108 results about "Spacial Analysis" patented technology

The invention discloses a territorial space planning optimization method and system based on three-dimensional modeling, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: obtaining multi-source territorial space data, constructing a three-dimensional space data set, carrying out the three-dimensional geometric modeling, and generating a territorial space three-dimensional model; carrying out multi-dimensional space analysis on the land space three-dimensional model, obtaining a space conflict feature set, carrying out planning constraint, and formulating a space optimization suggestion; executing the space optimization suggestion to perform three-dimensional dynamic modeling, generating a multi-time sequence planning simulation result to perform space planning evaluation, generating a land space planning score, performing planning compensation based on the backtracking space optimization suggestion, updating the space optimization suggestion, and obtaining a space planning optimization scheme. The technical problem that in the prior art, land space planning depends on two-dimensional data, the space conflict and the dynamic evolution process are difficult to comprehensively recognize, and the planning scheme is insufficient in scientificity is solved, and the technical effect of improving the space conflict recognition precision and the planning decision scientificity is achieved.

The invention provides a multi-source information fusion data spatial analysis method and system based on a knowledge graph, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the quality calibration and fusion of multi-source heterogeneous data, and obtaining standardized fusion data; a city system knowledge graph is constructed, the city system knowledge graph comprises city element nodes with category and hierarchical relations and association strength of the city element nodes, and city function communities are constructed and optimized through a modularity optimization method; an urban element diffusion wave equation model is constructed based on an urban system knowledge graph, an urban element state propagation rule is analyzed, urban function division is performed, an urban system coupling differential equation set model is established to predict urban element state change, an urban system vulnerability index is constructed, and urban system toughness analysis is performed. According to the method, multi-source city data can be effectively fused, the city system knowledge graph is constructed, and decision support is provided for city space planning and management.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention belongs to the technical field of public safety monitoring, and particularly relates to an intelligent monitoring system and method based on vision and language fusion. According to the method, multi-dimensional spatial analysis is carried out on a space-time sequence image, hierarchical feature information can be extracted, context association analysis is carried out on text data, semantic coding information is generated, then image features and text entity description are in butt joint through a multi-stage interaction channel of the hierarchical feature information and the semantic coding information, and the text entity description is extracted. And performing trend consistency calibration, eliminating phase deviation on a time sequence, predicting a future trend of hierarchical features and semantic coding, generating joint characterization data, dynamically correcting evaluation boundary parameters by combining environment feedback parameters on the basis, and finally evaluating the current joint characterization data according to the corrected evaluation boundary parameters. And determining the public safety state level in the target scene, thereby realizing the intelligent monitoring process of the public safety information in the target scene.

The invention relates to the technical field of data processing, and discloses a data processing method and application based on multi-source data fusion. The method comprises the following steps: acquiring multi-source time sequence data (including sensor, geographic space, user behavior data and the like) and space vector base map data of a target area; de-noising and normalizing the multi-source time series data, extracting an associated feature map through a heterogeneous model, generating a regional data object set based on base map adaptive segmentation, and extracting multi-dimensional features; optimizing a data object set through spatial topology verification, feature matching screening and weighted fusion; and generating statistics, space-time coupling analysis and a dynamic prediction result by utilizing optimized data, or generating a visual map supporting interaction by mapping a base map. The method improves the multi-source data fusion precision and spatial analysis capability, is suitable for multi-field cross-modal decision support, and can be used for scenes of smart cities, environment monitoring and the like.

The invention discloses a new quality productivity space analysis method based on machine learning, relates to the technical field of urban planning, and constructs a new quality productivity space index system, constructs a plurality of machine learning models for optimization, and provides a new quality productivity space analysis method based on machine learning. A machine learning model with the best prediction and generalization effect is selected to analyze the influence of each element on the new quality productivity space, an SHAP model is used for explaining and analyzing, and an SHAP value explaining mechanism for calculating the marginal contribution degree of each feature based on the Shapley value theory is introduced; the contribution degree of key indexes such as enterprise facility density and night light intensity to new quality productivity space distribution is quantitatively analyzed, and collaborative optimization of a machine learning model in prediction precision and interpretability is achieved; and in combination with spatial visualization analysis, a priority intervention basis is provided for urban planning, and finally, accurate quantitative analysis of a nonlinear effect and an element interaction effect is realized.

The invention relates to the technical field of value accounting, and discloses an ecological system total production value accounting method based on GIS spatial analysis, and the method comprises the steps: carrying out the accounting of regional basic data and satellite image data, and constructing a bidirectional dynamic semantic mapping library corresponding to land change investigation land parcel classification-ecological system type; internet of Things monitoring data and statistical report data are obtained, and a standardized data set is obtained through space-time fusion of spatial interpolation-boundary correction and a sliding window smoothing method; constructing an urban area-typical district double-level accounting index system, and synchronously calculating a real object quantity and a value quantity; a model correction factor is called based on a GIS, indexes are quantified to grid units, a result is obtained through neighborhood analysis, and after parameters are calibrated, a multi-year ecological value trend map is generated in combination with the GIS; and outputting a customized result according to scenes such as ecological compensation, and outputting a final result after the customized result is qualified through three-level quality control verification. According to the method, the requirements of practical affairs such as ecological compensation and EOD projects on accounting precision and practicability can be met.

The invention discloses an agricultural drought risk assessment method. The method comprises the following steps: dividing a target area into a plurality of assessment units according to geographic space raster data of the target area; agricultural disaster damage data of each evaluation unit is collected, and a quantitative agricultural loss rate is obtained by adopting a K-Means classification method and through moisture-yield coefficient correction; the total output value of the target area and the agricultural output value and the regional output value of each evaluation unit are extracted, an agricultural exposure degree model is constructed, and the agricultural exposure degree of the target area is obtained; constructing an agricultural drought risk assessment model by using the drought risk, the agricultural loss rate and the agricultural exposure degree, and obtaining agricultural drought risk pattern grid data of each assessment unit by using a neighborhood spatial analysis-main filtering method and a spatial difference method; and distributing the agricultural drought risk pattern grid data of each evaluation unit in a target area according to geographic space grid data of the target area to obtain an agricultural drought risk pattern of the target area. The agricultural drought risk is accurately quantified, high-vulnerability and high-risk regions are defined, and a scientific basis is provided for agricultural resource allocation optimization, disaster prevention and reduction strategy formulation and drought emergency management.

The invention discloses an urban inland inundation simulation prediction method and system based on interpretable machine learning, and the method comprises the steps: obtaining multi-driving-factor data, and obtaining urban inland inundation data through the combination of physical model simulation; constructing a spatial analysis unit of the city, taking multi-driving factor data as an input feature and waterlogging data as an output target variable, training the candidate machine learning models, comparing the prediction precision of each candidate machine learning model which is completely trained, and determining an optimal prediction model; and based on the optimal prediction model, key driving factors and a nonlinear influence mechanism thereof are identified in combination with an interpretability method, the influence of interaction among different factors on the waterlogging risk is analyzed, and finally, a prevention and control and treatment scheme is formulated and output. According to the method, the specific influence of each driving factor on urban waterlogging and the interaction relationship among the factors are disclosed, and a scientific basis is provided for urban climate risk treatment and prevention and control.

The invention discloses a rural land utilization evaluation system and method, and relates to the technical field of land management, and the system comprises a spatial analysis module which obtains a spatial distance value, a land utilization type value and a regional distribution value based on a land unit division result, carries out the superposition calculation of a spatial incidence relation matrix, and extracts the conflict intensity. According to the method, through comprehensive calculation of the spatial distance, the land utilization type and the regional distribution, the land conflict intensity and the dynamic characteristics thereof are quantitatively analyzed, the problem that the land resource conflict distribution is not clearly identified is solved, in the dynamic prediction process, the land dynamic characteristics are quantified through space and ecological connectivity difference operation, and the prediction accuracy is improved. According to the method, land utilization change requirements, economic value and ecological connectivity normalization processing and weighted calculation are effectively coped with, dynamic balance of economic benefits and ecological protection is achieved, limitation of single-dimension evaluation is avoided, and reasonable allocation of land resources is strengthened by adjusting the proportion of economic and ecological parameters through optimization verification and reducing evaluation deviation.

The invention discloses a database-based space conflict treatment method and device and a medium, and relates to the technical field of electric digital data processing. The method comprises the steps of determining conflicting pattern spots with conflicts in space pattern spots through a preset space analysis function according to a geometrical relationship between the space pattern spots corresponding to a planning land in a space database; analyzing the spatial relationship and the ownership planning relationship corresponding to the conflict pattern spots to determine the conflict intensity corresponding to each conflict pattern spot; based on the spatial relationship network, determining a hotspot conflict area with spatial aggregation according to the conflict intensity; determining treatment priorities corresponding to the conflict pattern spots according to a descending order of the conflict intensity, and adjusting the treatment priorities of the conflict pattern spots according to conflict linkage paths among the conflict pattern spots in the hotspot conflict area; and generating a space governance path in the hotspot conflict area through the adjusted governance priority, so as to realize the space conflict governance of the planned land through the space governance path.

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

The invention discloses a space-time carbon flux digital map construction method, which comprises the steps of three-dimensional vector information acquisition, space-time carbon flux accounting database establishment, carbon flux space-time analysis, carbon flux influence factor identification and space-time carbon flux digital map display. Real-time and high-precision carbon flux data of a target area are measured through a carbon flux measurement system, a space-time carbon flux accounting database is constructed, carbon flux time analysis and space analysis are carried out to judge the carbon flux space-time distribution trend of the measured area, and a carbon flux space-time geographic weighting model is used for identifying carbon flux influence factors. A carbon flux digital map is displayed on the large digital screen in real time; according to the method, high-time and high-space-precision carbon flux accounting and influence factor identification of the measurement area can be realized, and detection, management and prediction of the space-time carbon flux of the target area are realized in a precise quantification, visualization and artificial intelligence manner.

The invention provides an AI-based security risk analysis method, and relates to the technical field of data processing, and the method comprises the steps: dynamically determining three spatial reference positions through a spatial topological relation analysis algorithm based on spatial coordinates in a business data result; generating a dynamic risk partition based on the spatial reference position; executing a space grid division operation on the dynamic risk partition to form a discretized space analysis unit; calculating a grid deformation gradient tensor as a risk distribution adjustment value based on the geometric deformation parameters of the spatial analysis unit; and generating spatial risk thermodynamic data points describing spatial risk distribution according to the structured semantic information, the knowledge enhancement context, the business data result and the risk distribution adjustment value. According to the method, the risk analysis target, the key entity and the spatial constraint condition can be accurately extracted, and the convenience of man-machine interaction and the accuracy of semantic understanding are improved.

The invention discloses a power transmission line path planning method and system based on a natural language and a knowledge graph, and the method comprises the steps: analyzing a natural language path planning target through a large language model, obtaining an intention classification result and a named entity, mapping the intention classification result and the named entity to a business rule knowledge graph for reasoning, and generating a task step sequence; receiving confirmation or modification of a user on each task step to obtain each target task step; searching a combination corresponding to each target task step from the GIS operation knowledge graph by utilizing a large language model, and disassembling the combination into a GIS operator call chain; and calling a corresponding GIS tool through an MCP protocol, executing spatial analysis operation in the GIS operator calling chain corresponding to each target task step according to the sequence of all the target task steps, and generating a path planning result of the power transmission line. According to the invention, full automation, process interpretability and man-machine interaction cooperation of power transmission line path planning are realized, and the power transmission line path planning efficiency is improved.

The invention provides an urban rail transit station development intensity multi-objective optimization method in a TOD mode, and relates to the technical field of urban planning and intelligent transportation, and the method comprises the steps: analyzing the spatial relation between various types of land and urban function nodes according to the data after standardization processing, and carrying out the location value evaluation; constructing a development intensity evaluation model according to the location value evaluation, calculating the initial average plot ratio of each type of land, and deducing an initial development intensity parameter in combination with the current situation data; establishing a development intensity optimization target system according to a TOD planning principle, performing iterative optimization on the initial development intensity parameters by adopting a multi-target genetic algorithm, and outputting an optimal solution set; and carrying out comparative analysis on the optimal solution set and a current planning index, and obtaining a plot ratio index in combination with a regional development planning requirement. According to the method, a technical framework of subjective and objective collaborative optimization can be constructed by fusing GIS spatial analysis, an analytic hierarchy process and a non-dominated sorting genetic algorithm.

The invention relates to the technical field of computer-aided planning, and discloses a territorial space planning decision-making method and system based on multi-dimensional influence assessment, and the method sequentially comprises the steps: multi-source data fusion and problem diagnosis, multi-dimensional influence quantitative assessment, scheme collaborative optimization, and scheme comprehensive screening and decision-making. The system corresponds to the method. According to the method, through multi-source data fusion processing, the data reliability is improved, and a solid foundation is provided for subsequent problem identification; according to a quantitative index and a GIS spatial analysis technology, key problem region identification is more targeted, and the defect of generalization adjustment of a traditional qualitative method is avoided; through traffic, environment and municipal dimension parallel evaluation, the efficiency bottleneck of serial evaluation is avoided, and the evaluation period is shortened; a Pareto candidate scheme set generated through a multi-objective evolutionary algorithm is combined with a comprehensive screening mechanism to ensure that a final scheme is balanced between performance requirements and constraint conditions, and the feasibility of planning landing is enhanced.

The invention particularly relates to a spatial analysis method based on large model agent driving. According to the spatial analysis method based on large model agent driving, syntactic analysis and intention recognition are carried out on a spatial problem described by a natural language through a large model, and spatial-temporal clustering, network analysis and a field simulation algorithm module are dynamically matched; packaging a heterogeneous algorithm into a standardized service component based on a model context protocol (MCP), and automatically constructing an analysis process chain; and integrating a GIS engine and a visual template library, generating a dynamic thematic map according to the processed spatio-temporal data through the visual engine, and superposing multi-dimensional analysis annotations. According to the spatial analysis method based on large model agent driving, the full-chain intelligent capability from data processing to decision support is constructed, the efficiency and precision of spatio-temporal evolution modeling are remarkably improved, meanwhile, the system integration and maintenance cost is reduced, the method is suitable for high-complexity scenes needing real-time spatio-temporal data analysis, and the method is suitable for popularization and application. And the method has remarkable technical innovation and industrial application value.

The invention relates to a water environment knowledge graph construction method and device based on time and space, and relates to the technical field of water environment data processing, and the method comprises the steps: processing and integrating data based on multi-source data related to a water environment, taking the key attribute of each data as a reference, carrying out the analysis and series connection of entities, and generating the entities, and constructing an entity data model, and determining a spatial relationship and a time sequence relationship through spatial analysis and time sequence analysis between entities. And finally, performing space correlation optimization on each entity according to the space relationship, performing time correlation optimization on each entity according to the time sequence relationship, and further combining space-time correlation optimization to establish a multi-dimensional water environment knowledge graph. Therefore, according to the embodiment, the knowledge graph comprehensively considering the time and space characteristics is established according to the complexity requirement of the water environment information, and the knowledge graph can be used for achieving comprehensive monitoring and management of the water environment.

The invention discloses a GeoJSON-oriented geospatial analysis execution method and system, and relates to the technical field of geographic information science and artificial intelligence. One embodiment of the method comprises the following steps: acquiring a GeoJSON file to be processed; acquiring a natural language task instruction input by a user, performing semantic understanding on the task instruction by using a first language model, extracting an operation target and a constraint condition, and generating an overall task; disassembling the whole task into a plurality of logically ordered sub-tasks to form a sub-task sequence; calling a function library API or generating a script code, executing the subtask, and generating a subtask result; continuously receiving and evaluating the generated sub-task results, and updating the sub-tasks which are not executed completely according to the evaluation result of the sub-task results until all the sub-tasks are completed; and integrating the subtask results to generate an output result. In this way, efficient, reliable and controllable automated geospatial analysis driven by natural languages is achieved.

The invention relates to the technical field of building information analysis, and discloses a space design optimization method and system based on three-dimensional voxelization, a terminal and a storage medium, and the method comprises the steps: determining a three-dimensional coordinate according to a building planning scheme, and converting the three-dimensional coordinate into a voxel grid, so as to obtain a voxel code of each three-dimensional coordinate; obtaining rule information, constructing voxels corresponding to the rule information on the voxel codes, and according to the compliance rule, performing weighting on the rule information; and determining the voxel density of the voxel code according to the weight so as to predict a potential problem at the voxel code, thereby performing real-time adjustment on the building planning scheme. According to the method, through the spatial analysis process of three-dimensional voxelization, multi-source data are fused and weighted through voxels, automatic optimization of each type of data is achieved, and high-precision, dynamic and multi-target spatial analysis and scheme optimization are achieved.

The invention relates to the technical field of network geographic information systems, and discloses a visual modeling method, system and device for a spatial analysis process and a medium. The method comprises the following steps: constructing a visual component library; in response to a canvas interaction instruction input by a user, performing parameter configuration and component connection based on the visual component library to obtain workflow logic of the canvas; analyzing the workflow logic in real time based on a directed acyclic graph to generate a spatial analysis code and obtain an analysis result of the spatial analysis code; and performing result visualization and export on the analysis result based on the visual component library. According to the method, component dragging, parameter configuration and connection mechanisms suitable for WebGIS spatial analysis are defined, and visual workflow construction is achieved. Based on a directed acyclic graph process analysis algorithm, efficient and executable spatial analysis codes are automatically generated, and dynamic optimization and parallel computing are supported, so that visual modeling and automatic execution of a spatial analysis process are realized.

The invention discloses a backtracking algorithm-based large equipment room position searching method, equipment and medium, and the method comprises the steps: obtaining a large equipment room candidate position set generated through spatial analysis, and building a candidate position hierarchical evaluation mechanism; the hierarchical evaluation mechanism comprises a hard constraint layer, a soft constraint layer and an optimization target layer; constructing a weighted directed graph based on the candidate positions satisfying the hard constraint layer, and determining the weight of each node in the weighted directed graph based on the comprehensive evaluation result of the soft constraint layer and the optimization target layer; nodes in the weighted directed graph represent candidate positions and virtual nodes of each room; constructing an unweighted undirected avoidance graph, and identifying a spatial conflict relationship among all candidate positions; and searching an optimal path in the weighted directed graph and the unweighted undirected avoidance graph through a backtracking algorithm in combination with a heuristic rule, generating a position combination of the large equipment rooms, eliminating virtual nodes in the position combination, and restoring to a final arrangement scheme of the positions of the large equipment rooms.

The invention discloses a city-edge-rural regional structure identification method based on stacked ensemble learning, and belongs to the field of geographic information systems and artificial intelligence, and the method comprises the steps: determining an optimal identification scale through comprehensively analyzing the statistical characteristics of multi-source geographic index feature data on different spatial grid scales; on the optimal identification scale grid, a training set is constructed through manual labeling, an integrated learning model of a diversified unsupervised clustering base model and meta-model fusion strategy is constructed, and based on the training set, the constructed integrated learning model is trained, evaluated and optimized to obtain a final integrated learning model. And precise division of urban, marginal area and rural regional space structures is realized. On the basis of fully utilizing the diversity of the base model and dynamic weight adjustment, the classification precision is remarkably improved, the modular design and the self-adaptive adjustment mechanism can flexibly adapt to geographical environments of different regions, and scientific and reliable spatial analysis support is provided for region development and planning decision.

The invention relates to the technical field of data analysis, in particular to a land resource management data analysis method and system based on cloud computing, and the method comprises the following steps: obtaining a difference time node plot number and a purpose field, recognizing a number evolution chain and establishing mapping, extracting a land class sequence to generate a stability label, analyzing the spatial continuity to construct an attribution path, and analyzing the attribution path. And indexing a utilization rate fluctuation trend paragraph, and identifying a purpose fusion direction to generate a fusion layer set. According to the method, through joint comparison of a serial number structure, coordinate overlapping and a purpose field, a stable land class transition chain is established, it is ensured that land class time sequence features are effectively described, the contiguous direction of a space dot matrix and the consistency of land class affiliation are combined, splicing of the space affiliation relation between land parcels is achieved on the regional level, and the splicing efficiency of the space affiliation relation between land parcels is improved. The identification precision and the spatial expression definition of the land type change are improved, and the spatial analysis capability of the land type change trend and the dynamic perception capability of the land utilization efficiency are effectively enhanced.

The invention relates to the technical field of urban planning management, and particularly provides a cross-scale planning key point generation method and system based on NLP and knowledge graph reasoning, and the method comprises the steps: receiving and analyzing a natural language demand of a user, extracting entity information and an instruction intention, and converting the entity information and the instruction intention into a structured instruction; constructing a planning domain knowledge graph containing a planning index constraint relation, a cross-scale planning association rule and a cross-professional parameter mapping relation; extracting a target planning index from the structured instruction, and calling knowledge graph association reasoning to generate an index association set containing association indexes and an adjustment range; performing collaboration verification on the index association set, including cross-scale linkage verification based on upper planning data and GIS spatial analysis and adaptation verification based on knowledge graph cross-professional parameter mapping; and generating a structured planning key point report according to the index association set passing the verification. The method improves the planning key point generation efficiency and compliance, and is suitable for the scenes of urban planning, land development and the like.

The invention relates to the technical field of soil data processing, and discloses a soil data simulation method integrating machine learning and spatial analysis cooperation. The method comprises the following steps: acquiring multi-source soil data of a target area, determining spatial characteristic parameters of each soil data unit, calculating machine learning characteristic indexes and generating an analog input set; then, dynamic evolution parameters are determined by using a pre-trained machine learning model, and a collaborative simulation map is marked and generated in the spatial analysis model; and then performing data consistency detection, and simulating and generating soil data based on a detection result. And if the parameter conflict is detected, fusing the parameters according to the multi-source data priority. After the simulation result is generated, monitoring the attribute difference value of the soil data unit and the adjacent unit in real time, and dynamically correcting the parameter when the threshold value is exceeded. According to the method, multi-source data are integrated, machine learning and spatial analysis technologies are utilized, the precision and reliability of soil data simulation are improved, and powerful support is provided for soil research and related fields.

The invention relates to a ship encountering sector space risk degree space analysis method, which comprises the following steps of: establishing a ship encountering sector space model by taking a ship as a center and taking a detection distance R set by a ship-borne radar as a radius; according to the time gradient range, a sea area space, threatening the main ship, of the target ship is established, judgment is carried out according to the approaching coefficient when the two ship fields are tangent, namely the scaling multiple when the distance is closest, and the scaling multiple lt when the distance is closest; 1, continuing the following steps; determining an encounter fan-shaped space set GP after the target ship G is segmented and an encounter fan-shaped space set QP after the main ship Q is segmented; preliminarily judging the relative positions of the two ships according to the DCPA of the ship and the target ship; the minimum vector distance of the two ships is determined according to the relative heading of the two ships, and the space approaching degree of the combined sector GP is calculated. The method is an analysis method which can simplify a ship encounter space vector structure and accurately excavate and extract encounter space situations and space features of ships in different directions.

The invention discloses a method for generating an intangible cultural heritage map under the assistance of artificial intelligence, and the method comprises the steps: firstly collecting non-immortal item information from a multi-source heterogeneous data source, associating a space-time tag, and extracting non-immortal gene features; associating the non-perpetual development data with the environmental data by using a knowledge extraction technology; three types of dynamic relation layers are constructed in real time by adopting natural language processing, conflict information is merged through a knowledge fusion technology, and data accuracy is guaranteed; according to the three types of entity relation layers, combining time axis slicing and GIS spatial analysis, and utilizing a sequence modeling algorithm to reconstruct a non-genetic gene cross-spatiotemporal propagation path; and finally, mapping the propagation path to a non-abandoned map, and dynamically displaying related coordinates in a highlight manner according to non-abandoned keywords input by tourists. According to the method, intelligent reconstruction and visualization of the non-genetic genetic propagation path are realized, personalized services are provided for tourists, and the method has great significance in promoting inheritance and propagation of non-genetic culture.