Water ecosystem risk assessment system and method based on human activities and biological invasion
By acquiring and processing a variety of data to calculate the impact index of human activities and ecosystems, combined with satellite and drone monitoring, the dynamic reflection and monitoring accuracy problems of traditional water ecosystem assessment systems are solved, and efficient and low-cost risk assessment is achieved.
Patent Information
- Application Number
- CN202510727088.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-03
- Publication Date
- 2025-08-26
AI Technical Summary
Traditional water ecosystem risk assessment systems rely on static indicators and local artificial sampling data, which is difficult to dynamically reflect the impact of human activities and biological invasion on water ecosystems. The monitoring range is limited and costly, and the data acquisition method is single, making it difficult to adjust and improve monitoring accuracy in real time.
The data acquisition module is used to obtain population, industrial pollution, agricultural pollution and catch data, as well as water ecological, remote sensing and image data, pre-process and feature identification through the data processing module, calculate human activity pressure and ecosystem health index, combine the risk assessment module to calculate the risk assessment index, and use satellite and drone modules to dynamically monitor phytoplankton coverage to optimize the drone allocation strategy.
Dynamic risk assessment of water ecosystems has been achieved, monitoring accuracy and scope has been improved, costs have been reduced, and protection measures can be adjusted in a timely manner.
Smart Images
Figure CN120542934A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of ecosystem data collection and processing, and specifically relates to a water ecosystem risk assessment system and method based on human activities and biological invasions. Background Art
[0002] When developing aquatic ecosystem protection strategies and evaluating the effectiveness of ecological and environmental governance, a risk assessment system is needed to analyze the impact of different influencing factors on aquatic ecosystems and their risk status. Traditional risk assessment systems rely on static indicators and local manual sampling data, making it difficult to dynamically reflect the impact of human activities and biological invasions on aquatic ecosystems.
[0003] Furthermore, monitoring modules (such as ground-based stations) in traditional risk assessment systems are costly and inefficient, with limited monitoring coverage and a single data acquisition method. This makes it difficult to accurately capture rapid ecological changes, such as the spread of red tides and phytoplankton blooms. Furthermore, existing risk assessment systems struggle to monitor aquatic ecosystems using imagery and adjust satellite remote sensing strategies in real time based on ecological changes. Data updates lag, and monitoring accuracy needs to be improved. Summary of the Invention
[0004] The purpose of the present invention is to provide a water ecosystem risk assessment system and method based on human activities and biological invasion in order to solve the above problems.
[0005] The present invention achieves the above-mentioned purpose through the following technical solutions: A water ecosystem risk assessment system based on human activities and biological invasions, the system includes: Data acquisition module, used to obtain population data, industrial pollution data, agricultural pollution data and fish catch data, as well as water ecology data, water surface image data and water remote sensing data; Data processing module, used for preprocessing and feature recognition of water ecological data, water surface image data and water remote sensing data; An analysis and prediction module is used to calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data, and fish catch data, and to calculate the ecosystem health index based on the processing results of aquatic ecological data, water surface image data, and aquatic remote sensing data; The human activity pressure index includes the population change rate , Industrial Pressure Index , Agricultural Stress Index and the intensity of fishing activities ; The ecosystem health index includes the biotic niche overlap index , impact rate of invasive species , water pollution rate , water landform change rate , resource change rate , water surface pollution rate , preliminary productivity coefficient and coral bleaching rates ; A risk assessment module is used to calculate a risk assessment index based on the human activity pressure index and the ecosystem health index. , risk assessment index The calculation formula is as follows: in, , , , , , , , , , , and are the weight coefficients of various human activity pressure indices and ecosystem health indices respectively.
[0006] As a further optimization solution of the present invention, the population change rate The calculation formula is as follows: ; in, for Population data around the target waters at all times, for Population data around the target water area at the moment; The Industrial Stress Index The calculation formula is as follows: ; in, for Time to The amount of wastewater discharged at any given time, for Time to The amount of wastewater discharged at any given time, for Time to The time span of the moment, for Time to The time span of the moment; The Agricultural Stress Index The calculation formula is as follows: ; in, for Time to The amount of nitrogen lost at each moment, for Time to The amount of nitrogen lost at each moment, for Time to The amount of phosphorus loss at each moment, for Time to The amount of phosphorus loss at each moment; Intensity of fishing activity The calculation formula is as follows: ; in, for Time to The catch at the moment, for Time to The catch of the moment.
[0007] As a further optimization scheme of the present invention, the data processing module pre-processes the water ecological data and inputs the pre-processed water ecological data into the maximum entropy model for model training. The analysis and prediction module analyzes and predicts the distribution area of the target water biological species based on the trained model and the currently collected water ecological data, obtains the prediction results, and calculates the biological niche overlap index based on the prediction results. , the biological niche overlap index The calculation formula is as follows: ; in, For species In water resources The distribution frequency of species In water resources The distribution frequency under , H is the total number of resources.
[0008] As a further optimization scheme of the present invention, the analysis and prediction module converts the probability value of species occurrence in the grid unit into a species distribution map according to the prediction results, compares the historical species distribution map with the predicted species distribution map, and marks the species that only appear in the predicted species distribution map as potential invasive species. The impact rate of invasive species is The calculation formula is as follows: ; in, is the total number of potentially invasive species, is the total number of animals in the target water area.
[0009] As a further optimization solution of the present invention, the data acquisition module includes a satellite module for collecting water area remote sensing data, a drone module for collecting water surface image data, and a central control module for dispatching drones, and the drone module is composed of multiple drones; The central control module divides the target water area into multiple grid units, and the area of each grid unit is After the satellite module collects remote sensing data of the target water area, the data processing module obtains the phytoplankton distribution map based on the remote sensing data and analyzes the phytoplankton coverage of each grid unit. The central control module allocates drones based on the phytoplankton coverage and enables the drones to collect water surface image data.
[0010] As a further optimization solution of the present invention, drones are distributed directly above the grid units covered with phytoplankton, and the image data collection range of the drones is greater than When the UAV detects that phytoplankton enters a blank grid cell, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the UAV through the central control module.
[0011] As a further optimization scheme of the present invention, the drones allocated directly above the grid cells are divided into monitoring drone U1 and backup drone U2, the grid cells directly below the monitoring drone U1 are covered with phytoplankton, the blank grid cells are divided into high-risk grid cells and low-risk grid cells, and the grid cells directly below the backup drone U2 are high-risk grid cells.
[0012] As a further optimization scheme of the present invention, if the grid cell adjacent to the blank grid cell is covered with phytoplankton, the blank grid cell is a high-risk grid cell. When the backup drone U2 detects that the high-risk grid cell is covered with phytoplankton, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the drone through the central control module.
[0013] As a further optimization solution of the present invention, if the grid cells adjacent to the blank grid cells If the area is covered with phytoplankton, the blank grid cell is a high-risk grid cell. When the backup drone U2 detects a high-risk grid cell When the area is covered with phytoplankton, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the drone through the central control module.
[0014] A risk assessment method for aquatic ecosystems based on human activities and biological invasions includes the following steps: Obtain population data, industrial pollution data, agricultural pollution data, fish catch data, as well as water ecology data and water remote sensing data; Preprocess and identify features of water ecological data and water remote sensing data, obtain water surface image data, and preprocess and identify features of water surface image data; Calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data, and fish catch data, and calculate the ecosystem health index based on the processing results of aquatic ecological data, water surface image data, and aquatic remote sensing data; Calculate the risk assessment index based on the human activity pressure index and ecosystem health index .
[0015] The beneficial effects of the present invention are: The data analysis module of the present invention estimates a human activity pressure index and an ecosystem health index including the impact rate of invasive organisms based on population data, industrial pollution data, agricultural pollution data, and fish catch data, as well as water area ecological data, water surface image data, and water area remote sensing data. The module also estimates a risk assessment index for the aquatic ecosystem based on the aforementioned indices, thereby analyzing the impact of human activities and biological invasions on the aquatic ecosystem and facilitating the adjustment of protective measures for the aquatic ecosystem. The data acquisition module of the present invention includes a satellite module and a drone module. The satellite module collects water area remote sensing data, and the drone modules are reasonably allocated according to the analysis results of the water area remote sensing data. The drones are allocated according to the data collected by the satellite modules, and the data update process of the satellite modules is adjusted according to the water surface image data collected by the drones, so as to timely adjust the drone allocation strategy, thereby improving the real-time detection accuracy of the water surface image data and reducing the monitoring cost. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 is a system block diagram of the present invention; Figure 2 is a flow chart of the water ecosystem risk assessment of the present invention; Figure 3 This is the drone distribution map of Example 1 of the present invention; Figure 4 This is a sample diagram of phytoplankton diffusion in Example 1 of the present invention; Figure 5 This is a UAV distribution map according to Example 2 of the present invention; Figure 6 This is a distribution map of drones according to Example 3 of the present invention. DETAILED DESCRIPTION
[0017] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.
[0018] Example 1 like Figure 1 and Figure 2 As shown, this embodiment relates to a water ecosystem risk assessment system based on human activities and biological invasions, which includes a data acquisition module, a data processing module, an analysis and prediction module, and a risk assessment module. Among them, the data acquisition module is used to obtain population data, industrial pollution data, agricultural pollution data, and fish catch data, as well as water area ecological data, water surface image data, and water area remote sensing data. Among them, the water area ecological data includes biological data, water quality data, resource data, and environmental data of the water area to be tested. Biological data includes the number and type of species of organisms in the water area to be tested, water quality data includes salinity and pollutant concentration, resource data includes fishery resources, mining resources, and renewable resources, and environmental data includes water surface height, temperature and humidity, wind speed, dissolved oxygen, and sediment quality. The data processing module is used to preprocess and identify features of water area ecological data, water surface image data, and water area remote sensing data. It should be noted that the acquisition of water surface image data is carried out after the preprocessing and feature recognition of water area remote sensing data.
[0019] The analysis and prediction module is used to calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data and fish catch data, and calculate the ecosystem health index based on the processing results of water ecological data, water surface image data and water remote sensing data. The risk assessment module is used to calculate the risk assessment index based on the human activity pressure index and the ecosystem health index. The system is based on Time to The data at that moment is historical data. Always After a moment, Time to The data at the moment is the current data obtained in real time. In this embodiment, the water area to be measured is the ocean. In other embodiments, the water area to be measured can also be adjusted to a water ecosystem such as a river, a lake, or a wetland.
[0020] Human activity pressure index includes population change rate , Industrial Pressure Index , Agricultural Stress Index and the intensity of fishing activities The human activity stress index is calculated as follows: (1) Based on the census data, obtain Population data around the target water area at the moment, and Population data around the target waters at the moment. Population change rate The calculation formula is as follows: ; in, for Population data around the target waters at all times, for Population data around the target waters at the moment.
[0021] (2) Based on the pollution source census data, obtain Time to Time-of-day wastewater discharge data, and Time to Wastewater discharge data at the moment. Industrial Pressure Index The calculation formula is as follows: ; in, for Time to The amount of wastewater discharged at any given time, for Time to The amount of wastewater discharged at any given time, for Time to The time span of the moment, for Time to The time span of the moment; (3) Obtained from pollution source census data Time to The amount of nitrogen loss from farmland around the target water area at each moment, Time to The amount of nitrogen loss from farmland around the target water area at each moment, Time to The amount of phosphorus loss from farmland surrounding the target water area at the time, and Time to The amount of phosphorus loss from farmland around the target water area at the time. Agricultural Pressure Index The calculation formula is as follows: ; in, for Time to The amount of nitrogen loss from farmland around the target water area at each moment, for Time to The amount of nitrogen loss from farmland around the target water area at each moment, for Time to The amount of phosphorus loss from farmland around the target water area at each moment, for Time to The amount of phosphorus loss from farmland around the target water area at each moment, for Time to The time span of the moment, for Time to The time span of the moment.
[0022] (4) Based on the data registered by the fishery management department, obtain Time to The catch at the time, and Time to Catch at any given moment. Intensity of fishing activity The calculation formula is as follows: ; in, for Time to The catch at the moment, for Time to The catch at the moment, for Time to The time span of the moment, for Time to The time span of the moment.
[0023] Ecosystem health indices include the niche overlap index , impact rate of invasive species , water pollution rate , water landform change rate , resource change rate , water surface pollution rate , preliminary productivity coefficient and coral bleaching rates The Ecosystem Health Index is calculated as follows: (1) The data acquisition module inputs the biological data and environmental data in the water ecological data into the data processing module, preprocesses them through the data processing module, and inputs the preprocessed data into the maximum entropy model for model training. Then, the data processing module preprocesses the currently collected biological data and environmental data, and the analysis and prediction module inputs the preprocessed data into the trained model to analyze and predict the distribution area of biological species in the target water area to obtain the prediction results. According to the prediction results, the probability value of species occurrence in each grid unit is converted into a species distribution map, and the biological niche overlap index is calculated based on the prediction results. , biological niche overlap index The calculation formula is as follows: ; in, For species In water resources The distribution frequency of species In water resources The distribution frequency under , H is the total number of resources.
[0024] (2) The analysis and prediction module converts the probability values of species occurrence in the grid cells into species distribution maps based on the prediction results, compares the historical species distribution maps with the predicted species distribution maps, and marks the species that only appear in the predicted species distribution maps as potential invasive species. The impact rate of invasive species is The calculation formula is as follows: ; in, is the total number of potentially invasive species, is the total number of animals in the target waters; (3) Based on the water quality data in the water ecological data, the water pollution rate Analyze and calculate the water pollution rate The calculation formula is as follows: ; in, The first The concentration of pollutants, The first Standard limits for pollutants, is the total number of pollutants in the target waters; (4) By analyzing the water surface height in the environmental data, the change in sea surface height can be analyzed, and then the rate of change of water landform can be calculated. , water landform change rate The calculation formula is as follows: ; in, for Time to The change in sea level height at a given moment, for Time to The time span of the moment; (5) By analyzing resource data including fishery resources, mining resources and renewable resources, the resource change rate can be calculated. , resource change rate The calculation formula is as follows: ; in, for Time to The change of water resources at each moment, for Time to The time span of the moment; (6) The data acquisition module includes a satellite module and a drone module. The satellite module cooperates with the drone module to acquire water surface image data, thereby monitoring the growth of red tide and green tide, and calculating the water surface pollution rate based on the monitoring results. , water surface pollution rate The calculation formula is as follows: ; in, is the coverage area of the red tide area, is the coverage area of the green tide area, is the influencing factor of the red tide area, is the influencing factor of the green tide area, is the total surface area of the target water area; (7) Monitor the growth of phytoplankton through the satellite module and the drone module, and calculate the preliminary productivity coefficient based on the monitoring results , preliminary productivity coefficient The calculation formula is as follows: ; in, yes The phytoplankton area at the time, for The phytoplankton area at the time, is the chlorophyll concentration, is the water surface temperature, η is the conversion coefficient, η=3.7; (8) Monitor the coral reef bleaching area based on water remote sensing data and calculate the coral reef bleaching change rate , the rate of change in coral bleaching The calculation formula is as follows: ; in, for The area of coral bleaching at the moment, for When there is no coral reef in the waters to be tested, the coral reef bleaching rate The value of is zero.
[0025] After processing and analyzing the above human activity pressure index and ecosystem health index, the risk assessment index Conduct an assessment, risk assessment index The calculation formula is as follows: in, is the comprehensive index of human activity pressure, which is obtained by weighting each human activity pressure index; is the comprehensive ecosystem health index, which is obtained by weighting each ecosystem health index; , , , , , , , , , , and Represents the population change rate , Industrial Pressure Index , Agricultural Stress Index , intensity of fishing activities , biological niche overlap index , impact rate of invasive species , water pollution rate , water landform change rate , resource change rate , water surface pollution rate , preliminary productivity coefficient and coral bleaching rates The weight coefficient of risk assessment index. The larger the number, the higher the risk level of the water ecosystem. is 0.15, the weight coefficient is 0.1, the weight coefficient is 0.1, the weight coefficient is 0.1, the weight coefficient is 0.1, the weight coefficient is 0.15, the weight coefficient is 0.1, the weight coefficient is 0.1, the weight coefficient is 0.1, the weight coefficient is 0.15, the weight coefficient is 0.15, the weight coefficient is 0.15. Risk Assessment Index The risk threshold is 60%, when the risk assessment index When the danger threshold is exceeded, the system alerts the assessor.
[0026] The data acquisition module includes a satellite module for collecting remote sensing data of the water area, an unmanned aerial vehicle (UAV) module for collecting water surface image data, and a central control module for dispatching UAVs. The UAV module is composed of multiple UAVs. The growth of the aforementioned phytoplankton, including the aforementioned red tide and green tide, is monitored through the satellite module and the UAV module.
[0027] The central control module divides the target water area into multiple grid cells, and the area of each grid cell is After the satellite module collects remote sensing data of the target water area, the data processing module obtains the phytoplankton distribution map based on the remote sensing data and analyzes the phytoplankton coverage of each grid unit. The central control module allocates drones based on the phytoplankton coverage and enables the drones to collect water surface image data. Drones are distributed directly above the grid units covered with phytoplankton, and the image data collection range of the drones is larger than ,When the UAV detects that phytoplankton enters a blank grid cell, the satellite ,module collects water area remote sensing data again, the data ,processing module analyzes the phytoplankton coverage again, and ,reallocates the UAV through the central control module.
[0028] Specifically, Figure 3 and Figure 4 For example, Figure 3 The figure shows the distribution of phytoplankton detected by the satellite module and the distribution results of the drone. The rectangle surrounded by the dotted line is the grid unit, and the shaded part in the figure is the phytoplankton distribution map. Figure 3As shown, the satellite module detected that grid cells A24, A33, A34, A42, A43, A44, A52, A53, A63, A64, and A73 were all covered with phytoplankton. A drone was deployed directly above each of the above 12 grid cells. The above-deployed drones were monitoring drones U1, and the undeployed drones were on standby at the drone base station. The monitoring drone U1 adjusted its altitude at the corresponding grid cell, and its data collection range was greater than , ensuring that the monitoring drone U1 can monitor the grid cells adjacent to the grid cells directly below it within a certain range.
[0029] When the phytoplankton grows to the blank grid unit and the phytoplankton that grows to the blank grid unit is at the boundary of the data collection range of the monitoring drone U1, the central control module collects water area remote sensing data again through the satellite module, the data processing module analyzes the phytoplankton coverage again, and reallocates the drone through the central control module. Figure 4 As shown in the figure, the solid-line box represents the data collection range boundary of monitoring drone U1. Phytoplankton grows into blank grid cell A23 and covers the data collection range boundary of monitoring drone U1. At this point, the satellite module once again collects remote sensing data of the water area. Monitoring drone U1 can monitor phytoplankton at close range, improving the coverage area monitoring accuracy of red tide and green tide, and continuously and comprehensively tracking the effectiveness of red tide and green tide control.
[0030] Example 2 like Figure 5 As shown, this embodiment relates to another aquatic ecosystem risk assessment system based on human activities and biological invasions. This system differs from Example 1 in that it also includes a backup drone U2. Both monitoring drone U1 and backup drone U2 perform real-time monitoring of the grid cells directly below them. Specifically, the drones assigned directly above the grid cells are divided into monitoring drone U1 and backup drone U2. The grid cells directly below monitoring drone U1 are covered with phytoplankton, and the blank grid cells are divided into high-risk grid cells and low-risk grid cells. The grid cells directly below backup drone U2 are high-risk grid cells.
[0031] If the grid cells adjacent to the blank grid cells are covered with phytoplankton, then the blank grid cells are high-risk grid cells. When the backup drone U2 detects that the high-risk grid cells are covered with phytoplankton, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the drones through the central control module. This embodiment can lower the height of the drone above the grid cell, and the data acquisition range of the monitoring drone U1 and the backup drone U2 is no less than the area of the grid cell. When using the same image acquisition device, the monitoring accuracy of this embodiment is higher. In addition, under the same monitoring accuracy requirements, this embodiment can use a lower-cost image acquisition device. However, in this embodiment, a large number of backup drones U2 are required, and the overall cost is higher than the previous embodiment.
[0032] Example 3 like Figure 6 As shown, this embodiment relates to another water ecosystem risk assessment system based on human activities and biological invasions. The difference between this system and Example 2 is that this embodiment only sets a backup drone U2 above some blank grid cells covered with phytoplankton.
[0033] Specifically, if the grid cells adjacent to the blank grid cells If the area is covered with phytoplankton, the blank grid cell is a high-risk grid cell. In this embodiment, When the backup drone U2 detects a high-risk grid cell When the area is covered with phytoplankton, the satellite module collects water remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and redistributes the drones through the central control module. Figure 6 The phytoplankton coverage in grid unit A43 and grid units A63 and A64 in the figure exceeds Therefore, a backup UAV U2 is configured above grid unit A32, grid units A62, A65, and grid units A72, A74, and A75. Compared with the previous embodiment, the number of backup UAVs U2 used in this embodiment is greatly reduced, which helps to save the overall cost of the system.
[0034] Example 4 This embodiment relates to a method for assessing water ecosystem risk based on human activities and biological invasions. The method is applicable to the water ecosystem risk assessment system in Examples 1-3. The water ecosystem risk assessment method includes the following steps: Step 1: Obtain population data, industrial pollution data, agricultural pollution data, fish catch data, as well as aquatic ecological data and aquatic remote sensing data. Population data, industrial pollution data, agricultural pollution data, and fish catch data are used to calculate the human activity pressure index, while aquatic ecological data and aquatic remote sensing data are used to calculate the ecosystem health index.
[0035] Step 2: Preprocess and identify features of the aquatic ecological data and aquatic remote sensing data, obtain water surface image data, and perform preprocessing and feature identification on the water surface image data. This preprocessing includes cleaning and standardizing the aquatic ecological data, denoising and image enhancement of the water surface image data, and denoising, geometric correction, and radiometric correction of the aquatic remote sensing data. It should be noted that after preprocessing and feature identification of the aquatic remote sensing data, the water surface image data is collected by the data acquisition module.
[0036] Step 3: Based on the formula in Example 1, calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data and fish catch data, and calculate the ecosystem health index based on the processing results of water ecological data, water surface image data and water remote sensing data. The human activity pressure index includes the population change rate , Industrial Pressure Index , Agricultural Stress Index and the intensity of fishing activities Ecosystem health indices include the niche overlap index , impact rate of invasive species , water pollution rate , water landform change rate , resource change rate , water surface pollution rate , initial productivity coefficient and coral bleaching rates .
[0037] Step 4: Based on the formula in Example 1, calculate the risk assessment index according to the human activity pressure index and the ecosystem health index When the risk assessment index When dangerous thresholds are exceeded, an alert is sent to assessors.
[0038] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.
Claims
1. A water ecosystem risk assessment system based on human activities and biological invasions, characterized by: The system includes: Data acquisition module, used to obtain population data, industrial pollution data, agricultural pollution data and fish catch data, as well as water ecology data, water surface image data and water remote sensing data; Data processing module, used for preprocessing and feature recognition of water ecological data, water surface image data and water remote sensing data; An analysis and prediction module is used to calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data, and fish catch data, and to calculate the ecosystem health index based on the processing results of aquatic ecological data, water surface image data, and aquatic remote sensing data; The human activity pressure index includes the population change rate , Industrial Pressure Index , Agricultural Stress Index and the intensity of fishing activities ; The ecosystem health index includes the biotic niche overlap index , impact rate of invasive species , water pollution rate , water landform change rate , resource change rate , water surface pollution rate , preliminary productivity coefficient and coral bleaching rates ; A risk assessment module is used to calculate a risk assessment index based on the human activity pressure index and the ecosystem health index. , risk assessment index The calculation formula is as follows: in, , , , , , , , , , , and are the weight coefficients of various human activity pressure indices and ecosystem health indices respectively.
2. The water ecosystem risk assessment system according to claim 1, characterized in that: The population change rate The calculation formula is as follows: ； in, for Population data around the target waters at all times, for Population data around the target water area at the moment; The Industrial Stress Index The calculation formula is as follows: ； in, for Time to The amount of wastewater discharged at any given time, for Time to The amount of wastewater discharged at any given time, for Time to The time span of the moment, for Time to The time span of the moment; The Agricultural Stress Index The calculation formula is as follows: ； in, for Time to The amount of nitrogen lost at each moment, for Time to The amount of nitrogen lost at each moment, for Time to The amount of phosphorus loss at each moment, for Time to The amount of phosphorus loss at each moment; Intensity of fishing activity The calculation formula is as follows: ； in, for Time to The catch at the moment, for Time to The catch of the moment.
3. The water ecosystem risk assessment system according to claim 1, characterized in that: The data processing module pre-processes the water ecological data and inputs the pre-processed water ecological data into the maximum entropy model for model training. The analysis and prediction module analyzes and predicts the distribution area of the target water biological species based on the trained model and the currently collected water ecological data, obtains the prediction results, and calculates the biological niche overlap index based on the prediction results. , the biological niche overlap index The calculation formula is as follows: ； in, For species In water resources The distribution frequency of species In water resources The distribution frequency under , H is the total number of resources.
4. The water ecosystem risk assessment system according to claim 3, characterized in that: The analysis and prediction module converts the probability value of species occurrence in the grid unit into a species distribution map based on the prediction results, compares the historical species distribution map with the predicted species distribution map, and marks the species that only appear in the predicted species distribution map as potential invasive species. The invasive biological impact rate The calculation formula is as follows: ； in, is the total number of potentially invasive species, is the total number of animals in the target water area.
5. The water ecosystem risk assessment system according to claim 1, characterized in that: The data acquisition module includes a satellite module for collecting water area remote sensing data, a drone module for collecting water surface image data, and a central control module for dispatching drones, and the drone module is composed of multiple drones; The central control module divides the target water area into multiple grid units, and the area of each grid unit is After the satellite module collects remote sensing data of the target water area, the data processing module obtains the phytoplankton distribution map based on the remote sensing data and analyzes the phytoplankton coverage of each grid unit. The central control module allocates drones based on the phytoplankton coverage and enables the drones to collect water surface image data.
6. The water ecosystem risk assessment system according to claim 5, characterized in that: UAVs are distributed directly above the grid cells covered with phytoplankton, and the image data collection range of the UAVs is larger than When the UAV detects that phytoplankton enters a blank grid cell, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the UAV through the central control module.
7. The water ecosystem risk assessment system according to claim 5, characterized in that: The drones allocated directly above the grid cells are divided into monitoring drone U1 and backup drone U2. The grid cells directly below the monitoring drone U1 are covered with phytoplankton. The blank grid cells are divided into high-risk grid cells and low-risk grid cells. The grid cell directly below the backup drone U2 is a high-risk grid cell.
8. The water ecosystem risk assessment system according to claim 7, characterized in that: If the grid cell adjacent to the blank grid cell is covered with phytoplankton, the blank grid cell is a high-risk grid cell. When the backup drone U2 detects that the high-risk grid cell is covered with phytoplankton, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the drone through the central control module.
9. The water ecosystem risk assessment system according to claim 7, characterized in that: If the grid cells adjacent to the blank grid cells If the area is covered with phytoplankton, the blank grid cell is a high-risk grid cell. ; When the backup drone U2 detects a high-risk grid cell When the area is covered with phytoplankton, the satellite module collects water area remote sensing data again, the data processing module analyzes the phytoplankton coverage again, and reallocates the drone through the central control module.
10. A method for risk assessment of aquatic ecosystems based on human activities and biological invasions, characterized in that: The water ecosystem risk assessment system according to any one of claims 1 to 9 comprises the following steps: Obtain population data, industrial pollution data, agricultural pollution data, fish catch data, as well as water ecology data and water remote sensing data; Preprocess and identify features of water ecological data and water remote sensing data, obtain water surface image data, and preprocess and identify features of water surface image data; Calculate the human activity pressure index based on population data, industrial pollution data, agricultural pollution data, and fish catch data, and calculate the ecosystem health index based on the processing results of aquatic ecological data, water surface image data, and aquatic remote sensing data; Calculate the risk assessment index based on the human activity pressure index and ecosystem health index .