Multi-element ecological environment admission intelligent analysis system based on AI large model

Abstract

The invention relates to the technical field of environment detection, in particular to a multi-element ecological environment admission intelligent analysis system based on an AI large model, and the system comprises a remote sensing unit which is used for collecting the atmospheric environment data, land images and water area images of each land area; the data collection unit is used for planning a collection route of the remote sensing unit; the data integration unit is used for obtaining a land idle value and a water area parameter and generating an atmosphere evaluation value; the model training unit is used for evaluating the environment project to generate an environment project admission value; the analysis and evaluation unit is used for generating a water source sensitive area according to the water quality evaluation value and correcting the environment project admission value based on the water source sensitive area to obtain an admission correction value so as to determine a land parcel area in which the environment project can be set; according to the invention, the above units cooperate with each other, so that the accuracy of the multi-element ecological environment admission intelligent analysis system based on the AI large model is further improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental detection technology, and in particular to a multi-factor ecological environment access intelligent analysis system based on an AI large model. Background Art

[0002] Water resources are unevenly distributed geographically, which requires some regions to focus on the carrying capacity of water resources when making ecological and environmental access decisions, so as to avoid development restrictions or ecological damage due to water shortages. Protecting the integrity and stability of water ecosystems is one of the important goals of ecological and environmental access. In the current ecological and environmental protection and economic development work, the ecological and environmental and economic development departments undertake a large number of industry project approval and decision-making tasks and the responsibility of optimizing the business environment. The traditional approval and decision-making methods and the optimization of environmental services mainly rely on the experience of human experts to make judgments, which have many limitations and low efficiency. On the one hand, the knowledge and experience of experts are limited, and it is difficult to To comprehensively consider the multi-faceted impact of the project; on the other hand, there may be subjective differences between different experts, resulting in a lack of consistency in the approval decision-making results; thirdly, the timeliness of the manual judgment results also determines the degree of optimization of the business environment. Therefore, there is an urgent need for an intelligent ecological environment access analysis system that can comprehensively consider multiple factors and automatically, efficiently and accurately evaluate the impact of industry projects on the ecological environment, and provide scientific basis and efficient support for the approval decisions and investment promotion work of the ecological environment and economic development departments. The AI ​​big model can integrate multi-source data such as water resources, land resources, climate, and ecosystems, and explore the complex relationships and potential laws between various factors.

[0003] Chinese patent application publication number: CN118840680A discloses a method for identifying water surface algae based on drone images. The invention provides a method for identifying water surface algae based on drone images, including the following steps: step S101, collecting water surface images through drones and constructing a water area image dataset; step S102, training a water surface area semantic segmentation model based on the water area image dataset; step S103, constructing a water surface algae dataset; step S104, training a water surface algae recognition model based on the water surface algae dataset; step S105, inputting the water surface image with the background removed into the trained water surface algae recognition model, and outputting the water surface algae detection result; the invention adds feature receptive fields in both channel and space directions to the water surface area semantic segmentation model, thereby improving the recognition accuracy of the water area and reducing background interference, and adds a convolution layer at the output end of the water surface algae recognition model to increase the range of the receptive field, thereby improving the accuracy of algae recognition.

[0004] Chinese patent application publication number: CN117557166A discloses a multimodal and real-time data user data environment intelligent monitoring system. The invention provides a multimodal and real-time data user data environment intelligent monitoring system, which specifically relates to the field of environmental detection technology, including a water surface image analysis module, a water surface ecological risk assessment module, a water quality appearance analysis module, a water quality appearance quality assessment module, an ecological risk analysis module, an ecological risk assessment module, and a control module. Multimodal information about water quality is acquired through real-time collection. Based on the acquired water quality color difference, floating matter content, heavy metal excess coefficient, and ecological parameter offset coefficient, a control instruction is generated to control the water quality. Based on the water surface plant risk status before and after the control, the water quality appearance quality, and the ecological risk status, a control quality assessment coefficient is obtained. When the water quality control quality assessment coefficient is lower than the preset value, an early warning is issued to the management personnel to prompt control abnormalities and water quality abnormalities, which solves the problem of insufficient timely water quality monitoring and lack of long-term monitoring of water quality status in the prior art.

[0005] However, the above method has the following problems: it fails to comprehensively evaluate the water source situation by combining multiple environmental factors, and fails to comprehensively evaluate the environmental conditions by combining multiple environmental factors and water source conditions. Summary of the invention

[0006] To this end, the present invention provides a multi-factor ecological environment access intelligent analysis system based on an AI large model to overcome the problems in the prior art that the water source situation cannot be comprehensively evaluated by combining multiple environmental factors, and the environmental conditions cannot be comprehensively evaluated by combining multiple environmental factors and water source conditions.

[0007] To achieve the above objectives, the present invention provides a multi-factor ecological environment access intelligent analysis system based on an AI big model, comprising: A remote sensing unit, which is used to collect atmospheric environment data, land images and water images of each block area; A data collection unit connected to the remote sensing unit for planning a collection route of the remote sensing unit based on environmental demand data of the environmental project and historical environmental data of each of the land areas, wherein the environmental demand data includes a water source demand value, a land area demand value, and an estimated atmospheric pollution value; a data integration unit connected to the remote sensing unit, for processing the land image and the water area image to obtain a land vacancy value and a water area parameter, analyzing the atmospheric environment data to generate an atmospheric evaluation value, and adjusting the acquisition state of the remote sensing unit according to the water area image, wherein the water area parameter includes a water body type, a water quality evaluation value and a water area; A model training unit, which is respectively connected to the remote sensing unit and the data integration unit, is used to construct an ecological environment model according to the water area parameters, the land idle value, and the atmospheric evaluation value, and evaluate the environmental project by combining the ecological environment model and the environmental demand data to generate an environmental project access value; An analysis and evaluation unit, which is connected to the model training unit, is used to generate a water source sensitive area according to the water quality evaluation value, and correct the environmental project access value based on the water source sensitive area to obtain an access correction value, so as to determine the plot area where the environmental project can be established.

[0008] Furthermore, the data integration unit includes: A water body type subunit, which is connected to the remote sensing unit, is used to identify the plant species in the water area image, collect the plant distribution characteristics and growth conditions to generate a plant density parameter to determine the water body type, and the water body type includes artificial water areas and natural water areas.

[0009] Furthermore, the data integration unit also includes: A water quality evaluation subunit, which is connected to the remote sensing unit, is used to determine the water quality pollution situation based on the identification of the water surface color and water surface transparency in the water area image, and adjust the remote sensing unit to enter the detection and collection state to capture the polluted water area image when the water quality pollution state is determined, and generate the water quality evaluation value by combining the water surface ripples and water surface reflection conditions in the polluted water area image.

[0010] Furthermore, the data integration unit also includes: A water area subunit, which is connected to the remote sensing unit, is used to analyze the water area image by an edge detection algorithm to obtain a water area contour line, and adjust the remote sensing unit to enter the detection and collection state directly above the water area contour line to continuously capture a number of water area contour images, and determine the water area by combining a number of the water area contour images.

[0011] Furthermore, the data integration unit also includes: A land processing subunit, which is connected to the remote sensing unit, is used to calculate the land idle value based on the identification of the artificial building area in the land image and the area of the plot area; An atmospheric processing subunit, which is connected to the remote sensing unit, is used to generate the atmospheric evaluation value by combining the atmospheric environment data and the atmospheric estimated pollution value.

[0012] Furthermore, the model training unit constructs the ecological environment model according to the water body type, the water area, the land idle value, and the atmospheric evaluation value.

[0013] Furthermore, the model training unit compares the same environmental parameter items in the ecological environment model and the environmental demand data to obtain a number of ecological environment difference rates, and summarizes the several ecological environment difference rates to generate the environmental project access value.

[0014] Furthermore, the analysis and evaluation unit generates the water source sensitive area according to the water quality evaluation value, wherein: If the water quality evaluation value is less than or equal to the preset evaluation value, determining that the land area corresponding to the water quality evaluation value is the water source sensitive area; The preset evaluation value is negatively correlated with the water area.

[0015] Furthermore, the analysis and evaluation unit corrects the environmental project access value based on the water source sensitive area to obtain an access correction value to determine the land area where the environmental project can be established, wherein: If the access correction value is greater than or equal to the preset access value, it is determined that the land area corresponding to the access correction value can be used to establish the environmental project; The preset admission value is negatively correlated with the area of ​​the plot.

[0016] Furthermore, the data collection unit compares the environmental demand data of the environmental project with the historical environmental data of each of the land areas to obtain a number of environmental demand difference rates, sorts the environmental demand difference rates from small to large, and generates a collection route for the remote sensing unit.

[0017] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention generates plant density parameters to determine the type of water body by collecting plant distribution characteristics and growth conditions through identifying plant species in water area images. Different water body types (such as rivers, lakes, wetlands, etc.) often have unique plant community structures. Lakes are dominated by submerged plants and floating-leaf plants, while wetlands are dominated by emergent plants. By accurately identifying plant species and analyzing their distribution and density, it is possible to understand the ecological characteristics of water bodies in more detail, thereby improving the accuracy of water body type determination and avoiding the limitations of single indicators (such as water body area, depth, etc.). The growth status and distribution characteristics of plants are a comprehensive reflection of the ecological environment of water bodies. Healthy water bodies can usually support diverse and well-growing plant communities. If the water body is polluted or ecologically If water bodies are damaged, the types, quantity and growth conditions of plants will change. Understanding the distribution and growth of plants in water bodies will help to formulate targeted ecological protection and restoration measures. This method based on image recognition and data analysis requires the use of advanced computer vision, machine learning and other technologies, which has promoted the application and development of related technologies in the field of ecological monitoring. By long-term monitoring of changes in plant density parameters and growth conditions, the evolution trend of aquatic ecosystems can be predicted. With the influence of climate change or human activities, the types and distribution of plants in water bodies may change. Grasping these changing trends in advance will help to take corresponding countermeasures, reduce adverse effects, and protect the stability of aquatic ecosystems, effectively improving the accuracy of the multi-factor ecological environment access intelligent analysis system based on the AI ​​large model.

[0018] Furthermore, the present invention determines the water pollution situation by identifying the water surface color and water surface transparency in the water area image, and at the same time adjusts the flight altitude of the remote sensing unit to hover over the water surface, and evaluates the water pollution situation by collecting the water surface ripple image generated by the remote sensing unit blowing the water surface and the water surface reflection situation. The water surface color and transparency are intuitive manifestations of water pollution. Different pollutants will cause the water surface color to change and the transparency to decrease, while the ripple image generated by the remote sensing unit blowing the water surface and the water surface reflection situation provide additional information from a dynamic and optical perspective. The remote sensing unit can be flexibly deployed quickly in different waters and different locations, and image data can be collected in real time. By analyzing these data in real time, changes in water quality can be discovered in a timely manner, especially for sudden pollution incidents. Monitoring is of great significance. Using remote sensing units to collect images for water quality assessment does not require direct contact with water bodies, avoiding direct contact between inspectors and polluted water bodies and ensuring the safety of inspectors. Image data is intuitive and visual. Through image processing and analysis technology, information such as water surface color, transparency, ripple shape and reflection can be quantified, which is convenient for analysis and comparison. Compared with traditional water quality detection methods, the use of remote sensing units for water quality monitoring reduces costs to a certain extent, reduces the manpower, material and time costs of manual sampling, and also reduces the sample processing and testing costs of laboratory analysis. Moreover, remote sensing units can be reused, which reduces costs and further improves the accuracy of the multi-factor ecological environment access intelligent analysis system based on AI large models.

[0019] Furthermore, the present invention hovers over the water surface by adjusting the flight altitude of the remote sensing unit, and determines the area of the water area by collecting the image after the water surface ripples generated by the remote sensing unit blowing the water surface collide with the water area contour line. Traditional water area measurement methods, such as those based on satellite remote sensing images, may be affected by factors such as image resolution and cloud cover, resulting in limited measurement accuracy. By using the remote sensing unit to collect images of the collision between the water surface ripples and the water area contour line at close range, higher-resolution information can be obtained, the water area boundary can be clearly identified, and thus the water area can be calculated more accurately, reducing measurement errors. For some water areas with complex terrain and irregular shapes, such as the curved sections of rivers and the shorelines of lakes, traditional measurement methods may have difficulty accurately defining the boundaries. The remote sensing unit can flexibly adjust its flight altitude and position to take pictures close to the water surface, which can better capture the true contours of these complex water areas and thus accurately measure their areas. Even at the edges of water areas covered by vegetation or blocked by obstacles, clear images can be obtained by adjusting the shooting angle, improving the adaptability of the measurement. The collected images intuitively show the situation of the water surface ripples and the water area contour line. Through image processing techniques, such as edge detection and contour extraction algorithms, the water area boundary can be easily identified, and then the area can be calculated. Compared with some traditional measurement methods, this image-based measurement method has simpler and faster data processing, can improve work efficiency, and further enhances the accuracy of the multi-factor ecological environment access intelligent analysis system based on the AI large model.

[0020] Furthermore, the present invention evaluates the ecological environment of each plot area by combining water parameters, land parameters and atmospheric parameters in the ecological environment, and determines suitable plot areas in combination with the environmental requirements of the project. By comprehensively considering water parameters, land parameters and atmospheric parameters, it is possible to fully and deeply understand the ecological environment conditions of each plot area, avoid the one-sidedness of single parameter evaluation, make the evaluation results more scientific and accurate, and provide a reliable basis for subsequent decision-making. When evaluating a plot of land, not only the fertility of the land is considered, but also the pollution of the surrounding water bodies and the self-purification capacity of the atmosphere, so as to more objectively judge the ecological environment quality of the plot of land. Different projects have different requirements for the environment. By combining the environmental requirements of the project with the ecological environment parameters of each plot area, a reasonable match between the project and the environment can be achieved. For production projects with high water quality requirements, plots with excellent surrounding water quality and no pollution can be accurately screened out; for manufacturing projects that are sensitive to the atmospheric environment, plots with good air quality and low concentration of atmospheric pollutants can be selected. This evaluation and selection method helps to protect ecological and environmental sensitive areas. By identifying plots of land with important ecological value, inappropriate project development in these areas can be avoided, thereby protecting biodiversity and the integrity of the ecosystem. At the same time, reasonable project site selection can reduce damage to the ecological environment, promote the rational use of resources, achieve a virtuous interaction between economic development and ecological and environmental protection, and promote sustainable development. The evaluation and selection of plot areas based on ecological and environmental parameters and project environmental requirements can help optimize regional planning. According to the ecological and environmental characteristics of different plots and project requirements, the layout of various projects can be reasonably arranged to achieve optimal allocation of resources. Industrial projects can be concentrated in areas with large environmental capacity and complete infrastructure, and ecological agricultural projects can be arranged in plots with fertile soil and sufficient water resources. The overall development efficiency and quality of the region are improved, and the accuracy of the multi-factor ecological and environmental access intelligent analysis system based on the AI ​​large model is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a structural block diagram of the multi-factor ecological environment access intelligent analysis system based on the AI ​​big model of the present invention; Figure 2 It is a structural block diagram of a data integration unit according to an embodiment of the present invention; Figure 3 A determination diagram for determining a water source sensitive area according to an embodiment of the present invention; Figure 4 A determination diagram for determining a land area where an environmental project can be established according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0024] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0025] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] See also Figure 1 As shown, it is a structural block diagram of a multi-factor ecological environment access intelligent analysis system based on an AI big model of the present invention. An embodiment of the present invention provides a multi-factor ecological environment access intelligent analysis system based on an AI big model, including: A remote sensing unit, which is used to collect atmospheric environment data, land images and water images of each block area; A data collection unit connected to the remote sensing unit is used to plan the collection route of the remote sensing unit based on the environmental demand data of the environmental project and the historical environmental data of each block area, wherein the environmental demand data includes water source demand value, land area demand value and atmospheric pollution estimated value; A data integration unit is connected to the remote sensing unit and is used to process the land image and the water area image to obtain the land vacancy value and the water area parameter, analyze the atmospheric environment data to generate the atmospheric evaluation value, and adjust the acquisition state of the remote sensing unit according to the water area image. The water area parameter includes the water body type, the water quality evaluation value and the water area; A model training unit, which is connected to the remote sensing unit and the data integration unit respectively, is used to construct an ecological environment model according to water area parameters, land vacancy values ​​and atmospheric evaluation values, and to evaluate environmental projects in combination with the ecological environment model and environmental demand data to generate environmental project access values; The analysis and evaluation unit is connected to the model training unit and is used to generate a water source sensitive area according to the water quality evaluation value, and to correct the environmental project access value based on the water source sensitive area to obtain the access correction value to determine the land area where the environmental project can be established.

[0027] It is understandable that the remote sensing unit is provided with a number of drones to collect images and data.

[0028] See also Figure 2 As shown, it is a structural block diagram of a data integration unit according to an embodiment of the present invention, and the data integration unit includes: The water body type subunit is connected to the remote sensing unit and is used to identify plant species in water area images, collect plant distribution characteristics and growth conditions to generate plant density parameters to determine the water body type, which includes artificial water areas and natural water areas.

[0029] In implementation, the density of plants is calculated based on the distribution range and the number of plants. The density can be measured by the number of plants per unit area, for example, the number of plants per square meter is calculated.

[0030] It is understandable that for different types of plants, their density parameters can be calculated separately, or the comprehensive density parameter of the entire plant community can be calculated. The comprehensive density parameter can be a weighted average of the densities of various plants, and the weight can be determined according to the relative importance of the plants.

[0031] It is understandable that the type of water body can be determined based on plant density parameters or comprehensive density parameters combined with existing water body type classification standards or experience. For example, eutrophic water bodies in artificial waters have a higher density of aquatic plants, especially some plant species with strong pollution resistance; while the plant density in clean water bodies in natural waters is relatively low, and is dominated by some plants with higher water quality requirements.

[0032] Specifically, the present invention generates plant density parameters to determine the type of water body by identifying plant species in water area images, collecting plant distribution characteristics and growth conditions. Different water body types (such as rivers, lakes, wetlands, etc.) often have unique plant community structures. Lakes are dominated by submerged plants and floating-leaf plants, while wetlands are dominated by emergent plants. By accurately identifying plant species and analyzing their distribution and density, it is possible to understand the ecological characteristics of water bodies in more detail, thereby improving the accuracy of water body type determination and avoiding the limitations of single indicators (such as water body area, depth, etc.). The growth status and distribution characteristics of plants are a comprehensive reflection of the ecological environment of water bodies. Healthy water bodies can usually support diverse and well-growing plant communities. If the water body is polluted or ecologically damaged, the species of plants will be The types, numbers and growth conditions of plants in water bodies will change. Understanding the distribution and growth of plants in water bodies will help to formulate targeted ecological protection and restoration measures. This method based on image recognition and data analysis requires the use of advanced computer vision, machine learning and other technologies, which has promoted the application and development of related technologies in the field of ecological monitoring. By long-term monitoring of changes in plant density parameters and growth conditions, the evolution trend of aquatic ecosystems can be predicted. With the influence of climate change or human activities, the types and distribution of plants in water bodies may change. Grasping these changing trends in advance will help to take corresponding countermeasures, reduce adverse effects, and protect the stability of aquatic ecosystems, effectively improving the accuracy of the multi-factor ecological environment access intelligent analysis system based on the AI ​​large model.

[0033] Specifically, the data integration unit also includes: The water quality evaluation subunit is connected to the remote sensing unit and is used to determine the water pollution situation based on the water surface color and water surface transparency in the water area image, and to adjust the remote sensing unit to enter the detection and acquisition state to capture the polluted water area image when the water quality is determined to be polluted, and to generate a water quality evaluation value based on the water surface ripples and water surface reflection in the polluted water area image.

[0034] It can be understood that the correspondence between color features and the degree of water pollution is established. By referring to existing research results or conducting field sampling analysis, the type and degree of water pollution corresponding to different color features can be determined. In the HSV (hue, saturation, value) color mode, the darker the color, the smaller the V (value) value; the transparency is judged by calculating the gray value distribution of pixels in the image. The water body with higher transparency has a higher gray value, while the water body with lower transparency has a lower gray value; the weighted average method is used to assign different weights according to the degree of influence of the V (value) value and the gray value on the water pollution situation, and the comprehensive pollution index is calculated. The comprehensive pollution index = gray value x0.6 + V (value) value x0.4x100, and the comprehensive pollution index is compared with the preset standard value. If the comprehensive pollution index is less than or equal to the preset standard value, the water quality is judged to be polluted; if the comprehensive pollution index is greater than the preset standard value, the water quality is judged to be clean.

[0035] In a specific embodiment, the preset standard value is set to 152 (calculated when the gray value is 200 and the V (brightness) value is 80%). If the comprehensive pollution index is 183 and is greater than the preset standard value, the water quality is determined to be polluted; if the comprehensive pollution index is 137 and is less than the preset standard value, the water quality is determined to be clean.

[0036] It can be understood that the preset standard value is positively correlated with the water flow rate. The faster the water flow rate, the higher the self-cleaning ability, the higher the transparency, the lighter the color, the larger the grayscale value and V (brightness) value, and the larger the preset standard value. Therefore, the preset standard value is positively correlated with the water flow rate.

[0037] It can be understood that the water quality evaluation value = 100-(AxWxF+BxUxG), where A and B are coefficients, generally A is taken as 0.1, B is 0.05, W is the weight of the ripple frequency, U is the weight of the reflection intensity, W+U=1, generally W is taken as 0.4, V is 0.6, F is the water surface ripple frequency (pieces / minute), G is the water surface reflection intensity (brightness component of CIE Lab color space, ranging from 0-100); the above parameters are taken as numerical values ​​in the formula for calculation.

[0038] In practice, F was measured to be 120 and G to be 80, so the water quality evaluation value = 100-(0.1x0.4x120+0.05x0.6x80) = 92.8.

[0039] The detection and collection state is that the UAV hovers above the water surface at a preset distance and shoots directly below. The preset distance is positively correlated with the propeller speed of the UAV.

[0040] It is understandable that when the drone hovers above the water surface, the drone propeller can steadily blow the water surface to form ripples. When the water source is polluted, the water surface ripple period is less than that of normal water surface ripples. When the water surface ripples touch the edge of the water area contour, the ripples will become turbulent, thereby detecting the accurate edge of the water area contour.

[0041] It is understandable that, when the propeller area of ​​the drone is constant, the greater the propeller speed, the greater the blowing force generated, and the larger the ripples on the water surface. At this time, the drone's altitude can be adjusted to obtain stable water waves, so the preset distance is positively correlated with the propeller speed of the drone.

[0042] Specifically, the present invention determines the water pollution situation by identifying the water surface color and water surface transparency in the water area image, and at the same time adjusts the flight altitude of the remote sensing unit to hover over the water surface, and evaluates the water pollution situation by collecting the water surface ripple image generated by the remote sensing unit blowing the water surface and the water surface reflection situation. The water surface color and transparency are intuitive manifestations of water pollution. Different pollutants will cause the water surface color to change and the transparency to decrease, while the ripple image generated by the remote sensing unit blowing the water surface and the water surface reflection situation provide additional information from a dynamic and optical perspective. The remote sensing unit can be flexibly and quickly deployed in different waters and different locations, and image data can be collected in real time. By analyzing these data in real time, changes in water quality can be discovered in a timely manner, especially for sudden pollution incidents. Monitoring is of great significance. Using remote sensing units to collect images for water quality assessment does not require direct contact with water bodies, avoiding direct contact between inspectors and polluted water bodies and ensuring the safety of inspectors. Image data is intuitive and visual. Through image processing and analysis technology, information such as water surface color, transparency, ripple shape and reflection can be quantified, which is convenient for analysis and comparison. Compared with traditional water quality detection methods, the use of remote sensing units for water quality monitoring reduces costs to a certain extent, reduces the manpower, material and time costs of manual sampling, and also reduces the sample processing and testing costs of laboratory analysis. Moreover, remote sensing units can be reused, which reduces costs and further improves the accuracy of the multi-factor ecological environment access intelligent analysis system based on AI large models.

[0043] Specifically, the data integration unit also includes: The water area subunit is connected to the remote sensing unit and is used to analyze the water image through an edge detection algorithm to obtain the water contour line, and to adjust the remote sensing unit to enter the detection and acquisition state directly above the water contour line to continuously shoot several water contour images, and determine the water area by combining several water contour images.

[0044] It can be understood that the water area contour is determined by the remote sensing unit entering the detection and acquisition state just above the water area contour line and continuously photographing a number of water area contour images in which reflected ripples are generated by ripples colliding with the water area shore.

[0045] Specifically, the present invention determines the area of ​​the water area by adjusting the flying altitude of the remote sensing unit to hover over the water surface, and collecting images of the collision between the water surface ripples generated by the remote sensing unit blowing the water surface and the water area contour line. Traditional water area measurement methods, such as measurements based on satellite remote sensing images, may be affected by factors such as image resolution and cloud cover, resulting in limited measurement accuracy. However, using the remote sensing unit to collect images of the collision between water surface ripples and water area contour lines at close range can obtain higher resolution information and clearly identify the water area boundary, thereby more accurately calculating the water area and reducing measurement errors. For some water areas with complex terrain and irregular shapes, such as the curved sections of rivers and the coastlines of lakes, traditional measurement methods may find it difficult to accurately define the boundaries, and the remote sensing unit can flexibly Actively adjusting the flight altitude and position and shooting close to the water surface can better capture the true contours of these complex waters, thereby accurately measuring their area. Even at the edge of the water area covered by vegetation or blocked by obstacles, clear images can be obtained by adjusting the shooting angle, thereby improving the adaptability of the measurement. The collected images intuitively show the water surface ripples and the water area contour lines. Through image processing technology, such as edge detection, contour extraction and other algorithms, the water area boundary can be easily identified, and then the area can be calculated. Compared with some traditional measurement methods, this image-based measurement method has simpler and faster data processing, which can improve work efficiency and further improve the accuracy of the multi-factor ecological environment access intelligent analysis system based on AI large models.

[0046] Specifically, the data integration unit also includes: A land processing subunit, which is connected to the remote sensing unit, is used to calculate the land vacancy value based on the area of ​​artificial buildings and the area of ​​the land plot in the land image; It can be understood that the vacant land value = artificial building area / area of ​​the plot area.

[0047] The atmosphere processing subunit is connected to the remote sensing unit and is used to generate an atmosphere evaluation value by combining the atmospheric environment data and the atmospheric estimated pollution value.

[0048] It is understandable that the atmospheric evaluation value = (|Estimated atmospheric pollution value - atmospheric environmental data| / atmospheric environmental data). Where i is the number of items of atmospheric environmental data collected, and n is the total number of items of atmospheric environmental data collected.

[0049] Specifically, the model training unit constructs an ecological environment model based on water body type, water area, land vacancy value and atmospheric evaluation value.

[0050] Specifically, the model training unit compares the same environmental parameter items in the ecological environment model and the environmental demand data to obtain several ecological environment difference rates, and summarizes several ecological environment difference rates to generate environmental project access values.

[0051] It can be understood that the environmental project access value = (|water source demand value - water area| / water area) + (|land area demand value - land idle value| / land idle value) + (|estimated atmospheric pollution value - atmospheric evaluation value| / atmospheric evaluation value).

[0052] During implementation, the water source demand value is set at 95 square meters, the water area is 100 square meters, the land area demand value is 120 square meters, the land vacancy value is 240 square meters, the estimated atmospheric pollution value is 48, and the atmospheric evaluation value is 56, then the environmental project access value is 0.69.

[0053] See also Figure 3 As shown, it is a determination diagram for determining water source sensitive areas according to an embodiment of the present invention. The analysis and evaluation unit generates water source sensitive areas according to the water quality evaluation value, wherein: If the water quality evaluation value is less than or equal to the preset evaluation value, the land area corresponding to the water quality evaluation value is determined to be a water source sensitive area; If the water quality evaluation value is greater than the preset evaluation value, the land area corresponding to the water quality evaluation value is determined to be a non-water source sensitive area.

[0054] In a specific embodiment, the preset evaluation value is set to 95. If the water quality evaluation value is 84.4, which is less than the preset evaluation value, then the land area corresponding to the water quality evaluation value is determined to be a water source sensitive area; If the water quality evaluation value is 96.3, which is greater than the preset evaluation value, then the land area corresponding to the water quality evaluation value is determined to be a non-water source sensitive area.

[0055] The preset evaluation value is negatively correlated with the water area.

[0056] It is understandable that the larger the water area, the greater the probability of pollution, so the preset evaluation value is negatively correlated with the water area.

[0057] See also Figure 4 As shown, it is a determination diagram for determining a land area where an environmental project can be established in an embodiment of the present invention. The analysis and evaluation unit corrects the environmental project access value based on the water source sensitive area to obtain an access correction value to determine the land area where the environmental project can be established, wherein: If the access correction value is less than or equal to the preset access value, it is determined that the land area corresponding to the access correction value can be used to establish an environmental project; If the access correction value is greater than the preset access value, it is determined that the land area corresponding to the access correction value cannot be used to establish an environmental project; It can be understood that the environmental project access value is corrected based on the water source sensitive area, where, if the land area corresponding to the water quality evaluation value is determined to be a water source sensitive area, the environmental project access value is multiplied by 2 to generate an access correction value; if the land area corresponding to the water quality evaluation value is determined to be a non-water source sensitive area, the environmental project access value remains unchanged.

[0058] In a specific embodiment, the preset access value is set to 0.5, and if the access correction value is 0.23, which is less than the preset access value, it is determined that the land area corresponding to the access correction value can be used to establish an environmental project; If the access correction value is 0.69, which is greater than the preset access value, it is determined that the land area corresponding to the access correction value cannot be used to establish an environmental project; The preset entry value is negatively correlated with the area of ​​the plot.

[0059] It is understandable that the larger the area of ​​the plot, the more environmental factors there are, so the preset entry value is negatively correlated with the area of ​​the plot.

[0060] Specifically, the data collection unit compares the environmental demand data of the environmental project with the historical environmental data of each block area to obtain a number of environmental demand difference rates, sorts the environmental demand difference rates from small to large, and generates a collection route for the remote sensing unit.

[0061] Specifically, the present invention evaluates the ecological environment of each plot area by combining water parameters, land parameters and atmospheric parameters in the ecological environment, and determines suitable plot areas in combination with the environmental requirements of the project. By comprehensively considering water parameters, land parameters and atmospheric parameters, it is possible to fully and deeply understand the ecological environment conditions of each plot area, avoid the one-sidedness of single parameter evaluation, make the evaluation results more scientific and accurate, and provide a reliable basis for subsequent decision-making. When evaluating a plot of land, not only the fertility of the land is considered, but also the pollution of the surrounding water bodies and the self-purification capacity of the atmosphere, so as to more objectively judge the ecological environment quality of the plot of land. Different projects have different requirements for the environment. By combining the environmental requirements of the project with the ecological environment parameters of each plot area, a reasonable match between the project and the environment can be achieved. For production projects with high water quality requirements, plots with excellent surrounding water quality and no pollution can be accurately screened out; for manufacturing projects that are sensitive to the atmospheric environment, plots with good air quality and low concentration of atmospheric pollutants can be selected. This evaluation and selection method helps to protect ecological and environmental sensitive areas. By identifying plots of land with important ecological value, inappropriate project development in these areas can be avoided, thereby protecting biodiversity and the integrity of the ecosystem. At the same time, reasonable project site selection can reduce damage to the ecological environment, promote the rational use of resources, achieve a virtuous interaction between economic development and ecological and environmental protection, and promote sustainable development. The evaluation and selection of plot areas based on ecological and environmental parameters and project environmental requirements can help optimize regional planning. According to the ecological and environmental characteristics of different plots and project requirements, the layout of various projects can be reasonably arranged to achieve optimal allocation of resources. Industrial projects can be concentrated in areas with large environmental capacity and complete infrastructure, and ecological agricultural projects can be arranged in plots with fertile soil and sufficient water resources. The overall development efficiency and quality of the region are improved, and the accuracy of the multi-factor ecological and environmental access intelligent analysis system based on the AI ​​large model is further improved.

[0062] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-factor ecological environment access intelligent analysis system based on AI big model, characterized by: include: A remote sensing unit, which is used to collect atmospheric environment data, land images and water images of each block area; A data collection unit connected to the remote sensing unit for planning a collection route of the remote sensing unit based on environmental demand data of the environmental project and historical environmental data of each of the land areas, wherein the environmental demand data includes a water source demand value, a land area demand value, and an estimated atmospheric pollution value; a data integration unit connected to the remote sensing unit, for processing the land image and the water area image to obtain a land vacancy value and a water area parameter, analyzing the atmospheric environment data to generate an atmospheric evaluation value, and adjusting the acquisition state of the remote sensing unit according to the water area image, wherein the water area parameter includes a water body type, a water quality evaluation value and a water area; A model training unit, which is connected to the remote sensing unit and the data integration unit respectively, and is used to construct an ecological environment model according to the water area parameter, the land vacancy value and the atmospheric evaluation value, and evaluate the environmental project in combination with the ecological environment model and the environmental demand data to generate an environmental project access value; An analysis and evaluation unit is connected to the model training unit and is used to generate a water source sensitive area according to the water quality evaluation value, and to correct the environmental project access value based on the water source sensitive area to obtain an access correction value to determine the land area where the environmental project can be established.

2. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 1 is characterized in that: The data integration unit comprises: The water body type subunit is connected to the remote sensing unit and is used to identify the plant species in the water area image, collect plant distribution characteristics and growth conditions to generate plant density parameters to determine the water body type, and the water body type includes artificial water areas and natural water areas.

3. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 2 is characterized in that: The data integration unit also includes: A water quality evaluation subunit is connected to the remote sensing unit, and is used to determine the water pollution situation based on identifying the water surface color and water surface transparency in the water area image, and to adjust the remote sensing unit to enter the detection and acquisition state to capture the polluted water area image when the water quality is determined to be polluted, and to generate the water quality evaluation value in combination with the water surface ripples and water surface reflection in the polluted water area image.

4. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 3 is characterized in that: The data integration unit also includes: The water area subunit is connected to the remote sensing unit, and is used to analyze the water image through an edge detection algorithm to obtain a water contour line, and to adjust the remote sensing unit to enter the detection and acquisition state directly above the water contour line to continuously shoot a plurality of water contour images, and determine the water area by combining the plurality of water contour images.

5. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 4 is characterized in that: The data integration unit also includes: A land processing subunit connected to the remote sensing unit, for calculating the land vacancy value based on identifying the artificial building area in the land image and the area of ​​the land plot area; The atmosphere processing subunit is connected to the remote sensing unit and is used to generate the atmosphere evaluation value by combining the atmospheric environment data and the atmospheric estimated pollution value.

6. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 5 is characterized in that: The model training unit constructs the ecological environment model according to the water body type, the water area, the land vacancy value and the atmosphere evaluation value.

7. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 6 is characterized in that: The model training unit compares the same environmental parameter items in the ecological environment model and the environmental demand data to obtain a number of ecological environment difference rates, and summarizes the several ecological environment difference rates to generate the environmental project access value.

8. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 7 is characterized in that: The analysis and evaluation unit generates the water source sensitive area according to the water quality evaluation value, wherein: If the water quality evaluation value is less than or equal to the preset evaluation value, determining that the land area corresponding to the water quality evaluation value is the water source sensitive area; The preset evaluation value is negatively correlated with the water area.

9. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 8 is characterized in that: The analysis and evaluation unit corrects the environmental project access value based on the water source sensitive area to obtain an access correction value to determine the land area where the environmental project can be established, wherein: If the access correction value is greater than or equal to the preset access value, it is determined that the land area corresponding to the access correction value can be used to establish the environmental project; The preset admission value is negatively correlated with the area of ​​the plot.

10. The multi-factor ecological environment access intelligent analysis system based on AI big model according to claim 9 is characterized in that: The data collection unit compares the environmental demand data of the environmental project with the historical environmental data of each of the land areas to obtain a number of environmental demand difference rates, sorts the environmental demand difference rates from small to large, and generates a collection route for the remote sensing unit.

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