Base station investigation method and system based on edge calculation

Abstract

The invention provides a base station reconnaissance method and system based on edge calculation. Wherein one or more target areas are selected from a base station network coverage area, and each target area comprises at least one candidate base station position; at least one edge computing device is deployed in the target area, the collected environment parameter data are analyzed in real time through the edge computing device, instant feedback information is generated, and the instant feedback information is uploaded to a cloud server; coordinating data sharing and collaboration among the edge computing devices, and constructing an environment parameter map of the target area in combination with instant feedback information; and generating a site selection recommendation report of the candidate base station positions based on the environmental parameter atlas in combination with the historical base station data and the user behavior mode. According to the method, the positions of the candidate base stations are comprehensively determined through the environment parameter atlas, the historical base station data and the user behavior pattern analysis, so that the site selection quality and the site selection efficiency are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of base station survey, and in particular to a base station survey method and system based on edge computing. Background Art

[0002] With the rapid development of mobile communication technology and the increasing demands for network coverage and service quality, the rational layout of base stations has become a key issue facing operators. To ensure efficient operation of communication networks and continuously improve user experience, base station surveys have become a key step in the construction of communication infrastructure.

[0003] Currently, base station site selection relies primarily on manual site surveys and empirical judgment, supplemented by some automated tools. These tools typically require uploading large amounts of field data to a central server for processing, followed by analysis and decision-making by a team of experts.

[0004] Since data needs to be uploaded from edge devices to the cloud for processing and then the results are returned to on-site staff, this process may cause long delays, affecting the timeliness of decision-making, thereby resulting in poor site selection quality and low site selection efficiency. Summary of the Invention

[0005] The embodiments of the present application provide a base station survey method and system based on edge computing to solve the problems of poor site selection quality and low site selection efficiency in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a base station survey method based on edge computing, which is applied to a cloud server, including: Select one or more target areas from the base station network coverage area, wherein the target area includes at least one candidate base station location; Deploy at least one edge computing device in the target area to perform real-time analysis on the collected environmental parameter data through the edge computing device, generate instant feedback information, and upload the instant feedback information to a cloud server. The instant feedback information includes signal coverage, locations of potential interference sources, and dynamic changes in the surrounding environment. Coordinate data sharing and collaboration between the edge computing devices, and construct an environmental parameter map of the target area based on the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; Based on the environmental parameter map, combined with historical base station data and user behavior patterns, a site selection recommendation report for candidate base station locations is generated, wherein the site selection recommendation report includes an optimal installation location and optimization suggestions.

[0007] Furthermore, the coordinating data sharing and collaboration between the edge computing devices includes: Encrypting the communication link between the edge computing devices using a security protocol to establish a secure communication link; On the basis of establishing a secure communication link, data synchronization processing is performed on the edge computing devices according to the data update frequency of the edge computing devices to achieve real-time exchange and synchronization of environmental parameter data between the edge computing devices; The method further comprises: A conflict detection algorithm is used to process data conflicts that occur during data sharing between the edge computing devices to ensure data consistency between the edge computing devices.

[0008] Furthermore, the construction of an environmental parameter map of the target area in combination with the instant feedback information includes: Using a data fusion algorithm, the instant feedback information is fused with existing environmental parameter data to obtain an environmental parameter map; According to the latest immediate feedback information, the environmental parameter map is updated in real time to obtain a dynamically updated environmental parameter map.

[0009] Furthermore, the instant feedback information is fused with the existing environmental parameter data using a data fusion algorithm to obtain an environmental parameter map, including: Generate preliminary environmental parameter maps based on existing environmental parameter data; On the preliminary environmental parameter map, marking and locating the interference source according to the potential interference source data in the instant feedback information; According to the signal coverage in the instant feedback information, performing signal coverage mapping processing on the preliminary environmental parameter map to generate a signal coverage heat map; Based on the dynamic change data of the surrounding environment in the instant feedback information, dynamically update the preliminary environmental parameter map; Generates environmental parameter maps containing interference source location information, signal coverage heat maps, and dynamically updated data.

[0010] Furthermore, the generating of a site selection recommendation report for candidate base station locations based on the environmental parameter map in combination with historical base station data and user behavior patterns includes: Retrieving historical base station data of existing base stations related to the target area from a cloud database, including signal strength, coverage, and user access volume; Analyze user behavior patterns within the target area, including user activity times, activity locations, and communication needs; Evaluate the suitability scores of candidate base station locations based on the environmental parameter map, the historical base station data, and the user behavior patterns to obtain evaluation results; The evaluation results are sorted and a site selection recommendation report containing the best installation location and optimization suggestions is generated based on the sorting results.

[0011] Furthermore, the suitability score of the candidate base station location is evaluated based on the environmental parameter map, the historical base station data, and the user behavior pattern to obtain an evaluation result, including: Building a comprehensive evaluation model for evaluating the suitability of base station installation locations based on the historical base station data, user behavior patterns, and environmental parameter maps; Using a machine learning algorithm to train the comprehensive evaluation model so that the comprehensive evaluation model can accurately predict the signal coverage quality, user access efficiency, and overall network performance of the candidate base station location; Applying the trained comprehensive evaluation model to each candidate base station location to calculate a suitability score for each candidate base station location, wherein the suitability score reflects the expected service quality when the base station is installed at the corresponding location; The suitability score of each candidate base station location is calculated using the following formula: ; in, Indicates the location of the candidate base station The suitability score of Indicates the location of the candidate base station The exponential form of the environmental parameter spectrum score; Indicates the location of the candidate base station The exponential form of the historical base station data score; Indicates the location of the candidate base station The index form of user behavior pattern score; are exponential coefficients, which are used to adjust the influence of the environmental parameter map score, the historical base station data score, and the user behavior pattern score; are weight coefficients, representing the relative importance of the environmental parameter map score, historical base station data score, and user behavior pattern score in the final suitability score; Find the optimal weights using the least squares method : , is the candidate base station location, is the actual performance score of the candidate base station location; Environmental parameter map score: , is the candidate base station location The distance to the nearest source of interference; is the maximum permissible distance from the interference source; is the base of natural logarithms The exponential function of It's location the degree to which the surrounding environment is dynamically changing; It is the average level of environmental dynamics; is the candidate base station location Coverage ratio on the signal coverage heat map, value range is the attenuation coefficient; Historical base station data scores: , is the candidate base station location Average signal strength; is the strongest signal strength; is the candidate base station location coverage; is the maximum coverage; is the candidate base station location The number of users accessing is the maximum number of user accesses; is the coefficient of each score of historical base station data; User behavior pattern score: , is the candidate base station location The proportion of user active time; is the total active time; is the candidate base station location Percentage of user activity locations; It is the main activity location; is the candidate base station location The proportion of communication needs; is the total communication demand; It is the coefficient of each score of the user behavior pattern.

[0012] Furthermore, the method further comprises: Based on the site selection recommendation report and the corresponding immediate feedback information, specific guidance information is provided to the on-site survey personnel. The specific guidance information includes optimal path planning, on-site equipment deployment suggestions and interference source avoidance solutions to optimize the base station installation process.

[0013] In a second aspect, an embodiment of the present application provides a base station survey system based on edge computing, including: A selection module, configured to select one or more target areas from within the base station network coverage area, wherein the target area includes at least one candidate base station location; a deployment module, configured to deploy at least one edge computing device within the target area to analyze the collected environmental parameter data in real time through the edge computing device, generate instant feedback information, and upload the instant feedback information to a cloud server. The instant feedback information includes signal coverage, locations of potential interference sources, and dynamic changes in the surrounding environment; A construction module is used to coordinate data sharing and collaboration between the edge computing devices, and to construct an environmental parameter map of the target area based on the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; A generation module is used to generate a site selection recommendation report for candidate base station locations based on the environmental parameter map, combined with historical base station data and user behavior patterns, wherein the site selection recommendation report includes the best installation location and optimization suggestions.

[0014] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a base station survey method based on edge computing as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a base station survey method based on edge computing as described in the first aspect.

[0016] In an embodiment of the present application, one or more target areas are selected from the base station network coverage area, and the target area includes at least one candidate base station location; at least one edge computing device is deployed in the target area to perform real-time analysis of the collected environmental parameter data through the edge computing device, generate instant feedback information, and upload the instant feedback information to the cloud server; coordinate data sharing and collaboration between edge computing devices, and build an environmental parameter map of the target area based on the instant feedback information; based on the environmental parameter map, combined with historical base station data and user behavior patterns, generate a site selection recommendation report for the candidate base station location. The present application comprehensively determines the candidate base station location through environmental parameter maps, historical base station data, and user behavior pattern analysis, thereby improving the site selection quality and efficiency.

[0017] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flowchart of a base station survey method based on edge computing provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a base station survey system based on edge computing provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0021] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0023] Figure 1 A flow chart of a base station survey method based on edge computing is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Select one or more target areas from a base station network coverage area, wherein the target area includes at least one candidate base station location; This step aims to identify network coverage areas that require optimization. These areas may be areas with poor signal quality or high user density, and are selected by analyzing existing network performance indicators (such as signal strength and call drop rate).

[0024] In this embodiment, for example, a commercial district in the city center is selected as the target area because it is densely populated and has high network quality requirements. By analyzing the network performance report provided by the operator, it is found that there are many weak signal areas in the area, so it is decided to select them as the optimization targets.

[0025] 102. Deploy at least one edge computing device in the target area to perform real-time analysis on the collected environmental parameter data through the edge computing device, generate instant feedback information, and upload the instant feedback information to a cloud server. The instant feedback information includes signal coverage, locations of potential interference sources, and dynamic changes in the surrounding environment. In this step, edge computing devices are installed in the selected target area. These devices are responsible for collecting surrounding environmental parameters (such as electromagnetic field strength and noise level) and performing preliminary processing to ensure rapid response and reduce data transmission delays.

[0026] In this example, assume that several small edge computing nodes are installed within the commercial district selected in the previous step. Each node is equipped with sensors to monitor the electromagnetic environment. Upon detecting a sudden drop in signal strength in a specific area, the edge computing device can quickly identify the possible cause (such as a new interference source) and send this information to the cloud platform in real time.

[0027] 103. Coordinate data sharing and collaboration between the edge computing devices, and construct an environmental parameter map of the target area based on the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; In this step, all edge computing devices share data, which is then processed by algorithms to form a comprehensive map reflecting the signal coverage and interference conditions within the target area. This helps to more intuitively understand the communication environment of the entire area.

[0028] In this embodiment, using the data collected in the previous step, the development team can use GIS technology to create a detailed electronic map. This map not only shows the signal strength at each location, but also marks the location of major interference sources and their impact range. In addition, the map can be dynamically updated to reflect the latest impacts of environmental changes.

[0029] Optionally, the "coordinating data sharing and collaboration between the edge computing devices" in step 103 includes: using a security protocol to encrypt the communication link between the edge computing devices to establish a secure communication link; on the basis of establishing a secure communication link, according to the data update frequency of the edge computing device, performing data synchronization processing on the edge computing device to achieve real-time exchange and synchronization of environmental parameter data between the edge computing devices; the method also includes: using a conflict detection algorithm to handle data conflicts that occur during the data sharing process of the edge computing devices to ensure data consistency between the edge computing devices.

[0030] Security protocol: refers to a series of rules and technologies used to protect the security of data transmission, such as TLS / SSL, which can prevent data from being eavesdropped or tampered with during transmission.

[0031] Data update frequency: refers to the frequency at which edge computing devices send the latest data to other devices regularly or irregularly based on their own working status or changes in the external environment.

[0032] Data synchronization: refers to the process of keeping the same type of data on different devices consistent, ensuring that all relevant parties have the latest information.

[0033] Conflict detection algorithm: An algorithm used to identify and resolve problems that may arise when multiple users or systems attempt to modify the same resource at the same time.

[0034] First, secure protocols are used to encrypt the communication links between edge computing devices, ensuring data security during transmission and preventing unauthorized access or data leakage. Secondly, based on the data update frequency of each edge computing device, synchronization strategies are dynamically adjusted to ensure timely and accurate data dissemination across all relevant devices. Finally, conflict detection algorithms are deployed to automatically identify and resolve conflicts caused by multiple devices simultaneously updating the same data, maintaining data consistency.

[0035] In this embodiment, assume that in a smart city project, multiple edge computing devices (such as smart cameras and environmental monitoring stations) are distributed across the city, collecting information such as traffic flow and air quality. To improve data processing efficiency and service quality, these devices need to frequently exchange and synchronize information.

[0036] All devices are configured with the TLS 1.3 protocol, and authentication and encryption are performed before each data transmission to ensure that only legitimate recipients can decrypt and read the data. Secondly, considering the different data update requirements of different devices (for example, smart cameras generate new data every second, while environmental monitoring stations update once an hour), the system adopts an adaptive synchronization mechanism, which dynamically adjusts the synchronization cycle based on the actual update frequency of each device, ensuring data freshness while reducing unnecessary network burden. Furthermore, during a routine operation, two adjacent environmental monitoring stations uploaded new measurements of PM2.5 concentrations at the same location almost simultaneously. The system's built-in conflict detection algorithm quickly identified this situation and automatically selected the correct data version based on preset rules (such as data with a newer timestamp being prioritized), avoiding analytical errors caused by data inconsistencies.

[0037] By implementing the above solution, not only is the data security between edge computing devices strengthened, the efficiency and accuracy of data synchronization improved, but also the data conflict problem is effectively resolved, providing a solid foundation for building accurate environmental parameter maps, thereby improving the intelligence level and service quality of smart city management.

[0038] Optionally, the "combining the instant feedback information to construct an environmental parameter map of the target area" in step 103 includes: using a data fusion algorithm to fuse the instant feedback information with existing environmental parameter data to obtain an environmental parameter map; and updating the environmental parameter map in real time based on the latest instant feedback information to obtain a dynamically updated environmental parameter map.

[0039] Data fusion algorithm: A method that combines information from multiple sensors or data sources to produce more accurate and reliable results than a single source could provide.

[0040] Instant feedback information: refers to data obtained in real time from various sensors or devices, which reflects the latest status of the current environment.

[0041] Environmental parameter map: A visualization tool used to show the spatial distribution of various environmental parameters (such as signal strength, interference distribution, etc.) in a specific area.

[0042] First, a data fusion algorithm is used to fuse the immediate feedback information with the existing environmental parameter data to generate an initial environmental parameter map. Second, the environmental parameter map is updated in real time based on the latest immediate feedback information to ensure that the map always reflects the latest environmental status.

[0043] In this embodiment of the present application, assume that a smart city project needs to construct an environmental parameter map that reflects the city's signal coverage and interference distribution. The project deploys multiple edge computing devices, including signal towers, environmental monitoring stations, and mobile devices (such as smartphones).

[0044] First, historical data, including signal strength and interference levels, is collected from signal towers and environmental monitoring stations. Next, real-time signal strength and interference data is uploaded from mobile devices (such as smartphones). These two types of data are then fused using a Kalman filter. The Kalman filter is a recursive least-squares estimation method that effectively handles noise and uncertainty.

[0045] Assume that the existing data and immediate feedback information are expressed as and , then the fused data It can be expressed as: ; in, is the Kalman gain, which can be adjusted according to actual conditions.

[0046] Furthermore, mobile devices upload signal strength and interference data every 5 minutes. Whenever new instant feedback information is received, the system will immediately update the environmental parameter map using the above data fusion algorithm.

[0047] Suppose at a certain moment, the system receives a new set of immediate feedback information , then the updated environmental parameter map It can be expressed as: ; By implementing the above solution, the accuracy and real-time performance of environmental parameter maps can be significantly improved. Specifically, through real-time updates, environmental parameter maps can rapidly reflect the latest environmental changes, providing timely support for decision-making. Furthermore, data fusion algorithms can effectively integrate information from multiple data sources, reducing noise and uncertainty and improving map accuracy. Furthermore, this solution can flexibly adapt to different application scenarios, effectively integrating the instant feedback it provides, whether for fixed or mobile devices.

[0048] 104. Based on the environmental parameter map, combined with historical base station data and user behavior patterns, generate a site selection recommendation report for candidate base station locations, wherein the site selection recommendation report includes an optimal installation location and optimization suggestions.

[0049] In this step, based on the information obtained in the first three steps, combined with historical data and expected user demand, specific recommendations are made for adding or adjusting base station locations. These recommendations are intended to improve overall network efficiency and service quality.

[0050] In this example, based on the environmental parameter map constructed earlier, analysts discovered a significant signal blind spot in the northwest corner of a commercial district. Considering the expected population growth in this area over the next few years, they recommended adding a small base station there. Furthermore, for some existing base stations, they also proposed power adjustments and antenna orientation optimization to improve the user experience within their service areas.

[0051] Optionally, step 104 of "generating a site selection recommendation report for candidate base station locations based on the environmental parameter map, combined with historical base station data and user behavior patterns" includes: retrieving historical base station data of existing base stations related to the target area from a cloud database, including signal strength, coverage, and user access volume; analyzing user behavior patterns within the target area, including user activity time, activity location, and communication needs; evaluating the suitability score of candidate base station locations based on the environmental parameter map, the historical base station data, and the user behavior patterns to obtain an evaluation result; sorting according to the evaluation results, and generating a site selection recommendation report containing the best installation location and optimization suggestions based on the sorting results.

[0052] Environmental parameter map: refers to a map of information such as the terrain, building density, and population distribution of the target area obtained through technical means such as the Geographic Information System (GIS).

[0053] Historical base station data: including but not limited to indicators such as signal strength, coverage, and user access volume, reflecting the actual operating status of the base station.

[0054] User behavior patterns: Through data analysis of user usage habits, we can understand users' activity time, activity location and communication needs.

[0055] Suitability score: A quantitative evaluation of candidate base station locations based on comprehensive consideration of environmental parameters, historical base station performance, and user needs.

[0056] First, relevant data on all active base stations in the target area, such as signal strength, coverage, and user access, is collected from a cloud database. Next, big data analytics are used to identify patterns in user activity within the target area, including peak hours and popular locations. Environmental parameter maps are then combined with historical base station data and user behavior patterns, and a weighted scoring model is used to calculate the suitability score for each candidate location. Finally, all candidate locations are ranked based on their suitability scores, and the highest-scoring locations are selected as optimal installation locations. Optimization recommendations are then provided.

[0057] In this example, assume that a city plans to add new 5G base stations to improve network coverage and service quality in the city center. The project team first collected data from a cloud database on signal strength, coverage, and user access for all base stations in the area over the past year. Then, through data analysis, they discovered that user traffic peaks between 5 p.m. and 9 p.m. daily, with most users concentrated near commercial streets.

[0058] The project team used GIS technology to create an environmental parameter map of the area. Combining historical base station data and user behavior patterns, they used the following formula to calculate the suitability score for each candidate location: Suitability score Environmental adaptability Signal coverage capability User demand satisfaction in, and Represent the weight values ​​of environmental adaptability, signal coverage capability and user demand satisfaction, and adjust them according to actual conditions. For example, assuming the weights of environmental adaptability, signal coverage capability and user demand satisfaction are and 0.3, for a candidate location, its environmental adaptability score is 80, signal coverage score is 70, and user demand satisfaction score is 90, then the suitability score of the location is: Suitability score ; This allows the project team to evaluate multiple candidate locations and ultimately select the top-scoring locations as the optimal locations for new base stations. Furthermore, the report provides specific optimization recommendations for each selected location, such as adjusting antenna angles to expand coverage or adding frequency bands to increase data rates.

[0059] Optionally, the data fusion algorithm is used to fuse the instant feedback information with existing environmental parameter data to obtain an environmental parameter map, including: generating a preliminary environmental parameter map based on the existing environmental parameter data; marking and locating the interference source on the preliminary environmental parameter map based on the potential interference source data in the instant feedback information; performing signal coverage range drawing on the preliminary environmental parameter map based on the signal coverage range in the instant feedback information to generate a signal coverage heat map; dynamically updating the preliminary environmental parameter map based on the surrounding environment dynamic change data in the instant feedback information; and generating an environmental parameter map containing interference source location information, a signal coverage heat map and dynamically updated data.

[0060] Data fusion algorithm: A method that combines information from multiple sensors or data sources to produce more accurate and reliable results than a single source could provide.

[0061] Instant feedback information: refers to data obtained in real time from various sensors or devices, which reflects the latest status of the current environment.

[0062] Environmental parameter map: A visualization tool used to show the spatial distribution of various environmental parameters (such as signal strength, interference distribution, etc.) in a specific area.

[0063] Potential interference source data: refers to the location and strength information of sources that may interfere with signal transmission.

[0064] Signal Coverage Heat Map: A visualization tool that shows the distribution of signal coverage and strength within a specific area.

[0065] Dynamic changes in the surrounding environment: refers to factors in the environment that change over time, such as changes in the location of mobile devices, the addition or removal of buildings, etc.

[0066] First, a preliminary environmental parameter map is generated based on the existing environmental parameter data. Secondly, based on the potential interference source data in the immediate feedback information, interference source locations are marked and located on the preliminary environmental parameter map. Furthermore, based on the signal coverage range in the immediate feedback information, a signal coverage range mapping process is performed on the preliminary environmental parameter map to generate a signal coverage heat map. Then, based on the dynamic change data of the surrounding environment in the immediate feedback information, the preliminary environmental parameter map is dynamically updated. Finally, a final environmental parameter map is generated, which includes interference source location information, a signal coverage heat map, and dynamically updated data.

[0067] In this embodiment of the application, it is assumed that an environmental parameter map reflecting signal coverage and interference distribution needs to be constructed in an urban area. Multiple edge computing devices are deployed in the project, including signal towers, environmental monitoring stations, and mobile devices (such as smartphones).

[0068] First, historical data, including signal strength and interference levels, is collected from signal towers and environmental monitoring stations. This historical data is used to generate a preliminary environmental parameter map, showing basic information about signal coverage and interference distribution. Next, data on potential interference sources, including their location and strength, is uploaded in real time from mobile devices (such as smartphones). This interference source data is used to mark and locate the source on the preliminary environmental parameter map. For example, an interference source at coordinates (100, 200) has an interference strength of 80 dBm. Furthermore, signal coverage data, including signal strength and coverage range, is uploaded in real time from mobile devices. This data is then mapped on the preliminary environmental parameter map to generate a signal coverage heat map. For example, the signal strength at location (150, 250) is 75 dBm, with a coverage range of 300 meters. Next, dynamic environmental change data, such as changes in the mobile device's location or new buildings, is uploaded in real time from mobile devices. The preliminary environmental parameter map is updated in real time based on this dynamic change data. For example, if a mobile device moves from location (100,100) to (200,200), its signal coverage and interference information needs to be updated. Finally, a final environmental parameter map containing the interference source location information, signal coverage heat map, and dynamically updated data is generated.

[0069] By implementing the above solution, the accuracy and real-time performance of environmental parameter maps can be significantly improved. Through real-time updates, environmental parameter maps can quickly reflect the latest environmental changes, providing timely support for decision-making. Furthermore, by utilizing data fusion algorithms, combining immediate feedback information with existing environmental parameter data, the generated maps are more accurate and reliable. Furthermore, the maps not only display basic information about signal coverage and interference distribution, but also include information on interference source locations and dynamically updated data, providing more comprehensive information support. Finally, this solution can flexibly adapt to different application scenarios, effectively integrating the immediate feedback information it provides, whether for fixed or mobile devices.

[0070] Optionally, the suitability score of the candidate base station location is evaluated based on the environmental parameter map, the historical base station data and the user behavior pattern to obtain an evaluation result, including: constructing a comprehensive evaluation model for evaluating the suitability of the base station installation location based on the historical base station data, user behavior patterns and environmental parameter map; training the comprehensive evaluation model using a machine learning algorithm so that the comprehensive evaluation model can accurately predict the signal coverage quality, user access efficiency and overall network performance of the candidate base station location; applying the trained comprehensive evaluation model to each candidate base station location to calculate the suitability score of each candidate base station location, wherein the suitability score reflects the expected service quality when the base station is installed at the corresponding location.

[0071] Comprehensive evaluation model: A mathematical model used to comprehensively evaluate the suitability of base station installation locations, taking into account multiple factors such as historical base station data, user behavior patterns, and environmental parameter maps.

[0072] Machine learning algorithm: An algorithm that learns patterns from a training data set and can be used to predict performance indicators on unknown data.

[0073] Signal coverage quality: refers to the signal coverage range and strength of the candidate base station location, reflecting the availability and stability of the signal.

[0074] User access efficiency: refers to the user access speed and success rate at the candidate base station location, reflecting the convenience of users in using the network.

[0075] Overall network performance: This reflects the overall performance of the base station installation location by comprehensively considering signal coverage quality, user access efficiency, and other relevant indicators.

[0076] First, a comprehensive evaluation model for assessing the suitability of base station locations is constructed based on historical base station data, user behavior patterns, and environmental parameter maps. Next, a machine learning algorithm is used to train the comprehensive evaluation model, enabling it to accurately predict signal coverage quality, user access efficiency, and overall network performance at candidate base station locations. Finally, the trained comprehensive evaluation model is applied to each candidate base station location to calculate a suitability score for each candidate location. This score reflects the expected quality of service when the base station is installed at that location.

[0077] In this embodiment of the present application, it is assumed that in a base station site selection project in a new urban area, the suitability of multiple candidate base station locations needs to be evaluated. The specific steps are as follows: First, historical base station data (signal strength, coverage, user access volume), user behavior patterns (activity time, activity location, communication needs), and environmental parameter maps (signal coverage heat map, interference source location information). Signal coverage quality, user access efficiency, and overall network performance. Secondly, a large amount of historical base station data and user behavior pattern data are collected from the cloud database, combined with the environmental parameter map to form a training data set. Select a suitable machine learning algorithm, such as Random Forest, Support Vector Machine (SVM), or Neural Network. Use the training data set to train the comprehensive evaluation model and optimize the model parameters so that it can accurately predict the various performance indicators of the candidate base station location. For example, assuming the random forest algorithm is used, the trained model can be expressed as: ; in, is the suitability score, Input features include signal strength, coverage, user access volume, activity time, activity location, and communication needs. We then select several candidate base station locations, such as locations A, B, and C. Based on the environmental parameter maps, historical base station data, and user behavior patterns for each candidate location, we extract the corresponding input features. Using the trained comprehensive evaluation model, we calculate the suitability score for each candidate location.

[0078] For example, suppose the input features at position A are: Signal strength: 85dBm, coverage range: 600 meters, user access volume: 600 users / hour, event time: 9:00 AM to 5:00 PM, event location: commercial area, communication requirement: high-speed internet. These characteristics are input into the trained model, and the suitability score for Location A is calculated to be 8.5 (out of 10). Similarly, the suitability scores for Locations B and C are calculated: Location B: 8.0, Location C: 7.5. Finally, the scores are ranked: Location A > Location B > Location C. A site selection recommendation report is generated, recommending Location A as the optimal installation location and providing detailed optimization suggestions, such as increasing antenna height and optimizing signal direction.

[0079] By implementing the above solution, a comprehensive evaluation model is constructed and trained using machine learning algorithms, providing a scientific basis for decision-making and improving the accuracy and reliability of the assessment. This approach also comprehensively considers multiple factors, including historical base station data, user behavior patterns, and environmental parameter maps, ensuring a comprehensive and systematic assessment. Furthermore, it accurately predicts signal coverage quality, user access efficiency, and overall network performance at candidate base station locations, providing strong support for decision-making. Finally, it not only recommends the optimal installation location but also provides specific optimization suggestions, helping to improve base station performance and user experience.

[0080] This application takes into account that in the existing technology, the site selection evaluation of base stations mainly relies on the experience of engineers and limited historical data. The traditional evaluation method lacks systematic and quantitative evaluation indicators, making it difficult to fully reflect the suitability of the candidate base station location. In addition, relying solely on historical base station data and simple user behavior pattern analysis, it fails to fully utilize the rich information provided by the environmental parameter map. Traditional methods are difficult to accurately predict the signal coverage quality, user access efficiency and overall network performance of candidate base station locations, resulting in unscientific and unreliable decisions on base station site selection. In order to solve the above problems, an embodiment of the present invention proposes an optional solution, which constructs a comprehensive evaluation model and uses a machine learning algorithm for training to accurately predict the various performance indicators of the candidate base station location, thereby generating a scientific site selection recommendation report. In particular, the suitability score of each candidate base station location is calculated by a specific mathematical formula, which further improves the accuracy and reliability of the evaluation.

[0081] The options are as follows: Optionally, applying the trained comprehensive evaluation model to each candidate base station location to calculate a suitability score for each candidate base station location includes: The suitability score of each candidate base station location is calculated using the following formula: ; in, Indicates the location of the candidate base station The suitability score of Indicates the location of the candidate base station The exponential form of the environmental parameter spectrum score; Indicates the location of the candidate base station The exponential form of the historical base station data score; Indicates the location of the candidate base station The index form of user behavior pattern score; are exponential coefficients, which are used to adjust the influence of the environmental parameter map score, the historical base station data score, and the user behavior pattern score; are weight coefficients, representing the relative importance of the environmental parameter map score, historical base station data score, and user behavior pattern score in the final suitability score; Find the optimal weights using the least squares method : , is the candidate base station location, is the actual performance score of the candidate base station location; Environmental parameter map score: , is the candidate base station location The distance to the nearest source of interference; is the maximum permissible distance from the interference source; is the base of natural logarithms The exponential function of It's location the degree to which the surrounding environment is dynamically changing; It is the average level of environmental dynamics; is the candidate base station location Coverage ratio on the signal coverage heat map, value range is the attenuation coefficient; Historical base station data scores: , is the candidate base station location Average signal strength; is the strongest signal strength; is the candidate base station location coverage; is the maximum coverage; is the candidate base station location The number of users accessing is the maximum number of user accesses; is the coefficient of each score of historical base station data; User behavior pattern score: , is the candidate base station location The proportion of user active time; is the total active time; is the candidate base station location Percentage of user activity locations; It is the main activity location; is the candidate base station location The proportion of communication needs; is the total communication demand; It is the coefficient of each score of the user behavior pattern.

[0082] Comprehensive evaluation model: A mathematical model used to comprehensively evaluate the suitability of base station installation locations, taking into account multiple factors such as historical base station data, user behavior patterns, and environmental parameter maps.

[0083] Machine learning algorithm: An algorithm that learns patterns from a training data set and can be used to predict performance indicators on unknown data.

[0084] Suitability score: A comprehensive score that reflects the expected quality of service when a base station is installed in a corresponding location.

[0085] Environmental parameter map score: reflects the score of the environmental parameters of the candidate base station location (such as signal coverage, interference distribution, etc.).

[0086] Historical base station data score: reflects the score of the candidate base station location's historical base station data (such as signal strength, coverage, user access volume, etc.).

[0087] User behavior pattern score: A score that reflects the user behavior pattern (such as activity time, activity location, communication needs, etc.) at the candidate base station location.

[0088] Least squares method: A commonly used optimization method used to find the best weight coefficients so that the sum of squared errors between the predicted value and the actual value is minimized.

[0089] First, a comprehensive evaluation model for assessing the suitability of base station installation locations was constructed based on historical base station data, user behavior patterns, and environmental parameter maps. Next, a machine learning algorithm was used to train the comprehensive evaluation model, enabling it to accurately predict signal coverage quality, user access efficiency, and overall network performance at candidate base station locations.

[0090] In this embodiment of the present application, it is assumed that in a base station site selection project in a new urban area, the suitability of three candidate base station locations A, B, and C needs to be evaluated. The specific steps are as follows: First, input historical base station data (signal strength, coverage, user access volume), user behavior patterns (activity time, activity location, communication needs), and environmental parameter maps (signal coverage heat map, interference source location information). Signal coverage quality, user access efficiency, and overall network performance are measured. Next, a large amount of historical base station data and user behavior pattern data is collected from the cloud database and combined with the environmental parameter map to form a training dataset. A random forest algorithm is selected. The comprehensive evaluation model is trained using the training dataset, optimizing the model parameters to enable accurate prediction of various performance indicators at candidate base station locations. Further candidate locations are identified: Locations A, B, and C. Based on the environmental parameter map, historical base station data, and user behavior patterns for each candidate location, the corresponding input features are extracted. For example, the input features of location A are: signal strength: 85dBm, coverage range: 600 meters, user access volume: 600 users / hour, activity time: 9:00 am to 5:00 pm, activity location: commercial area, communication requirement: high-speed Internet, distance to the nearest interference source: 100 meters, degree of dynamic change of the surrounding environment: 0.2, and signal coverage ratio: 0.8. The suitability score of location A is calculated using the above formula: ; Similarly, calculate the suitability scores for locations B and C: Location B: 2.75, Location C: 2.60 Sort by scoring results: Position A > Position B > Position C.

[0091] Generate a site selection recommendation report, recommending location A as the best installation location, and provide detailed optimization suggestions, such as increasing antenna height and optimizing signal direction.

[0092] Implementing the above solution significantly improves the scientific nature and reliability of base station site selection. By building a comprehensive evaluation model and training it with a machine learning algorithm, a scientific basis for decision-making is provided, enhancing the accuracy and reliability of the assessment. Furthermore, by comprehensively considering multiple factors, including historical base station data, user behavior patterns, and environmental parameter maps, the evaluation is comprehensive and systematic. Furthermore, accurate predictions of signal coverage quality, user access efficiency, and overall network performance at candidate base station locations provide strong support for decision-making. Finally, the model not only recommends the optimal installation location but also provides specific optimization suggestions, helping to improve base station performance and user experience.

[0093] Optionally, it also includes: Based on the site selection recommendation report and the corresponding immediate feedback information, specific guidance information is provided to the on-site survey personnel. The specific guidance information includes optimal path planning, on-site equipment deployment suggestions and interference source avoidance solutions to optimize the base station installation process.

[0094] Site Recommendation Report: A professional recommendation document for base station site selection based on a comprehensive evaluation of GIS data, historical data, user needs, and other information.

[0095] Instant feedback information: information about the actual situation on site uploaded in real time by on-site survey personnel through mobile devices, including but not limited to photos, videos, text descriptions, etc.

[0096] Optimal Path Planning: Based on site conditions and traffic regulations, an algorithm is used to calculate the best route from the starting point to the target location, aiming to save time and cost.

[0097] On-site equipment deployment recommendations: Recommendations on equipment type, quantity, layout, etc. based on specific site conditions to ensure optimal performance.

[0098] Interference source avoidance plan: Identify and evaluate interference factors that may affect the normal operation of base stations, and propose corresponding countermeasures or measures to mitigate the impact of these interferences.

[0099] First, the project team will screen multiple candidate sites based on pre-set criteria and compile a recommended site selection report. Subsequently, field surveyors will be dispatched to each candidate location to record and upload detailed site conditions using smart terminals. Next, based on the site selection report and immediate feedback, a path planning algorithm will be used to determine the optimal route for the construction team to reach the selected site. Further, based on the results of the field survey, engineers will provide detailed recommendations on the quantity, model, and installation location of the required equipment. Finally, effective preventive and mitigation measures will be developed for potential interference factors on site.

[0100] In this example, assume a telecommunications company plans to add a new 5G base station in City A. After preliminary analysis, three possible construction sites were identified. The company dispatched technicians to conduct detailed on-site inspections of the three locations and uploaded relevant data via a mobile app. After receiving this information, the backend system automatically analyzed the strengths and weaknesses of each location and developed a specific construction plan for each.

[0101] For example, at Location B, the system identified a large hospital nearby, posing a potential risk of electromagnetic interference. It then planned a route for the technician to avoid peak-hour traffic congestion and recommended the use of antennas with enhanced interference resistance and the installation of additional shielding around the base station. Furthermore, the system provided a detailed equipment list and installation instructions to ensure a smooth operation.

[0102] Implementation of this solution significantly improves the efficiency and success rate of base station construction projects, reduces the additional costs incurred by insufficient preparatory work, and ensures network service quality. Through precise route planning and appropriate equipment configuration, various obstacles encountered during construction can be effectively avoided or reduced, accelerating project progress. Furthermore, preventative measures for potential interference issues help maintain a favorable communication environment and meet users' growing data transmission needs.

[0103] Figure 2 A schematic diagram of the structure of a base station survey system based on edge computing is provided for an embodiment of the present application. Figure 2 As shown, the system includes: A selection module 21 is configured to select one or more target areas from the base station network coverage area, wherein the target area includes at least one candidate base station location; A deployment module 22 is configured to deploy at least one edge computing device in the target area to perform real-time analysis of the collected environmental parameter data through the edge computing device, generate instant feedback information, and upload the instant feedback information to a cloud server. The instant feedback information includes signal coverage, locations of potential interference sources, and dynamic changes in the surrounding environment. A construction module 23 is used to coordinate data sharing and collaboration between the edge computing devices, and to construct an environmental parameter map of the target area based on the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; The generating module 24 is configured to generate a site selection recommendation report for candidate base station locations based on the environmental parameter map in combination with historical base station data and user behavior patterns, wherein the site selection recommendation report includes an optimal installation location and optimization suggestions.

[0104] Figure 2The base station survey system based on edge computing can perform Figure 1 The implementation principle and technical effects of the base station survey method based on edge computing described in the illustrated embodiment will not be repeated here. The specific manner in which each module and unit performs operations in the base station survey system based on edge computing in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated here.

[0105] In one possible design, Figure 2 The base station survey system based on edge computing of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0106] The processing component 32 is used to: select one or more target areas from the base station network coverage area, the target area including at least one candidate base station location; deploy at least one edge computing device in the target area to perform real-time analysis on the collected environmental parameter data through the edge computing device, generate instant feedback information, and upload the instant feedback information to the cloud server; coordinate data sharing and collaboration between edge computing devices, and construct an environmental parameter map of the target area based on the instant feedback information; based on the environmental parameter map, combined with historical base station data and user behavior patterns, generate a site selection recommendation report for the candidate base station location.

[0107] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0108] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0109] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0110] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0111] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0112] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0113] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A base station survey method based on edge computing in the illustrated embodiment.

[0114] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0115] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0116] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A base station survey method based on edge computing, characterized in that: Applied to cloud servers, including: Select one or more target areas from the base station network coverage area, wherein the target area includes at least one candidate base station location; Deploy at least one edge computing device in the target area to analyze the collected environmental parameter data in real time through the edge computing device, generate instant feedback information, and upload the instant feedback information to the cloud server, wherein the instant feedback information includes signal coverage, potential interference source location, and dynamic changes of the surrounding environment; Coordinate data sharing and collaboration between the edge computing devices, and construct an environmental parameter map of the target area in combination with the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; Based on the environmental parameter map, combined with historical base station data and user behavior patterns, a site selection recommendation report for candidate base station locations is generated, and the site selection recommendation report includes an optimal installation location and optimization suggestions.

2. The method according to claim 1, characterized in that The coordinating data sharing and collaboration between the edge computing devices includes: Using a security protocol, encrypting the communication link between the edge computing devices to establish a secure communication link; On the basis of establishing a secure communication link, according to the data update frequency of the edge computing device, the edge computing device is synchronized to realize real-time exchange and synchronization of environmental parameter data between the edge computing devices; The method further comprises: A conflict detection algorithm is used to process data conflicts that occur during data sharing among the edge computing devices to ensure data consistency among the edge computing devices.

3. The method according to claim 1, characterized in that The step of constructing an environmental parameter map of the target area in combination with the instant feedback information includes: Using a data fusion algorithm, the instant feedback information is fused with the existing environmental parameter data to obtain an environmental parameter map; According to the latest instant feedback information, the environmental parameter map is updated in real time to obtain a dynamically updated environmental parameter map.

4. The method according to claim 3, characterized in that The instant feedback information is fused with the existing environmental parameter data using a data fusion algorithm to obtain an environmental parameter map, including: Generate a preliminary environmental parameter map based on the existing environmental parameter data; On the preliminary environmental parameter map, according to the potential interference source data in the instant feedback information, perform interference source position marking and positioning processing; According to the signal coverage in the instant feedback information, performing signal coverage mapping processing on the preliminary environmental parameter map to generate a signal coverage heat map; Based on the dynamic change data of the surrounding environment in the instant feedback information, dynamically update the preliminary environmental parameter map; Generates environmental parameter maps containing interference source location information, signal coverage heat maps, and dynamically updated data.

5. The method according to claim 4, characterized in that The generating of a site selection recommendation report for a candidate base station location based on the environmental parameter map in combination with historical base station data and user behavior patterns includes: Retrieving historical base station data of existing base stations related to the target area from a cloud database, including signal strength, coverage, and user access volume; Analyze user behavior patterns in the target area, including user activity time, activity location, and communication needs; According to the environmental parameter map, the historical base station data and the user behavior pattern, evaluating the suitability score of the candidate base station location to obtain an evaluation result; The evaluation results are sorted and a site selection recommendation report containing the best installation location and optimization suggestions is generated based on the sorting results.

6. The method according to claim 5, characterized in that The step of evaluating the suitability score of the candidate base station location according to the environmental parameter map, the historical base station data, and the user behavior pattern to obtain an evaluation result includes: Based on the historical base station data, user behavior patterns, and environmental parameter maps, a comprehensive evaluation model for evaluating the suitability of base station installation locations is constructed; The comprehensive evaluation model is trained using a machine learning algorithm so that the comprehensive evaluation model can accurately predict the signal coverage quality, user access efficiency, and overall network performance of the candidate base station location; Applying the trained comprehensive evaluation model to each candidate base station location to calculate a suitability score for each candidate base station location, wherein the suitability score reflects the expected service quality when the base station is installed at the corresponding location; The suitability score of each candidate base station location is calculated using the following formula: ; in, Indicates the location of candidate base stations The suitability score of Indicates the location of candidate base stations The exponential form of the environmental parameter spectrum score; Indicates the location of candidate base stations The exponential form of the historical base station data score; Indicates the location of candidate base stations The index form of user behavior pattern score; are exponential coefficients, which are used to adjust the influence of environmental parameter map scores, historical base station data scores, and user behavior pattern scores; are weight coefficients, representing the relative importance of the environmental parameter map score, historical base station data score, and user behavior pattern score in the final suitability score; Find the optimal weights by using the least squares method : , is the candidate base station location, is the actual performance score of the candidate base station location; Environmental parameter map score: , is the candidate base station location The distance to the nearest source of interference; is the maximum permissible distance from the interference source; is the base of natural logarithms The exponential function of It's location The degree to which the surrounding environment is dynamically changing; It is the average level of environmental dynamics; is the candidate base station location Coverage ratio on the signal coverage heat map, value range is the attenuation coefficient; Historical base station data scores: , is the candidate base station location Average signal strength; is the strongest signal strength; is the candidate base station location coverage; is the maximum coverage; is the candidate base station location The number of users accessing is the maximum number of user accesses; is the coefficient of each score of historical base station data; User behavior pattern score: , is the candidate base station location The proportion of active time of users; is the total active time; is the candidate base station location Percentage of user activity locations; It is the general activity location; is the candidate base station location The proportion of communication needs; is the total communication demand; It is the coefficient of each score of the user behavior pattern.

7. The method according to claim 5, characterized in that The method further comprises: Based on the site selection recommendation report and the corresponding instant feedback information, specific guidance information is provided to the on-site survey personnel, and the specific guidance information includes optimal path planning, on-site equipment deployment suggestions and interference source avoidance solutions to optimize the base station installation process.

8. A base station survey system based on edge computing, characterized in that: include: A selection module, configured to select one or more target areas from the base station network coverage area, wherein the target area includes at least one candidate base station location; A deployment module, configured to deploy at least one edge computing device in the target area, so as to analyze the collected environmental parameter data in real time through the edge computing device, generate instant feedback information, and upload the instant feedback information to a cloud service end, wherein the instant feedback information includes signal coverage, location of potential interference sources, and dynamic changes of the surrounding environment; A construction module is used to coordinate data sharing and collaboration between the edge computing devices, and to construct an environmental parameter map of the target area in combination with the instant feedback information, wherein the environmental parameter map reflects the signal coverage and interference distribution of the entire target area; A generation module is used to generate a site selection recommendation report for candidate base station locations based on the environmental parameter map, combined with historical base station data and user behavior patterns, wherein the site selection recommendation report includes an optimal installation location and optimization suggestions.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a base station survey method based on edge computing as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a base station survey method based on edge computing as described in any one of claims 1 to 7 is implemented.

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