113 results about "Ecological index" patented technology

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further 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 remote sensing, and discloses a remote sensing ecological index assessment method based on deep learning, which comprises the following steps: acquiring Landsat remote sensing image data and land utilization data of a research area, and extracting remote sensing ecological index factors of the land utilization data and the preprocessed Landsat remote sensing image data; normalizing the remote sensing ecological index factors and performing dimension reduction analysis to obtain dimension-reduced remote sensing ecological index factors, inputting the dimension-reduced remote sensing ecological index factors into a time sequence convolutional network model, and generating a comprehensive feature vector through causal convolution, extended convolution and global average pooling operations; performing linear combination on the comprehensive feature vector and the principal component analysis feature value, and constructing a time sequence convolutional neural network remote sensing ecological index model for evaluating the ecological environment quality condition of the research area; according to the method, the accuracy, the reliability and the practicability of remote sensing ecological index evaluation are improved by adding the remote sensing ecological landmark factors reflecting human disturbance and water environment change and constructing the time sequence convolutional neural network remote sensing ecological index model.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a forestry surveying and mapping method based on remote sensing big data, which comprises the following steps: integrating remote sensing images, unmanned aerial vehicle images, temperature sensor data and humidity sensor data to perform space-time alignment processing, filling missing data through time interpolation, and performing abnormal value detection at the same time; and combining the processed data to calculate ecological indexes of the forest area, and outputting a forest health ecological assessment result. According to the method, by integrating remote sensing images, unmanned aerial vehicle images and temperature and humidity sensor data, the accuracy and integrity of data are improved, species identification and ecological function contribution analysis are achieved, key species and contribution of the key species to an ecological system can be accurately identified, and by analyzing the dynamic change trend of forest resources and climate influence, the forest resources can be accurately identified. According to the method, resource configuration can be effectively optimized, the management efficiency and the risk management and control capability are improved, the precision and timeliness of forest resource monitoring, assessment and management are improved, and the sustainable utilization and ecological protection capabilities are enhanced.

The invention discloses a multi-scale fusion ecological hydrological interaction quantification method, and relates to the technical field of ecological hydrology, and the method comprises the steps: 1, collecting environment driving data and remote sensing data; 2, preprocessing the remote sensing data, and carrying out hydrological process dynamic monitoring and ecological parameter collaborative inversion in combination with environment driving data to respectively obtain hydrological data and ecological indexes; 3, constructing a spatio-temporal data set by using the hydrological data, the ecological indexes and the environment driving data; 4, constructing a bidirectional LSTM neural network model, and performing training optimization by using the spatio-temporal data set to obtain a water volume change predicted value, lag time and corresponding ecological variables; 5, lag effect analysis is carried out according to the water volume change predicted value, the lag time and the corresponding ecological variables, and an interaction quantification network diagram of the ecological hydrological elements is constructed. According to the method, the dynamic coupling relation of the ecological hydrological process of the sand lake basin is quantified, and theoretical support is provided for resource optimization management and ecological restoration engineering.

The invention provides a grassland degradation and recovery condition evaluation method integrating an ecological structure and a service index, and the method comprises the steps: constructing a grassland ecological index data set based on multi-source remote sensing data, soil data and meteorological data, and carrying out the inversion of a single-factor monitoring index comprising an ecological system structure index and an ecological system service index; identifying an ecosystem service beam, and performing ecological partitioning on the research area by using the ecosystem service beam; determining the weight of the single-factor monitoring index of each ecological partition, and calculating a grassland health and service comprehensive index according to the weight of the single-factor monitoring index; and evaluating grassland degradation and recovery grades by using the grassland health and service comprehensive indexes. According to the method, the ecological system structure and the service indexes are comprehensively considered, and the comprehensiveness of the indexes is ensured; meanwhile, the difference of grassland ecosystem functions of different regions is fully considered, the index weight is adjusted according to the region characteristics, and the scientificity and reliability of the evaluation result are remarkably improved.

The invention provides a toxic element pollution ecological risk evaluation method based on soil nematode ecological indexes, and belongs to the technical field of toxic element pollution ecological risk evaluation. The technical problem to be solved is to provide a toxic element pollution ecological risk evaluation method based on soil nematode ecological indexes. According to the technical scheme, multi-period and multi-section sampling is conducted on soil polluted by toxic elements of different degrees in a region, and the sampled soil is used for soil potential toxic element content detection, soil physicochemical property detection and soil nematode community structure analysis; respectively calculating characteristic indexes of potential toxic element pollution in the soil, wherein the characteristic indexes comprise a pollution factor, a Nemero index, a pollution load index and a potential ecological risk index; analyzing a soil nematode community structure, and respectively calculating evaluation indexes of the soil nematode community structure; the method is applied to toxic element pollution ecological risk evaluation.

The invention provides a mining area ecological restoration guidance system based on ecological big data. Relates to the technical field of environmental engineering and big data application, and comprises a data acquisition and fusion module used for acquiring remote sensing images, unmanned aerial vehicle aerial photography, ground sensor and historical monitoring data according to a unified space-time coordinate system and outputting fusion data; the prediction and risk quantification module is used for constructing a space-time deep learning model based on the fused data and outputting an ecological index prediction value and a corresponding risk probability; and the strategy optimization module is used for generating a restoration scheme according to the ecological index prediction value and the corresponding risk probability through a multi-target reinforcement learning model. According to the mining area ecological restoration guidance system based on ecological big data, ecological benefits, economic cost and residual risks can be considered synchronously, and a quantifiable and comparable optimal parameter combination is provided for a mining area restoration scheme.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention relates to the technical field of ecological health assessment, and discloses a lake and reservoir water ecological health assessment technical method, which comprises the following steps: S1, acquiring survey monitoring data of a lake and a reservoir, including biological data and water quality data, and constructing a lake and reservoir water ecological index system; s2, determining a score level according to a total standard score value of each hierarchical level index score of the lake and reservoir water ecological index system; and S3, determining the water ecological health level of the lake and the reservoir based on the score value corresponding to each score level. According to the invention, a comprehensive lake and reservoir water ecological index system is constructed based on various biological data and water quality data. The index system comprises multiple levels such as phytoplankton, zooplankton and organic matter, the ecological health state of the lake or reservoir can be comprehensively evaluated from multiple dimensions, and the limitation of single index evaluation is avoided.

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 specifically discloses a long-time-sequence high-frequency ecological environment quality space-time differentiation and driving analysis method, and relates to the technical field of remote sensing and ecological environment. The method comprises the following steps: determining an evaluation area, and constructing a macroscopic ecological safety risk evaluation framework; ecological indexes of greenness, humidity, temperature and dryness are calculated, and a time sequence data set is constructed; reconstructing a time sequence data set and constructing a remote sensing ecological index model based on the reconstructed time sequence data set; verifying the precision of the model, and evaluating the reconstruction precision by taking the screened high-quality pixels as true values; analyzing the spatial and temporal change trend and significance output by the model by using slope estimation and trend test, outputting future change continuity by using a Hurst index analysis model, and measuring spatial autocorrelation output by the model by using a Moran index; remote sensing ecological index evolution factors are researched by means of an optimal parameter geographic detector. According to the invention, the precision and timeliness of ecological assessment are improved, and decision support is provided for ecological management.

The invention relates to the technical field of environment monitoring, in particular to a seawall ecologicalization real-time monitoring method based on multi-source data fusion. The method comprises the following steps: acquiring structure monitoring data, ecological monitoring data and water quality monitoring data of a seawall through an Internet of Things sensor, and acquiring remote sensing data of a seawall area in combination with satellite remote sensing and an unmanned aerial vehicle image; performing space-time registration and fusion on different data sources to generate a unified multi-source fusion data set; identifying a red tide abnormal event based on the fused data set, and constructing a red tide influence conduction model for predicting the damage degree of the red tide to the ecological system and the influence of the red tide to the stability of the embankment body; establishing a coupling evaluation model of ecological indexes and structural indexes, calculating a coupling coefficient by using a fuzzy comprehensive evaluation method, dynamically adjusting ecological and structural weights according to a model output result, and calculating a seawall comprehensive health index; graded early warning information is automatically generated according to a preset threshold value, and collaborative monitoring and intelligent early warning of seawall ecology and structure safety are achieved.

The invention relates to the technical field of ecological environment monitoring and resource scheduling, and discloses an ecological scheduling monitoring method and system based on multi-source remote sensing fusion. The method comprises the following steps: performing time-space consistency correction and fusion on multi-source data, and extracting initial ecological parameters; a feature enhancement parameter set and adaptive inversion parameters are obtained through optimization of a convolutional neural network and a genetic algorithm, key ecological indexes are inverted, and real-time ecological state description is generated; based on real-time ecological state adjustment model input, predicting an ecological change trend, and calculating to obtain an optimization decision variable; a water resource distribution scheme is generated, the distribution balance degree is evaluated, and final scheduling output is formed through iterative optimization; a final scheduling result is fed back to a data integration link, and updating and loop optimization of the preliminarily aligned data set are achieved. According to the invention, the integration of ecological monitoring and water resource scheduling under multi-source data fusion can be realized, and the balance of water resource distribution and the real-time performance of ecological system regulation and control are improved.

The invention relates to the technical field of ecological environment monitoring, in particular to a grassland environment resource database construction method, which comprises the following steps of: arranging a multi-modal sensor in a fractal multi-scale grid and synchronously collecting; obtaining a standard frame by adopting quantum random walk compression and space-time registration; extracting a topological bar code through neuromorphic pulse coding and persistent coherent topology analysis, and generating a unified potential space tensor and a missing mask in combination with weighted mutual attention and optimal transmission; constructing an energy function containing carbon and nitrogen conservation and topological deviation, and reconstructing a complete ecological submerged space tensor through Hamiltonian Monte Carlo-diffusion combined sampling; ecological indexes and abnormal events are reasoned in the Shenchang differential graph database, sampling scheduling is driven by prediction errors, closed-loop updating of data, models and sampling is achieved, and therefore the grassland monitoring precision and early warning timeliness are improved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention provides a forest sustainable operation monitoring method. The method comprises the following steps: acquiring species census information; acquiring an image representing the soil thickness; the images are acquired by a plurality of sensing devices arranged in the forest; determining an ecological index at the current time according to the species general survey information and the image; setting display colors for the ecological indexes of different grades; a long-strip-shaped display strip is arranged in the visual interface; a plurality of time values are set below the display bar; and filling the display strips with display colors corresponding to the levels of the ecological indexes at different times. According to the method, the soil thicknesses of the forest at different moments can be remotely collected, the ecological indexes of the forest are determined in combination with species general survey information, manual field collection of the soil thicknesses is not needed, the monitoring efficiency is improved, meanwhile, the ecological indexes at different moments are displayed through the display strips, and the monitoring result display method is more flexible.

The invention discloses a regional ecological environment quality evaluation method based on neighborhood granular fuzzy c-means clustering, and belongs to the technical field of electrical digital data processing, and the method comprises the steps: calculating and reconstructing a remote sensing time sequence MODIS data set based on a cloud platform, standardizing ecological factors, dividing ecological factor remote sensing images into n categories based on neighborhood granular fuzzy c-means clustering, and obtaining a comprehensive remote sensing ecological index PRSEI based on the MODIS by using an improved entropy weight method, and obtaining mining area ecological environment quality changes in year intervals according to the PRSEI of the research area. According to the method, the entropy weight method and the neighborhood fuzzy c-means clustering algorithm are effectively fused, all remote sensing pixel information of all ecological factors is reserved, the process is clear, and complex system problems such as remote sensing ecology and the like can be better analyzed and solved.

The invention discloses a mining area ecological index intelligent evaluation platform, and relates to the technical field of ecological evaluation, and the mining area ecological index intelligent evaluation platform comprises the following steps: obtaining multi-source remote sensing data, and obtaining an initial ecological index; performing ecological factor fusion according to the initial ecological indexes to obtain comprehensive ecological factors; performing principal component dimensionality reduction according to the comprehensive ecological factors to obtain principal component features; performing cross-cycle stability calculation according to the principal component characteristics to obtain a stability index; performing spatial overlay analysis according to the stability index to obtain an ecological distribution pattern; and performing multi-scale coupling analysis according to the ecological distribution pattern to obtain a mining area ecological index. According to the method, the cross-period stability index is calculated based on the principal component characteristics, the change trend of the mining area ecosystem in multiple time periods is comprehensively reflected, the accuracy of dynamic monitoring is improved, the scientificity and interpretability of ecological assessment are improved, and the systematicness and comprehensiveness of ecological assessment are enhanced.

The invention relates to the technical field of remote sensing monitoring, and discloses a remote sensing dynamic monitoring method for a sensitive ecological region and an embankment along a river, which comprises the following steps: dividing the sensitive ecological region and the embankment along the river, and collecting long-time-sequence multi-source remote sensing data and ground actual measurement data; preprocessing the Landsat series satellite image data, calculating an ecological index, performing regression analysis, and constructing a comprehensive ecological index monitoring model; the Landsat series satellite image data and the actually measured water level data are fused through a spatial interpolation method, and a water level safety threshold model is constructed; the method comprises the following steps: preprocessing Sentinel-1 / 3 satellite data, screening image pairs subjected to interference processing, and calculating a differential interference phase to solve terrain deformation rates of a sensitive ecological region and an embankment along a river; performing weight assignment and scoring grade setting on the ecological state, the water level state and the terrain state according to an importance principle, and performing summation through a weighted average method to obtain an overall ecological environment safety condition; the method improves the monitoring precision of the ecological environment.

The invention provides an improved remote sensing ecological index calculation method based on spatio-temporal data fusion. The method comprises the following steps: collecting remote sensing data of a research area; preprocessing the collected remote sensing data; performing mask processing on the water body in the processed remote sensing data; performing space-time fusion on the preprocessed remote sensing data; according to the remote sensing data after fusion processing, index data used for representing improved remote sensing ecological indexes are calculated respectively; the method comprises the following steps: acquiring aerosol optical thickness for representing air quality and night light data for representing human activity intensity on a gee platform, and resampling the two index data; performing parameter standardization processing on the index data to obtain standardized data; principal component analysis is adopted, principal component extraction is carried out according to the standardized data, and an evaluation index result is obtained. Through the spatio-temporal data fusion technology, the spatio-temporal resolution of the data can be improved, and the problem that index selection of the remote sensing ecological indexes is lack of comprehensiveness is solved.

The invention relates to the technical field of marine ecological environment monitoring, in particular to a marine ecological environment assessment early warning system based on big data and AI, which comprises a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module. Wherein the multi-source data fusion module is used for collecting multi-source data in real time; the dynamic feature extraction module is used for extracting 72-hour change gradients of the three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculating and outputting a meteorological disturbance coefficient; the ecological index generation module is used for generating a water quality deterioration index and a biological stress index; and the coupling early warning generation module is used for outputting an ecological risk level signal. According to the method, meteorological interference is reduced in real time, water quality deterioration and biological stress indexes are subjected to weighted fusion, the marine ecological environment risk is accurately evaluated, and scientific support is provided for ecological protection and pollution early warning.

The invention discloses a fruit tree ecological index assessment method based on an orthoimage and three-dimensional point cloud data, and the method comprises the following steps: obtaining an orthoimage map and three-dimensional point cloud data of a fruit tree region, carrying out the individual tree segmentation of the orthoimage map, and determining the individual tree region of each fruit tree; constructing a three-dimensional point cloud data set on the single tree area, performing point cloud denoising and layering, and outputting an independent layered three-dimensional point cloud of a single fruit tree; and calculating the core ecological index of the fruit tree by using the independent layered three-dimensional point cloud, so that the scheme is suitable for efficient monitoring and evaluation of a large-scale orchard, and the evaluation precision and efficiency are remarkably improved.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention discloses a territorial space monitoring system based on remote sensing data modeling. The system comprises a data collection module, a remote sensing feature modeling module, a time sequence change detection module, an ecological safety evaluation module and a comprehensive monitoring module. The invention belongs to the technical field of remote sensing data modeling analysis, and the system achieves the dynamic perception and evaluation of the surface change and ecological risk through the standardized collection of multi-source remote sensing data, the construction of a land parcel feature structured model, and the adoption of an improved causal change detection and multi-scale ecological index evaluation method. Innovation points comprise a differentiable feature selection mechanism of geographical perception depth coding, a double-flow change detection model and an ecological risk circuit simulation technology, a data-driven and full-flow closed-loop high-integration monitoring platform is constructed, and the monitoring precision and the response efficiency are improved.

The invention provides a mining area ecological restoration engineering evaluation system based on multi-source data fusion. The system comprises a data acquisition module which is used for acquiring multi-source heterogeneous original data related to a mining area environment; and the data preprocessing and fusion module is used for carrying out space-time alignment, format standardization and feature extraction on the multi-source original data and generating a unified ecological index data set based on a multi-scale deep learning fusion framework, and the preprocessing and fusion module comprises a dynamic credibility evaluation sub-module, a weighted fusion sub-module and an intelligent analysis module. The mining area ecological restoration project evaluation system based on multi-source data fusion can automatically explore an optimal restoration parameter combination in ecological health index amplification and cost-benefit tradeoff, realizes online adjustment and dynamic feedback of a scheme, and greatly improves project decision efficiency and restoration effect.

The invention discloses a water ecological index data processing method and system combining the Internet of Things and artificial intelligence, and belongs to the technical field of water body monitoring and analysis, and the method comprises the steps: grouping sensors, synchronizing a clock, and forming a space grouping data flow; comparing the grouped data with the steady-state baseline in real time, triggering high-density event acquisition, and outputting context data pulses; obtaining and standardizing mapping environment data, and generating a multi-dimensional spatio-temporal feature cube; dynamically distributing weights to calculate a water ecological index, quantifying credibility, and outputting a dynamic water ecological index; and when the credibility is lower than a threshold value, analyzing parameter contributions and tracing abnormities, generating and issuing a calibration strategy, and realizing grouping self-calibration and sampling adjustment. According to the method, multi-source collaborative fusion, dynamic weight distribution and an intelligent self-correction mechanism are adopted, the real-time performance, intelligence and evaluation accuracy of water ecology monitoring can be improved, and efficient and reliable data support and early warning capacity are provided for complex water environment changes.

The invention provides an ocean protection area ecological quality dynamic monitoring method and system based on a GEE platform, and the method comprises the steps: S1, calling remote sensing data in a first preset time period and a preset sea area through the GEE platform, extracting ecological indexes from the remote sensing data to form an ecological monitoring index set, and constructing a time sequence of the ecological indexes according to a pixel point unit; s2, constructing a fluctuation feature vector of the pixel point for the ecological index time sequence corresponding to each pixel point, and inputting the fluctuation feature vector of each pixel point into a clustering algorithm to divide a sensitive level area of the preset sea area; s3, constructing a region index weight adjustment model, calculating the influence degree of each ecological index in each sensitive level region on the ecological quality, and generating a region adaptive weight table; and S4, extracting the ecological indexes corresponding to the pixel points, generating fusion index values of the pixel points according to a preset fusion method, inputting the fusion index values into the fuzzy neural network model, and mapping the pixel points into ecological quality grades.

The invention relates to the technical field of intelligent environmental protection monitoring, in particular to a method for intelligently monitoring and analyzing land utilization change in an ecological protection red line, which comprises the following steps of: acquiring a historical remote sensing image, extracting pixel-level vegetation and artificial indexes, constructing an ecological index time sequence data set, and constructing an ecological index time sequence data set based on the data set; a dynamic baseline prediction model fusing a long-term trend term, a seasonal periodic term and a standard deviation is established for each pixel, personalized prediction of a theoretical state of the pixel is realized, abnormality diagnosis is performed through comparison with a confidence interval output by the model, and an abnormal pixel is marked as a collaborative abnormal pixel, so that comprehensive risk assessment is carried out, and the risk assessment efficiency is improved. The method comprises the steps of calculating risk scores of all indexes and carrying out weighted fusion to obtain a comprehensive risk index, then dividing risk levels, outputting an anomaly diagnosis report, finally, carrying out reverse calibration on model parameters based on confirmed anomaly cases, and mining risk modes from case characteristics through unsupervised clustering.

The invention relates to the technical field of ecological environment data monitoring, and further relates to a mine greening supervision method based on remote sensing image semantic segmentation. The method comprises the following steps: step 1, based on original ecological indexes of a mine, establishing a six-column time sequence matrix for each mine landscape patch; 2, at each monitoring moment, constraint non-negative matrix factorization is completed on the time sequence matrix through minimum K-L divergence iteration, and the target is to disassemble the time sequence matrix into a product of a potential ecological function module weight matrix and a potential ecological function module index contribution matrix; and 3, carrying out weighted average on the resource-liability balance degree by using the area of each mine landscape patch so as to evaluate the ecological restoration effect of the mine. According to the invention, dynamic, fine and structured monitoring and evaluation of the mine ecological restoration process are realized.