UAV base station deployment method, device, computer equipment and storage medium

Abstract

The present invention relates to the field of geographic information technology, and in particular to a method, device, computer equipment and storage medium for deploying a drone base station. The method comprises: obtaining remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data and remote sensing inversion data of building height in a target area; obtaining land pollution source monitoring demand data according to the remote sensing extraction data of the land pollution sources and the long-term remote sensing inversion data; conducting water quality monitoring demand assessment according to the long-term remote sensing inversion data to obtain water quality monitoring demand data; conducting drone base station coverage level assessment according to the land use data and the remote sensing inversion data of the building height to obtain drone base station coverage level data; constructing a drone base station site selection model, and obtaining drone base station deployment location data and level data according to the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data and the drone base station site selection model.

Description

Technical Field

[0001] This invention relates to the field of geographic information technology, and in particular to a method, apparatus, computer equipment, and storage medium for deploying unmanned aerial vehicle (UAV) base stations. Background Technology

[0002] Because sewage discharge from outlets into the sea is often sporadic, intermittent, and random, and the on-site drainage is often closely related to the tide level, existing drone monitoring methods cannot detect sudden sewage discharge events in a timely manner, making it difficult to conduct efficient environmental pollution monitoring. Summary of the Invention

[0003] Based on this, the purpose of the present invention is to provide a method, apparatus, computer equipment and storage medium for deploying unmanned aerial vehicle (UAV) base stations. Based on the constructed UAV base station site selection model and the obtained land pollution source monitoring demand data, water quality monitoring demand data and UAV base station coverage level data of the target area, the method generates UAV base station deployment location data and level data. Starting from the needs of maximizing the water area monitoring area and the most efficient land pollution source monitoring, the method aims to meet the monitoring needs of sudden, dynamic and land-sea coordinated monitoring, and improve the timeliness and efficiency of environmental pollution monitoring.

[0004] In a first aspect, embodiments of this application provide a method for deploying a drone base station, comprising the following steps:

[0005] Obtain remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data, and building height remote sensing inversion data of the target area;

[0006] Based on the remote sensing data extracted from land pollution sources and the long-term remote sensing inversion data, an assessment of land pollution monitoring needs is conducted to obtain land pollution source monitoring needs data.

[0007] Water quality monitoring needs assessment is conducted based on the long-term remote sensing inversion data to obtain water quality monitoring needs data.

[0008] Based on the land use data and building height remote sensing inversion data, the coverage level of UAV base stations is assessed to obtain UAV base station coverage level data.

[0009] A drone base station site selection model is constructed. Based on the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model, drone base station deployment location data and level data are obtained.

[0010] Secondly, embodiments of this application provide a drone base station deployment device, including:

[0011] The data acquisition module is used to acquire remote sensing extraction data of land pollution sources, long-term time-series remote sensing inversion data, land use data, and building height remote sensing inversion data of the target area;

[0012] The land pollution source monitoring demand data calculation module is used to assess the land pollution monitoring demand based on the land pollution source remote sensing extracted data and long-term time series remote sensing inversion data, and obtain land pollution source monitoring demand data.

[0013] The water quality monitoring demand data calculation module is used to assess water quality monitoring demand based on the long-term remote sensing inversion data and obtain water quality monitoring demand data.

[0014] The UAV base station coverage level data calculation module is used to evaluate the UAV base station coverage level based on the land use data and building height remote sensing inversion data, and obtain UAV base station coverage level data.

[0015] The drone base station deployment module is used to construct a drone base station site selection model. Based on the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model, it obtains drone base station deployment location data and level data.

[0016] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the UAV base station deployment method as described in the first aspect.

[0017] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the UAV base station deployment method as described in the first aspect.

[0018] In this application embodiment, a method, apparatus, computer equipment, and storage medium for deploying unmanned aerial vehicle (UAV) base stations are provided. Based on the constructed UAV base station site selection model and the obtained land pollution source monitoring demand data, water quality monitoring demand data, and UAV base station coverage level data of the target area, the deployment location data and level data of UAV base stations are generated. Starting from the needs of maximizing the water area monitoring area and the most efficient land pollution source monitoring, this method meets the monitoring needs of sudden, dynamic, and land-sea coordinated monitoring, and improves the timeliness and efficiency of environmental pollution monitoring.

[0019] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1A flowchart illustrating a method for deploying a drone base station according to an embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating step S2 of a drone base station deployment method provided in one embodiment of this application.

[0022] Figure 3 This is a flowchart illustrating step S3 of a drone base station deployment method provided in one embodiment of this application.

[0023] Figure 4 This is a flowchart illustrating step S4 of a drone base station deployment method provided in one embodiment of this application.

[0024] Figure 5 This is a flowchart illustrating step S5 of a drone base station deployment method provided in one embodiment of this application.

[0025] Figure 6 This is a flowchart illustrating step S5 of a drone base station deployment method provided in one embodiment of this application.

[0026] Figure 7 This is a schematic diagram of the structure of a drone base station deployment device provided in one embodiment of this application;

[0027] Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0030] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0031] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for deploying a drone base station according to an embodiment of this application. The method includes the following steps:

[0032] S1: Obtain remote sensing extraction data of land pollution sources, long-term time-series remote sensing inversion data, land use data, and building height remote sensing inversion data of the target area.

[0033] The execution entity of the UAV base station deployment method is the deployment equipment of the UAV base station deployment method (hereinafter referred to as the deployment equipment). In an optional embodiment, the deployment equipment may be a computer device, a server, or a server cluster composed of multiple computer devices.

[0034] In this embodiment, the deployed equipment obtains remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data, and building height remote sensing inversion data of the target area. Specifically, the target area is the nearshore estuary area flowing into the sea. The land pollution source remote sensing extraction data includes data on the distribution of sewage outlet locations and land pollution source area data. The long-term remote sensing inversion data includes water quality remote sensing inversion data and spectral remote sensing inversion data. The water quality remote sensing inversion data includes several water quality parameters, and the spectral remote sensing inversion data includes reflectance and center wavelength of several bands, including green light band, red light band, red-edge band 2, and near-infrared band.

[0035] S2: Based on the remote sensing data extracted from land pollution sources and the long-term remote sensing inversion data, conduct an assessment of the monitoring needs for land pollution sources to obtain monitoring needs data for land pollution sources.

[0036] In this embodiment, the deployed device performs a land-based pollution monitoring demand assessment based on the land-based pollution source remote sensing extraction data and long-term time-series remote sensing inversion data to obtain land-based pollution source monitoring demand data, thereby assessing the spatial distribution pattern of land-based pollution sources and evaluating the degree of monitoring demand for nearshore land-based pollution entering the sea in estuaries. The land-based pollution sources include, but are not limited to, factories, aquaculture areas, farmland, and sea / water surface.

[0037] Please see Figure 2 , Figure 2 The flowchart of S2 in the UAV base station deployment method provided in one embodiment of this application is shown below, including steps S21 to S23:

[0038] S21: Based on the location distribution data of the sewage outlets and the water quality remote sensing inversion data, obtain the sewage discharge abnormal event data.

[0039] In this embodiment, the deployment device obtains sewage discharge anomaly event data from the sewage outlet location distribution data in the land pollution source remote sensing extraction data and the water quality remote sensing inversion data in the long time series remote sensing inversion data. The sewage discharge anomaly event data includes the probability of sewage discharge anomalies for several sewage outlets.

[0040] Specifically, the deployment device uses the location parameters of several sewage outlets in the sewage outlet location distribution data and the water quality remote sensing inversion data in the long-term remote sensing inversion data to count the frequency of abnormal water quality events around several sewage outlets, obtain the probability of abnormal sewage discharge events at several sewage outlets, and construct the sewage outlet abnormal discharge event data.

[0041] S22: Based on the location parameters of several sewage outlets in the sewage outlet location distribution data and the preset buffer range threshold, construct several sewage outlet buffer areas; based on the land pollution source area data, obtain the area proportion of several land pollution sources in the several sewage outlet buffer areas.

[0042] In this embodiment, the deployment device constructs several sewage outlet buffer areas based on several sewage outlet location parameters in the sewage outlet location distribution data and a preset buffer range threshold. Specifically, the buffer range threshold can be set to 100 meters. The deployment device expands with several sewage outlet location parameters as the center and the buffer range threshold as the radius to construct several sewage outlet buffer areas.

[0043] Based on the land pollution source area data, the deployed equipment confirms the area data of several land pollution sources in several sewage outlet buffer areas, and obtains the area ratio of several land pollution sources in several sewage outlet buffer areas.

[0044] S23: Multiply the area proportion of the land pollution sources by the corresponding weight parameters of the land pollution sources to obtain a product result of several land pollution sources; accumulate the product results of several land pollution sources in the same sewage outlet buffer area to obtain a cumulative result of several sewage outlet buffer areas; multiply the cumulative result of several sewage outlet buffer areas by the probability of abnormal sewage discharge events of the corresponding sewage outlets to obtain sub-data of land pollution source monitoring needs in several sewage outlet buffer areas, and construct the land pollution source monitoring needs data.

[0045] In this embodiment, the deployment device, based on the area proportion of several land-based pollution sources in several sewage outlet buffer zones, the probability of corresponding sewage discharge anomaly events in the sewage outlet discharge anomaly event data, and a preset land-based pollution source monitoring demand calculation algorithm, multiplies the area proportion of the land-based pollution sources with the corresponding weight parameters of the land-based pollution sources to obtain the product result of several land-based pollution sources in several sewage outlet buffer zones. The product results of several land-based pollution sources in the same sewage outlet buffer zone are then accumulated to obtain the accumulated result of several sewage outlet buffer zones. Finally, the accumulated result of several sewage outlet buffer zones is multiplied with the probability of corresponding sewage discharge anomaly events to obtain the land-based pollution source monitoring demand sub-data for several sewage outlet buffer zones, thus constructing the land-based pollution source monitoring demand data. The land-based pollution source monitoring demand calculation algorithm is as follows:

[0046]

[0047] In the formula, For the sub-data required for monitoring land-based pollution sources, This represents the probability of abnormal sewage discharge events at the sewage outlet. The first buffer zone for sewage outlets q The area proportion of each land-based pollution source Q This refers to the number of land-based pollution sources within the sewage outlet buffer zone. For the first q Weight parameters for each land-based pollution source.

[0048] S3: Based on the long-term remote sensing inversion data, conduct a water quality monitoring demand assessment to obtain water quality monitoring demand data.

[0049] In this embodiment, the deployed equipment performs a water quality monitoring demand assessment based on the long-term remote sensing inversion data to obtain water quality monitoring demand data, thereby assessing the spatial distribution pattern of water quality parameters and evaluating the degree of monitoring demand for nearshore seawater entering the estuary. The water quality parameters include, but are not limited to, chlorophyll a, total nitrogen, total phosphorus, and chemical oxygen demand.

[0050] Please see Figure 3 , Figure 3 The flowchart of S3 in the UAV base station deployment method provided in one embodiment of this application is shown below, including steps S31 to S33:

[0051] S31: Divide the water area in the target area into several water area grid areas; based on several water quality parameters in the water quality remote sensing inversion data, obtain the water quality parameter exceedance data of several water area grid areas.

[0052] In this embodiment, the deployment device divides the water area in the target area into several water grid areas.

[0053] The deployed equipment obtains water quality parameter exceedance data for several water grid areas based on several water quality parameters in the water quality remote sensing inversion data. The water quality parameter exceedance data includes several water quality parameter exceedance frequencies.

[0054] S32: Based on the reflectance and center wavelength of several bands in the spectral remote sensing inversion data, obtain the red tide index of several water grid areas; perform normalized mean processing on the red tide index of several water grid areas to obtain the normalized mean red tide index of several water grid areas.

[0055] In this embodiment, the deployment device calculates the red tide index based on the reflectance and center wavelength of the green band, red band, red-edge 2 band, and near-infrared band in the spectral remote sensing inversion data, using a preset red tide index calculation algorithm to obtain the red tide index of several water grid areas. The red tide index calculation algorithm is as follows:

[0056]

[0057] In the formula, Red tide index The reflectivity of the red-edge band 2. The reflectivity is in the red light band. The reflectivity is in the near-infrared band. The reflectivity in the green light band, The center wavelength of the red-edge band 2. It is the center wavelength of the red light band. It is the center wavelength of the near-infrared band.

[0058] The equipment is deployed to perform normalized mean processing on the red tide index of several water grid areas, resulting in the normalized mean red tide index of several water grid areas.

[0059] S33: Multiply the frequency of water quality parameters exceeding the standard with the corresponding weight parameters of the water quality parameters to obtain the product results of several water quality parameters in several water area grid regions; accumulate the product results of several water quality parameters in the same water area grid region to obtain the accumulated results of several water area grid regions; multiply the accumulated results of several water area grid regions with the red tide index after normalization mean processing to obtain water quality monitoring demand sub-data for several water area grid regions, and construct the water quality monitoring demand data.

[0060] In this embodiment, the deployment device, based on the red tide index after normalized mean processing of several water grid areas, water quality parameter exceedance data, and a preset water quality monitoring demand calculation algorithm, multiplies the water quality parameter exceedance frequency with the corresponding water quality parameter weight parameter to obtain the product result of several water quality parameters for several water grid areas. The product results of several water quality parameters within the same water grid area are then accumulated to obtain the accumulated result for several water grid areas. This accumulated result is then multiplied by the corresponding red tide index after normalized mean processing to obtain water quality monitoring demand sub-data for several water grid areas, thus constructing the water quality monitoring demand data. The water quality monitoring demand calculation algorithm is as follows:

[0061]

[0062] In the formula, For water quality monitoring needs, sub-data The red tide index is the red tide index after normalization of the mean. For the first k Frequency of exceeding water quality parameters K The number of water quality parameters for the grid area of ​​the water body. For the first k Weight parameters for each water quality parameter.

[0063] S4: Based on the land use data and building height remote sensing inversion data, conduct an assessment of the coverage level of UAV base stations to obtain UAV base station coverage level data.

[0064] In this embodiment, the deployment device evaluates the coverage level of UAV base stations based on the land use data and building height remote sensing inversion data to obtain UAV base station coverage level data, so as to consider the applicability of UAV remote sensing monitoring in the nearshore estuary and evaluate the UAV monitoring level of the area.

[0065] Please see Figure 4 , Figure 4 The flowchart of S4 in the UAV base station deployment method provided in one embodiment of this application is shown below, including steps S41 to S42:

[0066] S41: Divide the target area into several building grid areas; based on the land use data, sum the area proportions of several land use types in the several building grid areas to obtain the land use type proportion data of the several building grid areas; based on the remote sensing inversion data of building height, obtain the building density of the several building grid areas.

[0067] In this embodiment, the deployment device divides the target area into several building grid areas, obtains the area proportion of several land use types in several building grid areas based on the land use data, and accumulates the area proportions of several land use types in several building grid areas to obtain the land use type proportion data of several building grid areas; and obtains the building density of several building grid areas based on the remote sensing inversion data of building height.

[0068] S42: If the land use type proportion data meets the land use type proportion threshold and the building density meets the building density threshold, obtain the secondary data of drone base station coverage in the building grid area; otherwise, obtain the primary data of drone base station coverage in the building grid area; based on the land use type proportion data and building density of several building grid areas, obtain the drone base station coverage level sub-data of several building grid areas, and construct the drone base station coverage level data.

[0069] The drone base station coverage level sub-data includes primary drone base station coverage data and secondary drone base station coverage data. These two data indicate the types of drones that can be deployed within the building grid area. Specifically, the primary drone base station coverage data indicates that only small drone base stations can be set up within the building grid area for deploying small drones. The secondary drone base station coverage data indicates that both small and large drone base stations can be set up within the building grid area for deploying small drones, such as multi-rotor drones, and large drones, such as fixed-wing drones.

[0070] In this embodiment, the deployment device obtains secondary data on drone base station coverage of the building grid area based on the land use type proportion data, building density, preset land use type proportion threshold, and building density threshold of the plurality of building grid areas. If the land use type proportion data meets the land use type proportion threshold and the building density meets the building density threshold, the deployment device obtains secondary data on drone base station coverage of the building grid area; otherwise, it obtains primary data on drone base station coverage of the building grid area, and obtains secondary data on drone base station coverage of the plurality of building grid areas.

[0071] S5: Construct a drone base station site selection model. Based on the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model, obtain drone base station deployment location data and level data.

[0072] In this embodiment, the deployment equipment constructs a drone base station site selection model. Based on the land-based pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model, it obtains drone base station deployment location data and level data. Based on the constructed drone base station site selection model and the obtained land-based pollution source monitoring demand data, water quality monitoring demand data, and drone base station coverage level data for the target area, it generates drone base station deployment location data and level data. Starting from the needs of maximizing the water area monitoring area and maximizing the efficiency of land-based pollution source monitoring, it aims to meet the monitoring needs of sudden, dynamic, and land-sea coordinated monitoring, thereby improving the timeliness and efficiency of environmental pollution monitoring.

[0073] Specifically, the deployment equipment constructs a multi-objective, multi-type optimization model as the UAV base station site selection model; please refer to [link to relevant documentation]. Figure 5 , Figure 5 The flowchart of S5 in the drone base station deployment method provided in one embodiment of this application is shown below, including steps S51 to S52:

[0074] S51: Construct a multi-objective requirement function.

[0075] In this embodiment, the deployment device constructs a multi-objective demand function, which includes an objective function for maximizing the demand for land-based pollution source monitoring, an objective function for maximizing the demand for water quality monitoring, and an objective function for minimizing the distance from the UAV base station to the center of demand for land-based pollution sources. The objective function for maximizing the demand for land-based pollution source monitoring is as follows:

[0076]

[0077] In the formula, To maximize the objective function of land-based pollution source monitoring needs, For the firsti The drone coverage tag data for each demand point is used to indicate whether the demand point is covered by the first drone. t Coverage of various types of drones, For the first i Data required for monitoring land-based pollution sources at each demand point.

[0078] Specifically, the drone coverage tag data is as follows:

[0079]

[0080] In the formula, For the first i The first demand point and the first j The shortest distance between candidate points for t The maximum coverage radius of each type of drone, for the monitoring needs of land pollution sources, the objective function for maximizing the monitoring needs of land pollution sources, and the specific needs points and candidate points in the drone coverage tag data are the sewage outlet buffer areas.

[0081] The objective function for maximizing the water quality monitoring demand is:

[0082]

[0083] In the formula, To maximize the objective function of water quality monitoring needs, For the first i Water quality monitoring demand sub-data for each demand point; for water quality monitoring demand, the demand points and candidate points in the objective function for maximizing water quality monitoring demand are specifically water area grid regions.

[0084] The objective function for minimizing the distance between the drone base station and the demand center of the land-based pollution source is:

[0085]

[0086] In the formula, Let the objective function be minimizing the distance from the drone base station to the demand center of land-based pollution sources. m The number of candidate points. For the first j The drone tag data for each candidate point is used to indicate whether the candidate point has a first... t Types of drones For the first j The candidate point and the first v The shortest distance between demand centers of land-based pollution sources; the demand centers of land-based pollution sources can be obtained by deploying equipment using density-based clustering algorithms based on remote sensing data extracted from the land-based pollution sources.

[0087] Specifically, the tag data set for the drone is as follows:

[0088]

[0089] For the distance requirement from the drone base station to the center of land pollution source demand, the objective function for minimizing the distance from the drone base station to the center of land pollution source demand, as well as the demand points and candidate points in the drone's set label data, are specifically the building grid area.

[0090] S52: Construct constraints.

[0091] In this embodiment, the deployment device establishes constraints, including a drone base station coverage level constraint and a candidate point quantity constraint. The drone base station coverage level constraint is as follows:

[0092]

[0093] In the formula, For the first i The drone level tag data for each demand point is used to indicate the type of drones covered by drones at that demand point. For the first i Sub-data on the coverage level of drone base stations at each demand point; to ensure that the location of drone base stations for the corresponding drone type is consistent with the coverage level of drones within that coverage area.

[0094] Specifically, the drone rating label data is as follows:

[0095]

[0096] For drone rating label data, the required points and candidate points are specifically building grid areas.

[0097] The constraint on the number of candidate points is as follows:

[0098]

[0099] In the formula, For the first t The number of drones of each type N The number of candidate points to be finally selected, to limit the number of candidate points to be finally selected.

[0100] Please see Figure 6 , Figure 6 The flowchart of S5 in the drone base station deployment method provided in one embodiment of this application is shown below, including steps S53-S54, as follows:

[0101] S53: Based on the objective functions for maximizing the demand for land pollution source monitoring, maximizing the demand for water quality monitoring, and minimizing the distance between the UAV base station and the demand center of land pollution sources in the UAV base station site selection model, construct the target demand adaptation function.

[0102] In this embodiment, the deployment device constructs a target demand adaptation function based on the objective functions for maximizing land pollution source monitoring demand, maximizing water quality monitoring demand, and minimizing the distance from the UAV base station to the center of land pollution source demand in the UAV base station site selection model. The target demand adaptation function is as follows:

[0103]

[0104] In the formula, F This is the adaptive function for the target requirement.

[0105] S54: Based on the land pollution source monitoring demand data, water quality monitoring demand data, UAV base station coverage level data, and the UAV base station coverage level constraints and candidate point number constraints in the UAV base station site selection model, a multi-objective genetic algorithm is used to iteratively solve the target demand fitness function to obtain UAV base station deployment location data and level data.

[0106] In this embodiment, the deployment device, based on the land-based pollution source monitoring demand data, water quality monitoring demand data, UAV base station coverage level data, and the UAV base station coverage level constraints and candidate point number constraints in the UAV base station site selection model, employs a multi-objective genetic algorithm to iteratively solve the target demand adaptation function to obtain UAV base station deployment location data and level data. It deeply considers the urgent needs and technical challenges of monitoring land-based pollution entering the sea in near-shore estuaries, constructing a complete UAV site selection scheme technical route. The results can effectively meet the dynamic monitoring needs of land-based pollution entering the sea in near-shore estuaries and the water monitoring needs.

[0107] Specifically, the deployment equipment employs a chromosome encoding method, treating a set of solutions as a single chromosome. Initial solutions are selected from several candidate base station locations using the K-MEANS algorithm. Based on the fitness function, a roulette wheel selection method is used to choose individuals with high fitness for genetic operations. Genetic operations include single-point crossover and multi-point crossover, exchanging the gene information of each individual to generate new individuals. These individuals are then mutated to introduce new information, undergoing multi-band iteration. Through selection, crossover, and mutation operations, individuals with higher fitness are iterated. Iteration stops when the fitness reaches a satisfactory level, thus solving the objective function and obtaining UAV base station deployment location data and level data.

[0108] Please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of a drone base station deployment device according to an embodiment of this application. The device can be implemented entirely or partially through software, hardware, or a combination of both. The device 7 includes:

[0109] The data acquisition module 71 is used to acquire remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data, and remote sensing inversion data of building height in the target area;

[0110] The land pollution source monitoring demand data calculation module 72 is used to assess the land pollution monitoring demand based on the land pollution source remote sensing extracted data and long-term remote sensing inversion data, and obtain land pollution source monitoring demand data.

[0111] The water quality monitoring demand data calculation module 73 is used to assess the water quality monitoring demand based on the long-term remote sensing inversion data and obtain water quality monitoring demand data.

[0112] The drone base station coverage level data calculation module 74 is used to evaluate the drone base station coverage level based on the land use data and building height remote sensing inversion data, and obtain drone base station coverage level data.

[0113] The drone base station deployment module 75 is used to construct a drone base station site selection model. Based on the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model, it obtains drone base station deployment location data and level data.

[0114] In this embodiment, a data acquisition module obtains remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data, and building height remote sensing inversion data of the target area. A land pollution source monitoring demand data calculation module assesses the land pollution monitoring demand based on the extracted and long-term remote sensing data to obtain land pollution source monitoring demand data. A water quality monitoring demand data calculation module assesses the water quality monitoring demand based on the long-term remote sensing inversion data to obtain water quality monitoring demand data. A drone base station coverage level data calculation module assesses the drone base station coverage level based on the land use data and building height remote sensing inversion data to obtain drone base station coverage level data. A drone base station deployment module constructs a drone base station site selection model and obtains drone base station deployment location and level data based on the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data, and the drone base station site selection model. Based on the constructed UAV base station site selection model and the obtained land pollution source monitoring demand data, water quality monitoring demand data, and UAV base station coverage level data of the target area, UAV base station deployment location data and level data are generated. Starting from the needs of maximizing the water area monitoring area and the most efficient land pollution source monitoring, this aims to meet the monitoring needs of sudden, dynamic, and land-sea coordinated monitoring, and improve the timeliness and efficiency of environmental pollution monitoring.

[0115] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81; the computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 81. Figures 1 to 6 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0116] The processor 81 may include one or more processing cores. The processor 81 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the UAV base station deployment device 7 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 82, and by calling data stored in the memory 82. Optionally, the processor 81 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 81 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 81 and may be implemented as a separate chip.

[0117] The memory 82 may include random access memory (RAM) or read-only memory. Optionally, the memory 82 may include a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 82 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 82 may also be at least one storage device located remotely from the aforementioned processor 81.

[0118] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 6 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0119] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0120] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0122] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0126] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for deploying a drone base station, characterized in that: The following steps are involved: Obtaining remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data and remote sensing inversion data of building height in the target area, wherein the remote sensing extraction data of land pollution sources includes the location distribution data of sewage outlets and the area data of land pollution sources; the long-term remote sensing inversion data includes water quality remote sensing inversion data and spectral remote sensing inversion data; the water quality remote sensing inversion data includes several water quality parameters; the spectral remote sensing inversion data includes the reflectance and central wavelength of several bands; Obtaining abnormal discharge event data of sewage outlets according to the sewage outlet location distribution data and the water quality remote sensing inversion data, wherein the abnormal discharge event data of sewage outlets includes the abnormal discharge event probabilities of several sewage outlets; According to the several sewage outlet location parameters in the sewage outlet location distribution data and the preset buffer zone range threshold, several sewage outlet buffer areas are constructed; according to the land pollution source area data, the area proportions of several land pollution sources in several sewage outlet buffer areas are obtained; The area proportion of the land pollution source is multiplied by the weight parameter of the corresponding land pollution source to obtain the product results of several land pollution sources; the product results of several land pollution sources in the same sewage outlet buffer area are accumulated to obtain the accumulated results of several sewage outlet buffer areas; the accumulated results of several sewage outlet buffer areas are multiplied by the probability of abnormal discharge events of the corresponding sewage outlet to obtain the land pollution source monitoring demand sub-data of several sewage outlet buffer areas, and the land pollution source monitoring demand data is constructed; Dividing the water area in the target area into a plurality of water area grid areas; obtaining water quality parameter exceeding standard data of the plurality of water area grid areas according to the plurality of water quality parameters in the water quality remote sensing inversion data, wherein the water quality parameter exceeding standard data includes a plurality of water quality parameter exceeding standard frequencies; According to the reflectance and central wavelength of several bands in the spectral remote sensing inversion data, the red tide index of several water grid areas is obtained; the red tide index of several water grid areas is normalized and averaged to obtain the red tide index of several water grid areas after normalized average processing; The frequency of exceeding the standard of the water quality parameter is multiplied by the weight parameter of the corresponding water quality parameter to obtain the product results of the water quality parameters of the water grid areas; the product results of the water quality parameters of the same water grid area are accumulated to obtain the accumulated results of the water grid areas; the accumulated results of the water grid areas are multiplied by the corresponding red tide index after normalized mean processing to obtain the water quality monitoring demand sub-data of the water grid areas, and the water quality monitoring demand data is constructed; Performing a UAV base station coverage level assessment based on the land use data and building height remote sensing inversion data to obtain UAV base station coverage level data; Construct a UAV base station site selection model, and obtain the UAV base station deployment location data and level data based on the land pollution source monitoring demand data, water quality monitoring demand data, UAV base station coverage level data and the UAV base station site selection model.

2. The method for deploying a drone base station according to claim 1, characterized in that: The land use data includes area data of several land use types, and the building height remote sensing inversion data includes building area data; The method of evaluating the coverage level of the UAV base station according to the land use data and the remote sensing inversion data of the building height to obtain the coverage level data of the UAV base station includes the following steps: The target area is divided into a plurality of building grid areas, and according to the land use data, the area proportions of a plurality of land use types in the plurality of building grid areas are accumulated to obtain the land use type proportion data of the plurality of building grid areas; according to the building height remote sensing inversion data, the building density of the plurality of building grid areas is obtained; If the land use type proportion data meets the land use type proportion threshold, and the building density meets the building density threshold, the secondary data of the drone base station coverage of the building grid area is obtained; otherwise, the primary data of the drone base station coverage of the building grid area is obtained; based on the land use type proportion data and building density of several building grid areas, the drone base station coverage level sub-data of several building grid areas are obtained to construct the drone base station coverage level data.

3. The method for deploying a drone base station according to claim 2, characterized in that: The method of obtaining the deployment location data and level data of the drone base station according to the land pollution source monitoring demand data, the water quality monitoring demand data, the drone base station coverage level data and the drone base station site selection model comprises the following steps: According to the objective function of maximizing the demand for land pollution source monitoring, the objective function of maximizing the demand for water quality monitoring, and the objective function of minimizing the distance from the drone base station to the demand center of the land pollution source in the drone base station site selection model, a target demand adaptation function is constructed; According to the land pollution source monitoring demand data, water quality monitoring demand data, UAV base station coverage level data, and the UAV base station coverage level constraints and candidate point quantity constraints in the UAV base station site selection model, a multi-objective genetic algorithm is adopted to iteratively solve the target demand adaptation function to obtain the UAV base station deployment location data and level data.

4. A device applied to the method for deploying a drone base station according to claim 1, characterized in that: include: The data acquisition module is used to obtain remote sensing extraction data of land pollution sources, long-term remote sensing inversion data, land use data and remote sensing inversion data of building height in the target area; A land pollution source monitoring demand data calculation module is used to evaluate the land pollution monitoring demand based on the land pollution source remote sensing extraction data and long-term remote sensing inversion data to obtain the land pollution source monitoring demand data; A water quality monitoring demand data calculation module is used to evaluate water quality monitoring demand based on the long-term remote sensing inversion data to obtain water quality monitoring demand data; A drone base station coverage level data calculation module is used to evaluate the drone base station coverage level based on the land use data and the building height remote sensing inversion data to obtain the drone base station coverage level data; The drone base station deployment module is used to construct a drone base station site selection model, and obtain the drone base station deployment location data and level data according to the land pollution source monitoring demand data, water quality monitoring demand data, drone base station coverage level data and the drone base station site selection model.

5. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the drone base station deployment method as described in any one of claims 1 to 3 are implemented.

6. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the drone base station deployment method according to any one of claims 1 to 3 are implemented.

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