Aquaculture non-point source pollution river-entering load determination method and device and storage medium

Abstract

The invention provides an aquaculture non-point source pollution river entering load determination method and device and a storage medium. Comprising the steps of obtaining drainage basin boundary data, river distribution data and river communication data of a to-be-determined drainage basin to determine all aquaculture ponds of the to-be-determined drainage basin; remote sensing data of the aquaculture pond are obtained, and the remote sensing data comprise the initial water surface size, the aquaculture pond characteristics, the water surface area change and the water surface organic matter content change; according to the characteristics of the culture pond, determining a corresponding culture variety; according to the water surface area change and the water surface organic matter content change, the corresponding pond drying time is determined; performing pollutant monitoring on the aquaculture pond according to the pond drying time to obtain corresponding pollutant data; determining a pollutant output coefficient corresponding to each breeding variety according to the pollutant data and the breeding varieties; according to the pollutant output coefficient and the initial water surface size, determining river entering load data of the to-be-determined drainage basin; the method and the device are used for improving pollution load evaluation accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method, device, and storage medium for determining the river input load of non-point source pollution from aquaculture. Background Art

[0002] Currently, the area of aquaculture is extensive and the distribution is complex. Accurately estimating the river input load of non-point source pollution from aquaculture is the key basis for formulating prevention and control strategies for non-point source pollution in small and medium-sized river basins. Therefore, determining the river input load of non-point source pollution from aquaculture has become a promising direction.

[0003] In the prior art, there are mainly two methods for determining the river input load of non-point source pollution from aquaculture: one is based on statistical data, by statistically calculating the aquaculture area in the river basin and combining the pollutant output coefficient of aquaculture, the river input load of non-point source pollution from aquaculture in the river basin is calculated; the other is to use remote sensing technology, by retrieving the area of aquaculture ponds in the region from remote sensing images, and then combining Geographic Information System (GIS) technology and the pollutant output coefficient of aquaculture to estimate the pollution load in the river basin.

[0004] However, for some regions, there are a wide variety of aquaculture species, including rice field shrimp, crab, yellow catfish, California perch, rice field eel, the four major Chinese carps, intensively cultured crucian carp and other categories. The prior art methods have not fully considered the differences between different aquaculture species, and since the prior art cannot determine the dry pond time of a single aquaculture pond, it can only provide a rough annual load estimate, and cannot provide the specific spatio-temporal distribution information of aquaculture pollution emissions, resulting in the technical problem of low accuracy in the assessment of the river input load of non-point source pollution from aquaculture. Summary of the Invention

[0005] The method, device, and storage medium for determining the river input load of non-point source pollution from aquaculture provided by the present application are used to achieve the effect of improving the accuracy rate of pollution load assessment.

[0006] In a first aspect, the present application provides a method for determining the river input load of non-point source pollution from aquaculture, including:

[0007] Obtaining the basin boundary data, river distribution data, and river connectivity data of the basin to be determined to determine all aquaculture ponds in the basin to be determined;

[0008] Obtaining remote sensing data of the aquaculture ponds, where the remote sensing data includes the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change;

[0009] Determining the corresponding aquaculture species according to the aquaculture pond characteristics;

[0010] Determine the corresponding dry pond time according to the change in water surface area and the change in the content of organic matter in the water surface;

[0011] Monitor the pollutants in the aquaculture pond to obtain the corresponding pollutant data;

[0012] Determine the corresponding pollutant output coefficient for each aquaculture variety according to the pollutant data and the aquaculture variety;

[0013] Determine the river input load data of the basin to be determined according to the dry pond time, the pollutant output coefficient and the initial water surface size, where the river input load data includes the time distribution data and the spatial distribution data of the river input load.

[0014] In a possible implementation, the aquaculture varieties include common aquaculture, Micropterus salmonides aquaculture, Pelteobagrus fulvidraco aquaculture, rice field crayfish aquaculture, Chinese mitten crab aquaculture and Monopterus albus aquaculture.

[0015] In a possible implementation, the aquaculture pond characteristics include anti-escape facilities, paddy fields, the number of cages, the number of aerators per mu of water surface and feeding machines;

[0016] Correspondingly, determine the corresponding aquaculture variety according to the aquaculture pond characteristics, including:

[0017] Identify whether there are anti-escape facilities within the preset range of the aquaculture pond;

[0018] If there are anti-escape facilities within the preset range of the aquaculture pond, then identify whether there is a paddy field in the aquaculture pond. If there is a paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is rice field crayfish aquaculture. If there is no paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is Chinese mitten crab aquaculture;

[0019] If there are no anti-escape facilities within the preset range of the aquaculture pond, then identify the number of cages in the aquaculture pond. If the number of cages is greater than the preset cage threshold, then determine that the aquaculture variety of the aquaculture pond is Monopterus albus aquaculture. If the number of cages is not greater than the preset cage threshold, then identify the number of aerators per mu of water surface in the aquaculture pond;

[0020] If the number of aerators per mu of water surface in the aquaculture pond is not greater than the preset threshold of the number of aerators per mu of water surface, then determine that the aquaculture variety of the aquaculture pond is common aquaculture;

[0021] If the number of aerators per mu of water surface in the aquaculture pond is greater than the preset threshold of the number of aerators per mu of water surface, then identify whether there is a feeding machine within the preset range of the aquaculture pond. If there is a feeding machine within the preset range of the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is Pelteobagrus fulvidraco aquaculture. If there is no feeding machine within the preset range of the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is Micropterus salmonides aquaculture.

[0022] In a possible implementation, determining the corresponding dry pond time according to the change in water surface area and the change in water surface organic matter content includes:

[0023] If the change in water surface area is greater than the preset water surface area change threshold, it is determined that the aquaculture pond has undergone dry pond operation, and the middle value of the two satellite transit times corresponding to the change in water surface area is determined as the dry pond time of the aquaculture pond;

[0024] If the change in water surface area is not greater than the preset water surface area change threshold, then compare the change in water surface organic matter content with the preset water surface organic matter content change threshold;

[0025] If the change in water surface organic matter content is greater than the preset water surface organic matter content change threshold, it is determined that the aquaculture pond has undergone dry pond operation, and the middle value of the two satellite transit times corresponding to the change in water surface organic matter content is determined as the dry pond time of the aquaculture pond;

[0026] If the change in water surface organic matter content is not greater than the preset water surface organic matter content change threshold, it is determined that the aquaculture pond has not undergone dry pond operation.

[0027] In a possible implementation, pollutant monitoring is carried out on the aquaculture pond according to the dry pond time to obtain corresponding pollutant data, including:

[0028] Monitor the aquaculture pond to obtain the chemical oxygen demand concentration data and water depth data of the aquaculture pond.

[0029] In a possible implementation, determining the pollutant output coefficient corresponding to each aquaculture variety according to the pollutant data and the aquaculture variety includes:

[0030] For each aquaculture variety, determine n aquaculture ponds, where n is any positive integer;

[0031] Calculate the average value of the chemical oxygen demand concentration data of the n aquaculture ponds;

[0032] Calculate the average value of the water depth data of the n aquaculture ponds;

[0033] Determine the pollutant output coefficient corresponding to each aquaculture variety according to the average value of the chemical oxygen demand concentration data and the average value of the water depth data.

[0034] In a possible implementation, determining the river input load data of the watershed to be determined according to the dry pond time, the pollutant output coefficient, and the initial water surface size includes:

[0035] Determine the aquaculture area corresponding to the aquaculture variety according to the initial water surface size of the aquaculture pond corresponding to the aquaculture variety;

[0036] Determine the spatial distribution data of the river input load of the basin to be determined according to the average value of the aquaculture area and the pollutant output coefficient;

[0037] Determine the time distribution data of the river input load of the basin to be determined according to the dry pond time.

[0038] In a second aspect, the present application provides a device for determining the river input load of aquaculture non-point source pollution, including:

[0039] A first processing module, configured to obtain the basin boundary data, river distribution data, and river connectivity data of the basin to be determined, so as to determine all aquaculture ponds in the basin to be determined;

[0040] An acquisition module, configured to acquire remote sensing data of the aquaculture ponds, where the remote sensing data includes the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change;

[0041] A second processing module, configured to determine the corresponding aquaculture species according to the aquaculture pond characteristics;

[0042] A third processing module, configured to determine the corresponding dry pond time according to the water surface area change and the water surface organic matter content change;

[0043] A fourth processing module, configured to monitor pollutants in the aquaculture ponds to obtain corresponding pollutant data;

[0044] A first determination module, configured to determine the pollutant output coefficient corresponding to each aquaculture species according to the pollutant data and the aquaculture species;

[0045] A second determination module, configured to determine the river input load data of the basin to be determined according to the pollutant output coefficient and the initial water surface size, where the river input load data includes time distribution data and spatial distribution data.

[0046] In a possible implementation manner, the second processing module is further configured to:

[0047] Aquaculture species;

[0048] The aquaculture species include common aquaculture, Micropterus salmonides aquaculture, Pelteobagrus fulvidraco aquaculture, rice-crayfish culture, Chinese mitten crab culture, and Monopterus albus culture.

[0049] In a possible implementation manner, the aquaculture pond characteristics include anti-escape facilities, paddy fields, the number of cages, the number of aerators per mu of water surface, and feeding machines;

[0050] Correspondingly, the second processing module is further configured to:

[0051] Identify whether there are anti-escape facilities within the preset range of the aquaculture pond;

[0052] If there is an anti-escape facility within the preset range of the aquaculture pond, identify whether there is a paddy field in the aquaculture pond. If there is a paddy field in the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is rice-crayfish culture. If there is no paddy field in the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is river crab culture;

[0053] If there is no anti-escape facility within the preset range of the aquaculture pond, identify the number of cages in the aquaculture pond. If the number of cages is greater than the preset cage threshold, determine that the aquaculture variety of the aquaculture pond is ricefield eel culture. If the number of cages is not greater than the preset cage threshold, identify the number of aerators per mu of water surface in the aquaculture pond;

[0054] If the number of aerators per mu of water surface in the aquaculture pond is not greater than the preset aerator threshold per mu of water surface, determine that the aquaculture variety of the aquaculture pond is ordinary aquaculture;

[0055] If the number of aerators per mu of water surface in the aquaculture pond is greater than the preset aerator threshold per mu of water surface, identify whether there is a feeding machine within the preset range of the aquaculture pond. If there is a feeding machine within the preset range of the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is yellow catfish culture. If there is no feeding machine within the preset range of the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is California bass culture.

[0056] In a possible implementation manner, the third processing module is further configured to:

[0057] If the change in water surface area is greater than the preset water surface area change threshold, determine that the aquaculture pond has undergone a pond drying operation, and determine the intermediate value of the two satellite transit times corresponding to the change in water surface area as the pond drying time of the aquaculture pond;

[0058] If the change in water surface area is not greater than the preset water surface area change threshold, compare the change in water surface organic matter content with the preset water surface organic matter content change threshold;

[0059] If the change in water surface organic matter content is greater than the preset water surface organic matter content change threshold, determine that the aquaculture pond has undergone a pond drying operation, and determine the intermediate value of the two satellite transit times corresponding to the change in water surface organic matter content as the pond drying time of the aquaculture pond;

[0060] If the change in water surface organic matter content is not greater than the preset water surface organic matter content change threshold, determine that the aquaculture pond has not undergone a pond drying operation.

[0061] In a possible implementation manner, the fourth processing module is further configured to:

[0062] Monitor the aquaculture pond to obtain the chemical oxygen demand concentration data and water depth data of the aquaculture pond.

[0063] In a possible implementation, the first determination module is further configured to:

[0064] For each aquaculture variety, determine n aquaculture ponds, where n is any positive integer;

[0065] Calculate the average value of the chemical oxygen demand concentration data of the n aquaculture ponds;

[0066] Calculate the average value of the water depth data of the n aquaculture ponds;

[0067] Determine the pollutant output coefficient corresponding to each aquaculture variety according to the average value of the chemical oxygen demand concentration data and the average value of the water depth data.

[0068] In a possible implementation, the second determination module is further configured to:

[0069] Determine the aquaculture area corresponding to the aquaculture variety according to the initial water surface size of the aquaculture pond corresponding to the aquaculture variety;

[0070] Determine the spatial distribution data of the river input load of the watershed to be determined according to the aquaculture area and the average value of the pollutant output coefficient;

[0071] Determine the time distribution data of the river input load of the watershed to be determined according to the dry pond time.

[0072] In a third aspect, the present application provides a device for determining the river input load of aquaculture non-point source pollution, including: a memory, a processor;

[0073] The memory stores computer execution instructions;

[0074] The processor executes the computer execution instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0075] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the above first aspect and / or various possible implementations of the first aspect.

[0076] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above first aspect and / or various possible implementations of the first aspect.

[0077] A method, device, and storage medium for determining the river input load of aquaculture non-point source pollution provided by this application can comprehensively cover all aquaculture ponds within the basin by obtaining the detailed boundaries, river distributions, and connectivity data of the basin to be determined, ensuring the integrity of the analysis. By using remote sensing technology to obtain the initial water surface size, characteristics, area changes, and organic matter content changes of the aquaculture ponds, the accuracy and timeliness of the data are improved, providing a reliable basis for subsequent pollutant monitoring and load calculation. Further, by using remote sensing technology and data analysis means, a large amount of data can be efficiently and quickly obtained and processed, greatly shortening the time cycle for load determination. In addition, the aquaculture species are determined according to the characteristics of the aquaculture ponds, and the dry pond time is determined by combining the water surface area change and the water surface organic matter content change. This process ensures its scientificity and rationality, can more accurately reflect the actual situation of the aquaculture ponds, and moreover, by combining the data obtained from pollutant monitoring and determining the pollutant output coefficient according to the aquaculture species, the accuracy of the load calculation is ensured, achieving the effect of improving the accuracy rate of pollution load assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0079] Figure 1 It is a schematic diagram of the architecture of an application data processing system provided by an embodiment of this application;

[0080] Figure 2 It is a flowchart of the method for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application Figure 1 ;

[0081] Figure 3 It is a flowchart of the method for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application Figure 2 ;

[0082] Figure 4 It is a flowchart of the method for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application Figure 3 ;

[0083] Figure 5 It is a flowchart of the method for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application Figure 4 ;

[0084] Figure 6 It is a schematic diagram of the structure of the device for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application;

[0085] Figure 7 It is a schematic diagram of the structure of the device for determining the river input load of aquaculture non-point source pollution provided by an embodiment of this application.

[0086] Through the above-mentioned accompanying drawings, specific embodiments of the present application have been shown, and there will be a more detailed description hereinafter. These drawings and written descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0087] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. 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 the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0088] In view of some regions, there are a wide variety of aquaculture species, covering multiple categories such as rice field crayfish, crabs, yellow catfish, California bass, rice field eel, the four major Chinese carps, intensively cultured crucian carp, etc. The existing technical methods have not fully considered the differences between different aquaculture species. Moreover, since the existing technology cannot yet determine the dry pond time of a single aquaculture pond, it can only provide a rough annual load estimate and cannot provide the specific spatio-temporal distribution information of aquaculture pollution emissions. There is a technical problem of low accuracy in the assessment of aquaculture non-point source pollution load.

[0089] In view of the above problems, a method, device and storage medium for determining the non-point source pollution load into rivers in aquaculture provided by the present application can comprehensively cover all aquaculture ponds in the basin by obtaining the detailed boundaries, river distributions and connectivity data of the basin to be determined, ensuring the integrity of the analysis. By using remote sensing technology to obtain the initial water surface size, characteristics, area changes and organic matter content changes of the aquaculture ponds, the accuracy and timeliness of the data are improved, providing a reliable basis for subsequent pollutant monitoring and load calculation. Further, by using remote sensing technology and data analysis means, a large amount of data can be efficiently and quickly obtained and processed, greatly shortening the time cycle for load determination. In addition, determining the aquaculture species according to the characteristics of the aquaculture ponds and combining the water surface area changes and water surface organic matter content changes to determine the dry pond time reflects its scientificity and rationality, can more accurately reflect the actual situation of the aquaculture ponds, and by combining the data obtained from pollutant monitoring and determining the pollutant output coefficient according to the aquaculture species, the accuracy of load calculation is ensured, achieving the effect of improving the accuracy rate of pollution load assessment.

[0090] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0091] Figure 1 FIG. is a schematic diagram of an application data processing system architecture provided by an embodiment of the present application. The application data processing system is a computer device. As Figure 1 shown, the above architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0092] It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the application data processing system architecture. In other feasible embodiments of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements, which can be specifically determined according to the actual application scenario and will not be limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0093] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0094] The processing device 102 can obtain the watershed boundary data, river distribution data, and river connectivity data of the watershed to be determined to determine all aquaculture ponds in the watershed to be determined; obtain the remote sensing data of the aquaculture ponds, where the remote sensing data includes the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change; determine the corresponding aquaculture species according to the aquaculture pond characteristics; determine the corresponding dry pond time according to the water surface area change and the water surface organic matter content change; monitor the pollutants in the aquaculture ponds to obtain the corresponding pollutant data; determine the pollutant output coefficient corresponding to each aquaculture species according to the pollutant data and the aquaculture species; and determine the river input load data of the watershed to be determined according to the pollutant output coefficient and the initial water surface size.

[0095] The display device 103 can also be a touch display screen or the screen of a terminal device, which is used to receive user instructions while displaying the above content to achieve interaction with the user.

[0096] It should be understood that the above processing device can be implemented by a processor reading instructions in a memory and executing the instructions, or can be implemented by a chip circuit.

[0097] In addition, the network architecture and service scenarios described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those of ordinary skill in the art will know that with the evolution of the network architecture and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0098] Figure 2 Schematic flow of the method for determining the river input load of non-point source pollution from aquaculture provided in the embodiments of the present application Figure 1 , such as Figure 2 shown, the method for determining the river input load of non-point source pollution from aquaculture provided in this embodiment includes:

[0099] S201. Obtain the watershed boundary data, river distribution data, and river connectivity data of the watershed to be determined to determine all aquaculture ponds in the watershed to be determined;

[0100] Specifically, accurately obtain the watershed boundary data, river distribution data, and river connectivity data of the watershed to be determined through digital elevation model (DEM) data and / or authoritative data from professional departments, and further determine all aquaculture ponds in the watershed to be determined based on these data.

[0101] S202. Obtain the remote sensing data of the aquaculture ponds, where the remote sensing data includes the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change;

[0102] Use satellite remote sensing technology to obtain the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change of the aquaculture ponds.

[0103] S203. Determine the corresponding aquaculture species according to the aquaculture pond characteristics;

[0104] According to the aquaculture pond characteristics, use the visual interpretation method to identify the aquaculture species in the aquaculture ponds.

[0105] Optionally, the aquaculture species include common aquaculture, Micropterus salmonides aquaculture, Pelteobagrus fulvidraco aquaculture, rice-crayfish culture, Chinese mitten crab culture, and Monopterus albus culture.

[0106] S204. Determine the corresponding dry-pond time according to the water surface area change and the water surface organic matter content change;

[0107] Use remote sensing image data to inversely analyze the water surface area change and the water surface organic matter content change of the aquaculture ponds, and use this to determine the dry-pond time of the aquaculture ponds.

[0108] S205. Monitor the pollutants in the aquaculture ponds to obtain the corresponding pollutant data;

[0109] According to the determined dry-pond time of the aquaculture pond, conduct pollutant monitoring on the aquaculture pond to further obtain the pollutant data of the aquaculture pond.

[0110] S206. Determine the pollutant output coefficient corresponding to each aquaculture variety according to the pollutant data and the aquaculture variety.

[0111] Determine the pollutant output coefficient corresponding to each aquaculture variety according to the pollutant data and the aquaculture variety of the aquaculture pond.

[0112] S207. Determine the river input load data of the basin to be determined according to the dry-pond time, the pollutant output coefficient, and the initial water surface area.

[0113] In this embodiment, the river input load data includes time distribution data and spatial distribution data. Among them, the time distribution data includes the pond sewage discharge time, and the spatial distribution data includes the location information of the aquaculture pond.

[0114] Determine the river input load data of the basin to be determined according to the pollutant output coefficient corresponding to each aquaculture variety, the dry-pond time of the aquaculture pond, and the initial water surface area.

[0115] A method for determining the river input load of aquaculture non-point source pollution provided by the present application can comprehensively cover all aquaculture ponds in the basin by obtaining the detailed boundaries, river distributions, and connectivity data of the basin to be determined, ensuring the integrity of the analysis. By using remote sensing technology to obtain the initial water surface area, characteristics, area changes, and organic matter content changes of the aquaculture pond, the accuracy and timeliness of the data are improved, providing a reliable basis for subsequent pollutant monitoring and load calculation. Further, by using remote sensing technology and data analysis means, a large amount of data can be obtained and processed efficiently and quickly, greatly shortening the time cycle for load determination. In addition, determining the aquaculture variety according to the characteristics of the aquaculture pond, and combining the water surface area change and the water surface organic matter content change to determine the dry-pond time, this process reflects its scientificity and rationality, can more accurately reflect the actual situation of the aquaculture pond, and by using the data obtained from pollutant monitoring and combining the aquaculture variety to determine the pollutant output coefficient, the accuracy of load calculation is ensured, achieving the technical effect of improving the accuracy rate of pollution load assessment.

[0116] Figure 3 It is a schematic flow chart of the method for determining the river input load of aquaculture non-point source pollution provided by the embodiment of the present application Figure 2 , such as Figure 3 shown. On the basis of the above embodiment, this embodiment elaborates in detail on the process of determining the aquaculture variety. Among them, the characteristics of the aquaculture pond include anti-escape facilities, paddy fields, the number of cages, the number of aerators per mu of water surface, and feeding machines. This process method includes:

[0117] S301. Identify whether there is an anti-escape facility within the preset range of the aquaculture pond;

[0118] In this embodiment, the anti-escape facilities include but are not limited to anti-escape nets and cement boards.

[0119] Judge whether there is an anti-escape facility within the preset range of the aquaculture pond.

[0120] S302. If there is an anti-escape facility within the preset range of the aquaculture pond, then identify whether there is a paddy field in the aquaculture pond. If there is a paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is rice-crayfish culture. If there is no paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is river crab culture;

[0121] If it is determined that there is an anti-escape facility within the preset range of the aquaculture pond, then further judge whether there is a paddy field in the aquaculture pond. If there is, then determine that the aquaculture variety of the aquaculture pond is rice-crayfish culture. Otherwise, determine that the aquaculture variety of the aquaculture pond is river crab culture.

[0122] S303. If there is no anti-escape facility within the preset range of the aquaculture pond, then identify the number of cages in the aquaculture pond. If the number of cages is greater than the preset cage threshold, then determine that the aquaculture variety of the aquaculture pond is eel culture. If the number of cages is not greater than the preset cage threshold, then identify the number of aerators per mu of water surface in the aquaculture pond;

[0123] If it is determined that there is no anti-escape facility within the preset range of the aquaculture pond, then further judge the number of cages in the aquaculture pond. If the number of cages is greater than the preset cage threshold, then determine that the aquaculture variety of the aquaculture pond is eel culture. Otherwise, identify the number of aerators per mu of water surface in the aquaculture pond.

[0124] S304. If the number of aerators per mu of water surface in the aquaculture pond is not greater than the preset threshold of the number of aerators per mu of water surface, then determine that the aquaculture variety of the aquaculture pond is ordinary aquaculture;

[0125] In this embodiment, ordinary aquatic products refer to the four major Chinese carps and / or intensively cultured crucian carps.

[0126] After determining that the number of aerators per mu of water surface in the aquaculture pond is not greater than the preset threshold of the number of aerators per mu of water surface, then determine that the aquaculture variety of the aquaculture pond is ordinary aquaculture.

[0127] S305. If the number of aerators per mu of water surface in an aquaculture pond is greater than the preset threshold of the number of aerators per mu of water surface, identify whether there is a feeding machine within the preset range of the aquaculture pond. If there is a feeding machine within the preset range of the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is yellow catfish farming. If there is no feeding machine within the preset range of the aquaculture pond, determine that the aquaculture variety of the aquaculture pond is California bass farming.

[0128] After determining that the number of aerators per mu of water surface in the aquaculture pond is greater than the preset threshold of the number of aerators per mu of water surface, further determine whether there is a feeding machine within the preset range of the aquaculture pond. If there is, determine that the aquaculture variety of the aquaculture pond is yellow catfish farming. Otherwise, determine that the aquaculture variety of the aquaculture pond is California bass farming.

[0129] The method for determining the river input load of aquaculture non-point source pollution provided by the embodiments of the present application can more accurately determine the actual aquaculture variety of the aquaculture pond through detailed environmental feature identification (such as the presence or absence of anti-escape facilities, paddy fields, number of cages, number of aerators, and feeding machines). This accuracy ensures the reliability of the subsequent determination of pollutant output coefficients and the calculation of river input loads. At the same time, it considers various aquaculture modes and conditions, including rice-crayfish co-culture, river crab farming, yellow eel cage farming, common aquaculture, yellow catfish farming, and California bass farming, etc., covering a wide range of aquaculture practices, and thus ensures the applicability of this method in different regions and different aquaculture conditions, thereby achieving the technical effect of improving the accuracy of pollution load assessment.

[0130] Figure 4 It is a flow schematic diagram of the method for determining the river input load of aquaculture non-point source pollution provided by the embodiments of the present application Figure 3 , such as Figure 4 shown. On the basis of the above embodiments, this embodiment details the determination process of the dry pond time, including:

[0131] S401. If the change in water surface area is greater than the preset threshold of water surface area change, determine that the aquaculture pond has undergone dry pond operation, and determine the mid-value of the two satellite transit times corresponding to the change in water surface area as the dry pond time of the aquaculture pond;

[0132] If the change in the water surface area of the aquaculture pond is greater than the preset threshold of water surface area change, determine that the aquaculture pond has undergone dry pond, and take the mid-value of the two satellite transit times corresponding to the change in water surface area as the dry pond time of the aquaculture pond.

[0133] S402. If the change in water surface area is not greater than the preset threshold of water surface area change, compare the change in water surface organic matter content with the preset threshold of water surface organic matter content change;

[0134] In this embodiment, the change in the organic matter content of the water surface refers to the absolute value of the difference in the organic matter content of the water surface before and after within the satellite revisit period.

[0135] If the change in the water surface area of the aquaculture pond is less than or equal to the preset water surface area change threshold, then continue to numerically compare the change in the organic matter content of the water surface with the preset change threshold of the organic matter content of the water surface.

[0136] S403. If the change in the organic matter content of the water surface is greater than the preset change threshold of the organic matter content of the water surface, then determine that the aquaculture pond has been dried out, and determine the mid-value of the two satellite transit times corresponding to the change in the organic matter content of the water surface as the dry-out time of the aquaculture pond;

[0137] After determining that the change in the organic matter content of the water surface is greater than the preset change threshold of the organic matter content of the water surface, then determine that the aquaculture pond has been dried out, and take the mid-value of the two satellite transit times corresponding to the change in the organic matter content of the water surface.

[0138] S404. If the change in the organic matter content of the water surface is not greater than the preset change threshold of the organic matter content of the water surface, then determine that the aquaculture pond has not been dried out.

[0139] After determining that the change in the organic matter content of the water surface is less than or equal to the preset change threshold of the organic matter content of the water surface, then determine that the aquaculture pond has not been dried out.

[0140] The method for determining the riverine load of non-point source pollution from aquaculture provided by the embodiments of the present application can accurately determine whether the aquaculture pond has been dried out through the change in the water surface area and the change in the organic matter content recorded by the satellite transit time. This method of judgment based on actual observation data ensures the reliability of the subsequent determination of the pollutant export coefficient and the calculation of the riverine load. At the same time, the mid-value of the satellite transit time is used as the basis for determining the dry-out time, ensuring the timeliness and accuracy of the time judgment. In addition, compared with the traditional manual on-site investigation method, by relying on satellite remote sensing and data analysis technology, not only the labor cost and time cost are greatly reduced, but also a larger range and more continuous monitoring can be achieved, further improving the monitoring efficiency and achieving the technical effect of improving the accuracy of pollution load assessment.

[0141] Figure 5 It is a schematic flow of the method for determining the riverine load of non-point source pollution from aquaculture provided by the embodiments of the present application Figure 4 , as Figure 5 shown, on the basis of the above embodiments, this embodiment further elaborates on the process of determining the riverine load data of the watershed to be determined. Among them, the pollutant data includes the chemical oxygen demand (COD) concentration data and the water depth data of the aquaculture pond, and the determination method includes:

[0142] S501. Monitor aquaculture ponds to obtain chemical oxygen demand concentration data and water depth data of the aquaculture ponds;

[0143] Continuously monitor the aquaculture ponds to further obtain chemical fluorine content concentration data and water depth data of the aquaculture ponds.

[0144] S502. For each aquaculture species, determine n aquaculture ponds, where n is any positive integer;

[0145] For each aquaculture species within the basin, randomly select n aquaculture ponds.

[0146] S503. Calculate the average value of the chemical oxygen demand concentration data of the n aquaculture ponds;

[0147] Respectively calculate the average value of the chemical oxygen demand concentration data of the above n aquaculture ponds.

[0148] S504. Calculate the average value of the water depth data of the n aquaculture ponds;

[0149] Continue to calculate the average value of the water depth data of the above n aquaculture ponds.

[0150] S505. According to the average value of the chemical oxygen demand concentration data and the average value of the water depth data, determine the pollutant output coefficient corresponding to each aquaculture species;

[0151] Specifically, taking the aquaculture species of Pelteobagrus fulvidraco as an example, according to the average value of the chemical oxygen demand concentration data and the average value of the water depth data, through the following formula, determine the pollutant output coefficient corresponding to Pelteobagrus fulvidraco:

[0152]

[0153] Among them, O COD is the average COD output coefficient per mu of Pelteobagrus fulvidraco (kg / mu), P 均 is the average value of the COD concentration (mg / L) when the three Pelteobagrus fulvidraco fish ponds are dried, H 均 is the average water depth (m) before the three Pelteobagrus fulvidraco fish ponds are dried, 666.667 is a constant, indicating that there are 666.667 m 2 in one mu of land; 1000 is a constant, used to convert the unit of COD concentration from mg / L to kg / m 3 .

[0154] It should be noted that if the aquaculture species is rice field crayfish, the average drainage depth and drainage pollutant concentration during the two main drainage processes of rice field crayfish farming need to be monitored separately.

[0155] S506. Determine the aquaculture area corresponding to the aquaculture species according to the initial water surface area of the aquaculture pond corresponding to the aquaculture species;

[0156] Further determine the aquaculture area corresponding to the aquaculture species according to the initial water surface area of the aquaculture pond corresponding to the aquaculture species.

[0157] S507. Determine the spatial distribution data of the river input load of the basin to be determined according to the average value of the aquaculture area and the pollutant output coefficient;

[0158] According to the average value of the aquaculture area and the pollutant output coefficient, the river input amount of a certain specific pollutant in the aquaculture ponds of the basin to be determined can be obtained through the following formula:

[0159]

[0160] Wherein, Q i is the river input amount of a certain specific pollutant in the aquaculture pond (kg), O i is the average output coefficient per mu of a certain specific pollutant in the aquaculture pond (kg / mu); S i is the area of the aquaculture pond (m 2 ); i can be but is not limited to COD, ammonia nitrogen, total phosphorus or total nitrogen.

[0161] Since the pollutants input into the river from the aquaculture ponds are considered to be discharged into the river closest to the aquaculture ponds, the spatial distribution data of the river input load of the basin to be determined can be further determined according to the river input amount of a certain specific pollutant in the aquaculture ponds.

[0162] S508. Determine the time distribution data of the river input load of the basin to be determined according to the dry pond time.

[0163] Specifically, further determine the pond sewage discharge time of the basin to be determined according to the dry pond time.

[0164] The method for determining the river input load of aquaculture non-point source pollution provided by the embodiments of the present application provides a systematic framework for evaluating the impact of aquaculture non-point source pollution on rivers by comprehensively considering chemical oxygen demand concentration data, water depth data, aquaculture species and aquaculture area, which helps to more comprehensively understand the sources and distributions of pollutants. At the same time, by monitoring multiple aquaculture ponds and calculating the average value, the contingency and errors of single pond data can be reduced, the accuracy of pollutant concentration and water depth data can be improved, which helps to improve the accuracy and reliability of pollutant output coefficients. In addition, determining the pollutant output coefficient based on actual monitoring data ensures that it can be adjusted and optimized according to the aquaculture practices and conditions of specific regions, improves the flexibility of the evaluation process, and achieves the technical effect of improving the accuracy rate of pollution load evaluation.

[0165] Figure 6This is a schematic structural diagram of a device for determining the river input load of non-point source pollution in aquaculture provided by an embodiment of the present application. The device of this embodiment can be in the form of software and / or hardware. As Figure 6 shown, the device 600 for determining the river input load of non-point source pollution in aquaculture provided by an embodiment of the present application includes: a first processing module 601, an acquisition module 602, a second processing module 603, a third processing module 604, a fourth processing module 605, a first determination module 606, and a second determination module 607:

[0166] The first processing module 601 is configured to obtain the watershed boundary data, river distribution data, and river connectivity data of the watershed to be determined, so as to determine all aquaculture ponds in the watershed to be determined;

[0167] The acquisition module 602 is configured to obtain remote sensing data of the aquaculture ponds, where the remote sensing data includes the initial water surface size, aquaculture pond characteristics, water surface area change, and water surface organic matter content change;

[0168] The second processing module 603 is configured to determine the corresponding aquaculture species according to the aquaculture pond characteristics;

[0169] The third processing module 604 is configured to determine the corresponding dry pond time according to the water surface area change and the water surface organic matter content change;

[0170] The fourth processing module 605 is configured to monitor the pollutants in the aquaculture ponds to obtain the corresponding pollutant data;

[0171] The first determination module 606 is configured to determine the pollutant output coefficient corresponding to each aquaculture species according to the pollutant data and the aquaculture species;

[0172] The second determination module 607 is configured to determine the river input load data of the watershed to be determined according to the dry pond time, the pollutant output coefficient, and the initial water surface size, where the river input load data includes time distribution data and spatial distribution data.

[0173] In a possible implementation manner, the second processing module 603 is further configured to:

[0174] Aquaculture species;

[0175] The aquaculture species include common aquaculture, Micropterus salmonides aquaculture, Pelteobagrus fulvidraco aquaculture, rice-crayfish culture, Chinese mitten crab culture, and Monopterus albus culture.

[0176] In a possible implementation manner, the aquaculture pond characteristics include anti-escape facilities, paddy fields, the number of cages, the number of aerators per mu of water surface, and feeding machines;

[0177] Correspondingly, the second processing module 603 is further configured to:

[0178] Identify whether there is an escape prevention facility within the preset range of the aquaculture pond;

[0179] If there is an escape prevention facility within the preset range of the aquaculture pond, then identify whether there is a paddy field in the aquaculture pond. If there is a paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is rice-crayfish culture. If there is no paddy field in the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is river crab culture;

[0180] If there is no escape prevention facility within the preset range of the aquaculture pond, then identify the number of cages in the aquaculture pond. If the number of cages is greater than the preset cage threshold, then determine that the aquaculture variety of the aquaculture pond is ricefield eel culture. If the number of cages is not greater than the preset cage threshold, then identify the number of aerators per mu of water surface in the aquaculture pond;

[0181] If the number of aerators per mu of water surface in the aquaculture pond is not greater than the preset aerator threshold per mu of water surface, then determine that the aquaculture variety of the aquaculture pond is ordinary aquaculture;

[0182] If the number of aerators per mu of water surface in the aquaculture pond is greater than the preset aerator threshold per mu of water surface, then identify whether there is a feeding machine within the preset range of the aquaculture pond. If there is a feeding machine within the preset range of the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is yellow catfish culture. If there is no feeding machine within the preset range of the aquaculture pond, then determine that the aquaculture variety of the aquaculture pond is California bass culture.

[0183] In a possible implementation manner, the third processing module 604 is further configured to:

[0184] If the change in water surface area is greater than the preset water surface area change threshold, then determine that the aquaculture pond has undergone a dry pond operation, and determine the middle value of the two satellite transit times corresponding to the change in water surface area as the dry pond time of the aquaculture pond;

[0185] If the change in water surface area is not greater than the preset water surface area change threshold, then compare the change in water surface organic matter content with the preset water surface organic matter content change threshold;

[0186] If the change in water surface organic matter content is greater than the preset water surface organic matter content change threshold, then determine that the aquaculture pond has undergone a dry pond operation, and determine the middle value of the two satellite transit times corresponding to the change in water surface organic matter content as the dry pond time of the aquaculture pond;

[0187] If the change in water surface organic matter content is not greater than the preset water surface organic matter content change threshold, then determine that the aquaculture pond has not undergone a dry pond operation.

[0188] In a possible implementation manner, the fourth processing module 605 is further configured to:

[0189] Monitor aquaculture ponds to obtain data on the chemical oxygen demand concentration and water depth in the aquaculture ponds.

[0190] In a possible implementation manner, the first determination module 606 is further configured to:

[0191] For each aquaculture variety, determine n aquaculture ponds, where n is any positive integer;

[0192] Calculate the average value of the chemical oxygen demand concentration data of the n aquaculture ponds;

[0193] Calculate the average value of the water depth data of the n aquaculture ponds;

[0194] Determine the pollutant output coefficient corresponding to each aquaculture variety according to the average value of the chemical oxygen demand concentration data and the average value of the water depth data.

[0195] In a possible implementation manner, the second determination module 607 is further configured to:

[0196] Determine the aquaculture area corresponding to the aquaculture variety according to the initial water surface area of the aquaculture pond corresponding to the aquaculture variety;

[0197] Determine the spatial distribution data of the river input load of the to-be-determined basin according to the aquaculture area and the average value of the pollutant output coefficient;

[0198] Determine the time distribution data of the river input load of the to-be-determined basin according to the dry pond time.

[0199] The device for determining the river input load of aquaculture non-point source pollution provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0200] Figure 7 This is a schematic structural diagram of the device for determining the river input load of aquaculture non-point source pollution provided in the embodiments of the present application. As Figure 7 shown, the electronic device 700 provided in this embodiment includes: at least one processor 701 and a memory 702. Optionally, the device 700 further includes a communication component 703. Among them, the processor 701, the memory 702, and the communication component 703 are connected through a bus.

[0201] In the specific implementation process, at least one processor 701 executes the computer execution instructions stored in the memory 702, so that at least one processor 701 executes the above method.

[0202] The specific implementation process of the processor 701 can refer to the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0203] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0204] The memory may include a high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk memory.

[0205] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

[0206] This application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0207] This application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the above method is implemented.

[0208] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0209] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0210] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the couplings or direct couplings or communication connections shown or discussed among each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0211] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0212] Furthermore, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can physically exist alone, or two or more units can be integrated in one unit.

[0213] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs and other various media that can store program codes.

[0214] Those of ordinary skill in the art will understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disk, or optical disc that can store program code.

[0215] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for determining the load of aquaculture non-point source pollution entering a river, characterized in that: include: Acquire watershed boundary data, river distribution data and river connectivity data of the watershed to be determined, so as to determine all aquaculture ponds in the watershed to be determined; Acquiring remote sensing data of the aquaculture pond, wherein the remote sensing data includes initial water surface size, aquaculture pond characteristics, changes in water surface area, and changes in organic matter content in the water surface; Determine the corresponding breeding species according to the characteristics of the breeding pond; Determining the corresponding pond drying time according to the changes in the water surface area and the organic matter content in the water surface; Conducting pollutant monitoring on the aquaculture pond to obtain corresponding pollutant data; Determine the pollutant output coefficient corresponding to each of the aquaculture species according to the pollutant data and the aquaculture species; The river inflow load data of the to-be-determined river basin are determined according to the dry pond time, the pollutant output coefficient and the initial water surface size, wherein the river inflow load data include the time distribution data of the river inflow load and the spatial distribution data of the river inflow load.

2. The method according to claim 1, characterized in that The cultured species include common aquaculture, California bass culture, yellow catfish culture, rice-shrimp culture, river crab culture and yellow eel culture.

3. The method according to claim 2, characterized in that The characteristics of the breeding ponds include escape prevention facilities, rice fields, number of cages, number of water surface aerators per mu, and feeders; Accordingly, the determining of the corresponding breeding species according to the characteristics of the breeding pond includes: Identifying whether there are escape prevention facilities within a preset range of the aquaculture pond; If there are escape prevention facilities within the preset range of the aquaculture pond, then identify whether there is a rice field in the aquaculture pond; if there is a rice field in the aquaculture pond, determine that the aquaculture species in the aquaculture pond is rice-shrimp aquaculture; if there is no rice field in the aquaculture pond, determine that the aquaculture species in the aquaculture pond is river crab aquaculture; If there is no anti-escape facility within the preset range of the aquaculture pond, the number of cages in the aquaculture pond is identified; if the number of cages is greater than a preset cage threshold, it is determined that the aquaculture pond is breeding rice field eels; if the number of cages is not greater than the preset cage threshold, the number of water surface aerators per mu in the aquaculture pond is identified; If the number of water surface aerators per mu in the aquaculture pond is not greater than the preset water surface aerator threshold per mu, it is determined that the aquaculture species in the aquaculture pond is ordinary aquaculture; If the number of water surface aerators per mu in the aquaculture pond is greater than a preset water surface aerator threshold per mu, it is identified whether there is a feeding machine within the preset range of the aquaculture pond. If there is a feeding machine within the preset range of the aquaculture pond, it is determined that the breeding species in the aquaculture pond is yellow catfish breeding. If there is no feeding machine within the preset range of the aquaculture pond, it is determined that the breeding species in the aquaculture pond is California bass breeding.

4. The method according to claim 1, characterized in that: Determining the corresponding pond drying time according to the change in the water surface area and the change in the organic matter content on the water surface includes: If the water surface area change is greater than a preset water surface area change threshold, it is determined that the aquaculture pond has been dried up, and the middle value of the two satellite transit times corresponding to the water surface area change is determined as the drying time of the aquaculture pond; If the water surface area change is not greater than the preset water surface area change threshold, then comparing the water surface organic matter content change with the preset water surface organic matter content change threshold; If the change in the water surface organic matter content is greater than the preset water surface organic matter content change threshold, it is determined that the aquaculture pond has been dried up, and the middle value of the two satellite transit times corresponding to the change in the water surface organic matter content is determined as the drying time of the aquaculture pond; If the change in the water surface organic matter content is not greater than the preset water surface organic matter content change threshold, it is determined that the aquaculture pond is not dry-ponded.

5. The method according to any one of claims 1 to 4, characterized in that: The pollutant monitoring of the aquaculture pond to obtain corresponding pollutant data includes: The aquaculture pond is monitored to obtain chemical oxygen concentration data and water depth data of the aquaculture pond.

6. The method according to claim 5, characterized in that Determining the pollutant output coefficient corresponding to each of the aquaculture species according to the pollutant data and the aquaculture species includes: For each of the aquaculture species, n aquaculture ponds are determined, wherein n is an arbitrary positive integer; Calculating the average value of the chemical oxygen concentration data of the n aquaculture ponds; Calculating the average of the water depth data of the n aquaculture ponds; The pollutant output coefficient corresponding to each of the aquaculture species is determined according to the average value of the chemical oxygen concentration data and the average value of the water depth data.

7. The method according to any one of claims 1 to 4, characterized in that: The determining of the river load data of the to-be-determined river basin according to the dry pond time, the pollutant output coefficient and the initial water surface size includes: Determining the breeding area corresponding to the breeding species according to the initial water surface size of the aquaculture pond corresponding to the breeding species; Determining the spatial distribution data of the river inflow load of the to-be-determined watershed according to the average values ​​of the aquaculture area and the pollutant output coefficient; According to the dry pond time, the time distribution data of the river inflow load of the to-be-determined watershed is determined.

8. A device for determining the load of aquaculture non-point source pollution entering a river, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for determining the river load of aquaculture non-point source pollution as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for determining the load of aquaculture non-point source pollution entering a river as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the method for determining the load of aquaculture non-point source pollution entering a river as described in any one of claims 1 to 7.

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