138 results about "Spatial analysis" patented technology

The invention relates to the technical field of underwater robots, in particular to an underwater robot cluster path planning method and system based on a dung beetle algorithm, and the method comprises the steps: obtaining a coordinate division region to construct an environment model, calculating and extracting an obstacle distance to fuse ocean current interference to generate a dynamic obstacle avoidance strategy, and evaluating the path speed similarity to generate a candidate path. And acquiring an offset angle difference, normalizing environmental factors to generate a correction path, and calculating a curvature fusion collision probability to generate a global path control instruction. According to the method, a dynamic model is constructed based on Cartesian coordinate division, the spatial analysis precision is improved, a sonar distance and ocean current interference are fused to generate a dynamic obstacle avoidance strategy, the trajectory smoothness is enhanced, candidate paths are screened by using vector similarity and Monte Carlo simulation, the collaboration is optimized, disturbance is reduced through coordinate offset and ocean current correction, and the accuracy of the dynamic obstacle avoidance strategy is improved. The curvature and collision probability are combined to balance safe energy consumption, and multi-dimensional parameter fusion improves cluster adaptability, precision, efficiency and energy utilization rate.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention relates to the technical field of big data processing, in particular to a hydrological information forecast data collection automatic processing method, which comprises the steps of multi-source data automatic acquisition and access, data quality intelligent diagnosis processing and abnormity early warning, multi-index comprehensive drought evaluation model construction and drought trend intelligent prediction. GIS spatial analysis and visual display are carried out, and drought evolution is dynamically reflected; and user-defined configuration and adaptive learning are carried out. The equipment state can be displayed, and related personnel can be prompted to maintain the observation station in time; existing hydrological data of the hydrological bureau are fully applied, rainfall, river flow, soil moisture content and other single items are normatively determined to determine the drought level, and meanwhile, according to multiple indexes, comprehensive drought judgment is performed according to the proportion, and based on the GIS technology, an interpolation method and a proximity association method are utilized to determine a Xinjiang whole administrative division plane drought map; and according to the analysis data, automatically sorting out soil moisture content brief report materials.

The invention discloses an urban flood mapping and driving factor analysis method based on multi-source remote sensing data, and the method comprises the steps: respectively extracting an optical semi-permanent water body and an SAR semi-permanent water body based on a global surface water body data set and a Sentinel-1 image, and taking the intersection of the optical semi-permanent water body and the SAR semi-permanent water body to generate an enhanced semi-permanent water body mask; extracting a water body in a flood period by using the Sentinel-1 image to obtain flood water body distribution, and performing water body extraction precision verification in combination with the Sentinel-2 image; the precision of the SRTM DEM is improved based on a DEM error prediction model of a Transform network, and a corrected DEM is generated; and constructing a multi-dimensional spatial analysis model based on the elevation, gradient and surface coverage type of the corrected DEM, and identifying spatial distribution characteristics and a terrain driving mechanism of the flood water body. According to the method, comprehensive analysis is performed in combination with terrain factors, and the spatial precision and timeliness of flood risk assessment can be improved.

The invention discloses an ecological environment intelligent pre-auditing method based on partition management and control and capacity control, and relates to the technical field of ecological environment informatization management. According to the method, parallel superposition is carried out on multiple types of ecological environment sensitive layers, dimension reduction processing and weighted integration are carried out on the multi-dimensional space conflict factors through principal component analysis, a quantifiable ecological sensitivity risk score is generated, the single logic that a traditional method can only judge whether overlapping exists or not is effectively overcome, fine expression of multi-layer composite conflicts is achieved, and the risk assessment efficiency is improved. And the resolution and judgment basis of spatial analysis are enhanced. A pollution factor-control unit-emission period three-dimensional capacity comparison model is established, and for capacity accounting requirements of multiple pollutant types, multiple space areas and multiple time dimensions, limitation of static threshold comparison of a traditional method is broken through, and dynamic adaptability and accuracy of capacity evaluation under a complex emission structure are realized.

The invention discloses a tourism highway landscape segmentation method and device and a readable storage medium, and the method comprises the steps: firstly, presetting visual sampling points along a highway, employing a multi-azimuth visual collection strategy, and collecting image data and space coordinate information of vegetation, a water body, an artificial structure and a natural landform under a preset illumination condition; dividing the landscape resources into a natural class, a humanity class and a composite class through a classification model based on the visual feature parameters; performing level division on landscape resources by adopting a multi-dimensional quantitative evaluation system and combining visual uniqueness, ecological integrity and culture identification degree; calculating spatial buffer distances of different levels of landscape resources through a landscape sensitivity analysis model; and finally, based on the spatial continuity, the level distribution characteristics and the spatial buffer distance of the landscape category, generating a landscape segmentation boundary of the tourist highway through a GIS spatial analysis tool. According to the method, the scientificity and reasonability of segmentation are improved, and technical support is provided for tourism highway landscape planning.

The invention discloses a data processing method applied to digital twinborn hydraulic engineering, and relates to the technical field of hydraulic engineering, the decision reliability of the digital twinborn hydraulic engineering in a rainstorm scene is significantly improved through algorithm architecture reconstruction, a two-channel cooperation mechanism deeply couples water level time sequence monitoring and rainfall space analysis, and the accuracy of data processing is improved. A graph convolutional network is used for extracting drainage basin level rainfall thermodynamic diagram features, self-attention dynamic noise filtering is combined, space-time alignment of meteorological data and ground sensing is achieved, false alarms caused by equipment interference or data asynchronization are restrained from the source, it is ensured that an early warning instruction is only triggered under the real flood situation condition, and the early warning efficiency is improved. The edge intelligent closed-loop decision breaks through the cloud dependence bottleneck, the lightweight model locally generates a scheduling strategy at a gate station node, the reinforcement learning driven gate control takes the water level deviation as an optimization target, and the loss of frequent actions on equipment is synchronously constrained, so that the flood discharge response is changed from passive rule execution to active risk stabilization.

The invention discloses a three-dimensional quantitative dynamic checking method for the flood prevention capacity of a transformer substation based on digital twinning and multi-source coupling, and belongs to the technical field of assessment and checking of the flood prevention capacity of the transformer substation. Multi-source data such as high-resolution satellite remote sensing, unmanned aerial vehicle laser radar, GIS spatial analysis and hydrological monitoring are integrated, a transformer substation digital twin and distributed hydrological-hydrodynamic coupling model is constructed, and a disaster-causing mechanism three-dimensional quantitative index system covering danger, exposure and vulnerability is established. According to the technical scheme, a full-process technical closed loop of data acquisition, model simulation, quantitative checking, three-dimensional visualization and dynamic early warning is realized, different scenes such as regions lacking data and transformed substations are adapted, the flood prevention capability level, weak point distribution and flood risk evolution situation of the substations are accurately output, and scientific support is provided for flood prevention transformation design and emergency disposal.

The invention discloses a plot soil pollution condition survey information acquisition and analysis system, relates to the technical field of plot soil information acquisition and analysis, and aims to solve the problem of unclear soil pollution condition analysis. According to the method, abstract sample detection data is converted into a law conclusion with spatial significance through accurate association, standard processing, visual display and deep analysis, the accuracy and systematicness of spatial analysis are ensured, the practicability of a result is enhanced through visualization, and the accuracy and accuracy of the result are improved by combining the spatial distribution law of sample analysis. A pollution core area is delimited for an aggregation area, key parameters are counted, classified implementation of target area, physical and chemical property and pollutant detection is provided for traceability and key treatment, various sample sensors are accurately matched with detection items, a full-dimension detection system of basic attributes and pollution characteristics is formed, and the detection accuracy is improved. The grid point distribution method or the snakelike point distribution method is combined with the marked GPS coordinates, the sampling depth and the serial number, so that the point location distribution is uniform, and the space positioning traceability of the sample is realized.

The invention discloses a station site selection and leveling design collaborative decision-making method and system based on spatial analysis, and belongs to the technical field of constructional engineering.According to the method, through topographic data processing, candidate station range screening, station and slope collaborative design, optimization adjustment and iterative optimization, and through combination of high-precision topographic data, topographic classification and a topographic analysis algorithm, the site selection and leveling design collaborative decision-making efficiency is improved. Areas suitable for construction stations can be accurately screened out, unavailable lands can be effectively avoided, and risks are reduced; according to the method, the automatic collaborative optimization of the station position and the slope design is realized, the mutual influence among the station parameters, the slope parameters and various constraint conditions is fully considered, the iterative optimization is carried out by taking the minimum leveling cost as the target, and the station construction cost is effectively reduced.

The invention discloses a mountain torrent disaster high-danger area quantitative identification method based on ensemble learning, and belongs to the field of mountain torrent disaster prevention and control, and the method comprises the steps: building a mountain torrent disaster danger influence factor database of disaster-inducing factors-disaster-pregnancy environments-disaster-bearing bodies based on a disaster system theory; an HEC-RAS two-dimensional hydrodynamic model is constructed to simulate flood elements under rainfall in different scenes, a multi-scale evaluation unit and GIS spatial analysis are combined, a dynamic evaluation method based on submerging elements and a danger level threshold value is established, mountain torrent disaster risk evaluation of a multi-dimensional disaster-bearing body is achieved, and the influence of rainfall characteristics on the disaster danger degree is analyzed; scene rainfall characteristics and disaster-pregnant environment factors are integrated as input variables, a training data set is constructed through index screening and discretization processing, and a disaster-bearing body-oriented mountain torrent disaster risk assessment model is constructed based on a Stacking algorithm framework. Through the technical scheme of the invention, the timeliness of mountain torrent disaster dynamic danger assessment is remarkably improved.

The invention discloses an ore body prediction method based on three-dimensional geological modeling and geological big data analysis. The method comprises the following steps: S1, establishing a database of a deep edge research area of a mine; s2, establishing a three-dimensional geological model of the research area; s3, constructing a three-dimensional intelligent prediction model of the demonstration area, taking a known ore body model as prior information and geology and geophysical variables as three-dimensional integrated information based on three-dimensional variable data of strata, ore bodies, fractures, geochemical exploration and geophysical exploration in the three-dimensional geologic model, and carrying out target area prediction work; and S4, delineating a prospecting target area, and delineating a favorable prospecting target area by utilizing the constructed three-dimensional intelligent prospecting prediction model and combining geological information of the research area. According to the method, the complementarity of multi-source heterogeneous data is fully utilized, and scientific basis and technical support are provided for quantitative prediction of the deep concealed ore body in combination with the spatial analysis capability of three-dimensional geological modeling and the high efficiency of big data analysis.

Methods and systems for identifying stormwater management opportunities in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, a method for identifying stormwater management opportunities comprises gathering spatial data for a region; rasterizing the spatial data into a set of raster data layers; forming a plurality of benefit maps using subsets of the set of raster data layers, wherein the plurality of benefit maps corresponds to a plurality of benefit categories; generating an opportunity map by combining a subset of the plurality of benefit maps using weights corresponding to the plurality of benefit categories; and displaying the opportunity map on a user interface.

A spatial analysis method for vector and raster data, applied on a server side, includes: receiving vector data and raster data from a client; performing gridded representation of the vector and raster data respectively in a reference coordinate system based on a first partitioning level to obtain grids related to the vector data and grids related to the raster data, wherein the first partitioning level is used to indicate the granularity level of grid division; and performing spatial analysis on the vector and raster data based on the grids related to the vector and raster data. In this way, by unifying the representation of both vector and raster data through gridded representation during spatial analysis, the spatial distribution of vector and raster data can be represented using data of a single modality, reducing the difficulty of cross-modal spatial analysis of vector and raster data. Furthermore, the method can filter out computationally irrelevant grids during spatial analysis, improving the efficiency of spatial analysis.

The application belongs to the technical field of height data analysis, and provides a DEM space model height point automatic extraction method, the application carries out clustering on the height data of sampling points by setting different clustering radii, judges the terrain integrity of each clustering range, and divides subspaces; analyzes the reference value of different subspaces on the height interpolation of the projection position point, obtains the reference height value of the projection position point; then analyzes the distance interval condition of each height data in the same region in different subspaces, adjusts the coordinate position of the projection position point, and obtains the corrected DEM space model; finally, the difference condition between the water flow trend and the actual terrain in the corrected DEM space model is analyzed, the reference height value of part of the projection position points is adjusted, and the twice corrected DEM space model is obtained. The accuracy of the interpolation height points of different positions is improved by the method, and the accuracy of the DEM space model construction is improved.

The invention discloses a laterite nickel ore resource management method. The method comprises the steps of multi-source data integration and three-dimensional geological modeling, modular mining design and boundary optimization, refined mining and grade control, and digital ore blending and production monitoring. According to the method, a multi-software cooperative working platform is constructed, and three-dimensional geological modeling and spatial analysis technologies are integrated, so that a high-precision geological model is established; on the basis of the high-precision geologic model, a modular mining design and a dynamic road system are innovatively adopted to realize high efficiency and flexibility of the mining process; meanwhile, an ore dilution prediction model is established through a big data analysis technology, grade control and ore blending optimization are carried out on the whole process of ore from mining to transportation, and stable quality of a final product is ensured.

A finite element-based ore deposit drilling information processing and analysis method and device are described herein. The information processing method includes: dividing finite elements based on related information of an exploration borehole, and constructing corresponding sectional finite element planes; and performing three-dimensional (3D) spatial co-position stacking on the sectional finite element planes, and constructing scanning analytic finite element profiles through spatial analysis. According to the method, geological borehole information can be visually analyzed and presented in a sectional and layered mode, and the method has excellent practical value and indicating significance for accurate delineation of prospecting target areas, accurate mining, dressing and smelting, and structural metallogenic research.

The invention discloses an artificial wetland space optimization arrangement method and system, a non-point source pollution model is constructed based on a hydrological response unit, target meteorological data, target pollution data and an SWAT model, and the simulation precision of the non-point source pollution process can be improved. Climate scene data is constructed through future climate data, a preset path rule and a simple quantile algorithm, and the influence of climate change on pollution load can be reflected. Furthermore, a wetland optimization model is constructed based on a non-point source pollution model and a target function, the scientificity of an optimization strategy is enhanced, candidate wetland areas are identified by a spatial analysis algorithm based on a complex terrain index CTI, and the maximum potential area of each candidate wetland area is counted, so that efficient identification of potential arrangement areas can be realized. The constructed wetland optimization regulation and control scheme is generated based on the preset genetic algorithm, and the constructed wetland space optimization arrangement is performed according to the scheme, so that the optimization and practicability of the constructed wetland layout can be realized.

The invention relates to a spatial analysis single cell state modeling method and system based on domain self-adaption and layered fine tuning, and the method comprises the steps: obtaining multiple immunofluorescence images and single cell segmentation masks, and constructing a no-label data set; constructing a mask auto-encoder composed of a ViT encoder and a linear decoder, adding a classification token in front of the image, carrying out field adaptive training on the mask auto-encoder based on the unlabeled data set, learning the classification token, and obtaining a field adaptive weight of the ViT encoder; obtaining a labeled data set; constructing a state embedding generation model, wherein the state embedding generation model comprises a shared ViT backbone network and a two-stage classifier; a classification token is added in front of an image feature sequence in the labeled data set, hierarchical training is carried out on the state embedding generation model, and the classification token is learned; and inputting the cell image blocks into the trained state embedding generation model, outputting a classification result and cell state embedding, and carrying out interpretability analysis. Compared with the prior art, the method has the advantages that accurate cell classification can be realized, and cell state representation with biological interpretability can be generated.

The application discloses a method for determining optimal positions of synchronous differential base stations for aerial remote sensing flight, and belongs to the technical field of aerial remote sensing. The method comprises the following steps: performing spatial analysis on vector data of a working area, quantitatively calculating distance average distance and maximum distance parameters, calculating average distances of each position from the working area within a given maximum base station distance threshold range by using python, outputting results, performing spatial analysis on the evaluation distance results and road data, extracting a range with a certain threshold distance from the road, and combining surface land cover classification data and road vector data according to base station erection requirements of different scales, surface cover, water area and forest area which are not suitable for base station erection, open source DEM data, and the optimal base station erection position or the nearest position of the base station erection is circled, so as to guarantee the differential processing precision of aerial remote sensing POS data.

The invention discloses a building vector graph multi-scene intelligent simplification method and system, and belongs to the technical field of geographic information systems. Comprising the following steps: establishing a four-level data system comprising an original data layer, an engineering cache layer, a display cache layer and a terminal view layer; monitoring data change and user interaction events through an event bus; the event is responded, the current application scene is intelligently recognized, and the corresponding simplified parameter configuration is dynamically loaded; based on the configuration, executing simplification logic on the target building vector graph; querying a topological relation to carry out cooperative processing on the contours of the adjacent buildings; and synchronously updating a processing result to the four-level data system. Through four-stage data caching, dual-drive triggering and scene self-adaptive judgment, efficient, high-quality and self-adaptive processing of building simplification in multiple scenes such as cartographic integration, real-time display and spatial analysis is realized, and the system flexibility, the resource utilization rate and the user experience are improved.

The invention relates to the technical field of underground structure displacement measurement, and discloses a structure displacement monitoring method and system based on an intelligent sensor. The method comprises the following steps: acquiring an initial inclination angle signal, an acceleration signal and a gyroscope static signal; generating a zero initial depth reference point; constructing a depth time sequence; the fuzzy logic controller FLC executes real-time compensation adjustment; constructing an equal-interval sampling point sequence; and calculating accumulative displacement based on a weight curvature corrected dual-channel displacement fusion mechanism. The technical problem that in the prior art, a displacement estimation method depending on tilt angle sensor integration or acceleration sensor accumulation is difficult to realize high-precision structural displacement measurement especially in a soft soil layer scene with multi-source signal drift and probe speed fluctuation is solved. Due to the fact that a speed increment adjusting mechanism driven by the fuzzy logic controller is introduced, dynamic deviation correction and depth displacement decoupling modeling of multi-sensor data are achieved, and the spatial analysis precision of a displacement monitoring result is improved.

This application relates to the fields of artificial intelligence and computer technology, specifically providing a risk decision-making method and system for the entire lifecycle of power data. The method includes: acquiring multi-source heterogeneous log data from the entire lifecycle of a power information system; using an unsupervised feature extraction module based on a variational autoencoder to extract latent feature vectors and determine the VAE reconstruction error; performing spatiotemporal feature extraction on the latent feature vectors to obtain the temporal behavior probability from the time-series analysis stream and the spatial graph embedding distance from the spatial analysis stream; and constructing a dynamic decision-making module based on a Bayesian network, using the VAE reconstruction error, temporal behavior probability, and spatial graph embedding distance as multi-source evidence nodes for the dynamic decision-making module to calculate the posterior probability of risk events. This application, through the collaborative fusion of spatiotemporal features and causal probabilistic inference, can significantly improve the safety risk perception and dynamic control capabilities of power data throughout the entire process of acquisition, transmission, and processing.

This invention discloses a method for selecting airdrop areas in high-altitude mountainous air transport. Based on data using raster images, raster pyramids, and vector hybrid data, and after dimensionless data processing, this invention pre-selects all candidate areas through GIS spatial analysis and constructs an evaluation index system that comprehensively considers the necessary specific needs for airdrop area selection in high-altitude air transport logistics. Then, the AHP decision analysis method is used to select candidate areas within the analysis scope and calculate evaluation values. Finally, K-Means clustering is used to group the candidate areas through iterative calculation, realizing a data-driven method for accurate airdrop area selection in high-altitude mountainous areas. The K-Means clustering grouping, calculated through iterative calculation, serves as the final result, providing a reasonable basis for site selection decisions. This invention offers accurate site selection results and good programming support, and can be widely applied in air transport, emergency management, and other departments to conduct airdrop area site selection data integration, information system construction, and new theoretical research.

The invention provides a karst basin underground water vertical layering resource quantity evaluation method, and relates to the technical field of hydrogeology, and the method comprises the following steps: S1, dividing a karst underground water vertical zone, and sequentially dividing a research area into four areas from top to bottom according to stratum lithology, karst development degree and drainage point elevation; s2, calculating the distribution area of each region on the elevation by using the spatial analysis function of arcgis software; s3, according to field survey unified measurement data, spring points with different flows in different areas are selected, and then the spring domain range of each area is delineated, so that the average runoff modulus of each area is determined; s4, recalculating the underground water resource quantity of each region according to the area of each region and the corresponding average runoff modulus; and S5, generating a vertical distribution evaluation result according to the underground water resource quantity of each region. The method can make up the defects of the technology at the present stage by vertically obtaining the water resource quantity, and is suitable for popularization.

The invention belongs to the technical field of forest fire monitoring and early warning, and particularly relates to an extreme climate prediction method based on a large warm period weighted FWI index and application. According to the specific technical scheme, the method comprises the following steps: acquiring climate data of a great warm period, and calculating FWI index components in each time period according to the climate data of the great warm period; the method comprises the following steps: acquiring forest vegetation data and topographic data by utilizing satellite remote sensing, performing space matching on the forest vegetation data and the topographic data, and calculating a vegetation-topographic collaborative weight factor; calculating a weighted FWI index based on the vegetation-terrain collaborative weight factor; and analyzing the relationship between the weighted FWI index and the occurrence frequency, intensity and duration of the extreme climate event by adopting a time sequence analysis method and a space analysis method, and visualizing the analysis result. The application blank of vegetation and topographic factors in large warm period FWI calculation is filled, so that the scientificity and accuracy of extreme climate prediction are improved.

The application discloses an automatic generation method of an invasive species prevention and control grade map, comprising the following steps: acquiring suitable habitat data of a target species in a target area and spatial data of at least two human interference factors; respectively performing spatial analysis on the suitable habitat data and the data of each human interference factor, dividing a risk grade, and obtaining a plurality of single-factor risk grade maps; weighting and superimposing the plurality of single-factor risk grade maps, calculating a comprehensive risk distribution according to a comprehensive evaluation rule, and generating a prevention and control grade map through reclassification; the method can be encapsulated as a geographic processing model to realize one-key automatic generation. Through systematic integration of natural suitable habitats and human interference factor systems, the application solves the problems of the existing prevention and control division methods, such as disconnection with actual conditions, difficulty in quickly responding to multi-scenario analysis, strong subjectivity and low repeatability, and realizes high-precision fitting of the division results with actual conditions, as well as an objective, stable and repeatable division process.

The application discloses an intelligent monitoring system for Eriocheir sinensis breeding environment, and particularly relates to the technical field of intelligent monitoring of aquaculture, which collects data through multiple climbing cage monitoring modules, calculates average weight according to the quantity and weight of image recognition and corrects the climbing cage frequency, carries out spatial analysis based on the corrected frequency to obtain a spatial aggregation global index and a local aggregation index, fuses to generate a spatial aggregation degree characteristic value, compares and calculates a deviation degree index by a deviation calculation module, and selectively starts the regulation and control equipment of different spatial regions according to the deviation degree index size by a regulation and control execution module; the climbing cage frequency is corrected through image and weight data, the single semantic problem of frequency data is solved, the spatial aggregation degree characteristic value is compared with the historical benchmark, the dynamic correlation between the environment and the behavior is realized, the deviation degree index is classified and partitioned for regulation and control, the execution lag problem is solved, and the precise intervention and energy consumption optimization are realized.

The application belongs to the field of spatial analysis, and is a dynamic multi-dimensional industrial space chain analysis and evaluation method, comprising the following steps: S1, collecting data to form a dynamic multi-source industrial space data set containing extended attributes after preprocessing; S2, fusing the dynamic multi-source industrial space data set with high-precision GIS data, industrial chain basic topology and remote sensing image data, updating the industrial chain topology relationship and optimizing the fusion features to form a dynamic fusion data set; S3, constructing a multi-target industrial space structure algorithm containing economic and environmental protection targets based on the dynamic fusion data set, realizing real-time iterative optimization and integrating an abnormal early warning function; S4, calculating a multi-dimensional agglomeration effect index through the multi-target real-time optimization algorithm, simulating industrial migration paths under multi-policy scenarios and visualizing; S5, carrying out two-dimensional coupling evaluation of sustainability and economy based on the scenario simulation results, generating feedback suggestions and optimizing the previous steps.