Surveying and mapping geographic information map data acquisition method and system

Abstract

The invention relates to the technical field of map data acquisition, in particular to a surveying and mapping geographic information map data acquisition method and system, and the method comprises the steps: building a space-time reference network: generating an underground-earth surface space-time reference network for connecting the underground with the earth surface through acousto-optic joint calibration; generating a multi-source correction parameter: fusing the underground inertial navigation data, the surface atmospheric refraction parameter and the equipment temperature drift coefficient, and calculating a multi-source correction parameter set for coordinate system conversion and error compensation; space-time evolution element collection: calibrating a mobile collection device in real time by using the multi-source correction parameter set, synchronously obtaining underground pipeline point cloud, an earth surface building image and a construction machinery track, and generating a space-time evolution element body with a space-time version label; and dynamic topology evolution modeling: analyzing an element change sequence in the spatio-temporal evolution element body, and constructing a dynamic evolution topology model. According to the method, differential analysis and a conflict backtracking strategy are combined, so that the adaptive updating and decision support capability of the map data in a construction disturbance scene is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of map data acquisition, and in particular to a method and system for acquiring surveying and mapping geographic information map data. Background Art

[0002] Against the backdrop of the rapid development of urban underground space, surveying and mapping geographic information faces challenges such as the integration of heterogeneous underground and surface environments, frequent dynamic evolution of elements, and high incidence of construction disturbances. Traditional geographic information collection methods mostly rely on a single GNSS positioning or inertial navigation system, which makes it difficult to cover underground scenes. In addition, the collection of various data sources (such as point clouds, images, and trajectories) often suffers from spatiotemporal asynchrony, accuracy drift, and version confusion, which cannot meet the needs of high-precision surveying and mapping and temporal change tracking. In addition, existing methods usually only establish static map structures and lack the ability to dynamically model element state changes and topological relationship evolution.

[0003] The existing technology has not yet formed a unified surveying and mapping framework that connects "underground-surface-construction activities", especially in terms of coordinate system unification, spatiotemporal precision correction and feature versioning. At the same time, in the face of frequent construction areas or large-scale engineering scenarios, it is difficult to effectively manage the differential evolution between multi-source data, and there is a lack of topological conflict detection and early warning capabilities. Therefore, there is an urgent need for a surveying and mapping geographic information data collection method and system that integrates multi-source perception and supports dynamic evolution modeling to achieve high-precision, version-traceable full-life cycle surveying and mapping in a dynamic environment. Summary of the Invention

[0004] The present invention provides a method and system for collecting surveying and mapping geographic information map data.

[0005] A method for collecting surveying and mapping geographic information map data comprises the following steps: S1, construction of a spatiotemporal reference network: deploying sonar beacon arrays in the underground space and setting up GNSS reference stations on the surface, and generating an underground-surface spatiotemporal reference network connecting the underground and the surface through joint acoustic and optical calibration; S2, multi-source correction parameter generation: Based on the underground-surface spatiotemporal reference network, the underground inertial navigation data, the surface atmospheric refraction parameters and the equipment temperature drift coefficient are integrated to calculate the multi-source correction parameter set for coordinate system conversion and error compensation; S3, spatiotemporal evolution element acquisition: using the multi-source correction parameter set to calibrate the mobile acquisition equipment in real time, synchronously acquire underground pipeline point clouds, surface building images, and construction machinery trajectories, and generate spatiotemporal evolution element volumes with spatiotemporal version labels; S4, dynamic topology evolution modeling: analyzing the element change sequence in the spatiotemporal evolution element body, and constructing a dynamic evolution topology model describing the history-current-future relationship of the elements based on the version difference algorithm.

[0006] Optionally, the S1 includes: S11, underground sonar array layout: a regular tetrahedron array consisting of four sonar transmitters is laid out on the tunnel sidewall, with controlled transmitter spacing and array volume; S12, Surface reference station setup: Install a GNSS reference station on the surface directly above each sonar array, close to the sonar geometric center, to reduce the coordinate conversion error between the underground and the surface; S13, combined acoustic and optical calibration: acoustic and laser signals are emitted simultaneously, and both signals are received by a mobile device. When the time and space alignment conditions are met, they are recorded as calibration points, and a coordinate transformation model including rotation, translation, and temperature drift compensation is established; S14, reference network generation: collect multiple valid calibration points, calculate the conversion model parameters through the least squares method, and build a unified spatiotemporal reference network for underground and surface areas.

[0007] Optionally, the S13 includes: S131, Joint Signal Transmission: The underground sonar transmitter emits an acoustic signal of a specific frequency, while the surface laser emits a green laser beam vertically downward, providing a dual-source physical signal for underground-surface joint calibration; S132, precise synchronous reception: Use a mobile calibration vehicle to synchronously receive the acoustic signal and the laser spot. When the two signals meet the set synchronization and alignment conditions in time and space, the point is recorded as a valid joint calibration point; S133, coordinate transformation modeling: Based on multiple joint calibration points, a transformation model from the underground coordinate system to the surface coordinate system is established. The model includes a rotation matrix, a translation vector, and compensation items caused by temperature changes.

[0008] Optionally, the S2 includes: S21, Reference Network Parameter Extraction: Extract the rotation matrix, translation vector, and temperature drift compensation vector from the underground-surface spatiotemporal reference network as the basic parameters for multi-source correction; S22, multi-source data fusion modeling: Construct a coordinate correction model. After inputting the original underground coordinates, rotation, translation, temperature drift, atmospheric refraction, and inertial drift compensation are applied in sequence to output the corrected coordinate results. S23, parameter calculation: Calculate the atmospheric delay and inertial drift error separately, and solve the coupling matrix of atmospheric and inertial errors through multi-point fitting to improve the accuracy of the correction model; S24, parameter set encapsulation: package all model parameters into structured data to generate a multi-source calibration parameter set.

[0009] Optionally, the S23 includes: S231, atmospheric delay calculation: Calculate the delay error caused by atmospheric refraction during the propagation of the GNSS signal based on the surface pressure, temperature, humidity, and altitude information of the acquisition point; S232, Inertial Drift Error Compensation: Calculate the drift error of the inertial navigation system by comparing the angular velocity measured by the IMU with the true angular velocity inferred by the sonar array. This is then converted into a drift correction in the spatial coordinates using the error transfer matrix. S233, multi-source parameter fitting and solution: Using multiple original coordinates and reference coordinates with known correspondences, the least squares adjustment method is used to jointly fit and solve the atmospheric refraction correction matrix and the inertial drift coupling matrix to achieve optimal estimation of the model parameters.

[0010] Optionally, the S3 includes: S31, real-time data calibration: Use the generated multi-source correction parameters to calibrate the real-time position and attitude information of the mobile acquisition device to compensate for errors caused by temperature changes, atmospheric refraction and inertial drift; S32, simultaneous multi-source data acquisition: Based on the calibrated pose information, three types of data are collected simultaneously, including underground pipeline point clouds, surface building images, and construction machinery trajectories; S33, generating spatiotemporal version labels: assigning a unique spatiotemporal version label to each type of collected feature. The spatiotemporal version label includes the feature type, collection timestamp, and version number. The version number is automatically incremented when the feature location changes. S34, feature body construction: encapsulate the calibrated point cloud, image, trajectory and label information into a complete spatiotemporal evolution feature body.

[0011] Optionally, the S32 includes: S321, Underground Pipeline Point Cloud Collection: Use laser scanning equipment to obtain 3D point cloud data of underground pipelines, and convert the original local coordinates into global coordinates based on the calibration posture of the equipment; S322, surface building image acquisition: using an oblique camera to acquire a sequence of building images, and calculating their spatial positioning information using the device's exterior orientation elements; S323, Construction Machinery Trajectory Collection: Integrate GNSS and IMU positioning data to obtain the position of the construction machinery at each time point and perform corrections to form a complete motion trajectory set.

[0012] Optionally, the S4 includes: S41, identifying element changes: extracting version information at each time point from element data to determine which elements have undergone significant changes; S42, calculate element differences: calculate the differences of geometric shapes and attributes separately, and combine them into an overall change degree index; S43, constructing a topological relationship graph: establishing a state node for each element of a time node, establishing a topological connection based on spatial overlapping relationships, and adding future prediction nodes based on historical change trends; S44, Generate Dynamic Topology Model: Integrate all nodes, connection relationships and spatiotemporal indexes to generate a dynamic topology model that can track historical, current and predicted states; S45, check and handle conflicts: When there is a position conflict or time logic error between the new data and the historical data, the processing mechanism is automatically triggered.

[0013] Optionally, the S43 includes: S431, constructing state graph nodes and edges: establishing corresponding nodes for the states of each element at different times, and establishing directed edges between versions at adjacent time points to form the basic structure of the evolution path; S432, determine spatial relationships and establish connections: Determine spatial relationships between elements at the same time. If there is significant overlap in geometric areas, establish bidirectional connecting edges indicating "adjacency" or "interaction" to construct spatial hierarchical topological connections. S433, add future prediction path: predict the future evolution trend of the element based on its past change frequency, and add a prediction edge pointing from the current state node to the future time point.

[0014] A surveying and mapping geographic information map data acquisition system, used to implement the above-mentioned surveying and mapping geographic information map data acquisition method, includes the following modules: The space-time reference network construction module deploys a sonar beacon array in the underground space and a GNSS reference station on the surface. Through the joint acoustic and optical calibration mechanism, an underground-surface space-time reference network connecting the underground and the surface is generated. Multi-source correction parameter generation module: Based on the time-space reference network, it integrates underground inertial navigation data, surface atmospheric refraction parameters and equipment temperature drift coefficient to calculate the multi-source correction parameter set required for coordinate system conversion and error compensation; The spatiotemporal evolution feature acquisition module applies multi-source correction parameter sets to the real-time calibration of mobile acquisition equipment, and simultaneously collects data such as underground pipeline point clouds, surface building images, and construction machinery trajectories to construct a spatiotemporal evolution feature volume with spatiotemporal version labels. Dynamic topology evolution modeling module: parses the element change sequence in the spatiotemporal evolution element body, executes the version difference algorithm, and constructs a dynamic evolution topology model that describes the evolution path of the historical state, current state and future state of each element.

[0015] Beneficial effects of the present invention: The present invention proposes a method for collecting surveying and mapping geographic information map data that connects the underground and the surface, constructs an underground-surface spatiotemporal reference network, realizes the unified calibration of multi-source sensor data in a heterogeneous environment, and effectively solves the problems of limited underground space surveying and mapping and the inability of surface and underground positioning systems to coordinate in the existing technology; at the same time, based on the joint acoustic and optical calibration and multi-source error compensation mechanism, it greatly improves the coordinate accuracy and spatiotemporal consistency in the dynamic collection process, meeting the needs of high-precision geographic information collection in complex environments.

[0016] This invention innovatively introduces the spatiotemporal evolution element modeling and topological evolution mechanism with version labels. By constructing a dynamic evolution topology map and a confidence matrix, it realizes the structural expression of the surveying and mapping object from a static state to a three-state fusion of "history-current situation-future", solving the problems of untraceable dynamic updates of multi-source elements and unpredictable topological evolution. At the same time, combined with differential analysis and conflict backtracking strategies, it improves the adaptive update and decision-making support capabilities of map data in construction disturbance scenarios, and has good scalability and engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 2 is a system module diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, a method for collecting surveying and mapping geographic information map data includes the following steps: S1, construction of a spatiotemporal reference network: deploying sonar beacon arrays in the underground space and setting up GNSS reference stations on the surface, and generating an underground-surface spatiotemporal reference network connecting the underground and the surface through joint acoustic and optical calibration; S1 specifically includes: S11, Underground Sonar Array Layout: Set up sonar beacon groups on the inner wall of the tunnel or underground pipeline corridor. Each group of beacons is arranged according to the geometric structure of a regular tetrahedron to ensure that it forms a structurally stable and directional array in three-dimensional space. Each group includes four sonar transmitters, and their spatial coordinates are expressed as: ; in, It is The three-dimensional coordinates of the sonar transmitter; To ensure adequate signal coverage and array stability, the spatial spacing between adjacent sonar transmitters is Tunnel height Within a certain proportion range, it can be expressed as: ; in, Indicates the clearance height of a tunnel or pipe gallery. The height of standard tunnels such as subways and pipe galleries is usually greater than 2.5 meters. This ensures space for equipment layout and meets the requirements of human traffic and equipment operation. The volume of the regular tetrahedron formed by four sonar transmitters , Small volume will reduce the stereo positioning capability and increase the risk of signal interference; volume greater than 2 cubic meters can achieve stable three-dimensional positioning performance. The distance between adjacent sonars ensures that the array has a certain density for high-precision positioning without causing spatial overlap or interference. Experience shows that 0.5-0.8 times the tunnel height is the optimal geometric stability configuration range. S12, surface reference station setting: In order to achieve the unification of the underground positioning system and the surface coordinate system, a GNSS reference station is deployed on the surface just above each set of sonar beacons to provide a global reference spatial positioning benchmark. Its coordinates are , the geometric center of the sonar beacon group is calculated by the positions of the four transmitters and is expressed as In order to minimize the datum conversion error and ensure the geometric consistency of the surface and underground measurement points in the vertical projection, the horizontal distance between the GNSS reference point and the sonar geometric center is constrained to meet the constraint conditions and ensure that the vertical mapping offset does not exceed 10% of the tunnel height, which is expressed as: ; in, is the three-dimensional coordinate of the surface GNSS reference station, are the geometric center coordinates of the four sonar transmitters; S13, combined acoustic and optical calibration: This involves synchronizing the reception and positioning of acoustic and laser signals to achieve combined calibration of the underground-surface measurement system. This involves three steps: (1) Transmitting: The sonar transmitter emits a frequency of At the same time, the surface laser projects a wavelength of A green laser beam; in, , commonly used positioning sonar frequency range, The attenuation is small in cement environment, and the penetration and positioning accuracy are well balanced, allowing The error is the frequency tolerance range of the device, is the laser wavelength, which makes the two physical signals intersect in space. , which is the most stable green laser wavelength on the market, suitable for visual positioning and high-contrast projection, and the supporting receiving device has high sensitivity; (2) Calibration: The mobile calibration vehicle receives the acoustic wave signal and the laser spot synchronously underground. When both conditions are met at the same time, it is recorded as a valid joint calibration point, that is, the time difference of the acoustic wave arrival is lower than the time difference threshold. When the distance between the center of the laser spot and the center of the acoustic wave receiver is lower than the distance threshold , the point is considered to be a valid joint calibration point and can be used for subsequent coordinate system registration modeling; in, It is the maximum time difference between different sonar receivers receiving the same signal. It is the time difference threshold. The propagation speed of sound waves in the air is about 340m / s. If it is less than the time difference threshold, the accuracy requirement of 0.1 level can be met. is the spatial distance between the center of the laser spot and the center of the acoustic wave receiver, is the distance threshold, which is the maximum allowable spatial deviation after the GNSS horizontal positioning error is combined with the acoustic positioning error, ensuring that the two signals actually point to the same physical point; (3) Establish a benchmark conversion model from underground to surface: Construct an underground coordinate system based on the collected joint calibration point pairs To the surface coordinate system The conversion relationship is expressed as: ; in, It is the rotation matrix from the underground coordinate system to the surface coordinate system, ensuring that the rotation transformation does not introduce scale or nonlinear distortion errors and satisfies the Euler transformation characteristics. It is the translation vector from underground to the surface, indicating the offset between the origins, with a value range of 0-100. It is the temperature drift compensation vector, which represents the error correction coefficient caused by thermal expansion. The value is based on the linear expansion coefficient of the structural material (such as concrete, rail) multiplied by the reference length (usually 10-30). A higher compensation coefficient should be set in the temperature-sensitive area. is the rate of change of ambient temperature, ranging from , which indicates the temperature variation per unit time. The temperature variation in the tunnel environment during day and night and equipment operation generally does not exceed ±5°C per hour. This range covers typical urban underground working conditions. S14, datum network generation: obtain no less than 12 joint calibration points to ensure the robustness and solvability of model parameter calculation, and use the least squares adjustment method to solve the parameters. , and build an underground-surface spatiotemporal reference network covering the acquisition area. The adjustment model is expressed as: ; in, For the The spatial coordinates of the joint calibration points in the underground, It is The spatial coordinates of the joint calibration points on the surface, is the total number of joint calibration points, 3 degrees of freedom for the rotation matrix + 3 degrees of freedom for the translation vector + 3 degrees of freedom for temperature drift compensation = 9 unknowns. At least 12 points are required to build a redundant measurement system to ensure least squares solvability and robustness.

[0023] S2, multi-source correction parameter generation: Based on the underground-surface spatiotemporal reference network, the multi-source correction parameter set for coordinate system conversion and error compensation is calculated by integrating underground inertial navigation data, surface atmospheric refraction parameters, and equipment temperature drift coefficients; S2 specifically includes: S21, Reference Network Parameter Extraction: Extract the core parameters for subsequent multi-source data correction from the constructed underground-surface spatiotemporal reference network, including the rotation matrix , translation vector And the temperature drift compensation coefficient vector ; S22, multi-source data fusion modeling: Establish a three-dimensional spatial coordinate correction model that integrates underground inertial navigation data, surface atmospheric refraction parameters, and equipment temperature drift coefficients, expressed as: ; in, is the original underground coordinate, are the corrected coordinates, is the rate of change of ambient temperature, is the atmospheric refraction delay, It is the inertial navigation angular velocity drift. The drift of high-precision inertial navigation system is controlled within 0.01. If it exceeds this value, obvious integral cumulative error will occur and it needs to be corrected in time. , is the atmospheric refraction correction matrix , which reflects the coupling error of atmospheric components to GNSS coordinates, is obtained by least squares fitting, maintaining numerical stability and physical rationality. is the inertial navigation error coupling matrix , the value range is 0.01-1.0, mapping angular velocity drift to coordinate error. The value range is affected by the stability and installation direction of the IMU, and is obtained by multi-point fitting to ensure that the error is controlled at the millimeter-centimeter level. is the temperature drift term, It is an atmospheric item. is the inertia term; S23, parameter calculation: Calculate atmospheric refraction delay, inertial navigation drift compensation and matrix parameters, including: (1) Calculation of atmospheric refraction delay: The Hopfield model is used to estimate the atmospheric delay effect on the GNSS signal during propagation, especially considering the influence of air pressure, temperature, humidity and altitude on the refraction effect, which is expressed as: ; in, is the surface air pressure, ranging from 850 to 1050. The absolute surface temperature ranges from 273°C to 320°C, corresponding to a temperature of 0–47°C. This is the range of common outdoor data collection environments. The GNSS base station meteorological module provides this data in real time. It is the altitude of the acquisition equipment, with a value range of 0-3. Map data acquisition is often carried out in low-altitude areas, and DEM interpolation results or GNSS elevation are provided directly. is the humidity correction factor, , the value range is 1.0-1.26, is the relative humidity, ranging from 0 to 100. hour, , used to introduce the effect of water vapor content on refraction in the Hopfield model; (2) Inertial navigation drift compensation: Considering the position drift caused by the integration error of the IMU during long-term operation, the inertial angular velocity drift is calculated, and the true angular velocity is calculated by the differential of the sonar beacon vector, which is expressed as: ; ; in, is the angular velocity measured by IMU, and its value range is , depends on the speed of the equipment posture change, the common operating equipment rotation does not exceed ±10, is the true angular velocity inferred from the geometric changes of the sonar array, and its value range is , which is calculated in real time from the geometric change rate of the sonar array, reflects the actual attitude change and serves as a reference for IMU drift correction. is the IMU error transfer matrix , the value range is 0.8-1.2, is the spatial position vector of the sonar beacon, which is used to calculate geometric differentials and derive the true angular velocity. The beacons must not be collinear to ensure that the spatial vector cross product is valid; (3) Matrix parameter calculation: In order to obtain the optimal solution of the atmospheric refraction matrix and the inertial coupling matrix, the least squares adjustment method is used to solve the atmospheric matrix and the inertia matrix , construct the objective function, expressed as: ; in, The number of calibration points should include at least 9 degrees of freedom (3 rotations + 3 translations + 3 compensations) parameter solutions. More than 15 points can provide redundancy, improve robustness and solution accuracy. It is The three-dimensional coordinates of the original observation points (underground equipment collection values), the value range is 0-500, the coordinates of the field scanning points, The first Reference coordinates, It is The ambient temperature change rate of the original observation point, It is The atmospheric refraction delay of the point ranges from 0.2 to 2.5. It is Point IMU angular velocity drift vector, the difference between the measured angular velocity and the reference estimated angular velocity. The larger the value, the more serious the IMU integral drift. The precision inertial navigation system error is less than 0.01. S24, parameter set encapsulation: All parameters involved in modeling and correction are structured and encapsulated to form a standardized "multi-source correction parameter set". The encapsulation format uses JSON structure, which is easy to embed into the acquisition system and share on multiple platforms.

[0024] S3, spatiotemporal evolution feature acquisition: Utilize multi-source calibration parameter sets to calibrate mobile acquisition equipment in real time, synchronously acquire underground pipeline point clouds, surface building images, and construction machinery trajectories, and generate spatiotemporal evolution feature volumes with spatiotemporal version labels; S3 specifically includes: S31, real-time data calibration: The position and posture of the acquisition device are corrected through the proposed multi-source correction parameter set to ensure that the original coordinates are accurately expressed in a unified spatial reference system and that the data collected at each moment is accurately expressed in a unified spatial reference system. Get the device's raw pose data , and introduces multi-factor error compensation items such as rotation, translation, temperature drift, atmospheric refraction and inertial navigation drift in real time to form a comprehensive correction model, which is expressed as: ; in, yes The original device pose at the moment, derived from the initial measurement results of IMU or GNSS, is the rough positioning to be corrected. The range is set according to the operating range of the device. , It is a comprehensive correction model that reflects the accurate coordinates in the standard surface reference system. The target coordinates generated after multi-source error compensation serve as the basis for spatial registration of downstream data. It is the real-time temperature change rate, the temperature fluctuation range caused by the common day and night temperature difference and equipment heating, which is measured in real time by the temperature sensor. It is the real-time atmospheric refraction delay, calculated based on the Hopfield model. Its value range fluctuates with humidity, altitude, and air pressure, and can affect GNSS signals by up to several meters. is the real-time IMU angular velocity drift, , the value range is ; S32, Multi-source Data Synchronous Collection: Synchronize the collection of multiple spatial and temporal elements of underground and surface dimensions to ensure one-to-one correspondence between spatial location and timestamps. Specifically, it includes: (1) Underground pipeline point cloud acquisition: Use laser scanner to obtain underground pipeline point cloud data In each point The original coordinates are in the local coordinate system of the scanner and need to be globally transformed after the device calibrates the posture. The transformation model is expressed as: ; in, is the point cloud coordinate in the local coordinate system, and its value range is , relative coordinates relative to the center of the scanner body, often used for point cloud preprocessing inside the device, It is the global point cloud coordinate after the device posture transformation, and the three-dimensional point consistent with the surface reference system, used for map modeling or spatial overlay. It is a pose transformation operator, which means transforming local coordinates into global coordinates. It is usually a rigid body transformation, which is a rotation followed by a translation. It is a common method for 3D data registration. It's time The corrected pose of the acquisition device in the Earth's surface reference system (rotation, translation, temperature drift, etc. have been compensated) is a three-dimensional global coordinate. (2) Surface building image acquisition: image sequence acquired by oblique photography camera , whose exterior orientation elements are solved by the corrected device posture and expressed as: ; in, It is the longitude coordinate of the oblique photography camera in the global coordinate system. The value range is 0-500000, corresponding to the projected longitude of the photography center point. The value varies with the coordinate system and is generally expressed in geographic coordinates or projected coordinates. It is the camera's latitude coordinate, ranging from 0 to 500000, corresponding to the projection latitude of the photography center point. It is the elevation of the camera relative to the ground, which usually depends on the flight altitude of the UAV or the height of the ground station, and satisfies the effective viewing angle range of oblique imaging. is the camera pitch angle, ranging from , which indicates the downward / upward rotation angle of the lens. A positive value indicates an upward shot, while a negative value indicates a downward shot. It is one of the external orientation elements. Is the camera roll angle, describing the rotation around the front and rear axes, and its value range is , which represents the rotation of the camera around the front-back axis (X axis). It does not change much during stable flight and is used for image distortion correction in high dynamic environments. is the camera yaw angle, which describes the rotation direction around the vertical axis and indicates the camera orientation (azimuth). It is used for image stitching and alignment in map projection. It is the global position and attitude of the device after multi-source error correction, providing pose information under a unified reference, which is used to solve the spatial positioning and attitude recovery (exterior orientation elements) of the image; (3) Construction machinery trajectory collection: The GNSS and IMU positioning system outputs the trajectory of the construction machinery. Position at the moment , after correction, the trajectory set is constructed and expressed as: ; in, It is construction machinery at all times The original positioning coordinates, ranging from 0 to 1000, are output from the GNSS / IMU fusion system and represent the current position of the device for subsequent correction and trajectory generation. It is a set of construction machinery trajectory points, a path set consisting of multiple corrected postures at consecutive moments, used in construction path management, change analysis, safety assessment and other scenarios. It is a construction time series, which represents the time index of the acquisition node. It can be equal interval or dynamic interval, and is used for trajectory sequence sorting and spatiotemporal version label synchronization; S33, spatiotemporal version tag generation: To achieve unique identification of features in different time and space states, a tag system with millisecond-level timestamp and version number structure is designed. The spatiotemporal version tag consists of three parts, represented as follows: ; in, It is the element type code (such as PL for pipeline, BLD for building, EQ for machinery), which is used to distinguish different types of spatiotemporal elements to facilitate subsequent classification management and version tracking. It is a collection timestamp, an accurate time tag, which helps in time sequence sorting and change comparison. It is the version number, which starts at V0 and increases when changes occur. It supports historical version backtracking and differential comparison of elements and adapts to dynamic construction environments. Version updates are determined based on changes in element positions, and the rules are as follows: ; in, It is the current historical version number of the feature, which identifies the status version of the current feature. It is the new version number obtained after the judgment. If the conditions are met, it remains unchanged. Otherwise, it is called The function generates the next version, is the positioning accuracy of the benchmark network, Represents the version number increment function, is the position change tolerance threshold, ranging from 0.03 to 0.15; S34, element body construction: Structural packaging of calibrated underground point clouds, building images, machine trajectories and label information to form a unified spatiotemporal evolution element body , its structural form is expressed as: ; in, It is a unified spatiotemporal evolution element body, whose internal data structure is expressed in four-dimensional tensors, including three-dimensional spatial position and time, attribute fields, and version history. It is a corrected surface image sequence that provides building information and visual features.

[0025] S4, dynamic topology evolution modeling: Analyze the feature change sequence in the spatiotemporal evolution feature body, and build a dynamic evolution topology model that describes the history-current-future relationship of the features based on the version difference algorithm.

[0026] S4 specifically includes: S41, Identifying element changes: From spatiotemporal evolution to element bodies Extract the version labels of each element at different times and construct its time series. By comparing the status at different times, filter out the status changes that exceed the change recognition threshold. Elements, generate change series , expressed as: ; in, It is A unique identifier for a geographic feature, distinguishing different feature entities, such as underground pipelines, surface buildings, etc. For the A time node represents the time point of sampling or version recording, supporting dynamic evolution analysis. It is an element At the moment The spatiotemporal version tag is used to record the version identity of the feature state evolution. It is a factor change sequence, which represents the change record of a factor between two consecutive time points. It is a state change fusion indicator with a value range of 0-1. It is used to judge whether it constitutes a valid evolution by integrating space and attribute changes. The change recognition threshold is fixed at 0.8. Only changes greater than this value are considered state changes to prevent oversensitivity. S42, Calculate element differences: For each pair of modified versions, perform differential analysis to quantify the changes, including: (1) Spatial difference calculation: By calculating the Hausdorff distance between the geometric shapes of the versions, the differences in spatial structure are obtained and normalized to offset the influence of the element scale on the degree of difference, which is expressed as: ; in, It is an element The degree of spatial variation, the larger the value, the more significant the change in spatial structure. yes and The Hausdorff distance measures the maximum offset between two geometric objects and reflects the degree of spatial deformation. It is an element At the moment The geometric representation of Is a collection object The diagonal length of the minimum enclosing rectangle is used to normalize the Hausdorff distance; (2) Attribute difference calculation: based on the weight of each attribute ,Comparing the changes in attribute values ​​in the two versions, the normalized weighted matching rate is used to reflect the attribute similarity, and the degree of attribute change is expressed by its complement, which is expressed as: ; in, It is the degree of change of feature attributes, with a value range of 0-1. The larger the value, the more changes in semantic and geometric attributes. is the attribute weight coefficient, which ranges from 0 to 1 and sums to 1. It reflects the degree of influence of the attribute on the overall change. Usually, the geometric attribute has a larger weight and the semantic attribute has a smaller weight. It is an attribute value equality function, which takes a value of 0 or 1 to determine whether the attribute value has changed. It is Attributes at the moment The value of is used to reflect non-geometric attributes such as pipeline material, diameter, and purpose; (3) Change fusion calculation: spatial difference and attribute difference are combined through weight coefficients Perform linear fusion to obtain a unified state change indicator, which is expressed as: ; in, is the spatial change weight factor, with a fixed value of 0.6, which balances the contribution of spatial differences and attribute differences to the total change. The default is to favor spatial changes. S43, constructing a topological relationship graph: After completing the state change calculation, further construct the spatiotemporal evolution graph structure and mine the topological connection relationship between elements, specifically including: (1) Constructing a directed acyclic graph , including nodes and the edge , where the node , indicating the element In time state, side , indicating the version evolution relationship, It is a topological evolution directed graph; (2) Establishment of topological connection relationship: Determine whether two elements overlap in space at the same time. When the overlapping area exceeds the area threshold, When the minimum ratio is defined, a bidirectional topological edge is established between them, which is expressed as: ; in, It is the geometric overlap area of ​​the elements, which is used to determine whether the elements have spatial adjacency. It is the topological connection area threshold, with a default value of 0.15, which controls whether to establish a spatial connection relationship. It represents the intersection area ratio; (3) Future state prediction connection: To support future trend modeling, at the current time node Add a prediction edge to connect to the next prediction time node , where the prediction interval is determined by the historical change frequency, expressed as: ; in, is the current status timestamp, is the predicted state timestamp, The time step for state prediction; S44, Generate Dynamic Topology Model: Integrate the historical, current, and predicted relationships to form a complete dynamic evolution topology model, expressed as: ; in, For spatiotemporal index structures (such as four-dimensional R-tree), is the topological edge confidence matrix, ranging from 0 to 1, , It is a dynamically evolving topology model; S45, check and handle conflicts: To ensure the logical consistency of the model, the system performs conflict detection on the relationship between the newly added nodes and the historical version nodes. When a geometric, semantic or temporal logic conflict occurs, the automatic backtracking mechanism is triggered, the historical topology state is called and the conflict resolution function is executed. , responding to abnormal situations such as logical contradictions and geometric conflicts, executing rollback or warning, expressed as: , (historical version backtracking); in, It is an element In time The status node at the moment indicates the current status of the feature and is used to determine whether it conflicts with the old version. It is an element In time The state node at a certain moment indicates the historical state and is used for version conflict comparison. It is a conflict type marker variable, indicating whether there is a topological conflict or logical contradiction. The value is a non-empty set or an empty set. If it is not empty, it means that there is a conflict, triggering the version backtracking mechanism.

[0027] like Figure 2 As shown, a surveying and mapping geographic information map data acquisition system is used to implement the above-mentioned surveying and mapping geographic information map data acquisition method, including the following modules: The space-time reference network construction module deploys a sonar beacon array in the underground space and a GNSS reference station on the surface. Through the joint acoustic and optical calibration mechanism, an underground-surface space-time reference network connecting the underground and the surface is generated. Multi-source correction parameter generation module: Based on the space-time reference network, it integrates underground inertial navigation data, surface atmospheric refraction parameters, and equipment temperature drift coefficient to calculate the multi-source correction parameter set required for coordinate system conversion and error compensation; The spatiotemporal evolution feature acquisition module applies multi-source correction parameter sets to the real-time calibration of mobile acquisition equipment, and simultaneously collects data such as underground pipeline point clouds, surface building images, and construction machinery trajectories to construct a spatiotemporal evolution feature volume with spatiotemporal version labels. Dynamic topology evolution modeling module: parses the element change sequence in the spatiotemporal evolution element body, executes the version difference algorithm, and constructs a dynamic evolution topology model that describes the historical state, current state, and future state evolution path of each element.

[0028] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0029] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for collecting surveying and mapping geographic information map data, characterized in that: The following steps are involved: S1, construction of a spatiotemporal reference network: deploying sonar beacon arrays in the underground space and setting up GNSS reference stations on the surface, and generating an underground-surface spatiotemporal reference network connecting the underground and the surface through joint acoustic and optical calibration; S2, multi-source correction parameter generation: Based on the underground-surface spatiotemporal reference network, the underground inertial navigation data, the surface atmospheric refraction parameters and the equipment temperature drift coefficient are integrated to calculate the multi-source correction parameter set for coordinate system conversion and error compensation; S3, spatiotemporal evolution element acquisition: using the multi-source correction parameter set to calibrate the mobile acquisition equipment in real time, synchronously acquire underground pipeline point clouds, surface building images, and construction machinery trajectories, and generate spatiotemporal evolution element volumes with spatiotemporal version labels; S4, dynamic topology evolution modeling: analyzing the element change sequence in the spatiotemporal evolution element body, and constructing a dynamic evolution topology model describing the history-current-future relationship of the elements based on the version difference algorithm.

2. A method for collecting surveying and mapping geographic information map data according to claim 1, characterized in that: Said S1 comprises: S11, underground sonar array layout: a regular tetrahedron array consisting of four sonar transmitters is laid out on the tunnel sidewall, with controlled transmitter spacing and array volume; S12, Surface reference station setup: Install a GNSS reference station on the surface directly above each sonar array, close to the sonar geometric center, to reduce the coordinate conversion error between the underground and the surface; S13, combined acoustic and optical calibration: acoustic and laser signals are emitted simultaneously, and both signals are received by a mobile device. When the time and space alignment conditions are met, they are recorded as calibration points, and a coordinate transformation model including rotation, translation, and temperature drift compensation is established; S14, reference network generation: collect multiple valid calibration points, calculate the conversion model parameters through the least squares method, and build a unified spatiotemporal reference network for underground and surface areas.

3. A method for collecting surveying and mapping geographic information map data according to claim 2, characterized in that: The S13 includes: S131, Joint Signal Transmission: The underground sonar transmitter emits an acoustic signal of a specific frequency, while the surface laser emits a green laser beam vertically downward, providing a dual-source physical signal for underground-surface joint calibration; S132, precise synchronous reception: Use a mobile calibration vehicle to synchronously receive the acoustic signal and the laser spot. When the two signals meet the set synchronization and alignment conditions in time and space, the point is recorded as a valid joint calibration point; S133, coordinate transformation modeling: Based on multiple joint calibration points, a transformation model from the underground coordinate system to the surface coordinate system is established. The model includes a rotation matrix, a translation vector, and compensation items caused by temperature changes.

4. A method for collecting surveying and mapping geographic information map data according to claim 3, characterized in that: The S2 includes: S21, Reference Network Parameter Extraction: Extract the rotation matrix, translation vector, and temperature drift compensation vector from the underground-surface spatiotemporal reference network as the basic parameters for multi-source correction; S22, multi-source data fusion modeling: Construct a coordinate correction model. After inputting the original underground coordinates, rotation, translation, temperature drift, atmospheric refraction, and inertial drift compensation are applied in sequence to output the corrected coordinate results. S23, parameter calculation: Calculate the atmospheric delay and inertial drift error separately, and solve the coupling matrix of atmospheric and inertial errors through multi-point fitting to improve the accuracy of the correction model; S24, parameter set encapsulation: package all model parameters into structured data to generate a multi-source calibration parameter set.

5. A method for collecting surveying and mapping geographic information map data according to claim 4, characterized in that: The S23 includes: S231, atmospheric delay calculation: Calculate the delay error caused by atmospheric refraction during the propagation of the GNSS signal based on the surface pressure, temperature, humidity, and altitude information of the acquisition point; S232, Inertial Drift Error Compensation: Calculate the drift error of the inertial navigation system by comparing the angular velocity measured by the IMU with the true angular velocity inferred by the sonar array. This is then converted into a drift correction in the spatial coordinates using the error transfer matrix. S233, multi-source parameter fitting and solution: Using multiple original coordinates and reference coordinates with known correspondences, the least squares adjustment method is used to jointly fit and solve the atmospheric refraction correction matrix and the inertial drift coupling matrix to achieve optimal estimation of the model parameters.

6. A method for collecting surveying and mapping geographic information map data according to claim 5, characterized in that: The S3 includes: S31, real-time data calibration: Use the generated multi-source correction parameters to calibrate the real-time position and attitude information of the mobile acquisition device to compensate for errors caused by temperature changes, atmospheric refraction and inertial drift; S32, simultaneous multi-source data acquisition: Based on the calibrated pose information, three types of data are collected simultaneously, including underground pipeline point clouds, surface building images, and construction machinery trajectories; S33, generating spatiotemporal version labels: assigning a unique spatiotemporal version label to each type of collected feature. The spatiotemporal version label includes the feature type, collection timestamp, and version number. The version number is automatically incremented when the feature location changes. S34, feature body construction: encapsulate the calibrated point cloud, image, trajectory and label information into a complete spatiotemporal evolution feature body.

7. A method for collecting surveying and mapping geographic information map data according to claim 6, characterized in that: The S32 includes: S321, Underground Pipeline Point Cloud Collection: Use laser scanning equipment to obtain 3D point cloud data of underground pipelines, and convert the original local coordinates into global coordinates based on the calibration posture of the equipment; S322, surface building image acquisition: using an oblique camera to acquire a sequence of building images, and calculating their spatial positioning information using the device's exterior orientation elements; S323, Construction Machinery Trajectory Collection: Integrate GNSS and IMU positioning data to obtain the position of the construction machinery at each time point and perform corrections to form a complete motion trajectory set.

8. A method for collecting surveying and mapping geographic information map data according to claim 7, characterized in that: The S4 includes: S41, identifying element changes: extracting version information at each time point from element data to determine which elements have undergone significant changes; S42, calculate element differences: calculate the differences of geometric shapes and attributes separately, and combine them into an overall change degree index; S43, constructing a topological relationship graph: establishing a state node for each element of a time node, establishing a topological connection based on spatial overlapping relationships, and adding future prediction nodes based on historical change trends; S44, Generate Dynamic Topology Model: Integrate all nodes, connection relationships and spatiotemporal indexes to generate a dynamic topology model that can track historical, current and predicted states; S45, check and handle conflicts: When there is a position conflict or time logic error between the new data and the historical data, the processing mechanism is automatically triggered.

9. A method for collecting surveying and mapping geographic information map data according to claim 8, characterized in that: The S43 includes: S431, constructing state graph nodes and edges: establishing corresponding nodes for the states of each element at different times, and establishing directed edges between versions at adjacent time points to form the basic structure of the evolution path; S432, determine spatial relationships and establish connections: Determine the spatial relationships of elements at the same time. If there is significant overlap in geometric areas, establish bidirectional connecting edges indicating "adjacency" or "interaction" to build topological connections at the spatial level. S433, add future prediction path: predict the future evolution trend of the element based on its past change frequency, and add a prediction edge pointing from the current state node to the future time point.

10. A surveying and mapping geographic information map data acquisition system, used to implement a surveying and mapping geographic information map data acquisition method according to any one of claims 1 to 9, characterized in that: Includes the following modules: The space-time reference network construction module deploys a sonar beacon array in the underground space and a GNSS reference station on the surface. Through the joint acoustic and optical calibration mechanism, an underground-surface space-time reference network connecting the underground and the surface is generated. Multi-source correction parameter generation module: Based on the time-space reference network, it integrates underground inertial navigation data, surface atmospheric refraction parameters and equipment temperature drift coefficient to calculate the multi-source correction parameter set required for coordinate system conversion and error compensation; The spatiotemporal evolution feature acquisition module applies multi-source correction parameter sets to the real-time calibration of mobile acquisition equipment, and simultaneously collects data such as underground pipeline point clouds, surface building images, and construction machinery trajectories to construct a spatiotemporal evolution feature volume with spatiotemporal version labels. Dynamic topology evolution modeling module: parses the element change sequence in the spatiotemporal evolution element body, executes the version difference algorithm, and constructs a dynamic evolution topology model that describes the evolution path of the historical state, current state and future state of each element.

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