Base station site selection method, device, storage medium and program product

Abstract

This application provides a base station site selection method, device, storage medium, and program product, relating to the field of communication technology. It can comprehensively and quantitatively evaluate the adaptability of flight routes and sensing resources to achieve more effective base station site selection, including: acquiring signal information of existing base stations and flight route information of UAVs; determining at least one candidate site based on the signal information of existing base stations and flight route information of UAVs; determining a base station site selection scheme based on at least one candidate site; wherein, the evaluation index of the base station site selection scheme is greater than or equal to a first threshold; the evaluation index is used to evaluate the construction cost and signal coverage.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a base station site selection method, device, storage medium, and program product. Background Technology

[0002] With the rapid development of drone technology, its application in the low-altitude field is becoming increasingly widespread, deeply covering many important areas such as logistics and transportation, environmental monitoring, and disaster assessment.

[0003] Stable communication and sensing support are crucial for the safe and efficient completion of missions by unmanned aerial vehicles (UAVs). Therefore, the compatibility between flight paths and sensing resources becomes a key factor affecting UAV flight performance and mission success rate. However, traditional base station site selection methods often overlook the complex relationship between sensing resources and flight paths, making UAVs prone to communication interruptions and being trapped in sensing blind spots during actual flight. Summary of the Invention

[0004] This application provides a base station site selection method, device, storage medium, and program product, which can comprehensively and quantitatively evaluate the adaptability of air routes and sensing resources, thereby achieving more effective base station site selection.

[0005] In a first aspect, this application provides a base station site selection method, the method comprising: acquiring signal information of an existing base station and flight path information of a UAV; determining at least one candidate site based on the signal information of the existing base station and the flight path information of the UAV; determining a base station site selection scheme based on the at least one candidate site; wherein, the evaluation index of the base station site selection scheme is greater than or equal to a first threshold; the evaluation index is used to evaluate the construction cost and signal coverage.

[0006] Understandably, traditional base station site selection schemes do not consider the compatibility between base station sensing resources and UAV flight paths. This application, however, fully utilizes signal information from existing base stations and UAV flight path information to evaluate candidate sites that meet the construction requirements but have not yet been built. Based on this, the optimal base station site selection scheme is determined by comprehensively considering both construction costs and signal coverage, successfully filling the gap in traditional base station site selection schemes that consider the aerial communication aspect, and improving the service quality and efficiency of air transport and mobile communications.

[0007] One possible implementation involves the UAV's flight path information, including the UAV's flight path itself. Based on the signal information of existing base stations and the UAV's flight path information, at least one candidate site is determined, including: dividing the UAV's flight path into multiple flight segments; for each flight segment, determining the base station signal strength in the airspace where each flight segment is located based on the signal information of existing base stations; determining M target areas that do not meet the UAV's communication requirements based on the base station signal strength in the airspace where each flight segment is located and the minimum signal strength required by the UAV on each flight segment; each target area includes one flight segment; M is an integer greater than or equal to 1; and determining at least one candidate site based on the M target areas.

[0008] Another possible implementation involves determining at least one candidate site based on M target regions, including: performing clustering operations on the M target regions to obtain X cluster centers; wherein, a cluster center is a point in a cluster whose local density is greater than or equal to a second threshold, and / or, a cluster center is a point whose cluster distance is greater than or equal to a third threshold; N is an integer greater than or equal to 1 and less than or equal to M; and determining at least one candidate site based on the positions of the X cluster centers.

[0009] Another possible implementation is that the distance between the candidate site and the X cluster centers is less than or equal to a preset distance.

[0010] Another possible implementation involves determining a base station location scheme based on at least one candidate site, including: determining at least one candidate base station location scheme based on at least one candidate site; the candidate base station location scheme includes one or more selected sites; wherein the selected site is any one of the at least one candidate site; for each candidate base station location scheme in the at least one candidate base station location scheme, calculating the evaluation index for each candidate base station location scheme; and determining the base station location scheme based on the evaluation index for each candidate base station location scheme.

[0011] Another possible implementation involves determining at least one candidate base station location scheme based on at least one candidate site, including: using a genetic algorithm to determine at least one candidate base station location scheme based on at least one candidate site.

[0012] Another possible approach is to determine the evaluation metrics based on a weighted sum of website construction cost and coverage metrics.

[0013] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the construction cost index is determined based on the following parameters: the number of selected base stations included in the base station site selection scheme, and the number of at least one candidate base station.

[0014] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the coverage index is determined based on the following parameters: the number of target areas that one or more selected base stations can cover, and the total number of target areas; wherein, the target area is the area where the signal strength of the existing base stations cannot meet the communication needs of the UAV.

[0015] Another possible implementation method is to use the signal information of existing base stations, including at least one of the following: signal strength, coverage area, and signal frequency band.

[0016] Another possible implementation method is to use the drone's flight path information, which includes at least one of the following: takeoff point, landing point, flight path, communication requirements, sensing frequency, and sensing latency.

[0017] Secondly, this application provides a base station site selection device, which includes an acquisition module and a processing module. The acquisition module is used to acquire signal information of existing base stations and flight path information of unmanned aerial vehicles (UAVs). The processing module is used to determine at least one candidate site based on the signal information of existing base stations and the flight path information of UAVs; and to determine a base station site selection scheme based on the at least one candidate site. The evaluation index of the base station site selection scheme is greater than or equal to a first threshold; the evaluation index is used to evaluate the construction cost and signal coverage.

[0018] One possible implementation involves the UAV's flight path information, including the UAV's flight path itself. The processing module is specifically used to: divide the UAV's flight path into multiple flight segments; for each flight segment, determine the base station signal strength in the airspace where each flight segment is located based on the signal information of existing base stations; based on the base station signal strength in the airspace where each flight segment is located and the minimum signal strength required by the UAV on each flight segment, determine M target areas that do not meet the UAV's communication requirements; each target area includes one flight segment; M is an integer greater than or equal to 1; and based on the M target areas, determine at least one candidate site.

[0019] Another possible implementation is that the processing module is specifically used to: perform clustering operations on M target regions to obtain X cluster centers; wherein, the cluster center is a point in the cluster whose local density is greater than or equal to a second threshold, and / or, the cluster center is a point whose cluster distance is greater than or equal to a third threshold; N is an integer greater than or equal to 1 and less than or equal to M; and determine at least one candidate site based on the location of the X cluster centers.

[0020] Another possible implementation is that the distance between the candidate site and the X cluster centers is less than or equal to a preset distance.

[0021] Another possible implementation is that the processing module is specifically used to: determine at least one candidate base station location scheme based on at least one candidate site; the candidate base station location scheme includes one or more selected sites; wherein, the selected site is any one of the at least one candidate site; for each candidate base station location scheme in the at least one candidate base station location scheme, calculate the evaluation index of each candidate base station location scheme respectively; and determine the base station location scheme based on the evaluation index of each candidate base station location scheme.

[0022] Another possible implementation is that the processing module is specifically used to: use a genetic algorithm to determine at least one candidate base station site selection scheme based on at least one candidate site.

[0023] Another possible approach is to determine the evaluation metrics based on a weighted sum of website construction cost and coverage metrics.

[0024] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the construction cost index is determined based on the following parameters: the number of selected base stations included in the base station site selection scheme, and the number of at least one candidate base station.

[0025] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the coverage index is determined based on the following parameters: the number of target areas that one or more selected base stations can cover, and the total number of target areas; wherein, the target area is the area where the signal strength of the existing base stations cannot meet the communication needs of the UAV.

[0026] Another possible implementation method is to use the signal information of existing base stations, including at least one of the following: signal strength, coverage area, and signal frequency band.

[0027] Another possible implementation method is to use the drone's flight path information, which includes at least one of the following: takeoff point, landing point, flight path, communication requirements, sensing frequency, and sensing latency.

[0028] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0029] Fourthly, this application provides a chip system applied to a base station addressing device; the chip system includes one or more interface circuits and one or more processors. The interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the base station addressing device and send signals to the processor of the base station addressing device, the signals including software instructions stored in the memory. When the processor executes the software instructions, it causes the electronic device to perform the method described in the first aspect.

[0030] Fifthly, this application provides a readable storage medium comprising: software instructions; when the software instructions are executed in an electronic device, they cause the electronic device to implement the method described in the first aspect.

[0031] In a sixth aspect, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps of the relevant method described in the first aspect above, so as to implement the method of the first aspect above.

[0032] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0033] Figure 1 A flowchart illustrating a base station site selection method provided in this application;

[0034] Figure 2 A flowchart illustrating another base station location method provided in this application;

[0035] Figure 3 A flowchart illustrating another base station location method provided in this application;

[0036] Figure 4 A flowchart illustrating another base station location method provided in this application;

[0037] Figure 5 This is a schematic diagram of the composition of a base station location selection device provided in this application;

[0038] Figure 6 This is a schematic diagram of the composition of an electronic device provided in this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0041] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0042] With the rapid development of drone technology, its application in the low-altitude domain is becoming increasingly widespread, covering important fields such as logistics transportation, environmental monitoring, and disaster assessment. Drone flight requires stable communication and sensing support to ensure safe and efficient mission completion. Therefore, the compatibility between flight paths and sensing resources has become one of the key factors affecting flight performance and mission success rate. Traditional base station site selection methods neglect the complex relationship between sensing resources and flight paths, potentially leading to communication interruptions and sensing blind spots during drone flight. Therefore, a comprehensive quantitative assessment of the compatibility between flight paths and sensing resources is essential to optimize base station site selection.

[0043] In terms of existing technological solutions, Integrated Sensing and Communication (ISAC) technology demonstrates dual advantages in wireless communication and environmental perception. It achieves integrated fusion across multiple dimensions, including hardware, spectrum, algorithms, and software, strongly supporting the development of the low-altitude economy. Especially in UAV scenarios, its key technologies include integrated waveform design, beamforming technology, UAV swarm cooperative interference management, and UAV swarm resource allocation and trajectory planning. Meanwhile, regarding the issue of communication base station site selection, academia and industry have conducted extensive research. It is necessary to comprehensively consider factors such as communication coverage, capacity, and quality, aiming for optimal cost or maximum coverage. When constructing models, it can be treated as a coverage problem. For example, the basic model of the coverage problem is widely used in traditional multi-base station site selection, and many scholars have conducted optimization research based on heuristic algorithms.

[0044] However, in the field of integrated communication and sensing technology, achieving optimal performance balance through resource allocation and trajectory planning for UAV swarms in resource-constrained environments remains a challenge. Furthermore, research on communication environment modeling and communication quality evaluation is insufficient in complex 5G communication scenarios and low-altitude cellular mobile network communication path loss estimation. Many studies rely on empirical models with significant errors, and the evaluation of signal quality rarely considers the combined impact of multiple factors, making it difficult to provide comprehensive evaluation standards.

[0045] Based on this, this application provides a base station site selection method that fully utilizes the signal information of existing base stations and UAV flight path information to evaluate candidate sites that meet the construction conditions but have not yet been built. On the one hand, by establishing refined evaluation criteria, evaluation errors are reduced; on the other hand, by comprehensively considering both construction costs and signal coverage, the optimal base station site selection scheme is determined, ensuring both efficient communication services and reasonable cost control. In summary, the base station site selection method provided in this application fills the gap in traditional base station site selection schemes that consider the airborne communication layer, and improves the service quality and efficiency of air transport and mobile communications.

[0046] The base station location method provided in this application embodiment can be executed by an electronic device.

[0047] For example, the electronic device can be a server, such as a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster.

[0048] For example, the electronic device can be a terminal device, such as a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, cellular phone, personal digital assistant (PDA), augmented reality (AR) / virtual reality (VR) device, etc. This application embodiment does not impose any special limitations on the specific form of the terminal device.

[0049] Figure 1 This is a flowchart illustrating a base station location selection method provided in an embodiment of this application. Figure 1 As shown, the base station site selection method provided in this application specifically includes the following steps:

[0050] S101. Obtain signal information from existing base stations and flight path information from drones.

[0051] In some embodiments, the signal information of an existing base station includes at least one of the following: signal strength, coverage area, and signal frequency band.

[0052] In some embodiments, the flight path information of the UAV includes at least one of the following: takeoff point, landing point, flight path, communication requirements, sensing frequency, and sensing latency.

[0053] For example, signal information of existing base stations can be obtained in the following ways: by using professional equipment or software for testing, such as using a signal tester; or by contacting the local operator to provide signal information of existing base stations.

[0054] For example, the flight path information of UAVs in the airspace can be collected in the following ways: by monitoring with a radar system; or by obtaining UAV operation information provided by a cooperating UAV manufacturer.

[0055] S102. Based on the signal information of the existing base stations and the flight path information of the UAV, determine at least one candidate site.

[0056] S103. Based on at least one candidate site, determine the base station site selection scheme.

[0057] Among them, the evaluation index of the base station site selection scheme is greater than or equal to the first threshold; the evaluation index is used to evaluate the construction cost and signal coverage.

[0058] The base station site selection method provided by this invention matches and analyzes the flight path of UAVs with sensor resources, realizes the coordinated optimization of base station site selection and resource allocation, and significantly improves the utilization efficiency of low-altitude network resources.

[0059] In some embodiments, the drone's route information includes the drone's flight path, such as... Figure 2 As shown, step S102 can be implemented as steps S1021 to S1024:

[0060] S1021. Divide the flight path of the UAV into multiple flight segments.

[0061] For example, the urban low-altitude airspace is divided into multiple small areas, each of which is called a signal grid; the flight path of the UAV is divided according to the signal grid to form multiple small segments, each of which is called a flight path segment.

[0062] For example, the size of the signal grid is determined based on the signal coverage of the existing base stations and the accuracy of the drone's flight path.

[0063] For example, if a single base station has stable signal strength within a height of 300 meters, meaning its signal coverage is 300 meters, then the upper limit of the height for signal grid division should be set to 300 meters.

[0064] For example, the flight path accuracy of a drone is affected by environmental complexity. That is, the higher the environmental complexity, the higher the accuracy requirement for the drone's flight path. Therefore, in the vertical direction, it can be divided into two layers. For example, the first layer (0-30 meters) has a higher environmental complexity, and the signal grid can be divided into smaller ones (such as 10 meters × 10 meters). The second layer (30-300 meters) has a lower environmental complexity, and the signal grid can be appropriately enlarged (such as 50 meters × 50 meters).

[0065] For example, each signal grid is marked and its corresponding signal strength, signal-to-noise ratio, latency, and route requirements are monitored.

[0066] Understandably, this dynamic signal grid partitioning mechanism improves the accuracy, efficiency, and adaptability of base station site selection assessment by dividing the geographic space into dynamically adjustable grid units and monitoring real-time signal parameters (such as signal strength, signal-to-noise ratio, latency, and environmental interference).

[0067] S1022. For each of the multiple flight segments, determine the base station signal strength in the airspace where each flight segment is located based on the signal information of the existing base stations.

[0068] For example, determining the base station signal strength in the airspace where each flight path segment is located based on the signal information of existing base stations can be achieved as follows:

[0069] A1. Determine the signal strength of each existing base station in the airspace of each flight path segment based on the signal information of existing base stations.

[0070] A2. Calculate the average signal strength of the airspace where each flight segment is located as the base station signal strength of the airspace where each flight segment is located.

[0071] S1023. Based on the base station signal strength in the airspace where each flight segment is located and the minimum signal strength required by the UAV on each flight segment, determine M target areas that do not meet the communication requirements of the UAV.

[0072] Wherein, a target area includes a flight path; M is an integer greater than or equal to 1.

[0073] For example, if the base station signal strength in the airspace where a certain flight path is located is lower than the minimum signal strength required by the drone on that flight path, then the signal grid where the flight path is located is the target area.

[0074] For example, step S1023 further includes: marking the target area and determining the signal strength difference (i.e., the difference between the base station signal strength in the airspace where the flight path is located and the minimum signal strength required by the UAV on the flight path).

[0075] S1024. Based on the M target areas, determine at least one candidate site.

[0076] In some embodiments, step S1024 may be implemented as follows:

[0077] B1. Perform clustering operations on M target regions to obtain X cluster centers.

[0078] Among them, the cluster center is a point where the local density in the cluster is greater than or equal to the second threshold, and / or the cluster center is a point where the cluster distance is greater than or equal to the third threshold; N is an integer greater than or equal to 1 and less than or equal to M.

[0079] Exemplarily, the M target regions can be represented as the set P = {P1, P2, …, P

[0083] }.

[0080] Exemplarily, step A1 can be implemented as the following steps A11 - A13:

[0081] B11. For any P i , P j ∈P, i, j ∈ M, calculate the distance d and between them. ij .

[0082] Exemplarily, d ij satisfies the following formula (I):

[0083]

[0084] B12. For any P i , calculate its local density ρ i and the cluster distance σ i .

[0085] Exemplarily, ρ i satisfies the following formula (II):

[0086]

[0087] Among them, d ij is the distance that does not satisfy the distance between the grid center and other grid centers that do not satisfy, d c is the distance threshold.

[0088] Exemplarily, d c = d [λh] .

[0089] Among them, the array d = {d ij , 0 < i < j < M} and the d ij in the array d is arranged in descending order, h is the total length of the array d, and λ is a coefficient less than 1.

[0090] Exemplarily, λ is 0.7.

[0091] Exemplarily, σ i satisfies the following formula (III):

[0092] <00002Where, d ij The distance between the grid center that does not meet the requirement and other grid centers that do not meet the requirement.

[0094] B13. Select the local density ρ based on the decision map. i Greater than or equal to the second threshold and the cluster distance σ i Target regions that are greater than or equal to the third threshold are used as cluster centers.

[0095] For example, the horizontal axis of the decision graph represents local density, the vertical axis represents cluster distance, and the cluster center is the point located in the upper right corner of the decision graph.

[0096] B2. Based on the locations of the X cluster centers, determine at least one candidate site.

[0097] In some embodiments, the distance between a candidate site and X cluster centers is less than or equal to a preset distance.

[0098] For example, given the location information of multiple available construction sites, sites that meet the following conditions are selected from these available construction sites: the distance between the site and at least one of the given X cluster centers is less than or equal to a preset distance threshold, and these selected eligible construction sites are determined as candidate sites.

[0099] This invention proposes a cluster-based base station site selection method. This method uses a decision graph approach based on local density and cluster distance to dynamically select cluster centers from a set of grids where the signal does not meet the requirements. This can reduce the number of redundant base stations and thus reduce the cost of deploying base stations.

[0100] In some embodiments, such as Figure 3 As shown, step S103 can be implemented as steps S1031 to S1033:

[0101] S1031. Based on at least one candidate site, determine at least one candidate base station site selection scheme.

[0102] In some embodiments, the candidate base station site selection scheme includes one or more selected sites; wherein the selected site is any one of at least one candidate site.

[0103] In some embodiments, the site construction cost index is determined based on the following parameters: the number of selected base stations included in the base station site selection scheme, and the number of at least one candidate base station.

[0104] In some embodiments, the coverage index is determined based on the following parameters: the number of target areas that one or more selected base stations can cover, and the total number of target areas; wherein, the target area is an area where the base station signal strength of the existing base stations cannot meet the communication needs of the UAV.

[0105] In some embodiments, step S1031 may be implemented as: using a genetic algorithm to determine at least one candidate base station location scheme based on at least one candidate site.

[0106] S1032. For each candidate base station location scheme in at least one candidate base station location scheme, calculate the evaluation index for each candidate base station location scheme.

[0107] S1033. Based on the evaluation indicators of each candidate base station site selection scheme, determine the base station site selection scheme.

[0108] In some embodiments, the evaluation metrics are determined based on a weighted sum of website construction cost metrics and coverage metrics.

[0109] For example, the evaluation index satisfies the following formula (iv):

[0110] Formula (IV) for f = w1f1 + w2f2

[0111] Where w1 is the cost indicator weight, f1 is the website building cost indicator, w2 is the coverage indicator weight, and f2 is the coverage indicator.

[0112] For example, the website construction cost index satisfies the following formula (V):

[0113]

[0114] Where N is the number of candidate base stations and N0 is the number of selected sites.

[0115] For example, the coverage metric satisfies the following formula (vi):

[0116]

[0117] Where M is the number of target areas, and M0 is the number of areas covered by the selected site in the target areas.

[0118] For example, determining whether a target area is covered by a selected site can be based on the following steps:

[0119] C1. Define the coverage rules.

[0120] For example, the candidate site set is The target region set is Where N is the number of candidate sites and M is the number of target areas.

[0121] The coverage area of ​​the selected site can be represented by the site coordinates (x, y, y). i ,y i ,z i Let R be the center of a circle and R be the radius. Therefore, base station s i Represented as si =(x i ,y i ,z i ,R),i∈N.

[0122] The center point of the target region can be represented as

[0123] For example, let p be the center point of the target region. j Base station s i The covered event is r ij The corresponding probability of occurrence is P(r) ij ), P(r ij It satisfies the following distribution rule:

[0124]

[0125] C2. Calculate the Euclidean distance d(s) between the center point coordinates of the target area and the selected station coordinates. i ,p j ).

[0126] For example, d(s) i ,p j It satisfies the following formula (VII):

[0127]

[0128] C3. Based on the calculation results, we obtain M0.

[0129] For example, if the Euclidean distance between the center point coordinates of the target area and the coordinates of any selected station is less than or equal to R, the target area is considered to be covered, and the value of M0 is incremented by 1; if the Euclidean distance between the center point coordinates of the target area and the coordinates of all selected stations is greater than R, the target area is considered not to be covered, and the value of M0 is not incremented.

[0130] This invention proposes a base station site selection evaluation method based on multi-objective optimization. It constructs a comprehensive evaluation model for coverage and construction cost using a weighted index method and introduces a genetic algorithm to achieve global optimization of candidate solutions. Compared to single-index evaluation, this method has significant advantages in efficiency improvement, cost control, and solution robustness.

[0131] The base station location method of this application is described below with a specific embodiment.

[0132] like Figure 4 As shown, the base station site selection method provided in this application includes the following steps:

[0133] S201. Obtain signal information from existing base stations and flight path information from UAVs.

[0134] S202. Divide the signal grid and segment the UAV's flight path to obtain multiple flight segments, each flight segment being located within a signal grid.

[0135] S203. Calculate the average signal strength within each signal grid.

[0136] S204. Determine the required signal strength threshold within each signal grid based on the UAV's flight path.

[0137] S205. Determine whether the average signal strength within each signal grid meets the signal strength threshold required by the UAV.

[0138] Specifically, if the average signal strength within the signal grid does not meet the signal strength threshold required by the UAV, it is marked as a signal not satisfying the grid. The set of center points of the signal not satisfying the grid can be represented as P = {P1, P2, ..., P}. M Then proceed to step S206; if all signal strength thresholds required by the drone are met, proceed to step S208.

[0139] S206, Clustering signals do not satisfy the grid.

[0140] Specifically, for signals that do not satisfy the grid center point P i Calculate the local density and cluster distance to obtain the corresponding (ρ) i , σ i The clustering process involves combining signals with relatively large local density and cluster distance, selecting the non-grid center points as cluster centers, and assigning the remaining non-grid center points to the clusters containing the nearest cluster centers.

[0141] S207. Use a location selection algorithm to determine the location of the base station.

[0142] Specifically, sites closer to the cluster center are selected as candidate sites, and all candidate sites constitute a candidate site set; each candidate base station location scheme in the set is evaluated; new candidate base station location schemes are generated by combining mutation, evolution, and selection operations in the genetic algorithm and evaluated; finally, the candidate base station location scheme with the highest evaluation index value is selected as the optimal base station location scheme.

[0143] For example, the pseudocode of the above addressing algorithm is shown in Table 1:

[0144] Table 1 Pseudocode for Base Station Location Selection Method

[0145]

[0146]

[0147] S208. Generate an assessment report and site selection plan.

[0148] Specifically, the assessment report and site selection plan include the existing signal quality, grid areas that do not meet signal requirements, clustering results, new base station locations, and estimated construction costs.

[0149] For example, signal quality can be presented using a signal quality heatmap.

[0150] The foregoing primarily describes the solutions of the embodiments of this disclosure from a methodological perspective. It is understood that, in order to achieve the aforementioned functions, the base station location device includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure.

[0151] This disclosure embodiment can divide the base station addressing device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one functional module. The integrated module can be implemented in hardware or software. It should be noted that the module division in this disclosure embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. The following description uses the example of dividing each functional module according to each function.

[0152] For example, Figure 5 This is a schematic diagram illustrating the composition of a base station location selection device provided in an embodiment of this application. Figure 5 As shown, the base station site selection device 400 includes an acquisition module 401 and a processing module 402. The acquisition module 401 is used to acquire signal information of existing base stations and flight path information of UAVs. The processing module 402 is used to determine at least one candidate site based on the signal information of existing base stations and the flight path information of UAVs; and to determine a base station site selection scheme based on the at least one candidate site. The evaluation index of the base station site selection scheme is greater than or equal to a first threshold; the evaluation index is used to evaluate the construction cost and signal coverage.

[0153] One possible implementation is that the UAV's flight path information includes the UAV's flight path; the processing module 402 is specifically used to: divide the UAV's flight path into multiple flight segments; for each of the multiple flight segments, determine the base station signal strength in the airspace where each flight segment is located based on the signal information of the established base stations; based on the base station signal strength in the airspace where each flight segment is located and the minimum signal strength required by the UAV on each flight segment, determine M target areas that do not meet the UAV's communication requirements; one target area includes one flight segment; M is an integer greater than or equal to 1; and determine at least one candidate site based on the M target areas.

[0154] Another possible implementation is that the processing module 402 is specifically used to: perform clustering operations on M target regions to obtain X cluster centers; wherein, the cluster center is a point in the cluster whose local density is greater than or equal to a second threshold, and / or, the cluster center is a point whose cluster distance is greater than or equal to a third threshold; N is an integer greater than or equal to 1 and less than or equal to M; and determine at least one candidate site based on the location of the X cluster centers.

[0155] Another possible implementation is that the distance between the candidate site and the X cluster centers is less than or equal to a preset distance.

[0156] Another possible implementation is that the processing module 402 is specifically used to: determine at least one candidate base station location scheme based on at least one candidate site; the candidate base station location scheme includes one or more selected sites; wherein, the selected site is any one of the at least one candidate site; for each candidate base station location scheme in the at least one candidate base station location scheme, calculate the evaluation index of each candidate base station location scheme respectively; and determine the base station location scheme based on the evaluation index of each candidate base station location scheme.

[0157] Another possible implementation is that the processing module 402 is specifically used to: use a genetic algorithm to determine at least one candidate base station site selection scheme based on at least one candidate site.

[0158] Another possible approach is to determine the evaluation metrics based on a weighted sum of website construction cost and coverage metrics.

[0159] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the construction cost index is determined based on the following parameters: the number of selected base stations included in the base station site selection scheme, and the number of at least one candidate base station.

[0160] Another possible implementation is that the base station site selection scheme includes one or more selected base stations; the coverage index is determined based on the following parameters: the number of target areas that one or more selected base stations can cover, and the total number of target areas; wherein, the target area is the area where the signal strength of the existing base stations cannot meet the communication needs of the UAV.

[0161] Another possible implementation method is to use the signal information of existing base stations, including at least one of the following: signal strength, coverage area, and signal frequency band.

[0162] Another possible implementation method is to use the drone's flight path information, which includes at least one of the following: takeoff point, landing point, flight path, communication requirements, sensing frequency, and sensing latency.

[0163] When the functions of the integrated modules described above are implemented in hardware, this disclosure provides a possible structure for the electronic device involved in the above embodiments. For example... Figure 6 As shown, the electronic device 500 includes: a processor 502 and a bus 504. Optionally, the electronic device 500 may also include a memory 501; optionally, the electronic device 500 may also include a communication interface 503.

[0164] Processor 502 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this application. Processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this disclosure. Processor 502 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0165] Communication interface 503 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0166] The memory 501 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0167] In one possible implementation, the memory 501 can exist independently of the processor 502. The memory 501 can be connected to the processor 502 via a bus 504 and is used to store instructions or program code. When the processor 502 calls and executes the instructions or program code stored in the memory 501, it can implement the base station addressing method provided in this embodiment. In another possible implementation, the memory 501 can also be integrated with the processor 502.

[0168] Bus 504 can be an extended industry standard architecture (EISA) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0169] In an exemplary embodiment, this application also provides a readable storage medium storing program instructions thereon; when the program instructions are executed by an electronic device, the electronic device performs the method described in the foregoing embodiments. The readable storage medium can be a non-transitory readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0170] In an exemplary embodiment, this application also provides a computer program product that, when run on an electronic device, causes the electronic device to execute the aforementioned related method steps to implement the base station location method in the above embodiments.

[0171] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A base station site selection method, characterized in that, The method includes: Acquire signal information from existing base stations and flight path information of UAVs; the flight path information of the UAVs includes the flight path of the UAVs; The flight path of the drone is divided into multiple flight segments; For each of the multiple flight segments, the base station signal strength in the airspace where each flight segment is located is determined based on the signal information of the established base stations; Based on the base station signal strength in the airspace where each flight segment is located and the minimum signal strength required by the UAV on each flight segment, M target areas that do not meet the communication requirements of the UAV are determined; each target area includes one flight segment; M is an integer greater than or equal to 1; Based on the M target areas, at least one candidate site is determined; Based on the at least one candidate site, a base station site selection scheme is determined; wherein, the evaluation index of the base station site selection scheme is greater than or equal to a first threshold; the evaluation index is used to evaluate the site construction cost and signal coverage.

2. The method according to claim 1, characterized in that, The step of determining the at least one candidate site based on the M target regions includes: Clustering is performed on the M target regions to obtain N cluster centers; wherein, the cluster center is a point in the cluster whose local density is greater than or equal to a second threshold, and / or, the cluster center is a point whose cluster distance is greater than or equal to a third threshold; and N is an integer greater than or equal to 1 and less than or equal to M. Based on the locations of the N cluster centers, at least one candidate site is determined.

3. The method according to claim 2, characterized in that, The distance between the candidate site and the N cluster centers is less than or equal to a preset distance.

4. The method according to claim 1, characterized in that, The step of determining a base station site selection scheme based on the at least one candidate site includes: Based on the at least one candidate site, at least one candidate base station site selection scheme is determined; the candidate base station site selection scheme includes one or more selected sites; wherein, the selected site is any one of the at least one candidate site; For each of the at least one candidate base station location schemes, calculate the evaluation index for each candidate base station location scheme. The base station location scheme is determined based on the evaluation indicators of each candidate base station location scheme.

5. The method according to claim 4, characterized in that, The step of determining at least one candidate base station site selection scheme based on the at least one candidate site includes: A genetic algorithm is used to determine the location scheme of the at least one candidate base station based on the at least one candidate site.

6. The method according to claim 1, characterized in that, The evaluation indicators are determined based on a weighted sum of website construction cost and coverage indicators.

7. The method according to claim 6, characterized in that, The base station location scheme includes one or more selected base stations; The site construction cost index is determined based on the following parameters: the number of selected base stations included in the base station site selection scheme, and the number of at least one candidate base station.

8. The method according to claim 6, characterized in that, The base station location scheme includes one or more selected base stations; The coverage rate is determined based on the following parameters: the number of target areas that the one or more selected base stations can cover, and the total number of target areas; wherein, the target area is an area where the base station signal strength of the existing base stations cannot meet the communication needs of the UAV.

9. The method according to claim 1, characterized in that, The signal information of the established base stations includes at least one of the following: Signal strength, coverage area, and signal frequency band.

10. The method according to claim 1, characterized in that, The flight path information of the UAV includes at least one of the following: Takeoff point, landing point, flight path, communication requirements, sensing frequency, and sensing latency.

11. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory stores instructions that the processor can execute; When the processor is configured to execute the instructions, it causes the electronic device to implement the method as described in any one of claims 1-10.

12. A readable storage medium, characterized in that, The readable storage medium includes: software instructions; When the software instructions are executed in an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-10.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the method as described in any one of claims 1-10.

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