Power control method and system for optimizing signal coverage, medium and electronic equipment

Abstract

The invention provides a power control method and system for optimizing signal coverage, a medium and electronic equipment, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring an environment data set of each base station in a target area, wherein the target area comprises a plurality of base stations; establishing a target three-dimensional environment model based on the environment data set; calculating target transmitting power of each base station based on the target three-dimensional environment model; and controlling each base station to carry out power adjustment according to the corresponding target transmitting power. By implementing the technical scheme provided by the invention, the uniformity of signal coverage of each base station can be improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a power control method, system, medium and electronic device for optimizing signal coverage. Background Art

[0002] With the rapid development of wireless communication technology, base station signal coverage optimization has become the key to improving network service quality and user experience. Especially in densely populated urban areas and complex terrain environments, how to effectively manage and optimize the signal transmission power of base stations to ensure stable and extensive coverage is a challenge in current wireless network management.

[0003] In traditional wireless network management, base station power control is usually adjusted in a static or semi-dynamic fixed manner, mainly relying on experience settings or simple feedback adjustment strategies. However, this traditional base station power control method will lead to uneven signal coverage, especially in the central area of ​​the city. Due to the obstruction of high-rise buildings, the base station signal is often difficult to penetrate to the ground layer, resulting in uneven signal coverage on the ground layer. Summary of the invention

[0004] The present application provides a power control method, system, medium and electronic device for optimizing signal coverage, which can improve the uniformity of signal coverage.

[0005] In a first aspect, the present application provides a power control method for optimizing signal coverage, the method comprising:

[0006] Collecting environmental data sets of each base station in a target area, wherein the target area includes a plurality of base stations;

[0007] Establishing a target three-dimensional environment model based on the environment data set;

[0008] Calculating the target transmission power of each base station based on the target three-dimensional environment model;

[0009] Control each of the base stations to perform power adjustment according to the corresponding target transmission power.

[0010] By adopting the above technical scheme, by collecting the environmental data set of each base station in the target area, the environmental data set reflects the actual physical environment of each base station, and then establishing a target three-dimensional environmental model based on the environmental data set, the target three-dimensional environmental model can fully reflect the signal propagation environment of each base station, and on the basis of obtaining the target three-dimensional environmental model, the target transmission power of each base station is calculated, and the target transmission power is obtained under the condition of fully considering the actual physical propagation environment of each base station, which can ensure that the transmission power of each base station matches the actual environment. Finally, each base station is controlled to adjust the power according to the corresponding target transmission power, which solves the problem of base station power adjustment in a fixed manner in the prior art, and realizes that each base station in the target area can be adjusted to the optimal target transmission power according to the actual environment, thereby improving the uniformity of signal coverage of each base station.

[0011] Optionally, the collecting of the environmental data set of each base station in the target area includes:

[0012] Determining the geographical location of each base station in the target area;

[0013] For any target base station, collecting raw environmental data within a preset range of the geographical location of the target base station;

[0014] The original environmental data is preprocessed to obtain an environmental data set.

[0015] By adopting the above technical solution, the geographical location of each base station in the target area is first determined, and then for each target base station, the original environmental data is collected within the preset range of its geographical location, and finally the original environmental data is preprocessed to obtain an accurate and standardized environmental data set corresponding to each target base station.

[0016] Optionally, the environmental data set includes building data, user data, meteorological data and signal data, the user data includes user equipment, and establishing a target three-dimensional environmental model based on the environmental data set includes:

[0017] Constructing a first three-dimensional environment model based on the building data and the meteorological environment in the environment data set;

[0018] Based on the user data and the signal data, a propagation simulation of a wireless signal is performed in the first three-dimensional environment model to obtain a second three-dimensional environment model, wherein the second three-dimensional environment model includes a propagation path from each base station to the user equipment;

[0019] Calculating the path loss of each of the propagation paths;

[0020] The propagation path and the corresponding path loss are mapped to the second three-dimensional environment model to obtain a target three-dimensional environment model.

[0021] By adopting the above technical solution, a preliminary first three-dimensional environmental model is first constructed using building and meteorological environment data, and then a wireless signal propagation simulation is performed. During the simulation, user equipment data and signal characteristics are considered to obtain a second three-dimensional environmental model containing each signal propagation path from the base station to the user equipment. The path loss is calculated for each propagation path, and finally the propagation path and path loss are mapped back to the three-dimensional model to form a target three-dimensional environmental model. The target three-dimensional environmental model integrates rich environmental data, including topography, buildings, weather, user distribution and wireless signal characteristics, and can comprehensively simulate the wireless signal propagation effect in the target area and reflect the actual physical propagation environment of each base station.

[0022] Optionally, the calculating the path loss of each of the propagation paths includes:

[0023] Calculating the path loss of each of the propagation paths according to a preset path loss formula;

[0024] The path loss formula is:

[0025] L(d)=L0+10nlog 10 (d / d0)+X σ ;

[0026] Where L(d) is the path loss at a distance of d meters from the base station, L0 is the path loss at a reference distance of d0 meters, n is the path loss exponent, and X σ is a Gaussian distributed random variable representing shadow fading.

[0027] By adopting the above technical solution, the path loss formula takes into account the path loss at the reference distance, the propagation distance, the path loss exponent and the random variable representing the shadow fading. This comprehensive consideration of various influencing factors from theoretical models to actual environments can accurately calculate the actual loss of the propagation path in a complex environment.

[0028] Optionally, after establishing the target three-dimensional environment model based on the environment data set, the method further includes:

[0029] Comparing the virtual signal of each base station in the three-dimensional environment model with the actual signal data to obtain a comparison result;

[0030] Adjusting the three-dimensional environment model based on the comparison result to obtain a corrected target three-dimensional environment model;

[0031] The calculating the target transmission power of each base station based on the signal data of each base station and the target three-dimensional environment model includes:

[0032] The target transmission power of each base station is calculated based on the signal data of each base station and the corrected target three-dimensional environment model.

[0033] By adopting the above technical solution, after the initial target three-dimensional environment model is established, the virtual signal of each base station in the model is compared with the actual signal data to check the accuracy of the model. The comparison result reflects the deviation between the model and the actual environment. Then, the three-dimensional model is adjusted and optimized according to the comparison result to make it more accurately reflect the actual physical propagation environment.

[0034] Optionally, the calculating the target transmit power of each base station based on the target three-dimensional environment model includes:

[0035] Obtaining the maximum transmit power of each of the base stations;

[0036] Determining the path loss and signal quality change of the propagation path of each base station in the target three-dimensional environment model;

[0037] Substituting the maximum transmit power of each base station and the corresponding path loss and signal quality change into a preset transmit power calculation formula to obtain a target transmit power of each base station;

[0038] The preset transmit power calculation formula is:

[0039] P i (t) = P max -L i (t)+η·ΔQ i (t);

[0040] Among them, P i (t) is the target transmit power of the ith base station at time t, P max is the maximum transmit power, L i (t) is the path loss of the ith base station at time t, η is the preset adjustment coefficient, η∈[0.1,1], ΔQ i (t) is the signal quality change of the ith base station at time t.

[0041] By adopting the above technical solution, a target power calculation formula is developed that considers multiple factors. This formula reasonably considers factors affecting the target power, such as base station power constraints, propagation losses, and signal quality changes. It can calculate accurate and reasonable target transmission power based on the specific environmental conditions and propagation characteristics of each base station.

[0042] Optionally, the controlling each of the base stations to perform power adjustment according to the corresponding target transmit power includes:

[0043] Based on the target three-dimensional environment model, identifying the hot spots and blind spots of signal coverage in the target area;

[0044] Acquire a first current transmission power of a first base station corresponding to the hot spot area, and a second current transmission power of a second base station corresponding to the blind spot area;

[0045] Controlling the first base station to perform power adjustment based on the target transmit power corresponding to the first base station and the first current transmit power;

[0046] Obtain the neighboring base stations of the second base station, and control the second base station to perform power adjustment based on the target transmit power corresponding to the second base station, the second current transmit power, and the current transmit power of the neighboring base station.

[0047] By adopting the above technical solution and distinguishing the differentiated adjustment methods for hot spots and blind spots, different adjustment strategies can be adopted according to different regional coverage conditions to avoid new problems caused by blind unified adjustments. For base stations in hot spots, direct adjustment is performed according to their target transmission power. For base stations in blind spots, comprehensive adjustment is performed after considering their target power and the current power of adjacent base stations, which can effectively improve the uniformity of signal coverage.

[0048] In a second aspect of the present application, a power control system for optimizing signal coverage is provided, the system comprising:

[0049] A data collection module, used to collect environmental data sets of each base station in a target area, wherein the target area includes multiple base stations;

[0050] A model building module, used to build a target three-dimensional environment model based on the environment data set;

[0051] A power calculation module, used to calculate the target transmission power of each base station based on the target three-dimensional environment model;

[0052] The power adjustment module is used to control each base station to perform power adjustment according to the corresponding target transmission power.

[0053] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0054] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0055] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0056] The present application collects an environmental data set of each base station in a target area, which reflects the actual physical environment in which each base station is located. A target three-dimensional environmental model is then established based on the environmental data set. The target three-dimensional environmental model can fully reflect the signal propagation environment of each base station. On the basis of obtaining the target three-dimensional environmental model, the target transmission power of each base station is calculated. The target transmission power is obtained under the condition of fully considering the actual physical propagation environment of each base station, which can ensure that the transmission power of each base station matches the actual environment. Finally, each base station is controlled to adjust the power according to the corresponding target transmission power, which solves the problem of base station power adjustment in a fixed manner in the prior art, and realizes that each base station in the target area can be adjusted to the optimal target transmission power according to the actual environment, thereby improving the uniformity of signal coverage of each base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flowchart of a power control method for optimizing signal coverage provided by an embodiment of the present application;

[0058] Figure 2 It is a module schematic diagram of a power control system for optimizing signal coverage provided by an embodiment of the present application;

[0059] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0060] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0061] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0062] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0063] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0064] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0065] Please refer to Figure 1 , a flowchart of a power control method for optimizing signal coverage is proposed, the method can be implemented by a computer program, can be implemented by a single-chip microcomputer, and can also be run on a power control system for optimizing signal coverage. The computer program can be integrated in a computer device, or can be run as an independent tool application. Specifically, the method includes steps 10 to 40, and the above steps are as follows:

[0066] Step 10: Collect environmental data sets of each base station in the target area, where the target area includes multiple base stations.

[0067] In the embodiment of the present application, the target area refers to a specific geographical range where signal coverage optimization is required. This area usually includes multiple base stations, which can be a certain area of ​​a city, a business park, or a large building complex. The selection of the target area depends on the optimization needs of the network operator, which may be an area with poor signal coverage or an area with dense users that needs to improve network performance. Among them, the rest of the target includes multiple base stations, and a base station refers to a wireless access point in a mobile communication network. It is a key device that connects mobile devices (such as mobile phones, tablets, etc.) to the core network. The base station is responsible for transmitting and receiving wireless signals in a specific area to provide mobile communication services to users.

[0068] The environmental data set refers to a series of related data collected in order to accurately simulate and optimize signal coverage. In the embodiment of the present application, the environmental data set includes but is not limited to the following types of data: building data, user data, meteorological data, and signal data, etc.

[0069] Specifically, first determine the precise geographic location of each base station in the target area through a network management system or GPS positioning technology. Then, for each target base station, collect raw environmental data within a preset range of its geographic location. This preset range is usually the theoretical coverage radius of the base station, which may vary depending on the type of base station and the environment in which it is located. The raw environmental data includes building data (such as the location, height, and material of the building), user data (such as the distribution and usage patterns of user devices), meteorological data (such as temperature, humidity, and precipitation), and signal data (such as current signal strength and quality). The raw environmental data is then preprocessed, which includes steps such as data cleaning, data standardization, and data fusion. Through these preprocessing steps, a structured and standardized environmental data set can be obtained.

[0070] Based on the above embodiment, as an optional embodiment, the step of collecting the environmental data set of each base station in the target area may further include the following steps:

[0071] Step 101: Determine the geographical location of each base station in the target area.

[0072] Specifically, the precise location information of the base station is the basis for building an accurate three-dimensional environmental model and optimizing signal coverage. The accurate location of the base station not only affects the accuracy of the signal propagation simulation, but is also directly related to the effectiveness of the subsequent power regulation strategy. In order to accurately determine the geographical location of each base station, this embodiment adopts a multi-source data fusion method. First, the initial location information of the base station is obtained from the network management system, which usually includes the latitude and longitude coordinates of the base station. However, considering that the data in the network management system may have errors or may not be updated in time, further verification and precision are required, and each base station is located on the spot using GPS technology to obtain the accurate geographical location of each base station.

[0073] In another feasible embodiment, in order to cope with complex urban environments, especially in areas with dense high-rise buildings where GPS signals may be blocked or produce multipath effects, this embodiment also combines field measurements and geographic information system (GIS) data to measure the relative position of the base station and surrounding fixed landmarks (such as building corners, road intersections, etc.), and compare and calibrate these measurement results with high-precision GIS map data, thereby further improving the accuracy of the base station location.

[0074] Step 102: For any target base station, collect raw environmental data within a preset range of the geographical location of the target base station.

[0075] Specifically, for any target base station in the target area, the preset range of the target base station is determined. The preset range is based on the theoretical coverage radius of the target base station, and this radius may vary depending on the base station type (such as macro base station, micro base station) and the environment (such as city center, suburbs). For example, for a typical macro base station in an urban environment, the preset range may be a circular area with a radius of 1-2 kilometers centered on the base station. After determining the preset range, this embodiment uses a combination of multiple methods to collect raw environmental data to ensure the comprehensiveness and accuracy of the data.

[0076] First, high-resolution satellite images and aerial photography are used to obtain the overall topography and building distribution of the target area. After orthorectification and georeferencing, these image data can provide accurate surface information. Next, drones equipped with lidar systems are used for low-altitude mapping to obtain high-precision three-dimensional point cloud data, which can accurately describe the height, shape, and distribution of vegetation of buildings, providing a basis for building a detailed three-dimensional environmental model.

[0077] At the ground level, a vehicle-mounted mobile measurement system is used to scan along the main roads within a preset range to obtain more detailed street-level environmental data, including building facade details, road width, traffic facilities and other information. At the same time, fixed environmental monitoring stations are deployed at key locations to continuously collect meteorological data, such as temperature, humidity, wind speed, precipitation, etc., all of which may affect signal propagation. In order to obtain data on user distribution and network usage, this embodiment extracts statistical information such as user density, traffic distribution, and mobility patterns within a preset range from the network management system. In addition, by deploying professional RF test equipment, grid-based signal strength and quality measurements are performed within a preset range to obtain the current signal coverage, such as using signal strength measurement equipment (such as road test vehicles, drones, etc.) to monitor the signal strength emitted by base stations in real time, as well as feedback signals received by base stations, calculate signal-to-noise ratios and interference levels, and evaluate signal quality. After the acquisition is completed, all original environmental data are synchronized in time and space to ensure data consistency and comparability.

[0078] Step 103: preprocess the original environmental data to obtain an environmental data set.

[0079] Specifically, since the original data usually contains noise, redundancy and inconsistency, direct use may affect the accuracy of subsequent modeling and optimization. The embodiment of the present application can improve the data quality through preprocessing, ensure that the environmental data set can accurately reflect the actual situation of the target area, and provide a reliable foundation for the subsequent three-dimensional environmental model construction and signal coverage optimization.

[0080] First, the original environmental data is cleaned, which includes removing outliers, processing missing data, and eliminating duplicate records. For outliers, this embodiment uses statistical methods such as Z-score or quartile-based methods to identify them, and combines domain knowledge to determine whether they need to be removed or corrected. For missing data, interpolation methods (such as linear interpolation or spline interpolation) or machine learning-based prediction methods (such as K nearest neighbors or random forests) can be used to fill them according to the degree of missingness and data type. Duplicate records are identified and merged by comparing timestamps and spatial coordinates.

[0081] Next is data standardization and normalization. Since the original environmental data comes from different sources and measurement equipment, there may be problems such as inconsistent units and large scale differences. This embodiment uses methods such as Min-Max normalization or Z-score normalization to convert different types of data into a unified scale range. This not only helps the stability of subsequent data processing algorithms, but also improves the comparability between different environmental factors.

[0082] Finally, the data from different sources (such as satellite images, LiDAR point clouds, ground measurement data, etc.) are fused. This embodiment uses geographic information system (GIS) technology as an integration platform to align all data into a unified coordinate system and time frame through spatial registration and time synchronization. In this process, special attention is paid to handling the resolution differences between different data sources, and resampling technology is used to ensure the consistency of the data. Then, the processed data is converted into a structured format suitable for subsequent three-dimensional environmental model construction and signal propagation simulation. This includes converting discrete point cloud data into a gridded elevation model, organizing building information into a vector format, and converting user distribution and signal strength data into raster layers, etc., and finally the original environmental data is converted into a high-quality environmental data set.

[0083] Step 20: Establish a target three-dimensional environment model based on the environment dataset.

[0084] In the embodiment of the present application, the target three-dimensional environment model refers to a digital three-dimensional representation constructed based on the collected and preprocessed environmental data set, which comprehensively and accurately describes the physical environment and wireless signal propagation characteristics of the target area.

[0085] Specifically, the process of establishing the target three-dimensional environment model is a multi-stage, progressive process. First, the basic three-dimensional model is constructed using the pre-processed terrain data and building information. This stage uses the digital elevation model (DEM) technology, combined with the triangular irregular network (TIN) algorithm, to accurately reproduce the terrain undulations of the target area. At the same time, computer-aided design technology is used to build an accurate building model on the basic terrain based on the geometric data and material information of the building. This process also includes adding other surface features such as vegetation and water bodies to ensure the integrity of the model. The base station information is then integrated into the model, and each base station is accurately located, including its geographical coordinates, antenna height, azimuth and other key parameters. At the same time, the user distribution data is superimposed on the model in the form of a heat map to intuitively display the spatial distribution of user density. After the basic three-dimensional environment is constructed, this embodiment introduces a ray tracing algorithm to simulate the propagation of wireless signals. This step takes into account complex physical phenomena such as multipath propagation, reflection, and diffraction. The ray tracing algorithm simulates the signal sent by each base station and calculates its propagation path in three-dimensional space, including direct paths and various reflection and diffraction paths. This process not only takes into account the blocking effect of buildings, but also the impact of different materials on the signal. In order to improve the accuracy of the model, this embodiment also calculates the path loss corresponding to each relay path. Finally, all these elements—terrain, buildings, vegetation, base stations, user distribution, signal propagation paths, and path losses—are integrated to obtain the target three-dimensional environment model.

[0086] Based on the above embodiment, as an optional embodiment, the step of establishing the target three-dimensional environment model based on the environment data set may further include the following steps:

[0087] Step 201: construct a first three-dimensional environment model based on the building data and the meteorological environment in the environment data set.

[0088] Specifically, first, the terrain undulations of the target area are reproduced using high-precision digital elevation model data. This process uses a triangular irregular network algorithm to convert discrete elevation points into a continuous terrain surface. Then, based on the building data, computer-aided design technology is used to accurately construct a three-dimensional building model on the basis of the terrain. These building models not only include geometric shapes and sizes, but also contain material information, such as concrete, glass or metal, etc. These material properties are crucial for subsequent signal propagation simulations. While constructing the building model, this embodiment also takes into account other surface features, such as vegetation, water bodies and road networks. These elements are integrated into the model by combining vector data and raster data. In particular, for large vegetation areas, such as parks or woods, voxelization technology is used to simulate their three-dimensional structures to more accurately reflect their impact on signal propagation.

[0089] In addition, this embodiment uses historical data and real-time data provided by the meteorological data station to create a dynamic meteorological layer, which includes three-dimensional distribution of parameters such as temperature, humidity, air pressure and precipitation. Through the interpolation algorithm, the discrete meteorological observation data is extended to the three-dimensional space of the entire target area. It is particularly noted that this embodiment takes into account the influence of urban heat island effect and terrain on local meteorology, and locally adjusts the meteorological data through the micro-meteorological model to more accurately reflect the meteorological changes in the complex urban environment. In order to improve the accuracy and availability of the model, this embodiment adopts multi-resolution modeling technology, and uses higher resolution data and more sophisticated modeling methods in signal coverage key areas, such as densely populated areas or areas where signals are susceptible to interference, while in other areas, a relatively low resolution is used to balance computational efficiency and model accuracy. A first three-dimensional environmental model is constructed based on the building data in the environmental data set and the meteorological environment. The first three-dimensional environmental model is a basic model that can accurately reflect the physical structure and atmospheric conditions of the target area, and provides a reliable environmental background for subsequent signal propagation simulation and power optimization.

[0090] Step 202: Based on the user data and the signal data, a propagation simulation of the wireless signal is performed in the first three-dimensional environment model to obtain a second three-dimensional environment model, where the second three-dimensional environment model includes a propagation path from each base station to the user equipment.

[0091] Specifically, user data is first integrated into the first three-dimensional environmental model. The user data includes information such as the geographical distribution, mobility pattern and usage behavior of user devices. By adopting the kernel density estimation algorithm, the discrete user location data is converted into a continuous user density distribution map, and it is superimposed on the first three-dimensional environmental model as a dynamic layer. This method can not only reflect the static user distribution, but also capture the spatiotemporal variation characteristics of user density through time series analysis. Then, based on the actual collected signal data, this embodiment adopts a hybrid propagation model to simulate the propagation of wireless signals. This hybrid model combines deterministic methods and statistical methods to balance computational efficiency and simulation accuracy. Specifically, in large-scale propagation simulation, the COST-231Hata model is adopted. The model is locally calibrated and can better adapt to the characteristics of the target area. For small-scale fading, a statistical model based on measured data, such as Rayleigh distribution or Rician distribution, is introduced to simulate multipath effects and shadow fading.

[0092] In the signal propagation simulation process, this embodiment uses ray tracing technology to accurately simulate and determine the propagation path from each base station to the user equipment. This process begins with accurately locating the three-dimensional coordinates of each base station and user equipment in the first three-dimensional environment model, and using the spherical wavefront method to launch a large number of rays for each base station. The number of rays is dynamically adjusted according to the complexity of the environment and the required accuracy. Next, the system tracks the propagation of each ray in the three-dimensional environment, including multiple propagation mechanisms such as direct path, reflection, diffraction and scattering. When reflection occurs on the surface of a building, the reflection path is calculated according to the law of reflection; the UTD (Uniform Theory of Diffraction) model is applied to the edge of the building to calculate the diffraction path; for irregular surfaces such as vegetation, the scattering effect on the signal is considered. The system records all valid ray paths reaching the user equipment, and each path contains information such as path type, length, phase change and amplitude attenuation. For each pair of base stations and user equipment, all valid paths are comprehensively considered, and the total field strength is calculated using the vector superposition method. Based on the signal strength threshold, the main contributing path is screened out, and the path with a signal strength of more than -20dB of the strongest path is usually selected. Taking into account the mobility of users, the simulation process also includes the impact of the Doppler effect on path characteristics. For each determined propagation path, key parameters are calculated, including path loss, arrival time, arrival angle, and launch angle, and finally a second three-dimensional environment model containing the propagation paths from each base station to the user equipment is obtained.

[0093] Step 203: Calculate the path loss of each propagation path.

[0094] Specifically, the path loss of each propagation path is calculated according to a preset path loss formula. The path loss formula is: L(d) = L0 + 10nlog 10 (d / d0)+X σ ; Where L(d) is the path loss at a distance of d meters from the base station, L0 is the path loss at a reference distance of d0 meters, n is the path loss index, and X σ is a Gaussian distributed random variable, representing shadow fading. This formula takes into account factors such as distance attenuation, environmental characteristics, and random fading, and can more accurately describe the propagation characteristics of wireless signals in complex environments.

[0095] When using the path loss formula to calculate the path loss of each propagation path, it is first necessary to determine the various parameters in the formula. The reference distance d0 is usually selected as 1 meter or 100 meters, depending on the network deployment environment. L0 can be obtained through actual measurement or theoretical calculation, which reflects the basic attenuation characteristics of the signal when it propagates over a short distance. The path loss exponent n is a key parameter that varies with the environment. For example, it may be close to 2 in open areas, and may be as high as 4 or more in urban environments with dense buildings. This embodiment analyzes factors such as building density and height in the first three-dimensional environment model, combines actual measurement data, and uses a machine learning algorithm to dynamically adjust the n value to adapt to the characteristics of different areas. For the shadow fading term X σ , this embodiment takes into account its random nature and uses the Monte Carlo method to generate random values ​​that conform to the Gaussian distribution. The value of σ is also adjusted based on environmental characteristics and measured data, usually between 4 and 10 dB. In order to improve computing efficiency, the system pre-generates a large-scale Gaussian distribution random number pool and directly calls it during the calculation process. In actual calculations, the system traverses each propagation path in the second three-dimensional environmental model. For each path, its length d is first determined, and then d and predetermined parameters are substituted into the path loss formula. Taking into account the differences in propagation characteristics of signals in different frequency bands, this embodiment sets corresponding parameter sets for different frequency bands (such as 700MHz, 2.6GHz, 3.5GHz, etc.). For high-frequency signals (such as millimeter waves), additional factors such as atmospheric absorption and rainfall attenuation are also considered, and these effects can be reflected by adding additional terms to the formula.

[0096] Step 204: Map the propagation path and the corresponding path loss to the second three-dimensional environment model to obtain a target three-dimensional environment model.

[0097] Specifically, based on the second three-dimensional environment model, a unique data structure is created for each determined propagation path, which contains the starting point (base station location), the end point (user equipment location), the coordinates of the intermediate reflection or diffraction point, the path type (direct, reflection, diffraction or a combination thereof), the path length, and the calculated path loss value. Then, these path data are organized using an efficient spatial indexing algorithm (such as an R-tree or an octree) for fast retrieval and update. In the mapping process, a multi-level visualization technique is used to represent various propagation paths in three-dimensional space with lines of different colors and thicknesses, where the color can encode the path type and the line thickness reflects the signal strength. The path loss information is intuitively presented in the model through a color gradient, for example, a gradient from green to red is used to represent the path loss from low to high, thereby obtaining a target three-dimensional environment model that integrates multi-dimensional information such as the physical environment, user distribution, signal propagation path, and path loss.

[0098] Based on the above embodiment, as an optional embodiment, after the target three-dimensional environment model is established based on the environment data set, a process of correcting the target three-dimensional environment model may also be included. The specific process is as follows:

[0099] Specifically, it first involves the collection and processing of virtual signal and actual signal data. Virtual signal data is directly extracted from the three-dimensional environment model. These data are generated based on the parameters and algorithms of the model, including signal strength, coverage, interference level and other indicators. Actual signal data is obtained through multiple channels such as measurement equipment in the network, user terminal feedback and special signal test vehicles. To ensure the comparability of data, the system needs to synchronize the two types of data in time and space and unify the format.

[0100] After the data preparation is completed, the system adopts a multi-dimensional comparative analysis method. This includes signal strength distribution comparison, coverage boundary comparison, interference level assessment, etc. For signal strength distribution, the system uses heat map visualization technology to display virtual and actual signal strengths on the same map, intuitively showing the difference. Coverage boundary comparison is quantified by calculating the overlap and deviation of virtual and actual coverage areas. Interference level assessment focuses on the cell edges and overlapping areas, comparing the consistency of interference conditions predicted by the virtual model with actual measurement results. Based on the comparative analysis, the system generates a detailed comparison result report, which not only contains the difference values ​​of various indicators, but also includes the spatial distribution of the differences, time change trends, and possible cause analysis. For example, if the actual signal strength in a certain area is significantly lower than the model prediction, the report will mark the area and provide possible explanations, such as the shielding effect of new buildings or inaccurate terrain data.

[0101] The target 3D environment model is adjusted based on the comparison results. If the comparison result shows that the difference is small, no adjustment is required. Otherwise, the target 3D environment model needs to be adjusted. The adjustment process uses an iterative optimization method to gradually correct the model parameters and algorithms. First, identify the areas and parameters with the largest differences, and give priority to adjusting these parts. The adjustments may include updating terrain data, correcting building material parameters, adjusting propagation model coefficients, etc. After each adjustment, the system regenerates virtual signal data and compares it with actual data to evaluate the effect of the adjustment. In order to avoid overfitting, a cross-validation mechanism is also introduced. The system divides the entire target area into multiple sub-areas, uses data from some areas to adjust the model, and then uses data from the remaining areas to verify the adjustment effect, ensuring the generalization of the model adjustment so that the corrected target 3D environment model can adapt to a wider range of scenarios.

[0102] Step 30: Calculate the target transmission power of each base station based on the target three-dimensional environment model.

[0103] In the embodiment of the present application, the target transmit power refers to the ideal transmit power value that each base station should adopt, which is obtained through optimization calculation. This power value is intended to achieve the best signal coverage effect while balancing network performance and energy efficiency.

[0104] Specifically, the relevant information of each base station is extracted from the target three-dimensional environment model, including its geographical location, user distribution within the coverage area, maximum transmission power, surrounding environment characteristics, and signal quality changes. Then, the propagation path and corresponding path loss from each base station to each user device within its coverage area are analyzed, and the target transmission power of each base station is calculated based on this information. The power configuration is precise and personalized, and the transmission power of each base station can be optimized according to its unique environment and network conditions. And by considering the path loss and signal quality changes, the system can dynamically respond to environmental changes and user demand fluctuations, improving the adaptability and service quality of the network.

[0105] Based on the above embodiment, as an optional embodiment, the step of calculating the target transmission power of each base station based on the target three-dimensional environment model may further include the following steps:

[0106] Step 301: Obtain the maximum transmission power of each base station.

[0107] Step 302: Determine the path loss and signal quality change of the propagation path of each base station in the target three-dimensional environment model.

[0108] Specifically, the maximum transmission power of each base station is obtained. This parameter is usually determined by the hardware specifications of the base station equipment. At the same time, the relevant regulations of the regulatory authorities must also be considered. The maximum transmission power can be obtained by querying the network management database or directly communicating with the base station equipment. Then, the path loss information of the propagation path of each base station is extracted from the target three-dimensional environment model. This process involves analyzing all major propagation paths within the coverage area of ​​each base station. Considering that there are a large number of propagation paths in the actual network, this method uses a weighted average to comprehensively calculate the path loss of each base station. The allocation of weights is based on multiple factors, including user density, business needs, and path importance. For example, for propagation paths in user-dense areas or areas with high traffic demand, the system will assign higher weights to ensure that these key areas are fully considered in power optimization.

[0109] While determining the path loss, the system also needs to calculate the signal quality change. This parameter reflects the dynamic changes in network performance and is the key to achieving adaptive power adjustment. The calculation of the signal quality change is based on comparing the network performance indicators at the current moment with the previous moment, such as signal-to-noise ratio, throughput, or user satisfaction. The system calculates the change amplitude and trend of these indicators by analyzing the network performance monitoring data. In order to capture changes at different time scales, this method considers both short-term fluctuations and long-term trends, and uses techniques such as exponential moving average to smooth the data to avoid excessive impact of instantaneous fluctuations on power adjustment.

[0110] Step 303: Substitute the maximum transmit power of each base station and the corresponding path loss and signal quality change into a preset transmit power calculation formula to obtain the target transmit power of each base station.

[0111] Specifically, after obtaining the maximum transmit power, path loss and signal quality change of each base station, these parameters are substituted into a preset transmit power calculation formula to calculate the target transmit power of each base station.

[0112] The preset transmit power calculation formula is: P i (t) = P max -L i (t)+η·ΔQ i (t);

[0113] Among them, P i (t) is the target transmit power of the ith base station at time t, P max is the maximum transmit power, L i (t) is the path loss of the ith base station at time t, η is the preset adjustment coefficient, η∈[0.1,1], ΔQ i (t) is the signal quality change of the ith base station at time t.

[0114] When calculating the target transmission power of each base station, first extract the maximum transmission power P of each base station from the database. max , this value is usually determined by the base station hardware specifications and regulatory requirements. i (t) is extracted from the modified target 3D environment model, which reflects the attenuation of the signal during propagation. i(t) is calculated by comparing the network performance indicators (such as signal-to-noise ratio or throughput) at the current moment with the previous moment. The selection of the adjustment coefficient η has an important impact on the power calculation results. The system determines its value based on the network type, coverage area characteristics and optimization goals. After the parameters are prepared, the system begins to calculate the target transmit power of each base station one by one. The calculation process uses parallel processing technology to process data from multiple base stations at the same time to improve efficiency. For each base station, the system first checks the validity of the parameters to ensure that all input data is within a reasonable range, and then substitutes these parameters into the preset formula for calculation. In order to cope with the complexity and dynamics of the network environment, the system introduces an adaptive mechanism in the calculation process. For example, for areas where high interference is detected, the system may dynamically adjust the η value to make the power adjustment more sensitive to quickly respond to interference changes. For areas at the edge of the network, the system may appropriately relax the power cap to ensure coverage.

[0115] Step 40: Control each base station to perform power adjustment according to the corresponding target transmission power.

[0116] Specifically, it first involves establishing a secure and reliable communication link connecting the central control system and each base station, which usually uses an existing network management system (NMS) or a dedicated control channel. The system needs to ensure the security and reliability of communication, using encrypted transmission and error detection mechanisms to prevent unauthorized access or data tampering. After establishing the communication link, the system begins to send power adjustment instructions to each base station. These instructions contain the target transmit power value and the time window for performing the adjustment. In order to avoid network interruption or sudden performance degradation, the system adopts a gradual adjustment strategy. Instead of adjusting the power to the target value at one time, the system divides the adjustment process into multiple small steps, each step adjusts a small amount of power, and leaves enough time interval between each step to monitor the network response. During the power adjustment process, the system monitors network performance indicators such as signal strength, coverage, interference level and user experience quality in real time. These real-time data are obtained through base station feedback or user equipment reports. If any anomalies are detected, such as a sudden drop in signal strength in a certain area or a significant increase in interference, the system can immediately suspend or roll back the power adjustment of the relevant base stations. In order to handle complex network topologies, the system also adopts a collaborative adjustment strategy. When adjusting the power of a base station, it also considers the status and planned adjustments of adjacent base stations. This collaborative approach can minimize the impact of power adjustment on the overall network balance and avoid the creation of new coverage blind spots or interference hotspots.

[0117] Based on the above embodiment, as an optional embodiment, the step of controlling each base station to perform power adjustment according to the corresponding target transmit power may further include the following steps:

[0118] Step 401: Based on the target three-dimensional environment model, identify the hot spots and blind spots of signal coverage in the target area.

[0119] Specifically, the signal strength data in the target three-dimensional environment model is comprehensively analyzed. The system uses a high-performance computing cluster to grid the entire target area. Each grid point contains detailed information such as signal strength, signal-to-noise ratio, and interference level. The analysis process uses a multi-threshold classification method to classify grid points into different levels according to preset signal strength and quality standards. For example, areas with signal strength exceeding -70dBm and signal-to-noise ratio above 20dB are marked as hot spots, while areas with signal strength below -100dBm or signal-to-noise ratio below 5dB are identified as blind spots. During the identification process, special attention is also paid to dynamically changing areas. By comparing data from different time periods, the system can discover patterns in signal coverage changes over time, such as the difference between weekdays and weekends, or the impact of specific events (such as large gatherings) on signal coverage. This dynamic analysis helps operators develop more flexible network optimization strategies.

[0120] Step 402: Obtain a first current transmission power of a first base station corresponding to a hot spot area, and a second current transmission power of a second base station corresponding to a blind spot area.

[0121] Specifically, all base stations corresponding to the hot spot area are regarded as the first base station, and all base stations corresponding to the blind spot area are regarded as the second base station. After determining the relevant base stations, the system queries the first current transmission power corresponding to each first base station and the second current transmission power corresponding to each second base station in real time through the network management interface or by directly communicating with the base station.

[0122] Step 403: Based on the target transmit power corresponding to the first base station and the first current transmit power, control the first base station to perform power adjustment.

[0123] Specifically, for any first base station, the pre-calculated target transmit power is compared with the obtained first current transmit power to obtain the specific value that needs to be adjusted. This process not only takes into account the difference in absolute power values, but also analyzes dynamic factors such as the current network load and user distribution to ensure the rationality of the adjustment. For example, if the current period is the peak usage period of the network, the system may adopt a more conservative adjustment strategy to avoid sudden impact on the user experience. After determining the adjustment amount, the system sends a power adjustment instruction to the first base station through a secure and reliable communication link. This communication process uses an encrypted transmission protocol to ensure the security and integrity of the instructions. At the same time, the system also implements a multiple authentication mechanism to prevent unauthorized power adjustment operations. The instruction contains not only the target power value, but also the time window and adjustment steps for performing the adjustment to achieve a smooth transition.

[0124] Step 404: Obtain neighboring base stations of the second base station, and control the second base station to perform power adjustment based on the target transmit power corresponding to the second base station, the second current transmit power, and the current transmit power of the neighboring base stations.

[0125] Specifically, for any second base station, the network topology information in the target three-dimensional environment model is used, combined with the geographic location data and signal coverage, to identify all neighboring base stations with potential signal overlap or mutual influence with each second base station. After determining the neighboring base stations, the system obtains the current transmit power of these neighboring base stations in real time through the network management interface or direct communication. At the same time, the system also collects other key parameters of the neighboring base stations, such as antenna direction, tilt angle, current load, etc., to comprehensively evaluate the surrounding network environment. The collection of these data adopts a distributed architecture to ensure that a large number of base station information can be obtained quickly and in parallel. Next, the system performs power adjustment calculations. This calculation process not only considers the target transmit power and current transmit power of the second base station itself, but also takes the current transmit power of the neighboring base station as an important input parameter. The system uses a multi-objective optimization algorithm and considers the following aspects at the same time: 1) improve signal coverage in blind spot areas; 2) avoid new interference to the coverage area of ​​neighboring base stations; 3) optimize the energy efficiency of the overall network. The algorithm weighs these goals and calculates the optimal power adjustment plan.

[0126] In a feasible embodiment, a multi-objective optimization method of a genetic algorithm can be used to calculate the optimal power adjustment solution, for example: define the decision variable as X=(P2, P n1 , P_ n2 , ..., P ni ), where P2 represents the transmission power of the second base station, P ni represents the transmission power of the ith neighboring base station. Define three objective functions: f1(X) = signal coverage in the blind spot area, f2(X) = interference level in the neighboring area, and f3(X) = network energy efficiency. Among them, f1(X) = Σ(S i ) / A total , S i is the number of grids whose signal strength exceeds the threshold, A total is the total number of grids in the blind spot area; f2(X)=Σ(I j ) / N,I j is the number of grids in the adjacent area whose interference exceeds the threshold, N is the total number of grids in the adjacent area; f3(X)=Σ(C k ) / Σ(P k ), C k is the capacity of the kth base station, P k is the transmission power of the kth base station. Constraints: Set the constraints for power adjustment: P min ≤P i ≤Pmax , where P min and P max are the minimum and maximum transmission powers allowed by the base station, respectively. Then, a genetic algorithm is used to obtain a solution that balances the three objectives as the optimal power adjustment scheme by setting the maximum number of iterations or the convergence threshold as the termination condition.

[0127] After determining the adjustment plan, the system adopts a progressive power adjustment strategy, and the adjustment process is broken down into multiple small steps, and the amplitude of each adjustment step is within the preset safety range. Between each adjustment step, the system monitors network performance indicators in real time, including signal strength, coverage, user connection quality, etc. If any abnormality is detected, the system can immediately pause or roll back the adjustment. In order to coordinate the power adjustment of the second base station and the adjacent base station, an embodiment of the present application also provides a dynamic negotiation mechanism. While adjusting the power of the second base station, the system sends a coordination request to the adjacent base station. The adjacent base station may fine-tune its own power or antenna parameters accordingly to cooperate with the coverage optimization of the second base station. This coordinated adjustment ensures that while solving the blind spot problem, it will not cause significant interference to the overall network architecture.

[0128] See also Figure 3 , is a schematic diagram of a module of a power control system for optimizing signal coverage provided in an embodiment of the present application. The power control system for optimizing signal coverage may include: a data acquisition module, a model building module, a power calculation module and a power regulation module, wherein:

[0129] A data collection module, used to collect environmental data sets of each base station in a target area, wherein the target area includes a plurality of base stations;

[0130] A model building module, used to build a target three-dimensional environment model based on the environment data set;

[0131] A power calculation module, used to calculate the target transmission power of each base station based on the target three-dimensional environment model;

[0132] The power adjustment module is used to control each base station to perform power adjustment according to the corresponding target transmission power.

[0133] Optionally, the data acquisition module is also used to determine the geographical location of each base station in the target area; for any target base station, collect original environmental data within a preset range of the geographical location of the target base station; and perform data preprocessing on the original environmental data to obtain an environmental data set.

[0134] Optionally, the model building module is also used to construct a first three-dimensional environmental model based on the building data and meteorological environment in the environmental data set; perform wireless signal propagation simulation in the first three-dimensional environmental model based on the user data and the signal data to obtain a second three-dimensional environmental model, wherein the second three-dimensional environmental model includes a propagation path from each base station to the user device; calculate the path loss of each propagation path; map the propagation path and the corresponding path loss to the second three-dimensional environmental model to obtain a target three-dimensional environmental model.

[0135] Optionally, the model building module is further used to calculate the path loss of each of the propagation paths according to a preset path loss formula; the path loss formula is: L(d) = L0 + 10nlog 10 (d / d0)+X σ ; Where L(d) is the path loss at a distance of d meters from the base station, L0 is the path loss at a reference distance of d0 meters, n is the path loss index, and X σ is a Gaussian distributed random variable representing shadow fading.

[0136] Optionally, a power control system for optimizing signal coverage further includes a model correction module, which is used to compare the virtual signal of each base station in the three-dimensional environment model with the actual signal data to obtain a comparison result; adjust the three-dimensional environment model based on the comparison result to obtain a corrected target three-dimensional environment model; and calculate the target transmission power of each base station based on the signal data of each base station and the target three-dimensional environment model, including: calculating the target transmission power of each base station based on the signal data of each base station and the corrected target three-dimensional environment model.

[0137] Optionally, the power calculation module is further used to obtain the maximum transmission power of each base station; determine the path loss and signal quality change of the propagation path of each base station in the target three-dimensional environment model; substitute the maximum transmission power of each base station and the corresponding path loss and signal quality change into a preset transmission power calculation formula to obtain the target transmission power of each base station; the preset transmission power calculation formula is: P i (t) = P max -L i (t)+η·ΔQ i (t); where P i (t) is the target transmit power of the ith base station at time t, P max is the maximum transmit power, L i (t) is the path loss of the ith base station at time t, η is the preset adjustment coefficient, η∈[0.1,1], ΔQ i(t) is the signal quality change of the ith base station at time t.

[0138] Optionally, the power adjustment module is also used to identify hotspot areas and blind spot areas of signal coverage in the target area based on the target three-dimensional environment model; obtain the first current transmission power of the first base station corresponding to the hotspot area, and the second current transmission power of the second base station corresponding to the blind spot area; control the first base station to perform power adjustment based on the target transmission power corresponding to the first base station and the first current transmission power; obtain the neighboring base stations of the second base station, and control the second base station to perform power adjustment based on the target transmission power corresponding to the second base station, the second current transmission power and the current transmission power of the neighboring base stations.

[0139] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0140] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a power control method for optimizing signal coverage in the above embodiment. The specific execution process can be found in the specific description of the above embodiment and will not be repeated here.

[0141] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0142] The communication bus 302 is used to realize the connection and communication between these components.

[0143] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0144] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0145] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0146] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a power control method for optimizing signal coverage.

[0147] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a power control method for optimizing signal coverage stored in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0152] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0153] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0154] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A power control method for optimizing signal coverage, characterized in that: The method comprises: Collecting environmental data sets of each base station in a target area, wherein the target area includes a plurality of base stations; Establishing a target three-dimensional environment model based on the environment data set; Calculating the target transmission power of each base station based on the target three-dimensional environment model; Control each of the base stations to perform power adjustment according to the corresponding target transmission power.

2. The power control method for optimizing signal coverage according to claim 1, characterized in that: The environmental data set of each base station in the target area is collected, including: Determining the geographical location of each base station in the target area; For any target base station, collecting raw environmental data within a preset range of the geographical location of the target base station; Data preprocessing is performed on each of the original environmental data to obtain an environmental data set of each of the base stations.

3. The power control method for optimizing signal coverage according to claim 1, characterized in that: The environmental data set includes building data, user data, meteorological data and signal data, the user data includes user equipment, and establishing a target three-dimensional environmental model based on the environmental data set includes: Constructing a first three-dimensional environment model based on the building data and the meteorological environment in the environment data set; Based on the user data and the signal data, a propagation simulation of a wireless signal is performed in the first three-dimensional environment model to obtain a second three-dimensional environment model, wherein the second three-dimensional environment model includes a propagation path from each base station to the user equipment; Calculating the path loss of each of the propagation paths; The propagation path and the corresponding path loss are mapped to the second three-dimensional environment model to obtain a target three-dimensional environment model.

4. The power control method for optimizing signal coverage according to claim 3, characterized in that: The calculating the path loss of each of the propagation paths comprises: Calculating the path loss of each of the propagation paths according to a preset path loss formula; The path loss formula is: L(d)=L0+10nlog 10 (d / d0)+X σ ; Where L(d) is the path loss at a distance of d meters from the base station, L0 is the path loss at a reference distance of d0 meters, n is the path loss exponent, and X σ is a Gaussian distributed random variable representing shadow fading.

5. The power control method for optimizing signal coverage according to claim 1, characterized in that: After the target three-dimensional environment model is established based on the environment data set, the method further includes: Comparing the virtual signal of each base station in the three-dimensional environment model with the actual signal data to obtain a comparison result; Adjusting the three-dimensional environment model based on the comparison result to obtain a corrected target three-dimensional environment model; The calculating the target transmission power of each base station based on the signal data of each base station and the target three-dimensional environment model includes: The target transmission power of each base station is calculated based on the signal data of each base station and the corrected target three-dimensional environment model.

6. The power control method for optimizing signal coverage according to claim 1, characterized in that: The calculating the target transmission power of each base station based on the target three-dimensional environment model includes: Obtaining the maximum transmit power of each of the base stations; Determining the path loss and signal quality change of the propagation path of each base station in the target three-dimensional environment model; Substituting the maximum transmit power of each base station and the corresponding path loss and signal quality change into a preset transmit power calculation formula to obtain a target transmit power of each base station; The preset transmit power calculation formula is: P i (t)=P max -L i (t)+η·ΔQ i (t); Among them, P i (t) is the target transmit power of the ith base station at time t, P max is the maximum transmit power, L i (t) is the path loss of the ith base station at time t, η is the preset adjustment coefficient, η∈[0.1,1], ΔQ i (t) is the signal quality change of the ith base station at time t.

7. The power control method for optimizing signal coverage according to claim 1, characterized in that: The controlling each of the base stations to perform power adjustment according to the corresponding target transmit power includes: Based on the target three-dimensional environment model, identifying the hot spots and blind spots of signal coverage in the target area; Acquire a first current transmission power of a first base station corresponding to the hot spot area, and a second current transmission power of a second base station corresponding to the blind spot area; Controlling the first base station to perform power adjustment based on the target transmit power corresponding to the first base station and the first current transmit power; Obtain the neighboring base stations of the second base station, and control the second base station to perform power adjustment based on the target transmit power corresponding to the second base station, the second current transmit power, and the current transmit power of the neighboring base station.

8. A power control system for optimizing signal coverage, characterized in that: The system comprises: A data collection module, used to collect environmental data sets of each base station in a target area, wherein the target area includes a plurality of base stations; A model building module, used to build a target three-dimensional environment model based on the environment data set; A power calculation module, used to calculate the target transmission power of each base station based on the target three-dimensional environment model; The power adjustment module is used to control each base station to perform power adjustment according to the corresponding target transmission power.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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