A dynamic analysis system for concentration of particulate organic carbon in water body

Abstract

The present application relates to the field of environmental remote sensing and carbon monitoring technology, in particular to a water body particulate organic carbon concentration dynamic analysis system, the system comprises a data acquisition and fusion module, an information conversion and optimization module, a prediction analysis module, and a display and storage module; the position and quantity of data sampling points in the water body monitoring area are determined, and the data acquisition and fusion module obtains multi-dimensional detection data of the water body monitoring area according to the position and quantity; the converted multi-dimensional detection information is output through the information conversion and optimization module; the prediction analysis module analyzes the color index and particulate organic carbon concentration of different data sampling points based on the converted multi-dimensional detection information; the display and storage module receives the detection information, the color index and the particulate organic carbon concentration in real time to realize the monitoring and analysis of the water body particulate organic carbon concentration. The dynamic analysis system combines remote sensing technology and carbon monitoring technology to realize the dynamic monitoring and accurate analysis of the water body particulate organic carbon concentration.

Description

Technical Field

[0001] This invention relates to the field of environmental remote sensing and carbon monitoring technology, specifically to a dynamic analysis system for particulate organic carbon concentration in water. Background Technology

[0002] Against the backdrop of escalating global climate change and increasing emphasis on ecological and environmental protection, the accurate determination of the concentration of particulate organic carbon in water bodies, as a key component of the carbon cycle, is of great scientific significance and practical value for assessing the carbon sink function of water bodies, revealing the intrinsic mechanisms of the carbon cycle in water bodies, and formulating watershed carbon neutrality strategies.

[0003] Traditional site sampling and monitoring methods require significant manpower, resources, and time to transport samples to laboratories for analysis and processing. The entire monitoring process is lengthy, taking a considerable amount of time from sampling to obtaining final results, making it difficult to meet current monitoring needs for particulate organic carbon (POC) concentration in water bodies. Traditional methods are constrained by multiple factors such as geographical conditions and cost, resulting in a limited number of sampling points and difficulties in achieving uniform and reasonable distribution. Consequently, traditional methods cannot comprehensively and accurately reflect the actual distribution of POC concentration in the entire water body. This is particularly true for large water bodies such as lakes and reservoirs, where traditional monitoring methods cannot achieve dynamic monitoring and cannot promptly capture the dynamic trends of POC concentration changes, thus failing to provide technical support for carbon sequestration monitoring and management decisions in aquatic ecosystems.

[0004] Furthermore, in practical applications, data monitoring conditions, actual environmental conditions, and data management methods all affect the accuracy and quality of monitoring data. In practical applications, the amount of monitoring data is growing exponentially, and traditional monitoring methods cannot efficiently process and analyze it, making it difficult to extract valuable information and provide efficient, accurate, and comprehensive data support for monitoring particulate organic carbon concentration in water bodies.

[0005] In conclusion, in order to overcome the limitations of traditional monitoring methods and achieve efficient, accurate, and large-scale dynamic monitoring of particulate organic carbon concentration in water bodies, it is necessary to construct a new monitoring and analysis system to meet the actual needs of carbon sink monitoring and ecological environmental protection. Summary of the Invention

[0006] Given the limitations of existing monitoring methods, such as high resource investment, long monitoring cycles, limited spatial coverage, and insufficient data processing capabilities, this invention provides a dynamic analysis system for particulate organic carbon concentration in water bodies. This system aims to achieve efficient, accurate, and dynamic monitoring of particulate organic carbon concentration in water bodies, acquire effective detection data from the monitoring area, and enable intelligent analysis of particulate organic carbon concentration. This system will support dynamic monitoring of carbon cycles in lakes and other water bodies, as well as water quality monitoring and assessment. The system includes a data acquisition and fusion module, an information conversion and optimization module, a predictive analysis module, and a display and storage module. It determines the location and data volume of data sampling points within the water monitoring area. The data acquisition and fusion module obtains multi-dimensional detection data of the water body monitoring area based on the location and quantity; the information conversion and optimization module transforms and synthesizes the multi-dimensional detection data to output converted multi-dimensional detection information of the water body monitoring area; the prediction and analysis module analyzes the color index and particulate organic carbon concentration of different data sampling points in the water body monitoring area based on the converted multi-dimensional detection information; the display and storage module receives the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in real time to realize dynamic monitoring and analysis of particulate organic carbon concentration in the water body monitoring area.

[0007] The various modules of this invention work together to acquire, process, and analyze data in real time, enabling dynamic monitoring and intelligent analysis of particulate organic carbon concentration in the water monitoring area. This allows for timely understanding of the changing trends in the carbon cycle of water bodies, ultimately achieving efficient and accurate monitoring of particulate organic carbon concentration in water bodies.

[0008] Optionally, determining the location and number of data sampling points within the water body monitoring area includes: collecting water area data, topographic information, and water flow characteristic information of the water body monitoring area; the data acquisition and fusion module determining the location of data sampling points within the water body monitoring area by referring to the water area data, topographic information, and water flow characteristic information; constructing a formula for calculating the number of sampling points in a sub-region in the data acquisition and fusion module; and using the data sampling point locations and the formula for calculating the number of sampling points in a sub-region to design the number of data sampling points within the water body monitoring area. The sampling point location and quantity design of this invention can more accurately and comprehensively reflect the actual situation of the water body monitoring area, and data analysis and processing can more accurately obtain information such as the concentration of particulate organic carbon in the water.

[0009] Optionally, the data acquisition and fusion module obtains multi-dimensional detection data of the water monitoring area based on the location and the quantity, including: the data acquisition and fusion module combining the data acquisition device, the location, and the quantity to obtain initial multispectral image data of different data sampling points within the water monitoring area; establishing radiometric calibration models for different bands in the data acquisition and fusion module; obtaining calibrated data of different data sampling points within the water monitoring area based on the different radiometric calibration models and the initial multispectral image data; and integrating the calibrated data of the different data sampling points to obtain multi-dimensional detection data of the water monitoring area.

[0010] The radiometric calibration model of this invention can process the initial data of multispectral images, convert the initial data into more accurate physical quantities, and enable the acquired data to more realistically reflect the water condition.

[0011] Optionally, the transformation and synthesis of the multi-dimensional detection data through the information conversion and optimization module to output the converted multi-dimensional detection information of the water monitoring area includes: constructing a spectral image conversion model in the information conversion and optimization module based on human visual characteristics and the HIS transformation method; the information conversion and optimization module performs conversion analysis on the multi-dimensional detection data through the spectral image conversion model to obtain the brightness component, chromaticity component, and saturation component of different data sampling points within the water monitoring area. This invention converts multispectral image data from RGB space to HIS space, achieving the separation of brightness (lightness), chromaticity, and saturation information, allowing for independent processing and analysis of different components, thus improving the flexibility and targeting of data processing.

[0012] Optionally, the transformation and synthesis of the multi-dimensional detection data by the information conversion and optimization module to output the converted multi-dimensional detection information of the water monitoring area includes: obtaining a high spatial resolution panchromatic image based on the multi-dimensional detection data; the information conversion and optimization module obtaining the mean and variance of the high spatial resolution panchromatic image based on the high spatial resolution panchromatic image; and the information conversion and optimization module performing contrast stretching processing on the high spatial resolution panchromatic image based on the mean and variance to obtain a processed panchromatic image. This invention performs contrast stretching processing on the high spatial resolution panchromatic image based on the mean and variance, which can effectively improve the dynamic range of the image, making the image grayscale distribution more uniform while expanding the dynamic range of the image.

[0013] Optionally, the transformation and synthesis of the multi-dimensional detection data through the information conversion and optimization module to output the transformed multi-dimensional detection information of the water monitoring area includes: constructing an image synthesis model in the information conversion and optimization module based on the HIS transformation method, the transformed multi-dimensional detection information, and the processed panchromatic image; the information conversion and optimization module uses the image synthesis model to perform inverse transformation and synthesis on the multi-dimensional detection data, and obtains new pixel values ​​for the red band, green band, and blue band corresponding to different data sampling points within the water monitoring area; the information conversion and optimization module outputs the transformed multi-dimensional detection information of the water monitoring area based on the new pixel values ​​for the red band, green band, and blue band. This invention has strong adaptability and can cope with different types and environments of water monitoring scenarios. By processing multi-dimensional detection data through an image synthesis model to obtain new pixel values ​​for the red, green, and blue bands, the system can operate stably in various complex environments, improving the system's versatility and practicality.

[0014] Optionally, the predictive analysis module analyzes the color index and particulate organic carbon concentration of different data sampling points within the water body monitoring area based on the converted multi-dimensional detection information, including: the predictive analysis module analyzes the remote sensing reflectance of different data sampling points within the water body monitoring area based on the converted multi-dimensional detection information; constructs a color index analysis function in the predictive analysis module based on the remote sensing reflectance; and sets water body band reference information using the converted multi-dimensional detection information and the remote sensing reflectance. The converted multi-dimensional detection information of this invention includes spatial, spectral, and other information, which can comprehensively consider various characteristics of the water body, reduce errors caused by a single data source, and improve the accuracy of the analysis results.

[0015] Optionally, the predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information. This includes: the predictive analysis module substituting the water body band reference information into the color index analysis function for derivation and analysis to obtain the color index at different data sampling points within the water monitoring area. The color index analysis function of this invention can more accurately reveal the intrinsic relationship between water color and optical properties, providing a more reliable information basis for subsequent carbon concentration prediction.

[0016] Optionally, the predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information, including: establishing a particulate organic carbon concentration prediction regression model in the predictive analysis module based on the transformed multi-dimensional detection information and the color index; and performing a fitting analysis based on the particulate organic carbon concentration prediction regression model and the color index to obtain the particulate organic carbon concentration at different data sampling points within the water monitoring area. This invention establishes a regression model based on multi-dimensional detection information and the color index, making the model prediction results easier to understand and interpret.

[0017] Optionally, the display and storage module receives the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in real time to achieve dynamic monitoring and analysis of particulate organic carbon concentration in the water monitoring area. This includes: the display and storage module storing the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in the system database; the display and storage module visually displaying the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in chart form; and the display and storage module updating and optimizing the chart form in real time based on the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration. The display and storage module of this invention updates and optimizes in real time based on the system information visualization charts, enabling timely understanding of the latest changes in different indicators within the water monitoring area, and achieving real-time monitoring and dynamic control of the water environment. Attached Figure Description

[0018] Figure 1 This is a flowchart of the dynamic analysis system for particulate organic carbon concentration in water according to the present invention; Figure 2 This is a flowchart illustrating the information conversion and optimization module of the present invention; Figure 3 This is a tabular diagram illustrating the concentration of particulate organic carbon in water according to the present invention. Figure 4 This is a structural diagram of the dynamic analysis system for particulate organic carbon concentration in water according to the present invention. Detailed Implementation

[0019] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0020] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0021] To achieve dynamic monitoring and accurate analysis of particulate organic carbon concentration in water bodies, obtain precise information on the water monitoring area, and realize intelligent analysis of water carbon concentration, providing technical support for dynamic monitoring of carbon cycle and water quality control in lakes and other water bodies, this invention provides a dynamic analysis system for particulate organic carbon concentration in water bodies. (See attached image for details.) Figure 1 The above system includes the following steps: A dynamic analysis system for particulate organic carbon concentration in water includes a data acquisition and fusion module, an information conversion and optimization module, a predictive analysis module, and a display and storage module.

[0022] S1. Determine the location and number of data sampling points within the water body monitoring area. The data acquisition and fusion module above obtains multi-dimensional detection data of the water body monitoring area based on the location and number. The specific implementation details are as follows: Before monitoring the concentration of particulate organic carbon in water, it is necessary to first determine the location and number of data sampling points within the water monitoring area. The location and number of data sampling points directly affect the accuracy and representativeness of the monitoring data.

[0023] The first step involves collecting basic information about the water monitoring area based on the data acquisition and fusion module. This includes, but is not limited to, water area data, topographic information, and water flow characteristics. In one optional embodiment, water area data is obtained using Geographic Information System (GIS) technology combined with satellite remote sensing imagery and field measurements. The size of the water area affects the overall layout and number of sampling points; larger water areas require more sampling points to ensure comprehensive monitoring.

[0024] In one optional embodiment, topographic data of the water body's surroundings, such as the distribution of mountains, plains, and hills, as well as the topographic features inside the water body, such as water depth and underwater topographic relief, are collected. Topography can affect water flow and material distribution. In mountainous rivers, the water flow is rapid and the material mixing is relatively uniform. In still water bodies such as lakes, there are significant differences in water quality at different depths.

[0025] In one alternative embodiment, the characteristics of the water body, such as water flow velocity, flow direction, and water exchange cycle, were understood. The change in water flow velocity at the river inlet plays an important role in the transport and distribution of particulate organic carbon in the water body, and particulate organic carbon will correspondingly undergo deposition or resuspension.

[0026] The second step, after obtaining the above basic information, is for the data acquisition and fusion module to determine the location of data sampling points within the water body monitoring area by referring to water area data, topographic information and water flow characteristics.

[0027] This embodiment comprehensively considers factors such as the distribution characteristics of the water body monitoring area, the monitoring targets, and practical operability. Simultaneously, based on the characteristics of water body morphology, such as the curvature of rivers, the shape and flow direction of lakes, and the pollution status, the approximate locations of sampling points are initially determined. In areas with more severe pollution, the density of sampling points can be appropriately increased to more accurately grasp the distribution of pollutants.

[0028] Data sampling points can be distributed uniformly or non-uniformly based on water characteristics. Uniform distribution is suitable for areas with relatively uniform water characteristics, ensuring uniform coverage of the entire monitoring area; non-uniform distribution is suitable for areas with significant differences in water characteristics, and weights can be set according to factors such as water pollution level and water flow velocity to make data sampling points more concentrated in key areas.

[0029] Furthermore, the embodiments processed the latitude and longitude of different data sampling points within the water monitoring area, marking the locations of different data sampling points according to a unified format. In one specific embodiment, the latitude and longitude data of different data sampling points were processed to obtain location information in a unified format, and sampling points were then set. The longitude is Latitude is The geographic coordinates were converted into the corresponding planar coordinates of the water monitoring area using a geographic coordinate transformation formula. The above-mentioned geographic coordinate transformation formula can be the Gauss-Kruger projection formula, etc. In other embodiments, a suitable formula can be selected for transformation according to the geographical location and projection requirements of the water monitoring area, so as to facilitate subsequent data processing and analysis.

[0030] In the third step, to allocate the number of sampling points more reasonably, a formula for calculating the number of sampling points in sub-regions was constructed in the data acquisition and fusion module.

[0031] In this embodiment, the total area of ​​the water monitoring area is set to be... Furthermore, based on its complexity and monitoring accuracy requirements, the water monitoring area is divided into... The above-mentioned sub-regions need to be divided according to factors such as topography, water flow characteristics, and pollution status. Rivers can be divided according to different river sections, and lakes can be divided according to different regions.

[0032] In an optional embodiment, in the case of non-uniform distribution, let the first... The weight of each sub-region is The number of sampling points in this sub-region is The number of sampling points in the sub-regions can be determined by a calculation formula based on the number of sampling points in the data acquisition and fusion module.

[0033] The formula for calculating the number of sampling points in the above sub-region satisfies the following relationship: , in, For the first Number of sampling points in each sub-region For the first Setting weights for each sub-region For the first The total number of sub-regions Assign weights to different sub-regions. The approximate number of sampling points is calculated and determined based on the preset total number of sampling points. The above weights can be adjusted according to the actual situation. Higher weights can be assigned to severely polluted sub-regions to increase the number of sampling points in these regions, thereby obtaining more monitoring data and facilitating effective analysis of particulate organic carbon concentration in these sub-regions.

[0034] The fourth step, based on the data acquisition and fusion module, uses the location of data sampling points and the number of sampling points in sub-regions to calculate the number of data sampling points within the water body monitoring area.

[0035] The implementation example calculates the number of sampling points in different sub-regions according to the formula for calculating the number of sampling points in each sub-region. The total number of sampling points in the water monitoring area can be obtained by adding up the number of sampling points in each sub-region. In practical applications, the total number of sampling points can be adjusted appropriately according to monitoring needs and resource conditions to meet monitoring accuracy requirements without wasting resources.

[0036] Through the above implementation steps, the data acquisition and fusion module can scientifically and rationally determine the location and number of data sampling points within the water monitoring area, providing a foundation for subsequent monitoring of particulate organic carbon concentration in water.

[0037] Since the location and number of data sampling points directly affect the accuracy and representativeness of monitoring data, this embodiment determines the sampling point locations by using basic information about the water body monitoring area and comprehensively considering other influencing factors. This allows the sampling point layout to better reflect the actual water conditions, ensuring that the collected data truly reflects the water body status. Simultaneously, the calculation formula for the number of sampling points in sub-regions in this embodiment can divide the water body monitoring area into sub-regions based on the complexity and monitoring accuracy requirements, and allocate the number of sampling points according to the weight of each sub-region, enabling a more accurate understanding of the actual conditions in different areas of the water body. Furthermore, the latitude and longitude of the sampling points are processed and converted into a unified format of planar coordinates, facilitating subsequent data processing and analysis, making the data more standardized and consistent, and contributing to improved data analysis efficiency and result accuracy.

[0038] Then, after determining the location and number of data sampling points within the water monitoring area, the data acquisition and fusion module will obtain multi-dimensional detection data of the water monitoring area based on the above information.

[0039] The first step, data acquisition and fusion module, combines data acquisition devices, locations, and quantities to obtain initial multispectral image data from different data sampling points within the water monitoring area.

[0040] This embodiment rationally configures the multispectral remote sensing equipment in the system. A suitable data platform was selected and relevant parameters were set. Equipment capable of acquiring multispectral image data of lake water was chosen. This equipment can automatically acquire multispectral images according to preset parameters and task requirements, providing a foundation for subsequent carbon concentration analysis. Simultaneously, the geographical location and monitoring needs of the water monitoring area were fully considered, and multispectral remote sensing platforms such as satellites or drones were selected. Satellite remote sensing has advantages such as wide coverage and stable data acquisition cycles, making it suitable for acquiring large-scale lake water images. Drone remote sensing features high flexibility and adjustable resolution, allowing for detailed monitoring of local areas. Furthermore, based on the actual needs of the water monitoring area, various parameters of the multispectral remote sensing equipment can be rationally set, covering spectral range, resolution, etc., to ensure the effective acquisition of initial multispectral image data from different data sampling points. The embodiment sets the multispectral remote sensing equipment to acquire the first... Each band image is ,in These are the image pixel coordinates.

[0041] The second step involves establishing radiometric calibration models for different bands in the data acquisition and fusion module, and obtaining calibrated data from different data sampling points within the water monitoring area based on the radiometric calibration models for different bands and the initial multispectral image data.

[0042] This embodiment processes the initial data from the acquired multispectral images, further checking for any missing images to ensure complete coverage of the entire monitored area. This prevents incomplete data due to missing images, which could affect the accuracy of subsequent particulate organic carbon concentration analysis. Simultaneously, a mask image with the same number of rows and columns is created for each calibration image using a calibration reflective panel image. The creation of these mask images facilitates subsequent image processing and analysis, accurately distinguishing between valid and invalid data areas.

[0043] In this embodiment, radiometric calibration models for different bands are established in the data acquisition and fusion module. The average grayscale value in the mask image is calculated based on the initial multispectral image data. Then, the radiometric calibration coefficients for different bands are calculated according to the different band radiometric calibration models. The calculation formulas for the different band radiometric calibration models satisfy the following relationship: , in, These are the radiation calibration coefficients for different wavebands. For band The standard reflectance proportionality coefficient, These are the numberings for different frequency bands. It is the set of pixels with a grayscale value of 255 in the grayscale mask. For set The total number of pixels in the middle. The spectral radiance of the calibration image after radiometric correction.

[0044] By performing radiometric calibration and radiometric correction simultaneously for each band, the above steps can eliminate radiometric distortions generated by the sensor and lens during imaging, and convert image grayscale values ​​into spectral radiance. In this embodiment, the original image pixel value is set to Therefore, spectral radiance Compared with the original image pixel values The relationship can be analyzed using the formula relating spectral radiance to the pixel values ​​of the original image.

[0045] The relationship between spectral radiance and the pixel value of the original image satisfies the following: , in, For spectral radiance, The first parameter to adjust for the equipment. These are the original image pixel values. This is the second adjustment parameter for the device. The above linear relationship conversion conforms to the conversion principle between pixel values ​​and actual radiance during sensor imaging.

[0046] Next, the spectral radiance Radiation calibration coefficients for different frequency bands Multiplying them together gives the absolute reflectance ratio at different data sampling point locations. The above formula for calculating the absolute reflectance proportionality coefficient satisfies the following relationship: , in, This is the absolute reflectivity proportionality coefficient. For different spectral bands, the radiation calibration coefficients are... For spectral radiance. In an optional embodiment, the first... Each band in position The absolute reflectance proportionality coefficient is This allows us to obtain multispectral reflectance information from different data collection locations within the water monitoring area, thus obtaining calibrated data from different data sampling points.

[0047] The aforementioned radiometric calibration models for different spectral bands can process the initial data of the acquired multispectral images, check the integrity of each spectral band image, and then establish a mask image to distinguish between valid and invalid data areas. Based on the radiometric calibration coefficient and radiometric correction processing, the radiometric distortion in the sensor and lens imaging process is eliminated, and an accurate absolute reflectance ratio coefficient is obtained. This is beneficial for obtaining calibrated data from different data sampling points, ensuring the accuracy and reliability of the data.

[0048] The third step involves using the data acquisition and fusion module to integrate the calibrated data from different data sampling points to obtain multi-dimensional detection data for the water monitoring area.

[0049] The initial multispectral imagery data at the data sampling point locations includes water body detection and survey data corresponding to different data sampling points. In this embodiment, multispectral and multitemporal data acquired by different types of sensors at the data sampling points, along with water body detection and survey data, are combined based on the location information of the different data sampling points. Spatial registration allows us to obtain multi-source data corresponding to different data sampling points.

[0050] In one embodiment, an affine transformation method is used for spatial registration. Let the original data coordinates be... The target coordinates are Meanwhile, the above affine transformation process satisfies the following relationship: , By solving the transformation parameters , , , , , Achieve accurate data registration and generate multi-source data from different data sampling points.

[0051] By fusing calibrated data from different sampling points, the actual condition of water bodies can be more accurately described and monitored. This reduces or suppresses the incompleteness, uncertainty, and error associated with monitoring water bodies or the environment from a single information source, thereby maximizing the use of information from various sources. This provides a solid information foundation for the analysis of particulate organic carbon concentration in water bodies, making the system more practical and feasible. In one embodiment, when fusing calibrated data from different sampling points, let the fused multi-dimensional detection data be... This embodiment adopts a weighted fusion method that combines the reliability and importance of different data sources to set weights. Based on this, the information provided by various information sources can be utilized to the maximum extent to obtain multi-dimensional detection data of the water monitoring area.

[0052] Through the above implementation steps, the data acquisition and fusion module can accurately obtain multi-dimensional detection data of the water monitoring area based on the location and number of data sampling points, providing reliable technical methods and information basis for subsequent analysis of particulate organic carbon concentration in the water.

[0053] This embodiment integrates calibrated data from different data sampling points, fusing multispectral, multi-temporal, and water body detection and survey data based on location information to obtain multi-source data. This maximizes the use of information from various sources, reduces the incompleteness, uncertainty, and error of a single information source, and more accurately describes and monitors the actual condition of water bodies, providing a solid information foundation for the analysis of particulate organic carbon concentration in water bodies.

[0054] S2. The information conversion and optimization module transforms and synthesizes the multi-dimensional detection data to output the converted multi-dimensional detection information of the water monitoring area. The specific implementation details are as follows: The first step is to construct a spectral image conversion model in the information conversion and optimization module based on the characteristics of human visual perception and the HIS transformation method.

[0055] In practical applications, the human eye has different abilities to distinguish between brightness, chroma, and saturation of images. The ability to distinguish brightness is significantly higher than that to distinguish chroma and saturation. The implementation example refers to the human eye visual characteristics to optimize the HIS transformation method. Based on the processing method of human eye visual characteristics, the transformed information is more in line with human visual cognitive habits, which helps to identify and extract key information more quickly and accurately in subsequent analysis and improve analysis efficiency.

[0056] The embodiment selects spatially registered three-band multispectral images with low spatial resolution based on multidimensional detection data, and treats them as blue (B), green (G), and red (R) bands, respectively. Simultaneously, the pixel value of the multispectral image is set to... Next, a spectral image conversion model was constructed using the HIS transform method. The aforementioned three-band multispectral image was then converted into brightness (I), chromaticity (H), and saturation (S) components. The analytical expressions for different components in the spectral image conversion model are as follows: The lightness component analysis formula in the spectral image conversion model: , in, The brightness components are R, G, and B, representing the pixel values ​​of the red, green, and blue bands, respectively. These brightness components reflect the overall brightness information of the image, and averaging the pixel values ​​of the three bands provides a clear indication of the image's brightness.

[0057] The chromaticity component analysis formula in the spectral image conversion model is as follows: , in, For chromaticity components, G and B are the pixel values ​​in the green and blue bands, respectively. These are intermediate calculation variables in the chromaticity component analysis process. The aforementioned chromaticity components are mainly used to describe the types of colors in an image, comprehensively considering the relationship between the red, green, and blue bands. The above formula can accurately distinguish the characteristics of different colors.

[0058] Intermediate calculation variables in the colorimetric component analysis process It can reflect the angle between vectors in the color space, and thus quantify the directional characteristics of color in the RGB space. The above intermediate calculation variables It is mainly calculated from the pixel values ​​of the red (R), green (G), and blue (B) bands using the inverse cosine function, while satisfying the following calculation relationship: , in, R, G, and B are intermediate calculation variables in the chromaticity component analysis process, representing the pixel values ​​of the red, green, and blue bands, respectively.

[0059] The above numerator This reflects the overall deviation of the red band relative to the green and blue bands; the denominator term As a normalization factor, it ensures that the input to the inverse cosine function is controlled within... Within the range, intermediate calculation variables By converting segmented logic into chromaticity components, color types can be distinguished, such as blue tones and green tones, thereby ensuring the continuity of the color sequence in the monitoring image.

[0060] The saturation component analysis formula in the spectral image conversion model is as follows: , in, For saturation components, For the minimum value function, R, G, and B are the pixel values ​​of the red, green, and blue bands, respectively. The above saturation components reflect the vividness of colors in the image, and their values ​​can be set between 0 and 1, where a larger value indicates a more vivid color, and a smaller value indicates a duller color.

[0061] By converting multispectral images into brightness, chromaticity, and saturation components using a spectral image conversion model, the spectral characteristics of the original multispectral images can be preserved. These spectral characteristics can reflect the presence and concentration of different substances in the water body. Preserving spectral characteristics helps to obtain more accurate information related to particulate organic carbon in the water body, which plays an important role in the accurate analysis of particulate organic carbon concentration in the water body.

[0062] The second step, the information conversion and optimization module, uses a spectral image conversion model to convert and analyze multi-dimensional detection data, thereby obtaining the brightness component, color component, and saturation component of different data sampling points within the water monitoring area.

[0063] The third step involved extracting a high spatial resolution panchromatic image based on multi-dimensional detection data.

[0064] The fourth step, the information conversion and optimization module, analyzes the mean and variance of the high spatial resolution panchromatic image based on the aforementioned high spatial resolution panchromatic image.

[0065] In this embodiment, panchromatic images are set in the multi-dimensional detection data. The pixel value ,in , The above and These represent the number of rows and columns of the panchromatic image, respectively.

[0066] Furthermore, the mean and variance of the panchromatic image P satisfy the following relationship: , in, For panchromatic images The mean, For panchromatic images the number of rows, For panchromatic images The number of columns, For panchromatic images pixel values, For panchromatic images The variance.

[0067] Simultaneous brightness component image The mean and variance satisfy the following relationship: , in, For brightness component image The mean, For brightness component image the number of rows, For brightness component image The number of columns, For brightness component image pixel values, For brightness component image The variance; The fifth step, the information conversion and optimization module, performs contrast stretching on the high spatial resolution panchromatic image based on the mean and variance to obtain the processed panchromatic image.

[0068] The embodiment introduces a contrast stretching method, based on which high spatial resolution panchromatic images in multi-dimensional detection data are processed. Contrast stretching can adjust the distribution range of pixel values ​​to match the brightness component image. With similar means and variances, the above steps can enhance the correlation between images.

[0069] Based on the above mean values, panchromatic images Contrast stretching can be used to obtain the processed panchromatic image. And satisfy the following relationship: , This embodiment will use the processed high spatial resolution panchromatic image. Replaces brightness component image However, the chroma and saturation components need to remain unchanged.

[0070] Furthermore, the embodiment performs contrast stretching on the high spatial resolution panchromatic image to make it have a similar mean and variance to the brightness component image. Then, the processed panchromatic image is used to replace the brightness component image for image synthesis. The resulting color image has improved spatial resolution, which can more clearly present the details of the water monitoring area and make the information from different data sampling points more accurate, thereby enhancing the reliability and accuracy of water monitoring information.

[0071] The sixth step involves constructing an image synthesis model in the information conversion and optimization module based on the HIS transformation method, the converted multi-dimensional detection information, and the processed panchromatic image. Then, the image synthesis model is used to perform inverse transformation and synthesis on the multi-dimensional detection data, and obtain the new pixel values ​​of the red band, green band, and blue band corresponding to different data sampling points in the water monitoring area.

[0072] The embodiment combines the inverse transformation in the transformation method to process the lightness component. The chroma component (H) and saturation component (S) can be converted again to obtain the result. , , Band.

[0073] The image synthesis model constructed in this embodiment satisfies the following relationship: when hour, , , The bands satisfy the following relationship: , when hour, , , The bands satisfy the following relationship: , when hour, , , The bands satisfy the following relationship: , The seventh step, information conversion and optimization module, outputs converted multi-dimensional detection information of the water monitoring area based on the new pixel values ​​of the red, green, and blue bands. In one embodiment, the above-mentioned new... , , By performing color synthesis on the bands, a color image with improved spatial resolution can be obtained, which in turn yields multi-dimensional detection information after conversion from different data sampling points in the water monitoring area.

[0074] The information conversion and optimization module further integrates the converted multi-dimensional detection information from different data sampling points, enabling rapid output of the converted multi-dimensional detection information for the water monitoring area. The final image synthesis of the converted multi-dimensional detection information not only retains the spectral characteristics of the original multispectral image but also improves clarity and spatial resolution, further enhancing the reliability and accuracy of the water monitoring information.

[0075] Furthermore, this embodiment provides a flowchart illustrating the information conversion and optimization module. Please refer to the attached diagram for details. Figure 2 , Figure 2 The system clearly displays the data flow and processing relationships between each step, facilitating practical operation and troubleshooting, and further ensuring the practical performance and feasibility of a dynamic analysis system for particulate organic carbon concentration in water.

[0076] This embodiment describes the complete process of spectral image conversion model, multi-dimensional detection data conversion and analysis, high spatial resolution panchromatic image processing, inverse transformation and synthesis of the image synthesis model, and finally outputting the converted multi-dimensional detection information. The above process and specific implementation details facilitate practical operation, ensuring the actual performance and feasibility of the system, and enabling efficient data processing for dynamic analysis of particulate organic carbon concentration in water. The information conversion and optimization module in this embodiment can integrate converted multi-dimensional detection information from different data sampling points, quickly outputting the converted multi-dimensional detection information for the water monitoring area. This allows the system to comprehensively and holistically present the situation of the water monitoring area, providing richer and more complete data support for dynamic analysis of particulate organic carbon concentration in water.

[0077] S3. The predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information. The specific implementation details are as follows: The predictive analysis module in this embodiment mainly analyzes the color index and particulate organic carbon (POC) concentration of different data sampling points in the water monitoring area based on the transformed multi-dimensional detection information. It reduces the limitations and uncertainties in the remote sensing inversion process by constructing model algorithms, and provides accurate data support for water environment monitoring.

[0078] The first step, the predictive analysis module, analyzes the remote sensing reflectance of different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information.

[0079] The predictive analysis module first analyzes the remote sensing reflectance of different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information. This is because remote sensing reflectance (…) ) and the total absorption coefficient of water body ( ) and backscattering coefficient ( There is a close relationship between them, which can be expressed as: , The above relationship shows that remote sensing reflectance can comprehensively reflect the absorption and scattering characteristics of light by particulate matter in water, laying the foundation for subsequent color index analysis. In constructing the color index analysis function, this embodiment fully considers the total absorption coefficient (...). ) and backscattering coefficient ( The ratio of ) can ensure that the color index can accurately quantify the optical properties of water.

[0080] Based on the above relationship analysis, the remote sensing reflectance of different data sampling points in the water monitoring area is analyzed. According to its close correlation with the total absorption coefficient and backscattering coefficient of the water body, it can comprehensively reflect the absorption and scattering characteristics of particulate matter in the water body, and fully obtain the optical information of the water body, laying a solid foundation for subsequent analysis.

[0081] The second step involves constructing a color index analysis function in the prediction and analysis module based on remote sensing reflectance, and setting water body band reference information using multi-dimensional detection information after conversion and remote sensing reflectance.

[0082] Construct a color index analysis function: This embodiment constructs a color index analysis function in the prediction and analysis module based on remote sensing reflectance. It is primarily based on the color index of three-band differences, while also incorporating the remote sensing reflectance of the water monitoring area. The color index analysis function is constructed using different bands of the spectrum. This function can quantify the complex relationship between the optical properties of water and the concentration of particulate organic carbon (POC). The expression for the color index analysis function in this embodiment is as follows: , in, This represents the color index analysis function. Indicates the wavelength in the water monitoring area Remote sensing reflectance, Indicates the wavelength in the water monitoring area Remote sensing reflectance, Indicates the wavelength in the water monitoring area Remote sensing reflectance.

[0083] The numerator of the color index analysis function is the band difference. Through combined calculations of specific wavebands, it can keenly reflect the differences in absorption and scattering of particulate matter in water, while preserving the effect of independent changes in the backscattering coefficient. This plays a crucial role in accurately capturing changes in the optical properties of water. The denominator consists of wavebands and... It plays a key role in normalization. By standardizing molecular results, it effectively reduces the impact of factors such as lighting conditions or observation angle on the color index calculation results, and further improves the stability and reliability of color index analysis results.

[0084] In order to enable the color index analysis function to better adapt to the needs of actual water body monitoring, three representative analysis bands were determined as water body band reference information by analyzing the converted multi-dimensional detection information and the actual situation of water body and environment.

[0085] The first reference band is set to and The first reference band belongs to the blue-green band and is highly sensitive to chlorophyll and suspended matter in water. Chlorophyll is an important component of phytoplankton in water, and changes in its content can reflect the degree of eutrophication. Suspended matter includes silt, organic debris, etc., which have a significant impact on the optical properties and ecological environment of water. Therefore, [the band is selected...] Wavelength bands help to accurately capture the optical characteristics of these key substances in water.

[0086] Second reference band and The second reference band is the green band. The green band holds a unique position in water optics research, providing important information about the distribution and optical properties of particulate matter in water, and offering crucial reference data for calculating the color index.

[0087] Third reference band and The third reference band is the red band. This band is highly sensitive to inorganic suspended solids in water bodies. The content and distribution of inorganic suspended solids are of great significance for assessing the degree of water pollution and the quality of the ecological environment. By introducing... The band can further improve the quantitative description of the optical properties of water bodies by the color index.

[0088] A comprehensive analysis of the three reference bands (490nm blue-green band, 555nm green band, and 670nm red band) shows that the combination of the three bands can reflect the optical characteristics of water bodies from different perspectives, further improve the quantitative description of the optical characteristics of water bodies by the color index, provide richer information for subsequent analysis, help assess the degree of water pollution and the quality of the ecological environment, and accurately capture the optical characteristics of key substances in water bodies.

[0089] The third step, the predictive analysis module, substitutes the water body band reference information into the color index analysis function for derivation and analysis, thus obtaining the color index of different data sampling points within the water body monitoring area.

[0090] By substituting the aforementioned water body band reference information into the color index analysis function for derivation and analysis, the color index of different data sampling points within the water body monitoring area can be obtained. In one embodiment, based on the transformed multi-dimensional detection information, water body analysis band information, and the color index analysis function, the color index of each data sampling point is accurately calculated.

[0091] Based on the remote sensing reflectance of the three analytical bands mentioned above, and combined with the color index analysis function, the color index of different data sampling points in the water monitoring area can be accurately quantified. Since the color index is essentially a function of the total absorption coefficient of the water body, and remote sensing reflectance is directly proportional to both the total absorption coefficient and the backscattering coefficient, the color index can retain effects that are independently related to changes in the backscattering coefficient. Furthermore, the form of the band difference does not cancel out molecular changes, thus ensuring that the color index accurately reflects the optical properties of the water body, further providing a reliable basis for subsequent inversion of particulate organic carbon concentration.

[0092] Further integration of color indices from different data sampling points can yield a set of color indices for the water monitoring area.

[0093] The example uses different data sampling points The spectral characteristic data is accurately input into the color index analysis function, and after calculation, each data sampling point is output. Color index and This is represented. To facilitate system data storage and management, different data sampling points... The color index is stored in the form of a matrix or table, with each data sampling point... For each unique color index, the above storage method satisfies the following relationship: , The above matrix or table format can clearly present the color index distribution of each data sampling point in the water monitoring area, providing a convenient and reliable basis for subsequent monitoring of particulate organic carbon concentration and dynamic analysis of water bodies.

[0094] The fourth step, after obtaining the color indices from different data sampling points, involves establishing a regression model for predicting particulate organic carbon (POC) concentration in the predictive analysis module based on the transformed multi-dimensional detection information and the color indices. This model establishes a mathematical relationship between the color indices and measured POC concentration data in water bodies, enabling accurate prediction of POC concentration in water.

[0095] The regression model for predicting particulate organic carbon concentration in this embodiment satisfies the following relationship: , in, POC concentration, The first fitting coefficient, Color index The second fitting coefficient, The third fitting coefficient.

[0096] Based on the transformed multi-dimensional detection information and color index, a regression model for predicting particulate organic carbon concentration is established. This model can establish a mathematical relationship between the color index and the measured POC concentration data in water bodies, providing an effective tool for accurately predicting the concentration of particulate organic carbon in water bodies.

[0097] Wherein, POC concentration represents the concentration of particulate organic carbon in the water body; the first fitting coefficient reflects the influence of the squared term of the color index on the POC concentration; the color index is the key input variable of the model; the second fitting coefficient reflects the contribution of the linear term of the color index to the POC concentration; and the third fitting coefficient is mainly used to correct other influencing factors and errors in the model.

[0098] In this embodiment, in order to train and obtain the above three fitting coefficients ( The least squares method is used to estimate parameter values. By minimizing the sum of squared errors between the measured values ​​and the model's predicted values, the optimal fitting coefficients are sought, ensuring that the particulate organic carbon concentration prediction regression model best fits the measured data. Furthermore, in other embodiments, methods such as random forests and support vector machines can be used to train the fitting coefficients, further improving the prediction accuracy and generalization ability of the particulate organic carbon concentration prediction regression model.

[0099] The fifth step involves fitting analysis based on the particulate organic carbon concentration prediction regression model and color index to obtain the particulate organic carbon concentration at different data sampling points within the water monitoring area.

[0100] Based on the established regression model for predicting particulate organic carbon concentration, and according to different data sampling points The corresponding color index can be calculated using the aforementioned predictive regression model to obtain the color index for different data sampling points. The corresponding POC concentration prediction results show that the above process makes full use of the quantitative relationship between color index and POC concentration. Through model calculation, the color index information is converted into specific POC concentration values, thus realizing the scientific prediction of particulate organic carbon concentration in water.

[0101] Further integration of POC concentration prediction results from different data sampling points can yield POC concentration prediction results for the water monitoring area.

[0102] This embodiment integrates the POC concentration prediction results corresponding to different data sampling points, and the relevant prediction results are also stored and presented in the form of a matrix or table as follows.

[0103] , The above integration method can comprehensively and intuitively display the distribution of particulate organic carbon concentration at various locations within the water body monitoring area, providing important data support and decision-making basis for water body carbon sink assessment, pollution control and ecological protection.

[0104] Through the above steps and analysis methods, the predictive analysis module can accurately analyze the color index and particulate organic carbon concentration of different data sampling points in the water monitoring area based on the transformed multi-dimensional detection information. This effectively reduces the limitations and uncertainties in the remote sensing inversion process, providing a scientific, reliable, and practical analysis method and solution for the field of water environment monitoring, and helps to improve the quality and level of water environment monitoring.

[0105] S4. The display and storage module receives the converted multi-dimensional detection information, color index, and particulate organic carbon concentration in real time, realizing dynamic monitoring and analysis of particulate organic carbon concentration in the water monitoring area. The specific implementation details are as follows: The display and storage module maintains a continuous communication connection with the data conversion module, acquiring multi-dimensional detection information in real time. This information covers multiple indicators affecting water particulate organic carbon concentration, such as water temperature, pH, and dissolved oxygen, as well as color indices and particulate organic carbon concentration data. Upon receiving relevant data, the module immediately stores it in the system database, ensuring data timeliness and accuracy. Furthermore, the data conversion module maintains continuous communication with other modules, enabling real-time acquisition of indicators affecting water particulate organic carbon concentration, such as water temperature, pH, and dissolved oxygen, as well as color indices and particulate organic carbon concentration data, providing an information foundation for subsequent analysis and dynamic monitoring.

[0106] To facilitate intuitive viewing and analysis of data, the display and storage module visualizes the received multi-dimensional detection information, color index, and particulate organic carbon concentration in various chart formats. In one embodiment, a line chart is used to show the trend of particulate organic carbon concentration over time, clearly illustrating its dynamic changes; a scatter plot is used to present the relationship between the color index and particulate organic carbon concentration, facilitating subsequent analysis of their correlation; and a bar chart is used to compare particulate organic carbon concentrations at different monitoring points, helping to identify areas with significant concentration differences. These charts not only have basic display functions but also support interactive operations such as zooming, panning, and data point querying to obtain more detailed information. For an optional embodiment of the tabular display format of particulate organic carbon concentration, please refer to [link to optional embodiment]. Figure 3 The prediction results are presented in tabular form, and the concentration of particulate organic carbon at different locations is collected in the table. This provides an intuitive reference for the analysis of the overall particulate organic carbon concentration in the water monitoring area and the particulate organic carbon concentration in local areas, making the information on particulate organic carbon concentration in water more intuitive and comprehensive.

[0107] Furthermore, to more intuitively display the concentration distribution of particulate organic carbon in water, the module presents the prediction results in map form. In one embodiment, based on different ranges of particulate organic carbon concentration, it is mapped as a color image and overlaid on a Geographic Information System (GIS) map. Low-concentration areas are displayed in blue, and as the concentration increases, the color gradually transitions to green, yellow, orange, until high-concentration areas are displayed in red. This color coding method can quickly identify areas with high concentrations, providing an intuitive reference for the analysis of particulate organic carbon concentration in water. Simultaneously, the map display function also supports zooming and panning operations to view detailed information for different sub-regions, and allows users to click on specific locations to obtain the specific concentration value and other relevant detection information for that point.

[0108] The display and storage module updates charts and maps in real time based on received multi-dimensional detection information, color indices, and particulate organic carbon concentration. Whenever new data arrives, the module automatically recalculates relevant statistical indicators and updates data points and curves in the charts, as well as the color distribution on the map, ensuring users always see the latest monitoring results. Furthermore, the module features intelligent optimization capabilities, automatically adjusting chart display methods and parameters based on data characteristics and user habits. This includes automatically selecting appropriate axis ranges and adjusting chart color schemes to improve data visualization and user experience.

[0109] In this embodiment, the charts and maps are updated in real time based on the received data, and relevant statistical indicators are automatically recalculated. The data points, curves and color distribution are updated to ensure that the latest monitoring results are always visible, and the dynamic changes in the concentration of particulate organic carbon in the water are grasped in a timely manner. This improves the data visualization effect and makes data analysis more efficient and convenient.

[0110] Please see Figure 4 In an optional embodiment, the present invention also provides a dynamic analysis system for particulate organic carbon concentration in water. This system includes a data acquisition and fusion module, an information conversion and optimization module, a predictive analysis module, and a display and storage module. These modules are interconnected to implement the specific steps of the relevant embodiments of the dynamic analysis system for particulate organic carbon concentration in water provided by the present invention. The dynamic analysis system for particulate organic carbon concentration in water of the present invention has a complete structure, is objective and stable, and enhances the overall applicability and practical application capability of the present invention.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A dynamic analysis system for particulate organic carbon concentration in water, characterized in that, The system includes a data acquisition and fusion module, an information conversion and optimization module, a predictive analysis module, and a display and storage module; The location and number of data sampling points within the water body monitoring area are determined, and the data acquisition and fusion module obtains multi-dimensional detection data of the water body monitoring area based on the location and the number. The information conversion and optimization module transforms and synthesizes the multi-dimensional detection data to output the converted multi-dimensional detection information of the water body monitoring area. The predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the converted multi-dimensional detection information. The display and storage module receives the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in real time, enabling dynamic monitoring and analysis of the particulate organic carbon concentration in the water monitoring area.

2. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The determination of the location and number of data sampling points within the water body monitoring area includes: Collect water area data, topographic information, and water flow characteristic information of the water monitoring area; The data acquisition and fusion module determines the location of data sampling points within the water body monitoring area by referring to the water area data, the topographic information, and the water flow characteristic information. A formula for calculating the number of sampling points in a sub-region is constructed in the data acquisition and fusion module. The data acquisition and fusion module uses the location of the data sampling points and the number of sampling points in the sub-region to calculate the number of data sampling points within the water body monitoring area.

3. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The data acquisition and fusion module obtains multi-dimensional detection data of the water monitoring area based on the location and the quantity, including: The data acquisition and fusion module combines the data acquisition device, the location, and the quantity to obtain initial multispectral image data of different data sampling points within the water monitoring area; Different band radiometric calibration models are established in the data acquisition and fusion module; The data acquisition and fusion module obtains calibrated data from different data sampling points within the water monitoring area based on the different band radiometric calibration models and the initial multispectral image data. The data acquisition and fusion module integrates the calibrated data from different data sampling points to obtain multi-dimensional detection data for the water monitoring area.

4. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The process of transforming and synthesizing the multi-dimensional detection data through the information conversion and optimization module to output the converted multi-dimensional detection information of the water monitoring area includes: Based on the characteristics of human visual perception and the HIS transformation method, a spectral image conversion model is constructed in the information conversion and optimization module. The information conversion and optimization module performs conversion and analysis on the multi-dimensional detection data through the spectral image conversion model to obtain the brightness component, color component, and saturation component of different data sampling points in the water body monitoring area.

5. The dynamic analysis system for particulate organic carbon concentration in water according to claim 4, characterized in that, The process of transforming and synthesizing the multi-dimensional detection data through the information conversion and optimization module to output the converted multi-dimensional detection information of the water monitoring area includes: A high spatial resolution panchromatic image is obtained based on the multi-dimensional detection data; The information conversion and optimization module obtains the mean and variance of the high spatial resolution panchromatic image based on the high spatial resolution panchromatic image. The information conversion and optimization module performs contrast stretching on the high spatial resolution panchromatic image based on the mean and the variance to obtain the processed panchromatic image.

6. The dynamic analysis system for particulate organic carbon concentration in water according to claim 5, characterized in that, The process of transforming and synthesizing the multi-dimensional detection data through the information conversion and optimization module to output the converted multi-dimensional detection information of the water monitoring area includes: Based on the HIS transformation method, the transformed multi-dimensional detection information, and the processed panchromatic image, an image synthesis model is constructed in the information transformation and optimization module. The information conversion and optimization module uses the image synthesis model to perform inverse transformation and synthesis on the multi-dimensional detection data, and obtains the new pixel values ​​of the red band, green band and blue band corresponding to different data sampling points in the water monitoring area; The information conversion and optimization module outputs converted multi-dimensional detection information of the water monitoring area based on the new pixel values ​​of the red band, the green band, and the blue band.

7. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information, including: The predictive analysis module analyzes the remote sensing reflectance of different data sampling points within the water body monitoring area based on the transformed multi-dimensional detection information. A color index analysis function is constructed in the prediction and analysis module based on the remotely sensed reflectance. The predictive analysis module uses the converted multi-dimensional detection information and the remote sensing reflectance to set water body band reference information.

8. The dynamic analysis system for particulate organic carbon concentration in water according to claim 7, characterized in that, The predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information, including: The predictive analysis module substitutes the water body band reference information into the color index analysis function for derivation and analysis to obtain the color index of different data sampling points within the water body monitoring area.

9. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The predictive analysis module analyzes the color index and particulate organic carbon concentration at different data sampling points within the water monitoring area based on the transformed multi-dimensional detection information, including: Based on the converted multidimensional detection information and the color index, a regression model for predicting particulate organic carbon concentration is established in the prediction and analysis module. The particulate organic carbon concentration is obtained by fitting the predicted regression model and the color index to obtain the particulate organic carbon concentration at different data sampling points in the water monitoring area.

10. The dynamic analysis system for particulate organic carbon concentration in water according to claim 1, characterized in that, The display and storage module receives the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in real time, enabling dynamic monitoring and analysis of particulate organic carbon concentration in the water monitoring area, including: The display and storage module stores the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration into the system database; The display and storage module visualizes the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration in the form of charts; The display and storage module updates and optimizes the chart format in real time based on the converted multi-dimensional detection information, the color index, and the particulate organic carbon concentration.

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