1439 results about "Spatial relationship" patented technology

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.

The described aspects and implementations enable online lane estimation and tracking in autonomous vehicle and driver assistance applications. In one implementation, disclosed is a method and a system to perform the method that includes receiving perception data indicative of driving lanes and reference objects in a driving environment of a vehicle, identifying target destinations corresponding to the driving lanes, determining candidate driving paths connecting a starting location with one of the target destinations, selecting a target driving path from the candidate driving paths based on geometric properties of each candidate driving path and a spatial relationship of a respective candidate driving path to the reference objects, and causing a motion of the vehicle to be modified based on the target driving path.

The invention relates to the technical field of building risk monitoring, in particular to an urban building safety evaluation method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a synthetic aperture radar image, an optical remote sensing image, a hyperspectral image, topographic data and building contour data of a to-be-evaluated region; based on the optical remote sensing image and the building contour data, identifying spatial form change information of each target building, and generating change parameters used for describing structure adjustment features; generating an interferogram based on the synthetic aperture radar image, extracting a corresponding deformation monitoring point, and generating a settlement parameter reflecting the displacement change of the building; based on the hyperspectral image and the topographic data, identifying spatial relationship information of the target building and the surrounding environment risk body, and generating external influence parameters; and based on the change parameter, the settlement parameter, the external influence parameter and the preset evaluation model, generating a safety evaluation result of each target building, thereby improving the efficiency of urban building safety evaluation.

The invention belongs to the technical field of three-dimensional scene modeling, and discloses a dual-branch diffusion three-dimensional scene generation method based on a multi-modal semantic graph, which comprises the following steps of: firstly, receiving multi-modal data such as sketches, texts, automatic completion instructions and scene general knowledge, extracting features and fusing the features into a unified multi-modal semantic graph; utilizing a graph neural network and an attention mechanism to enhance semantic graph features, and optimizing physical constraints through a physical engine; complementing the missing visual modality and graph structure relationship; performing quality scoring on the scene based on semantics, spatial relationships and physical constraints; and finally, respectively generating a spatial layout and a geometric shape through a double-branch diffusion model, and ensuring the coordination of the layout and the shape. The method has the advantages of multi-modal information fusion, physical rationality guarantee, high structure complementation capability, high-quality score optimization and efficient generation process, and is suitable for three-dimensional scene modeling requirements in the fields of virtual reality, augmented reality, robots and the like.

An artificial intelligence system based on spatial-temporal information pairs is provided by the present disclosure. By integrally deploying paired vision, auditory and olfactory acquisition devices, a device that can collect the data within a 720-degree area is constructed, and the multi-dimensional continuous spatial-temporal information pairs such as positions, morphologies, motion states, sounds and odors from the ambient environment of the acquisition device or a same spatial object in the environment are recorded in real time. These information pairs can not only contain spatial relationships and a clock attribute, but also contain rich label attributes, for example, identifiers of acquisition devices, and a name, a category and a behavior pattern of the spatial object.

The present invention relates to the technical field of algae proliferation risk prediction. Disclosed is a machine learning model-based algae proliferation risk prediction method. The present invention comprises collecting water quality data, geographic information and historical algae proliferation event data and preprocessing the collected data. In the present invention, a graph structure between monitoring points is constructed on the basis of spatial features, each monitoring point is represented as a node in a graph, and a relationship between nodes is represented as an edge, so that spatial dependency between monitoring points can be effectively captured. The weight of each edge is defined as the reciprocal of the distance between the nodes, so that the model can more accurately reflect the effect of adjacent monitoring points on each other. By constructing the graph structure to represent the relationship between the monitoring points, large-scale spatial data can be efficiently processed. A graph neural network is capable of performing a convolution operation on a graph structure, effectively aggregating information of adjacent nodes, thereby improving the spatial relationship expression capability and the model prediction accuracy.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

The invention relates to the technical field of power equipment monitoring, in particular to a ground wire state intelligent monitoring method based on multi-modal information fusion, which comprises the following steps: acquiring ground wire multi-modal monitoring data including image data, inertial sensing data and time sequence task data; a deep learning target detection algorithm is adopted for the image data to identify key components in the image, wherein the key components at least comprise a wire clamp, a connecting bolt and a hanging point; the preliminary judgment is based on the identified key components, the spatial relation features, geometric features and visual features of all the components are analyzed, and a preliminary result of the state of the grounding wire is generated according to a preset judgment rule; fusion verification: performing multi-modal fusion verification on the judged preliminary result, the attitude data and the time sequence task data, and outputting a final state judgment result; according to the invention, by fusing the image data, the inertial sensing data and the time sequence task data, accurate judgment and graded early warning of the hitching state of the ground wire are realized, and the safety risk of electric power overhaul operation is reduced.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention discloses a short-term wind speed prediction method for multiple offshore wind power plants, relates to the technical field of power system intellectualization, and constructs a dynamic graph structure fusing the correlation between geographic distance and wind speed according to the correlation between the geographic position of a target area and the wind speed, namely a multi-wind-plant connected graph, and represents the spatial topological relation of a wind power plant group. A wind power plant group is mapped into a node network by constructing a dynamic graph structure fusing geographic distance and wind speed correlation, spatial dependence intensity between nodes is quantized by using a weighted adjacent matrix, spectral domain convolution operation is carried out by a graph convolution network based on a normalized Laplacian matrix, and the spatial dependence intensity between nodes is quantized by using a normalized Laplacian matrix. Efficient neighborhood feature aggregation is achieved through Chebyshev polynomial approximation, complex spatial association caused by geographic position difference and meteorological condition interaction can be accurately captured, the defect of non-Euclidean spatial relationship modeling in a traditional method is overcome, and the representation capacity of the spatial dependency relationship in the multi-wind-power-plant environment is remarkably improved.

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

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention discloses a test pit volume measurement system based on three-dimensional laser scanning, relates to the technical field of engineering measurement, and mainly solves the technical problems that a traditional test pit volume measurement method is low in efficiency, poor in precision and greatly influenced by human factors. According to the technical scheme, the system is characterized in that a three-dimensional laser scanner with the scanning distance being 0.5-150 m and the point cloud density being 10000-500000 points per square meter is adopted to scan a test pit area from a plurality of sites; recognizing four symmetrically arranged targets through a point cloud registration module in the control processing unit to realize multi-site cloud data fusion; after denoising processing is carried out by using the point cloud filtering module, a surface model is constructed by using the triangulation network modeling module, and finally, the closed volume is calculated based on the spatial relationship between the triangulation network model and the fitted reference plane through the volume calculation module. The system is mainly used for accurately measuring the earthwork volume in filling and excavation projects in the fields of water conservancy, traffic, mining industry and the like.

The invention discloses a water quality parameter prediction method and system based on multi-sensor data fusion, and the method comprises the steps: collecting and preprocessing original dissolved oxygen data and turbidity data, and obtaining dissolved oxygen data and turbidity data in a plurality of time windows; according to the dissolved oxygen data and the turbidity data in each time window, calculating a water quality feature vector, a dynamic convolution kernel and an abnormal discrimination probability of each sensor in the time window, and performing traversal elimination based on the abnormal discrimination probability to obtain a sensor sequence; according to geographic coordinates and water flow directions of sensors corresponding to data points in the sensor sequence, calculating spatial relationship weights among the sensors, according to the water quality feature vectors, calculating frequency dynamic features, and combining the spatial relationship weights and the frequency dynamic features to generate global feature vectors; and calculating a water quality parameter tensor according to the global feature vector and the long-short term memory network, and generating a water quality parameter thermodynamic diagram. According to the method, the problem of insufficient prediction precision caused by poor spatial collaboration of single-point abnormal data and multi-source data is solved.

The invention relates to the technical field of control, in particular to a double-arm robot operation skill learning method and system based on big language model reasoning. The method comprises the following steps: firstly, carrying out context semantic modeling on an input natural language task instruction based on a large language model to generate a task semantic graph; in combination with a semantic entity in the task semantic graph and visual perception data collected by a robot, determining a three-dimensional space position of a target object through a multi-modal matching model, and constructing an environment semantic graph containing object nodes and spatial relation edges; generating an action sequence by using a language model according to the task semantic map and the environment semantic map, and generating a collaborative operation strategy based on the two-arm tail end state and an obstacle map; and finally, collecting feedback data of the sensor in real time when the action sequence is executed. According to the method provided by the invention, the understanding and execution capability of the two-arm robot on the unstructured natural language instruction is remarkably improved.

The embodiment of the invention provides a multi-modal low-altitude grid data dynamic indexing method and device for improving a cache mechanism. The method comprises the following steps: mapping multi-modal low-altitude grid data to a unified vector space, and constructing a cross-modal semantic association hybrid index for the mapped low-altitude grid data; dividing the mapped low-altitude grid data into a plurality of dynamic sub-regions according to data distribution density and data access popularity, and managing a mixed index corresponding to each dynamic sub-region by combining a Loki log index fragmentation mechanism and tagged metadata; the mapped low-altitude grid data is cached by adopting an EHCD hierarchical multi-level cache architecture; and carrying out cross-modal low-altitude grid data retrieval by combining SIMD instruction set parallelization vector similarity calculation and GPU acceleration complex space relation operation. In this way, the real-time retrieval efficiency, the system expansibility and the energy efficiency ratio of the low-altitude grid data can be remarkably improved.

The invention discloses an infrared small target detection method based on a multi-scale rotation deformable attention module. The infrared small target detection method comprises the following steps: step 1, collecting an infrared image; 2, extracting and enhancing high-frequency feature representation of the original infrared image; 3, performing deep feature extraction on the high-frequency feature representation, constructing multi-scale feature representation by adopting a feature pyramid network, and performing feature enhancement to obtain multi-scale enhanced features with rich semantic information and spatial details; 4, a multi-scale and multi-direction comprehensive module is adopted, the local space relation between multi-scale enhanced features is extracted, feature representation of the rotating target is enhanced, and self-adaptive positioning and recognition of the target in different directions and sizes are achieved; and 5, dynamically adjusting the target feature weight, obtaining features fusing multi-scale, space and task adaptive information, outputting a target category and a rotating frame parameter by adopting a detection head, and performing training optimization of the detection head. According to the invention, the detection precision of the infrared rotating target is improved.

The invention relates to a land space planning and surveying and mapping engineering collaborative measurement method, which comprises the following steps: collecting multi-source surveying and mapping data of a target area, and constructing a live-action three-dimensional semantic model bearing ground feature semantics through fusion processing; key planning elements are extracted based on the model, the spatial relation of the key planning elements is quantified, and a spatial relation graph with geometric entities as nodes and the spatial relation as edges is generated; the planning management and control terms are formalized into computable logic assertions, and a machine executable planning rule base is constructed; performing collaborative traversal and logical reasoning on the spatial relation graph based on the rule base, automatically comparing and identifying violation situations, and generating a planning conflict report; and carrying out fusion mapping on the report and the three-dimensional model to generate a three-dimensional visual review layer for intuitively indicating the position, type and degree of the violation. According to the method, deep fusion of surveying and mapping data and planning rules is realized, planning conformity review can be automatically and intelligently completed, and the problems that the prior art depends on manpower, and is low in efficiency and different in standard are effectively solved.

A terminal according to one aspect of the present disclosure includes: a receiving section that receives configuration information not including spatial relation information and indicating a physical uplink control channel (PUCCH) resource having a lowest ID; and a control section that controls reception of a physical uplink shared channel (PUSCH) scheduled using downlink control information (DCI) format 0_0.

The present application discloses a method and system for identifying internal surface defects of a nuclear reactor pressure vessel, a computer device, and a computer-readable storage medium. The method comprises: collecting image data of the surface of a pressure vessel; constructing an illumination angle attention mechanism, using an image in a data set to establish a feature map, and producing a spatial attention map on the basis of a spatial relationship of the feature map; and using the illumination angle attention mechanism to improve a YOLOv5 model, and identifying surface defects of the pressure vessel by means of the improved YOLOv5. The present application has the characteristics of high defect identification efficiency, low missed detection rate, etc. The present application can effectively cope with common problems when defect detection is performed under different illumination conditions. This precise defect localization and identification capability is critical for ensuring safe operation of pressure vessels, particularly during routine maintenance and inspection.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention provides a drainage basin water quality abnormity tracing method and system based on time sequence fluctuation characteristics, and the method comprises the steps: taking each monitoring point in a drainage basin as a graph node, constructing a directed edge connected with the graph node according to the spatial relation between the monitoring points and the water flow direction, and forming a drainage basin graph network; acquiring historical monitoring data, extracting amplitude variation features and fluctuation features from the historical monitoring data, and calculating a time sequence fluctuation feature vector; traversing graph nodes with directed edge connection, constructing index pairs corresponding to the graph nodes, analyzing the correlation of the index pairs under different lag dimensions, and calculating response lag dimension coefficients; according to the time sequence fluctuation feature vector and the response lag dimension coefficient, calculating an abnormal correlation coefficient between graph nodes, and constructing an abnormal transmission path; and according to the abnormal transmission path, dividing a pollution troubleshooting area for a worker to carry out troubleshooting treatment. The water quality abnormal fluctuation excitation source position can be accurately positioned, and the water quality abnormal analysis efficiency and the abnormal source positioning precision are improved.

The invention relates to the technical field of optical measurement, in particular to a complex reflective object surface reconstruction method and system based on multi-system point cloud fusion, and the method comprises the steps: carrying out the joint calibration of the spatial relation among a camera, a projector and an LCD screen; projecting to a target object based on a projection grating phase method and a phase deflection operation path, and collecting structured light patterns of the two through a camera; phase solving is carried out on the structured light pattern collected by the camera, and a three-dimensional reconstruction result is obtained in combination with the joint calibration information; and world coordinate system matching and point cloud fusion are carried out on the three-dimensional reconstruction result based on the joint calibration information, and the three-dimensional morphology of the target object is generated. According to the method, the problems that in diffuse reflection reconstruction, patterns projected by a projector cannot be comprehensively collected due to exposure and height and gradient ambiguity cannot be solved by a monocular phase deflection technology in high reflection can be solved complementarily, high reflection and diffuse reflection objects can be solved, a complete reconstructed object can be obtained, and the reconstruction precision is improved.

The invention relates to the technical field of wireless charging, and discloses a wireless charging power adaptive adjustment method and system based on machine learning, and the method comprises the steps: collecting and preprocessing multi-dimensional parameter data of a charging environment; learning the data by using a recurrent neural network, and establishing a correlation model of environmental factors and transmission loss; modeling a charging environment based on a graph structure, and extracting spatial relationship characteristics by using a graph neural network; a neighborhood sampling and dynamic pruning technology optimization model is adopted, and a hierarchical power decision system is constructed; adjusting the charging power by adopting feed-forward control according to the output of the decision-making system; according to the invention, by introducing the deep learning technology, accurate prediction and dynamic adjustment of the charging power are realized, and the charging efficiency and stability are improved; collaborative optimization of multiple charging points is realized by adopting graph structure modeling; and the calculation complexity is reduced through an optimization technology, so that the system is suitable for a large-scale application scene.

The invention discloses a man-machine interaction method and system based on an intelligent sensor, belongs to the technical field of robots, solves the problem of misoperation caused by abnormal sensor data to a certain extent, and enables the system to still run stably through quality credibility evaluation and a dynamic weight distribution mechanism when part of sensors fail. Meanwhile, based on a dynamic safety boundary strategy of scene semantic understanding, the robot can automatically adjust motion constraints according to object attributes and spatial relations, and collaborative optimization of safety and operation efficiency is achieved in complex tasks.

The invention provides a machined part logistics transportation path planning management system, and relates to the technical field of data processing, and the system is used for reconstructing the spatial relationship of path nodes, splitting and combining the arrangement sequence and time sequence characteristics of the path nodes, and storing the path nodes in a database; calculating a dynamic quantitative index of a preset transportation path between a time dimension and a resource occupation dimension to obtain a time sequence tensor factor, identifying an offset track of a machined part between the preset transportation path and an actual transportation path, extracting transportation behavior characteristics between path nodes, calculating a time sequence disturbance degree of transportation path offset to a scheduling plan, and obtaining a scheduling result; the method comprises the steps of obtaining a scheduling influence coefficient, correcting a preset transportation path, adjusting the passing sequence and priority of path nodes, synchronously updating scheduling information, and carrying out linkage matching on path change information and a workshop plan to obtain linkage scheduling data. According to the invention, the whole-course data modeling and dynamic scheduling management of the transportation process of the workpieces are realized.

The invention relates to the technical field of rural space governance and intelligent decision making, in particular to a rural space planning method and related equipment, and the method comprises the steps: obtaining multi-source data of a target rural, the multi-source data comprising land data, population data and rural economic data of the target rural; performing fusion processing of space-time alignment on the multi-source data to obtain fusion data; constructing a spatio-temporal knowledge graph based on the fused data, and extracting entity attributes and spatial relationships; and inputting the entity attributes and the spatial relationship into a pre-trained planning optimization model, iteratively optimizing a rural space matching strategy according to rigid constraints, and outputting an optimal planning result, the technical problems that in the prior art, rural planning faces data splitting, element analysis isolation, planning scheme staticization and actual development requirements are disjointed, the decision making process depends on subjective experience, and economic-ecological benefit tradeoff is difficult to quantify are effectively solved.