data preprocessing on the input first building data, the historical first operating condition data, and the historical first base station layout configuration data to generate a first training set;

[0015] A graph neural network (GNN) integrated with a reinforcement learning framework is selected as the overall building layout configuration model architecture, specifically including: establishing a graph neural network (GNN), including: defining nodes, defining edges, and building a graph structure based on the defined nodes and variables; node definition includes: defining each floor as a node; node feature definition includes: floor height, area, shape and material properties, signal strength, and user density; edge definition includes: defining stairs and elevator shafts as edges; then combining the reinforcement learning framework with the GNN, including: defining an intelligent agent in the reinforcement learning framework, using the intelligent agent to observe the building data, working condition data, and historical base station layout of the target building, and generating base station layout adjustment decisions based on the base station layout output by the GNN; updating the state based on the agent's adjustment decision, and calculating the reward function, which is designed based on signal coverage and base station installation cost, until the reward function reaches its maximum value and the corresponding adjustment decision is output;

[0016] The first training set is used to train the graph neural network (GNN) of the constructed integrated reinforcement learning framework to obtain a trained building overall layout configuration model.

[0017] By adopting the above solution, taking into account the overall structure and material characteristics of the building, floor height, floor user density and other contents, and using the reinforcement learning framework and graph neural network as the basic framework of the building's overall layout configuration model, it can not only automatically adjust the base station configuration under different building structures and material conditions, improve the adaptability of the model, but also ensure maximum signal coverage while reducing installation costs.

[0018] Preferably, the method of using a deep learning algorithm to train and construct a single-story building layout configuration model specifically includes:

[0019] Performing data preprocessing on the input second building data, the historical second operating condition data, and the historical second base station layout configuration data to generate a second training set;

[0020] A hybrid neural network of CNN and LSTM is selected as the architecture of the single-story building layout configuration model, specifically including: constructing a CNN network model, and using the constructed CNN to process the spatial features of the second building data, including: identifying the spatial features of key grids in a two-dimensional grid image generated by converting the single-story layout information in the second building data, including: room locations, wall material distribution, and obstacle locations;

[0021] Construct an LSTM network model and use LSTM to process the time series data in the second operating condition data and the second base station layout configuration data, including the time series characteristics of user density and signal strength;

[0022] Define a multi-objective optimization function, including: a target optimization function generated by weighted calculation of signal coverage, signal capacity, and base station installation cost;

[0023] The constructed hybrid neural network of CNN and LSTM is trained using the second training set, and the parameters of the model are continuously adjusted during the training process so that the target optimization function reaches the optimal solution, thereby obtaining a trained single-story building layout configuration model.

[0024] By adopting the above solution, a CNN network is used to process spatial features such as room location, material distribution, and obstacle location within a single floor of a building, ensuring that the layout of base stations on a single floor of the building fully considers the impact of the physical environment and reduces signal blind spots. An LSTM network is also used to process the changing trends of floor user density and signal strength, dynamically adjusting the base station configuration. A multi-objective optimization function is introduced to balance the relationship between signal coverage, capacity, and base station installation costs, thereby achieving efficient resource utilization.

[0025] Preferably, it also includes:

[0026] Obtain the satisfaction of users in the target building with respect to signal quality feedback in real time, and determine whether the proportion of current user satisfaction exceeding the preset satisfaction number to the total number of user feedback is lower than the preset proportion. If so, expand the range of base stations around the location where the real-time signal quality is lower than the preset quality, and dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality according to the AI ​​model.

[0027] By adopting the above solution, considering the user feedback on the satisfaction of the base station layout in the target building, the configuration of these base stations is dynamically adjusted based on the user feedback mechanism to improve the signal coverage quality and stability.

[0028] Preferably, it also includes:

[0029] If, after expanding the base station range around the location where the real-time signal quality is lower than the preset quality and readjusting the base station configuration, the proportion of user satisfaction with signal quality feedback in the target building obtained over a period of time exceeds the preset satisfaction but is still lower than the preset proportion, the overall building layout configuration model and / or the single-layer building layout configuration model shall be optimized accordingly.

[0030] By adopting the above solution, considering that there are still limitations in continuously collecting user signal quality feedback and dynamically adjusting base station configuration based on user feedback, it can be preliminarily determined that there are errors in the base station layout determination model. Accordingly, new simulation data is introduced as the data source for incremental learning, so that the model can be gradually improved, responding to complex changes in building environments more accurately and optimizing base station layout.

[0031] Preferably, the optimization of the overall building layout configuration model and / or the single-layer building layout configuration model includes:

[0032] Create a virtual building signal communication scenario corresponding to a typical building structure, perform signal attenuation simulation, and generate signal coverage simulation data, which is used as incremental data between the first and second training sets to achieve incremental learning of the overall building layout configuration model and / or the single-layer building layout configuration model.

[0033] By adopting the above solution, a virtual building signal communication scenario corresponding to a typical building structure is created, the complex environment in reality is fully simulated, simulation data is obtained to supplement the training data, and incremental learning of the overall building layout configuration model and / or the single-layer building layout configuration model is achieved, thereby improving the model's adaptability to different types of buildings.

[0034] Preferably, the collected architectural data and working condition requirements of the target building are input into the overall building layout configuration model and the single-layer building layout configuration model respectively, further comprising: performing a preset proportion increase adjustment on the working condition requirements of the collected target building, and inputting the collected architectural data of the target building and the adjusted working condition requirements into the overall building layout configuration model and the single-layer building layout configuration model respectively.

[0035] By adopting the above solution and adjusting the working condition requirements of the target building by a preset proportion, we can more comprehensively consider the pressure brought by the possible increase in service demand or the growth in the number of users in the future, optimize the base station configuration in advance, and avoid the waste of resources and technical difficulties caused by frequent adjustments in the later stage.

[0036] In a second aspect, the present application provides a system for determining a base station layout configuration solution in an intelligent building, comprising:

[0037] The building training data collection and processing module is used to collect the building data, historical operating condition data and historical base station layout configuration data of several buildings; statistically obtain the first building data, historical first operating condition data and historical first base station layout configuration data belonging to the entire building, and the second building data, historical second operating condition data and historical second base station layout configuration data belonging to a single floor of the building;

[0038] a building overall layout configuration model acquisition module, configured to use the first building data, the historical first operating condition data, and the historical first base station layout configuration data as training data, and to train and construct a building overall layout configuration model using a deep learning algorithm. The building overall layout configuration model takes the first building data and the first operating condition data as input, and outputs the number, configuration, and vertical distribution of building base stations that meet a first preset requirement; the first preset requirement includes achieving a maximum value for a reward function that uses signal coverage and base station installation cost as reward factors.

[0039] A building single-story layout configuration model acquisition module is configured to use the second building data, the historical second operating condition data, and the historical second base station layout configuration data as training data, and to train and construct a building single-story layout configuration model using a deep learning algorithm. The input of the building single-story overall layout configuration model is the second building data and the second operating condition data, and the output is the number, configuration, and horizontal distribution of base stations on the building single floor that meets a second preset requirement; the second preset requirement includes achieving an optimal solution using a multi-objective optimization function of signal coverage, capacity, and base station installation cost;

[0040] The target building base station layout configuration acquisition module is used to collect the building data and working condition requirements of the target building and input them into the building overall layout configuration model and the building single-layer layout configuration model respectively, so as to comprehensively obtain the target building base station layout configuration;

[0041] The target building base station layout configuration adjustment module is used to use the sensor array deployed in the target building to monitor the signal quality of each location in the target building in real time, and use the AI ​​model to dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality, so that the location where the real-time signal quality is lower than the preset quality reaches the preset signal quality at the next moment. The configuration includes carrier frequency occupancy, antenna transmission power and beam direction.

[0042] By adopting the above solution, the overall building and single-layer layout configuration models trained with deep learning algorithms can more accurately predict and optimize the number and distribution of base stations. At the same time, a real-time monitoring mechanism is introduced to quickly respond to environmental changes, automatically adjust the working status of base stations, and improve the stability and reliability of the system.

[0043] In a third aspect, the present application provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0044] In a fourth aspect, the present application provides a computer device, which includes a memory, a processor, and a program stored and executable on the memory, and the program implements the steps of the above method when executed by the processor.

[0045] In summary, this application has the following beneficial effects:

[0046] 1. Build building and single-floor layout configuration models using deep learning algorithms to adapt to diverse and complex building environments. This allows for more accurate prediction of signal propagation characteristics and optimal base station layout solutions in complex building environments, significantly improving data transmission performance across deployed networks. Furthermore, a real-time monitoring mechanism is introduced to dynamically adjust base station configurations to address unexpected signal quality issues, further enhancing data transmission stability and reliability.

[0047] 2. Integrate user feedback mechanism to dynamically adjust base station configuration or incrementally train building overall and single-floor layout configuration models based on continuous monitoring of user feedback data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flowchart of a method for determining a base station layout configuration solution in an intelligent building according to a specific embodiment;

[0049] Figure 2 This is a structural diagram of a system for determining a base station layout and configuration solution in an intelligent building according to a specific embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] like Figure 1 As shown, the embodiment of the present application discloses a method for determining a base station layout configuration scheme in an intelligent building, and the specific steps include:

[0052] S1. Collect construction data, historical operating condition data, and historical base station layout configuration data of several buildings.

[0053] Specifically, building data, historical operating data, and historical base station layout configuration data are collected through various channels, such as architectural design drawings, construction records, and past network maintenance logs. To ensure the accuracy and completeness of the collected data, automated data collection tools, such as drones and 3D scanners, can be used to assist with manual verification and validation.

[0054] The collected building data includes but is not limited to the overall building data, such as the overall shape of the floor, building height, number of floors, floor height, wall materials, stairs, and door and window locations, etc. The single floor building data includes but is not limited to the room distribution, wall materials, and obstacle locations on the floor;

[0055] Historical operating condition data includes but is not limited to: user density distribution in buildings over a period of time, signal strength fluctuations, business demand data, equipment failure records, etc.; historical base station layout configuration data includes but is not limited to: the number, location, antenna type, and transmission power of base stations deployed in buildings over a period of time.

[0056] S2. Perform data preprocessing on the collected building data, historical operating condition data, and historical base station layout configuration data.

[0057] Specifically, in order to comprehensively and accurately train the model to automatically adjust the number and optimal layout of base stations, the collected data are classified and counted in advance to obtain the first building data belonging to the entire building, the historical first working condition data and the historical first base station layout configuration data, the second building data belonging to a single floor of the building, the historical second working condition data and the historical second base station layout configuration data, etc.

[0058] Among them, the first building data includes: overall indicators such as the overall building shape, building height, number of floors, floor height, wall material distribution and structural distribution; the first historical working condition data includes: the overall user density distribution of the building, signal strength distribution, signal interference distribution, whether the base station is faulty or not, etc.; the first historical base station layout configuration data includes: the overall number, location, antenna type and transmission power of base stations in the building, etc.

[0059] Among them, the second building data includes: indicators of a single floor such as the height and area of ​​a single floor, room distribution, wall material distribution, and obstacle location; the historical second working condition data includes: user density distribution, signal strength distribution, signal interference distribution, whether the base station is faulty, etc. on a single floor; the historical second base station layout configuration data includes: the number, location, antenna type, and transmission power of base stations on a single floor.

[0060] S3. Build and train a building overall layout configuration model.

[0061] Specifically, based on the deep learning algorithm, a suitable algorithm model is selected in advance to build a building overall layout configuration model, obtain the optimal number of base stations and their vertical distribution in the target building, so as to adapt to the signal attenuation caused by different building structures and material characteristics, and achieve deployment network performance optimization.

[0062] While optimizing the performance of base station deployment, the cost of base station deployment also needs to be considered to reduce the base station installation cost as much as possible. Therefore, the input of the building overall layout configuration model is further designed to be the first building data and the first working condition data, and the output is the basic output configuration such as the number, configuration and vertical distribution of building base stations that meet the first preset requirements, as well as the frequency band allocation, power allocation of the base stations; wherein, the first preset requirement includes the reward function with signal coverage and base station installation cost as reward factors reaching the maximum value, thereby ensuring that the determined building base station data and vertical distribution meet the building signal coverage requirements and balance the installation cost.

[0063] The first building data, the first historical working condition data and the first historical base station layout configuration data are used as training data, and divided into a training set and a test set; the training set is used to train the constructed building overall layout configuration model, and the test set is used to test whether the building overall layout configuration model meets the preset requirements.

[0064] S4. Build and train a single-story building layout configuration model.

[0065] Specifically, based on the deep learning algorithm, a suitable algorithm model is selected in advance to build a single-story overall floor layout configuration model for the building, and the optimal number and horizontal distribution of base stations on a single floor of the target building are obtained to adapt to the signal attenuation caused by the structure and material characteristics of the single-story building, thereby achieving deployment network performance optimization.

[0066] While optimizing the performance of base station deployment, it is also necessary to consider the working conditions requirements and the cost of base station deployment in a single-story building, and consider the base station deployment layout of a single floor as detailed as possible. Therefore, the input of the building single-story overall floor layout configuration model is further designed to be the second building data and the second working condition data, and the output is the basic output configuration such as the number, configuration and horizontal distribution of single-story base stations in the building that meets the second preset requirements, as well as the frequency band allocation, power allocation of the base station; wherein, the second preset requirement includes achieving the optimal solution through a multi-objective optimization function of signal coverage, capacity and base station installation cost, thereby ensuring that the determined single-story floor horizontal distribution meets the building signal coverage and specific working conditions requirements and balances the installation cost.

[0067] The second building data, the historical second working condition data and the historical second base station layout configuration data are used as training data, and divided into a training set and a test set; the constructed single-layer layout configuration model of the building is trained using the training set, and the single-layer layout configuration model of the building is tested using the test set to ensure that it meets the preset requirements.

[0068] S5. Collect the building data and working condition requirements of the target building, and input the building overall layout configuration model and the building single-layer layout configuration model to obtain the base station layout of the target building.

[0069] Specifically, the building data and working condition requirements of the target building are collected, including the building data and working condition requirements of the entire target building, and the building data and working condition requirements of a single floor of the target building; among them, the working condition requirements can determine the user density range and signal strength distribution requirements based on the historical working conditions of the target building; or based on the working condition requirements set by user input.

[0070] The architectural data and working condition requirements of the target building are collected and input into the overall building layout configuration model to obtain the number, configuration, and vertical distribution of base stations in the target building. For example, the overall number of base stations in the target building is 20, and the ratio of base stations in a 6-story building is 1:2:3:1:1:2.

[0071] The architectural data and working condition requirements of the target building are collected and input into the single-story building layout configuration model to obtain the number, configuration, and horizontal distribution of base stations in the single-story building. For example, the total number of base stations in a single-story building is 2, and the base stations are installed in two areas from east to west along the single-story building, with a ratio of 1:1.

[0072] The outputs of the overall building layout configuration model and the single-layer building layout configuration model are integrated to obtain the target building base station layout configuration. In order to avoid output contradictions, the priority between the number, configuration and vertical distribution of target building base stations and the number, configuration and horizontal distribution of single-layer building base stations can be determined according to user needs. For example, if the user pays more attention to the overall base station layout configuration of the building, a higher priority will be set for the number, configuration and vertical distribution of target building base stations. On the basis of meeting the number, configuration and vertical distribution of target building base stations, the number, configuration and horizontal distribution of single-layer building base stations will be further adjusted. Similarly, if the user pays more attention to the base station layout configuration of a single floor, a higher priority will be set for the number, configuration and vertical distribution of base stations on a single floor of the target building. On the basis of meeting the number, configuration and horizontal distribution of base stations on a single floor of the target building, the number, configuration and vertical distribution of base stations on the target building will be further adjusted.

[0073] S6. Conduct real-time monitoring of target buildings and dynamically adjust and optimize base station configurations.

[0074] To further improve the performance of the deployed network, a sensor array deployed within the target building monitors signal quality at every location in real time. This quality includes parameters such as signal strength and signal-to-noise ratio (SNR). Specifically, a certain number of micro-sensors can be deployed on each floor to monitor key indicators such as signal strength, noise level, and multipath effects at every location around the clock.

[0075] An AI model is used to dynamically adjust the configuration of base stations near locations where real-time signal quality falls below a preset quality level, so that the signal quality at those locations reaches the preset quality level at the next moment. This configuration includes carrier frequency occupancy, antenna transmit power, and beam direction. The AI ​​model uses a deep learning neural network, trained using historical data on the configuration of the base station to be adjusted, its preset surrounding base stations, and historically optimized adjustment plans, to generate an AI model that outputs base station configuration adjustment plans.

[0076] By adopting the above solution, a deep learning algorithm is used to build the overall building and single-layer layout configuration model to more accurately determine the optimal base station layout configuration solution in a complex building environment.

[0077] In a specific embodiment, in order to more accurately optimize base station layout based on signal propagation characteristics in complex building environments to improve the transmission performance of the deployed network, characteristic indicators of the overall building base station layout are integrated to adaptively design a model that can extract the overall building environment characteristics and signal transmission characteristics and optimize the layout output. The method uses a deep learning algorithm to train and construct the overall building layout configuration model, which specifically includes:

[0078] Data preprocessing is performed on the input first building data, the historical first operating condition data, and the historical first base station layout configuration data to generate a first training set; wherein the preprocessing includes data cleaning, feature extraction, and standardization.

[0079] The graph neural network (GNN) integrated with the reinforcement learning framework was selected as the overall building layout configuration model architecture, specifically including:

[0080] First, a graph neural network (GNN) is established; specifically, it includes: defining nodes, defining edges, and constructing a graph structure based on the defined nodes and variables; wherein, the node definition includes: defining each floor as a node, and also including base station sub-nodes and user sub-nodes on each floor; the node feature definition includes: attribute features such as floor height, area, shape and material properties, signal strength, and user density; the edge definition includes: defining stairs and elevator shafts as edges; based on the above-defined nodes and edges, a graph structure model of the entire building is constructed; the established graph neural network (GNN) is used to preliminarily obtain the base station layout, including the number of building base stations and their vertical distribution.

[0081] Secondly, the reinforcement learning framework is combined with GNN; specifically, an intelligent agent is defined in the reinforcement learning framework, and the base station layout problem is defined as a reinforcement learning task of the intelligent agent. The intelligent agent is used to observe the building data, working condition data and historical base station layout of the target building, and the base station layout adjustment decision is generated based on the GNN output base station layout, that is, adding, reducing or moving base stations; the state is updated according to the adjustment decision of the intelligent agent, and the reward function is calculated. The reward function is designed based on signal coverage and base station installation cost, such as setting a scoring standard for signal coverage and base station installation cost parameters and performing a weighted calculation until the reward function reaches the maximum value and the corresponding adjustment decision is output.

[0082] The first training set is used to train the graph neural network (GNN) built within the integrated reinforcement learning framework to obtain a trained building layout configuration model. Furthermore, the preprocessed data can be used to set up a first test set. The first training set and the first test set are combined to test the accuracy and optimize the building layout configuration model.

[0083] In a specific embodiment, to more accurately optimize base station layout based on signal propagation characteristics in complex building environments to improve the transmission performance of deployed networks, a model is adaptively designed to extract the environmental characteristics and signal transmission characteristics of a single floor and optimize the layout output by integrating characteristic indicators of base station layout on a single floor. The method utilizes a deep learning algorithm to train and construct a single-floor building layout configuration model, specifically including:

[0084] Data preprocessing is performed on the input second building data, the historical second operating condition data, and the historical second base station layout configuration data to generate a second training set.

[0085] A hybrid neural network of CNN and LSTM is selected as the single-story building layout configuration model architecture, specifically including:

[0086] First, a CNN network model is constructed, and the constructed CNN is used to process the spatial features of the second building data, including: identifying the spatial features of key grids in a two-dimensional grid image generated by converting the single floor layout information in the second building data, including: room location, wall material distribution, and obstacle location.

[0087] Secondly, an LSTM network model is constructed, and LSTM is used to process the time series data in the second operating condition data and the second base station layout configuration data, including the time series characteristics of user density and signal strength.

[0088] Again, based on the design of a hybrid neural network structure by combining CNN and RNN / LSTM, a multi-objective optimization function is defined as the model loss function, such as the target optimization function generated by weighted calculation of signal coverage, signal capacity and base station installation cost.

[0089] The constructed hybrid CNN and LSTM neural network is trained using the second training set. During training, the model parameters are continuously adjusted to achieve the optimal solution for the target optimization function, that is, to minimize the loss function, thereby obtaining a trained single-story building layout configuration model. Similarly, a second test set can be set up using the preprocessed data. The second training set and the second test set are combined to test the accuracy and optimize the training of the single-story building layout configuration model.

[0090] In a specific embodiment, a user feedback mechanism is designed. By utilizing a closed-loop feedback mechanism, not only existing layout defects are corrected in a timely manner, but also new experience and knowledge are continuously accumulated, so that the layout configuration solution determination model becomes increasingly intelligent and better adapts to more complex environmental changes. The method further includes:

[0091] Obtain the satisfaction of users in the target building with respect to signal quality feedback in real time, and determine whether the proportion of current user satisfaction exceeding the preset satisfaction number to the total number of user feedbacks is lower than the preset proportion. If it is lower, it indicates that the satisfaction with the current base station layout configuration is relatively high and needs to be further improved. Without making major adjustments to the base station layout, fine-tuning is performed based on the base station configuration output, thereby expanding the range of base stations around the locations where the real-time signal quality is lower than the preset quality, and dynamically re-adjusting the configuration of base stations around the locations where the real-time signal quality is lower than the preset quality according to the AI ​​model.

[0092] Among them, the base station range around the position where the real-time signal quality is lower than the preset quality can determine the size of the surrounding range according to the area range of a single floor of the intelligent building, such as setting it to 1 / 20 of the area range of a single floor; similarly, the base station range for controlling the expansion of the surrounding area can be determined according to the area range of a single floor of the intelligent building or a predetermined surrounding range, such as setting it to half the ratio of 1 / 20 of the area range of a single floor.

[0093] In addition, if low satisfaction persists even after expanding and adjusting surrounding base stations, model optimization is required from a broader base station layout perspective, including:

[0094] If, after expanding the base station range around the location where the real-time signal quality is lower than the preset quality and readjusting the base station configuration, the proportion of user satisfaction with signal quality feedback in the target building obtained over a period of time exceeds the preset satisfaction but is still lower than the preset proportion, then incremental learning can be used to optimize the overall building layout configuration model and / or the single-layer building layout configuration model.

[0095] In addition to continuing to acquire and collect architectural data, historical operating condition data, and historical base station layout configuration data of several buildings to increase the first training set and / or the second training set, the optimization of the overall building layout configuration model and / or the single-story building layout configuration model also includes:

[0096] Create a virtual building 3D model corresponding to a typical building structure and a signal communication scenario within the virtual building. Based on this, perform signal attenuation simulation to generate signal coverage simulation data related to several buildings. The corresponding building structure and the signal coverage simulation data as working condition data are divided and used as incremental data for the first training set and the second training set to achieve incremental learning of the overall building layout configuration model and / or the single-layer building layout configuration model.

[0097] In a specific embodiment, considering that during actual base station operation, there may be base station failures that may result in limited communication transmission, or considering the pressure brought by the possible increase in service demand or the growth in the number of users in the future, the base station configuration is optimized in advance to avoid resource waste and technical difficulties caused by frequent adjustments in the later stage. In the method, the building data and working condition requirements of the target building are collected and input into the building overall layout configuration model and the building single-story layout configuration model respectively, and further includes:

[0098] A preset proportion of the working condition requirements of the collected target building is adjusted, and the collected architectural data of the target building and the adjusted working condition requirements are input into the building overall layout configuration model and the building single-layer layout configuration model respectively.

[0099] The preset ratio is manually set, and the user density of the entire target building or a single floor is adjusted in advance by the preset ratio, such as adjusting the user density of M1 to a user density of 1.2 M1.

[0100] In addition, in order to more accurately adjust the operating conditions of the target building, deep learning algorithms can be used to determine the historical operating conditions based on the historical operating data of the target building, predict the operating conditions in the future, and dynamically adjust the operating conditions of the target building based on the predicted operating conditions.

[0101] like Figure 2 As shown, this embodiment discloses a system for determining a base station layout configuration solution in an intelligent building, specifically comprising:

[0102] The building training data collection and processing module 101 is used to collect building data, historical operating condition data, and historical base station layout configuration data of several buildings; statistically obtain first building data, historical first operating condition data, and historical first base station layout configuration data belonging to the entire building, and second building data, historical second operating condition data, and historical second base station layout configuration data belonging to a single floor of the building;

[0103] The building overall layout configuration model acquisition module 102 is configured to use the first building data, the historical first operating condition data, and the historical first base station layout configuration data as training data, and to train and construct a building overall layout configuration model using a deep learning algorithm. The input of the building overall layout configuration model is the first building data and the first operating condition data, and the output is the number, configuration, and vertical distribution of building base stations that meet a first preset requirement; the first preset requirement includes achieving a maximum value for a reward function with signal coverage and base station installation cost as reward factors.

[0104] A building single-story layout configuration model acquisition module 103 is configured to use the second building data, the historical second operating condition data, and the historical second base station layout configuration data as training data, and to train and construct a building single-story layout configuration model using a deep learning algorithm. The input of the building single-story overall layout configuration model is the second building data and the second operating condition data, and the output is the number, configuration, and horizontal distribution of base stations on the building single floor that meets a second preset requirement; the second preset requirement includes achieving an optimal solution using a multi-objective optimization function of signal coverage, capacity, and base station installation cost.

[0105] The target building base station layout configuration acquisition module 104 is used to collect the building data and working condition requirements of the target building and input them into the building overall layout configuration model and the building single-layer layout configuration model respectively, so as to comprehensively obtain the target building base station layout configuration;

[0106] The target building base station layout configuration adjustment module 105 is used to use the sensor array deployed in the target building to monitor the signal quality of each location in the target building in real time, and use the AI ​​model to dynamically adjust the configuration of the base stations around the location where the real-time signal quality is lower than the preset quality, so that the location where the real-time signal quality is lower than the preset quality reaches the preset signal quality at the next moment. The configuration includes carrier frequency occupancy, antenna transmission power and beam direction.

[0107] The system further comprises:

[0108] The target building base station layout configuration feedback module 106 is used to obtain the satisfaction of users in the target building for signal quality feedback in real time, and determine whether the proportion of current user satisfaction exceeding the preset satisfaction number to the total number of user feedback is lower than the preset proportion. If it is lower, the range of base stations around the position where the real-time signal quality is lower than the preset quality is expanded, and the configuration of base stations around the position where the real-time signal quality is lower than the preset quality is dynamically adjusted according to the AI ​​model; it is also used to optimize the overall building layout configuration model and / or the single-layer building layout configuration model if the proportion of user satisfaction with signal quality feedback in the target building obtained for a continuous period of time exceeds the preset satisfaction level after expanding the range of base stations around the position where the real-time signal quality is lower than the preset quality and readjusting the base station configuration.

[0109] The embodiment of the present application also discloses a computer-readable storage medium.

[0110] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed by the method for determining the base station layout configuration scheme in the intelligent building as described above. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0111] The embodiment of the present application also discloses a computer device.

[0112] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned method for determining the layout configuration scheme of base stations in the intelligent building.

[0113] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise stated, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for determining a base station layout configuration plan in an intelligent building, characterized in that: include: Collect construction data, historical operating data, and historical base station layout configuration data of several buildings; Statistically obtaining first building data, historical first operating condition data, and historical first base station layout configuration data belonging to the entire building, and second building data, historical second operating condition data, and historical second base station layout configuration data belonging to a single floor of the building; The first building data, the first historical operating condition data, and the first historical base station layout configuration data are used as training data, and a deep learning algorithm is used to train and construct a building overall layout configuration model. The input of the building overall layout configuration model is the first building data and the first operating condition data, and the output is the number, configuration, and vertical distribution of building base stations that meet the first preset requirement; the first preset requirement includes reaching the maximum value of a reward function with signal coverage and base station installation cost as reward factors; the second building data, the second historical operating condition data, and the second historical base station layout configuration data are used as training data, and a deep learning algorithm is used to train and construct a building single-story layout configuration model. The input of the building single-story layout configuration model is the second building data and the second operating condition data, and the output is the number, configuration, and horizontal distribution of building single-story base stations that meet the second preset requirement; the second preset requirement includes reaching the optimal solution of a multi-objective optimization function with signal coverage, capacity, and base station installation cost; Collect the building data and working condition requirements of the target building and input them into the building overall layout configuration model and the building single-layer layout configuration model respectively, and comprehensively obtain the layout configuration of the base station of the target building; The sensor array deployed in the target building is used to monitor the signal quality of each location in the target building in real time. The AI ​​model is used to dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality, so that the location where the real-time signal quality is lower than the preset quality reaches the preset signal quality at the next moment. The configuration includes carrier frequency occupancy, antenna transmission power and beam direction.

2. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 1, wherein: The method of using a deep learning algorithm to train and construct a building overall layout configuration model specifically includes: Performing data preprocessing on the input first building data, the historical first operating condition data, and the historical first base station layout configuration data to generate a first training set; A graph neural network (GNN) integrated with a reinforcement learning framework is selected as the overall building layout configuration model architecture, specifically including: establishing a graph neural network (GNN), including: defining nodes, defining edges, and constructing a graph structure based on the defined nodes and edges; node definition includes: defining each floor as a node; node feature definition includes: floor height, area, shape and material properties, signal strength, and user density; edge definition includes: defining stairs and elevator shafts as edges; then combining the reinforcement learning framework with the GNN, including: defining an intelligent agent in the reinforcement learning framework, using the intelligent agent to observe the building data, working condition data, and historical base station layout of the target building, and generating base station layout adjustment decisions based on the base station layout output by the GNN; updating the state based on the agent's adjustment decision, and calculating the reward function, which is designed based on signal coverage and base station installation cost, until the reward function reaches its maximum value and the corresponding adjustment decision is output; The first training set is used to train the graph neural network (GNN) of the constructed integrated reinforcement learning framework to obtain a trained building overall layout configuration model.

3. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 1, wherein: The method of using a deep learning algorithm to train and construct a single-layer building layout configuration model specifically includes: Performing data preprocessing on the input second building data, the historical second operating condition data, and the historical second base station layout configuration data to generate a second training set; A hybrid neural network of CNN and LSTM is selected as the architecture of the single-story building layout configuration model, specifically including: constructing a CNN network model, and using the constructed CNN to process the spatial features of the second building data, including: identifying the spatial features of key grids in a two-dimensional grid image generated by converting the single-story layout information in the second building data, including: room locations, wall material distribution, and obstacle locations; Construct an LSTM network model and use LSTM to process the time series data in the second operating condition data and the second base station layout configuration data, including the time series characteristics of user density and signal strength; Define a multi-objective optimization function, including: a target optimization function generated by weighted calculation of signal coverage, signal capacity, and base station installation cost; The constructed hybrid neural network of CNN and LSTM is trained using the second training set, and the parameters of the model are continuously adjusted during the training process so that the target optimization function reaches the optimal solution, thereby obtaining a trained single-story building layout configuration model.

4. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 1, wherein: Also includes: Obtain the satisfaction of users in the target building with respect to signal quality feedback in real time, and determine whether the proportion of current user satisfaction exceeding the preset satisfaction number to the total number of user feedback is lower than the preset proportion. If so, expand the range of base stations around the location where the real-time signal quality is lower than the preset quality, and dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality according to the AI ​​model.

5. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 4, characterized in that: Also includes: If, after expanding the base station range around the location where the real-time signal quality is lower than the preset quality and readjusting the base station configuration, the proportion of user satisfaction with signal quality feedback in the target building obtained over a period of time exceeds the preset satisfaction but is still lower than the preset proportion, the overall building layout configuration model and / or the single-layer building layout configuration model shall be optimized accordingly.

6. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 5, characterized in that: The optimization of the overall building layout configuration model and / or the single-layer building layout configuration model includes: Create a virtual building signal communication scenario corresponding to a typical building structure, perform signal attenuation simulation, and generate signal coverage simulation data, which is used as incremental data between the first and second training sets to achieve incremental learning of the overall building layout configuration model and / or the single-layer building layout configuration model.

7. The method for determining a layout configuration scheme of base stations in an intelligent building according to claim 1, wherein: The collecting of the target building's architectural data and operating requirements into the building's overall layout configuration model and the building's single-layer layout configuration model also includes: adjusting the operating requirements of the collected target building by a preset proportion, and inputting the collected target building's architectural data and the adjusted operating requirements into the building's overall layout configuration model and the building's single-layer layout configuration model.

8. A system for determining a base station layout configuration plan in an intelligent building, characterized in that: include: Building training data collection and processing module, used to collect construction data, historical working condition data and historical base station layout configuration data of several buildings; Statistically obtaining first building data, historical first operating condition data, and historical first base station layout configuration data belonging to the entire building, and second building data, historical second operating condition data, and historical second base station layout configuration data belonging to a single floor of the building; a building overall layout configuration model acquisition module, configured to use the first building data, the historical first operating condition data, and the historical first base station layout configuration data as training data, and to train and construct a building overall layout configuration model using a deep learning algorithm. The building overall layout configuration model takes the first building data and the first operating condition data as input, and outputs the number, configuration, and vertical distribution of building base stations that meet a first preset requirement; the first preset requirement includes achieving a maximum value for a reward function that uses signal coverage and base station installation cost as reward factors. A building single-story layout configuration model acquisition module is configured to use the second building data, the historical second operating condition data, and the historical second base station layout configuration data as training data, and to train and construct a building single-story layout configuration model using a deep learning algorithm. The input of the building single-story layout configuration model is the second building data and the second operating condition data, and the output is the number, configuration, and horizontal distribution of base stations on the building single floor that meets a second preset requirement; the second preset requirement includes achieving an optimal solution using a multi-objective optimization function of signal coverage, capacity, and base station installation cost; The target building base station layout configuration acquisition module is used to collect the building data and working condition requirements of the target building and input them into the building overall layout configuration model and the building single-layer layout configuration model respectively, so as to comprehensively obtain the target building base station layout configuration; The target building base station layout configuration adjustment module is used to use the sensor array deployed in the target building to monitor the signal quality of each location in the target building in real time, and use the AI ​​model to dynamically adjust the configuration of base stations around the location where the real-time signal quality is lower than the preset quality, so that the location where the real-time signal quality is lower than the preset quality reaches the preset signal quality at the next moment. The configuration includes carrier frequency occupancy, antenna transmission power and beam direction.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device includes a memory, a processor, and a program stored and executable on the memory, and when the program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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