Ecological quality analysis method, device and equipment based on offshore area
Through deep learning methods, the water quality and water depth prediction model was constructed, which solved the problems of quantitative evaluation of ecological quality in nearshore waters and the acquisition of spatial changes, and achieved efficient and accurate ecological quality analysis.
Patent Information
- Application Number
- CN202510077428.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-17
- Publication Date
- 2025-06-13
AI Technical Summary
It is difficult for the existing technology to conduct quantitative evaluation of ecological quality in nearshore waters and obtain spatial change information, and it mainly relies on qualitative evaluation of single or multi-index evaluation systems.
By obtaining water quality data, water depth data and band data in remote sensing images in nearshore waters, deep learning methods are used to build water quality prediction models and water depth prediction models, obtain water quality and water depth prediction data, and calculate the pollution flux into the sea based on these data.
Accurate and rapid quantitative evaluation of the ecological quality of nearshore waters and the acquisition of spatial change information, improving the efficiency and accuracy of ecological quality analysis.
Smart Images
Figure CN120146648A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of geographic information technology, and particularly to an ecological quality analysis method, device, equipment and storage medium based on the nearshore sea area. Background Art
[0002] Carrying out a fine evaluation of the ecological quality of the nearshore sea area is of great significance for improving the marine ecological environment. At present, the evaluation of the ecological quality of the nearshore sea area is mainly carried out by using a single-index evaluation system and a multi-index evaluation system. Most of these studies obtain a qualitative evaluation of the ecological quality by using statistical survey data such as statistical yearbooks, and cannot obtain a quantitative evaluation of the ecological quality and spatial variation information. Summary of the Invention
[0003] Based on this, the purpose of the present invention is to provide an ecological quality analysis method, device, equipment and storage medium based on the nearshore sea area. By obtaining water quality data, water depth data and band data in the remote sensing images of the nearshore sea area, and using deep learning methods, corresponding water quality prediction models and water depth prediction models are constructed to accurately and quickly obtain water quality prediction data and water depth prediction data of the nearshore sea area, and based on the water quality prediction data and water depth prediction data, pollution input flux prediction data of the nearshore sea area are obtained.
[0004] In a first aspect, an embodiment of the present application provides an ecological quality analysis method based on the nearshore sea area, including the following steps:
[0005] Obtain spectral reflectance data, land use data and the location data of the seawater aquaculture area of the target nearshore sea area;
[0006] Input the spectral reflectance data of the target nearshore sea area into a preset water quality inversion model to obtain water quality inversion data of the target nearshore sea area;
[0007] According to the land use data, the location data of the seawater aquaculture area and the water quality inversion data of the target nearshore sea area, use spatial interpolation methods to construct a multi-index parameter set of the target nearshore sea area, where the multi-index parameter set includes several index parameters corresponding to several aspect index parameter layers of several types;
[0008] Adopt the upper and lower bound sequence entropy value method, and according to the upper and lower bound parameters of several evaluation grades corresponding to several preset index parameters, obtain weight parameters corresponding to several index parameters of several aspect index parameter layers;
[0009] According to several index parameters of several aspect index parameter layers and the weight parameters corresponding to the index parameters, obtain an ecological quality comprehensive index, and based on the ecological quality comprehensive index, obtain the ecological quality analysis result of the target nearshore sea area.
[0010] In a second aspect, an embodiment of the present application provides an ecological quality analysis device based on the coastal sea area, including:
[0011] A data acquisition module, configured to acquire spectral reflection data, land use data, and seawater aquaculture area location data of a target coastal sea area;
[0012] A water quality inversion module, configured to input the spectral reflection data of the target coastal sea area into a preset water quality inversion model to obtain water quality inversion data of the target coastal sea area;
[0013] An index parameter set construction module, configured to construct a multi - index parameter set of the target coastal sea area by using a spatial interpolation method according to the land use data, seawater aquaculture area location data, and water quality inversion data of the target coastal sea area, where the multi - index parameter set includes a plurality of index parameters corresponding to several types of aspect index parameter layers;
[0014] A weight calculation module, configured to use the upper and lower bound sequence entropy value method to obtain weight parameters corresponding to the index parameters of several aspect index parameter layers according to the upper and lower bound parameters of several evaluation grades corresponding to the preset several index parameters;
[0015] An ecological quality analysis module, configured to obtain an ecological quality comprehensive index according to the index parameters of several aspect index parameter layers and the corresponding weight parameters of the index parameters, and obtain an ecological quality analysis result of the target coastal sea area according to the ecological quality comprehensive index.
[0016] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the ecological quality analysis method based on the coastal sea area as described in the first aspect are implemented.
[0017] In a fourth aspect, an embodiment of the present application provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ecological quality analysis method based on the coastal sea area as described in the first aspect are implemented.
[0018] In the embodiments of the present application, an ecological quality analysis method, device, equipment, and storage medium based on the coastal sea area are provided. Based on the spectral reflection data, land use data, and seawater aquaculture area location data in the remote sensing image of the coastal sea area obtained, a multi - index parameter set of the target coastal sea area is constructed by using a spatial interpolation method for constructing an ecological quality comprehensive index, making full use of the spatial information of the coastal sea area and improving the efficiency and accuracy of ecological quality analysis.
[0019] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Description of the Drawings
[0020] Figure 1 It is a schematic flowchart of an ecological quality analysis method based on the nearshore sea area provided for an embodiment of the present application;
[0021] Figure 2 It is a schematic flowchart of S6 in the ecological quality analysis method based on the nearshore sea area provided for another embodiment of the present application;
[0022] Figure 3 It is a schematic flowchart of S3 in the ecological quality analysis method based on the nearshore sea area provided for an embodiment of the present application;
[0023] Figure 4 It is a schematic flowchart of S4 in the ecological quality analysis method based on the nearshore sea area provided for an embodiment of the present application;
[0024] Figure 5 It is a schematic flowchart of S41 in the ecological quality analysis method based on the nearshore sea area provided for an embodiment of the present application;
[0025] Figure 6 It is a schematic flowchart of S42 in the ecological quality analysis method based on the nearshore sea area provided for an embodiment of the present application;
[0026] Figure 7 It is a schematic structural diagram of an ecological quality analysis device based on the nearshore sea area provided for an embodiment of the present application;
[0027] Figure 8 It is a schematic structural diagram of a computer device provided for an embodiment of the present application. Detailed Embodiments
[0028] Here, exemplary embodiments will be described in detail, and the examples 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.
[0029] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
[0030] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".
[0031] Please refer to Figure 1 , Figure 1 which is a schematic flow diagram of an ecological quality analysis method based on the nearshore sea area provided for an embodiment of this application. The method includes the following steps:
[0032] S1: Obtain the spectral reflectance data, land use data, and seawater aquaculture area location data of the target nearshore sea area.
[0033] The execution subject of the ecological quality analysis method based on the nearshore sea area is an analysis device for the ecological quality analysis method based on the nearshore sea area (hereinafter referred to as the analysis device). In an optional embodiment, the analysis device may be a computer device, which may be a server, or a server cluster formed by combining multiple computer devices.
[0034] In this embodiment, the analysis device can obtain the spectral reflectance data, land use data, and seawater aquaculture area location data input by the user. Among them, the spectral reflectance data includes the spectral reflectance of several bands; the land use data includes the land use degree classification index and the land classification area percentage of several land use types; the land use types include construction land, cultivated land, garden land, forest land, and water area; the land use degree classification index includes the land use degree classification indexes corresponding to urban settlement land level, agricultural land level, grass, forest, and water land level; the seawater aquaculture area location data includes the position coordinate parameters of several seawater aquaculture areas, and the seawater aquaculture area is an area constructed by expanding a preset radius distance centered on a preset seawater aquaculture point.
[0035] S2: Input the spectral reflectance data of the target inshore sea area into a preset water quality inversion model to obtain the water quality inversion data of the target inshore sea area.
[0036] The water quality inversion model is a neural network model trained by an artificial intelligence algorithm, with the spectral reflectance of bands as independent variables and water quality parameters as dependent variables.
[0037] In this embodiment, the analysis device inputs the spectral reflectance data of the target inshore sea area into a preset water quality inversion model, performs water quality inversion based on the spectral reflectance of several bands in the spectral reflectance data, and obtains the water quality inversion data of the target inshore sea area. Among them, the water quality inversion data includes several types of water quality inversion parameters, including inorganic nitrogen, reactive phosphate, dissolved oxygen, chemical oxygen demand, and chlorophyll a.
[0038] In an alternative embodiment, it further includes step S6: Construct the water quality inversion model. Step S6 is before step S2. Please refer to Figure 2 , Figure 2 which is a schematic flowchart of S6 in the ecological quality analysis method based on inshore sea areas provided by another embodiment of this application, including steps S61 - S62, specifically as follows:
[0039] S61: Obtain the spectral reflectance data and water quality sampling data of several sampling points in a sample inshore sea area; perform correlation analysis based on the spectral reflectance of several bands in the spectral reflectance data of several sampling points in the sample inshore sea area and several categories of water quality sampling parameters in the water quality sampling data to obtain the associated bands of several categories of water quality sampling parameters.
[0040] In this embodiment, the analysis device obtains the spectral reflectance data and water quality sampling data of several sampling points in a sample inshore sea area. Among them, the water quality sampling data includes several types of water quality sampling parameters.
[0041] The analysis device performs correlation analysis based on the spectral reflectance of several bands in the spectral reflectance data of several sampling points in the sample inshore sea area and several categories of water quality sampling parameters in the water quality sampling data to obtain the associated bands of several categories of water quality sampling parameters. The correlation analysis can be the Pearson analysis method, which is not limited here.
[0042] S62: According to the associated bands of water quality sampling parameters of several categories, combine the water quality sampling parameters of several categories at the same sampling point in the sample nearshore sea area with the spectral reflectance of the corresponding associated bands to construct a sample data set; divide the sample data set into a training data set and a test set, and train the neural network model to be trained according to the training data set and the test set to obtain a target neural network model as the water quality inversion model.
[0043] In this embodiment, the analysis device combines the water quality sampling parameters of several categories at the same sampling point in the sample nearshore sea area with the spectral reflectance of the corresponding associated bands according to the associated bands of water quality sampling parameters of several categories to construct a sample data set, where the sample data set includes several data groups at several sampling points, and the data group includes the water quality sampling parameters and the spectral reflectance of the corresponding associated bands.
[0044] The analysis device divides the sample data set into a training data set and a test set, and trains the neural network model to be trained according to the training data set and the test set to obtain a target neural network model as the water quality inversion model. In an optional embodiment, the analysis device uses the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) to evaluate the accuracy of the water quality inversion model. The closer the R2 value is to 1, the better the model fitting effect. The smaller the values of RMSE, MSE, and MAE, the higher the model accuracy.
[0045] S3: According to the land use data, seawater aquaculture area location data, and water quality inversion data of the target nearshore sea area, use the spatial interpolation method to construct a multi-index parameter set of the target nearshore sea area.
[0046] In this embodiment, the analysis device uses the spatial interpolation method to construct a multi-index parameter set of the target nearshore sea area according to the land use data, seawater aquaculture area location data, and water quality inversion data of the target nearshore sea area, where the multi-index parameter set includes several index parameters corresponding to several types of aspect index parameter layers.
[0047] The multi-index parameter set includes a pressure index parameter layer, a state index parameter layer, and a response index parameter layer; the index parameters of the pressure index parameter layer include a land use intensity index parameter and a distance from seawater aquaculture index parameter; the index parameters of the state index parameter layer include an inorganic nitrogen index parameter, a reactive phosphate index parameter, and a chemical oxygen demand index parameter; the index parameters of the response index parameter layer include a chlorophyll a index parameter, a dissolved oxygen index parameter, and a primary productivity index parameter.
[0048] For the land use intensity index parameters and the primary productivity index parameters, please refer to Figure 3 , Figure 3 FIG. is a schematic flow chart of S3 in the ecological quality analysis method based on the nearshore sea area provided by an embodiment of the present application, including steps S31 to S33, specifically as follows:
[0049] S31: According to a plurality of preset sampling points, a plurality of sampling areas of the target nearshore sea area are obtained. According to the land use data of the plurality of sampling areas, the land use degree grading indexes and the land grading area percentages of a plurality of land use types of the plurality of sampling areas are obtained.
[0050] In this embodiment, the analysis device obtains a plurality of sampling areas of the target nearshore sea area according to a plurality of preset sampling points, and obtains the land use degree grading indexes and the land grading area percentages of a plurality of land use types of the plurality of sampling areas according to the land use data of the plurality of sampling areas.
[0051] S32: According to the land use degree grading indexes, the land grading area percentages of a plurality of land use types of the plurality of sampling areas, and a preset land use intensity comprehensive index calculation algorithm, the land use intensity comprehensive indexes of the plurality of sampling areas are obtained, and the land use intensity comprehensive indexes of the plurality of sampling areas are subjected to spatial interpolation processing to obtain the land use intensity index parameters.
[0052] The land use intensity comprehensive index calculation algorithm is:
[0053]
[0054] In the formula, S is the land use intensity comprehensive index, A l is the land use degree grading index of the l-th land use type, B l is the land grading area percentage of the l-th land use type, and L is the number of land use types.
[0055] In this embodiment, the analysis device obtains the land use intensity comprehensive indexes of the plurality of sampling areas according to the land use degree grading indexes, the land grading area percentages of a plurality of land use types of the plurality of sampling areas, and a preset land use intensity comprehensive index calculation algorithm, and performs spatial interpolation processing on the land use intensity comprehensive indexes of the plurality of sampling areas to obtain the land use intensity index parameters.
[0056] S33: Obtain the primary productivity of several sampling areas according to the chlorophyll a in the water quality inversion data of several sampling areas and a preset primary productivity calculation algorithm, and perform spatial interpolation processing on the primary productivity of several sampling areas to obtain the primary productivity index parameter.
[0057] The primary productivity calculation algorithm is as follows:
[0058] pp = 302.2 * chla - 242.96
[0059] In the formula, pp is the primary productivity, and chla is the chlorophyll a.
[0060] In this embodiment, the analysis device obtains the primary productivity of several sampling areas according to the chlorophyll a in the water quality inversion data of several sampling areas and a preset primary productivity calculation algorithm, and performs spatial interpolation processing on the primary productivity of several sampling areas to obtain the primary productivity index parameter.
[0061] For the distance to seawater aquaculture distance index parameter, the analysis device obtains the distances between several sampling areas and several seawater aquaculture areas, takes the minimum distance parameter as the distance to seawater aquaculture, obtains several sampling areas, and performs spatial interpolation processing on the distances to seawater aquaculture of several sampling areas to obtain the distance to seawater aquaculture distance index parameter.
[0062] For the inorganic nitrogen index parameter, reactive phosphate index parameter, chemical oxygen demand index parameter, chlorophyll a index parameter, and dissolved oxygen index parameter, the analysis device obtains the inorganic nitrogen, reactive phosphate, chemical oxygen demand, and chlorophyll a of several sampling areas, and respectively performs spatial interpolation processing on the inorganic nitrogen, reactive phosphate, chemical oxygen demand, and chlorophyll a of several sampling areas to obtain the inorganic nitrogen index parameter, reactive phosphate index parameter, and chemical oxygen demand index parameter.
[0063] S4: Adopt the upper and lower bound sequence entropy method to obtain the weight parameters corresponding to several index parameters of several aspect index parameter layers according to the upper and lower bound parameters of several evaluation grades corresponding to several index parameters.
[0064] In this embodiment, the analysis device adopts the upper and lower bound sequence entropy method to obtain the weight parameters corresponding to several index parameters of several aspect index parameter layers according to the upper and lower bound parameters of several evaluation grades corresponding to several index parameters.
[0065] Please refer to Figure 4 , Figure 4Schematic flow diagram of S4 in the ecological quality analysis method based on the nearshore sea area provided by an embodiment of the present application, including steps S41 to S42, specifically as follows:
[0066] S41: Obtain the upper-bound sequence information entropy and lower-bound sequence information entropy corresponding to a number of index parameters according to the upper-bound parameters, lower-bound parameters of a number of evaluation levels corresponding to the preset number of index parameters and the preset upper and lower bound sequence information entropy calculation algorithm.
[0067] The upper and lower bound sequence information entropy calculation algorithm is:
[0068]
[0069] In the formula, is the upper-bound sequence information entropy of the j-th index parameter, n is the number of index parameters, M is the number of evaluation levels, A mj is the upper-bound parameter of the m-th evaluation level of the j-th index parameter, is the lower-bound sequence information entropy of the j-th index parameter, B mj is the lower-bound parameter of the m-th evaluation level of the j-th index parameter.
[0070] In this embodiment, the analysis device obtains the upper-bound sequence information entropy and lower-bound sequence information entropy corresponding to a number of index parameters according to the upper-bound parameters, lower-bound parameters of a number of evaluation levels corresponding to the preset number of index parameters and the preset upper and lower bound sequence information entropy calculation algorithm.
[0071] S42: Obtain the weight parameters corresponding to a number of index parameters corresponding to a number of aspect index parameter layers according to the upper-bound sequence information entropy, lower-bound sequence information entropy corresponding to a number of index parameters and the preset weight parameter calculation algorithm.
[0072] The weight parameter calculation algorithm is:
[0073]
[0074] In the formula, w j is the weight parameter corresponding to the j-th index parameter.
[0075] In this embodiment, the analysis device obtains the weight parameters corresponding to a number of index parameters corresponding to a number of aspect index parameter layers according to the upper-bound sequence information entropy, lower-bound sequence information entropy corresponding to a number of index parameters and the preset weight parameter calculation algorithm.
[0076] S5: Obtain the ecological quality comprehensive index according to a number of index parameters corresponding to a number of aspect index parameter layers and the weight parameters corresponding to the index parameters, and obtain the ecological quality analysis result of the target nearshore sea area according to the ecological quality comprehensive index.
[0077] In this embodiment, the analysis device obtains the comprehensive ecological quality index according to a number of index parameters corresponding to several aspect index parameter layers and the corresponding weight parameters of the index parameters, and obtains the ecological quality analysis result of the target coastal sea area according to the comprehensive ecological quality index.
[0078] Based on the spectral reflection data, land use data, and seawater aquaculture area location data in the remote sensing image of the coastal sea area obtained, a spatial interpolation method is used to construct a multi-index parameter set of the target coastal sea area for constructing the comprehensive ecological quality index, making full use of the spatial information of the coastal sea area and improving the efficiency and accuracy of ecological quality analysis.
[0079] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of S41 in the ecological quality analysis method based on the coastal sea area provided by an embodiment of the present application, including steps S51 to S52, specifically as follows:
[0080] S51: According to the type of the aspect index parameter layer, standardize the several index parameters corresponding to the aspect index parameter layer to obtain the several index parameters corresponding to the aspect index parameter layer after standardization processing.
[0081] In this embodiment, the analysis device standardizes the several index parameters corresponding to the aspect index parameter layer according to the type of the aspect index parameter layer to obtain the several index parameters corresponding to the aspect index parameter layer after standardization processing. By standardizing the index parameters, subsequent comparison and calculation can be made more convenient, improving the efficiency of ecological quality analysis in the coastal area.
[0082] For the positive index type, the greater it is, the greater the positive impact on the stability of the ecosystem. Conversely, for the negative index type, and for the moderate index type, the best effect is achieved when the index is close to a certain interval. Please refer to Figure 6 , Figure 6 which is a schematic flowchart of S51 in the ecological quality analysis method based on the coastal sea area provided by an embodiment of the present application, including steps S511 to S512, specifically as follows:
[0083] S511: If the type of the aspect index parameter layer is a positive index type, standardize the several index parameters corresponding to the aspect index parameter layer according to a preset positive index standardization algorithm to obtain the several index parameters corresponding to the aspect index parameter layer after standardization processing.
[0084] The positive index standardization algorithm is:
[0085]
[0086] Wherein, X ij is the j-th index parameter of the i-th aspect index parameter layer, and X' ij is the j-th index parameter of the i-th aspect index parameter layer after standardization processing, min(·) is the minimum value function, and max(·) is the maximum value function.
[0087] If the type of the aspect index parameter layer is a positive index type, in this embodiment, the analysis device performs standardization processing on several index parameters corresponding to the aspect index parameter layer according to a preset positive index standardization algorithm, and obtains several index parameters corresponding to the aspect index parameter layer after standardization processing.
[0088] S512: If the type of the aspect index parameter layer is a moderate index type, perform standardization processing on several index parameters corresponding to the aspect index parameter layer according to a preset moderate index standardization algorithm, and obtain several index parameters corresponding to the aspect index parameter layer after standardization processing.
[0089] The moderate index standardization algorithm is:
[0090]
[0091] Wherein, X' ij is the j-th index parameter of the i-th aspect index parameter layer after standardization processing.
[0092] If the type of the aspect index parameter layer is a moderate index type, in this embodiment, the analysis device performs standardization processing on several index parameters corresponding to the aspect index parameter layer according to a preset moderate index standardization algorithm, and obtains several index parameters corresponding to the aspect index parameter layer after standardization processing.
[0093] S52: Obtain the comprehensive ecological quality index according to several index parameters corresponding to the standardized processed several aspect index parameter layers, the corresponding weight parameters of the index parameters, and a preset ecological quality index algorithm, wherein the ecological quality index algorithm is:
[0094]
[0095] Wherein, U is the ecological quality index, and δ i is the weight parameter of the i-th aspect index parameter layer, and w ij is the corresponding weight parameter of the j-th index parameter of the i-th aspect index parameter layer, and X' ijThe j-th indicator parameter of the i-th aspect indicator parameter layer after standardization processing, N is the number of aspect indicator parameter layers, and n is the number of indicator parameters in the aspect indicator parameter layer.
[0096] Please refer to Figure 7 , Figure 7 FIG. 7 is a schematic structural diagram of an ecological quality analysis device based on the nearshore sea area provided by an embodiment of the present application. The device can implement all or part of the ecological quality analysis device based on the nearshore sea area through software, hardware, or a combination of both. The device 7 includes:
[0097] A data acquisition module 71, configured to acquire spectral reflection data, land use data, and seawater aquaculture area location data of a target nearshore sea area;
[0098] A water quality inversion module 72, configured to input the spectral reflection data of the target nearshore sea area into a preset water quality inversion model to obtain water quality inversion data of the target nearshore sea area;
[0099] An indicator parameter set construction module 73, configured to construct a multi-index parameter set of the target nearshore sea area by using a spatial interpolation method according to the land use data, seawater aquaculture area location data, and water quality inversion data of the target nearshore sea area, where the multi-index parameter set includes a plurality of indicator parameters corresponding to a plurality of types of aspect indicator parameter layers;
[0100] A weight calculation module 74, configured to use the upper and lower bound sequence entropy method to obtain weight parameters corresponding to a plurality of indicator parameters of a plurality of aspect indicator parameter layers according to the upper and lower bound parameters of a plurality of evaluation grades corresponding to a plurality of preset indicator parameters;
[0101] An ecological quality analysis module 75, configured to obtain an ecological quality comprehensive index according to a plurality of indicator parameters of a plurality of aspect indicator parameter layers and the corresponding weight parameters of the indicator parameters, and obtain an ecological quality analysis result of the target nearshore sea area according to the ecological quality comprehensive index.
[0102] In the embodiment of the present application, through a data acquisition module, spectral reflection data, land use data, and seawater aquaculture area location data of a target nearshore sea area are acquired; through a water quality inversion module, the spectral reflection data of the target nearshore sea area is input into a preset water quality inversion model to obtain water quality inversion data of the target nearshore sea area; through an index parameter set construction module, according to the land use data, seawater aquaculture area location data, and water quality inversion data of the target nearshore sea area, a spatial interpolation method is used to construct a multi - index parameter set of the target nearshore sea area, where the multi - index parameter set includes a number of index parameter layers corresponding to several types of aspect index parameters; through a weight calculation module, using the upper and lower bound sequence entropy method, according to the upper and lower bound parameters of several evaluation grades corresponding to a number of preset index parameters, weight parameters corresponding to a number of index parameters of several aspect index parameter layers are obtained; through an ecological quality analysis module, according to a number of index parameters of several aspect index parameter layers and the corresponding weight parameters of the index parameters, an ecological quality comprehensive index is obtained, and based on the ecological quality comprehensive index, an ecological quality analysis result of the target nearshore sea area is obtained. Based on the spectral reflection data, land use data, and seawater aquaculture area location data in the remote sensing image of the obtained nearshore sea area, a spatial interpolation method is used to construct a multi - index parameter set of the target nearshore sea area for constructing an ecological quality comprehensive index, which makes full use of the spatial information of the nearshore sea area and improves the efficiency and accuracy of ecological quality analysis.
[0103] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored on the memory 82 and executable on the processor 81; the computer device can store multiple instructions, and the instructions are suitable for being loaded and executed by the processor 81 to perform the method steps of the above - mentioned Figures 1 to 6 shown embodiment. The specific execution process can be referred to the specific description of the Figures 1 to 6 shown embodiment and will not be elaborated here.
[0104] Among them, the processor 81 may include one or more processing cores. The processor 81 uses various interfaces and circuits to connect various parts within the server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 82, and by invoking the data stored in the memory 82, it executes various functions of the ecological quality analysis device 7 based on the offshore sea area and processes data. Optionally, the processor 81 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 81 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the touch display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 81 and may be implemented separately by a single chip.
[0105] Among them, the memory 82 may include a random access memory (RAM) and may also include a read-only memory (ROM). Optionally, the memory 82 includes a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 82 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 82 may also be at least one storage device located far from the aforementioned processor 81.
[0106] The embodiment of the present application also provides a storage medium, which can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform the method steps of the above Figures 1 to 6 shown embodiments. The specific execution process can be referred to Figures 1 to 6 the specific description of the shown embodiments and will not be elaborated here.
[0107] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.
[0108] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0109] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.
[0110] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can 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. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.
[0111] The units described as separate components may or may not be physically separated, and the components displayed 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.
[0112] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.
[0113] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc.
[0114] The present invention is not limited to the above embodiments. If various modifications or deformations of the present invention do not depart from the spirit and scope of the present invention, and if these modifications and deformations fall within the scope of the claims of the present invention and equivalent technical scope, then the present invention also intends to include these modifications and deformations.
Claims
1. A method for analyzing ecological quality based on nearshore waters, characterized in that: The following steps are involved: Obtain spectral reflectance data, land use data, and marine aquaculture area location data of the target nearshore waters; Inputting the spectral reflectance data of the target nearshore waters into a preset water quality inversion model to obtain the water quality inversion data of the target nearshore waters; According to the land use data, marine aquaculture area location data and water quality inversion data of the target nearshore waters, a spatial interpolation method is used to construct a multivariate indicator parameter set of the target nearshore waters, wherein the multivariate indicator parameter set includes a plurality of indicator parameters corresponding to a plurality of types of aspect indicator parameter layers; Adopting the upper and lower bound sequence entropy method, according to the preset upper and lower bound parameters of the evaluation levels corresponding to the several indicator parameters, obtain the corresponding weight parameters of the several indicator parameters corresponding to the several aspect indicator parameter layers; According to several indicator parameters corresponding to several indicator parameter layers and corresponding weight parameters of the indicator parameters, an ecological quality comprehensive index is obtained, and according to the ecological quality comprehensive index, an ecological quality analysis result of the target nearshore waters is obtained.
2. The method for analyzing ecological quality based on nearshore waters according to claim 1, characterized in that: The water quality inversion data includes inorganic nitrogen, active phosphate, dissolved oxygen, chemical oxygen demand and chlorophyll a; the multivariate indicator parameter set includes a pressure indicator parameter layer, a state indicator parameter layer and a response indicator parameter layer; the indicator parameters of the pressure indicator parameter layer include land use intensity indicator parameters and distance to seawater aquaculture indicator parameters; the indicator parameters of the state indicator parameter layer include inorganic nitrogen indicator parameters, active phosphate indicator parameters and chemical oxygen demand indicator parameters; the indicator parameters of the response indicator parameter layer include chlorophyll a indicator parameters, dissolved oxygen indicator parameters and primary productivity indicator parameters.
3. The method for analyzing ecological quality based on nearshore waters according to claim 2 is characterized in that: The method of constructing a multivariate indicator parameter set of the target coastal waters by using a spatial interpolation method based on the land use data, the location data of the marine aquaculture area and the water quality inversion data of the target coastal waters comprises the following steps: According to a plurality of preset sampling points, a plurality of sampling areas of the target nearshore waters are obtained, and according to the land use data of the plurality of sampling areas, a land use degree classification index and a percentage of land classification area of a plurality of land use types of the plurality of sampling areas are obtained; According to the land use degree classification index of several land use types of several sampling areas, the percentage of land classification area and the preset land use intensity comprehensive index calculation algorithm, the land use intensity comprehensive index of several sampling areas is obtained, and the land use intensity comprehensive index of several sampling areas is spatially interpolated to obtain the land use intensity index parameter, wherein the land use intensity comprehensive index calculation algorithm is: In the formula, S is the comprehensive index of land use intensity, A l is the land use degree classification index of the lth land use type, B l is the percentage of land classification area of the lth land use type, and L is the number of land use types; According to the chlorophyll a in the water quality inversion data of the plurality of sampling areas and the preset primary productivity calculation algorithm, the primary productivity of the plurality of sampling areas is obtained, and the primary productivity of the plurality of sampling areas is spatially interpolated to obtain the primary productivity index parameter, wherein the primary productivity calculation algorithm is: pp = 302.2 * chla - 242.96 Where pp is the primary productivity and chla is chlorophyll a.
4. The method for analyzing ecological quality based on nearshore waters according to claim 3 is characterized in that: The method adopts the upper and lower bound sequence entropy value method, and obtains the corresponding weight parameters of several indicator parameters corresponding to several aspect indicator parameter layers according to the upper and lower bound parameters of several evaluation levels corresponding to the preset several indicator parameters, including the steps of: According to the upper limit parameters, lower limit parameters of the evaluation levels corresponding to the indicator parameters and the preset upper and lower limit sequence information entropy calculation algorithm, the upper limit sequence information entropy and the lower limit sequence information entropy corresponding to the indicator parameters are obtained, wherein the upper and lower limit sequence information entropy calculation algorithm is: In the formula, is the upper bound sequence information entropy of the jth indicator parameter, n is the number of indicator parameters, M is the number of evaluation levels, A mj is the upper bound parameter of the mth evaluation level of the jth indicator parameter, is the lower bound sequence information entropy of the jth indicator parameter, B mj is the lower bound parameter of the mth evaluation level of the jth indicator parameter; According to the upper bound sequence information entropy, lower bound sequence information entropy and a preset weight parameter calculation algorithm corresponding to the several indicator parameters, the weight parameters corresponding to the several indicator parameter layers are obtained, wherein the weight parameter calculation algorithm is: In the formula, w j is the weight parameter corresponding to the j-th indicator parameter.
5. The method for analyzing ecological quality based on nearshore waters according to claim 4 is characterized in that: The step of obtaining weight parameters corresponding to a plurality of indicator parameters corresponding to a plurality of aspect indicator parameter layers comprises the following steps: According to the type of the aspect indicator parameter layer, a plurality of indicator parameters corresponding to the aspect indicator parameter layer are standardized to obtain a plurality of indicator parameters corresponding to the plurality of aspect indicator parameter layers after the standardization; The ecological quality comprehensive index is obtained according to the several indicator parameters corresponding to the several indicator parameter layers after the standardization, the weight parameters corresponding to the indicator parameters and the preset ecological quality index algorithm, wherein the ecological quality index algorithm is: In the formula, U is the ecological quality index, δ ii is the weight parameter of the i-th aspect indicator parameter layer, w ij is the weight parameter corresponding to the jth indicator parameter of the i-th aspect indicator parameter layer, X′ ij is the jth indicator parameter of the i-th aspect indicator parameter layer after standardization, N is the number of aspect indicator parameter layers, and n is the number of indicator parameters in the aspect indicator parameter layer.
6. The method for analyzing ecological quality based on nearshore waters according to claim 5 is characterized in that: The types of the aspect indicator parameter layer include positive indicator types and negative indicator types; The step of performing standardization processing on a plurality of indicator parameters corresponding to the aspect indicator parameter layer according to the type of the aspect indicator parameter layer to obtain a plurality of indicator parameters corresponding to the plurality of aspect indicator parameter layers after the standardization processing comprises the following steps: If the type of the aspect indicator parameter layer is a positive indicator type, a plurality of indicator parameters corresponding to the aspect indicator parameter layer are standardized according to a preset positive indicator standardization algorithm to obtain a plurality of indicator parameters corresponding to the aspect indicator parameter layer after the standardization, wherein the positive indicator standardization algorithm is: Where, X ij is the jth indicator parameter of the i-th aspect indicator parameter layer, X′ ij is the jth indicator parameter of the i-th aspect indicator parameter layer after standardization, min(·) is the minimum value function, and max(·) is the maximum value function; If the type of the aspect indicator parameter layer is a moderation indicator type, a plurality of indicator parameters corresponding to the aspect indicator parameter layer are standardized according to a preset moderation indicator standardization algorithm to obtain a plurality of indicator parameters corresponding to the aspect indicator parameter layer after the standardization, wherein the moderation indicator standardization algorithm is: In the formula, X′ ij is the jth indicator parameter of the i-th aspect indicator parameter layer after standardization.
7. The method for analyzing ecological quality based on nearshore waters according to claim 6 is characterized in that: The step of inputting the spectral reflectance data of the target nearshore waters into a preset water quality inversion model to obtain the water quality inversion data of the target nearshore waters comprises the following steps: Obtain spectral reflectance data and water quality sampling data of several sampling points in the sample nearshore waters; perform correlation analysis based on the spectral reflectance of several bands in the spectral reflectance data of several sampling points in the sample nearshore waters and several categories of water quality sampling parameters in the water quality sampling data to obtain associated bands of several categories of water quality sampling parameters; According to the associated bands of several categories of water quality sampling parameters, several categories of water quality sampling parameters of the same sampling point in the sample nearshore waters are combined with the spectral reflectance of the corresponding associated bands to construct a sample data set; the sample data set is divided into a training data set and a test set, and the neural network model to be trained is trained according to the training data set and the test set to obtain a target neural network model as the water quality inversion model.
8. An ecological quality analysis device based on nearshore waters, characterized in that: include: A data acquisition module is used to obtain spectral reflectance data, land use data and marine aquaculture area location data of the target nearshore waters; A water quality inversion module, used to input the spectral reflectance data of the target nearshore waters into a preset water quality inversion model to obtain water quality inversion data of the target nearshore waters; An indicator parameter set construction module is used to construct a multivariate indicator parameter set of the target nearshore waters based on the land use data, seawater aquaculture area location data and water quality inversion data of the target nearshore waters by using a spatial interpolation method, wherein the multivariate indicator parameter set includes a plurality of indicator parameters corresponding to a plurality of types of aspect indicator parameter layers; A weight calculation module is used to obtain corresponding weight parameters of several indicator parameters corresponding to several aspect indicator parameter layers according to several preset upper and lower limit parameters of several evaluation levels corresponding to the indicator parameters by adopting the upper and lower limit sequence entropy value method; The ecological quality analysis module is used to obtain an ecological quality comprehensive index based on several indicator parameters corresponding to several indicator parameter layers and the corresponding weight parameters of the indicator parameters, and obtain the ecological quality analysis results of the target nearshore waters based on the ecological quality comprehensive index.
9. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the method for analyzing ecological quality based on nearshore waters as described in any one of claims 1 to 7 are implemented.
10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ecological quality analysis method based on the nearshore waters as described in any one of claims 1 to 7 are implemented.