160 results about "Geographic feature" patented technology

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention discloses a hydrological monitoring point dynamic planning method and system, and relates to the technical field of hydrological monitoring, and the method comprises the steps: collecting hydrological monitoring point parameters of a target region, and making an initial point planning scheme; and performing hydrological dynamic prediction on the region based on prediction of a future time zone to obtain a hydrological prediction result. And according to the result, performing redundancy adjustment on the initial point location scheme to generate a first adjustment scheme. And based on the monitoring demand and the prediction result, correcting the point location blind area to form a second adjustment scheme. And in combination with the geographic feature data and the damage risk threshold, compensation adjustment of the damaged point location is carried out, and a third adjustment scheme is generated. And integrating the above adjustment schemes, carrying out global optimization, and generating an optimized hydrological monitoring point layout. The technical problems of poor planning flexibility and low precision of the hydrological monitoring point positions in the prior art are solved, and the technical effects of improving the planning flexibility and precision of the hydrological monitoring point positions and improving the hydrological monitoring quality are achieved.

The invention discloses a method for enhancing geographical name and address translation precision by using context awareness and a geographic library, relates to the technical field of intelligent translation, and constructs a dynamic collaborative ambiguity resolution framework by fusing a context awareness mechanism and multi-dimensional attributes of a geographic knowledge base so as to break through semantic limitation of traditional geographical name and address translation. Specifically, firstly, dynamic association information implied in an address text is captured by utilizing context semantic coding of a non-geographical name text fragment, and structured feature embedding is performed on candidate entities in combination with a geographic knowledge base to extract multi-dimensional attributes of the candidate entities, so that cross-modal disambiguation decision is realized in a semantic aggregation stage; through a geographic feature cross validation mechanism guided by context semantics, the problem of translation ambiguity caused by static rule dependence and geographic attribute segmentation in the prior art is effectively solved, so that a more accurate multi-language address translation effect is achieved.

The invention relates to the technical field of machine vision, and discloses a surveying and mapping geographic information analysis method and system based on machine vision and a medium. The method comprises the following steps: preprocessing multi-source remote sensing data to obtain a standardized surveying and mapping data set, and extracting ground feature edge and texture information to obtain a multi-scale spatial feature and a semantic segmentation result; carrying out multi-source data integration to obtain a unified geographic element representation diagram, and carrying out geographic element identification on the unified geographic element representation diagram to obtain a surveying and mapping geographic element set; performing Bayesian inference of spatial uncertainty quantization and parameter uncertainty quantization on the surveying and mapping geographic element set to obtain a comprehensive uncertainty evaluation result; and constructing vectorized geographic information based on the surveying and mapping geographic element set and the comprehensive uncertainty evaluation result. According to the method and the device, the edge information of the geographic elements is effectively reserved, the identification precision of the geographic elements is improved, the geographic information expression is more structured, and automatic updating is realized.

The invention discloses a channel electronic fence calculation method and system based on an unmanned aerial vehicle. The method comprises the following steps: setting a channel basic fence according to geographic features of a channel; setting a lowest fence standard according to ship attributes; fusing the navigation channel basic fence and the lowest fence through a label priority mechanism to obtain a navigation electronic fence of the ship; acquiring channel monitoring data through an unmanned aerial vehicle group to obtain a real-time monitoring label; and dynamically adjusting the navigation electronic fence based on a preset rule engine and the real-time monitoring tag. According to the invention, dynamic adjustment of fence parameters is realized through linkage of the real-time monitoring label and the rule engine in cooperation with the unmanned aerial vehicle group; customizing a core protection area according to different types, loading states and maneuvering performance of cargo ships, passenger ships and the like; by constructing a multi-dimensional label system, multi-source data is converted into labels with unified semantics, and deep data fusion is achieved; and a machine learning model is introduced to dynamically calibrate the label, and a label system and a calibration rule are optimized.

The invention relates to an unmanned aerial vehicle flight path planning method based on semantic guidance and visual perception, and the method comprises the following steps: S1, obtaining original image information and a digital elevation model, and generating a standardized target region image, S2, inputting the standardized target region image into a trained improved U-Net semantic segmentation model, the improved U-Net semantic segmentation model outputs a pixel-level geographic feature classification result, S3, according to the geographic feature classification result, determining an obstacle and a passable area, and generating a binary grid map, the obstacle area being marked as 0, and the passable area being 1, and S4, generating an initial flight path based on the binary grid map, and if the initial flight path is blocked, executing the step S3. Triggering an online re-planning algorithm to generate an alternative path; the method has the advantages that lightweight calculation and high robustness are both considered, and the flight safety and the real-time decision-making capability in a complex scene are remarkably improved.

The invention relates to the technical field of visualization of an air pollution information distribution state, in particular to a visualization method based on the air pollution information distribution state. The method comprises the following steps: acquiring atmospheric pollution information through a remote sensing technology to obtain atmospheric pollution data, and constructing an atmospheric pollution geographic position topological graph in combination with atmospheric pollution geographic feature analysis; through diffusion analysis and distribution state analysis, a pollutant hot spot area is further identified, the acid rain forming probability and air quality attenuation data are calculated, and therefore the pollution degree of a high-concentration area is evaluated; atmospheric pollution risk assessment is carried out through analysis of atmospheric pollution information abnormal data, and an assessment result is uploaded to a cloud platform for real-time early warning; according to the method, the air pollution distribution is identified and optimized, so that the air pollution information is identified more accurately.

The invention provides a method and system for constructing a geographic entity based on a DLG topographic map, and the method comprises the steps: taking the DLG topographic map as a data basis, and carrying out the objectification processing of basic geographic information elements; the method comprises the following steps: constructing a data graph based on an association relationship between geographic information elements and a target geographic entity in dimensions such as spatial scale, geometric morphology, geographic features, attribute expression and the like, belonging to a mapping conversion standard, and extracting an intermediate entity library from an objectified result library; entity construction operators are assembled according to needs to complete entity production scheme configuration, processing such as assembly and fusion is carried out on primitive objects in the middle entity library based on an entity production scheme, and rapid production and construction of geographic entity achievements are achieved.

The invention provides a telephone traffic demand prediction method and system. The method comprises the steps that firstly, map data are obtained, grid generation processing is conducted on the map data, geographic grid features are extracted, the map data comprise one or more of point-shaped map data, linear map data and planar map data, and the geographic grid features comprise feature vectors describing various geographic indexes of each grid space; secondly, acquiring first telephone traffic data, and decomposing the first telephone traffic data according to the geographic grid characteristics to obtain second telephone traffic data; and finally, space traffic data and time sequence traffic data are generated according to the second traffic data and the geographic grid features, the space traffic data comprise data of traffic demand prediction of rasterized spatial distribution, and the time sequence traffic data comprise data of traffic demand prediction of normalized time sequence distribution. The method not only can support data-free cold start of energy-saving control of the wireless base station, but also can generate traffic demand prediction data of time-space domain decoupling according to geographic features and service modes of the wireless base station.

The invention discloses a spectrum allocation method and device based on graph data and deep reinforcement learning and a medium, and belongs to the field of electric power communication networks, and the method comprises the steps: fusing the multi-dimensional node features of an electric power communication air-ground ad hoc network, and calculating the weight of channel quality and interference coupling, thereby obtaining a spectrum allocation result of the electric power communication air-ground ad hoc network; constructing a dynamic graph structure model of a node and link relationship; constructing a time-space associated multi-dimensional spectrum map by fusing the geographic feature data, the frequency band service data, the channel feature data and the version information data; and on the basis of the dynamic graph structure model and the multi-dimensional spectrum map, coding dynamic topological characteristics through a graph convolutional network, and combining experience playback and a dynamic Q value updating mechanism, generating a spectrum allocation action meeting a power constraint and an interference threshold, and executing spectrum allocation of the electric power communication air-ground ad hoc network according to the spectrum allocation action. Therefore, by implementing the method, the robustness of spectrum allocation of the electric power communication air-ground ad hoc network can be improved.

The invention discloses a method for constructing a computable road network. Road data in the vector map are extracted, line string data or line segments obtained by splitting the line string data are used as connecting edges, geographic feature codes containing Geohash information are given to starting points and ending points of the connecting edges, and therefore a basic road network map is constructed. And extracting a middle vertex set of the basic road network diagram and an edge attribute set of middle vertex connection edges, and carrying out road network vertex optimization based on edge attributes to obtain an optimized computable road network. Geographic feature codes adopted by the method comprise spatial geographic information and have the characteristics of graph vertex coding, and the constructed road network graph can be used for graph calculation and verification and restoration of road connection relations. Furthermore, in combination with a representation point acquisition scheme provided by the invention, a road network map fusing facilities and equipment can be constructed, so that the application range of the computable road network is expanded. The computable road network constructed by the method is high in operation efficiency and suitable for different production environments.

The invention relates to the field of knowledge maps, and discloses a method and system for processing and predicting enterprise address data, and the method comprises the steps: building a knowledge base through cleaning multi-source heterogeneous address data, training an embedded enhancement model, carrying out the multi-granularity word segmentation through employing a Word-Pi ece algorithm and a field extension dictionary, and carrying out the prediction of the enterprise address data. A result is input into the model to generate an initial hierarchical label sequence, and after verification and completion of the place name recognition model, a standardized structured address is output based on a priority fusion rule; the system comprises a data cleaning and knowledge base construction module, a hierarchical prediction model training module, a multi-granularity word segmentation module, a hierarchical prediction module, a hierarchical verification and fusion module and a distributed computing cluster. The analysis accuracy is improved by fusing semantic and geographic features, and the dynamic knowledge base is combined to adapt to administrative division change; the processing efficiency is optimized by adopting a distributed calculation and acceleration technology; manual intervention is reduced through automatic verification, and standardized address data is generated.

The invention discloses a GIS-based cross-section diagram real-time synchronous display method and system, and relates to the technical field of geological modeling, and the method comprises the steps: importing multi-source heterogeneous geographic data, carrying out the standardized preprocessing, and generating a geographic information data set in a unified format; carrying out multi-dimensional feature extraction on the geographic information data set by adopting a feature level fusion algorithm, and constructing a multi-dimensional geographic feature model fusing geological structure, terrain elevation and earth surface coverage information; performing data optimization by adopting a spatial interpolation algorithm based on the multi-dimensional geographic feature model to generate a continuous geographic surface model; and performing parallel contour tracking and topological sorting on the discrete section line segment set, and generating a user-defined section diagram supporting interactive parameter adjustment in real time. According to the method, the multi-dimensional geographic feature model is constructed through the feature level fusion algorithm, semantic level association of the geologic structure, the terrain elevation and the earth surface coverage information is realized, the problem of pixel level fusion is solved, and the data space consistency error is reduced.

The invention discloses a three-dimensional geographic scene dynamic updating method, device and equipment based on space-time interpolation and a medium, and the method comprises the steps: collecting multi-source heterogeneous data, including a satellite image, an unmanned plane remote sensing image and ground sensor data; calculating the weight of a single data source according to the spatial resolution and the time resolution of the data; carrying out spatial interpolation and time interpolation calculation on spatial points of the three-dimensional scene in combination with the multi-source data weight; performing nonlinear compensation on the spatial interpolation and the time interpolation to obtain a compensated spatial point update value of the three-dimensional scene; and comparing interpolation results at different time points, detecting local change, and updating the change area of the three-dimensional scene. According to the method, the fusion quality is guaranteed through reasonable weights, and the weights are configured by integrating spatial resolution, time resolution, data quality and reference data similarity, so that three-dimensional scene basic data fusion is more fit for real geographic features.

The invention discloses a remote sensing image cloud type inversion model and system based on symmetric cross attention. The method comprises the following steps: acquiring satellite remote sensing image data, and extracting cloud spectrum, texture and shape features after processing; performing feature coding on longitude and latitude coordinates, terrain, underlying surfaces and time elements; constructing a symmetric cross attention module, and bidirectionally fusing the image, the geographic feature and the time feature as channel input; a ClodSat cloud product is used as a truth value, an Adam optimizer and a cross entropy loss function training model are adopted, and verification is carried out through indexes such as a Kappa coefficient and producer precision. The method overcomes the defect that a traditional method only depends on two-dimensional images and does not consider external driving elements, cloud types can be accurately classified, and inversion accuracy and efficiency are improved.

The invention relates to the field of image content understanding, and discloses an image geographic positioning system and method based on geographic feature extraction, and the system comprises a feature extraction module which is used for extracting a visual feature vector, a GPS feature vector, a text position description feature vector and a text scene description feature vector from multi-modal input; the comparative learning module is used for realizing multi-modal feature alignment through comparative learning of the visual features, the GPS features, the text position description features and the text scene description features; and the data set construction module is used for fusing the multi-modal features to generate geographic feature vectors and constructing a retrieval vector data set. By adopting the technical scheme of multi-modal feature fusion and cross-modal comparative learning, the technical effect of improving the geographic positioning precision and generalization ability is achieved. Compared with a scheme depending on a single mode or simple feature splicing in the prior art, the problem of semantic gaps caused by mode information splitting in a traditional method is solved.

The invention relates to the field of green building design, and particularly discloses a municipal green building design optimization method, which comprises the following steps of: firstly, extracting geographic position information from municipal green building design condition information input by a user, and obtaining a target area aerial view from a GIS (Geographic Information System) according to the geographic position information; further introducing an image processing technology based on deep learning to perform multi-scale feature extraction on the aerial view of the target area to capture geographic features of the target building area, and performing semantic analysis on budget limitation, function requirements, environment requirements, time frames and other information input by a user by using a natural language processing technology; and attention modulation is carried out on the geographic features of the target building area as design constraint conditions, so that the geographic features of the building area and the design constraint conditions are comprehensively considered, and a municipal green building design sketch is intelligently generated. According to the method, the design efficiency of municipal green buildings can be remarkably improved, the design period is shortened, and the innovativeness and quality of design achievements are ensured.

The invention relates to the technical field of geographic information data processing, in particular to a geographic information data processing system based on cloud computing, which comprises a data processing module, a division and fusion module, a difference detection module, a correlation analysis module, a feature prediction module and a data selection module. The data processing module preprocesses the received geographic information data; the division and fusion module performs iterative region division and fusion; the difference detection module detects a difference area; the association analysis module obtains an association relationship between the difference region and other regions; the feature prediction module obtains predicted geographic features of the difference region; and the data selection module selects final regional information data. According to the method, the data conflict region is identified through difference detection, the internal complex association of the geographic information data is mined, the geographic features of the difference region are predicted, the most suitable geographic information data is selected as the final data based on similarity calculation, and the intelligence and automation of the data processing process are realized.

The invention discloses a load spatial distribution characteristic combined carbon emission correlation modeling method and system, and relates to the technical field of carbon emission management and optimal scheduling, and the method comprises the steps: collecting node load data and geographic information data in a power system, carrying out the preprocessing, and building a correlation table of node loads and geographic characteristics; modeling the relevance between the load space distribution and the carbon emission factors based on a geographic weighted regression model and a spatial autoregression model, and constructing a relevance model between the load space distribution and the carbon emission factors; training and calibrating the correlation model, and evaluating the fitting effect and prediction precision of the model by adopting a cross validation method; and predicting the spatial distribution characteristics of the carbon emission of each region by using the correlation model, and implementing low-carbon scheduling optimization based on the load and carbon emission characteristics of different regions. According to the invention, through combination of the geographically weighted regression model and the spatial autoregression model, spatial refined modeling of carbon emission factors is realized, and a high-carbon emission density region can be accurately identified.

The invention relates to an animal epidemic disease monitoring data statistical analysis system and method. The method comprises the following steps: a multi-source data fusion module performs standardized fusion processing based on input case data, environment data and population data to obtain a spatio-temporal joint data matrix; a risk probability prediction module performs area scanning shape prediction on geographic feature data and population distribution data in the matrix, outputs a dynamic scanning area set, and performs environment and small sample correction to obtain risk probability distribution; and the hierarchical prevention and control visualization module performs spatial aggregation test based on the distribution, outputs a significant epidemic disease aggregation area, and generates a hierarchical prevention and control thermodynamic diagram in combination with an environment correction factor. According to the system, through multi-source data fusion, geographically adaptive dynamic window prediction and spatial aggregation test, the animal epidemic disease risk probability can be accurately analyzed, an epidemic disease aggregation area is output, a hierarchical prevention and control thermodynamic diagram is generated, and the animal epidemic disease monitoring level and the prevention and control visualization capability are further improved.

The invention relates to the technical field of electromagnetic environment monitoring, in particular to a radio electromagnetic environment real-time monitoring system based on urban traffic, which comprises a signal acquisition module, a grid compression module, a signal processing module, a signal positioning module and a situation filling module. According to the method, the signal acquisition frequency is dynamically adjusted, the data redundancy problem in the high-speed moving scene is optimized, the local signal capturing capability is improved, the plane coordinate system is utilized to divide grids, normalization processing is combined, the data storage pressure is reduced, the feature integrity is guaranteed, the threshold line is dynamically generated by adopting the moving average algorithm, and the data storage efficiency is improved. The method improves the signal-noise separation precision, reduces the misjudgment rate, corrects a signal level value in combination with the vehicle position and the grid center distance, reduces the positioning error of a signal source, adjusts the fitting weight in combination with the geographic feature gradient and the building height parameter, reduces the data distortion and the electromagnetic situation map blind area, and improves the positioning accuracy. And high-precision and low-delay panoramic monitoring of the electromagnetic environment in a complex urban area environment is realized.

The invention provides an intelligent traffic jam real-time analysis method, system, device and medium based on multi-dimensional spatial geographic features, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: collecting traffic data through combining non-traditional and traditional data collection devices, and transmitting the traffic data to a rear-end data center; the data center adopts a distributed computing framework to clean and integrate data, performs quality control, extracts multi-dimensional spatial features by using a GIS (Geographic Information System), calculates a congestion index and predicts a traffic congestion trend at the same time; and the back-end data center issues real-time congestion conditions and prediction trends through non-traditional and traditional data issuing channels, and pushes personalized congestion planning suggestions according to travel habits of users. According to the invention, a complete intelligent traffic jam real-time analysis solution is formed, the traffic jam problem is effectively relieved, and the intelligent level of traffic management is improved.

The invention relates to a flood prevention drainage method and device based on a digital intelligent scheduling algorithm, and relates to the technical field of flood prevention drainage. A relay; the number of the relays corresponds to that of the water pumps; the touch screen all-in-one machine is used for man-machine interaction and displaying the running state of the drainage device The liquid level sensor is used for detecting the water level height in real time; a control mainboard; the control mainboard is electrically connected with the touch screen all-in-one machine, the relay, the liquid level sensor, the variable frequency driver and the current sensor; the control mainboard obtains a weather condition sent by the external equipment; the control mainboard predicts the water level change trend through a water level prediction model based on the water level height, the weather condition and the geographic features of the drainage area and generates a drainage task. And the control main board determines the working sequence of all the water pumps performing the drainage task according to the drainage flow, the energy consumption and the current operation duration of each water pump, and controls the relays of the water pumps in the working state to be in the conduction state.

The invention relates to the technical field of airport noise prediction. The airport noise map isoline calculation method and system based on multi-source data fusion are provided, and the method comprises the following steps: carrying out space-time alignment fusion processing on multi-source heterogeneous data to generate a space-time synchronous standardized data set; processing through a track-noise coupling calculation unit to generate a single event noise value containing a meteorological correction factor; performing dynamic gridding accumulation calculation processing on the single event noise value to generate a noise evaluation quantity grid matrix with a time stamp; processing is carried out through a curvature-driven adaptive contour line generation unit, and a fidelity-optimized noise contour map is generated; and selecting a real-time lightweight channel or a distributed batch processing channel for processing based on the calculation mode decision-making unit, and outputting a noise contour map with optimized fidelity, so as to achieve the technical effects of improving noise positioning precision, reducing modeling errors in a dynamic flight environment and improving the fidelity of geographic features of contour lines.

The invention discloses a coastal disaster forecasting method and system. The method comprises the following steps: acquiring data of a target near-shore area, and carrying out data preprocessing and unstructured grid division; generating an open sea drive-near-shore response sample library by using the coupling numerical model; extracting the dominant lag step number of each open-sea factor for each node near the shore through mutual information analysis, performing time rearrangement on key factors, splicing with static geographic features, and constructing a structured input tensor; training a graph attention space-time fusion model taking the unstructured grid as a graph structure; in real-time forecasting, an implicit state assimilation mechanism based on observation errors is introduced, and the model state is dynamically corrected to achieve self-adaptive rolling forecasting. According to the invention, high-precision and high-efficiency near-shore disaster real-time early warning is realized.

The invention discloses a disaster risk early warning method and system combined with multi-source data analysis and electronic equipment, and relates to the technical field of data analysis. The method comprises the following steps: carrying out monitoring range screening according to real-time latitude and longitude coordinates to obtain K disaster risk monitoring subareas; executing multi-source information cross-platform crawling to obtain K pieces of disaster monitoring multi-source data; retrieving and outputting the geographic feature data of the K subareas from the open GIS data platform; acquiring disaster risk characteristics of K subareas; carrying out risk dispersion fitting on the disaster risk characteristics of the K subareas, and outputting disaster risk evolution characteristics; and performing spatial consistency analysis according to the disaster risk evolution characteristics and the position deviation of the real-time latitude and longitude coordinates, outputting real-time disaster risk early warning, and transmitting the real-time disaster risk early warning to a risk monitoring user through a mobile terminal. The technical problems of lagged response and insufficient accuracy of disaster risk early warning in the prior art are solved, and the technical effect of improving the timeliness and accuracy of disaster early warning is achieved.

A work vehicle performs self-traveling among a plurality of crop rows. The work vehicle includes: an exterior sensor to output sensor data indicating a distribution of geographic features around the work vehicle; and a controller to control self-traveling of the work vehicle. The controller detects two crop rows existing on opposite sides of the work vehicle based on the sensor data, and causes the work vehicle to travel along a path between the two crop rows. During travel, if an end of at least a crop row that corresponds to a turning direction between the two crop rows is detected based on the sensor data, the controller sets a coordinate system for turning travel that is fixed to a ground surface and a target point for the turning travel. The controller controls the turning travel toward the target point based on the coordinate system.

This framework provides scalable land use characterization using Points of Interest (POIs) and non-POI geographic features. Leveraging open-access POI data and hierarchical spatial structures, it generates high-dimensional embeddings that capture spatial and semantic characteristics of land use for areas of interest (AOIs). An OSM-tag-based representation harmonizes diverse data sources, while a neural network language model produces embeddings optimized for multi-scale land use classification across geographic regions. Supervised classification models validate the robustness of AOI embeddings, revealing variations in semantic salience for different land use types. Results demonstrate that combining POIs with non-POI features and tailoring spatial and semantic granularities enhance land use characterization. Future directions include augmenting data and integrating temporal dynamics to improve representational accuracy and capture land use patterns more effectively.

The invention provides a VPN traffic identification method and system based on multi-model fusion, and the method comprises the steps: splicing a Pcap file traffic feature vector with a geographic feature vector, carrying out the traffic class prediction through employing a random forest model and an LSTM network model, and splicing prediction results as the input of a meta-model, and obtaining a final prediction result. According to the invention, efficient network flow monitoring, feature extraction and decision support are realized. Cooperative work among the modules provides powerful technical support for network security monitoring, and can effectively deal with complex network security threats. According to the method, the Vmess and Shadowsockets effects are researched and judged through multi-model fusion, and the effect is improved by 5% compared with the effect of a single algorithm; by obtaining the first 100 frames of the Pcap packet and abandoning the subsequent flow, the research and judgment accuracy is not reduced, and the research and judgment time is shortened to 40% of the original time.

The invention provides a railway Beidou satellite signal simulation method and system based on artificial intelligence. The method comprises the steps of obtaining geographic data corresponding to a railway simulation line and ephemeris data corresponding to simulation time; through statistical analysis of geographic data, geographic features such as geometric features, terrain categories, geotechnical engineering parameters, geologic structure parameters, stratigraphic structure parameters and ground feature distribution features of the terrain are obtained; input of a pre-trained artificial intelligence model is obtained based on the geographic features and ephemeris data, so that multi-dimensional analog signal parameters are obtained; and performing navigation message filling by using the ephemeris data to generate a navigation message, and generating a Beidou satellite simulation signal based on the navigation message and the multi-dimensional simulation signal parameters. According to the invention, efficient, accurate and intelligent analog signals can be generated.