Simulation analysis method and system for influence of watershed landscape pattern evolution on water quality

Abstract

The invention discloses a simulation analysis method and system for the influence of watershed landscape pattern evolution on water quality, and relates to the technical field of water quality simulation analysis, and the method comprises the following steps: collecting water quality monitoring data including image data, landform data, meteorological data and river data; constructing a water quality output model through the pre-processed river data in combination with a multiple linear regression algorithm, and further obtaining a water quality evaluation index; based on the obtained water quality evaluation index, combining a neural network algorithm to construct a drainage basin water quality simulation analysis model; according to the method, the multispectral analysis technology, the multiple linear regression algorithm, the neural network algorithm and the simulation analysis technology are closely combined with the modern information technology, and therefore the simulation analysis report of the drainage basin water quality is generated. The problem that the traditional method is difficult to analyze the dynamic influence of basin landscape pattern evolution on water quality is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality simulation analysis, and in particular to a simulation analysis method and system for the impact of watershed landscape pattern evolution on water quality. Background Technique

[0002] With the rapid development of social economy, the change of landscape pattern leads to the change of surface runoff, which in turn has a negative impact on water quality. Traditional water quality monitoring methods mainly rely on field sampling and laboratory analysis. This method is not only costly and inefficient, but also difficult to comprehensively reflect the dynamic impact of watershed landscape pattern evolution on water quality. Therefore, it is of great practical significance to establish a simulation analysis method and system that can comprehensively consider watershed landscape pattern evolution and water quality change. By combining landscape pattern evolution data and water quality models, a simulation analysis model for the impact of watershed landscape pattern evolution on water quality is constructed to realize the simulation of water quality changes under different landscape pattern scenarios, providing a scientific basis for watershed water resource management and protection. Although there have been great advances in the direction of water quality simulation analysis in the prior art, there are still some problems to be optimized. The current water quality monitoring methods are single, resulting in inaccurate simulation analysis of water quality changes. Traditional methods only analyze and judge the water quality change situation through the properties of the water quality itself, which will lead to low accuracy of water quality change simulation analysis. Therefore, how to obtain the correlation between watershed landscape pattern evolution and water quality change in the water quality monitoring through simulation analysis and realize the simulation and prediction of the process from landscape pattern change to water quality index change is the problem to be solved by the present invention. For this reason, a simulation analysis method and system for the impact of watershed landscape pattern evolution on water quality are proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide a simulation analysis method and system for the impact of watershed landscape pattern evolution on water quality to solve the problems raised in the above background technique.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows: In the first aspect, a simulation analysis method for the impact of watershed landscape pattern evolution on water quality includes the following steps: Step 1: Collect water quality monitoring data including image data, topographic and geomorphic data, meteorological data, and river data, and preprocess the collected data to provide a data basis for the implementation of subsequent module functions. Step 2: Perform multispectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtain corridor pattern evolution data. Step 3: Identify landscape patches from different types of corridor images and count the number of landscape patches. Step 4: Combine the corridor pattern evolution data, the number of landscape patches, the preprocessed topographic and geomorphic data, and the meteorological data to analyze the corridor connectivity and landscape fragmentation degree during the landscape pattern evolution process; Step 5: Through the preprocessed river data, combine the multiple linear regression algorithm to construct a water quality output model, and then obtain the water quality assessment index; Step 6: Based on the obtained water quality assessment index, combine the neural network algorithm to construct a basin water quality simulation analysis model, and realize the analysis of the correlation between the landscape pattern evolution and water quality change in the water quality monitoring basin; Step 7: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water pollution, and then generate a basin water quality simulation analysis report.

[0005] A further improvement of the technical solution of the present invention lies in: in the said Step 1, the process of collecting water quality monitoring data includes: Deploy different types of collection devices to collect image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin. The collection devices include multi-spectral cameras, total stations, GPS altitude measuring instruments, tipping bucket rain gauges, temperature sensors, ultrasonic anemometers, ultrasonic current meters, electromagnetic flow meters, pH meters, dissolved oxygen meters, on-line chemical oxygen demand monitors, on-line biochemical oxygen demand monitors, and heavy metal monitors; The image data is the real-time remote sensing image of the water quality monitoring basin; the topographic and geomorphic data includes the slope and altitude of the water quality monitoring basin; the meteorological data includes the real-time precipitation, real-time temperature, and real-time wind speed of the water quality monitoring basin; the river data includes the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen content, real-time chemical oxygen demand, real-time biochemical oxygen demand, and real-time heavy metal element content of the water quality monitoring basin; Perform data cleaning and data standardization processing on the collected topographic and geomorphic data, meteorological data, and river data, perform image enhancement and image denoising processing on the collected image data, assign time stamps to the image data, topographic and geomorphic data, meteorological data, and river data, and adjust the assigned time dimension to achieve the synchronization of the collection time of the image data, topographic and geomorphic data, meteorological data, and river data; Integrate the preprocessed river data to generate a water quality monitoring data set, and divide the water quality monitoring data set into a training set and a test set, and the ratio of the training set to the test set is 8:2.

[0006] A further improvement of the technical solution of the present invention lies in: in the said Step 2, the process of obtaining the corridor pattern evolution data includes: Using ENVI, extract the near-infrared band reflectance and red band reflectance from the image data of the water quality monitoring basin, and then calculate the NDVI value to distinguish different types of corridor images. The different types of corridors include vegetation corridors, river corridors, and road corridors. The calculation process of the NDVI value includes: Wherein, is the near-infrared band reflectance, and R is the red band reflectance.

[0007] Specifically, when the NDVI value is between 0.2 and 1, it corresponds to a vegetation corridor; when the NDVI value is between -0.1 and 0, it corresponds to a river corridor; when the NDVI value is between 0 and 0.1, it corresponds to a road corridor; Based on the discrimination results of different types of corridor images, use GIS software to measure the length, width, area, and longitude and latitude coordinates of different types of corridors corresponding to each corridor image, and obtain the corridor pattern evolution data. The corridor pattern evolution data includes the length, width, area, and longitude and latitude coordinates of different types of corridors.

[0008] A further improvement of the technical solution of the present invention lies in: in the third step, the statistical process of the number of landscape patches includes: Adopt an object-oriented image segmentation method. According to the spectrum, texture, and shape of different types of corridor images, segment each corridor image into different image blocks, and by adjusting the segmentation parameters, make the segmented image blocks correspond to the landscape patches; Use GIS software to count the landscape patches segmented from each corridor image, and count the number of landscape patches in each corridor image.

[0009] A further improvement of the technical solution of the present invention lies in: in the fourth step, the analysis process of the corridor connectivity during the landscape pattern evolution includes: Assign weights to the corridor pattern evolution data and meteorological data, combine the weighted summation method, calculate the corridor connectivity index, and integrate the corridor connectivity index into the water quality monitoring dataset. The calculation process of the corridor connectivity index includes: Wherein, is the corridor connectivity index; , , and are the weights of the length, width, area, and longitude and latitude coordinates of different types of corridors respectively; , and are the weights of the real-time precipitation, temperature, and wind speed of the water quality monitoring basin respectively; , , and are the lengths, widths, areas, and longitude and latitude coordinates of different types of corridors respectively; , and are the real-time precipitation, temperature, and wind speed of the water quality monitoring basin respectively; Set the first threshold for corridor circulation and the second threshold for corridor circulation. When the corridor circulation index is lower than the first threshold for corridor circulation, the corresponding corridor has low circulation; when the corridor circulation index is between the first threshold for corridor circulation and the second threshold for corridor circulation, the corresponding corridor has general circulation; when the corridor circulation index is higher than the second threshold for corridor circulation, the corresponding corridor has high circulation.

[0010] A further improvement of the technical solution of the present invention lies in: in the step four, the analysis process of the landscape fragmentation degree during the landscape pattern evolution process includes: Assign weights to the number of landscape patches, topographic and geomorphic data, and meteorological data in each corridor image, and use the weighted average method to calculate the landscape fragmentation index, and integrate the landscape fragmentation index into the water quality monitoring dataset. The calculation process of the landscape fragmentation index includes: Among them, is the landscape fragmentation index; is the weight of the number of landscape patches in each corridor image, and are the weights of the slope and altitude of the water quality monitoring basin respectively; , and are the weights of the real-time precipitation, temperature, and wind speed of the water quality monitoring basin respectively; is the number of landscape patches in each corridor image; and are the slope and altitude of the water quality monitoring basin respectively; , and are the real-time precipitation, temperature, and wind speed of the water quality monitoring basin respectively; Set the first threshold for landscape fragmentation and the second threshold for landscape fragmentation. When the landscape fragmentation index is lower than the first threshold for landscape fragmentation, it corresponds to a low degree of landscape fragmentation; when the landscape fragmentation index is between the first threshold for landscape fragmentation and the second threshold for landscape fragmentation, it corresponds to a medium degree of landscape fragmentation; when the landscape fragmentation index is higher than the second threshold for landscape fragmentation, it corresponds to a high degree of landscape fragmentation.

[0011] A further improvement of the technical solution of the present invention lies in: in the step five, the analysis process of the water quality assessment index includes: Extract the river data from the water quality monitoring dataset. Using the training set data and combining with the multiple linear regression algorithm, take the river data as the input and the water quality assessment index as the output to learn the linear relationship between the river data and the water quality assessment index, and train the water quality assessment model; Input the test set data into the water quality assessment model, adjust the intercept term and regression coefficients of the water quality assessment model, optimize the water quality assessment model, deploy the optimized water quality assessment model to the system, combine with the current river data, output the corresponding water quality assessment index, and integrate this water quality assessment index into the water quality monitoring dataset; The expression of the water quality assessment model is: Where, is the water quality assessment index; , , , , , and are the regression coefficients of the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen, real-time chemical oxygen demand, real-time biochemical oxygen demand, and real-time heavy metal element content in the water quality monitoring basin respectively; , , , , , and are the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen, real-time chemical oxygen demand, real-time biochemical oxygen demand, and real-time heavy metal element content in the water quality monitoring basin respectively; and are the intercept term and error term of the water quality assessment model respectively.

[0012] A further improvement of the technical solution of the present invention lies in: in the sixth step, the construction process of the basin water quality simulation analysis model includes: Extract the corridor circulation index, landscape fragmentation index, and water quality assessment index from the water quality monitoring dataset. Combining with the training set data and the neural network algorithm, take the corridor circulation index and landscape fragmentation index as the input and the water quality assessment index as the output to learn the non-linear relationship between the corridor circulation index, landscape fragmentation index, and water quality assessment index, and train the basin water quality simulation analysis model; Input the test set data into the basin water quality simulation analysis model, compare the water quality assessment index output by the basin water quality simulation analysis model with the actual water quality assessment index, adjust the parameters of the basin water quality simulation analysis model, optimize the performance of the basin water quality simulation analysis model, and deploy the optimized basin water quality simulation analysis model to the system.

[0013] A further improvement of the technical solution of the present invention lies in that: in the seventh step, the generation process of the basin water quality simulation analysis report includes: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water quality pollution. Specifically, when the water quality assessment index is lower than 0.3, it corresponds to a low degree of water quality pollution; when the water quality assessment index is between 0.3 and 0.6, it corresponds to a medium degree of water quality pollution; when the water quality assessment index is higher than 0.6, it corresponds to a high degree of water quality pollution; Integrate the corridor pattern evolution data of each corridor, the number of landscape patches of each corridor, the water quality assessment index, and the degree of water quality pollution to generate a basin water quality simulation analysis report.

[0014] In the second aspect, a simulation analysis system for the impact of basin landscape pattern evolution on water quality is used to implement the above-mentioned simulation analysis method for the impact of basin landscape pattern evolution on water quality, including a water quality monitoring data acquisition module, a corridor identification module, a landscape patch identification module, a landscape pattern evolution analysis module, a water quality assessment module, a simulation analysis module, and an execution module. Among them, each module is communicatively connected; The water quality monitoring data acquisition module collects image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin through different types of acquisition devices, preprocesses the collected data, and then generates a water quality monitoring data set; The corridor identification module performs multi-spectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtains corridor pattern evolution data; The landscape patch identification module combines the object-oriented image segmentation method and GIS software to count the number of landscape patches in each corridor image; The landscape pattern evolution analysis module assigns weights to the corridor pattern evolution data and meteorological data, combines the weight summation method to calculate the corridor circulation index, and then analyzes the corridor circulation during the landscape pattern evolution; assigns weights to the number of landscape patches, topographic and geomorphic data, and meteorological data in each corridor image, and uses the weighted average method to calculate the landscape fragmentation index, and then analyzes the degree of landscape fragmentation during the landscape pattern evolution; The water quality assessment module uses the preprocessed water source data to construct a water quality assessment model through a multiple linear regression algorithm, and then outputs a water quality assessment index; The simulation analysis module combines the corridor circulation index, landscape fragmentation index, water quality assessment index, and neural network algorithm to learn the non-linear relationship between the corridor circulation index, landscape fragmentation index, and water quality assessment index, and constructs a basin water quality simulation analysis model; The execution module analyzes the degree of water quality pollution based on the output results of the basin water quality simulation analysis model, and then generates a basin water quality simulation analysis report.

[0015] The beneficial effects of the present invention are as follows: The simulation analysis method and system for the impact of watershed landscape pattern evolution on water quality in the present invention, compared with the traditional simulation analysis method and system for the impact of watershed landscape pattern evolution on water quality, the multi-spectral analysis technology, multiple linear regression algorithm, neural network algorithm, simulation analysis technology and modern information technology in the method of the present invention are closely combined. By using the neural network algorithm, the association between the evolution of the watershed landscape pattern of water quality monitoring and water quality changes is obtained, achieving real-time and comprehensive monitoring of water quality changes, solving the problem that the traditional method has a single monitoring method and is difficult to analyze the dynamic impact of watershed landscape pattern evolution on water quality, ensuring that the method in the present invention can refine the dynamic monitoring standard for the simulation analysis method and system of the impact of watershed landscape pattern evolution on water quality within a more accurate range, making the monitored data a more accurate indicator under the same conditions. The research and application of this method significantly enhance the intelligent level in the simulation analysis process of the impact of watershed landscape pattern evolution on water quality. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the simulation analysis method for the impact of watershed landscape pattern evolution on water quality of the present invention; Figure 2 It is a block diagram of the simulation analysis system for the impact of watershed landscape pattern evolution on water quality of the present invention. Detailed Embodiments

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0019] Embodiment 1, as Figure 1 shown, the present invention provides a simulation analysis method for the impact of watershed landscape pattern evolution on water quality, including the following steps: Step 1: Collect water quality monitoring data including image data, topographic and geomorphic data, meteorological data, and river data, and preprocess the collected data to provide a data basis for the implementation of subsequent module functions; Step 2: Perform multispectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtain the corridor pattern evolution data; Step 3: Identify landscape patches from different types of corridor images and count the number of landscape patches; Step 4: Combine the corridor pattern evolution data, the number of landscape patches, the preprocessed topographic and geomorphic data, and the meteorological data to analyze the corridor connectivity and landscape fragmentation during the landscape pattern evolution process; Step 5: Through the preprocessed river data, combined with the multiple linear regression algorithm, construct a water quality output model, and then obtain the water quality assessment index; Step 6: Based on the obtained water quality assessment index, combined with the neural network algorithm, construct a basin water quality simulation analysis model, and realize the analysis of the correlation between the landscape pattern evolution and water quality change in the water quality monitoring basin; Step 7: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water pollution, and then generate a basin water quality simulation analysis report.

[0020] In Step 1, the process of collecting water quality monitoring data includes: Deploy different types of collection devices to collect image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin. The collection devices include multispectral cameras, total stations, GPS altitude measuring instruments, tipping bucket rain gauges, temperature sensors, ultrasonic anemometers, ultrasonic current meters, electromagnetic flow meters, pH meters, dissolved oxygen meters, on-line chemical oxygen demand monitors, on-line biochemical oxygen demand monitors, and heavy metal monitors; The image data is the real-time remote sensing image of the water quality monitoring basin; the topographic and geomorphic data includes the slope and altitude of the water quality monitoring basin; the meteorological data includes the real-time precipitation, real-time temperature, and real-time wind speed of the water quality monitoring basin; the river data includes the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen content, real-time chemical oxygen demand, real-time biochemical oxygen demand, and real-time heavy metal element content of the river; Perform data cleaning and data standardization processing on the collected topographic and geomorphic data, meteorological data, and river data, perform image enhancement and image denoising processing on the collected image data, assign time stamps to the image data, topographic and geomorphic data, meteorological data, and river data, and adjust the assigned time dimensions to achieve the synchronization of the collection times of the image data, topographic and geomorphic data, meteorological data, and river data; Integrate the preprocessed river data to generate a water quality monitoring data set, and divide the water quality monitoring data set into a training set and a test set, and the ratio of the training set to the test set is 8:2.

[0021] In Step 2, the process of obtaining the corridor pattern evolution data includes: Extract the near-infrared band reflectance and red band reflectance from the image data of the water quality monitoring watershed using ENVI, and then calculate the NDVI value to distinguish different types of corridor images. Different types of corridors include vegetation corridors, river corridors, and road corridors. The calculation process of the NDVI value includes: Among them, is the near-infrared band reflectance, and R is the red band reflectance.

[0022] Specifically, when the NDVI value is between 0.2 and 1, it corresponds to a vegetation corridor; when the NDVI value is between -0.1 and 0, it corresponds to a river corridor; when the NDVI value is between 0 and 0.1, it corresponds to a road corridor; Based on the discrimination results of different types of corridor images, use GIS software to measure the length, width, area, and longitude and latitude coordinates of different types of corridors corresponding to each corridor image, and obtain the corridor pattern evolution data. The corridor pattern evolution data includes the length, width, area, and longitude and latitude coordinates of different types of corridors.

[0023] In step three, the statistical process of the number of landscape patches includes: Adopt an object-oriented image segmentation method. According to the spectrum, texture, and shape of different types of corridor images, segment each corridor image into different image blocks, and by adjusting the segmentation parameters, make the segmented image blocks correspond to the landscape patches; Use GIS software to count the landscape patches segmented from each corridor image, and count the number of landscape patches in each corridor image.

[0024] In step four, the analysis process of the corridor connectivity during the landscape pattern evolution includes: Assign weights to the corridor pattern evolution data and meteorological data, and combine the weighted summation method to calculate the corridor connectivity index, and integrate this corridor connectivity index into the water quality monitoring dataset. The calculation process of the corridor connectivity index includes: Among them, is the corridor connectivity index; , , and are the weights of the length, width, area, and longitude and latitude coordinates of different types of corridors respectively; , and are the weights of the real-time precipitation, temperature, and wind speed of the water quality monitoring watershed respectively; , , and The length, width, area, longitude and latitude coordinates of different types of corridors respectively; , and are the real-time precipitation, temperature and wind speed of the water quality monitoring basin respectively; Set the first threshold and the second threshold of corridor circulation. When the corridor circulation index is lower than the first threshold of corridor circulation, the corresponding corridor has low circulation; when the corridor circulation index is between the first threshold and the second threshold of corridor circulation, the corresponding corridor has general circulation; when the corridor circulation index is higher than the second threshold of corridor circulation, the corresponding corridor has high circulation.

[0025] In step four, the analysis process of the landscape fragmentation degree in the landscape pattern evolution process includes: Assign weights to the number of landscape patches, topographic and geomorphic data, and meteorological data in each corridor image. Using the weighted average method, calculate the landscape fragmentation index and integrate the landscape fragmentation index into the water quality monitoring dataset. The calculation process of the landscape fragmentation index includes: Among them, is the landscape fragmentation index; is the weight of the number of landscape patches in each corridor image, and are the weights of the slope and altitude of the water quality monitoring basin respectively; , and are the weights of the real-time precipitation, temperature and wind speed of the water quality monitoring basin respectively; is the number of landscape patches in each corridor image; and are the slope and altitude of the water quality monitoring basin respectively; , and are the real-time precipitation, temperature and wind speed of the water quality monitoring basin respectively; Set the first threshold and the second threshold of landscape fragmentation. When the landscape fragmentation index is lower than the first threshold of landscape fragmentation, the corresponding landscape fragmentation degree is low; when the landscape fragmentation index is between the first threshold and the second threshold of landscape fragmentation, the corresponding landscape fragmentation degree is medium; when the landscape fragmentation index is higher than the second threshold of landscape fragmentation, the corresponding landscape fragmentation degree is high.

[0026] In step five, the analysis process of the water quality assessment index includes: Extract the river data from the water quality monitoring dataset. Using the training set data, combined with the multiple linear regression algorithm, take the river data as the input and the water quality assessment index as the output, learn the linear relationship between the river data and the water quality assessment index, and train the water quality assessment model; Input the test set data into the water quality assessment model, adjust the intercept term and regression coefficients of the water quality assessment model, optimize the water quality assessment model, deploy the optimized water quality assessment model to the system, combine the current river data, output the corresponding water quality assessment index, and integrate the water quality assessment index into the water quality monitoring dataset; The expression of the water quality assessment model is: Where, is the water quality assessment index; , , , , , and are the regression coefficients of the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen, real-time chemical oxygen demand, real-time biochemical oxygen demand and real-time heavy metal element content in the water quality monitoring basin, respectively; , , , , , and are the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen, real-time chemical oxygen demand, real-time biochemical oxygen demand and real-time heavy metal element content in the water quality monitoring basin, respectively; and are the intercept term and error term of the water quality assessment model, respectively.

[0027] In step six, the construction process of the basin water quality simulation analysis model includes: Extract the corridor circulation index, landscape fragmentation index and water quality assessment index from the water quality monitoring dataset, combine the training set data with the neural network algorithm, use the corridor circulation index and landscape fragmentation index as inputs, use the water quality assessment index as the output, learn the non-linear relationship between the corridor circulation index, landscape fragmentation index and water quality assessment index, and train the basin water quality simulation analysis model; Input the test set data into the basin water quality simulation analysis model, compare the water quality assessment index output by the basin water quality simulation analysis model with the actual water quality assessment index, adjust the parameters of the basin water quality simulation analysis model, optimize the performance of the basin water quality simulation analysis model, and deploy the optimized basin water quality simulation analysis model to the system.

[0028] In step seven, the generation process of the basin water quality simulation analysis report includes: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water quality pollution. Specifically, when the water quality assessment index is lower than 0.3, it corresponds to a low water quality pollution degree; when the water quality assessment index is between 0.3 and 0.6, it corresponds to a medium water quality pollution degree; when the water quality assessment index is higher than 0.6, it corresponds to a high water quality pollution degree; Integrate the corridor pattern evolution data of each corridor, the number of landscape patches of each corridor, the water quality assessment index, and the degree of water quality pollution to generate a basin water quality simulation analysis report.

[0029] Example 2, as Figure 2 shown, on the basis of Example 1, the present invention provides a technical solution: a simulation analysis system for the impact of basin landscape pattern evolution on water quality, which is used to implement the above-mentioned simulation analysis method for the impact of basin landscape pattern evolution on water quality, including a water quality monitoring data acquisition module, a corridor identification module, a landscape patch identification module, a landscape pattern evolution analysis module, a water quality assessment module, a simulation analysis module, and an execution module. Among them, each module is communicatively connected; The water quality monitoring data acquisition module collects image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin through different types of acquisition devices, preprocesses the collected data, and then generates a water quality monitoring data set; The corridor identification module performs multi-spectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtains corridor pattern evolution data; The landscape patch identification module combines the object-oriented image segmentation method and GIS software to count the number of landscape patches in each corridor image; The landscape pattern evolution analysis module assigns weights to the corridor pattern evolution data and meteorological data, combines the weight summation method to calculate the corridor circulation index, and then analyzes the corridor circulation during the landscape pattern evolution; assigns weights to the number of landscape patches, topographic and geomorphic data, and meteorological data in each corridor image, and uses the weighted average method to calculate the landscape fragmentation index, and then analyzes the degree of landscape fragmentation during the landscape pattern evolution; The water quality assessment module uses the preprocessed water source data to construct a water quality assessment model through a multiple linear regression algorithm, and then outputs a water quality assessment index; The simulation analysis module combines the corridor circulation index, the landscape fragmentation index, the water quality assessment index, and the neural network algorithm to learn the non-linear relationship between the corridor circulation index, the landscape fragmentation index, and the water quality assessment index, and constructs a basin water quality simulation analysis model; The execution module analyzes the degree of water quality pollution based on the output results of the basin water quality simulation analysis model, and then generates a basin water quality simulation analysis report.

[0030] As described above, it is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

Claims

1. A simulation analysis method for the impact of the evolution of the basin landscape pattern on water quality, characterized in that: It includes the following steps: Step 1: Collect water quality monitoring data including image data, topographic and geomorphic data, meteorological data, and river data, and preprocess the collected data; Step 2: Conduct multispectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtain corridor pattern evolution data; Step 3: Identify landscape patches from different types of corridor images and count the number of landscape patches; Step 4: Combine the corridor pattern evolution data, the number of landscape patches, the preprocessed topographic and geomorphic data, and the meteorological data to analyze the corridor connectivity and landscape fragmentation degree during the landscape pattern evolution process; Step 5: Through the preprocessed river data, combine with the multiple linear regression algorithm to construct a water quality output model, and then obtain a water quality assessment index; Step 6: Based on the obtained water quality assessment index, combine with the neural network algorithm to construct a basin water quality simulation analysis model; Step 7: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water quality pollution, and then generate a basin water quality simulation analysis report.

2. The simulation analysis method for the impact of the evolution of the basin landscape pattern on water quality according to claim 1, characterized in that: In the above Step 1, the process of collecting water quality monitoring data includes: Deploy different types of collection devices to collect image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin. The collection devices include multispectral cameras, total stations, GPS altitude measuring instruments, tipping bucket rain gauges, temperature sensors, ultrasonic anemometers, ultrasonic current meters, electromagnetic flow meters, pH meters, dissolved oxygen meters, on-line chemical oxygen demand monitors, on-line biochemical oxygen demand monitors, and heavy metal monitors; The image data is the real-time remote sensing image of the water quality monitoring basin; the topographic and geomorphic data includes the slope and altitude of the water quality monitoring basin; the meteorological data includes the real-time precipitation, real-time temperature, and real-time wind speed of the water quality monitoring basin; the river data includes the real-time river velocity, real-time river flow, real-time pH value of the river, real-time dissolved oxygen content, real-time chemical oxygen demand, real-time biochemical oxygen demand, and real-time heavy metal element content of the river; Conduct data cleaning and data standardization processing on the collected topographic and geomorphic data, meteorological data, and river data, conduct image enhancement and image denoising processing on the collected image data, assign time stamps to the image data, topographic and geomorphic data, meteorological data, and river data, and adjust the assigned time dimensions to achieve the acquisition time synchronization of the image data, topographic and geomorphic data, meteorological data, and river data; Integrate the preprocessed river data to generate a water quality monitoring data set, and divide the water quality monitoring data set into a training set and a test set.

3. The simulation analysis method for the impact of the evolution of the basin landscape pattern on water quality according to claim 2, characterized in that: In the above Step 2, the process of obtaining corridor pattern evolution data includes: Use ENVI to extract the near-infrared band reflectance and red band reflectance from the image data of the water quality monitoring basin, and then calculate the NDVI value to distinguish different types of corridor images. The different types of corridors include vegetation corridors, river corridors, and road corridors; Based on the discrimination results of different types of corridor images, use GIS software to measure the length, width, area, and longitude and latitude coordinates of different types of corridors corresponding to each corridor image, and obtain the corridor pattern evolution data, where the corridor pattern evolution data includes the length, width, area, and longitude and latitude coordinates of different types of corridors.

4. The simulation analysis method for the impact of the evolution of the basin landscape pattern on water quality according to claim 3, wherein: In the third step, the statistical process of the number of landscape patches includes: Adopt an object-oriented image segmentation method. According to the spectrum, texture, and shape of different types of corridor images, segment each corridor image into different image blocks, and by adjusting the segmentation parameters, make the segmented image blocks correspond to the landscape patches; Use GIS software to count the landscape patches segmented from each corridor image, and count the number of landscape patches in each corridor image.

5. The simulation analysis method for the impact of the evolution of the basin landscape pattern on water quality according to claim 4, characterized in that: In the fourth step, the analysis process of the corridor connectivity during the landscape pattern evolution includes: Assign weights to the corridor pattern evolution data and meteorological data, and combine the weighted summation method to calculate the corridor connectivity index, and integrate the corridor connectivity index into the water quality monitoring dataset; Set the first threshold of corridor connectivity and the second threshold of corridor connectivity. When the corridor connectivity index is lower than the first threshold of corridor connectivity, the corresponding corridor connectivity is low; when the corridor connectivity index is between the first threshold and the second threshold of corridor connectivity, the corresponding corridor connectivity is average; when the corridor connectivity index is higher than the second threshold of corridor connectivity, the corresponding corridor connectivity is high.

6. The simulation analysis method for the impact of watershed landscape pattern evolution on water quality according to claim 5, wherein: In the fourth step, the analysis process of the landscape fragmentation degree during the landscape pattern evolution includes: Assign weights to the number of landscape patches, topographic and geomorphic data, and meteorological data in each corridor image, and use the weighted average method to calculate the landscape fragmentation index, and integrate the landscape fragmentation index into the water quality monitoring dataset; Set the first threshold of landscape fragmentation and the second threshold of landscape fragmentation. When the landscape fragmentation index is lower than the first threshold of landscape fragmentation, the corresponding landscape fragmentation degree is low; when the landscape fragmentation index is between the first threshold and the second threshold of landscape fragmentation, the corresponding landscape fragmentation degree is medium; when the landscape fragmentation index is higher than the second threshold of landscape fragmentation, the corresponding landscape fragmentation degree is high.

7. The simulation analysis method for the impact of the evolution of the watershed landscape pattern on water quality according to claim 6, characterized in that: In the fifth step, the analysis process of the water quality assessment index includes: Extract the river data from the water quality monitoring dataset, and use the training set data. Combine the multiple linear regression algorithm, use the river data as the input, and the water quality assessment index as the output, learn the linear relationship between the river data and the water quality assessment index, and train the water quality assessment model; Input the test set data into the water quality assessment model, adjust the intercept term and regression coefficient of the water quality assessment model, optimize the water quality assessment model, deploy the optimized water quality assessment model to the system, combine the current river data, output the corresponding water quality assessment index, and integrate the water quality assessment index into the water quality monitoring dataset.

8. The simulation analysis method for the impact of the evolution of the watershed landscape pattern on water quality according to claim 7, characterized in that: In the sixth step, the construction process of the basin water quality simulation analysis model includes: Extract the corridor circulation index, landscape fragmentation index, and water quality assessment index from the water quality monitoring dataset. Combine the training set data with the neural network algorithm, use the corridor circulation index and landscape fragmentation index as inputs, and the water quality assessment index as the output to learn the non-linear relationship among the corridor circulation index, landscape fragmentation index, and water quality assessment index, and train the basin water quality simulation analysis model; Input the test set data into the basin water quality simulation analysis model, compare the water quality assessment index output by the basin water quality simulation analysis model with the actual water quality assessment index, adjust the parameters of the basin water quality simulation analysis model, optimize the performance of the basin water quality simulation analysis model, and deploy the optimized basin water quality simulation analysis model into the system.

9. The simulation analysis method for the impact of watershed landscape pattern evolution on water quality according to claim 8, characterized in that: In step 7, the generation process of the basin water quality simulation analysis report includes: Based on the output results of the basin water quality simulation analysis model, analyze the degree of water pollution, integrate the corridor pattern evolution data of each corridor, the number of landscape patches of each corridor, the water quality assessment index, and the degree of water pollution to generate a basin water quality simulation analysis report.

10. A simulation analysis system for the impact of watershed landscape pattern evolution on water quality, which is used to implement the simulation analysis method for the impact of watershed landscape pattern evolution on water quality described in any one of the above claims 1-9, including a water quality monitoring data acquisition module, a corridor identification module, a landscape patch identification module, a landscape pattern evolution analysis module, a water quality assessment module, a simulation analysis module and an execution module, wherein, Each module is communicatively connected, characterized in that: The water quality monitoring data acquisition module collects image data, topographic and geomorphic data, meteorological data, and river data of the water quality monitoring basin through different types of acquisition devices, preprocesses the collected data, and then generates a water quality monitoring dataset; The corridor identification module performs multi-spectral analysis on the preprocessed image data to distinguish different types of corridor images, and then obtains the corridor pattern evolution data; The landscape patch identification module combines the object-oriented image segmentation method with GIS software to count the number of landscape patches in each corridor image; The landscape pattern evolution analysis module assigns weights to the corridor pattern evolution data and meteorological data, combines the weighted summation method to calculate the corridor circulation index, and then analyzes the corridor circulation during the landscape pattern evolution; assigns weights to the number of landscape patches in each corridor image, topographic and geomorphic data, and meteorological data, and uses the weighted average method to calculate the landscape fragmentation index, and then analyzes the degree of landscape fragmentation during the landscape pattern evolution; The water quality assessment module uses the preprocessed water source data to construct a water quality assessment model through the multiple linear regression algorithm, and then outputs the water quality assessment index; The simulation analysis module combines the corridor circulation index, landscape fragmentation index, water quality assessment index with the neural network algorithm to learn the non-linear relationship among the corridor circulation index, landscape fragmentation index, and water quality assessment index, and constructs a basin water quality simulation analysis model; The execution module analyzes the degree of water pollution based on the output results of the basin water quality simulation analysis model, and then generates a basin water quality simulation analysis report.

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