A UAV deployment method and system for watershed water ecological environment monitoring

Abstract

The present application relates to a method and system for deploying unmanned aerial vehicles (UAVs) for monitoring the water ecological environment of a river basin. After conducting a hydrological analysis on a target area, the target river basin is divided into a plurality of sub-basin units, and a water ecological environment evaluation index system is constructed for each sub-basin unit, thereby obtaining a comprehensive water ecological environment index. Based on the variation range of the comprehensive water ecological environment index, each sub-basin unit is divided into monitoring areas of different levels. Monitoring areas of different levels correspond to different monitoring demand points, thereby obtaining a multi-level demand point set with clear hierarchy, clear positioning, and the ability to clearly identify the objects to be monitored. Finally, the UAV performance data is used as a constraint condition, and the field stations in the monitoring area are combined as candidate UAV airport facilities and a multi-level demand point set. Based on the location set coverage model (LSCP), a UAV deployment plan is obtained, thereby constructing a multi-level UAV remote sensing network, which can achieve the establishment of the minimum number of UAV airport facilities to meet all monitoring needs.

Description

Technical Field

[0001] The present application relates to the technical field of watershed water ecological environment monitoring, and in particular to a method and system for deploying unmanned aerial vehicles (UAVs) for watershed water ecological environment monitoring. Background Art

[0002] UAV remote sensing is a modern science and technology that uses sensors of drones to collect electromagnetic wave information of target objects, and after processing and analysis, identifies the target objects and reveals their geometric and physical properties, relationships and laws of change. Based on drone remote sensing, ground data can be quickly obtained, and it has the advantages of flexibility, real-time and rapidity, low cost, high accuracy and high safety factor. At present, drone remote sensing monitoring has become an important means to achieve real-time dynamic monitoring. Establishing a drone remote sensing monitoring network within the basin can quickly obtain high-precision water ecological environment status information in real time, which is of great significance to the monitoring of the water ecological environment in the basin.

[0003] However, there are few studies on the layout of UAV remote sensing monitoring networks, and most of them are based on the maximum coverage model (MCLP) to carry out the layout of UAV airports. This layout method can meet the maximum monitoring needs under limited resources, but it cannot meet all monitoring needs and lacks fairness. In addition, most UAV airport layouts based on a single level have unclear levels and positioning, and lack the pertinence of the monitoring objects. Summary of the invention

[0004] In view of the problems in the prior art, this application provides a method for deploying drones for watershed water ecological environment monitoring, which can establish a minimum number of drone airport facilities to meet all monitoring needs, and has clear hierarchy, clear positioning and clear monitoring objects, including the following steps:

[0005] Conduct hydrological analysis of the target area based on DEM data and divide the sub-basin units;

[0006] Constructing a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index;

[0007] Calculating corresponding water ecological indicators, water environment indicators and ecological stress indicators for the divided sub-watershed units;

[0008] The water ecological environment comprehensive index is obtained by multiplying the water ecological index by the weight of the preset water ecological index layer, the water environment index by the weight of the preset water environment index layer, and the ecological stress index by the weight of the preset ecological stress index layer;

[0009] According to the variation range of the comprehensive index of water ecological environment in different periods, the sub-basin unit is divided into monitoring areas of several levels;

[0010] Selecting the geographical center of each monitoring area as a demand point, and obtaining a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to monitoring areas of each level;

[0011] Selecting the field stations in each monitoring area as candidate facilities for drone airports to obtain a set of candidate facilities for drone airports;

[0012] The drone performance data, the drone airport candidate facility point set and the multi-level demand point set are input into a preset location set coverage model to obtain a facility set covering the demand points at each level and generate a drone deployment plan.

[0013] The present application also provides a system for deploying drones for watershed water ecological environment monitoring, which is characterized by comprising a sub-watershed unit division module for performing hydrological analysis on the target area based on DEM data and dividing the sub-watershed units;

[0014] A water ecological environment evaluation index system construction module is used to construct a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index;

[0015] An index calculation module, used for calculating the corresponding water ecological index, water environment index and ecological stress index for the divided sub-basin units;

[0016] A water ecological environment comprehensive index calculation module, used to multiply the water ecological index by the weight of a preset water ecological index layer, the water environment index by the weight of a preset water environment index layer, and the ecological stress index by the weight of a preset ecological stress index layer, and add them together to obtain a water ecological environment comprehensive index;

[0017] A monitoring area division module is used to divide the sub-basin unit into monitoring areas of several levels according to the change range of the comprehensive index of water ecological environment in different periods;

[0018] A multi-level demand point set acquisition module is used to select the geographic center of each monitoring area as a demand point, and obtain a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to the monitoring areas of each level;

[0019] A drone airport candidate facility point set module is used to select field stations in each monitoring area as drone airport candidate facility points to obtain a drone airport candidate facility point set;

[0020] The drone deployment module is used to input the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into a preset location set coverage model, obtain the facility set covering the demand points at each level, and generate a drone deployment plan.

[0021] Compared with the prior art, the present application divides the target basin into multiple sub-basin units after conducting a hydrological analysis on the target area, and constructs a water ecological environment evaluation index system for each sub-basin unit, thereby obtaining a comprehensive water ecological environment index, and dividing each sub-basin unit into monitoring areas of different levels based on the change range of the comprehensive water ecological environment index. Monitoring areas of different levels correspond to different monitoring demand points, and a multi-level demand point set is obtained, which has a clear hierarchy, clear positioning, and can clearly identify the objects to be monitored; finally, the UAV performance data is used as a constraint condition, combined with the field stations in the monitoring area as candidate UAV airport facilities and a multi-level demand point set, based on the location set coverage model (LSCP), to obtain a UAV deployment plan, thereby constructing a multi-level UAV remote sensing network, which can achieve the establishment of the least number of UAV airport facilities to meet all monitoring needs.

[0022] In order to provide a clearer understanding of the present application, the specific implementation of the present application will be described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This application provides a flow chart of a method for deploying drones for watershed water ecological environment monitoring;

[0024] Figure 2 A flowchart of a method for dividing sub-basin units by a method for deploying drones for water ecological environment monitoring in a watershed is provided for this application;

[0025] Figure 3 A flowchart of a method for implementing index calculation for a method of deploying unmanned aerial vehicles for watershed water ecological environment monitoring in this application;

[0026] Figure 4 A flowchart of a method for determining indicator weights by using a method for deploying drones for watershed water ecological environment monitoring is provided for this application;

[0027] Figure 5 A method flow chart of a method for dividing monitoring areas by using a drone deployment method for watershed water ecological environment monitoring is provided for this application;

[0028] Figure 6 A method flow chart for generating a drone deployment plan for a drone deployment method for watershed water ecological environment monitoring in this application;

[0029] Figure 7A schematic diagram of a UAV deployment system for watershed water ecological environment monitoring is provided for this application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0031] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the application. It should be understood that the operations of the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowcharts, or may remove one or more operations from the flowcharts, under the guidance of the content of this application.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] Example 1

[0034] See also Figure 1 , Figure 1 This application provides a flow chart of a UAV deployment method for watershed water ecological environment monitoring.

[0035] The present application provides a method for deploying drones for watershed water ecological environment monitoring, which specifically includes the following steps:

[0036] S11: Conduct hydrological analysis on the target area based on DEM data and divide the sub-basin units;

[0037] S12: constructing a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index;

[0038] S13: Calculating corresponding water ecological indicators, water environment indicators and ecological stress indicators for the divided sub-watershed units;

[0039] S14: multiplying the water ecological index by the weight of a preset water ecological index layer, multiplying the water environment index by the weight of a preset water environment index layer, and multiplying the ecological stress index by the weight of a preset ecological stress index layer, and adding them together to obtain a comprehensive water ecological environment index;

[0040] S15: Dividing the sub-basin unit into monitoring areas of several levels according to the variation range of the comprehensive index of water ecological environment in different periods;

[0041] S16: Selecting the geographic center of each monitoring area as a demand point, and obtaining a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to monitoring areas of each level;

[0042] S17: Selecting the field stations in each monitoring area as candidate facilities for drone airports to obtain a set of candidate facilities for drone airports;

[0043] S18: Input the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into a preset location set coverage model to obtain a facility set covering the demand points at each level and generate a drone deployment plan.

[0044] Compared with the prior art, the present application divides the target basin into multiple sub-basin units by conducting hydrological analysis on the target area, and constructs a water ecological environment evaluation index system for each sub-basin unit, thereby obtaining a comprehensive water ecological environment index, and dividing each sub-basin unit into monitoring areas of different levels based on the change range of the comprehensive water ecological environment index. Monitoring areas of different levels correspond to different monitoring demand points, and a multi-level demand point set is obtained, which has a clear hierarchy, clear positioning, and can clearly identify the objects to be monitored; finally, the UAV performance data is used as a constraint condition, combined with the field stations in the monitoring area as candidate UAV airport facilities and a multi-level demand point set, based on the location set coverage model (LSCP), to obtain a UAV deployment plan, thereby constructing a multi-level UAV remote sensing network, which can achieve the establishment of the least number of UAV airport facilities to meet all monitoring needs.

[0045] In step S11, a hydrological analysis is performed on the target area based on the DEM data and the sub-basin units are divided.

[0046] Among them, the Digital Elevation Model (DEM) is a digital simulation of landforms and terrains using limited terrain elevation data. The DEM data can be downloaded from the geospatial data cloud. Figure 2 , Figure 2This application discloses a method flow chart of a method for implementing a method for deploying drones for watershed water ecological environment monitoring to divide sub-watershed units, which specifically includes the following steps:

[0047] S111: using a depression-filling tool of a hydrological analysis software to perform depression-filling processing on the DEM data to obtain a depression-free DEM;

[0048] S112: using the flow direction tool of the hydrological analysis software to obtain the water flow direction data of each grid of the non-depression DEM by using the maximum slope drop method on the non-depression DEM;

[0049] S113: Calculate the number of confluence grids of the non-depression DEM using the flow tool of the hydrological analysis software according to the water flow direction data of each grid of the non-depression DEM, where the number of confluence grids is the cumulative confluence amount;

[0050] S114: Based on a preset minimum cumulative runoff, using a grid calculator of the hydrological analysis software, extracting target river network grid data from grids whose confluence accumulation is greater than the preset minimum cumulative runoff;

[0051] S115: using the river linking tool of the hydrological analysis software to link the target river network grid data into a target river network;

[0052] S116: Use the watershed tool of the hydrological analysis software to divide the target river network into sub-basin units.

[0053] In this embodiment, a hydrological analysis tool ArcGIS is used to perform hydrological analysis on the target watershed and divide the target watershed into a plurality of sub-watershed units. The ArcGIS is ArcGIS software developed and maintained by the Environmental Systems Research Institute, Inc. of the United States.

[0054] In step S111, the DEM data is processed by using a depression-filling tool of the hydrological analysis software to obtain a depression-free DEM.

[0055] Among them, due to the terrain, there will be sunken areas in the DEM data. In the sunken areas, unreasonable or wrong water flow direction data will be obtained. Therefore, before calculating the water flow direction data, the DEM data must be filled with depressions to obtain the DEM data without depressions.

[0056] For step S112, the flow direction tool of the hydrological analysis software is used to obtain the water flow direction data of each grid of the non-depression DEM using the maximum slope drop method on the non-depression DEM.

[0057] Among them, the maximum slope drop method is a commonly used terrain analysis method, which is used to calculate the water flow accumulation matrix; the water flow accumulation matrix refers to the cumulative value of water flowing into a specific point from different positions in an area; by calculating the water flow accumulation matrix, the path of the water flow, the convergence area of ​​the water flow, the direction of the water flow and other information can be determined; the slope of each grid is determined by calculating its elevation difference, and the elevation difference can be obtained through the DEM; the direction of the water flow is determined according to the size of the slope. In the maximum slope drop method, the basic principle of calculating the water flow direction data is that water flows to a lower place, and the flow direction is limited to flowing along the direction of the maximum slope, that is, the greater the slope, the easier it is for the water to flow to a lower place.

[0058] For steps S114-S116, the minimum cumulative runoff determines the range of the sub-basin units of the target watershed, the density of the target river network increases as the minimum cumulative runoff decreases, and the range of the sub-basin units of the target watershed decreases as the density of the target river network increases. In other embodiments, other hydrological analysis software can also be used to perform hydrological analysis on the target watershed; according to actual needs, the preset minimum cumulative runoff can also be adjusted.

[0059] For step S12, a water ecological environment evaluation index system is constructed, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index. In other embodiments, the selection of the water ecological environment index can be adaptively modified according to actual needs, for example, the water ecological environment evaluation index can also include indicators such as aquatic habitats and water conservation area ecosystem quality.

[0060] For step S13, the corresponding water ecological index, water environment index and ecological stress index are calculated for the divided sub-watershed units.

[0061] Among them, in this embodiment, the water ecological indicators include vegetation coverage index, habitat quality index and water network density index; the water environment indicators include water quality index and water eutrophication index; the ecological stress indicators include cultivated land area ratio, construction land area ratio, population density and GDP. In other embodiments, according to actual needs, the selection of the water ecological indicators, water environment indicators and ecological stress indicators can be adaptively modified. For example, the water ecological indicators can also include zooplankton community structure, the water environment indicators can also include comprehensive pollution index, and the ecological stress indicators can also include ecological flow compliance rate.

[0062] The vegetation cover index is calculated as follows: Among them, the A veg is the normalization coefficient of vegetation coverage, and the reference value is 0.0121165124;i is the pixel gray value in the NDVI image, and n is the number of pixels in the vegetation coverage study area in the sub-basin unit.

[0063] It should be noted that the NDVI image can be obtained through a satellite. In this embodiment, the vegetation coverage study area refers to a sub-basin unit.

[0064] The habitat quality index is calculated as follows:

[0065]

[0066] Among them, the A bio is the normalization coefficient of the habitat quality index, and the reference value is 511.2642131067. It should be noted that, in this embodiment, the area is the sub-basin unit.

[0067] The water network density index is calculated as follows:

[0068] Water network density index =

[0069] (A rib × River length / region area + A lak × water area / region area + A res × water resources / regional area) / 3, where A riv is the normalization coefficient of river length, the reference value is 84.3704083981; A lak is the normalization coefficient of the water area, and the reference value is 591.7908642005; A res is the normalization coefficient of water resources, and its reference value is 86.3869548281. It should be noted that, in this embodiment, the area is the sub-basin unit.

[0070] The water quality index is obtained by calculating water quality indicators such as total phosphorus, total nitrogen, dissolved oxygen, ammonia nitrogen and chemical oxygen demand using the Nemerow comprehensive pollution index method. The specific calculation formula is: in Where P is the water quality index, P i is the pollution index of the i-th water quality indicator; Q i With T i are the measured values ​​and evaluation standard values ​​of each water quality index concentration; P imax is the maximum value among the pollution indexes of the water quality indicators, Of course, in other embodiments, the number of indicators selected for calculating the water quality index can be adaptively adjusted according to actual needs.

[0071] The water nutrient index is obtained by calculating the chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index through the comprehensive nutrient status index method. The specific calculation formula is: In the formula, TLI is the water nutrient index, TLI j is the nutritional status index of the jth index among the chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index, W j is the relevant weight of the nutritional status index of the jth indicator among the indicators such as chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index, m is the number of indicators, and in this embodiment, the value of m is 5;

[0072] Referring to the Technical Specifications for Surface Water Environmental Quality Assessment, the relevant weights of the nutritional status index of indicators such as chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index are W and W respectively. Chla =0.2663,W TP =0.1879,W TN =0.1790,W SD =0.1834,W IMn =0.1834;

[0073] The calculation formulas of the nutritional status index in the chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index are respectively: TLI Chla =10×(2.5+1.086×lnρ Chla ), TLI TP =10×(9.436+1.624×lnρ TP ), TLI TN =10×(5.453+1.694×lnρ TN ), TLI SD =10×(5.118+1.94×lnρ SD ), TLI IMn =10×(0.109+2.661×lnρ IMn ).

[0074] Where, TLI Chla 、TLI TP 、TLI TN 、TLI SD and TLI IMn are the nutritional status indices of chlorophyll a, total phosphorus, total nitrogen, transparency, and permanganate index; ρ Chla is the chlorophyll a concentration, ρ TP , TN and ρ IMn are total phosphorus concentration, total nitrogen concentration, and permanganate index, ρ SDOf course, in other embodiments, according to actual needs, the number of indicators selected for calculating the water body eutrophication index can be adaptively adjusted; and the water quality index can also be calculated by means of the coefficient of variation method and the like.

[0075] The cultivated land area ratio is calculated as follows: cultivated land area ratio = regional cultivated land area / regional total area, where the region is the sub-basin unit;

[0076] The construction land area ratio is calculated as follows:

[0077] Construction land area ratio = regional construction land area / regional total area, the regional total area is the regional area of ​​the sub-basin unit;

[0078] The population density is calculated in the following manner: population density = total population of the region / area of ​​the region. It should be noted that, in this embodiment, the region refers to a sub-basin unit.

[0079] The gross domestic product (GDP) reflects the degree of economic urbanization in a region, and the GDP data can be obtained from pre-stored statistical yearbook data of each region. In this application, the method for obtaining the GDP data includes not only the method described in the above embodiment, but other methods for obtaining the GDP data can also be applied to the solution of this application.

[0080] See also Figure 3 , Figure 3 The present application provides a method flow chart for implementing an index calculation method for a drone deployment method for watershed water ecological environment monitoring. In this embodiment, the following steps are also included:

[0081] S131: multiplying the plant coverage index by the relative weight of the plant coverage index, multiplying the habitat quality index by the relative weight of the habitat quality index, and multiplying the water network density index by the relative weight of the water network density index, and adding them together to obtain the water ecological index;

[0082] S132: multiplying the water quality index by the relative weight of the water quality index and multiplying the water eutrophication index by the relative weight of the water eutrophication index, and adding them together to obtain the water environment index;

[0083] S133: Multiply the cultivated land area ratio by the relative weight of the cultivated land area ratio, multiply the construction land area ratio by the relative weight of the construction land area, multiply the population density by the relative weight of the population density, and multiply the GDP by the relative weight of the GDP, and add them up to obtain the ecological stress index.

[0084] For step S131, the calculation formula of the water ecological index is specifically: Among them, W eci is the water ecological index, ω eco(l) is the relative weight of the lth water ecological indicator, for example, ω eco(1) is the relative weight of the first water ecological index, namely the plant coverage index, ω eco(2) is the relative weight of the second water ecological index, namely the habitat quality index, ω eco(3) is the relative weight of the third water ecological index, namely, the water network density index. The value of l is a positive integer less than or equal to s. In this embodiment, s is 3. Of course, the value of s can be adaptively adjusted according to actual needs.

[0085] For step S132, the calculation formula of the water environment index is specifically: Among them, W env is the water environment index, ω env(m) is the relative weight of the mth water environment indicator, for example, ω env(1) is the relative weight of the first water environment indicator, namely the water quality index, ω env(2) is the relative weight of the second water environment index, namely the water eutrophication index. The value of m is a positive integer less than or equal to k. In this embodiment, k is 2. Of course, the value of k can be adaptively adjusted according to actual needs.

[0086] For step S133, the calculation formula of the ecological stress index is specifically: Among them, W str is the ecological stress index, ω str(n) is the relative weight of the nth ecological stress indicator, for example, ω str(1) is the relative weight of the first water ecological stress indicator, i.e. the proportion of cultivated land area, ω str(2) is the relative weight of the second water ecological stress indicator, i.e., the proportion of construction land area, ω str(3) is the relative weight of the third water ecological stress index, namely population density, ω str(4) is the relative weight of the fourth water ecological stress indicator, namely GDP. The value of n is a positive integer less than or equal to t. In this embodiment, t is 4. Of course, the value of t can be adaptively adjusted according to actual needs.

[0087] For step S14, the water ecological index is multiplied by the weight of the preset water ecological index layer, the water environment index is multiplied by the weight of the preset water environment index layer, and the ecological stress index is multiplied by the weight of the preset ecological stress index layer, and the water ecological environment comprehensive index is added together. The specific formula is: WEEI = λ eco ×W eco +λ env ×W env+λ str ×W str , where WEEI is the comprehensive index of water ecological environment, λ eco is the weight of the preset water ecological index layer, λ env is the weight of the preset water environment index layer, λ str is the weight of the preset ecological stress indicator layer, W eco is the water ecological index, W env is the water environment index, W str It is the ecological stress indicator.

[0088] For steps S131-133 and step S14, please refer to Figure 4 , Figure 4 This is a flowchart of a method for determining the weight of indicators in a method for deploying drones for watershed water ecological environment monitoring in this application. In this embodiment, the analytic hierarchy process is used to determine the weights of the preset water ecological indicator layer, the preset water environment indicator layer, and the preset ecological stress indicator layer, as well as the relative weights of the plant coverage index, the habitat quality index, the water network density index, the water quality index, the water nutrient index, the construction land area, the population density, and the GDP, specifically including the following steps:

[0089] S141: construct a hierarchical structure, the hierarchical structure at least comprising a target layer, a criterion layer and a scheme layer; the constituent factors of the target layer at least include the comprehensive index of water ecological environment, the constituent factors of the criterion layer at least include the water ecological index, water environment index and ecological stress index, and the constituent factors of the scheme layer at least include the vegetation coverage index, habitat quality index, water network density index, water quality index, water eutrophication index, cultivated land area ratio, construction land area ratio, population density and GDP;

[0090] S142: using a 1-9 scaling method to determine the scaling value of each component under the criterion layer for the target layer, and constructing a judgment matrix A;

[0091] S143: Performing hierarchical single sorting on the judgment matrix A to obtain the maximum eigenvalue λ of the judgment matrix A max ;

[0092] S144: subtract the order of the judgment matrix from the maximum eigenvalue of the judgment matrix, and divide the result by the order of the judgment matrix - 1 to obtain a consistency index value CI of the judgment matrix;

[0093] S145: Obtaining a random consistency index standard value RI of the judgment matrix order m according to the AHP average random consistency index standard value table, and dividing the consistency index value CI by the random consistency index standard value RI to obtain a random consistency ratio of the judgment matrix A;

[0094] S146: When the random consistency ratio is less than or equal to 0.1, the hierarchical structure is sorted to obtain the weight of each component factor under the solution layer;

[0095] S147: Use the 1-9 scaling method to modify the scaling values ​​of the various components under the criterion layer for the target layer, and reconstruct the judgment matrix A.

[0096] For step S142, the 1-9 scaling method is used to determine the scaling value of each component under the criterion layer for the target layer, and a judgment matrix A is constructed.

[0097] In this embodiment, the judgment matrix A is specifically A=(a ij ) m×n , where the element a in the judgment matrix A ij >0, a ii =1. The judgment matrix represents the status of evaluating the relative importance of each component in the criterion layer to the component in the target layer. ij Indicates the relative importance of the i-type component and the j-type component in the criterion layer for the component in the target layer. ij Generally, there are five levels of scale, namely 1, 3, 5, 7, and 9. ij =1 means that for the constituent factors in the target layer, the i-type constituent factors in the criterion layer are as important as the j-type constituent factors; a ij =3 means that for the constituent factors in the target layer, the i-type constituent factors in the criterion layer are slightly more important than the j-type constituent factors; a ij =5 means that for the constituent factors in the target layer, the i-type constituent factors in the criterion layer are significantly more important than the j-type constituent factors; a ij =7 means that for the constituent factors in the target layer, the i-type constituent factors in the criterion layer are more important than the j-type constituent factors; a ij =9 For the components in the target layer, the i-type components in the criterion layer are more important than the j-type components. And a ii =1 means that for the constituent factors in the target layer, the same constituent factors in the criterion layer have the same importance. In other embodiments, according to actual needs, when the 5-level scale is not enough, a ijThe value of can be modified adaptively, for example, it can also take values ​​such as 2, 4, 6, 8, etc.

[0098] For step S143, the judgment matrix A is sorted hierarchically to obtain the eigenvector and maximum eigenvalue λ of the judgment matrix A. max The purpose of the single sorting is to determine the weight value of the importance order of the elements in the current level that are related to an element in the upper level. In this embodiment, the judgment matrix A is first multiplied by row elements, and then The power is obtained The specific formula is Where n is the order of the judgment matrix A. In this embodiment, n is 3. The order of the judgment matrix A can be adaptively adjusted according to actual needs. The normalized sorting weight vector is recorded as W, and the specific formula is: Where n is the order of the judgment matrix A. In this embodiment, n is 3. According to actual needs, the order of the judgment matrix A can be adaptively adjusted; the elements in W are the ranking weights of the relative importance of the constituent factors of the criterion layer to the target layer, then W = (W1, W2, ..., W n ) T That is the eigenvector. Finally, the calculation formula of the maximum eigenvalue is Wherein n is the order of the judgment matrix A. In this embodiment, n is 3, and the order of the judgment matrix A can be adaptively adjusted according to actual needs.

[0099] For step S144, the maximum eigenvalue λ of the judgment matrix A is max After subtracting the order of the judgment matrix, divide it by the order of the judgment matrix - 1 to obtain the consistency index value of the judgment matrix. The calculation formula is specifically as follows: Wherein, m is the order of the judgment matrix A, λ max is the maximum eigenvalue of the judgment matrix. In this embodiment, n is 3, and the order of the judgment matrix A can be adaptively adjusted according to actual needs.

[0100] For step S145, the random consistency index standard value corresponding to the judgment matrix order is obtained according to the AHP average random consistency index standard value table, and the random consistency ratio of the judgment matrix is ​​obtained by dividing the consistency index value CI by the RI value. The specific formula is:

[0101] For step S146: when the random consistency ratio is less than or equal to 0.1, the hierarchical structure is sorted in total to obtain the weights of each component factor under the solution layer. The total hierarchical sorting is to calculate the relative importance of all factors of a certain level to the highest level (target level). It should be noted that the total hierarchical sorting is to be performed from the highest level to the lowest level.

[0102] In this embodiment, for the target layer, the weights of the various components under the criterion layer can be determined after the hierarchical single sorting. The 1-9 scaling method is used to determine the scale values ​​of the various components under the solution layer for the water ecological indicators, water environment indicators and ecological stress indicators under the criterion layer, and the first judgment matrix, the second judgment matrix and the third judgment matrix are constructed respectively.

[0103] The first judgment matrix, the second judgment matrix and the third judgment matrix are sorted hierarchically to obtain the first sorting weight vector, the second sorting weight vector and the third sorting weight vector as well as the first maximum eigenvalue, the second maximum eigenvalue and the third maximum eigenvalue for the water ecological indicators, water environment indicators and ecological stress indicators under the criterion layer and each component factor under the scheme layer.

[0104] The first maximum eigenvalue, the second maximum eigenvalue and the third maximum eigenvalue are subjected to steps S144-S145 to obtain a first random consistency ratio, a second random consistency ratio and a third random consistency ratio respectively. If the first random consistency ratio, the second random consistency ratio and the third random consistency ratio are all less than or equal to 0.1, the first sorting weight vector, the second sorting weight vector and the third sorting weight vector are weighted based on the weights of the water ecological index, the water environment index and the ecological stress index under the criterion layer to obtain the weights of each component under the scheme layer; if any of the first random consistency ratio, the second random consistency ratio and the third random consistency ratio is greater than 0.1, the 1-9 scaling method is re-adopted to determine the scale values ​​of each component under the scheme layer for the water ecological index, the water environment index and the ecological stress index under the criterion layer, and the first judgment matrix, the second judgment matrix and the third judgment matrix are re-constructed.

[0105] In this embodiment, since the dimensions of the water ecological index, water environment index and ecological stress index are different and the magnitudes are quite different, when calculating the comprehensive index of the water ecological environment, from the perspective of numerical values, the index with a large numerical variation range has a large proportion in the comprehensive index of the water ecological environment, which leads to an increase in the error of the comprehensive index of the water ecological environment. Therefore, in order to avoid being affected by the dimensions and magnitudes of the water ecological index, the water environment index and ecological stress index are standardized by the range standard method, and the formula of the range standard method is:

[0106]

[0107] Wherein, the formula (1) is a positive indicator, and the formula (2) is a negative indicator; X′ is the value of the data after standardization, and its value range is [0,1], X is the original value of the indicator, and X max With X min In other embodiments, the standardization method can be adaptively modified according to actual needs. For example, when the maximum and minimum values ​​of an indicator are unknown, the Z-score standard method can be used to standardize the indicator.

[0108] For step S15, the sub-basin unit is divided into monitoring areas of several levels according to the variation range of the comprehensive index of water ecological environment in different periods.

[0109] Among them, see Figure 5 , Figure 5 This is a flowchart of a method for implementing a method for dividing monitoring areas by deploying drones for watershed water ecological environment monitoring. In this embodiment, the following steps are also included:

[0110] S151: Obtaining a comprehensive water ecological environment index before a preset measurement period;

[0111] S152: Obtaining a comprehensive water ecological environment index after a preset measurement period;

[0112] S153: subtracting the comprehensive water ecological environment index before the preset measurement period from the comprehensive water ecological environment index after the preset measurement period to obtain a water ecological environment status change index of the sub-basin unit during the measurement period;

[0113] S154: using the attribute classification tool of the hydrological analysis software to classify the water ecological environment status of the sub-basin unit into at least three ranges of change amplitude according to the water ecological environment status change index;

[0114] S155: According to at least three ranges of variation of the water ecological environment conditions of the sub-basin unit, the sub-basin unit is divided into at least three levels of monitoring areas.

[0115] In this embodiment, the preset measurement period is set to 3 months. Of course, the setting of the preset measurement period can be adaptively modified according to actual conditions. For example, if there is no obvious pollution source around the sub-basin unit, the preset measurement period can be adaptively extended to 6 months.

[0116] For step S153, the comprehensive water ecological environment index after the preset measurement period is subtracted from the comprehensive water ecological environment index before the preset measurement period to obtain the water ecological environment status change index of the sub-basin unit during the measurement period. The specific formula is: ΔWEEI=WEEI b -WEEI a , where ΔWEEI is the water ecological environment change index, WEEI b is the comprehensive water ecological environment index after the preset measurement period, WEEI a It is the comprehensive index of the water ecological environment before the preset measurement period.

[0117] For steps S154 and S155, in this embodiment, the hydrological analysis software is ArcGIS software, and the attribute classification tool in the ArcGIS software uses the natural breakpoint method to divide the water ecological environment status of the sub-basin unit into at least three ranges of variation; the natural breakpoint method is a univariate classification method based on cluster analysis, and the water ecological environment status change index is divided into three categories according to the distribution characteristics of the water ecological environment status change index, and the data breakpoints between classes are iteratively calculated to minimize the differences in the classes and maximize the differences between the classes, thereby completing the most appropriate classification. Corresponding to the three types of water ecological environment status change indexes, the water ecological environment status is divided into three ranges of variation, namely stable, changing and obvious changes; according to the three ranges of variation, the sub-basin unit is divided into a general ecological environment monitoring area, an important ecological environment monitoring area and an extremely important ecological environment monitoring area.

[0118] When the water ecological environment changes, it includes improvement and deterioration; when the water ecological environment changes significantly, it includes obvious improvement and obvious deterioration. The change of the water ecological environment is determined according to the water ecological environment change index.

[0119] In this embodiment, when the water ecological environment status change index of the sub-basin unit is less than -3, the water ecological environment status is significantly deteriorated, and the sub-basin unit needs to be divided into an extremely important ecological environment monitoring area; when the water ecological environment status change index of the sub-basin unit is less than or equal to -1, and greater than or equal to -3, the water ecological environment status is deteriorated, and the sub-basin unit needs to be divided into an important ecological environment monitoring area; when the water ecological environment status change index of the sub-basin unit is greater than -1, and less than or equal to 1, the water ecological environment status is stable, and the sub-basin unit needs to be divided into a general ecological environment monitoring area; when the water ecological environment status change index of the sub-basin unit is greater than 1, and less than or equal to 3, the water ecological environment status is improved, and the sub-basin unit needs to be divided into an important ecological environment monitoring area; when the water ecological environment status change index of the sub-basin unit is greater than 3, the water ecological environment status is significantly improved, and the sub-basin unit needs to be divided into an important ecological environment monitoring area. Of course, according to actual needs, the range of the water ecological environment change index can be adaptively adjusted to correspondingly adjust the water ecological environment status of the sub-basin unit.

[0120] In other embodiments, the number of categories of the water ecological environment status change index can be adaptively adjusted, for example, the water ecological environment status change index can be divided into four categories; after the number of categories of the water ecological environment status change index is adaptively adjusted, the way of dividing the amplitude of the water ecological environment comprehensive index can also be adaptively adjusted, for example, the amplitude of the water ecological environment comprehensive index can be divided into stable, slightly deteriorated, deteriorated and significantly deteriorated.

[0121] For step S155, when the division method of the water ecological environment status of the sub-basin unit is adjusted, the division method of the monitoring area of ​​the sub-basin unit can be adaptively adjusted.

[0122] For step S16, the geographical center of each monitoring area is selected as the demand point, and a multi-level demand point set is obtained according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to the monitoring areas of each level.

[0123] Among them, in this embodiment, the multi-level demand point set includes at least a first-level demand point, a second-level demand point and a third-level demand point, and the first-level demand point, the second-level demand point and the third-level demand point correspond to the geographical centers of the monitoring areas of the three different levels respectively. The geographical center of the extremely important ecological environment monitoring area is selected as the first-level demand point, the geographical center of the important ecological environment monitoring area is selected as the second-level demand point, and the geographical center of the general ecological environment monitoring area is selected as the third-level demand point.

[0124] This application extracts the geographic center of the sub-basin unit as the demand point, and abstractly represents the target sub-basin unit in the form of a point. In the location set coverage model (LSCP) used in this patent, as long as the drone airport facility point can cover the geographic center of the sub-basin, it means that the drone airport facility point can cover the demand point. The meaning of coverage means that there is a maximum coverage distance for any drone airport facility point. When the distance from the demand point to the drone airport facility point is within the standard distance, the candidate facility point can cover the demand point. Of course, when the division method of the monitoring area is adjusted, the division method of the multi-level demand points can be adaptively adjusted.

[0125] For step S17, field stations in each of the monitoring areas are selected as candidate facilities for drone airports to obtain a set of candidate facilities for drone airports.

[0126] Among them, in this embodiment, the field station is an important scientific and technological infrastructure, providing a large amount of direct, comprehensive and long-term positioning observation data of water, soil, air and living ecological elements of each ecosystem, and is a three-in-one field technology platform for long-term positioning monitoring of ecosystems, cross-regional and cross-disciplinary networked observation, experiment and research. The field station can be obtained by collecting the spatial distribution data of existing field stations in China, including the field stations of the Chinese Academy of Sciences, the field stations of the Academy of Forestry, the field stations of the Ministry of Agriculture and Rural Affairs, and the field stations of the Ministry of Education.

[0127] For step S18, the drone performance data, the drone airport candidate facility point set and the multi-level demand point set are input into a preset location set coverage model to obtain a facility set covering the demand points at each level and generate a drone deployment plan.

[0128] In this embodiment, the drone performance data includes the drone flight range, the drone endurance, and the drone transmission signal distance. Of course, the selection of the drone performance data can be adaptively adjusted according to actual needs. Figure 6 , Figure 6 A method flow chart for generating a drone deployment scheme for a drone deployment method for watershed water ecological environment monitoring in this application also includes the following steps:

[0129] S181: inputting the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into the preset location set coverage model, and obtaining the first drone airport facility point set N1, the second drone airport facility point set N2 and the third drone airport facility point set N3 whose drone flight ranges cover the first-level demand points, the second-level demand points and the third-level demand points respectively from the drone airport candidate facility point set;

[0130] S182: Select any drone airport facility point from the first drone airport facility point set N1 as a first-level drone airport facility point;

[0131] S183: From the second drone airport facility point set N2, select any drone airport facility point other than the union of the first drone airport facility point set N1 and the second drone airport facility point set N2 as a secondary drone airport facility point;

[0132] S184: From the third drone airport facility point set N3, select any drone airport facility point other than the union of the third drone airport facility point set N3 and the first drone airport facility point set N1, and other than the union of the third drone airport facility point set N3 and the second drone airport facility point set N2, as the third-level drone airport facility point.

[0133] Wherein, the expression of the location set coverage model (LSCP) is:

[0134]

[0135] Among them, I p is the set of p-th level demand points, J is the set of all candidate facility points; j is the jth candidate facility point; N is the set of all candidate facility points that can cover demand point i, d ij is the distance from the i-th demand point to the j-th facility point, and R is the flight radius of the drone.

[0136] The objective function (1) requires the minimum number of facilities to be built; the constraint (2) requires that each demand point be covered by at least one facility point, and the constraint (3) requires that the decision variable x j is a binary variable of 0,1. When the jth candidate facility point is selected, x j =1, otherwise, x j =0.

[0137] Example 2

[0138] The present application also provides a drone deployment system for watershed water ecological environment monitoring to implement the steps of the drone deployment method for watershed water ecological environment monitoring described in Example 1.

[0139] See also Figure 7 , Figure 7 This is a schematic diagram of a drone deployment system for watershed water ecological environment monitoring in this application. The drone deployment system for watershed water ecological environment monitoring includes: a sub-basin unit division module 11, a water ecological environment evaluation index system construction module 12, an index calculation module 13, a water ecological environment comprehensive index calculation module 14, a monitoring area division module 15, a multi-level demand point set acquisition module 16, a drone airport candidate facility point collection module 17 and a drone deployment module 18.

[0140] Wherein, the sub-basin unit division module 11 is used to perform hydrological analysis on the target area based on DEM data and divide the sub-basin units;

[0141] The water ecological environment evaluation index system construction module 12 is used to construct a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index;

[0142] The index calculation module 13 is used to calculate the corresponding water ecological index, water environment index and ecological stress index for the divided sub-basin units;

[0143] The water ecological environment comprehensive index calculation module 14 is used to multiply the water ecological index by the weight of the preset water ecological index layer, the water environment index by the weight of the preset water environment index layer, and the ecological stress index by the weight of the preset ecological stress index layer, and add them together to obtain the water ecological environment comprehensive index;

[0144] The monitoring area division module 15 is used to divide the sub-basin unit into monitoring areas of several levels according to the change range of the comprehensive index of water ecological environment in different periods;

[0145] The multi-level demand point set acquisition module 16 is used to select the geographical center of each monitoring area as a demand point, and obtain a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to the monitoring areas of each level;

[0146] The drone airport candidate facility point collection module 17 is used to select the field stations in each monitoring area as drone airport candidate facility points to obtain a drone airport candidate facility point collection;

[0147] The drone deployment module 18 is used to input the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into a preset location set coverage model, obtain a facility set covering the demand points at each level, and generate a drone deployment plan.

[0148] It should be noted that the data obtained when implementing a drone deployment method for watershed water ecological environment monitoring in a drone deployment system for watershed water ecological environment monitoring provided by this application are stored one-to-one in the storage of this system. When relevant calculations are required, the data required for the calculation can be directly obtained from the storage.

[0149] It should also be noted that the above-mentioned embodiment provides a drone deployment system for watershed water ecological environment monitoring. When implementing a drone deployment method for watershed water ecological environment monitoring, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the above-mentioned embodiment provides a drone deployment system for watershed water ecological environment monitoring and the drone deployment method for watershed water ecological environment monitoring in Example 1 belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.

[0150] The present application is not limited to the above-mentioned implementation modes. If various changes or modifications to the present application do not depart from the spirit and scope of the present application, and if these changes and modifications fall within the claims of the present application and the scope of equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for deploying unmanned aerial vehicles for watershed water ecological environment monitoring, characterized in that: The following steps are involved: Conduct hydrological analysis of the target area based on DEM data and divide the sub-basin units; Constructing a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index; Calculating corresponding water ecological indicators, water environment indicators and ecological stress indicators for the divided sub-watershed units; The water ecological environment comprehensive index is obtained by multiplying the water ecological index by the weight of the preset water ecological index layer, the water environment index by the weight of the preset water environment index layer, and the ecological stress index by the weight of the preset ecological stress index layer; According to the variation range of the water ecological environment comprehensive index in different periods, the sub-basin unit is divided into monitoring areas of several levels, including: obtaining the water ecological environment comprehensive index before a preset measurement period; Obtain the comprehensive index of water ecological environment after a preset measurement period; Subtract the comprehensive water ecological environment index before the preset measurement period from the comprehensive water ecological environment index after the preset measurement period to obtain the water ecological environment status change index of the sub-basin unit during the measurement period; According to the water ecological environment status change index, the water ecological environment status of the sub-basin unit is divided into at least three change ranges using the attribute classification tool of the hydrological analysis software; According to at least three ranges of variation of the water ecological environment conditions of the sub-basin unit, the sub-basin unit is divided into at least three levels of monitoring areas; Selecting the geographic center of each monitoring area as a demand point, respectively, and obtaining a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to monitoring areas of each level, respectively, wherein the multi-level demand point set includes at least a first-level demand point, a second-level demand point and a third-level demand point, and the first-level demand point, the second-level demand point and the third-level demand point correspond to the geographic centers of the monitoring areas of the three different levels, respectively; Selecting the field stations in each monitoring area as candidate facilities for drone airports to obtain a set of candidate facilities for drone airports; The drone performance data, the drone airport candidate facility point set and the multi-level demand point set are input into a preset location set coverage model to obtain a facility set covering the demand points at each level, and a drone deployment plan is generated, including the following steps: the drone performance data, the drone airport candidate facility point set and the multi-level demand point set are input into the preset location set coverage model, and a first drone airport facility point set, a second drone airport facility point set and a third drone airport facility point set whose drone flight ranges cover the first-level demand points, the second-level demand points and the third-level demand points respectively are obtained from the drone airport candidate facility point set. The expression of the location set coverage model (LSCP) is: Among them, I p is the set of p-th level demand points, J is the set of all candidate facility points; j is the jth candidate facility point; N i is the set of all candidate facilities that can cover demand point i, d ij is the distance from the i-th demand point to the j-th facility point, and R is the flight radius of the drone; Select any drone airport facility point from the first drone airport facility point set as a first-level drone airport facility point; From the second drone airport facility point set, select any drone airport facility point other than the union of the first drone airport facility point set and the second drone airport facility point set as a secondary drone airport facility point; From the third UAV airport facility point set, any UAV airport facility point other than the union of the third UAV airport facility point set and the first UAV airport facility point set, and any UAV airport facility point other than the union of the third UAV airport facility point set and the second UAV airport facility point set is selected as the third-level UAV airport facility point.

2. The method for deploying unmanned aerial vehicles for watershed water ecological environment monitoring according to claim 1 is characterized in that: The hydrological analysis of the target area based on the DEM data and the division of the sub-basin units also include the following steps: Use the depression-filling tool of the hydrological analysis software to fill the DEM data to obtain a depression-free DEM; Use the flow direction tool of the hydrological analysis software to obtain the water flow direction data of each grid of the non-depression DEM by using the maximum slope drop method on the non-depression DEM; According to the water flow direction data of each grid of the depression-free DEM, the flow tool of the hydrological analysis software is used to calculate the number of confluence grids of the depression-free DEM, and the number of confluence grids is the cumulative confluence amount; Based on a preset minimum cumulative runoff, using a grid calculator of the hydrological analysis software, extracting target river network grid data from grids whose confluence accumulation is greater than the preset minimum cumulative runoff; Use the river linking tool of the hydrological analysis software to link the target river network grid data into a target river network; The watershed tool of the hydrological analysis software is used to divide the target river network into sub-basin units.

3. The method for deploying unmanned aerial vehicles for watershed water ecological environment monitoring according to claim 1 is characterized in that: The water ecological index at least includes the vegetation coverage index, habitat quality index and water network density index; the water environment index at least includes the water quality index and water eutrophication index; the ecological stress index at least includes the proportion of cultivated land area, the proportion of construction land area, population density and GDP; The step of calculating the corresponding water ecological index, water environment index and ecological stress index for the divided sub-basin units includes: The vegetation cover index is calculated as follows: Among them, the A veg is the normalized coefficient of vegetation coverage; where P i is the gray value of the pixel in the NDVI image, and n is the number of pixels in the vegetation coverage study area in the sub-basin unit; The habitat quality index is calculated as follows: Among them, the A bio is the normalized coefficient of the habitat quality index, and the area is the sub-basin unit; The water network density index is calculated as follows: Water network density index = (A riv × River length / region area + A lak × water area / region area + A res × water resources / regional area) / 3, where A riv is the normalization coefficient of river length, A lak is the normalized coefficient of water area, A res is the normalization coefficient of water resources, and the area is the sub-basin unit; The water quality index is obtained by calculating total phosphorus, total nitrogen, dissolved oxygen, ammonia nitrogen and chemical oxygen demand using the Nemerow comprehensive pollution index method; The water nutrient index is obtained by calculating chlorophyll a, total phosphorus, total nitrogen, transparency and permanganate index using a comprehensive nutrient status index method; The cultivated land area ratio is calculated as follows: cultivated land area ratio = regional cultivated land area / regional total area, where the region is the sub-basin unit; The construction land area ratio is calculated as follows: Construction land area ratio = construction land area of ​​the region / total area of ​​the region, where the region is the sub-basin unit; The population density is calculated in the following manner: population density = total population of a region / area of ​​a region, where the region is the sub-basin unit.

4. The method for deploying unmanned aerial vehicles for watershed water ecological environment monitoring according to claim 3 is characterized in that: Calculating the corresponding water ecological index, water environment index and ecological stress index for the divided sub-basin units also includes the following steps: The water ecological index is obtained by multiplying the vegetation coverage index by the relative weight of the vegetation coverage index, the habitat quality index by the relative weight of the habitat quality index, and the water network density index by the relative weight of the water network density index; The water quality index is multiplied by the relative weight of the water quality index and the water eutrophication index is multiplied by the relative weight of the water eutrophication index, and then the water environment index is obtained by adding them together; The ecological stress index is obtained by multiplying the cultivated land area ratio by the relative weight of the cultivated land area ratio, multiplying the construction land area ratio by the relative weight of the construction land area, multiplying the population density by the relative weight of the population density, and multiplying the GDP by the relative weight of the GDP.

5. The method for deploying unmanned aerial vehicles for monitoring water ecological environment in a river basin according to claim 4, characterized in that: Determining the weights of the preset water ecological index layer, the preset water environment index layer and the preset ecological stress index layer, and the relative weights of the vegetation coverage index, the habitat quality index, the water network density index, the water quality index, the water eutrophication index, the construction land area, the population density and the GDP, further comprises the following steps: Constructing a hierarchical structure, the hierarchical structure at least includes a target layer, a criterion layer and a program layer; the constituent factors of the target layer at least include the comprehensive index of water ecological environment, the constituent factors of the criterion layer at least include the water ecological index, water environment index and ecological stress index, and the constituent factors of the program layer at least include the vegetation coverage index, habitat quality index, water network density index, water quality index, water eutrophication index, cultivated land area ratio, construction land area ratio, population density and GDP; Using the 1-9 scaling method to determine the scaling value of each component under the criterion layer for the target layer, and constructing a judgment matrix; After performing hierarchical single sorting on the judgment matrix, the maximum eigenvalue of the judgment matrix is ​​obtained; Subtracting the order of the judgment matrix from the maximum eigenvalue of the judgment matrix and dividing the result by the order of the judgment matrix-1 to obtain a consistency index value of the judgment matrix; Obtaining the random consistency index standard value corresponding to the order of the judgment matrix according to the AHP average random consistency index standard value table, and dividing the consistency index value by the random consistency index standard value to obtain the random consistency ratio of the judgment matrix; When the random consistency ratio is less than or equal to 0.1, the hierarchical structure is sorted in order to obtain the weights of the various components under the solution layer; When the random consistency ratio is greater than 0.1, the 1-9 scaling method is used to modify the scaling values ​​of each component under the criterion layer for the target layer, and the judgment matrix is ​​reconstructed.

6. The method for deploying unmanned aerial vehicles for watershed water ecological environment monitoring according to claim 1 is characterized in that: The water ecological indicators, water environment indicators and ecological stress indicators of the sub-basin units are standardized using the extreme value standard method.

7. A system for deploying drones for watershed water ecological environment monitoring, characterized in that: include Sub-basin unit division module, used to conduct hydrological analysis on the target area based on DEM data and divide sub-basin units; A water ecological environment evaluation index system construction module is used to construct a water ecological environment evaluation index system, wherein the water ecological environment evaluation index at least includes water ecological index, water environment index and ecological stress index; An index calculation module, used for calculating the corresponding water ecological index, water environment index and ecological stress index for the divided sub-basin units; A water ecological environment comprehensive index calculation module, used to multiply the water ecological index by the weight of a preset water ecological index layer, the water environment index by the weight of a preset water environment index layer, and the ecological stress index by the weight of a preset ecological stress index layer, and add them together to obtain a water ecological environment comprehensive index; The monitoring area division module is used to divide the sub-basin unit into several levels of monitoring areas according to the change range of the comprehensive index of water ecological environment in different periods, including: obtaining the comprehensive index of water ecological environment before a preset measurement period; Obtain the comprehensive index of water ecological environment after a preset measurement period; Subtract the comprehensive water ecological environment index before the preset measurement period from the comprehensive water ecological environment index after the preset measurement period to obtain the water ecological environment status change index of the sub-basin unit during the measurement period; According to the water ecological environment status change index, the water ecological environment status of the sub-basin unit is divided into at least three change ranges using the attribute classification tool of the hydrological analysis software; According to at least three ranges of variation of the water ecological environment conditions of the sub-basin unit, the sub-basin unit is divided into at least three levels of monitoring areas; A multi-level demand point set acquisition module is used to select the geographic center of each monitoring area as a demand point, and obtain a multi-level demand point set according to the level of each monitoring area, wherein the multi-level demand point set includes demand points of several levels corresponding to the monitoring areas of each level, wherein the multi-level demand point set includes at least a first-level demand point, a second-level demand point and a third-level demand point, and the first-level demand point, the second-level demand point and the third-level demand point correspond to the geographic centers of the monitoring areas of the three different levels respectively; A drone airport candidate facility point set module is used to select field stations in each monitoring area as drone airport candidate facility points to obtain a drone airport candidate facility point set; The drone deployment module is used to input the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into a preset location set coverage model to obtain a facility set covering the demand points of each level and generate a drone deployment plan, including the following steps: inputting the drone performance data, the drone airport candidate facility point set and the multi-level demand point set into the preset location set coverage model, obtaining the first drone airport facility point set, the second drone airport facility point set and the third drone airport facility point set whose drone flight ranges cover the first-level demand points, the second-level demand points and the third-level demand points respectively from the drone airport candidate facility point set, and the expression of the location set coverage model (LSCP) is: Among them, I p is the set of p-th level demand points, J is the set of all candidate facility points; j is the jth candidate facility point; N i is the set of all candidate facilities that can cover demand point i, d ij is the distance from the i-th demand point to the j-th facility point, and R is the flight radius of the drone; Select any drone airport facility point from the first drone airport facility point set as a first-level drone airport facility point; From the second drone airport facility point set, select any drone airport facility point other than the union of the first drone airport facility point set and the second drone airport facility point set as a secondary drone airport facility point; From the third UAV airport facility point set, any UAV airport facility point other than the union of the third UAV airport facility point set and the first UAV airport facility point set, and any UAV airport facility point other than the union of the third UAV airport facility point set and the second UAV airport facility point set is selected as the third-level UAV airport facility point.

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