Method and electronic equipment for setting up control network for tunnel construction in complex terrain

Abstract

The present disclosure relates to a method for setting up a control network for construction of a complex terrain tunnel and an electronic device. The control network is used for spatial positioning and navigation during the construction of the tunnel. The method includes: obtaining environmental point cloud data of a target tunnel opening collected by a drone; inputting the environmental point cloud data into a control point prediction model to obtain target placement position information for a GNSS control point and a total station control point, and the target placement position information is used to guide the placement of a GNSS device and a total station; after the GNSS device and the total station are placed, obtaining the first measurement data of the GNSS device and the second measurement data of the total station; determining the position information of each GNSS control point and each total station control point according to the first measurement data and the second measurement data; and forming a control network based on the position information. The accuracy of spatial positioning and navigation during the construction of a complex terrain tunnel is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the field of tunnel construction, and in particular to a method for setting up a control network for tunnel construction in complex terrain and electronic equipment. Background Art

[0002] Tunnel external plane control measurement is crucial for tunnel engineering design, site selection and construction. In complex tunnel environments, the traditional GNSS measurement method cannot guarantee the measurement accuracy of mountain tunnel entrances due to reasons such as obstruction by trees, poor Global Navigation Satellite System (GNSS) observation conditions and short line of sight, which brings risks to the correct penetration of the tunnel. Summary of the invention

[0003] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] A first aspect of the disclosed embodiments provides a method for setting up a control network for tunnel construction in complex terrain, wherein the control network is used for spatial positioning and navigation during the construction of the tunnel, and the control network includes a plurality of control points, wherein the plurality of control points include a plurality of GNSS control points and a plurality of total station control points, wherein for any of the control points, there is at least one other control point that has line of sight with the control point;

[0005] The method comprises:

[0006] Acquire environmental point cloud data of the target entrance of the tunnel collected by the drone;

[0007] Inputting the environmental point cloud data into a control point prediction model to obtain target placement position information for the GNSS control point and the total station control point, wherein the target placement position information is used to guide the placement of the GNSS device and the total station;

[0008] After the GNSS device and the total station are installed, obtaining first measurement data of the GNSS device and second measurement data of the total station;

[0009] Determine the position information of each of the GNSS control points and each of the total station control points according to the first measurement data and the second measurement data;

[0010] The control network is constructed based on the location information.

[0011] In some embodiments, the step of inputting the environmental point cloud data into a pre-trained control point prediction model to obtain predicted position information of the GNSS control point and the total station control point includes:

[0012] According to a preset granularity, the environmental point cloud data is sampled in a height dimension to obtain a plurality of layered data corresponding to different heights;

[0013] For each of the hierarchical data, determining a placement cost corresponding to the hierarchical data, the placement cost being used to indicate a minimum cost when placing the GNSS device at a height corresponding to the hierarchical data;

[0014] The placement location information corresponding to the hierarchical data with the lowest placement cost is determined as the target placement location information.

[0015] In some embodiments, for each of the hierarchical data, determining the placement cost corresponding to the hierarchical data comprises:

[0016] For each of the hierarchical data, determining a first position group that meets a first preset condition and has a lowest first placement cost, wherein the first position group is used to indicate a candidate installation position of each of the GNSS devices;

[0017] Determine, according to the first position group and the environmental point cloud data, a second position group that satisfies a second preset condition and has the lowest second placement cost, wherein the second position group is used to indicate a candidate installation position of each of the total stations;

[0018] Determine a placement cost corresponding to the hierarchical data according to the first placement cost and the second placement cost, wherein the placement location information corresponding to the hierarchical data includes the first location group and the second location group;

[0019] Among them, when the first preset condition is met, the distance between any two of the control points is within a preset range; when the second preset condition is met, for any one of the control points, there is at least one other control point that has line of sight with the control point.

[0020] In some embodiments, the number of the GNSS control points is equal to 3, and determining the first position group that satisfies the first preset condition and has the lowest first placement cost includes:

[0021] Performing two-dimensional quantization on the layered data to obtain a set of position coordinates in the layered data that can be used to place the GNSS device;

[0022] Based on a divide-and-conquer algorithm, a target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area is determined, and the target triplet is the first position group that satisfies the first preset condition and has the lowest placement cost.

[0023] In some embodiments, the determining, based on a divide-and-conquer algorithm, a target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area includes:

[0024] Sorting the position coordinate set according to the x-coordinates of the elements in the position coordinate set;

[0025] Determine the center coordinates of the position coordinate set according to the sorting result;

[0026] Dividing the position coordinate set into two subsets based on the center coordinate;

[0027] Determine a first largest triangle and a second largest triangle corresponding to the two subsets respectively;

[0028] Determine a third largest triangle formed by taking the central coordinates as vertices;

[0029] The coordinates of three vertices of the triangle with the largest area among the first largest triangle, the second largest triangle and the third largest triangle are determined as three elements of the target triplet.

[0030] In some embodiments, determining the first largest triangle and the second largest triangle corresponding to the two subsets respectively includes:

[0031] Recursively call the divide-and-conquer method to determine the first largest triangle and the second largest triangle corresponding to the two subsets, respectively, wherein the termination condition of the recursion is that the number of elements in the recursive subset is less than or equal to 3.

[0032] In some embodiments, determining the position information of each of the GNSS control points and each of the total station control points according to the first measurement data and the second measurement data includes:

[0033] Performing data calibration preprocessing on the first measurement data and the second measurement data to obtain third measurement data;

[0034] Performing data fusion preprocessing on the third measurement data to obtain fourth measurement data;

[0035] The position information of each of the GNSS control points and each of the total station control points is determined according to the fourth measurement data.

[0036] In some embodiments, the data calibration preprocessing includes at least one of the following:

[0037] Isosphere and tropospheric delay corrections for said first measurement data;

[0038] multipath correction for said first measurement data;

[0039] Line phase center correction for the first measurement data;

[0040] Precise point positioning of the first measurement data;

[0041] temperature and pressure correction for said second measurement data;

[0042] lens constant correction for the second measurement data;

[0043] centering error correction for said second measurement data;

[0044] prism constant correction for the second measurement data;

[0045] atmospheric refraction correction for said second measurement data;

[0046] Data filtering for the first measurement data and / or the second measurement data.

[0047] In some embodiments, the data fusion preprocessing includes at least one of the following:

[0048] The data format is unified;

[0049] Coordinate system conversion;

[0050] Timestamp alignment;

[0051] Data interpolation.

[0052] A second aspect of the present disclosure provides an electronic device, including:

[0053] a storage device having a computer program stored thereon;

[0054] A processing device is used to execute the computer program in the storage device to implement the steps of the method described in the first aspect.

[0055] Through the above technical scheme, environmental point cloud data can be collected by drones, and by inputting the point cloud data into the control point prediction model, the target placement position information of the optimal GNSS control points and the total station control points can be obtained, thereby guiding the placement of the control points, effectively improving the accuracy of spatial positioning and navigation during the construction of tunnels in complex terrain.

[0056] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0058] Figure 1 It is a flow chart of a method for setting up a control network for tunnel construction in complex terrain according to an exemplary embodiment.

[0059] Figure 2 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0060] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0061] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0062] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0063] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0064] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0065] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0066] It is worth mentioning that the total station is a high-precision measuring instrument that integrates functions such as angle measurement, distance measurement, and data processing. It measures the distance between two points by emitting and receiving electromagnetic waves (usually infrared or laser) and determines the angle through a built-in optical telescope. Its application scenarios can be used for short to medium distance measurement work, such as topographic mapping, land surveys, construction projects, tunnel and bridge construction, etc.

[0067] GNSS (Global Navigation Satellite System) uses satellite signals from global navigation satellite systems (such as GPS, GLONASS, Galileo, BeiDou, etc.) to determine the precise location of ground points. It can be used for geodetic surveying, geographic information systems (GIS), navigation, land management, environmental monitoring, etc. GNSS technology can provide continuous coverage and positioning services over a large area, even globally, without being restricted by the geographical environment. However, GNSS measurements may be affected by factors such as satellite signal obstruction, multipath effects, and atmospheric conditions, and appropriate measures need to be taken to ensure accuracy.

[0068] Although GNSS satellite positioning measurement has the advantages of high relative accuracy over long distances, no error accumulation, and high point accuracy, its accuracy is not as good as that of the total station in short-distance measurement. Therefore, it is necessary to extend the GNSS observation side length as much as possible. The accuracy of tunnel penetration depends on the coordinate accuracy of the control network points outside the tunnel and the azimuth accuracy of the entrance to the tunnel. These two factors are precisely limited by the natural conditions of the tunnel entrance and are difficult to change through human intervention. With the development of infrastructure, the length of tunnels has increased, which often leads to tunnel entrances and exits, inclined shaft openings, cross tunnel openings and other openings in complex environments such as high mountain canyons or deep valleys and old forests. It is difficult to select multiple GNSS control points in an ideal state. Therefore, the accuracy of the measurement results cannot be guaranteed, which brings great risks to the correct penetration of the tunnel.

[0069] In order to solve the problems existing in the related art, the embodiments of the present disclosure provide a method for setting up a control network for tunnel construction in complex terrain and an electronic device.

[0070] Figure 1This is a flow chart of a method for setting up a control network for tunnel construction in complex terrain according to an exemplary embodiment. The execution subject of the method can be any electronic device with information processing capabilities, such as a computer, a server, etc., and the embodiments of the present disclosure do not limit this. The control network is used for spatial positioning and navigation during the construction of the tunnel. The control network includes multiple control points, and the multiple control points include multiple GNSS control points and multiple total station control points. For any of the control points, there is at least one other control point that has line of sight with the control point.

[0071] It is understandable that two GNSS control points may not have line of sight with each other, and a GNSS control point (GNSS device) may have line of sight with only one total station. For any total station control point (total station), it may have line of sight with only one or more other total stations, or it may have line of sight with the GNSS control point.

[0072] For example, the control network may include three GNSS control points, none of which have a line-of-sight relationship. The control network also includes three total station control points. Total station 1 may have a line-of-sight relationship with total station 2 and GNSS device 1, total station 2 has a line-of-sight relationship with total station 1, total station 3 and GNSS device 2, and total station 3 has a line-of-sight relationship with total station 2 and GNSS device 3.

[0073] It is worth noting that the control network is composed of a series of measurement points (control points), which are precisely measured to determine their positions in space, usually including their coordinates (longitude, latitude, altitude) and directions (azimuth) relative to other control points. During tunnel construction, establishing and maintaining a control network to monitor construction progress and ensure construction quality is a basic task in tunnel engineering, and its accuracy is directly related to the quality and safety of tunnel construction. Through these measurements, engineers can ensure that the tunnel is accurately positioned and excavated according to the design, while avoiding possible deviations and errors during construction.

[0074] like Figure 1 As shown, the method includes:

[0075] S101, obtaining environmental point cloud data of a target entrance of the tunnel collected by a drone.

[0076] The target opening can be the expected opening of the tunnel. In the specific implementation, a drone equipped with a high-precision LiDAR system can be used to perform a flight scan of the tunnel opening area to obtain high-quality point cloud data. The point cloud data should cover the tunnel opening and its surrounding area, such as the area within 2,000 meters around it, to provide sufficient spatial information.

[0077] S102, inputting the environmental point cloud data into a control point prediction model to obtain target placement position information for the GNSS control point and the total station control point, wherein the target placement position information is used to guide the placement of the GNSS device and the total station.

[0078] Before inputting the environmental point cloud data into the control point prediction model, the point cloud data is preprocessed, such as performing denoising, filtering and ground removal operations to improve the quality of the point cloud data.

[0079] It can be understood that the control point prediction model can be used to determine the best locations for placing GNSS equipment and total stations around the target cave entrance, that is, when placing GNSS equipment and total stations according to the target placement location information, better measurement results can be achieved, or better measurement results can be achieved while maintaining a low placement cost.

[0080] In some embodiments, the control point prediction model may be a model that processes the environmental point cloud data based on a pre-configured algorithm to obtain the target placement location information. For example, it may be implemented based on the height dimension sampling and divide-and-conquer algorithm involved in the following embodiments. This method will be described in detail in the following embodiments and will not be repeated here.

[0081] In other embodiments, the control point prediction model can also be a model pre-trained based on machine learning, such as a decision tree, random forest or neural network, for predicting the optimal GNSS and total station control point positions. For example, the initial control point prediction model can be trained based on pre-collected sample data, and finally a model that can predict the target installation position of the GNSS device and the total station based on the environmental point cloud data is obtained.

[0082] After obtaining the target placement location information, staff can be arranged to actually place the GNSS equipment and the total station according to the coordinates of the target placement location information.

[0083] S103: After the GNSS device and the total station are installed, obtain first measurement data of the GNSS device and second measurement data of the total station.

[0084] Wherein, obtaining the first measurement data of the GNSS device may be a process of obtaining the coordinates of each GNSS control point (i.e., GNSS device) through a navigation satellite system, and obtaining the second measurement data of the total station may be a process of obtaining information such as the distance and azimuth of other control points that are in line of sight with the total station through each total station. That is, the first measurement information may include the coordinate information of each GNSS control point, and the second measurement information may include the distance information and range information between any two control points.

[0085] S104: Determine position information of each of the GNSS control points and each of the total station control points according to the first measurement data and the second measurement data.

[0086] It is understandable that there may be certain errors in the process of placing the GNSS device and the total station. Therefore, after the placement is completed, the specific position coordinates of the GNSS device and the total station, that is, the GNSS control point and the total station control point, can be further determined based on the first measurement data and the second measurement data actually collected.

[0087] S105. Construct the control network based on the location information.

[0088] That is, the precise position of each control point and the distance and range between each control point can be determined based on the position information, and then the spatial positioning and navigation of the tunnel construction can be carried out based on the control network.

[0089] Among them, the main functions of the control network include the following: Precise location of control points: measure and determine the precise geographic location of the tunnel entrance and key points around it. Coordinates of the tunnel entrance and exit: provide precise three-dimensional coordinates for the entrance and exit points of the tunnel to ensure that the tunnel is accurately penetrated as designed. Orientation of the tunnel axis: measure the precise direction of the tunnel axis, which is crucial for directional control during tunnel excavation. Topography and geomorphic features: measure the topography and geomorphology around the tunnel entrance in detail to provide necessary geographic information for engineering design and construction. The relative position of the tunnel entrance and the surrounding terrain: determine the relative position relationship between the tunnel entrance and the surrounding terrain features (such as mountains, rivers, buildings, etc.). Construction control points: during the tunnel construction process, establish and maintain a control network to monitor the construction progress and ensure the quality of construction. Tunnel internal control network: in addition to the portal control network, a control network also needs to be established inside the tunnel to guide the construction and installation of the tunnel's internal structure.

[0090] It should be noted that the GNSS device in the embodiment of the present disclosure may have some of the capabilities of a total station. For example, signals or electromagnetic waves can be interacted between the GNSS device and the total station. For example, the GNSS device can receive signals sent by the total station to determine the distance and direction between itself and the total station, and the total station can also receive signals sent by the GNSS device to determine the distance and direction between itself and the GNSS device.

[0091] In the above embodiment, environmental point cloud data can be collected by drones, and the point cloud data can be input into the control point prediction model to obtain the target placement position information of the optimal GNSS control points and the total station control points, thereby guiding the placement of the control points, effectively improving the accuracy of spatial positioning and navigation during the construction of tunnels in complex terrain.

[0092] In some embodiments, the step of inputting the environmental point cloud data into a pre-trained control point prediction model to obtain predicted position information of the GNSS control point and the total station control point includes:

[0093] According to a preset granularity, the environmental point cloud data is sampled in a height dimension to obtain a plurality of layered data corresponding to different heights;

[0094] For each of the hierarchical data, determining a placement cost corresponding to the hierarchical data, the placement cost being used to indicate a minimum cost when placing the GNSS device at a height corresponding to the hierarchical data;

[0095] The placement location information corresponding to the hierarchical data with the lowest placement cost is determined as the target placement location information.

[0096] It is understandable that, based on the measurement characteristics of GNSS equipment, when multiple GNSS equipment are installed at the same height, the measurement effect is better, while when the height difference between the installations is large, the measurement effect may not be improved. In addition, the installation cost may be positively correlated with the number of total stations and negatively correlated with the measurement effect, that is, when the installation cost is low, the number of total stations required to install the GNSS at the corresponding height may be low, and the measurement effect may be better.

[0097] Among them, the preset granularity can be, for example, 1 meter (m), that is, the environmental point cloud data can be sampled every one meter in the height dimension, and then layered point cloud data on the same height plane can be obtained. In this way, the installation height of the GNSS equipment can be effectively guaranteed to be relatively uniform, so as to ensure the measurement accuracy of the construction process.

[0098] In some embodiments, for each of the hierarchical data, determining the placement cost corresponding to the hierarchical data comprises:

[0099] For each of the hierarchical data, determining a first position group that meets a first preset condition and has a lowest first placement cost, wherein the first position group is used to indicate a candidate installation position of each of the GNSS devices;

[0100] Determine, according to the first position group and the environmental point cloud data, a second position group that satisfies a second preset condition and has the lowest second placement cost, wherein the second position group is used to indicate a candidate installation position of each of the total stations;

[0101] Determine a placement cost corresponding to the hierarchical data according to the first placement cost and the second placement cost, wherein the placement location information corresponding to the hierarchical data includes the first location group and the second location group;

[0102] Among them, when the first preset condition is met, the distance between any two of the control points is within a preset range; when the second preset condition is met, for any one of the control points, there is at least one other control point that has line of sight with the control point.

[0103] For example, the preset range may be 500-1000 meters, wherein a better measurement effect can be obtained when the distance between GNSS devices is 500-1000 meters. Alternatively, the preset range may also be set to other values ​​according to the measurement effect, which is not limited in the embodiments of the present disclosure.

[0104] Optionally, the first placement cost can be determined based on the spacing between GNSS devices or the area of ​​the triangle formed. For example, for a first position group [a, b, c], the first placement cost can be positively correlated with the value of ab+ac+bc. That is, when the spacing between GNSS devices is less than 1000 meters, the larger the spacing, the smaller the placement cost and the better the measurement effect.

[0105] Optionally, the second placement cost can be determined based on the number of total stations. For example, for a first position group [x, y, z], when the second condition is met, the minimum number of total stations required is 3. The second position group with the lowest second placement cost can be used to indicate the positions corresponding to the three total stations.

[0106] By adopting the above scheme, by minimizing the first placement cost and the second placement cost, the measurement accuracy of the control network can be effectively ensured while reducing the placement cost. By meeting the first and second preset conditions, the visibility and distance requirements between control points are ensured, thereby enhancing the reliability and effectiveness of the control network.

[0107] In some embodiments, the number of the GNSS control points is equal to 3, and determining the first position group that satisfies the first preset condition and has the lowest first placement cost includes:

[0108] Performing two-dimensional quantization on the layered data to obtain a set of position coordinates in the layered data that can be used to place the GNSS device;

[0109] Based on a divide-and-conquer algorithm, a target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area is determined, and the target triplet is the first position group that satisfies the first preset condition and has the lowest placement cost.

[0110] Among them, two-dimensional quantization can quantize continuous point cloud data into discrete two-dimensional coordinates, which can effectively reduce the amount of calculation. The divide-and-conquer algorithm can be solved by recursively decomposing the problem into smaller sub-problems, and then merging the solutions of these sub-problems to obtain the solution of the original problem. It can quickly and reliably determine the first position group in the hierarchical data that meets the first preset condition and has the lowest placement cost.

[0111] Using the above scheme, when the number of GNSS control points is fixed at 3, the placement of the control points is optimized using the divide-and-conquer algorithm. The optimal control point layout is found by maximizing the triangle area. The divide-and-conquer algorithm can efficiently divide and search the position coordinate set to determine the vertex coordinates of the largest triangle, further improving the optimization degree of control point placement.

[0112] In some embodiments, the determining, based on a divide-and-conquer algorithm, a target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area includes:

[0113] Sorting the position coordinate set according to the x-coordinates of the elements in the position coordinate set;

[0114] Determine the center coordinates of the position coordinate set according to the sorting result;

[0115] Dividing the position coordinate set into two subsets based on the center coordinate;

[0116] Determine a first largest triangle and a second largest triangle corresponding to the two subsets respectively;

[0117] Determine a third largest triangle formed by taking the central coordinates as vertices;

[0118] The coordinates of three vertices of the triangle with the largest area among the first largest triangle, the second largest triangle and the third largest triangle are determined as three elements of the target triplet.

[0119] First, the point set is sorted by x-coordinate, and the middle point in the sequence is found. The maximum triangle area is found in the left and right subsets respectively, and the maximum triangle area with the middle point as the vertex is calculated. Then, the three maximum areas are compared and the largest one is returned to obtain the target triplet.

[0120] Optionally, two other vertices of the current largest triangle are also maintained using a balanced binary search tree or a priority queue.

[0121] In some embodiments, determining the first largest triangle and the second largest triangle corresponding to the two subsets respectively includes:

[0122] Recursively call the divide-and-conquer method to determine the first largest triangle and the second largest triangle corresponding to the two subsets, respectively, wherein the termination condition of the recursion is that the number of elements in the recursive subset is less than or equal to 3.

[0123] In the above embodiment, the point set can be sorted according to the x coordinate, and the point set can be further divided into two subsets, and the middle point is selected as the dividing point. The middle point in the sorted point set is selected, and the middle point is used as a potential triangle vertex. The point set is divided into two parts, the left and the right. For each subset, the divide-and-conquer method is recursively applied to find the maximum triangle area. At the bottom of the recursion, each subset contains only a few points, and the maximum triangle area can be directly calculated. When backtracking upward, it is necessary to consider the maximum triangle area across the boundary line of the two subsets (i.e., the line where the middle point is located). Further, compare the maximum area in the subset and the maximum area across the boundary, and select the larger of the two as the solution to the current subproblem. When the recursion reaches only one point or no point, the recursion terminates. Finally, recurse to the top level, and the maximum triangle area of ​​the entire point set is obtained.

[0124] By adopting the above scheme, the divide-and-conquer method is recursively called to achieve an in-depth search of the subsets, ensuring that the largest triangle can be found in any subset, improving the accuracy and efficiency of the algorithm. The problem can be recursively decomposed into smaller sub-problems, and the maximum triangle area across subsets can be effectively calculated by maintaining the convex hull or half-plane, which can significantly reduce the amount of calculation.

[0125] In some embodiments, determining the position information of each of the GNSS control points and each of the total station control points according to the first measurement data and the second measurement data includes:

[0126] Performing data calibration preprocessing on the first measurement data and the second measurement data to obtain third measurement data;

[0127] Performing data fusion preprocessing on the third measurement data to obtain fourth measurement data;

[0128] The position information of each of the GNSS control points and each of the total station control points is determined according to the fourth measurement data.

[0129] By adopting the above scheme, the accuracy and consistency of the measurement data are improved through data calibration and fusion preprocessing, ensuring the precise determination of the position information of the GNSS control points and the total station control points.

[0130] In some embodiments, the data calibration preprocessing includes at least one of the following: detachment and tropospheric delay correction for the first measurement data; multipath effect correction for the first measurement data; line phase center correction for the first measurement data; precise point positioning for the first measurement data; temperature and air pressure correction for the second measurement data; lens constant correction for the second measurement data; centering error correction for the second measurement data; prism constant correction for the second measurement data; atmospheric refraction correction for the second measurement data; and data screening for the first measurement data and / or the second measurement data.

[0131] In some embodiments, data calibration preprocessing for the GNSS data (i.e., the first measurement data) may include one or more of the following:

[0132] Satellite selection, such as selecting satellites with better quality for data processing and excluding those with weak signals or significant errors;

[0133] Ionospheric and tropospheric delay corrections, such as applying models or observational data to correct for the effects of the ionosphere and troposphere on GNSS signals;

[0134] Multipath correction, by identifying and reducing multipath effects, for example by analyzing the angle of arrival of the signals or using specific algorithms;

[0135] Antenna phase center correction, used to correct antenna phase center deviation to ensure measurement accuracy;

[0136] Precise point positioning, such as using precise ephemeris and ground station data to improve the accuracy of GNSS positioning;

[0137] Data filtering to remove outliers and erroneous data, such as data quality issues caused by occlusion or interference.

[0138] In some embodiments, data calibration preprocessing for the total station data (i.e., the second measurement data) may include one or more of the following:

[0139] Temperature and air pressure correction: since temperature and air pressure can affect the measurement distance, correction is required based on environmental parameters;

[0140] Lens constant correction, such as correction based on a corrected lens constant, to ensure the accuracy of the measured angle;

[0141] Centering error correction, for example, if there is a centering error, correction can be performed to ensure the precise position of the measuring point;

[0142] Prism constant correction: if a reflecting prism is used, the prism constant needs to be corrected;

[0143] Atmospheric refraction correction: refraction correction of the measured distance according to atmospheric conditions;

[0144] Equipment calibration, which is used to ensure that the total station is properly calibrated to reduce systematic errors;

[0145] Data screening, checking data quality, such as excluding measurements that are outside the expected error range.

[0146] Repeated observation averaging. If multiple observations are made, the average value can be calculated to improve the stability and accuracy of the measurement results.

[0147] In some embodiments, the data fusion preprocessing includes at least one of the following:

[0148] The data format is unified to ensure that GNSS data and total station data have a unified format to facilitate fusion processing;

[0149] Coordinate system transformation, used to transform all data into the same coordinate system and reference frame;

[0150] Timestamp alignment, used to align the timestamps of two devices to ensure data synchronization;

[0151] Error assessment, e.g. evaluating the error characteristics of each measurement technique to provide error weights for the data fusion algorithm;

[0152] Data interpolation is used to interpolate missing data.

[0153] By adopting the above preprocessing scheme, various error sources are eliminated and the quality of measurement data is improved through data calibration preprocessing according to the characteristics of GNSS and total station data. Through data fusion preprocessing, the data format is unified, the time synchronization and coordinate system conversion problems are solved, and the data missing situation is handled through data interpolation, laying a solid foundation for subsequent data fusion.

[0154] Reference below Figure 2 , which shows a schematic diagram of the structure of an electronic device (such as a terminal device or a server) 200 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 2 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0155] like Figure 2As shown, the electronic device 200 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 202 or a program loaded from a storage device 208 to a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the electronic device 200 are also stored. The processing device 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0156] Typically, the following devices may be connected to the I / O interface 205: an input device 206 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 207 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 208 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 209. The communication device 209 may allow the electronic device 200 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 2 The electronic device 200 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0157] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 209, or installed from the storage device 208, or installed from the ROM 202. When the computer program is executed by the processing device 201, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0158] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0159] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0160] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0161] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes one or more steps of the method for setting up a complex terrain tunnel construction control network involved in the above embodiment.

[0162] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0163] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0164] The modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a module does not, in some cases, constitute a limitation on the module itself.

[0165] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0166] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0167] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0168] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0169] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. A method for setting up a control network for tunnel construction in complex terrain, characterized in that: The control network is used for spatial positioning and navigation during the construction of the tunnel. The control network includes a plurality of control points, including a plurality of global navigation satellite system GNSS control points and a plurality of total station control points. For any of the control points, there is at least one other control point that has line of sight with the control point. The method comprises: Acquire environmental point cloud data of the target entrance of the tunnel collected by the drone; Inputting the environmental point cloud data into a control point prediction model to obtain target placement position information for the GNSS control point and the total station control point, wherein the target placement position information is used to guide the placement of the GNSS device and the total station; After the GNSS device and the total station are installed, obtaining first measurement data of the GNSS device and second measurement data of the total station; Determine the position information of each of the GNSS control points and each of the total station control points according to the first measurement data and the second measurement data; The control network is formed based on the position information; the environmental point cloud data is input into a control point prediction model to obtain target placement position information for the GNSS control point and the total station control point, including: According to a preset granularity, the environmental point cloud data is sampled in a height dimension to obtain a plurality of layered data corresponding to different heights; For each of the hierarchical data, determining a placement cost corresponding to the hierarchical data, the placement cost being used to indicate a minimum cost when placing the GNSS device at a height corresponding to the hierarchical data; Determining the placement location information corresponding to the hierarchical data with the lowest placement cost as the target placement location information; and determining the placement cost corresponding to each hierarchical data, including: For each of the hierarchical data, determining a first position group that meets a first preset condition and has a lowest first placement cost, wherein the first position group is used to indicate a candidate installation position of each of the GNSS devices; Determine, according to the first position group and the environmental point cloud data, a second position group that satisfies a second preset condition and has the lowest second placement cost, wherein the second position group is used to indicate a candidate installation position of each of the total stations; Determine a placement cost corresponding to the hierarchical data according to the first placement cost and the second placement cost, wherein the placement location information corresponding to the hierarchical data includes the first location group and the second location group; Among them, when the first preset condition is met, the distance between any two of the control points is within a preset range; when the second preset condition is met, for any one of the control points, there is at least one other control point that has line of sight with the control point.

2. The method according to claim 1, characterized in that The number of GNSS control points is equal to 3, and determining a first position group that satisfies a first preset condition and has a lowest first placement cost includes: Performing two-dimensional quantization on the layered data to obtain a set of position coordinates in the layered data that can be used to place the GNSS device; Based on a divide-and-conquer algorithm, a target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area is determined, and the target triplet is the first position group that satisfies the first preset condition and has the lowest placement cost.

3. The method according to claim 2, characterized in that The method of determining the target triplet in the position coordinate set that satisfies the first preset condition and has the largest corresponding triangle area based on the divide-and-conquer algorithm includes: Sorting the position coordinate set according to the x-coordinates of the elements in the position coordinate set; Determine the center coordinates of the position coordinate set according to the sorting result; Dividing the position coordinate set into two subsets based on the center coordinate; Determine a first largest triangle and a second largest triangle corresponding to the two subsets respectively; Determine a third largest triangle formed by taking the central coordinates as vertices; The coordinates of three vertices of the triangle with the largest area among the first largest triangle, the second largest triangle and the third largest triangle are determined as three elements of the target triplet.

4. The method according to claim 3, characterized in that The determining of the first largest triangle and the second largest triangle respectively corresponding to the two subsets comprises: Recursively call the divide-and-conquer method to determine the first largest triangle and the second largest triangle corresponding to the two subsets, respectively, wherein the termination condition of the recursion is that the number of elements in the recursive subset is less than or equal to 3.

5. The method according to any one of claims 1 to 4, characterized in that: The determining, according to the first measurement data and the second measurement data, the position information of each of the GNSS control points and each of the total station control points comprises: Performing data calibration preprocessing on the first measurement data and the second measurement data to obtain third measurement data; Performing data fusion preprocessing on the third measurement data to obtain fourth measurement data; The position information of each of the GNSS control points and each of the total station control points is determined according to the fourth measurement data.

6. The method according to claim 5, characterized in that The data calibration preprocessing includes at least one of the following: Isosphere and tropospheric delay corrections for said first measurement data; multipath correction for said first measurement data; Line phase center correction for the first measurement data; Precise point positioning of the first measurement data; temperature and pressure correction for said second measurement data; lens constant correction for the second measurement data; centering error correction for said second measurement data; prism constant correction for the second measurement data; atmospheric refraction correction for said second measurement data; Data filtering for the first measurement data and / or the second measurement data.

7. The method according to claim 6, characterized in that The data fusion preprocessing includes at least one of the following: The data format is unified; Coordinate system conversion; Timestamp alignment; Data interpolation.

8. An electronic device, characterized in that: include: a storage device having a computer program stored thereon; A processing device, configured to execute the computer program in the storage device to implement the steps of the method according to any one of claims 1 to 7.

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