2432 results about "Ecological environment" patented technology

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention provides an ecological environment sensitive area dynamic change evaluation and prediction method based on machine learning, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: obtaining and preprocessing remote sensing image data, and extracting an environment element feature sequence; adopting a hierarchical memory distillation network to extract periodic characteristics of the long-term change sequence, and combining with the short-term change sequence to carry out space-time recombination; utilizing a graph neural network to construct a dynamic association graph to analyze an environmental element coupling relationship; and evaluating the sensitivity and performing early warning based on the sudden change amplitude and the cumulative change trend. According to the invention, the dynamic change characteristics of the environmental elements can be accurately captured, and the accuracy and timeliness of early warning of the ecological environment sensitive area are improved.

The invention discloses a forest land ecosystem health assessment system, and belongs to the technical field of environmental protection. Comprising the following modules: an intelligent sensing module for realizing omnibearing and high-resolution monitoring of the forest land ecological environment; the feature extraction module automatically extracts, standardizes and dynamically corrects the multi-dimensional features of the ecological system, and constructs a unified ecological index mapping model; the coupling analysis module is used for accurately identifying structural evolution characteristics and key conversion nodes of the ecological system; the health assessment module is used for constructing a dynamic self-adaptive ecological health assessment model, assessing the overall health state of the system and simulating the vulnerability and potential risk of the ecological system under different climate change scenes; the decision and visualization module is used for realizing efficient intelligent interaction and multi-scene decision analysis of ecological big data; and the restoration and regulation and control module intelligently generates a restoration path and an intervention strategy based on ecological risk assessment so as to improve the restoration capability and toughness of the ecological system.

The invention relates to the technical field of ecological environment monitoring, in particular to a multi-dimensional data analysis-based water body pollution monitoring and processing system, which comprises a dynamic data cube construction module, a pollution event slice extraction module, a multi-dimensional feature fingerprint coupling module, a pollution source reverse traceability module and a self-adaptive early warning trigger module. The method comprises the following steps: receiving water quality parameters of an internet of things sensor, meteorological data of a meteorological hydrological station and satellite remote sensing spectral data, and constructing a dynamic data cube of unified space-time coordinates and feature axes; identifying pollution event slices by using entropy change analysis and a self-adaptive threshold method; carrying out characteristic coupling analysis on the pollution event, and fusing a spectrum fluctuation mode, a physicochemical gradient and toxicity response to generate a pollution characteristic fingerprint; pollution source positioning and contribution weight estimation are realized by adopting a dynamic time warping algorithm, and corresponding disposal equipment is driven to start based on an early warning level, so that intelligent closed-loop control of pollution identification, source tracing and treatment is realized.

The invention discloses a coastal wetland vegetation degradation dynamic monitoring method based on multi-modal transfer learning, and belongs to the technical field of ecological environment monitoring, and the method comprises the steps: data acquisition: collecting coastal wetland region data through a plurality of sensors, feature extraction and fusion: preprocessing multi-source data, extracting space, time series and physical parameter features, and carrying out feature fusion; and fusing the data features of different modalities. According to the method, all-around and high-precision perception of the coastal wetland ecological environment is realized, the spatial-temporal resolution of data acquisition is improved, the response capability to complex environmental factors is enhanced, the generalization capability and prediction precision of the model are effectively improved, key environmental factors such as soil salinity can be incorporated into an evaluation system, and the evaluation accuracy is improved. The scientificity and pertinence of ecological risk identification are further improved, the efficiency and response speed of coastal wetland ecological management are improved, and powerful support is provided for formulating scientific and reasonable ecological protection and restoration measures.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention relates to the technical field of ecological environment prediction, in particular to an ecological environment prediction system and method, and the system and method employ artificial intelligence technologies such as multi-source data fusion, dynamic space-time modeling, cross-modal collaborative optimization, space-time deep learning, multi-modal learning, adaptive optimization, and prediction branches employing a Transform architecture neural network model. And the comprehensiveness, precision and practicability of ecological environment prediction are remarkably improved.

The invention, which relates to the technical field of ecological environment monitoring and intelligent management, discloses a real-time monitoring and early warning management system based on an ecological environment, comprising a multi-modal data acquisition module for acquiring and preprocessing multi-medium environment data, a multi-medium risk conduction modeling module for constructing a directed graph, and a dynamic modeling risk conduction process. The cross-medium risk conduction chain effect evaluation system has the advantages that the problem of cross-medium risk conduction chain effect evaluation deficiency is effectively solved, multi-modal data provides a data basis, the modeling module constructs a directed conduction relation graph, and the model construction module constructs a three-dimensional model and simulates pollutant migration and transformation and risk effects, and the intelligent early warning module generates early warnings, so that the cross-medium risk conduction chain effect evaluation system has the advantages that the cross-medium risk conduction chain effect evaluation deficiency problem is effectively solved; a graph neural network is used for dynamic modeling, a conduction path is presented, a digital twin engine simulates pollutant migration and transformation and a risk conduction effect, an intelligent early warning module carries out multi-level evaluation and timely early warning, a decision intervention module generates an optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-medium risk conduction.

According to the remote sensing image green roof intelligent identification method and system based on the improved Mask RCNN algorithm, the feature extraction capability of the algorithm is enhanced by introducing an attention mechanism, and the edge segmentation precision of a target is improved by constructing a joint loss function, so that precise detection of a green roof in a complex built environment is realized. According to the invention, the morphological parameter analysis module is synchronously integrated, geometric feature parameters such as the area, the perimeter and the shape index of the green roof can be automatically extracted, and integrated processing of green roof identification and feature parameter extraction is realized. Compared with a traditional method, the method can effectively solve the problems that target examples are difficult to distinguish, and environmental adaptability is poor, and provides a reliable technical means for green roof monitoring and ecological environment benefit evaluation.

The invention relates to the technical field of environmental monitoring, in particular to an ecological environment and environmental pollution monitoring system and method, and a multi-source heterogeneous data acquisition module comprehensively acquires water quality, weather and space monitoring data. The system comprises a topological mapping data preprocessing module which is used for abnormal data detection and data vacancy filling; the state evolution analysis module is used for constructing an environment state transition network and analyzing an environment state evolution rule; the environment comprehensive evaluation module is used for calculating a water safety level index and a water pollution condition index and generating an environment quality evaluation report; the pollution diffusion prediction module is used for predicting a pollutant diffusion path and concentration distribution and identifying a high-risk area; the intelligent early warning and visualization module is used for generating graded early warning information and visually displaying the environment state and the pollution diffusion condition through multi-dimensional data, so that the coverage and the data integrity of environment monitoring are remarkably improved, the environmental pollution problem can be timely found and dealt with, and the ecological environment safety is guaranteed.

The invention provides an ecological environment dynamic monitoring method based on big data, and relates to the technical field of environmental pollution detection, and the method comprises the steps: collecting multi-source sensor data of a fixed monitoring station in an urban region, and carrying out the time alignment and space registration of the multi-source sensor data, thereby generating a standardized environmental monitoring data set. And on the basis of the data set, constructing a pollutant transmission network between the monitoring points to obtain a regional pollutant distribution characteristic graph, calculating a diffusion trend coefficient of pollutants in combination with regional wind direction data, constructing a pollutant migration path network, and generating a key supervision region map. According to the method, the pollutant concentration distribution can be dynamically partitioned, and the mobile monitoring equipment is scheduled to carry out gradient type point distribution monitoring, so that the accurate positioning of the pollution source is realized; the accuracy of pollutant diffusion trend analysis and the precision of pollution source positioning are improved, dynamic supervision of key areas is facilitated, the efficiency of environment monitoring and the fine management level are improved, and therefore the risk of environmental pollution is effectively reduced.

The invention discloses an ecological environment detection method and system based on multispectral remote sensing fusion, and relates to remote sensing image processing. The method comprises the following steps: collecting multispectral remote sensing image data of a target area; performing multiband joint atmospheric correction processing on the multispectral remote sensing image according to the scattering coefficient, the atmospheric light value and the transmissivity of each band; performing foreground and background analysis on the corrected multispectral remote sensing image; fusing the vegetation area and the non-vegetation area of each wave band image by adopting different weight strategies to generate a multispectral fusion image; and extracting spectral features of the multispectral fusion image, constructing a standard vegetation spectral feature library, and identifying regions deviating from a standard vegetation spectrum through an anomaly detection algorithm according to the extracted spectral features to obtain various vegetation coverage rates. In view of low vegetation identification precision caused by direct foreground and background division of a multispectral remote sensing image under an atmospheric interference condition, vegetation division is performed after a clear image is obtained, so that the detection precision is improved.

The invention relates to the field of gene engineering for enhancing the cold resistance of plants, and relates to a recombinant expression vector, a recombinant strain and a primer pair of a pennisetum purpureum PpMYB2 gene, a method for improving the cold resistance of the plants and application of related materials in improving the cold resistance of the plants. One purpose of the invention is to provide application of the Pennisetum purpureum PpMYB2 gene and related materials thereof in improving the cold resistance of the plant, the amino acid sequence of the encoding protein of the Pennisetum purpureum PpMYB2 gene is as shown in SEQ ID NO.1, and the plant is arabidopsis thaliana or Pennisetum purpureum. Under the background that extreme low temperature events caused by global climate change are increasingly frequent, the cold resistance enhancing technology provided by the invention has important significance on guaranteeing the stability of agricultural production and reducing loss caused by low temperature. Through overexpression of the gene PpMYB2, the adaptability of perennial pennisetum purpureum to low-temperature stress can be remarkably improved, so that the planting range of perennial pennisetum purpureum is expanded to regions with lower temperature, and the survival and growth performance in different ecological environments is enhanced.

A method for delineating an ecological corridor based on a maximum similarity model determines ecosystem type according to the determined ecological source, and assigns a habitat suitability index; and according to a maximum similarity matrix between different ecosystems, assigns the habitat suitability index to peripheral pixels of the selected ecological source, to obtain a similar value of the habitat suitability index. The habitat suitability index is corrected according to the surface curvature data and population density data, and resistance surface data is calculated according to the corrected value. Based on the resistance surface data, an ecological corridor is delineated comprehensively to find one or more paths corresponding to a minimum cost distance as ecological corridors. The influence of ecological environment on ecological corridor establishment is considered more comprehensively, so that the ecological corridor is delineated more accurately, and applied into practice to maximum extent.

The invention discloses a space-air-ground integrated ecological environment monitoring data fusion method and system, and the method comprises the steps: obtaining the original data of heterogeneous multi-source environment monitoring in real time, carrying out the preprocessing, and carrying out the time-space data synchronization of the preprocessed data; performing data fusion on the synchronized data; the data fusion comprises the following steps: calculating initial fusion data by adopting a weighted average fusion method based on space-time weight to obtain an initial fusion result; acquiring a data quality factor, a space-time synchronization factor and a sensor stability factor, and determining an initial weight of each data source according to the data quality factor, the space-time synchronization factor and the sensor stability factor; dynamically adjusting the weight in real time through a Bayesian optimization strategy; and carrying out dynamic optimization on the data after real-time fusion based on an adaptive Kalman filtering algorithm. According to the method, the problem that a traditional data processing method is insufficient in multi-source heterogeneous data fusion capability is solved, and the utilization efficiency and value of the data are improved.

The invention provides a mine ecological restoration monitoring method based on multi-source data, and the method comprises the steps: obtaining the multi-source data, such as remote sensing monitoring data, ground monitoring data and engineering monitoring data, of a target region, carrying out the restoration feature extraction processing, generating restoration state data, and carrying out the restoration evaluation based on a preset restoration evaluation model. And performing restoration abnormity matching processing on the restoration state data, generating restoration abnormity response data, generating a restoration optimization instruction according to the restoration abnormity response data, and feeding back the restoration optimization instruction to the restoration execution module to trigger restoration adjustment operation, thereby realizing comprehensive and dynamic monitoring and intelligent regulation and control of the mine ecological restoration process. The accuracy and efficiency of restoration effect evaluation are improved, and rapid restoration of the mine ecological environment is promoted.

The invention discloses a multi-source remote sensing ecological production environment assessment system, and belongs to the field of ecological environment assessment. The system is composed of a data acquisition module, a data processing and fusion module, a generative artificial intelligence core engine, an ecological evaluation and decision support module and a user interaction interface, and all the modules are connected with one another, so that the overall function of the system is realized. According to the method, through a'representation-cognition-semantics' three-layer fusion architecture, the system can surpass traditional data piling, deep integration is carried out on multi-source information from bottom-layer features and spatial relationships to high-layer semantics, so that a complex interaction mechanism among ecological elements is more accurately understood and described, and the system is more efficient and efficient. And the overall cognitive ability and evaluation accuracy of the complex ecological system are remarkably improved. The system is based on a generative artificial intelligence core engine, not only can analyze the current situation, but also can simulate future scene evolution under different ecological management strategies.

The invention discloses an ecological environment monitoring method and system based on a geographic information technology, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: carrying out the multi-source heterogeneous data fusion of a multispectral image and point-like time series data of a water ecological protection target water area, and obtaining a three-dimensional water quality dynamic situation field; future trajectory prediction and water ecological health analysis are carried out on the three-dimensional water quality dynamic situation field to obtain a global water ecological health index thermodynamic cloud picture, pollution diffusion is carried out on the global water ecological health index thermodynamic cloud picture to obtain a pollutant trajectory matrix, ecological risk prediction is carried out based on the pollutant trajectory matrix, and the ecological risk prediction result is obtained. According to the method, the spatial-temporal dynamic risk early warning grade map is obtained, the restoration scheme is optimized based on the spatial-temporal dynamic risk early warning grade map, the restoration scheme optimal solution set is obtained, the timeliness and accuracy of ecological risk early warning are improved, and the economy of restoration decision is enhanced.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses a method and system for dynamically measuring and calculating carbon sink in the whole construction process of a wetland protection area, and belongs to the technical field of ecological environment monitoring, and the method comprises the following steps: respectively calculating potential carbon emissions of a building material production and transportation stage, a construction stage and an operation stage after project construction, and obtaining a carbon emission accounting result; a carbon flux model is established, and a carbon flux change data set of the whole project construction process is calculated and obtained according to wetland ecological monitoring data, meteorological data, biological and physiological parameters and construction activity parameters; according to the carbon emission accounting result and the carbon flux change data set, a comprehensive carbon sink measurement and calculation model of the whole construction process is constructed; the actual carbon sink data are compared to correct the measurement and calculation model, and the corrected measurement and calculation model is applied to carbon sink amount calculation in the whole construction process of different types of wetland protection area production and construction projects; comparing a model calculation result with results obtained by other authoritative calculation methods to verify the accuracy and reliability of the model; and the blank in the field of current wetland carbon sink measurement and calculation is filled.

The invention discloses a method for evaluating the influence of reservoir-water transfer project combined regulation on downstream ecological flow. The method comprises the following steps: step 1, collecting data of a research area; 2, constructing a water transfer project water source area natural water circulation model WEP-NHA; step 3, constructing a natural-social binary water circulation coupling model WEP-HA; 4, simulating the runoff of the water source area by using a WEP-HA model; 5, the system analyzes the change degree of the key control section water quantity and the ecological flow guarantee rate of the water source area under the condition that the reservoir and the water transfer project are overlapped. According to the method, system simulation of disturbance response of a complex system in a water source area can be achieved, the comprehensive influence of a complex water conservancy project system on water resources and the ecological environment is effectively revealed, high adaptability and predictability are achieved, the problem of regulation and storage effect simulation of a reservoir without data or with little data is effectively solved, and the method is suitable for popularization and application. And decision support is provided for scientifically formulating the water transfer scale and guaranteeing the ecological flow.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring are solved.

The invention discloses an evaluation method for dynamically monitoring carbon sink of a grassland and wetland ecosystem, which relates to the field of ecological environment monitoring and comprises the following steps: acquiring historical carbon flux observation data of a target area; dividing the target area into a plurality of space grid units, taking each grid unit as a node in the graph structure, and establishing graph edge connection between adjacent nodes; extracting ecological features for each node; calculating the edge weight of each edge in the graph structure according to the ecological characteristic difference between the adjacent nodes; inputting the graph structure containing the node features and the edge weights into a graph neural network model, and training the graph neural network model by using historical carbon flux observation data as labels; and predicting the carbon flux of an unobserved node or a future time period node by using the trained graph neural network model. The method solves the problem that a traditional model is large in estimation error of the carbon flux in the ecological boundary region, and can remarkably improve the carbon exchange prediction precision of the boundary region.

The invention discloses an ecological environment monitoring data processing method and system based on big data, and the method comprises the steps: obtaining multi-source heterogeneous ecological environment monitoring data in a set territorial scope, carrying out the data source screening and data preprocessing of the ecological environment monitoring data, and extracting multi-dimensional data features from a data source based on a data preprocessing result; constructing a multi-modal knowledge graph, and extracting semantic association of the multi-dimensional data features through an attention mechanism by adopting a CM-GAT cross-modal graph attention network to obtain an association feature vector; inputting the associated feature vector into an improved model for prediction, and outputting an environment state index; and according to the environmental state index, pollution degree judgment is carried out, an association mining algorithm is used to analyze an association relationship between a pollutant migration path and a potential pollution source, and an early warning result is output. The pollution source can be efficiently and rapidly analyzed and accurately locked, the pollutant migration path is clearly described, and active monitoring and early warning are achieved.

The invention relates to the technical field of animal monitoring, and provides a wild animal epidemic disease monitoring, prevention and control method and system based on artificial intelligence, and the system collects the video stream, the shell temperature, the air pathogen concentration, the sound characteristics, the VOCs spectrogram and other data of wild animals in real time through arranging multi-mode sensor nodes. Real-time reasoning is carried out through a wildness degree AI model in the edge calculation unit, the health state and epidemic disease risk of animals are evaluated, a multi-source risk knowledge graph and a graph neural network are combined, an epidemic situation occurrence probability threshold value is dynamically adjusted by the system, and accurate prevention and control instructions such as risk area division, isolation early warning and material putting schemes are generated; after the epidemic disease risk is confirmed, the system sends early warning information to a prevention and control center through various communication links, and continuously optimizes a prevention and control strategy through reinforcement learning. According to the method, intelligence and precision of epidemic disease prevention and control of wild animals are achieved, complex ecological environment changes can be coped with in real time, and prevention and control efficiency and accuracy are remarkably improved.

The invention discloses an ecological restoration effect evaluation method for a land and space comprehensive improvement area, and relates to the technical field of ecological environment monitoring. A terrain structure data set and a moisture dynamic data set are obtained after data analysis and image feature extraction; the method comprises the following steps: carrying out terrain disturbance evaluation by constructing a terrain disturbance response index Rtz, carrying out hydrological back-up state evaluation by constructing a hydrological back-up trend index Gsh when the terrain has an unsteady disturbance region, carrying out ecological adaptation evaluation by constructing an ecological adaptation comprehensive index SSZ when the hydrological back-up meets the standard, and generating a landform base map of a renovation region by calling DEM (Digital Elevation Model) data. And each evaluation result is subjected to visual partition display through GIS space rendering, and a repair effect report is automatically generated. According to the method, multi-dimensional quantitative evaluation of terrain disturbance, hydrological refilling and ecological adaptability of the renovation area is realized.

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment dynamic monitoring method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: 1, carrying out the multi-source data collection and preprocessing of a designated monitoring region, 2, carrying out the space-time alignment and fusion, 3, constructing a dual-channel generative adversarial network (DC-GAN), and 4, carrying out the multi-source remote sensing data fusion. The super-resolution is reconstructed, so that the acquired image data is enhanced; the method comprises the following steps: step 1, carrying an LSTM-RF hybrid model, step 2, constructing an LSTM-RF hybrid model, step 6, carrying out edge intelligent deployment: carrying NVIDIA Jetson AGX Orin at an unmanned aerial vehicle end, operating a lightweight student model (parameter quantity is compressed to 0.8 MB), and realizing real-time ground feature classification (delay lt; 300 ms). According to the invention, rain season monitoring is more complete through a meteorological adaptive weight distribution algorithm based on data fusion; the ground feature misjudgment rate is reduced through image-enhanced dual-channel GAN-SAR fusion reconstruction, and real-time early warning of ecological degradation is supported through an LSTM-RF hybrid model.

The invention relates to the technical field of microorganisms, in particular to a disease-resistant, growth-promoting and yield-increasing strain and a microbial agent and application thereof. The strain is a strain bacillus velezensis NB21 (CCTCC (China Center for Type Culture Collection) NO: M2024979) which is found autonomously; the strain can grow at low temperature, is strong in ecological environment adaptability, can be used for preparing a single microbial agent or a compound microbial agent, and can also be used for forming a composition with other functional organic and inorganic substances. The prepared microbial agent can significantly prevent and treat diseases, promote root development of crops such as rice and corn, enhance photosynthesis, improve system resistance, increase crop yield, enhance nutrient supply and improve soil micro-ecology. The strain is unique, strong in colonization ability and diversified in function, the prepared microbial agent fermentation production technology is mature, the cost is low, the application range is wide, the yield increasing and quality improving effects are outstanding, and the microbial agent has good application prospects.

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.