593 results about "Urban planning" patented technology

The invention relates to the technical field of geographic information systems, in particular to a GIS-based urban land planning analysis method and system, and aims to construct a multi-scale space-time cube model by integrating remote sensing, historical planning, mobile terminals and social media data, generate standardized urban temporary land data and improve the urban land planning analysis efficiency. Adaptive feature extraction is adopted to identify temporary land, a hierarchical classification system is established, abnormal changes are monitored through a time sequence model, an association diagram of the temporary land and an urban function network is constructed, an interaction relation is analyzed, finally, differentiated urban planning optimization suggestions are proposed, efficient configuration and management of urban land resources are assisted, and through fusion of multi-source data, the urban planning optimization suggestions are optimized. Multi-dimensional cognition of space, time, functions and relations is achieved, and the understanding depth of temporary land is fundamentally improved.

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

The invention discloses a remote sensing image open vocabulary segmentation method and system based on vision-language interaction. According to the method, the advantages of a multi-modal large language model and a semantic segmentation network based on a visual basic model are cooperatively utilized, language-pixel two-way mapping is taken as a core, five types of marks of images, texts, categories, objects and segmentation are introduced as carriers of cross-modal information, and three types of cross-modal fine-grained information interaction modules are taken as bridges, so that the cross-modal information interaction is realized. Bidirectional mapping and alignment of fine-grained information of the multi-modal large language model and the semantic segmentation network are realized, the open vocabulary segmentation capability of the semantic segmentation model is improved, and the method can adapt to different remote sensing scenes and category definitions. The method has the following advantages: the method has high performance, strong generalization ability and good expansibility, can provide any category of semantic segmentation maps for unlabeled target domain images based on instructions, and has high application value in the aspects of urban planning, map making, disaster response and the like.

The invention discloses an interactive sand table demonstration system based on artificial intelligence, and relates to the technical field of intelligent interactive sand table demonstration. Comprising a multi-mode perception module, a semantic intention recognition module, a three-dimensional simulation module, a decision feedback module and a visualization module. Accurate recognition and semantic analysis of a user instruction are realized by fusing various input modes such as voice, gestures and images; based on a knowledge graph and a deep learning model, user demands can be intelligently understood, and a three-dimensional sand table is driven to carry out dynamic interactive demonstration. The situation adaptation module automatically adjusts the sand table scene according to the environment and the demonstration progress, and the reality sense and immersion sense of demonstration are improved; the decision feedback module generates demonstration results and strategy suggestions in real time, and supports multi-dimensional and intelligent interactive experience; the system is widely applied to the fields of emergency drilling, military deduction, urban planning, educational training and the like, and the intelligent level and the operation convenience of sand table demonstration are remarkably improved.

The invention relates to the technical field of urban planning, in particular to an industrial space suitability optimization method and system, and the method comprises the steps: collecting geographic information data, industrial data, industrial service related data, enterprise information and infrastructure data as a multi-source data set, associating the multi-source data set with geographic space coordinates, and inputting the geographic space coordinates into a database; the adjusted weight coefficient is applied to the LSTM-ARIMA hybrid model, and updated future industry prediction data is obtained; generating optimized industrial land layout scheme data based on a preset parametric modeling rule; determining planning scale data selected by a user, and obtaining a unified database associated with cross-level data; and comparing the land utilization change condition and the enterprise activity state data with data in the unified database, and when it is detected that data updating lag exists, updating the latest data after verification and prediction filling to the unified database. According to the invention, the adaptability and the intelligent level of urban industrial space planning can be improved.

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 discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention discloses a remote sensing image segmentation method and system based on a high-similarity transmission attention mechanism. The method comprises the steps that a high-similarity transmission attention feature extraction network is constructed, multi-scale local features are extracted through preprocessing and a local exploration block, details are reserved in combination with jump connection, and high-similarity features are screened through an attention module; the features are fused with original features to obtain enhanced features; performing multi-scale up-sampling on the enhanced features to recover spatial information, optimizing details by combining coding features, and realizing model training by combining cross entropy and Dice loss; and inputting a target remote sensing image needing to be processed into the optimized model, outputting a segmentation mask image with the same size as the target input remote sensing image, and marking regions of different surface feature categories. According to the invention, the segmentation precision in a complex scene can be obviously improved; the generalization ability of the model is enhanced; the method provides powerful technical support for automatic processing of remote sensing images, and is suitable for practical applications such as urban planning and environment monitoring.

The invention belongs to the technical field of urban planning, and discloses an urban inland inundation toughness evaluation method and related equipment, rainfall event characteristic parameters are obtained through obtaining and analyzing rainfall early warning information, dynamic inland inundation toughness evaluation index weights are generated according to the parameters and a preset weight adjustment rule, and evaluation is executed according to the dynamic inland inundation toughness evaluation index weights. And finally, outputting an evaluation result containing weak link identification information, thereby effectively solving the problems of weight staticization and homogenization in the existing evaluation method. The evaluation index weight can be dynamically adjusted according to rainfall event characteristics and urban spatial heterogeneity, so that accurate evaluation of urban waterlogging toughness is realized, weak links are effectively identified, and a scientific basis is provided for accurate scheduling of emergency resources and effective implementation of disaster prevention measures.

The invention relates to the technical field of urban planning, and particularly discloses an urban planning construction land management and control system based on big data, and the system comprises the steps: obtaining geological environment, landform, engineering construction and human activity data through multi-source data collection; multi-source data fusion analysis is adopted to construct a construction land comprehensive evaluation field including geological stability, engineering suitability and environmental bearing capacity; a development suitability partition is generated based on a density peak value spatial clustering method; updating the development suitability level by dynamically monitoring the verification process; according to the method, accurate analysis and early warning of the three-dimensional deformation field are realized, and scientificity and safety of urban planning in a complex geological environment are improved.

The invention discloses an urban rail transit station site selection multi-objective optimization method and a related device, and belongs to the crossing field of urban planning and traffic engineering, and the method comprises the following steps: according to preprocessed urban road network data, screening crossing points of urban three-level roads and roads above the three-level roads as candidate stations; the coordinates of the candidate sites are coded; based on the preprocessed crowd travel origin and destination data, POI data, building data and land utilization data, quantifying the service people flow, facility accessibility and land development intensity of each candidate site, and forming a feature matrix of each candidate site; and constructing a multi-target traffic station site selection model, screening candidate stations meeting distance constraints through a greedy search algorithm according to the feature matrix, inputting the candidate stations to NSGA-II, and solving a multi-target optimization function of the multi-target traffic station site selection model to obtain an optimal planning result. The method can solve the problems that in the prior art, the target is single during site selection, and the inter-site distance constraint is not considered.

The invention discloses an intelligent city planning method and system based on multi-source remote sensing data fusion, and particularly relates to the technical field of city planning, and the method comprises the steps: S1, region division: dividing a planning region into M dynamic management grids based on GIS geographic coordinates and city function zoning data, carrying out the auxiliary recognition of topographic relief and building height through LiDAR point cloud data, and adjusting the boundary of the grids, establishing a bidirectional mapping relation between the grid and the multi-source sensing equipment; s2, multi-source data acquisition: constructing a city holographic data set through global coverage and a multi-modal sensing technology, acquiring target city data in real time, then executing multi-stage cleaning and space-time alignment, and performing normalization operation on each layer of data by adopting a standardized processing mode to obtain city data; according to the invention, an air-sky-ground-network global sensing network and a multi-level space-time grid framework are constructed, satellite remote sensing, unmanned aerial vehicle point cloud, ground sensors and urban operation data are deeply fused, and the limitation of single-source data of a traditional system is broken through.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention relates to a positioning device for urban planning topographic survey, and relates to the technical field of topographic survey equipment, the positioning device comprises a mounting body, a mounting plate, a mounting assembly, a leveling assembly and a locking assembly; a supporting telescopic rod and a metering telescopic rod are arranged at the bottom of the mounting body; the number of the supporting telescopic rods is at least three, and the fixed ends of the supporting telescopic rods are hinged to the installation body. The fixed end of the metering telescopic rod is fixedly connected with the mounting body; the surveying instrument is rotationally arranged on the mounting plate; the mounting assembly is arranged on the mounting body and is used for mounting the mounting plate on the mounting body; the leveling assembly is arranged on the mounting body and is used for enabling the surveying instrument to keep a horizontal state in a self-adaptive manner; the locking assembly is arranged on the surveying instrument and used for locking the surveying instrument after the surveying instrument is in the horizontal state. According to the invention, landform survey can be conveniently carried out on a city.

The invention relates to the technical field of urban planning, and particularly provides an intelligent site selection method and system based on a spatio-temporal data platform, and the method comprises the steps: collecting multi-source spatio-temporal data of key industry categories of a target region, constructing an enterprise demand feature set based on the multi-source spatio-temporal data, and building a quantitative demand standard based on the feature set; constructing an industrial ecology adaptation model based on the current industrial basic data of the target area, calculating the matching degree between the feature set and the industrial ecology of the target area through the industrial ecology adaptation model, and outputting candidate parks according to the matching degree by taking industrial ecology clustering as a target; establishing a multi-element adaptation relationship between the target enterprise and the candidate parks based on the quantitative demand standard, performing matching verification through a multi-dimensional compliance verification unit, and removing parks which do not meet the constraint condition of the quantitative demand standard; and based on the verification result, outputting a site selection scheme, finally realizing high matching of enterprise demands and public resources, improving site selection efficiency and quality, and assisting sustainable development of regional industries.

The invention provides a multi-conditional constraint fusion generation method and system for generating a planning intention graph. The method belongs to the technical field of urban planning and artificial intelligence crossing. The method comprises the following steps: acquiring sample data containing different territorial and different types of territorial space intention graphs, and cutting a planning graph sample into image segments by using an image cutting technology according to the sample data; generating a detailed description for each image segment by means of a visual model; refining the detailed description of the generated image segment by using a large language model, generating a standardized label group suitable for model training, and forming a multi-modal sample library containing image-text pairs as data to be analyzed; based on the primary screening and secondary screening evaluation results, the model parameters and the control conditions are adaptively adjusted, so that the model can continuously optimize the generation effect according to different design requirements and feedback information, and the adaptability and the flexibility of the model are improved.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses a Mama-based edge-refined remote sensing image semantic change detection method, and belongs to the technical field of remote sensing image change detection. In order to solve the problem of rough prediction edge caused by insufficient optimization of boundary region details in the feature extraction and fusion process of the existing method, the invention provides the following technical scheme: firstly, extracting multi-level features of a dual-temporal remote sensing image by using a twin Mama encoder backbone network; secondly, cross-time-phase feature interaction and difference feature extraction are carried out through a difference module based on Mamba; then, an edge-refined visual state space decoder is adopted, and expansion and corrosion operation and an attention mechanism are fused to reinforce edge information; meanwhile, the learning ability of the model to edge details is improved by combining a loss function strategy of depth boundary supervision and change region supervision. Experiments are verified based on a SECOND data set, the method is superior to an existing mainstream method in the aspects of precision, intersection-to-union ratio, F1 score and other indexes, the boundary precision and semantic segmentation effect of change detection are remarkably improved, and the method is suitable for urban planning, disaster assessment and other high-precision demand scenes.

This patent discloses innovative technologies for efficient and scalable collection, processing, and analysis of geospatial data. The proposed solutions encompass advanced hardware components, such as sensors, cameras, vehicles, satellites, LiDAR systems, and GPS devices, enabling high-precision data acquisition. The invention also introduces sophisticated software algorithms and techniques for processing and analyzing large volumes of geospatial data, including data fusion, feature extraction, image processing, and machine learning approaches. Additionally, the patent addresses the challenges of scalability and efficiency by optimizing data acquisition workflows, reducing processing time and resource requirements, and improving the accuracy and reliability of collected data. The disclosed technologies are designed to support a wide range of applications and use cases, such as mapping, surveying, navigation, urban planning, environmental monitoring, agriculture, disaster response, and infrastructure management. Furthermore, the patent explores methods for seamless integration with existing infrastructure, software platforms, and data management systems, enabling interoperability and data sharing. Overall, this invention offers innovative solutions for efficient and scalable geospatial data collection, processing, and analysis, with potential applications across various domains.

The invention relates to the technical field of urban planning, in particular to a charging facility planning method and device based on machine learning and a medium, and the method comprises the following steps: carrying out the grid division of a planning region, which comprises a plurality of charging service grids; carrying out vehicle ownership prediction and charging facility demand prediction to obtain a charging demand quantity; adopting a clustering algorithm to obtain a plurality of charging block areas with balanced charging demand quantity; and constructing an objective function by maximizing the station revenue of each charging block area, constructing constraint conditions by full coverage of charging demands, calling an aurora optimization algorithm to obtain an optimal planning scheme of the charging facilities, and completing the planning of the charging facilities by adopting the optimal planning scheme of the charging facilities. According to the method, the problems of incomplete analysis and unreasonable partition optimization in the prior art can be solved, and various influence factors of charging facility layout are comprehensively considered, so that efficient layout and resource allocation of a charging network are realized, and the overall quality of charging services and the user satisfaction are remarkably improved.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention belongs to the technical field of urban planning, and provides an urban park site selection decision-making method, system, equipment and medium in order to solve the problem that the existing heat island effect relieving technology faces multiple bottlenecks, and multi-dimensional indexes are endowed with combined weights based on a game theory method and subjected to hierarchical normalization processing; according to the conversion heat contribution degrees of different surface feature categories, decomposing the combined weight of the surface temperature to different surface features of the target area; enabling the normalized spatial distribution map corresponding to the surface temperature to correspond to different surface features, and obtaining a thermal contribution normalized distribution map of different surface features in the target area; the above steps are fused to generate a target area site selection suitability distribution map, and screening is performed to obtain an urban park site selection scheme, so that the sensitivity loss of existing'one-step 'weighting on a high heat source area is avoided, and it is ensured that a park site selection decision not only can alleviate a heat island core area in a targeted manner, but also can adapt to the diversity characteristics of an urban spatial pattern.

The invention discloses a GIS resource management method, system and device and a medium, and belongs to the technical field of resource management, and the method comprises the steps: obtaining a city planning map, constructing a city three-dimensional model, and collecting multi-source city feature information; constructing a multi-source data fusion matrix of a GIS graph and generating three layers of attribute tags; a spatial gravity model is constructed, and reachability analysis is carried out on urban resources; constructing a dynamic prediction model, and triggering a model iteration mechanism; and generating a resource health degree index based on a model iteration mechanism result, and triggering a resource allocation control signal. The method has the beneficial effects that through multi-source data fusion matrix construction, precise space-time association of geographic space, Internet of Things time sequence and visual feature data is realized, and the integrity and accuracy of data integration are improved; quantitative evaluation and prospective pre-judgment of resource supply and demand are realized based on reachability analysis of a space gravity model and a dynamic prediction model of a space-time diagram convolutional network.

The invention discloses a layout generation method and system for urban ruin boundary transformation and a storage medium, and relates to the field of urban planning and building design, and the method comprises the steps: obtaining the three-dimensional scanning data of a ruin region, extracting space control points, and recording the space coordinates of each space control point; with the space control points as the center, the sight line access range of each space control point is calculated based on the three-dimensional scanning data and the space coordinates; converting the sight line access range into a space potential energy value, and constructing a regional potential energy network; analyzing the regional potential energy network, generating a natural flow path in the region, determining a preset activity channel based on the natural flow path, and generating a space separation line; function partitions are divided according to the jump position of the potential energy value, and an initial space layout is generated; and adjusting the initial spatial layout based on the urban planning control index and the surrounding road network to obtain a boundary transformation layout scheme of the paleoruins region. By implementing the method, the layout generation efficiency of paleo-ruin boundary transformation can be improved.

The invention provides a risk prediction method based on city system evolution, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and city planning, and the method comprises the steps: obtaining city composition basic data matched with each city composition dimension; the city composition dimension comprises at least one of city land, city economy, population, house price and traffic dimension; and on the basis of the target city constraint rule, inputting the city composition basic data into a city evolution prediction model for city evolution prediction to obtain target city evolution data, and carrying out risk identification on the target city evolution data based on a city planning demand to obtain target city evolution risk data. According to the method, the evolution data of each dimension is predicted through the city evolution prediction model, and the risk identification of city system evolution is carried out based on the evolution data of each dimension and the city planning demand, so that the potential risk in the city system evolution process can be accurately positioned, and the risk prediction accuracy of city system evolution is improved.

The invention provides an urban traffic flow prediction method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: receiving traffic data, and carrying out the linear transformation processing through a full connection layer; the processed traffic data are input into multiple layers of time-space modules of the same structure for feature extraction, each layer of time-space module is composed of a time convolution module and a mixed time-varying graph module, and time features output by the time convolution module and spatial features output by the mixed time-varying graph module are spliced to form output of the time-space modules; parameterized learning is carried out on the mixed time-varying graph module; performing jump connection on the outputs of all the space-time modules to generate fused space-time features; and processing the fused spatial-temporal characteristics through a ReLU activation function, and transmitting a processing result to a full connection layer to obtain a final prediction result. According to the method, the urban traffic condition and the change trend can be accurately judged, and effective decision support is provided for urban planning and traffic management.

The present invention provides a visualization intelligent big data urban planning decision support management method and system, which relates to the technical field of big data management. The method includes: acquiring multi-dimensional data related to urban planning; preprocessing the multi-dimensional data; establishing a correlation analysis model between urban elements, and calculating the interaction influence factors between different urban element data through it to form an urban element correlation matrix; extracting features from the urban element correlation matrix, screening out urban element combinations, and calculating the weight values of different urban element combinations by using a causal inference method; performing site selection optimization calculation on the target planning area, and calculating the comprehensive fitness scores of each alternative area; sorting each alternative area, selecting the alternative area with the highest comprehensive fitness score as the target area, and generating a visualization urban planning decision report. The present invention can provide dynamic analysis content, which is convenient for providing decision-making basis for urban planning.

The invention discloses a low-altitude take-off and landing infrastructure grading site selection method, device and equipment, and relates to the technical fields of low-altitude economy, urban planning and the like, and the method comprises the steps: obtaining a low-altitude scene demand, and carrying out the quantification processing, and obtaining demand data; a facility grading system is established based on the demand data, configuration data corresponding to the facility grading system is obtained, and the facility grading system comprises at least three grades; performing overlay analysis on the limiting factor data, and determining a candidate region; and performing hierarchical analysis on the candidate region to obtain suitability distribution data under the low-altitude scene requirement, and performing solution based on the suitability distribution data and the configuration data to obtain site selection data corresponding to the facility grading system.

The invention relates to the technical field of surveying and mapping, in particular to a surveying and mapping device for urban planning, which comprises a surveying and mapping main body, the lower end of the supporting plate is movably connected with three supporting legs at equal intervals, the supporting legs have a telescopic function, and the upper end face of the supporting plate is sunken downwards to form a through hole; the cross section of the auxiliary base is in a T shape, the vertical portion of the auxiliary base penetrates through the through hole, the auxiliary base is connected with the supporting plate through a clamping piece, the clamping piece extends out of the lower side of the auxiliary base, and the transverse portion of the auxiliary base is attached to the lower end of the supporting plate; the connecting rope is connected with the lower end of the clamping piece, the outer end of the connecting rope is slidably connected with the functional piece, and the other end of the functional piece is connected with the lower end of the auxiliary seat; by means of the design, entity type point-to-point operation is achieved, the probability that point-to-point observation generates interference and the like due to factors such as light is effectively reduced, the operation time of point-to-point operation is effectively shortened, and the surveying and mapping effect and efficiency are effectively guaranteed.