Ecological environment inspection and detection information management method and system based on artificial intelligence

Abstract

The invention relates to an ecological environment inspection and detection information management method and system based on artificial intelligence, and the method comprises the following steps: carrying out the inspection and detection of water quality, air and soil samples collected by an ecological environment monitoring station, and obtaining environment detection data; performing environmental element association calculation based on the data to obtain element association data, and generating an environmental element distribution diagram through spatial-temporal distribution mapping; tracking a pollutant migration rule by using the distribution map, extracting pollutant migration characteristic data, and carrying out environmental impact assessment according to the pollutant migration characteristic data to form an environmental impact assessment report; according to the method, the monitoring area is subjected to risk distribution division according to the report, area environment risk distribution data is obtained, a targeted ecological environment management scheme is generated based on the data, and the technical problems that in the prior art, an effective correlation analysis means is lacked, and migration and transformation rules of pollutants among different media are difficult to reveal are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological environment, and particularly to an ecological environment inspection and detection information management method and system based on artificial intelligence. BACKGROUND

[0002] With the accelerating process of industrialization and urbanization, ecological environment problems are increasingly prominent. Environmental problems such as water pollution, air pollution and soil degradation pose a serious threat to human health and ecological system safety. In order to achieve effective environmental supervision and management, ecological environment monitoring sites are widely deployed to continuously collect multi-dimensional data such as water quality, air quality and soil composition.

[0003] The current environmental information management system has obvious shortcomings in data integration and deep mining. On the one hand, the monitoring data of environmental elements such as water quality, air, soil, etc. are often stored in isolation, lack effective correlation analysis means, and it is difficult to reveal the migration and transformation law of pollutants in different media. On the other hand, the existing technology is insufficient in describing the spatio-temporal distribution characteristics of environmental data, and cannot accurately present the diffusion path and influence range of pollutants, resulting in one-sided environmental impact assessment results and limited prediction ability. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application proposes an ecological environment inspection and detection information management method and system based on artificial intelligence.

[0005] The technical solution adopted by the present application is: On the one hand, the present application embodiment includes an ecological environment inspection and detection information management method based on artificial intelligence, comprising the following steps: inspecting and detecting water quality, air and soil samples collected by ecological environment monitoring sites to obtain environmental detection data; performing environmental element correlation calculation on the monitoring area based on the environmental detection data to obtain element correlation data, and performing spatio-temporal distribution mapping on the element correlation data to obtain an environmental element distribution map; tracking and analyzing the migration law of pollutants in the environmental element distribution map to obtain pollutant migration characteristic data, and performing environmental impact assessment based on the pollutant migration characteristic data to obtain an environmental impact assessment report; dividing the risk distribution of the monitoring area based on the environmental impact assessment report to obtain regional environmental risk distribution data, and generating an ecological environment management scheme based on the regional environmental risk distribution data.

[0006] Further, the inspecting and detecting water quality, air and soil samples collected by ecological environment monitoring sites to obtain environmental detection data comprises: The water quality, air and soil samples are subjected to multi-parameter spectral test detection to obtain sample spectral characteristic data, and the sample spectral characteristic data is subjected to wavelength resolution processing to obtain environmental element spectral fingerprint data, wherein the environmental element spectral fingerprint data includes water quality organic matter content characteristics, air particulate matter component characteristics and soil heavy metal content characteristics; Based on the environmental element spectral fingerprint data, element content quantitative analysis is performed on the samples to obtain environmental element concentration distribution data, and the environmental element concentration distribution data is taken as environmental detection data.

[0007] Further, based on the environmental detection data, environmental element correlation calculation is performed on the monitoring area to obtain element correlation data, including: The environmental detection data is subjected to environmental element coupling analysis to obtain element coupling matrix data, and the element coupling matrix data is subjected to parameter normalization processing to obtain environmental element correlation data; Based on the environmental element correlation data, environmental element transfer path analysis is performed on the monitoring area to obtain element transfer flux data, and the element transfer flux data is subjected to cumulative effect calculation to obtain element cumulative impact data; The element cumulative impact data is subjected to environmental element synergistic effect analysis to obtain element synergistic strength data, and the element synergistic strength data is subjected to regional difference calculation to obtain regional element difference data; Based on the regional element difference data, environmental element comprehensive evaluation is performed on the monitoring area to obtain element comprehensive evaluation data, and the element comprehensive evaluation data is subjected to weight distribution calculation to obtain element correlation data.

[0008] Further, the element correlation data is subjected to space-time distribution mapping to obtain an environmental element distribution map, including: The element correlation data is subjected to time sequence segmentation processing to obtain time period characteristic sequence data, and the time period characteristic sequence data is subjected to time scale refinement to obtain multi-scale time characteristic data; Based on the multi-scale time characteristic data, spatial grid division is performed on the monitoring area to obtain regional gridded data, and the regional gridded data is subjected to geographic attribute labeling to obtain geographic element distribution data; The geographic element distribution data is subjected to spatial interpolation operation to obtain continuous distribution field data, and the continuous distribution field data is subjected to boundary constraint processing to obtain boundary correction data; Based on the boundary correction data, spatio-temporal data fusion is performed on the monitoring area to obtain spatio-temporal feature fusion data, and the spatio-temporal feature fusion data is subjected to contour surface generation to obtain an environmental element distribution map.

[0009] Further, the migration rule of the pollutant in the environmental element distribution map is tracked and analyzed to obtain pollutant migration characteristic data, including: The medium interface of the environmental element distribution map is identified to obtain medium interface data, and the medium interface data is subjected to material flux calculation to obtain cross-medium transmission data, wherein the cross-medium transmission data includes water-air interface exchange flux, air-soil interface deposition flux and water-soil interface diffusion flux; Based on the cross-medium transmission data, the form transformation of the pollutant is analyzed to obtain pollutant form composition data, and the pollutant form composition data is subjected to chemical activity evaluation to obtain pollutant transformation characteristic data; The pollutant transformation characteristic data is subjected to migration pathway analysis to obtain pollutant migration path data, and the pollutant migration path data is subjected to diffusion kinetics analysis to obtain diffusion dynamic parameter data; Based on the diffusion dynamic parameter data, the enrichment process of the pollutant is analyzed to obtain pollutant accumulation effect data, and the pollutant accumulation effect data is subjected to migration characteristic induction to obtain pollutant migration characteristic data.

[0010] Further, based on the pollutant migration characteristic data, environmental impact assessment is performed to obtain an environmental impact assessment report, including: The pollutant migration characteristic data is subjected to long-term cumulative effect analysis to obtain pollutant cumulative dose data, and the pollutant cumulative dose data is subjected to time series analysis to obtain cumulative trend characteristic data; Based on the cumulative trend characteristic data, the monitoring area is subjected to multi-pollutant synergistic effect analysis to obtain pollutant synergistic effect data, and the pollutant synergistic effect data is subjected to toxicity effect evaluation to obtain toxicity superposition characteristic data; The toxicity superposition characteristic data is subjected to ecosystem response analysis to obtain ecological effect data, and the ecological effect data is subjected to sensitivity evaluation to obtain ecological sensitivity data; Based on the ecological sensitivity data, the environmental impact is comprehensively analyzed to obtain environmental impact comprehensive data, and the environmental impact comprehensive data is subjected to evaluation report preparation to obtain an environmental impact assessment report.

[0011] Further, based on the cumulative trend characteristic data, the monitoring area is subjected to multi-pollutant synergistic effect analysis to obtain pollutant synergistic effect data, including: The cumulative trend characteristic data is subjected to pollutant combination identification to obtain pollutant combination type data, and the pollutant combination type data is subjected to interaction mechanism analysis to obtain interaction mechanism data; Based on the interaction mechanism data, the pollutants are subjected to promotion effect analysis, promotion effect intensity data are obtained, and the promotion effect intensity data are subjected to inhibition effect evaluation, and inhibition effect parameter data are obtained; The inhibition effect parameter data are subjected to superposition effect calculation, effect superposition characteristic data are obtained, and the effect superposition characteristic data are subjected to synergy degree grading, and synergy level data are obtained; Based on the synergy level data, the monitoring area is subjected to action intensity distribution analysis, action intensity distribution data are obtained, and the action intensity distribution data are subjected to feature extraction, and pollutant synergy effect data are obtained.

[0012] Further, based on the environmental impact evaluation report, the monitoring area is subjected to risk distribution division, and regional environmental risk distribution data are obtained, including: The environmental impact evaluation report is subjected to risk factor aggregation analysis, and risk aggregation area data are obtained, and the risk aggregation area data are subjected to spatial partition processing, and risk partition characteristic data are obtained; Based on the risk partition characteristic data, the monitoring area is subjected to prevention and control unit division, and risk prevention and control unit data are obtained, and the risk prevention and control unit data are subjected to boundary optimization processing, and prevention and control boundary data are obtained; The prevention and control boundary data are subjected to risk propagation path analysis, and risk propagation characteristic data are obtained, and the risk propagation characteristic data are subjected to barrier area identification, and risk blocking area data are obtained; Based on the risk blocking area data, the monitoring area is subjected to risk level partitioning, and risk level distribution data are obtained, and the risk level distribution data are subjected to regional feature extraction, and regional environmental risk distribution data are obtained.

[0013] The application also provides an ecological environment inspection and detection information management system based on artificial intelligence, comprising: An inspection and detection module is configured to inspect and detect water quality, air and soil samples collected by an ecological environment monitoring station to obtain environmental detection data; A calculation module is configured to perform environmental factor correlation calculation on a monitoring area based on the environmental detection data to obtain factor correlation data, and perform spatio-temporal distribution mapping on the factor correlation data to obtain an environmental factor distribution map; An analysis module is configured to track and analyze the migration rule of pollutants in the environmental factor distribution map to obtain pollutant migration characteristic data, and perform environmental impact evaluation based on the pollutant migration characteristic data to obtain an environmental impact evaluation report; A division module is configured to divide a monitoring area based on the environmental impact evaluation report to obtain regional environmental risk distribution data, and generate an ecological environment management scheme based on the regional environmental risk distribution data.

[0014] The application provides an ecological environment inspection and detection information management method based on artificial intelligence, comprising the following steps: performing inspection and detection on water quality, air and soil samples collected by an ecological environment monitoring station to obtain environmental detection data; performing environmental element correlation calculation on a monitoring area based on the environmental detection data to obtain element correlation data, and performing space-time distribution mapping on the element correlation data to obtain an environmental element distribution map; performing tracking analysis on the migration rule of pollutants in the environmental element distribution map to obtain pollutant migration characteristic data, and performing environmental impact assessment based on the pollutant migration characteristic data to obtain an environmental impact assessment report; dividing the monitoring area based on the environmental impact assessment report to obtain regional environmental risk distribution data, and generating an ecological environment management scheme based on the regional environmental risk distribution data, which solves the technical problem that traditional technologies lack effective correlation analysis means and are difficult to reveal the migration and transformation rule of pollutants in different media, based on the environmental element distribution map, a machine learning or deep learning model is used to track and analyze the migration path, diffusion rate and transformation mechanism of pollutants, extract pollutant migration characteristic data, and significantly improve the prediction accuracy of long-term migration trend and potential diffusion risk. BRIEF DESCRIPTION OF DRAWINGS

[0015] The above and / or additional aspects and advantages of the application will become apparent and be readily understood from the following description, taken in conjunction with the following drawings, in which: Figure 1 A step flow chart of the ecological environment inspection and detection information management method based on artificial intelligence in the embodiments of the application; Figure 2 A structural block diagram of the ecological environment inspection and detection information management system based on artificial intelligence in the embodiments of the application; The purposes, functional characteristics and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0016] The embodiments of the application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, in which the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the application, and cannot be understood as a limitation of the application.

[0017] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by the upper, lower, front, rear, left, right and the like, is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0018] In the description of the present application, the meaning of several is one or more, the meaning of multiple is more than two, greater than, less than, more than and the like are understood as not including the number, above, below, within and the like are understood as including the number. If the first, second is described, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance of the indicated technical features or implying that the indicated technical features are the number or the order of the indicated technical features.

[0019] In the description of the present application, unless otherwise explicitly limited, the words such as setting, installing, connecting and the like should be broadly understood, and the person skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical scheme.

[0020] The embodiments of the present application will be further described below with reference to the drawings.

[0021] Referring to Figure 1 The embodiment of the present application provides an ecological environment inspection and detection information management method based on artificial intelligence, comprising the following steps: Step S1, the water quality, air and soil samples collected by the ecological environment monitoring station are tested and detected to obtain environmental detection data.

[0022] Specifically, for water quality, air and soil samples collected by ecological environment monitoring stations, the process of testing and detecting to obtain environmental detection data first requires sample collection at each ecological environment monitoring station according to the predetermined time interval and standard procedure, such as daily or weekly scheduled water quality sample collection, monthly scheduled soil sample collection, and irregular air sample collection according to air quality monitoring requirements. Subsequently, these samples are sent to the laboratory for precise determination of key indicators such as heavy metals, organic pollution in water, particulate matter concentration, harmful gas content in air, and nutrient status, pollutant residue in soil, etc. using advanced analytical instruments such as gas chromatography-mass spectrometry (GC-MS), atomic absorption spectrometer (AAS), etc. For example, in an ecological environment monitoring station set up around an industrial area in a city, technical personnel will regularly collect water samples from nearby rivers and lakes, collect air samples at different heights in the same area, and collect soil samples from farmland around the factory. After pretreatment, all samples are analyzed by the above-mentioned professional equipment to obtain a series of data sets reflecting the current ecological environment status of the area, including but not limited to dissolved oxygen in water, chemical oxygen demand (COD), sulfur dioxide (SO2) concentration in air, soil pH value and heavy metal content, etc. These detailed test results constitute the basic data for the subsequent step of environmental element correlation calculation. Based on these environmental detection data, further research on the relationship between environmental elements can be carried out to provide scientific basis for formulating effective environmental protection measures.

[0023] Step S2, based on the environmental detection data, environmental element correlation calculation is performed on the monitoring area to obtain element correlation data, and the element correlation data is mapped in time and space distribution to obtain an environmental element distribution map.

[0024] Specifically, based on the environmental detection data, environmental element correlation calculation is performed on the monitoring area to obtain element correlation data, and spatio-temporal distribution mapping is performed on the element correlation data to obtain an environmental element distribution map. First, the test results of water quality, air, and soil samples obtained from the ecological environment monitoring station are utilized. Key indicators such as ammonia nitrogen content in water, PM2.5 concentration in air, and heavy metal content such as lead and cadmium in soil are subjected to data normalization and standardization processing. Subsequently, a multivariate statistical model or machine learning algorithms such as random forest, neural network, etc. are applied to analyze the correlation and coupling relationship between different environmental media, identify potential correlation patterns between water pollution and atmospheric deposition, and soil pollutants and surrounding industrial emissions, and generate element correlation data containing interaction strength and influence path of each environmental element. On this basis, combined with the geographic coordinate information and sampling timestamp of the monitoring station, geographic information system (GIS) technology and spatio-temporal interpolation methods such as Kriging interpolation or inverse distance weighting method are used to continuously express the element correlation data in spatial dimension and dynamically reconstruct it in time dimension according to daily, weekly, or monthly granularity, and further generate an environmental element distribution map that can reflect the distribution characteristics of pollutants in different regions and time periods. For example, in the monitoring network around an industrial area in a city, when an increase in nitrate concentration in the water upstream of a river is found, combined with the changes in nitrogen oxide concentration in the air and nitrogen accumulation in the surrounding farmland soil during the same period, the contribution of atmospheric nitrogen deposition to water and soil nitrogen load can be identified through correlation calculation, and the migration and enrichment distribution map of nitrogen elements in water, air, and soil in this region can be generated through spatio-temporal distribution mapping technology, providing a visual basis for subsequent pollutant migration tracking. It should be noted that the monitoring area is the area covered by the ecological environment monitoring station, which can be a city, a village, a forest, a river basin, or any place that needs to be evaluated for environmental quality. The "ecological environment monitoring station" is a specific location set up in the area for collecting water quality, air, and soil samples. These stations are key nodes for obtaining environmental data.

[0025] Step S3, tracking analysis of the pollutant migration rule in the environmental element distribution map is performed to obtain pollutant migration characteristic data, and environmental impact assessment is performed based on the pollutant migration characteristic data to obtain an environmental impact assessment report.

[0026] Specifically, the migration characteristics of pollutants in the environmental element distribution map are tracked and analyzed to obtain pollutant migration characteristic data, and environmental impact assessment is performed based on the pollutant migration characteristic data to obtain an environmental impact assessment report. This process first relies on the previously constructed environmental element distribution map, and uses spatiotemporal analysis tools and models to analyze the concentration change trend of pollutants at different time points and geographical locations, as well as the transmission path and rate of pollutants between water, air, and soil. For example, in the above case of the industrial area around the city, when an increase in nitrate concentration is observed upstream of the river, historical environmental element distribution maps are used in combination with hydrological conditions such as flow rate, flow direction, and seasonal climate factors to calculate the diffusion speed and range of nitrate along the flow direction, and further explore its interaction with related substances in the air and soil. Subsequently, based on these tracking analysis results, i.e., pollutant migration characteristic data, a detailed environmental impact assessment plan is developed, which includes the impact on ecosystem health, such as the threat of water quality deterioration to aquatic biodiversity and the potential risk of excessive nitrate absorption by crops to human health. At the same time, considering that pollutants may cross administrative boundaries, multi-region monitoring data needs to be coordinated to comprehensively assess cross-regional environmental impact. Finally, all collected data and analysis conclusions are integrated to write a systematic environmental impact assessment report. This report not only describes the migration characteristics of pollutants and their specific impact on various components of the environment, but also proposes targeted management recommendations and preventive measures, aiming to provide scientific basis for decision-makers and promote effective solutions to environmental pollution problems.

[0027] Step S4, based on the environmental impact assessment report, the monitoring area is divided into risk distribution, and regional environmental risk distribution data is obtained. Based on the regional environmental risk distribution data, an ecological environment management scheme is generated.

[0028] Specifically, based on the environmental impact assessment report, the monitoring area is divided into risk distribution, and the regional environmental risk distribution data is obtained, and the ecological environment management scheme is generated based on the regional environmental risk distribution data. First of all, it is necessary to analyze the pollution migration characteristics, the affected ecosystem type and the human activity exposure scenario in the environmental impact assessment report in detail, and the monitoring area is divided into high, medium and low risk areas according to the pollution degree, ecological sensitivity and human health risk. In this process, through the geographic information system technology, the soil, water and air pollution concentration space data are superimposed with the land use map, ecological function zoning map, etc., to identify the key areas and fragile habitats most threatened by pollution, and the influence of long-term meteorological conditions and seasonal changes on the diffusion of pollutants is considered to ensure the scientificity and dynamics of the risk zoning. For example, in the foregoing urban industrial area scene, if the environmental impact assessment report points out that due to the historical legacy of heavy metal pollution, the water quality of a river section in a certain area is seriously deteriorated and the cadmium content of the surrounding farmland soil exceeds the standard, then according to this information, the river section and the farmland along the river can be divided into a high-risk area, and the upstream water area far away from the pollution source and the urban park not affected by pollution are divided into a low-risk area. Next, according to the obtained regional environmental risk distribution data, a targeted ecological environment management scheme is formulated, including implementing strict pollution control measures such as establishing a soil remediation demonstration base and setting up a river buffer zone to reduce agricultural non-point source pollution in the high-risk area, taking preventive protection strategies such as strengthening environmental protection propaganda and popularizing green agricultural technology in the medium-risk area, and focusing on maintaining the existing good environmental status in the low-risk area, so as to realize the overall improvement of the ecological environment quality in the whole monitoring area.

[0029] In a specific scenario, the water quality, air and soil samples collected by the ecological environment monitoring station are tested and detected to obtain environmental detection data, including: The water quality, air and soil samples are tested and detected by multi-parameter spectrum to obtain sample spectrum characteristic data, and the sample spectrum characteristic data is processed by wavelength resolution to obtain environmental element spectrum fingerprint data, wherein the environmental element spectrum fingerprint data includes water quality organic matter content characteristics, air particulate matter component characteristics and soil heavy metal content characteristics. Based on the environmental element spectrum fingerprint data, the element content of the sample is quantitatively analyzed to obtain environmental element concentration distribution data, and the environmental element concentration distribution data is taken as the environmental detection data.

[0030] Specifically, water quality, air and soil samples collected by an ecological environment monitoring station are tested and detected to obtain environmental detection data, including multi-parameter spectral testing and detection on the water quality, air and soil samples to obtain sample spectral feature data, and wavelength resolution processing on the sample spectral feature data to obtain environmental element spectral fingerprint data, wherein the environmental element spectral fingerprint data includes water quality organic matter content characteristics, air particulate matter component characteristics and soil heavy metal content characteristics; element content quantitative analysis is performed on the samples based on the environmental element spectral fingerprint data to obtain environmental element concentration distribution data, and the environmental element concentration distribution data is taken as the environmental detection data.The process first completes the on-site collection of water quality, air and soil samples at the ecological environment monitoring site, then transports the samples to a detection laboratory with spectral analysis capability, uses multi-parameter spectral detection equipment such as ultraviolet-visible absorption spectrometer, infrared spectrometer, laser-induced breakdown spectroscopy (LIBS) system and mass spectrometry combined spectral platform under standard environmental conditions, performs synchronous multi-channel spectral scanning on the samples, obtains original spectral response signals covering a specific waveband range (such as 200-900 nm), and forms sample spectral feature data containing optical characteristics such as absorption, scattering and fluorescence; then, wavelength resolution processing is performed on the sample spectral feature data, overlapping peaks are separated by high-resolution spectral deconvolution algorithm and wavelet transform technology, and characteristic absorption or emission spectral lines of different chemical components at specific wavelengths are identified, thereby extracting spectral fingerprint data of environmental elements with unique identification, for example, in water quality samples, the characteristics of dissolved organic matter and nitrate are identified and characterized by using absorption peaks at 270 nm and 220 nm, in air particulate matter samples, organic carbon, elemental carbon and sulfate components are distinguished according to the absorption mode in the near-infrared waveband, and in soil samples, the characteristic emission lines of heavy metal elements such as iron, lead, cadmium and zinc are identified by LIBS in the 300-500 nm interval, forming spectral fingerprint atlas corresponding to water quality organic matter content characteristics, air particulate matter component characteristics and soil heavy metal content characteristics; on this basis, a pre-established calibration model is used, which is based on the regression relationship between the spectral fingerprint and the measured concentration of a large number of standard samples with known concentration, and machine learning methods such as partial least squares (PLS) or support vector regression (SVR) are used to input the spectral fingerprint data of environmental elements into the model for inversion calculation, to realize quantitative analysis of the element content of each environmental element, and output specific concentration values such as COD in water 45 mg / L, organic carbon in PM2.5 in air 32%, and cadmium content in soil 0.8 mg / kg, and finally integrate all analysis results to form environmental element concentration distribution data covering spatial position, sampling time, detection items and corresponding concentration values; for example, in the application scenario of the aforementioned urban industrial area, the water quality sample collected from the river section in a certain monitoring has a strong absorption peak at 254 nm after multi-parameter spectral detection, which is confirmed as the characteristic of aromatic organic matter after wavelength resolution processing, and the concentration is calculated to be 12 mg / L by quantitative analysis combined with the calibration model, and the metal oxide emission characteristics in the 400-450 nm interval are identified in the surrounding air filter membrane sample, and the lead component concentration is found to be 1.5 μg / m by quantitative inversion. 3, the cadmium characteristic line at 656.5 nm was detected in the adjacent farmland soil sample by LIBS, and the content was 0.95 mg / kg through analysis, indicating that there was a potential heavy metal pollution risk. The above quantitative results jointly constitute the environmental detection data of the region in this monitoring, providing accurate and traceable data basis for subsequent environmental element correlation calculation and risk assessment.

[0031] In a specific scenario, based on the environmental detection data, environmental element correlation calculation is performed on the monitoring area to obtain element correlation data, including: Performing environmental element coupling analysis on the environmental detection data to obtain element coupling matrix data, and performing parameter normalization processing on the element coupling matrix data to obtain environmental element correlation data; Based on the environmental element correlation data, environmental element transfer path analysis is performed on the monitoring area to obtain element transfer flux data, and cumulative effect calculation is performed on the element transfer flux data to obtain element cumulative impact data; Performing environmental element synergistic effect analysis on the element cumulative impact data to obtain element synergistic strength data, and performing regional difference calculation on the element synergistic strength data to obtain regional element difference data; Based on the regional element difference data, environmental element comprehensive evaluation is performed on the monitoring area to obtain element comprehensive evaluation data, and weight distribution calculation is performed on the element comprehensive evaluation data to obtain element correlation data.

[0032] Specifically, based on the environment detection data, environment element correlation calculation is performed on the monitoring area to obtain element correlation data, including performing environment element coupling analysis on the environment detection data to obtain element coupling matrix data, and performing parameter normalization processing on the element coupling matrix data to obtain environment element correlation data; based on the environment element correlation data, environment element transfer path analysis is performed on the monitoring area to obtain element transfer flux data, and cumulative effect calculation is performed on the element transfer flux data to obtain element cumulative influence data; environment element synergistic effect analysis is performed on the element cumulative influence data to obtain element synergistic strength data, and regional difference calculation is performed on the element synergistic strength data to obtain regional element difference data; based on the regional element difference data, environment element comprehensive evaluation is performed on the monitoring area to obtain element comprehensive evaluation data, and weight distribution calculation is performed on the element comprehensive evaluation data to obtain element correlation data.The process is first based on environmental detection data obtained from the ecological environment monitoring station, which includes water quality organic matter content characteristics, air particulate matter component characteristics and soil heavy metal content characteristics and other multi-dimensional information. Through the construction of a multivariate time series model, the synchronicity and hysteresis of key parameters in different environmental media are analyzed to identify the dynamic response relationship between the changes in COD concentration in water and the fluctuations in NOx concentration in air and the rise in Cd content in soil, forming an element coupling matrix data reflecting the interaction strength of each environmental element. Subsequently, to eliminate the bias caused by the different dimensions and orders of magnitude of the indicators, the parameter normalization processing is performed on each value in the element coupling matrix data, and the minimum-maximum standardization or Z-score standardization method is used to unify all coupling coefficients to the interval [0, 1], so as to obtain comparable environmental element correlation data. On this basis, combined with geographic spatial data and hydrological and meteorological information, the migration path of pollutants between water, air and soil is deduced based on the environmental element correlation data, such as using wind field data to judge the process of atmospheric particulate matter settling on the surface soil, or using the surface runoff model to simulate the process of rainwater carrying soil pollutants into rivers, and then quantifying the transfer flux of pollutants between different media in unit time to generate element transfer flux data. Then, the element transfer flux data is subjected to time integration and spatial superposition to calculate the enrichment degree of pollutants in a specific region in the long-term cumulative process, such as the continuous accumulation of nitrogen in a river segment downstream of an industrial area due to the reception of surface runoff from upstream farmland for many months, forming element cumulative impact data. Subsequently, for the ecological effects under the joint action of multiple pollutants or environmental factors, the environmental element synergistic effect analysis is carried out to identify non-linear interaction mechanisms such as "acid rain aggravating soil heavy metal dissolution" or "high temperature promoting organic matter degradation but aggravating volatile pollutant diffusion", and the joint influence strength is evaluated using a synergistic index model to obtain element synergistic strength data. Further, the element synergistic strength data is combined with the geographical, ecological and socio-economic attributes of different sub-regions to compare the response differences between urban industrial areas, urban-rural junctions and ecological protection areas, such as the adsorption capacity of soil around industrial areas to atmospheric deposition being significantly higher than that of green areas, thereby calculating the regional element difference data. Then, based on the regional element difference data, the environmental state of each monitoring sub-region is scored as a whole using the analytic hierarchy process or fuzzy comprehensive evaluation method to form element comprehensive evaluation data reflecting the regional environmental quality level. Finally, combined with expert experience library and historical management effectiveness data, different weights are assigned to each evaluation index, such as higher water quality weight for drinking water sources and soil safety weight for agricultural land, and the weight distribution calculation is completed through the weighted summation algorithm, and finally the structured element correlation data is output for supporting subsequent spatio-temporal distribution mapping and risk assessment.For example, in the aforementioned urban industrial area scenario, when environmental detection data shows that the lead content in PM2.5 in the air of the area is rising, the surrounding soil lead concentration is also rising, and lead enrichment is detected in the nearby river sediment, the transmission path of “atmospheric deposition-soil adsorption-surface runoff scouring-water deposition” can be established through the above process, the transmission flux and cumulative impact of lead among the three phases are calculated, the synergistic diffusion strength is evaluated in combination with parameters such as rainfall frequency and wind direction, and regional difference calculation is performed according to the sensitivity differences of different functional areas (such as industrial areas, residential areas, farmland), and finally the element correlation data including spatial distribution weight and influence path weight is formed, providing data support for accurately formulating pollution prevention and control strategies.

[0033] In a specific scenario, the element correlation data is subjected to spatiotemporal distribution mapping to obtain an environmental element distribution map, including: The element correlation data is subjected to time series segmentation processing to obtain time period characteristic sequence data, and the time period characteristic sequence data is subjected to time scale refinement to obtain multi-scale time characteristic data; The monitoring area is subjected to spatial grid division based on the multi-scale time characteristic data to obtain regional gridded data, and the regional gridded data is subjected to geographic attribute labeling to obtain geographic element distribution data; The geographic element distribution data is subjected to spatial interpolation operation to obtain continuous distribution field data, and the continuous distribution field data is subjected to boundary constraint processing to obtain boundary correction data; The monitoring area is subjected to spatiotemporal data fusion based on the boundary correction data to obtain spatiotemporal characteristic fusion data, and the spatiotemporal characteristic fusion data is subjected to isosurface generation to obtain an environmental element distribution map.

[0034] Specifically, the element correlation data is subjected to space-time distribution mapping to obtain an environmental element distribution map, including time series segmentation processing on the element correlation data to obtain time period characteristic sequence data, time scale refinement on the time period characteristic sequence data to obtain multi-scale time characteristic data, spatial grid division on a monitoring area based on the multi-scale time characteristic data to obtain regional gridded data, geographical attribute labeling on the regional gridded data to obtain geographical element distribution data, spatial interpolation operation on the geographical element distribution data to obtain continuous distribution field data, boundary constraint processing on the continuous distribution field data to obtain boundary correction data, space-time data fusion on the monitoring area based on the boundary correction data to obtain space-time characteristic fusion data, and contour surface generation on the space-time characteristic fusion data to obtain the environmental element distribution map.The process is first based on previously generated element correlation data, which contains information on the coupling of pollutants between water quality, air and soil, transfer flux, cumulative impact and regional differences, and is processed in time dimension for time segmentation, dividing the continuous monitoring period into different time periods such as hour, day, week and month, extracting the change characteristics of each environmental element in each period, and forming time-labeled period characteristic sequence data; then, in order to adapt to the time response characteristics of different environmental processes, the period characteristic sequence data is refined in time scale, for example, in the pollution event burst period, the hourly resolution is used, and in the background concentration analysis, the monthly average is used, so as to build multi-scale time characteristic data covering short-term fluctuations and long-term trends; on this basis, combined with the geographical range and terrain characteristics of the monitoring area, according to the spatial position information corresponding to the multi-scale time characteristic data, the entire region is divided into regular or irregular spatial grids, usually using 100m x 100m or finer grid units, forming regional gridded data with a unified coordinate system; then, each grid unit is assigned a corresponding geographical attribute label, such as land use type (industrial land, farmland, residential area), water system distribution, elevation, slope and vegetation coverage, etc., binding physical space information with environmental element data to generate geographical element distribution data containing spatial semantic information; then, in order to solve the problem of data sparseness caused by uneven distribution of monitoring sites, spatial interpolation operation method is used for geographical element distribution data, such as Kriging interpolation, inverse distance weighting (IDW) or spatial prediction model based on machine learning, to extend the element correlation value of discrete sites to the entire regional grid, generating continuous distribution field data reflecting the spatial continuous change of pollutants; in order to improve the accuracy of spatial expression, the boundary constraint processing is carried out on the continuous distribution field data, using administrative boundaries, river direction, mountain barriers and other natural or artificial geographical boundaries as limiting conditions to correct the unreasonable diffusion phenomenon caused by extrapolation of interpolation algorithm, and to ensure that the pollutant distribution will not cross the actual insurmountable geographical barriers, so as to obtain boundary corrected data; on this basis, the multi-scale time characteristic data and the spatial data after boundary correction are spatio-temporal data fusion, through spatio-temporal Kriging, spatio-temporal autoregressive model or spatio-temporal graph neural network (ST-GNN) in deep learning, etc., integrating time evolution law and spatial distribution pattern, generating spatio-temporal characteristic fusion data that can dynamically reflect the change characteristics of pollutants at different times and spatial positions; finally, the spatio-temporal characteristic fusion data is processed to generate contour surface, using contour tracking algorithm or three-dimensional surface modeling technology to convert continuous numerical field into visual contour graphics, such as using different color blocks to represent soil heavy metal pollution concentration gradient, using contour to define air PM2.5 high value area, and using three-dimensional curved surface to display the spatial fluctuation of water organic matter concentration, and finally forming environmental element distribution map that comprehensively reflects the collaborative change of water quality, air and soil environmental elements in time and space dimensions.For example, in the application scenario of the aforementioned urban industrial area, when the element correlation data indicates that there is a pollution chain of “atmospheric lead deposition → soil enrichment → rainwater erosion → river diffusion” around a certain factory area, the lead concentration data in different time periods can be segmented and refined in time sequence through the above process, the pollution emission peak period and the background period are divided, the monitoring area is divided into 100-meter grids and labeled with geographical attributes such as industrial area, road, farmland and river, and the continuous distribution field of lead on the ground is generated by using Kriging interpolation, and the boundary is corrected combined with the river direction and the factory wall. Finally, the time evolution and spatial diffusion characteristics are fused to generate a dynamic isosurface map, which clearly shows the spatial range and time process of lead pollution diffusion from the factory to the surrounding area, providing high-precision spatial data support for subsequent pollutant migration tracking and risk assessment.

[0035] In specific scenarios, the pollution migration characteristics data is obtained by tracking and analyzing the pollution migration law in the environmental element distribution map, including: The medium interface data is obtained by identifying the medium interface in the environmental element distribution map, and the cross-medium transport data is obtained by calculating the mass flux of the medium interface data, wherein the cross-medium transport data includes water-air interface exchange flux, air-soil interface deposition flux and water-soil interface diffusion flux; The pollution form composition data is obtained by analyzing the form transformation of the pollution based on the cross-medium transport data, and the pollution transformation characteristic data is obtained by evaluating the chemical activity of the pollution form composition data; The pollution migration path data is obtained by analyzing the migration pathway of the pollution transformation characteristic data, and the diffusion dynamic parameter data is obtained by analyzing the diffusion kinetics of the pollution migration path data; The pollution accumulation effect data is obtained by analyzing the enrichment process of the pollution based on the diffusion dynamic parameter data, and the pollution migration characteristic data is obtained by summarizing the migration characteristics of the pollution accumulation effect data.

[0036] Specifically, the migration characteristics of the pollutants are obtained by tracking and analyzing the migration rules of the pollutants in the environmental element distribution map, including medium interface recognition on the environmental element distribution map to obtain medium interface data, and material flux calculation on the medium interface data to obtain cross-medium transmission data, wherein the cross-medium transmission data includes water-air interface exchange flux, air-soil interface deposition flux, and water-soil interface diffusion flux; form transformation analysis of the pollutants is performed based on the cross-medium transmission data to obtain pollutant form composition data, and chemical activity evaluation is performed on the pollutant form composition data to obtain pollutant transformation characteristic data; migration pathway analysis is performed on the pollutant transformation characteristic data to obtain pollutant migration path data, and diffusion kinetics analysis is performed on the pollutant migration path data to obtain diffusion dynamic parameter data; enrichment process analysis of the pollutants is performed based on the diffusion dynamic parameter data to obtain pollutant accumulation effect data, and migration characteristics are induced from the pollutant accumulation effect data to obtain the pollutant migration characteristic data. First, based on the generated environmental element distribution map, the spatial boundaries between different environmental media are recognized by using image segmentation and edge detection algorithms, such as the water surface line where the water body contacts the atmosphere, the ground surface line where the soil surface layer contacts the air, and the sediment interface between the river sediment and the overlying water, so as to extract the medium interface data with clear physical meaning; then, at the positions of the recognized interfaces, combined with meteorological data (such as wind speed, temperature, humidity), hydrological parameters (such as flow rate, turbulence intensity), and soil physical and chemical properties (such as porosity, adsorption coefficient), the mass transfer model is used to calculate the migration flux of the pollutants between different media per unit area per unit time, to form the cross-medium transmission data including water-air interface exchange flux (such as the volatilization rate of volatile organic compounds from water to air), air-soil interface deposition flux (such as the rate of heavy metals in atmospheric particulate matter entering the soil through dry and wet deposition), and water-soil interface diffusion flux (such as the release flux of pollutants in sediment to overlying water); on this basis, for the chemical form changes of the pollutants in the migration process, based on the concentration change trend and environmental conditions in the cross-medium transmission data, the form transformation analysis of the pollutants is carried out, such as analyzing the process of inorganic mercury in water being converted to methyl mercury under anaerobic conditions, or the conversion path of NO2 in the air forming nitrate ions after dissolving in water, identifying the proportion of different chemical forms (such as ionic state, complex state, particulate state, and organic combined state), and forming the pollutant form composition data; then, combined with the chemical properties of each form such as solubility, migration, and biological availability, the chemical activity evaluation is performed on the pollutant form composition data to judge the tendency of further reaction or biological accumulation of the pollutants in the environment, so as to generate the pollutant transformation characteristic data reflecting the reaction activity and ecological risk potential of the pollutants.Then, according to the pollutant transformation characteristic data, combined with the geographical spatial flow information and medium connectivity, the moving path of the pollutant from the source area to the receptor area is systematically analyzed, and the main migration channel is identified, such as the complete chain of "atmospheric emission → dry and wet deposition → soil adsorption → rainwater erosion → surface runoff → river transport → deposition to sediment", forming structured pollutant migration path data; Subsequently, the diffusion kinetics model, such as Fick's diffusion law, convection-diffusion equation or random walk simulation, is applied to the pollutant migration path data to fit the concentration decay curve and front advance speed of the pollutant in space, and the diffusion coefficient, migration rate, residence time and other key parameters are obtained by inversion, forming diffusion dynamic parameter data; On this basis, based on the diffusion dynamic parameter data, the long-term residence and enrichment process of the pollutant in the sensitive area is simulated, for example, to evaluate the annual accumulation trend of heavy metals in farmland soil, or the layering rule of persistent organic pollutants in lake sediments, and the model output is verified combined with time series monitoring data, to obtain pollutant accumulation effect data reflecting the degree of continuous accumulation of pollutants in a specific area; Finally, the pollutant accumulation effect data is systematically summarized and pattern extracted, and multi-dimensional information such as migration path, transformation characteristic, dynamic parameter and accumulation trend is integrated, and representative migration behavior patterns such as "long-distance transport dominated by atmospheric deposition" or "local diffusion driven by surface runoff" are extracted, and finally structured and complete pollutant migration characteristic data with clear parameters is formed. For example, in the application scenario of the aforementioned urban industrial area, when the environmental element distribution map shows that the lead concentration in the air around the factory is high, the lead content in the adjacent soil is distributed in a ring shape, and the lead concentration in the downstream river sediment continues to rise, the air-soil interface between the factory chimney and the surrounding surface can be identified through the above process, the dry deposition flux of lead particles is calculated to be 1.2 μg / (m 2 ·d), the analysis shows that it exists stably in the form of lead phosphate in the soil, enters the surface runoff in the form of dissolved state under rainwater erosion, the migration path is "atmosphere → soil → surface water → sediment", the transverse diffusion coefficient of lead in the soil is 0.03 cm 2 / s obtained by diffusion kinetics analysis, the migration rate in water is 5 m / h, long-term simulation shows that the lead in the sediment accumulates about 15% every five years, and finally the lead pollution in this area is summarized as "point source emission → atmospheric deposition → surface runoff → water deposition", which provides accurate behavior parameter support for subsequent environmental impact assessment.

[0037] In a specific scenario, environmental impact assessment is performed based on the pollutant migration characteristic data, and an environmental impact assessment report is obtained, including: Long-term accumulation effect analysis is performed on the pollutant migration characteristic data to obtain pollutant accumulation dose data, and time series analysis is performed on the pollutant accumulation dose data to obtain accumulation trend characteristic data; performing multi-pollutant synergistic effect analysis on the monitoring area based on the cumulative trend characteristic data to obtain pollutant synergistic effect data, and performing toxicity effect evaluation on the pollutant synergistic effect data to obtain toxicity superposition characteristic data; performing ecosystem response analysis on the toxicity superposition characteristic data to obtain ecological effect data, and performing sensitivity evaluation on the ecological effect data to obtain ecological sensitivity data; performing comprehensive analysis on environmental impact based on the ecological sensitivity data to obtain environmental impact comprehensive data, and performing evaluation report preparation on the environmental impact comprehensive data to obtain an environmental impact evaluation report.

[0038] Specifically, performing environmental impact evaluation based on the pollutant migration characteristic data to obtain an environmental impact evaluation report, including performing long-term cumulative effect analysis on the pollutant migration characteristic data to obtain pollutant cumulative dose data, and performing time series analysis on the pollutant cumulative dose data to obtain cumulative trend characteristic data; performing multi-pollutant synergistic effect analysis on the monitoring area based on the cumulative trend characteristic data to obtain pollutant synergistic effect data, and performing toxicity effect evaluation on the pollutant synergistic effect data to obtain toxicity superposition characteristic data; performing ecosystem response analysis on the toxicity superposition characteristic data to obtain ecological effect data, and performing sensitivity evaluation on the ecological effect data to obtain ecological sensitivity data; performing comprehensive analysis on environmental impact based on the ecological sensitivity data to obtain environmental impact comprehensive data, and performing evaluation report preparation on the environmental impact comprehensive data to obtain an environmental impact evaluation report. This process is first based on pollutant migration characteristic data, which contains information such as migration path, diffusion dynamic parameters, form transformation law and cumulative effect of pollutants in water, gas and soil media. By integrating the diffusion rate, residence time and concentration level in the migration path, the total exposure amount of pollutants borne by a specific area within a certain time scale, i.e., pollutant cumulative dose data, is calculated. For example, the cumulative input amount of cadmium in unit area of soil is calculated to be 2.8 mg / m 2;Subsequently, time series analysis was performed on the cumulative dose data of pollutants, using methods such as moving average, trend line fitting or ARIMA model to identify their growth or decay trends, and determine whether the pollution was in a stage of continuous aggravation, stability or gradual relief, forming cumulative trend characteristic data reflecting the dynamic development of pollution; On this basis, combined with information on other pollutants present in the monitoring area at the same time, such as lead, arsenic, polycyclic aromatic hydrocarbons, etc., the interaction matrix model was used to analyze the synergistic or antagonistic relationship of multiple pollutants in the process of migration and enrichment, such as analyzing the joint inhibition effect of cadmium and acid rain on soil microbial activity, or the synergistic toxicity of heavy metals and organic compounds in PM2.5 on the respiratory system, thereby generating pollutant synergistic effect data; Next, according to the toxicity parameters in the international general toxicity database (such as US EPA IRIS, WHO guideline), the toxicity effect of the pollutant synergistic effect data was evaluated, and the concentrations of different pollutants were converted by weighting according to the toxicity equivalent factor (TEF), to calculate the comprehensive toxicity of the pollutants to human health and organisms, such as converting the exposure of multiple heavy metals into "lead equivalent concentration", or converting multiple organic pollutants into "dioxin equivalent", forming toxicity superposition characteristic data; Then, the toxicity superposition characteristic data was input into the ecosystem response model to simulate the influence of pollutants on indicator species such as aquatic organisms, soil microbial communities, vegetation growth and birds, and to evaluate the disturbance degree of species diversity, food chain structure and ecological function, generating ecological effect data; On this basis, combined with regional ecological function zoning, species distribution map and habitat sensitivity level, the ecological effect data was evaluated for sensitivity, to judge the difference in tolerance of different sub-regions to pollution disturbance, such as identifying that the wetland in the lower reaches of the river is a high sensitivity area, while the rock exposed area in the upper reaches is a low sensitivity area, thereby forming ecological sensitivity data; Subsequently, based on the ecological sensitivity data, the weights of various environmental impact factors were allocated and integrated, and a multi-criteria decision analysis method was used to integrate factors such as pollution cumulative trend, toxicity intensity, ecological response and regional sensitivity, to generate comprehensive environmental impact data covering spatial distribution and temporal evolution; Finally, the environmental impact comprehensive data was structured and organized in a standardized format, combined with charts, contour maps, trend curves and textual explanations, to compile a complete environmental impact assessment report, including pollution cause analysis, impact range definition, key risk area identification, ecological fragile point prompt and management recommendations, etc.For example, in the aforementioned application scenario of urban industrial areas, when the pollutant migration characteristic data shows that lead continues to accumulate through the "atmospheric deposition-surface runoff-sediment enrichment" path, the lead concentration in the sediment increases by 40% over ten years, and forms a combined pollution with zinc and copper, through long-term cumulative effect analysis, it is concluded that the cumulative dose has reached the ecological risk threshold, time series analysis shows that the annual growth rate is 3.2%, multi-pollutant synergistic effect analysis reveals that its joint toxicity is 1.8 times that of single lead toxicity, toxicity superposition characteristic data shows that the LC50 value of benthic organisms is significantly reduced, ecosystem response analysis shows that the density of chironomid larvae decreases by 60%, ecological sensitivity assessment confirms that the river section is a high-sensitivity area of aquatic ecology, and finally the comprehensive analysis result is compiled into the environmental impact assessment report, and it is clearly pointed out that a settling tank needs to be added around the factory, the discharge time is limited, and the sediment remediation is carried out, etc. Control measures provide a scientific basis for ecological environment management.

[0039] In specific scenarios, based on the cumulative trend characteristic data, multi-pollutant synergistic effect analysis is performed on the monitoring area to obtain pollutant synergistic effect data, including: The cumulative trend characteristic data is subjected to pollutant combination recognition to obtain pollutant combination type data, and the pollutant combination type data is subjected to interaction mechanism analysis to obtain interaction mechanism data; Based on the interaction mechanism data, the promotion effect of the pollutant is analyzed to obtain promotion effect intensity data, and the promotion effect intensity data is subjected to inhibition effect evaluation to obtain inhibition effect parameter data; The inhibition effect parameter data is subjected to superposition effect calculation to obtain effect superposition characteristic data, and the effect superposition characteristic data is subjected to synergistic degree classification to obtain synergistic level data; Based on the synergistic level data, the action intensity distribution analysis of the monitoring area is performed to obtain action intensity distribution data, and the action intensity distribution data is subjected to feature extraction to obtain pollutant synergistic effect data.

[0040] Specifically, the environmental impact assessment is performed based on the pollutant migration characteristic data to obtain an environmental impact assessment report, including long-term cumulative effect analysis on the pollutant migration characteristic data to obtain pollutant cumulative dose data, time series analysis on the pollutant cumulative dose data to obtain cumulative trend characteristic data, multi-pollutant synergistic effect analysis on a monitoring area based on the cumulative trend characteristic data to obtain pollutant synergistic effect data, toxicity effect evaluation on the pollutant synergistic effect data to obtain toxicity superposition characteristic data, ecosystem response analysis on the toxicity superposition characteristic data to obtain ecological effect data, sensitivity evaluation on the ecological effect data to obtain ecological sensitivity data, comprehensive analysis on the environmental impact based on the ecological sensitivity data to obtain environmental impact comprehensive data, and report preparation on the environmental impact comprehensive data to obtain the environmental impact assessment report. This process is first based on the pollutant migration characteristic data, which contains information such as migration path, diffusion dynamic parameter, form transformation rule and cumulative effect of the pollutant in water, gas and soil media. By integrating the diffusion rate, residence time and concentration level in the migration path, the total exposure amount of the pollutant in a specific area within a certain time scale, i.e., the pollutant cumulative dose data, is calculated, for example, the cumulative input amount of cadmium in unit area of soil is 2.8 mg / m 2;Subsequently, time series analysis was performed on the cumulative dose data of pollutants, using methods such as moving average, trend line fitting or ARIMA model to identify their growth or decay trends, and determine whether the pollution was in a stage of continuous aggravation, stability or gradual relief, forming cumulative trend characteristic data reflecting the dynamic development of pollution; On this basis, combined with information on other pollutants present in the monitoring area at the same time, such as lead, arsenic, polycyclic aromatic hydrocarbons, etc., the interaction matrix model was used to analyze the synergistic or antagonistic relationship of multiple pollutants in the process of migration and enrichment, such as analyzing the joint inhibition effect of cadmium and acid rain on soil microbial activity, or the synergistic toxicity of heavy metals and organic compounds in PM2.5 on the respiratory system, thereby generating pollutant synergistic effect data; Next, according to the toxicity parameters in the international general toxicity database (such as US EPA IRIS, WHO guideline), the toxicity effect of the pollutant synergistic effect data was evaluated, and the concentrations of different pollutants were converted by weighting according to the toxicity equivalent factor (TEF), to calculate the comprehensive toxicity of the pollutants to human health and organisms, such as converting the exposure of multiple heavy metals into "lead equivalent concentration", or converting multiple organic pollutants into "dioxin equivalent", forming toxicity superposition characteristic data; Then, the toxicity superposition characteristic data was input into the ecosystem response model to simulate the influence of pollutants on indicator species such as aquatic organisms, soil microbial communities, vegetation growth and birds, and to evaluate the disturbance degree of species diversity, food chain structure and ecological function, generating ecological effect data; On this basis, combined with regional ecological function zoning, species distribution map and habitat sensitivity level, the ecological effect data was evaluated for sensitivity, to judge the difference in tolerance of different sub-regions to pollution disturbance, such as identifying that the wetland in the lower reaches of the river is a high sensitivity area, while the rock exposed area in the upper reaches is a low sensitivity area, thereby forming ecological sensitivity data; Subsequently, based on the ecological sensitivity data, the weights of various environmental impact factors were allocated and integrated, and a multi-criteria decision analysis method was used to integrate factors such as pollution cumulative trend, toxicity intensity, ecological response and regional sensitivity, to generate comprehensive environmental impact data covering spatial distribution and temporal evolution; Finally, the environmental impact comprehensive data was structured and organized in a standardized format, combined with charts, contour maps, trend curves and textual explanations, to compile a complete environmental impact assessment report, including pollution cause analysis, impact range definition, key risk area identification, ecological fragile point prompt and management recommendations, etc.For example, in the aforementioned application scenario of the urban industrial area, when the pollutant migration characteristic data shows that lead continues to accumulate through the "atmospheric deposition-surface runoff-sediment enrichment" path, the lead concentration in the sediment increases by 40% in ten years, and forms a combined pollution with zinc and copper, through long-term cumulative effect analysis, it is concluded that the cumulative dose has reached the ecological risk threshold, time series analysis shows that the annual growth rate is 3.2%, multi-pollutant synergistic effect analysis reveals that its joint toxicity is 1.8 times that of single lead toxicity, toxicity superposition characteristic data shows that the LC50 value of benthic organisms is significantly reduced, ecosystem response analysis shows that the density of chironomid larvae decreases by 60%, ecological sensitivity assessment confirms that the river section is a high-sensitivity area of aquatic ecology, and finally the comprehensive analysis result is compiled into the environmental impact assessment report, which clearly proposes to add a settling tank around the factory, limit the discharge time and carry out sediment remediation and other control measures, and provides a scientific basis for ecological environment management.

[0041] In a specific scenario, based on the environmental impact assessment report, the monitoring area is divided into risk distribution data, including: The environmental impact assessment report is subjected to risk element aggregation analysis to obtain risk aggregation area data, and the risk aggregation area data is subjected to spatial partition processing to obtain risk partition characteristic data; Based on the risk partition characteristic data, the monitoring area is divided into risk prevention and control unit data, and the risk prevention and control unit data is subjected to boundary optimization processing to obtain prevention and control boundary data; The prevention and control boundary data is subjected to risk propagation path analysis to obtain risk propagation characteristic data, and the risk propagation characteristic data is subjected to barrier area identification to obtain risk blocking area data; Based on the risk blocking area data, the monitoring area is divided into risk level partition data, and the risk level distribution data is subjected to regional characteristic extraction to obtain regional environmental risk distribution data.

[0042] Specifically, based on the cumulative trend characteristic data, a multi-pollutant synergistic effect analysis is performed on the monitoring area to obtain pollutant synergistic effect data, including performing pollutant combination identification on the cumulative trend characteristic data to obtain pollutant combination type data, and performing interaction mechanism analysis on the pollutant combination type data to obtain interaction mechanism data; based on the interaction mechanism data, a promotion effect analysis is performed on the pollutants to obtain promotion effect intensity data, and an inhibition effect evaluation is performed on the promotion effect intensity data to obtain inhibition effect parameter data; a superposition effect calculation is performed on the inhibition effect parameter data to obtain effect superposition characteristic data, and a synergistic degree grading is performed on the effect superposition characteristic data to obtain synergistic level data; based on the synergistic level data, an effect intensity distribution analysis is performed on the monitoring area to obtain effect intensity distribution data, and a feature extraction is performed on the effect intensity distribution data to obtain pollutant synergistic effect data. This process is first based on cumulative trend characteristic data, which reflects the concentration change law and growth or decay trend of different pollutants in the time dimension. By performing correlation analysis and clustering identification on the time series curves of multiple pollutants in the same monitoring area, it is determined which pollutants have synchronous rising or alternating fluctuation characteristics in space and time, so as to identify frequently coexisting pollutant combinations, for example, in urban industrial areas, it is found that the concentration curves of PM2.5, SO2 and NO x in the air rise synchronously in the winter heating period, or the cumulative trends of cadmium, lead and zinc in the soil show a high positive correlation, thereby forming pollutant combination type data including “heavy metal complex pollution”, “acid gas synergistic emission” and “organic-inorganic complex pollution” categories; then, for each identified pollutant combination type data, the interaction mechanism analysis is carried out in combination with environmental chemistry principles and existing research literature, to analyze the possible interaction mechanisms in the aspects of physical adsorption, chemical reaction or biological metabolism in the process of migration, transformation and ecological effect, for example, it is analyzed that acid gases (SO2, NO x ) can reduce the pH value of atmospheric particulate matter, thereby enhancing the solubility and bioavailability of heavy metals, or the presence of phosphate in soil can promote the co-precipitation of cadmium and lead to reduce their mobility, thereby generating interaction mechanism data describing the internal interaction paths of various combinations; on this basis, a quantitative model is constructed based on the interaction mechanism data to analyze the promotion effect between pollutants, for example, to simulate the promotion effect of NO xhow the presence of VOCs accelerates the generation of ozone from ozone, or how iron oxides catalyze the redox conversion of arsenic, the strength of the promotion effect is calculated by experimental data fitting or kinetic simulation, forming promotion effect strength data; at the same time, the inhibitory effect of some pollutants on other pollutants is evaluated, such as the competitive inhibition of high concentration of calcium ions on the absorption of cadmium by plants, or the reduction of heavy metal release by humic acid wrapping the surface of particulate matter, and the degree of inhibition is quantified and converted into comparable inhibition effect parameter data; then, the promotion effect strength data and the inhibition effect parameter data are superimposed, and the overall effect strength of each pollutant combination under specific environmental conditions is calculated by vector synthesis or weighted integration method, forming effect superposition characteristic data reflecting the direction and size of its comprehensive influence; then, according to the numerical range and ecological significance of the effect superposition characteristic data, threshold intervals are set for synergy degree classification, such as defining effect value greater than 1.5 as "strong synergy", between 0.8 and 1.5 as "moderate synergy", and less than 0.8 as "weak synergy or antagonism", generating synergy level data containing grade labels; then, the synergy level data is mapped to the spatial grid of the monitoring area, combined with the distribution of pollutant combinations at each sampling point, to analyze the action strength distribution and identify hotspots with significant synergistic effect, such as the downwind of industrial emission sources, river confluence or farmland irrigation area, forming spatialized action strength distribution data; finally, the action strength distribution data is subjected to pattern recognition and feature extraction, and representative synergistic pollution spatial patterns are extracted using principal component analysis or clustering algorithm, such as "high synergy zone around factory", "composite pollution area along river corridor", etc. Typical patterns, integrate their spatial location, involved pollutant species, action strength and dominant mechanism, and finally form structured pollutant synergy data. For example, in the application scenario of the aforementioned urban industrial area, when the cumulative trend characteristic data shows that the concentrations of cadmium and lead in the soil of the region have been rising synchronously for a long time, and the concentration of sulfate particulate matter in the air is also increasing, the "Cd-Pb-SO4 2- " composite pollution system is identified through pollutant combination recognition, and the action mechanism analysis reveals that sulfuric acid deposition leads to soil acidification, which in turn enhances the activity and plant absorbability of cadmium and lead, and the promotion effect strength analysis shows that the bioavailability of cadmium increases by about 40% under acidification conditions, the inhibition effect evaluation finds that high organic matter content can partially alleviate the process, the superposition effect calculation shows that the overall effect presents strong synergy characteristics, the synergy level is divided into "level III (strong synergy)", the action strength distribution analysis shows that the effect is most significant in the farmland southeast of the factory, and finally the pollutant synergy data of "acid deposition driven heavy metal synergistic pollution" in this region is generated through feature extraction, providing key input for subsequent toxicity effect evaluation and management scheme development.

[0043] The method for managing ecological environment inspection and detection information based on artificial intelligence in the embodiment of the application is described above, and the system for managing ecological environment inspection and detection information based on artificial intelligence in the embodiment of the application is described below. Please refer to Figure 2 One embodiment of the system for managing ecological environment inspection and detection information based on artificial intelligence in the embodiment of the application includes: An inspection and detection module 21 is configured to perform inspection and detection on water quality, air, and soil samples collected by an ecological environment monitoring site to obtain environment detection data. A calculation module 22 is configured to perform environment element correlation calculation on a monitoring area based on the environment detection data to obtain element correlation data, and perform space-time distribution mapping on the element correlation data to obtain an environment element distribution map. An analysis module 23 is configured to perform tracking analysis on a pollutant migration law in the environment element distribution map to obtain pollutant migration characteristic data, and perform environment impact assessment based on the pollutant migration characteristic data to obtain an environment impact assessment report. A division module 24 is configured to perform risk distribution division on the monitoring area based on the environment impact assessment report to obtain regional environment risk distribution data, and generate an ecological environment management scheme based on the regional environment risk distribution data.

[0044] In this embodiment, the specific implementation of each unit in the system embodiment described above is described in the method embodiment described above, and will not be described here.

Claims

1. A method for managing ecological environment inspection and testing information based on artificial intelligence, characterized in that, Includes the following steps: The water, air, and soil samples collected from ecological and environmental monitoring stations are tested and analyzed to obtain environmental monitoring data. Based on the environmental monitoring data, environmental element correlation calculations are performed on the monitoring area to obtain element correlation data, and the element correlation data is then mapped to a spatiotemporal distribution to obtain an environmental element distribution map. The migration patterns of pollutants in the environmental element distribution map are tracked and analyzed to obtain pollutant migration characteristic data. Based on the pollutant migration characteristic data, an environmental impact assessment is conducted to obtain an environmental impact assessment report. Based on the environmental impact assessment report, the monitoring area is divided into risk distribution areas to obtain regional environmental risk distribution data, and an ecological environment management plan is generated based on the regional environmental risk distribution data.

2. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, The process involves testing and analyzing water, air, and soil samples collected from ecological and environmental monitoring stations to obtain environmental monitoring data, including: Multi-parameter spectral testing was performed on the water, air, and soil samples to obtain sample spectral characteristic data. The sample spectral characteristic data was then processed with wavelength resolution to obtain environmental element spectral fingerprint data, which included water organic matter content characteristics, air particulate matter composition characteristics, and soil heavy metal content characteristics. Based on the spectral fingerprint data of the environmental elements, quantitative analysis of elemental content is performed on the sample to obtain the concentration distribution data of the environmental elements, and the concentration distribution data of the environmental elements is used as environmental monitoring data.

3. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, Based on the environmental monitoring data, environmental element correlation calculations are performed on the monitoring area to obtain element correlation data, including: Environmental element coupling analysis is performed on the environmental monitoring data to obtain element coupling matrix data, and parameter normalization processing is performed on the element coupling matrix data to obtain environmental element correlation data. Based on the environmental element correlation data, environmental element transmission path analysis is performed in the monitoring area to obtain element transmission flux data, and cumulative effect calculation is performed on the element transmission flux data to obtain element cumulative impact data. An environmental synergy analysis is performed on the cumulative impact data of the aforementioned elements to obtain synergy intensity data, and regional differences are calculated on the synergy intensity data to obtain regional element difference data. Based on the regional element difference data, a comprehensive environmental element evaluation is performed on the monitoring area to obtain element comprehensive evaluation data. Then, weight allocation calculation is performed on the element comprehensive evaluation data to obtain element correlation data.

4. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, The spatiotemporal distribution mapping of the aforementioned element association data yields an environmental element distribution map, including: The associated data of the elements is processed by time series segmentation to obtain time period feature sequence data, and the time period feature sequence data is refined by time scale to obtain multi-scale time feature data. Based on the multi-scale temporal feature data, the monitoring area is divided into spatial grids to obtain regional gridded data, and the regional gridded data is labeled with geographic attributes to obtain geographic element distribution data. Spatial interpolation is performed on the geographic element distribution data to obtain continuous distribution field data, and boundary constraint processing is performed on the continuous distribution field data to obtain boundary correction data; Based on the boundary correction data, spatiotemporal data fusion is performed on the monitoring area to obtain spatiotemporal feature fusion data, and isosurfaces are generated from the spatiotemporal feature fusion data to obtain an environmental element distribution map.

5. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, The migration patterns of pollutants in the environmental element distribution map are tracked and analyzed to obtain pollutant migration characteristic data, including: The environmental element distribution map is used to identify media interfaces to obtain media interface data, and the mass flux of the media interface data is calculated to obtain cross-media transport data. The cross-media transport data includes water-air interface exchange flux, air-soil interface settlement flux, and water-soil interface diffusion flux. Based on the cross-media transport data, speciation analysis of pollutants is performed to obtain speciation composition data of pollutants, and chemical activity assessment is performed on the speciation composition data of pollutants to obtain speciation characteristic data of pollutants. The pollutant transformation characteristic data are analyzed to obtain pollutant migration path data, and the pollutant migration path data are analyzed to obtain diffusion dynamic parameter data. Based on the diffusion dynamics parameter data, the enrichment process of pollutants is analyzed to obtain pollutant accumulation effect data, and the migration characteristics of the pollutant accumulation effect data are summarized to obtain pollutant migration characteristic data.

6. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, An environmental impact assessment is conducted based on the pollutant migration characteristic data to obtain an environmental impact assessment report, including: Long-term cumulative effect analysis was performed on the pollutant migration characteristic data to obtain pollutant cumulative dose data, and time series analysis was performed on the pollutant cumulative dose data to obtain cumulative trend characteristic data. Based on the cumulative trend characteristic data, a multi-pollutant synergistic effect analysis is performed on the monitoring area to obtain pollutant synergistic effect data, and the toxicity effect is assessed on the pollutant synergistic effect data to obtain toxicity superposition characteristic data. Ecosystem response analysis is performed on the superimposed toxicity characteristic data to obtain ecological effect data, and sensitivity assessment is performed on the ecological effect data to obtain ecological sensitivity data. Based on the ecological sensitivity data, a comprehensive analysis of the environmental impact is conducted to obtain comprehensive environmental impact data. An assessment report is then prepared based on the comprehensive environmental impact data to obtain an environmental impact assessment report.

7. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 6, characterized in that, Based on the cumulative trend characteristic data, a multi-pollutant synergistic effect analysis was performed on the monitoring area to obtain pollutant synergistic effect data, including: Pollutant combination identification is performed on the cumulative trend feature data to obtain pollutant combination type data, and the action mechanism analysis is performed on the pollutant combination type data to obtain interaction mechanism data; Based on the interaction mechanism data, the promoting effect of pollutants is analyzed to obtain the promoting effect intensity data, and the inhibitory effect is evaluated based on the promoting effect intensity data to obtain the inhibitory effect parameter data. The superposition effect of the inhibition parameter data is calculated to obtain superposition effect characteristic data, and the degree of synergy of the superposition effect characteristic data is graded to obtain synergy level data. Based on the synergistic level data, the intensity distribution of the pollutant interaction is analyzed in the monitoring area to obtain the intensity distribution data. Then, the features of the intensity distribution data are extracted to obtain the synergistic effect data of the pollutants.

8. The method for managing ecological environment inspection and testing information based on artificial intelligence according to claim 1, characterized in that, Based on the aforementioned environmental impact assessment report, the monitoring area is divided into risk distribution areas to obtain regional environmental risk distribution data, including: Risk factor clustering analysis was performed on the environmental impact assessment report to obtain risk clustering area data, and spatial partitioning was performed on the risk clustering area data to obtain risk partitioning characteristic data. Based on the risk zoning feature data, the monitoring area is divided into prevention and control units to obtain risk prevention and control unit data, and the risk prevention and control unit data is then subjected to boundary optimization processing to obtain prevention and control boundary data. Risk propagation path analysis is performed on the aforementioned prevention and control boundary data to obtain risk propagation characteristic data, and barrier area identification is performed on the aforementioned risk propagation characteristic data to obtain risk barrier area data; Based on the risk barrier area data, the monitoring area is divided into risk level zones to obtain risk level distribution data. Regional features are then extracted from the risk level distribution data to obtain regional environmental risk distribution data.

9. An artificial intelligence-based ecological environment inspection and monitoring information management system, characterized in that, include: The testing and inspection module is used to test and inspect water, air and soil samples collected from ecological and environmental monitoring stations to obtain environmental monitoring data. The calculation module is used to perform environmental element correlation calculations on the monitoring area based on the environmental monitoring data, obtain element correlation data, and perform spatiotemporal distribution mapping on the element correlation data to obtain an environmental element distribution map. The analysis module is used to track and analyze the migration patterns of pollutants in the environmental element distribution map, obtain pollutant migration characteristic data, and conduct environmental impact assessment based on the pollutant migration characteristic data to obtain an environmental impact assessment report. The segmentation module is used to segment the monitoring area into risk distributions based on the environmental impact assessment report, obtain regional environmental risk distribution data, and generate an ecological environment management plan based on the regional environmental risk distribution data.

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