Basin ecological risk monitoring method and device, electronic equipment and storage medium

Abstract

The invention relates to a drainage basin ecological risk monitoring method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining multi-source heterogeneous data and ecological risk detection data of a target drainage basin; dividing the target drainage basin into a plurality of hydrological response units; initializing the weight of each ecological risk detection index; determining the neighborhood radius and the minimum neighborhood sample number of each ecological risk detection index; clustering the first ecological risk detection data corresponding to each ecological risk detection index to obtain second ecological risk detection data; correcting the initial weight of each ecological risk detection index according to the second ecological risk detection data to obtain the target weight of each ecological risk detection index; according to the target weight and the first ecological risk detection data, the ecological risk index of each hydrological response unit of the target drainage basin is obtained, and the precision of ecological risk monitoring of each hydrological response unit of the target drainage basin is improved.

Description

Technical Field

[0001] This application relates to the technical field of ecological risk monitoring, and particularly to a method, device, electronic device, and storage medium for monitoring the ecological risk of a river basin. Background Art

[0002] As a complex integrated system connecting natural ecology and social economy, the health status of a river basin is directly related to water resource security, biodiversity maintenance, and the exertion of ecological service functions. By systematically evaluating the ecological risk of a river basin and identifying ecologically fragile and sensitive areas, it can provide a scientific basis for formulating differential management measures for the river basin.

[0003] In related technologies, the ecological risk monitoring method of a river basin mostly uses the fixed weight method to determine the weight of each ecological risk detection index.

[0004] However, assigning a fixed weight to each ecological risk detection index ignores the dynamics and spatial heterogeneity of the river basin ecosystem, which will lead to low accuracy in monitoring the ecological risk of the river basin. Summary of the Invention

[0005] Based on this, the purpose of this application is to provide a method, device, electronic device, and storage medium for monitoring the ecological risk of a river basin, which can improve the accuracy of monitoring the ecological risk of each hydrological response unit in the river basin.

[0006] According to the first aspect of the embodiments of this application, a method for monitoring the ecological risk of a river basin is provided, including the following steps: Obtain multi-source heterogeneous data and ecological risk detection data of the target river basin; the ecological risk detection data includes a number of ecological risk detection indexes and the index values of each ecological risk detection index; According to the multi-source heterogeneous data, divide the target river basin into a number of hydrological response units; standardize the index values of each ecological risk detection index in each hydrological response unit to obtain the first ecological risk detection data of each hydrological response unit; Initialize the weight of each ecological risk detection index to obtain the initial weight of each ecological risk detection index; Determine the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit; determine the minimum neighborhood sample number of each ecological risk detection index according to the pollution source density of each hydrological response unit; Cluster the first ecological risk detection data corresponding to each ecological risk detection index according to the neighborhood radius of each ecological risk detection index, the minimum number of neighborhood samples of each ecological risk detection index, and a preset density clustering method, to obtain the second ecological risk detection data of each ecological risk detection index; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data. Correct the initial weight of each ecological risk detection index according to the second ecological risk detection data to obtain the target weight of each ecological risk detection index; obtain the ecological risk index of each hydrological response unit in the target basin according to the target weight of each ecological risk detection index and the first ecological risk detection data of each hydrological response unit.

[0007] According to the second aspect of the embodiments of the present application, a basin ecological risk monitoring device is provided, including: A data acquisition module, configured to acquire multi-source heterogeneous data and ecological risk detection data of a target basin; the ecological risk detection data includes a plurality of ecological risk detection indexes and the index values of each ecological risk detection index. A data standardization module, configured to divide the target basin into a plurality of hydrological response units according to the multi-source heterogeneous data; perform standardization processing on the index values of each ecological risk detection index in each hydrological response unit to obtain the first ecological risk detection data of each hydrological response unit. A weight initialization module, configured to initialize the weight of each ecological risk detection index to obtain the initial weight of each ecological risk detection index. A neighborhood radius determination module, configured to determine the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit; determine the minimum number of neighborhood samples of each ecological risk detection index according to the pollution source density of each hydrological response unit. A data clustering module, configured to cluster the first ecological risk detection data corresponding to each ecological risk detection index according to the neighborhood radius of each ecological risk detection index, the minimum number of neighborhood samples of each ecological risk detection index, and a preset density clustering method, to obtain the second ecological risk detection data of each ecological risk detection index; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data. An ecological risk index acquisition module, configured to correct the initial weight of each ecological risk detection index according to the second ecological risk detection data to obtain the target weight of each ecological risk detection index; obtain the ecological risk index of each hydrological response unit in the target basin according to the target weight of each ecological risk detection index and the first ecological risk detection data of each hydrological response unit.

[0008] According to the third aspect of the embodiments of the present application, the embodiments of the present application provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method according to the first aspect are implemented.

[0009] According to the fourth aspect of the embodiments of the present application, the embodiments of the present application provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0010] The embodiments of the present application obtain multi-source heterogeneous data and ecological risk detection data of a target basin; the ecological risk detection data includes a number of ecological risk detection indicators and the indicator values of each ecological risk detection indicator; according to the multi-source heterogeneous data, the target basin is divided into a number of hydrological response units; the indicator values of each ecological risk detection indicator in each hydrological response unit are standardized to obtain the first ecological risk detection data of each hydrological response unit; the weights of each ecological risk detection indicator are initialized to obtain the initial weights of each ecological risk detection indicator; according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit, the neighborhood radius of each ecological risk detection indicator is determined; according to the pollution source density of each hydrological response unit, the minimum neighborhood sample number of each ecological risk detection indicator is determined; according to the neighborhood radius of each ecological risk detection indicator, the minimum neighborhood sample number of each ecological risk detection indicator, and a preset density clustering method, the first ecological risk detection data corresponding to each ecological risk detection indicator is clustered to obtain the second ecological risk detection data of each ecological risk detection indicator; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data; according to the second ecological risk detection data, the initial weights of each ecological risk detection indicator are corrected to obtain the target weights of each ecological risk detection indicator; according to the target weights of each ecological risk detection indicator and the first ecological risk detection data of each hydrological response unit, the ecological risk index of each hydrological response unit in the target basin is obtained. The present application determines the neighborhood radius of each ecological risk detection indicator and the minimum neighborhood sample number of each ecological risk detection indicator, clusters the first ecological risk detection data corresponding to each ecological risk detection indicator, identifies the second ecological risk detection data, and corrects the initial weights of the corresponding ecological risk detection indicators according to the second ecological risk detection data, improving the accuracy of the weights of the ecological risk detection indicators, thereby improving the accuracy of the ecological risk index of each hydrological response unit in the target basin, and further improving the accuracy of the ecological risk monitoring of each hydrological response unit in the target basin.

[0011] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application.

[0012] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Description of the Drawings

[0013] Figure 1 A flowchart of the watershed ecological risk monitoring method provided for an embodiment of this application; Figure 2 A structural block diagram of the watershed ecological risk monitoring device provided for an embodiment of this application; Figure 3 A structural schematic block diagram of the electronic device provided for an embodiment of this application. Detailed Embodiments

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0015] It should be clear that the described embodiments are only some embodiments of this application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0016] The terms used in the embodiments of this application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of this application. The singular forms "a", "the", and "said" used in the embodiments of 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.

[0017] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0018] In addition, in the description of this application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0019] Please refer to Figure 1 , which is a schematic flow chart of the watershed ecological risk monitoring method provided by an embodiment of this application. The watershed ecological risk monitoring method provided by the embodiment of this application includes the following steps: S10: Obtain multi-source heterogeneous data and ecological risk detection data of the target watershed; the ecological risk detection data includes a number of ecological risk detection indicators and the indicator values of each ecological risk detection indicator.

[0020] Among them, the target watershed is the watershed to be monitored for ecological risks.

[0021] Among them, the multi-source heterogeneous data includes, but is not limited to, land use change data, hydrological and water quality data, pollution source data, social and economic data, and environmental protection data.

[0022] In the embodiment of this application, remote sensing satellite technology and geographic information system technology are used to comprehensively obtain multi-dimensional heterogeneous data such as hydrometeorology, land use, and vegetation cover of the target watershed.

[0023] Based on the DPSIR (Driving force - Pressure - State - Impact - Response) model framework, a watershed ecological risk assessment system containing 5 levels and 18 core indicators is constructed, and the data sources cover ground monitoring stations, remote sensing inversion data, statistical yearbooks, and public data of government departments, etc.

[0024] Specifically, the ecological risk detection indicators include 5 levels: driving force indicators, pressure indicators, state indicators, impact indicators, and response indicators. Among them, the driving force indicators include social and economic driving factors such as population growth rate, GDP growth rate, urbanization rate, and industrial development level. The pressure indicators include human activity pressures such as industrial wastewater discharge, agricultural non-point source pollution load, soil erosion intensity, and biodiversity threat index. The state indicators include environmental state parameters such as water quality comprehensive index (COD, ammonia nitrogen, total phosphorus, etc.), vegetation coverage, soil erosion modulus, and ecological service function value. The impact indicators include ecological impact degrees such as ecological vulnerability index, biological integrity index, and biodiversity index. The response indicators include management response measures such as the proportion of environmental protection investment, sewage treatment rate, and the implemented area of ecological restoration projects.

[0025] S20: Divide the target basin into several hydrological response units according to multi-source heterogeneous data; standardize the index values of each ecological risk detection index in each hydrological response unit to obtain the first ecological risk detection data of each hydrological response unit.

[0026] Among them, a hydrological response unit (HRU) refers to an area with relatively uniform characteristics such as land use, soil type, and terrain conditions.

[0027] In the embodiment of the present application, multi-source heterogeneous data can be input into a distributed hydrological model to divide the target basin into several hydrological response units. Among them, the distributed hydrological model can be a SWAT (Soil and Water Assessment Tool) model.

[0028] To ensure the comparability of the index values of different ecological risk indicators, standardize the index values of each ecological risk detection index in each hydrological response unit. Among them, the standardization method can be the range standardization method or the Z-score standardization method.

[0029] S30: Initialize the weights of each ecological risk detection index to obtain the initial weights of each ecological risk detection index.

[0030] In the embodiment of the present application, the expert questionnaire method and relevant literature materials can be used to initialize the weights of each ecological risk detection index to obtain the initial weights of each ecological risk detection index. Among them, the sum of the initial weights of all ecological risk detection indexes is 1.

[0031] S40: Determine the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit; determine the minimum neighborhood sample number of each ecological risk detection index according to the pollution source density of each hydrological response unit.

[0032] Among them, the spatial location information of the hydrological response unit can be the geometric center location of the hydrological response unit.

[0033] Among them, the neighborhood radius is used to define the neighborhood range of data points, and the minimum neighborhood sample number is used to determine whether a data point belongs to a high-density area (cluster). Specifically, when the number of data points within the neighborhood range of a data point is greater than or equal to the minimum neighborhood sample number, it is determined that the data point belongs to a high-density area.

[0034] In the embodiments of the present application, each hydrological response unit includes the index values of multiple ecological risk detection indicators. In order to identify abnormal index values, it is necessary to determine the neighborhood radius and the minimum number of neighborhood samples of each ecological risk detection indicator.

[0035] S50: According to the neighborhood radius of each ecological risk detection indicator, the minimum number of neighborhood samples of each ecological risk detection indicator, and a preset density clustering method, cluster the first ecological risk detection data corresponding to each ecological risk detection indicator to obtain the second ecological risk detection data of each ecological risk detection indicator; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data.

[0036] Among them, the preset density clustering method is DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density-based noise-resistant clustering method). The algorithm parameters of DBSCAN include the neighborhood radius and the minimum number of neighborhood samples, which are used to divide the points in the dataset into several clustering clusters and can identify noise points. For the specific clustering process, reference can be made to the prior art and will not be elaborated here.

[0037] In the embodiments of the present application, set the neighborhood radius of each ecological risk detection indicator and the minimum number of neighborhood samples of each ecological risk detection indicator as the parameter values of the preset density clustering method, use the first ecological risk detection data corresponding to each ecological risk detection indicator as the input data of the preset density clustering method, and cluster the first ecological risk detection data corresponding to each ecological risk detection indicator to identify the second ecological risk detection data.

[0038] S60: According to the second ecological risk detection data, correct the initial weight of each ecological risk detection indicator to obtain the target weight of each ecological risk detection indicator; according to the target weight and the first ecological risk detection data, obtain the ecological risk index of each hydrological response unit in the target basin.

[0039] Among them, the ecological risk index is used to indicate the risk level of ecological risk monitoring.

[0040] In the embodiments of the present application, when a certain first ecological risk detection data of a hydrological response unit is abnormal, correct the initial weight of the ecological risk detection indicator corresponding to the first ecological risk detection data to obtain the target weight of the ecological risk detection indicator. When a certain first ecological risk detection data of a hydrological response unit is not abnormal, use the initial weight of the ecological risk detection indicator corresponding to the first ecological risk detection data as the target weight of the ecological risk detection indicator.

[0041] According to the target weights of each ecological risk detection index, the first ecological risk detection data of each hydrological response unit is weighted and summed to obtain the ecological risk index of each hydrological response unit in the target basin.

[0042] By comparing the ecological risk index of each hydrological response unit with the preset risk threshold, the risk level of each hydrological response unit can be determined. Specifically, when the ecological risk index of a hydrological response unit is greater than or equal to the first preset risk threshold, it is determined that the hydrological response unit is at a high risk level. When the ecological risk index of a hydrological response unit is greater than or equal to the second preset risk threshold and less than the first preset risk threshold, it is determined that the hydrological response unit is at a medium risk level. When the ecological risk index of a hydrological response unit is less than the second preset risk threshold, it is determined that the hydrological response unit is at a low risk level. Among them, the first preset risk threshold is greater than the second preset risk threshold. For example, the first preset risk threshold is 0.75 and the second preset risk threshold is 0.25.

[0043] Applying the embodiments of the present application, by obtaining multi-source heterogeneous data and ecological risk detection data of the target basin; the ecological risk detection data includes a number of ecological risk detection indicators and the indicator values of each ecological risk detection indicator; according to the multi-source heterogeneous data, the target basin is divided into a number of hydrological response units; the indicator values of each ecological risk detection indicator in each hydrological response unit are standardized to obtain the first ecological risk detection data of each hydrological response unit; the weights of each ecological risk detection indicator are initialized to obtain the initial weights of each ecological risk detection indicator; according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit, the neighborhood radius of each ecological risk detection indicator is determined; according to the pollution source density of each hydrological response unit, the minimum neighborhood sample number of each ecological risk detection indicator is determined; according to the neighborhood radius of each ecological risk detection indicator, the minimum neighborhood sample number of each ecological risk detection indicator, and a preset density clustering method, the first ecological risk detection data corresponding to each ecological risk detection indicator is clustered to obtain the second ecological risk detection data of each ecological risk detection indicator; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data; according to the second ecological risk detection data, the initial weights of each ecological risk detection indicator are corrected to obtain the target weights of each ecological risk detection indicator; according to the target weights of each ecological risk detection indicator and the first ecological risk detection data of each hydrological response unit, the ecological risk indices of each hydrological response unit in the target basin are obtained. The present application determines the neighborhood radius of each ecological risk detection indicator and the minimum neighborhood sample number of each ecological risk detection indicator, clusters the first ecological risk detection data corresponding to each ecological risk detection indicator, identifies the second ecological risk detection data, and corrects the initial weights of the corresponding ecological risk detection indicators according to the second ecological risk detection data, improving the accuracy of the weights of the ecological risk detection indicators, thereby improving the accuracy of the ecological risk indices of each hydrological response unit in the target basin, and further improving the accuracy of the ecological risk monitoring of each hydrological response unit in the target basin.

[0044] In one embodiment, the multi-source heterogeneous data includes digital elevation model data and land use classification data. The step of dividing the target basin into a number of hydrological response units according to the multi-source heterogeneous data in step S20 includes steps S201 to S206, which are specifically as follows: S201: Use the depression filling tool of the hydrological analysis software to perform depression filling on the digital elevation model data to obtain the digital elevation model data without depressions.

[0045] In the embodiments of the present application, due to topographical reasons, there may be sunken areas in the digital elevation model data, and unreasonable or incorrect water flow direction data may be obtained in the sunken areas. Therefore, before calculating the water flow direction data, the digital elevation model data needs to be filled to obtain digital elevation model data without depressions.

[0046] S202: Use the flow direction tool of the hydrological analysis software to obtain the water flow direction data of each grid of the digital elevation model data without depressions on the digital elevation model data without depressions by using the maximum slope method. Among them, the maximum slope method is a topographical analysis method used to calculate the flow accumulation matrix. The flow accumulation matrix refers to the cumulative value of the water volume flowing into a specific point from different positions in a region. By calculating the flow accumulation matrix, information such as the path of the water flow, the convergence area of the water flow, and the direction of the water flow can be determined.

[0047] In the embodiments of the present application, the elevation difference of each grid is obtained through the digital elevation model data. According to the elevation difference, the slope is determined. The direction of the water flow is determined according to the magnitude of the slope. In the maximum slope method, the basic principle of calculating the water flow direction data is that water flows to lower places, and the flow direction is limited to flow along the direction of the maximum slope, that is, the greater the slope, the easier the water flow is to flow to lower places. S203: According to the water flow direction data of each grid, use the flow tool of the hydrological analysis software to calculate the flow accumulation of the digital elevation model data without depressions.

[0048] Among them, in the process of surface runoff simulation, the flow accumulation is calculated based on the water flow direction data. Specifically, there is one unit of water volume at each point of the digital ground elevation model represented by grids. According to the natural law that water flows from high places to low places, the water volume flowing through each point is calculated according to the water flow direction data of the regional topography to obtain the flow accumulation of the region.

[0049] In the embodiments of the present application, the water flow direction data of each grid is input into the flow tool of the hydrological analysis software to obtain the flow accumulation of the digital elevation model data without depressions.

[0050] S204: Based on a preset minimum cumulative runoff, use the grid calculator of the hydrological analysis software to extract the water system network grid data from the grids where the flow accumulation is greater than the preset minimum cumulative runoff.

[0051] Among them, the minimum cumulative runoff determines the range of dividing the catchment unit of the target basin.

[0052] In the embodiment of the present application, the confluence accumulation of each grid is compared with a preset minimum cumulative runoff to determine the grids where the confluence accumulation is greater than the preset minimum cumulative runoff. Using the grid calculator of hydrological analysis software, these grids are calculated to obtain the grid data of the water system network.

[0053] S205: Extract the watershed line from the grid data of the water system network, and use the watershed line as the initial boundary constraint of the hydrological response unit.

[0054] In the embodiment of the present application, the gradient of the grid data of the water system network is calculated to identify the catchment area. The water injection process is simulated. When two catchment areas merge, a water retaining dam is built at the watershed, and the watershed line is output as the initial boundary constraint of the hydrological response unit.

[0055] S206: Using the land use classification data, calculate the coupling weight of each grid to construct a weight surface; perform spatial clustering on the weight surface to obtain a spatial clustering result; according to the spatial clustering result and the initial boundary constraint of the hydrological response unit, obtain several hydrological response units.

[0056] Among them, the land use classification data includes the terrain feature intensity and the land use sensitivity index. Among them, the terrain feature intensity refers to the spatial variation degree or complexity of the surface morphology (such as altitude, slope, aspect, terrain undulation degree, etc.). The land use sensitivity index (Land Use Sensitivity Index, abbreviated as LUSI) refers to the response degree of the land use method to the change of natural conditions or the interference of human activities.

[0057] In the embodiment of the present application, the calculation formula of the coupling weight is as follows:

[0058] Among them, represents the terrain feature intensity, represents the land use sensitivity index, which is determined based on the CN value (runoff curve number) in the SWAT model. represents the normalization coefficient.

[0059] In the embodiment of the present application, by constructing a weight surface, the accuracy of hydrological response unit identification can be improved.

[0060] In one embodiment, the step of standardizing the index values of each ecological risk detection index in each hydrological response unit in step S20 to obtain the first ecological risk detection data of each hydrological response unit includes steps S21~S22, specifically as follows: S21: Determine the index values of the ecological risk detection indices for several pixels corresponding to each hydrological response unit; based on the index values of the ecological risk detection indices of each pixel, obtain the average value and standard deviation of each ecological risk detection index. S22: Subtract the value of each ecological risk detection index from the average value to obtain a difference; divide the difference by the standard deviation to obtain the first ecological risk detection data after standardization for each hydrological response unit.

[0061] Among them, when collecting the ecological risk detection data of the target basin, it is obtained by using remote sensing image technology, and the minimum unit of data collection is a pixel. Therefore, the ecological risk detection data of each hydrological response unit is a certain number of pixel data, and each hydrological response unit corresponds to a certain number of pixels.

[0062] In the embodiment of the present application, the Z-score standardization method is used to standardize the ecological risk detection data of each hydrological response unit. Among them, the expression of the first ecological risk detection data obtained after standardization is:

[0063] Among them, represents the index value of the i-th ecological risk detection index for the j-th pixel within the hydrological response unit, represents the index value of the i-th ecological risk detection index for the j-th pixel within the hydrological response unit obtained after standard deviation processing, and respectively represent the average value and standard deviation of the index values of the i-th ecological risk detection index.

[0064] After standardizing the index values of each pixel within the hydrological response unit, for the same ecological risk detection index, average the index values obtained after standardization of all pixels within the hydrological response unit to obtain the first ecological risk detection data of the hydrological response unit.

[0065] In one embodiment, the step of determining the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit in step S40 includes S401 to S403, specifically as follows: S401: According to the spatial location information of each hydrological response unit, determine the spatial distance between each hydrological response unit; compare the spatial distances between each hydrological response unit to determine the maximum spatial distance. S402: According to the first ecological risk detection data of each hydrological response unit, determine the difference between the first ecological risk detection data corresponding to the same ecological risk detection index of each hydrological response unit. S403: Determine the neighborhood radius of each ecological risk detection indicator based on the difference between the first ecological risk detection data corresponding to the same ecological risk detection indicator of each hydrological response unit, the spatial distance between each hydrological response unit, and the maximum spatial distance.

[0066] In the embodiments of the present application, the calculation process of the neighborhood radius of each ecological risk detection indicator is as follows:

[0067] Wherein, represents the comprehensive measure of the distance and index value between the i-th hydrological response unit and the j-th hydrological response unit, represents the spatial distance between the i-th hydrological response unit and the j-th hydrological response unit, represents the maximum spatial distance. For the same ecological risk detection indicator, is the first ecological risk detection data of the i-th hydrological response unit, is the first ecological risk detection data of the j-th hydrological response unit, represents a coefficient.

[0068] Traverse each hydrological response unit, sort the comprehensive measures of the distance and index value between the current hydrological response unit and the remaining other hydrological response units in ascending order, and determine the comprehensive measure of the distance and index value ranked at the first preset position. Sort the comprehensive measures of the distance and index value ranked at the first preset position corresponding to each hydrological response unit in ascending order, and use the comprehensive measure of the distance and index value ranked at the second preset position as the neighborhood radius of this ecological risk detection indicator. Among them, the first preset position and the second preset position can be set according to actual needs.

[0069] In one embodiment, the step of determining the minimum neighborhood sample quantity of each ecological risk detection indicator according to the pollution source density of each hydrological response unit in step S40 includes S41 to S44, which are specifically as follows: S41: Compare the pollution source density of each hydrological response unit to determine the maximum pollution source density.

[0070] In the embodiments of the present application, the calculation formula of the pollution source density is as follows:

[0071] Wherein, n represents the number of pollution sources in the hydrological response unit, represents the Gaussian kernel density function, h represents the bandwidth, which is used to control the width of the Gaussian kernel density function, that is, the "influence range" around each data point, (x, y) represents the central coordinates of the hydrological response unit, represents the spatial coordinates of the i-th pollution source, Indicates the Euclidean distance.

[0072] S42: Obtain the normalized pollution source density of each hydrological response unit according to the pollution source density, the maximum pollution source density of each hydrological response unit, and a preset pollution source density threshold.

[0073] In the embodiment of the present application, the normalized pollution source density is expressed as:

[0074] Wherein, P represents the pollution source density of the hydrological response unit, represents the preset pollution source density threshold, represents the maximum pollution source density.

[0075] S43: Determine a number of initial neighborhood sample quantities according to a preset first neighborhood sample quantity threshold and the normalized pollution source density of each hydrological response unit.

[0076] In the embodiment of the present application, multiply the normalized pollution source density of each hydrological response unit by an adjustment amplitude coefficient to obtain a product result; subtract the preset first neighborhood sample quantity threshold from the product result to obtain a number of initial neighborhood sample quantities. Among them, the adjustment amplitude coefficient is a preset coefficient. For example, it can be a value between 3 and 5.

[0077] S44: Compare the preset second neighborhood sample quantity threshold and a number of initial neighborhood sample quantities to determine the maximum neighborhood sample quantity; use the maximum neighborhood sample quantity as the minimum neighborhood sample quantity of each ecological risk detection index.

[0078] In the embodiment of the present application, the expression of the minimum neighborhood sample quantity is:

[0079] Wherein, represents the preset second neighborhood sample quantity threshold, represents the preset first neighborhood sample quantity threshold, represents the adjustment amplitude coefficient, represents rounding down.

[0080] In one embodiment, the step of correcting the initial weight of each ecological risk detection index according to the second ecological risk detection data in step S60 to obtain the target weight of each ecological risk detection index includes S601 - S605, specifically as follows: S601: Count the quantity of the second ecological risk detection data corresponding to each ecological risk detection index and the quantity of the first ecological risk detection data corresponding to each ecological risk detection index.

[0081] S602: Sum the quantities of the second ecological risk detection data corresponding to each ecological risk detection index to obtain a first quantity; sum the quantities of the first ecological risk detection data corresponding to each ecological risk detection index to obtain a second quantity.

[0082] S603: Determine a ratio based on the first quantity and the second quantity.

[0083] S604: Obtain the intermediate weight of each ecological risk detection index based on the ratio and the initial weight of each ecological risk detection index.

[0084] In the embodiment of the present application, the expression of the intermediate weight is:

[0085] Wherein, represents the total sum of the quantities of the second ecological risk detection data corresponding to the i-th ecological risk detection index in all hydrological response units (the first quantity), represents the total sum of the quantities of the first ecological risk detection data corresponding to the i-th ecological risk detection index in all hydrological response units (the second quantity), represents the intermediate weight of the i-th ecological risk detection index, represents the initial weight of the i-th ecological risk detection index.

[0086] S605: Perform normalization processing on the intermediate weights of each ecological risk detection index to obtain the target weight of each ecological risk detection index.

[0087] In the embodiment of the present application, the expression of the target weight is:

[0088] Wherein, represents the target weight of the i-th ecological risk detection index.

[0089] In one embodiment, the step of obtaining the ecological risk index of each hydrological response unit in the target basin according to the target weight of each ecological risk detection index and the first ecological risk detection data of each hydrological response unit in step S60 includes S61~S62, specifically as follows: S61: Multiply the first ecological risk detection data of each ecological risk detection index of each hydrological response unit in the target basin by the target weight of each ecological risk detection index of each hydrological response unit in the target basin to obtain the ecological risk index of each ecological risk detection index of each hydrological response unit in the target basin.

[0090] S62: Summing up the ecological risk indices of each ecological risk detection index for each hydrological response unit in the target basin to obtain the ecological risk index of each hydrological response unit in the target basin.

[0091] In the embodiments of the present application, the expression of the ecological risk index of each hydrological response unit in the target basin is as follows:

[0092]

[0093] Wherein, represents the ecological risk index of the j-th hydrological response unit, represents the target weight of the i-th ecological risk detection index of the hydrological response unit j, represents the initial weight of the i-th ecological risk detection index of the hydrological response unit j.

[0094] The following are the device embodiments of the present application, which can be used to execute the content of the method in the embodiments of the present application. For the details not disclosed in the device embodiments of the present application, please refer to the content of the method in the embodiments of the present application.

[0095] Please refer to Figure 2 , which shows the structural schematic diagram of the basin ecological risk monitoring device provided by the embodiments of the present application. The basin ecological risk monitoring device 7 provided by the embodiments of the present application includes: A data acquisition module 71, configured to acquire multi-source heterogeneous data and ecological risk detection data of the target basin; the ecological risk detection data includes a number of ecological risk detection indices and the index values of each ecological risk detection index; A data standardization module 72, configured to divide the target basin into a number of hydrological response units according to the multi-source heterogeneous data; perform standardization processing on the index values of each ecological risk detection index in each hydrological response unit to obtain the first ecological risk detection data of each hydrological response unit; A weight initialization module 73, configured to initialize the weights of each ecological risk detection index to obtain the initial weights of each ecological risk detection index; A neighborhood radius determination module 74, configured to determine the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit; determine the minimum number of neighborhood samples of each ecological risk detection index according to the pollution source density of each hydrological response unit; The data clustering module 75 is configured to cluster the first ecological risk detection data corresponding to each ecological risk detection index according to the neighborhood radius of each ecological risk detection index, the minimum number of neighborhood samples of each ecological risk detection index, and a preset density clustering method, so as to obtain the second ecological risk detection data of each ecological risk detection index; the second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data. The ecological risk index obtaining module 76 is configured to correct the initial weight of each ecological risk detection index according to the second ecological risk detection data to obtain the target weight of each ecological risk detection index; and obtain the ecological risk index of each hydrological response unit in the target basin according to the target weight of each ecological risk detection index and the first ecological risk detection data of each hydrological response unit.

[0096] It should be noted that when the above-mentioned basin ecological risk monitoring device executes the basin ecological risk monitoring method, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above-mentioned functions can be allocated to different functional modules according to needs, 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 basin ecological risk monitoring device and the basin ecological risk monitoring method belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be elaborated here.

[0097] The following is an embodiment of the device of the present application, which can be used to execute the content of the method in the embodiment of the present application. For the details not disclosed in the embodiment of the device of the present application, please refer to the content of the method in the embodiment of the present application.

[0098] Please refer to Figure 3 , the present application also provides an electronic device 300, which can specifically be a computer, a mobile phone, a tablet computer, etc. In an exemplary embodiment of the present application, the electronic device 300 is a computer, and the computer may include: at least one processor 301, at least one memory 302, at least one display, at least one network interface 303, a user interface 304, and at least one communication bus 305.

[0099] Among them, the user interface 304 is mainly used to provide an input interface for the user and obtain the data input by the user. Optionally, the user interface may further include a standard wired interface and a wireless interface.

[0100] Among them, the network interface 303 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0101] Among them, the communication bus 305 is used to realize the connection and communication between these components.

[0102] Among them, the processor 301 may include one or more processing cores. The processor uses various interfaces and circuits to connect various parts within the entire electronic device. By running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by calling the data stored in the memory, it executes various functions of the electronic device and processes data. Optionally, the processor may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed in the display layer; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately by a single chip.

[0103] Among them, the memory 302 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory may also be at least one storage device located far from the aforementioned processor. Figure 3 For example, in a memory as a computer storage medium, there may be an operating system, a network communication module, a user interface module, and an operating application program.

[0104] The processor can be used to call the application program of the watershed ecological risk monitoring method stored in the memory and specifically execute the method steps of the above-mentioned embodiments. The specific execution process can refer to the specific description shown in the embodiments and will not be elaborated here.

[0105] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0106] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for monitoring the ecological risk of a river basin, characterized in that, The method includes the following steps: Obtain multi-source heterogeneous data and ecological risk detection data of the target basin; the ecological risk detection data includes a number of ecological risk detection indicators and the indicator values of each ecological risk detection indicator; According to the multi-source heterogeneous data, divide the target basin into a number of hydrological response units; standardize the indicator values of each ecological risk detection indicator in each hydrological response unit to obtain the first ecological risk detection data of each hydrological response unit; Initialize the weights of each ecological risk detection indicator to obtain the initial weights of each ecological risk detection indicator; According to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit, determine the neighborhood radius of each ecological risk detection indicator; according to the pollution source density of each hydrological response unit, determine the minimum neighborhood sample number of each ecological risk detection indicator; According to the neighborhood radius of each ecological risk detection indicator, the minimum neighborhood sample number of each ecological risk detection indicator, and a preset density clustering method, cluster the first ecological risk detection data corresponding to each ecological risk detection indicator to obtain the second ecological risk detection data of each ecological risk detection indicator; The second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data; According to the second ecological risk detection data, correct the initial weights of each ecological risk detection indicator to obtain the target weights of each ecological risk detection indicator; According to the target weights of each ecological risk detection indicator and the first ecological risk detection data of each hydrological response unit, obtain the ecological risk index of each hydrological response unit in the target basin.

2. The method for monitoring the ecological risk of a basin according to claim 1, wherein: The step of determining the neighborhood radius of each ecological risk detection indicator according to the first ecological risk detection data of each hydrological response unit and the spatial location information of each hydrological response unit includes: According to the spatial location information of each hydrological response unit, determine the spatial distance between each hydrological response unit; compare the spatial distances between each hydrological response unit to determine the maximum spatial distance; According to the first ecological risk detection data of each hydrological response unit, determine the difference between the first ecological risk detection data corresponding to the same ecological risk detection indicator of each hydrological response unit; According to the difference between the first ecological risk detection data corresponding to the same ecological risk detection indicator of each hydrological response unit, the spatial distance between each hydrological response unit, and the maximum spatial distance, determine the neighborhood radius of each ecological risk detection indicator.

3. The method for monitoring the ecological risk of a basin according to claim 1, wherein: The step of determining the minimum neighborhood sample number of each ecological risk detection indicator according to the pollution source density of each hydrological response unit includes: Compare the pollution source density of each of the hydrological response units to determine the maximum pollution source density; Based on the pollution source density of each of the hydrological response units, the maximum pollution source density, and a preset pollution source density threshold, obtain the pollution source density of each of the hydrological response units after normalization processing; Based on a preset first neighborhood sample quantity threshold and the pollution source density of each of the hydrological response units after normalization processing, determine a number of initial neighborhood sample quantities; Compare the preset second neighborhood sample quantity threshold and the number of initial neighborhood sample quantities to determine the maximum neighborhood sample quantity; use the maximum neighborhood sample quantity as the minimum neighborhood sample quantity of each of the ecological risk detection indicators.

4. The method for monitoring the ecological risk of a basin according to claim 1, wherein: The step of correcting the initial weight of each of the ecological risk detection indicators according to the second ecological risk detection data to obtain the target weight of each of the ecological risk detection indicators includes: Count the quantity of the second ecological risk detection data corresponding to each of the ecological risk detection indicators and the quantity of the first ecological risk detection data corresponding to each of the ecological risk detection indicators; Sum the quantity of the second ecological risk detection data corresponding to each of the ecological risk detection indicators to obtain a first quantity; sum the quantity of the first ecological risk detection data corresponding to each of the ecological risk detection indicators to obtain a second quantity; Determine a ratio according to the first quantity and the second quantity; Obtain the intermediate weight of each of the ecological risk detection indicators according to the ratio and the initial weight of each of the ecological risk detection indicators; Perform normalization processing on the intermediate weight of each of the ecological risk detection indicators to obtain the target weight of each of the ecological risk detection indicators.

5. The method for monitoring the ecological risk of a basin according to claim 1, wherein: The step of obtaining the ecological risk index of each hydrological response unit in the target basin according to the target weight of each of the ecological risk detection indicators and the first ecological risk detection data of each of the hydrological response units includes: Multiply the first ecological risk detection data of each of the ecological risk detection indicators of each hydrological response unit in the target basin by the target weight of each of the ecological risk detection indicators of each hydrological response unit in the target basin to obtain the ecological risk index of each of the ecological risk detection indicators of each hydrological response unit in the target basin; Sum the ecological risk indices of each of the ecological risk detection indicators of each hydrological response unit in the target basin to obtain the ecological risk index of each hydrological response unit in the target basin.

6. The method for monitoring the ecological risk of a basin according to any one of claims 1 to 5, wherein: The step of performing standardization processing on the index values of each of the ecological risk detection indicators in each of the hydrological response units to obtain the first ecological risk detection data of each of the hydrological response units includes: Determine the index values of the ecological risk detection indices for several pixels corresponding to each of the hydrological response units; based on the index values of the ecological risk detection indices of each pixel, obtain the average value and standard deviation of each ecological risk detection index. Subtract the index value of each ecological risk detection index from the average value to obtain a difference; divide the difference by the standard deviation to obtain the first ecological risk detection data for each hydrological response unit.

7. The method for monitoring basin ecological risk according to any one of claims 1 to 5, characterized in that: The multi-source heterogeneous data includes digital elevation model data and land use classification data. The step of dividing the target basin into several hydrological response units according to the multi-source heterogeneous data includes: Use the depression filling tool of the hydrological analysis software to perform depression filling on the digital elevation model data to obtain digital elevation model data without depressions. Use the flow direction tool of the hydrological analysis software to obtain the flow direction data of each grid of the digital elevation model data without depressions on the digital elevation model data without depressions by using the maximum slope drop method. According to the flow direction data of each grid, use the flow accumulation tool of the hydrological analysis software to calculate the flow accumulation of the digital elevation model data without depressions. Based on a preset minimum cumulative runoff, use the raster calculator of the hydrological analysis software to extract the river network raster data from the grids where the flow accumulation is greater than the preset minimum cumulative runoff. Extract the watershed line from the river network raster data and use the watershed line as the initial boundary constraint of the hydrological response unit. Use the land use classification data to calculate the coupling weight of each grid, construct a weight surface; perform spatial clustering on the weight surface to obtain a spatial clustering result; according to the spatial clustering result and the initial boundary constraint of the hydrological response unit, obtain several hydrological response units.

8. A basin ecological risk monitoring device, characterized in that Including: A data acquisition module for acquiring multi-source heterogeneous data and ecological risk detection data of the target basin; the ecological risk detection data includes several ecological risk detection indices and the index values of each ecological risk detection index. A data standardization module for dividing the target basin into several hydrological response units according to the multi-source heterogeneous data; performing standardization processing on the index values of each ecological risk detection index in each hydrological response unit to obtain the first ecological risk detection data for each hydrological response unit. A weight initialization module for initializing the weight of each ecological risk detection index to obtain the initial weight of each ecological risk detection index. A neighborhood radius determination module for determining the neighborhood radius of each ecological risk detection index according to the first ecological risk detection data of each hydrological response unit and the spatial position information of each hydrological response unit; determining the minimum neighborhood sample number of each ecological risk detection index according to the pollution source density of each hydrological response unit. A data clustering module, configured to cluster the first ecological risk detection data corresponding to each ecological risk detection index according to the neighborhood radius of each ecological risk detection index, the minimum number of neighborhood samples of each ecological risk detection index, and a preset density clustering method, so as to obtain the second ecological risk detection data of each ecological risk detection index; The second ecological risk detection data is the abnormal first ecological risk detection data in the first ecological risk detection data; An ecological risk index obtaining module, configured to correct the initial weight of each ecological risk detection index according to the second ecological risk detection data, so as to obtain the target weight of each ecological risk detection index; According to the target weight of each ecological risk detection index and the first ecological risk detection data of each hydrological response unit, the ecological risk index of each hydrological response unit in the target basin is obtained.

9. An electronic device, comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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