Underground space modeling method, device and terminal equipment

Abstract

The present invention provides an underground space modeling method, device and terminal equipment, the method comprising: obtaining a multi-source data set corresponding to a target underground space, the multi-source data set containing sampled data from multiple data sources; constructing an initial data model corresponding to the target underground space based on the sampled data from each data source, obtaining multiple underground space models, each underground space model corresponding to the sampled data from one data source; fusing the multiple underground space models based on the data priorities corresponding to the various data sources, obtaining a final space model corresponding to the target underground space. The underground space modeling method, device and terminal equipment provided by the present invention utilize sampled data from multiple data sources to model the underground space, which can effectively improve the accuracy of underground space modeling.

Description

Technical Field

[0001] The present invention belongs to the technical field of underground space, and more specifically, relates to an underground space modeling method, device and terminal equipment. Background Art

[0002] The invisibility of underground space has brought many adverse effects to engineering construction. Digital modeling has made underground space gradually transparent and has become an effective way to solve engineering construction problems. The modeling method of the existing technology generally uses various sampling data to carry out digital modeling of underground space. However, when sampling data, the sampling accuracy will be affected by the sampling equipment and the surrounding environment, resulting in the accuracy of the sampling data being unreliable. On this basis, the underground space model established is not accurate enough. Summary of the invention

[0003] The purpose of the present invention is to provide an underground space modeling method, device and terminal equipment to improve the accuracy of underground space modeling.

[0004] A first aspect of an embodiment of the present invention provides an underground space modeling method, comprising:

[0005] Acquire a multi-source data set corresponding to the target underground space, wherein the multi-source data set includes sampling data from multiple data sources;

[0006] Based on the sampling data of each data source, an initial data model corresponding to the target underground space is constructed respectively, so as to obtain a plurality of underground space models, each of which corresponds to the sampling data of one data source;

[0007] The multiple underground space models are fused based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space.

[0008] In a possible implementation, before constructing the initial data model corresponding to the target underground space based on the sampled data from each data source, the underground space modeling method further includes:

[0009] Mapping is performed on the sampled data in the multi-source data set to obtain a multi-source data set with a unified data structure.

[0010] In a possible implementation, an initial data model corresponding to a target underground space is constructed based on sampled data from a certain data source, and an underground space model corresponding to the sampled data from the data source is obtained, including:

[0011] Record the sampling data of this data source as target sampling data, calculate the matching degree between the target sampling data and the historical sampling data in the preset database, and record the historical sampling data with the highest matching degree with the target sampling data as the target historical sampling data;

[0012] If the matching degree of the target historical sampling data is greater than the preset matching threshold, each marginal surface model corresponding to the target historical sampling data is used as the target marginal surface model, and the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0013] In a possible implementation, the underground space modeling method further includes:

[0014] If the matching degree of the target historical sampling data is not greater than the preset matching threshold, a universal marginal surface model is obtained, and the universal marginal surface model is corrected based on the target sampling data, and the corrected universal marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0015] In a possible implementation, the merging of the multiple underground space models based on the data priorities corresponding to the various data sources to obtain a final space model corresponding to the target underground space includes:

[0016] sorting the plurality of underground space models according to data priorities corresponding to various data sources;

[0017] The two adjacent underground space models are merged in order of increasing data priority until the final space model corresponding to the target underground space is obtained;

[0018] Among them, the method for fusing two adjacent underground space models is:

[0019] Assigning a first weight to an underground space model with a higher data priority among two adjacent underground space models, and assigning a second weight to an underground space model with a lower data priority among two adjacent underground space models, wherein the first weight is greater than the second weight;

[0020] Two adjacent underground space models are fused based on the first weight and the second weight to obtain a fused underground space model.

[0021] In a possible implementation, the underground space modeling method further includes:

[0022] Count the physical equipment corresponding to the target underground space and establish a digital twin model of each physical equipment, wherein the physical equipment includes sensor equipment, communication equipment, and control equipment;

[0023] The digital twin models of various physical devices are combined according to the connection relationship of each physical device to obtain the digital twin model corresponding to the target underground space;

[0024] The state of the target underground space is predicted based on the digital twin model corresponding to the target underground space, and the state prediction result is marked on the final space model.

[0025] In a possible implementation, the underground space modeling method further includes:

[0026] The final annotated spatial model is sent to the display device for three-dimensional display.

[0027] A second aspect of an embodiment of the present invention provides an underground space modeling device, comprising:

[0028] A data acquisition module, used to acquire a multi-source data set corresponding to a target underground space, wherein the multi-source data set includes sampled data from multiple data sources;

[0029] A model building module is used to build an initial data model corresponding to the target underground space based on the sampled data of each data source, so as to obtain multiple underground space models, each of which corresponds to the sampled data of one data source;

[0030] The model fusion module is used to fuse the multiple underground space models based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space.

[0031] According to a third aspect of an embodiment of the present invention, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned underground space modeling method when executing the computer program.

[0032] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned underground space modeling method are implemented.

[0033] The underground space modeling method, device and terminal device provided by the present invention have the following beneficial effects:

[0034] Different from the method of directly using a certain sampling data to model underground space in the prior art, the present invention uses sampling data from multiple data sources to model underground space respectively. On this basis, each underground space is modeled based on the data priority of various data sources to obtain the final underground space model. In other words, the present invention uses multi-source data to correct the deviation that may be caused by a single sampling data, thereby obtaining a more accurate modeling result and effectively improving the accuracy of underground space modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A schematic diagram of a flow chart of an underground space modeling method provided by an embodiment of the present invention;

[0037] Figure 2 A structural block diagram of an underground space modeling device provided by an embodiment of the present invention;

[0038] Figure 3 A schematic block diagram of a terminal device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0039] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0041] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of an underground space modeling method provided by an embodiment of the present invention, the method comprising:

[0042] S101: Acquire a multi-source data set corresponding to a target underground space, where the multi-source data set includes sampling data from multiple data sources.

[0043] In this embodiment, the multi-source data set may specifically include image data of the target underground space collected by different hardware devices (mainly referring to camera devices), point cloud data of the target underground space collected by different hardware devices (mainly referring to laser scanning devices), data generated by other data collection or software, etc. Among them, a type of data of the target underground space collected by a hardware device is sampled data as a data source (for example, image data of the target underground space collected by device A, point cloud data of the target underground space collected by device B, image data of the target underground space collected by device C, and point cloud data of the target underground space collected by device D all belong to sampled data from different data sources). This solution obtains as much data as possible to reduce the impact of sampling equipment, sampling environment, etc. on modeling accuracy.

[0044] S102: constructing initial data models corresponding to the target underground space based on the sampled data from each data source, and obtaining a plurality of underground space models, each of which corresponds to the sampled data from one data source.

[0045] In this embodiment, an initial data model corresponding to the target underground space is constructed based on the sampling data from each data source, so that an underground space model corresponding to the sampling data from each data source can be obtained, that is, multiple underground space models are obtained, and each underground space model corresponds to the sampling data from one data source.

[0046] In this embodiment, before constructing the initial data model corresponding to the target underground space based on the sampled data of each data source, the underground space modeling method further includes:

[0047] Data cleaning is performed on the sampled data from each data source.

[0048] Among them, data cleaning of sampled data from a certain data source includes:

[0049] Cluster the sampled data from this data source using a clustering algorithm, and filter the sampled data from this data source based on the clustering results. The specific steps may be:

[0050] S0: The sampled data from this data source is taken as the data set X to be processed, where the number of samples in the data set to be processed is m, and x=1, k=0 is set.

[0051] S1: Select the xth sample from the data set to be processed as the current processing sample, calculate the Mahalanobis distance between the feature vector of the current processing sample and the feature vector of each sample in the sampling data of this data source, and select all samples whose Mahalanobis distance is less than a preset clustering radius, and count the number of all samples whose Mahalanobis distance is less than the preset clustering radius; wherein the preset clustering radius is a pre-set distance value related to the number of samples in the data set to be processed.

[0052] S2: Determine whether the number of all samples whose Mahalanobis distance is less than the preset clustering radius is greater than the preset clustering number. If the number of all samples whose Mahalanobis distance is less than the preset clustering radius is greater than the preset clustering number, add the currently processed sample as a core sample to the core sample set Ω. Determine whether x is equal to m. If x = m, execute step S3. If x < m, let x = x + 1, and return to execute step S1.

[0053] S3: If the core sample set Ω is empty, the clustering ends. If the core sample set is not empty, execute step S4.

[0054] S4: In the core sample set, randomly select a core sample o, initialize the current cluster core object queue Ωcur = {o}, initialize the class serial number k = k + 1, initialize the current cluster sample set C k = {o}, update the unvisited sample set X = X - {o}.

[0055] S5: If the current cluster core object queue Ωcur is empty, the current clustering cluster C k is generated. Update the cluster partition C = {C 1 , C 2 ,..., C k}, update the core sample set Ω = Ω - C k , and return to execute step S3; if the current cluster core object queue Ωcur is not empty, update the core sample set Ω = Ω - C k .

[0056] S6: Take out a core object o′ from the current cluster core object queue Ωcur, find the set of samples in the pending data set whose Mahalanobis distance from this core object o′ is less than the preset clustering radius through the preset clustering radius, denoted as N∈(o′), let Δ = N∈(o′) ∩ X, update the current cluster sample set C k = C k ∪ Δ, update the unvisited sample set X = X - Δ, update Ωcur = Ωcur ∪ (Δ ∩ Ω) - o′, and return to execute step S5.

[0057] S7: After the clustering is completed, the output result is: the cluster partition C = {C 1 , C 2 ,..., C kOn this basis, sample points whose Mahalanobis distance from the central sample point in each cluster is greater than a preset threshold are screened out, sample points whose Mahalanobis distance from the central sample point in each cluster is greater than a preset distance value are deleted as abnormal points, and clusters whose sample quantity is less than a preset quantity value are deleted as abnormal clusters to perform data cleaning, wherein the preset threshold value is less than a preset clustering radius, and the preset quantity value can be set according to the quantity of sampled data of this data source.

[0058] In summary, the embodiment of the present invention further screens the sampled data by combining the clustering algorithm with the Mahalanobis distance, thereby further ensuring the reliability of the sampled data and facilitating improving the modeling accuracy.

[0059] S103: Multiple underground space models are integrated based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space.

[0060] In this embodiment, the data priority corresponding to various data sources refers to the modeling accuracy of the sampled data corresponding to the various data sources, wherein the modeling accuracy of the sampled data corresponding to the various data sources is positively correlated with the data priority corresponding to the various data sources. In other words, the higher the modeling accuracy, the greater the corresponding data priority. Specifically, the data priority of drilling data is greater than the data priority of point cloud data, and the data priority of point cloud data is greater than the data priority of image data.

[0061] From the above, it can be concluded that, unlike the prior art method of directly using certain sampling data to model underground space, the embodiment of the present invention uses sampling data from multiple data sources to model underground space respectively. On this basis, each underground space is modeled based on the data priority of various data sources to obtain the final underground space model. In other words, the embodiment of the present invention uses multi-source data to correct the deviation that may be caused by a single type of sampling data, thereby obtaining a more accurate modeling result and effectively improving the accuracy of underground space modeling.

[0062] In a possible implementation, before constructing the initial data model corresponding to the target underground space based on the sampled data from each data source, the underground space modeling method further includes:

[0063] The sampling data in the multi-source data set is mapped to obtain a multi-source data set with a unified data structure.

[0064] In this embodiment, the sampled data from each data source in a multi-source data set are usually heterogeneous. Therefore, the present invention can map the sampled data from each data source based on a preset mapping table before modeling to obtain a multi-source data set with a unified data structure. Subsequent processing can be performed based on the multi-source data set with a unified data structure.

[0065] In a possible implementation, an initial data model corresponding to a target underground space is constructed based on sampled data from a certain data source, and an underground space model corresponding to the sampled data from the data source is obtained, including:

[0066] The sampling data from this data source is recorded as target sampling data, the matching degree between the target sampling data and the historical sampling data in the preset database is calculated, and the historical sampling data with the highest matching degree with the target sampling data is recorded as the target historical sampling data.

[0067] If the matching degree of the target historical sampling data is greater than the preset matching threshold, each marginal surface model corresponding to the target historical sampling data is used as the target marginal surface model, and the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0068] In this embodiment, it is mainly described in detail how to obtain the corresponding underground space model according to a certain sampling data. Among them, the method for calculating the matching degree between the target sampling data and the historical sampling data is: calculating the Chebyshev distance between the target sampling data and the historical sampling data, and taking the Chebyshev distance between the target sampling data and the historical sampling data as the matching degree between the target sampling data and the historical sampling data.

[0069] In this embodiment, the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is assembled to obtain the underground space model corresponding to the sampling data of the data source, which can be described in detail as follows:

[0070] The boundary of the target boundary surface model is re-determined according to the target sampling data, and the revised target boundary surface model is spliced ​​by fitting, so as to obtain the underground space model corresponding to the sampling data of this data source.

[0071] That is to say, in this embodiment, the model of the existing data can be directly used as the basic model based on the matching degree between the target sampling data and the existing data, thereby effectively improving the modeling efficiency.

[0072] In a possible implementation, the underground space modeling method further includes:

[0073] If the matching degree of the target historical sampling data is not greater than the preset matching threshold, the universal marginal surface model is obtained, and the universal marginal surface model is corrected based on the target sampling data. The corrected universal marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0074] In this embodiment, if the matching degree corresponding to the target historical sampling data is not greater than the preset matching threshold, it means that there is no model suitable for the target sampling data in the existing database. At this time, the general marginal surface model can be directly obtained, and the model can be established based on the general marginal surface model.

[0075] In a possible implementation, multiple underground space models are fused based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space, including:

[0076] Multiple underground space models are sorted according to the data priorities corresponding to various data sources.

[0077] The two adjacent underground space models are fused in ascending order of data priority until the final space model corresponding to the target underground space is obtained.

[0078] Among them, the method for fusing two adjacent underground space models is:

[0079] A first weight is assigned to the underground space model with a higher data priority in two adjacent underground space models, and a second weight is assigned to the underground space model with a lower data priority in two adjacent underground space models, wherein the first weight is greater than the second weight.

[0080] Two adjacent underground space models are fused based on the first weight and the second weight to obtain a fused underground space model.

[0081] In this embodiment, it is known that various data sources have corresponding data priorities, and the underground space model corresponds one-to-one to the sampled data of various data sources. Therefore, each underground space model and the data priority also correspond one-to-one. On this basis, each underground space model can be sorted according to the data priority.

[0082] In this embodiment, each underground space model is set to be represented by Sx (Sx specifically represents a set of a series of spatial coordinate points), where x represents a sorting sequence number (the larger the sequence number, the higher the data priority), x ranges from 1 to n, and n is the number of underground space models. On this basis, two adjacent underground space models are merged in order of increasing data priority until the final space model corresponding to the target underground space is obtained, which can be described in detail as follows:

[0083] First, S1 and S2 are fused to obtain S2*, then S2* and S3 are fused to obtain S3*, then S3* and S4 are fused to obtain S4*, and so on, until Sn* (that is, the final spatial model corresponding to the target underground space) is obtained.

[0084] In this embodiment, two adjacent underground space models are set to be Sy and Sz (where y is greater than z). On this basis, the method for fusing two adjacent underground space models can be described in detail as follows:

[0085] A first weight a1 is set for Sy, and a second weight a2 is set for Sz, wherein a1 is greater than a2.

[0086] S*=Sy×a1+Sz×a2 is used to fuse the two adjacent underground space models Sy and Sz to obtain the fused underground space model S*. That is, the fusion of the two underground space models is essentially the fusion of corresponding spatial coordinate points. For example, the spatial coordinate points contained in Sy are {sy1, sy2, …, sym}, where m is the set number of spatial points in the underground space model (that is, m points are set to achieve underground space modeling), and the spatial coordinate points contained in Sz are {sz1, sz2, …, szm}, where sy1 corresponds to sz1, sy2 corresponds to sz2, and so on. The spatial coordinate points of S* obtained by fusing the underground space models Sy and Sz are {sy1×a1+sz1×a2, sy2×a1+sz2×a2, …, sym×a1+szm×a2}.

[0087] In a possible implementation, the underground space modeling method further includes:

[0088] The drilling data, profile data, and contour data of the target underground space are obtained, and a three-dimensional geological structure model is established based on the drilling data, profile data, and contour data. The three-dimensional geological structure model is fused with the final spatial model of the target underground space to obtain the fused final spatial model.

[0089] Optionally, the underground space modeling method may also include: constructing a geological attribute model of the target underground space based on the geological structure model of the target underground space and the physical and mechanical property data of rock and soil, constructing a pipeline model of the target underground space based on the pipeline data of the target underground space, constructing a structure model corresponding to the target underground space based on the geometric data corresponding to the underground structures of the target underground space, constructing a water system model corresponding to the target underground space based on the geometric data corresponding to the water system of the target underground space, and constructing a subway tunnel model corresponding to the target underground space based on the geometric data of the subway tunnel corresponding to the target underground space; finally, the final spatial model, geological attribute model, pipeline model, structure model, groundwater system model, and subway tunnel model of the target underground space are integrated to obtain the final display model of the target underground space.

[0090] In a possible implementation, the underground space modeling method further includes:

[0091] Count the physical equipment corresponding to the target underground space and establish a digital twin model of each physical equipment, where the physical equipment includes sensor equipment, communication equipment, and control equipment.

[0092] The digital twin models of each physical device are combined according to the connection relationship between the physical devices to obtain the digital twin model corresponding to the target underground space.

[0093] The state of the target underground space is predicted based on the digital twin model corresponding to the target underground space, and the state prediction results are marked on the final space model.

[0094] In this embodiment, the sensing device may include a temperature sensor, a humidity sensor, a gas sensor, etc., the communication device may be a wireless communication device or a wired communication device, and the control device may be a single chip microcomputer or a digital processor, etc.

[0095] In this embodiment, the state prediction of the target underground space can be achieved based on the digital twin model, and the state prediction structure can be marked on the final space model to more intuitively display the spatial structure and spatial state of the target underground space.

[0096] In a possible implementation, the underground space modeling method further includes:

[0097] The final annotated spatial model is sent to the display device for three-dimensional display.

[0098] In this embodiment, the display device may be a display device provided by the execution subject, or may be an externally connected display device, which is not limited in this embodiment.

[0099] Corresponding to the underground space modeling method of the above embodiment, Figure 2 This is a structural block diagram of an underground space modeling device provided by an embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown. Figure 2 The underground space modeling device 20 includes: a data acquisition module 21, a model construction module 22, and a model fusion module 23.

[0100] The data acquisition module 21 is used to acquire a multi-source data set corresponding to the target underground space, and the multi-source data set includes sampling data from multiple data sources.

[0101] The model building module 22 is used to build initial data models corresponding to the target underground space based on the sampled data of each data source, so as to obtain multiple underground space models, each of which corresponds to the sampled data of one data source.

[0102] The model fusion module 23 is used to fuse multiple underground space models based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space.

[0103] In a possible implementation, the model building module 22 is further configured to perform the following steps before respectively building the initial data model corresponding to the target underground space based on the sampled data from each data source:

[0104] The sampling data in the multi-source data set is mapped to obtain a multi-source data set with a unified data structure.

[0105] In a possible implementation, the model building module 22 is specifically used to:

[0106] The sampling data from this data source is recorded as target sampling data, the matching degree between the target sampling data and the historical sampling data in the preset database is calculated, and the historical sampling data with the highest matching degree with the target sampling data is recorded as the target historical sampling data.

[0107] If the matching degree of the target historical sampling data is greater than the preset matching threshold, each marginal surface model corresponding to the target historical sampling data is used as the target marginal surface model, and the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0108] In a possible implementation, the model building module 22 is specifically used to:

[0109] If the matching degree of the target historical sampling data is not greater than the preset matching threshold, the universal marginal surface model is obtained, and the universal marginal surface model is corrected based on the target sampling data. The corrected universal marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

[0110] In a possible implementation, the model fusion module 23 is specifically used for:

[0111] Multiple underground space models are sorted according to the data priorities corresponding to various data sources.

[0112] The two adjacent underground space models are fused in ascending order of data priority until the final space model corresponding to the target underground space is obtained.

[0113] Among them, the method for fusing two adjacent underground space models is:

[0114] A first weight is assigned to the underground space model with a higher data priority in two adjacent underground space models, and a second weight is assigned to the underground space model with a lower data priority in two adjacent underground space models, wherein the first weight is greater than the second weight.

[0115] Two adjacent underground space models are fused based on the first weight and the second weight to obtain a fused underground space model.

[0116] In a possible implementation, the model fusion module 23 is further used for:

[0117] Count the physical equipment corresponding to the target underground space and establish a digital twin model of each physical equipment, where the physical equipment includes sensor equipment, communication equipment, and control equipment.

[0118] The digital twin models of each physical device are combined according to the connection relationship between the physical devices to obtain the digital twin model corresponding to the target underground space.

[0119] The state of the target underground space is predicted based on the digital twin model corresponding to the target underground space, and the state prediction results are marked on the final space model.

[0120] In a possible implementation, the model fusion module 23 is further used for:

[0121] The final annotated spatial model is sent to the display device for three-dimensional display.

[0122] See also Figure 3 , Figure 3 A schematic block diagram of a terminal device provided by an embodiment of the present invention. Figure 3 The terminal 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0123] It should be understood that in the embodiment of the present invention, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0124] The input device 302 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0125] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0126] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present invention can execute the implementation methods described in the first and second embodiments of the underground space modeling method provided in the embodiment of the present invention, and can also execute the implementation methods of the terminal described in the embodiment of the present invention, which will not be repeated here.

[0127] In another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, wherein the program instructions are executed by a processor to implement all or part of the processes in the above-mentioned embodiment method, and may also be completed by instructing the relevant hardware through the computer program, wherein the computer program may be stored in a computer-readable storage medium, and wherein the computer program may be executed by the processor to implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the contents contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signal and telecommunication signal.

[0128] The computer-readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed terminals and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for modeling underground space, characterized in that: include: Acquire a multi-source data set corresponding to the target underground space, wherein the multi-source data set includes sampling data from multiple data sources; Based on the sampling data of each data source, an initial data model corresponding to the target underground space is constructed respectively, so as to obtain a plurality of underground space models, each of which corresponds to the sampling data of one data source; The multiple underground space models are merged based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space; An initial data model corresponding to the target underground space is constructed based on the sampled data from a certain data source, and an underground space model corresponding to the sampled data from the data source is obtained, including: Record the sampling data of this data source as target sampling data, calculate the matching degree between the target sampling data and the historical sampling data in the preset database, and record the historical sampling data with the highest matching degree with the target sampling data as the target historical sampling data; If the matching degree of the target historical sampling data is greater than the preset matching threshold, each marginal surface model corresponding to the target historical sampling data is used as the target marginal surface model, and the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source; The fusing of the multiple underground space models based on the data priorities corresponding to the various data sources to obtain a final space model corresponding to the target underground space includes: sorting the plurality of underground space models according to data priorities corresponding to various data sources; The two adjacent underground space models are merged in order of increasing data priority until the final space model corresponding to the target underground space is obtained; Among them, the method for fusing two adjacent underground space models is: Assigning a first weight to an underground space model with a higher data priority among two adjacent underground space models, and assigning a second weight to an underground space model with a lower data priority among two adjacent underground space models, wherein the first weight is greater than the second weight; Two adjacent underground space models are fused based on the first weight and the second weight to obtain a fused underground space model.

2. The underground space modeling method according to claim 1, characterized in that: Before constructing the initial data model corresponding to the target underground space based on the sampled data of each data source, the underground space modeling method further includes: Mapping is performed on the sampled data in the multi-source data set to obtain a multi-source data set with a unified data structure.

3. The underground space modeling method according to claim 1, characterized in that: The underground space modeling method further includes: If the matching degree of the target historical sampling data is not greater than the preset matching threshold, a universal marginal surface model is obtained, and the universal marginal surface model is corrected based on the target sampling data, and the corrected universal marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source.

4. The underground space modeling method according to any one of claims 1 to 3, characterized in that: The underground space modeling method further includes: Count the physical equipment corresponding to the target underground space and establish a digital twin model of each physical equipment, wherein the physical equipment includes sensor equipment, communication equipment, and control equipment; The digital twin models of various physical devices are combined according to the connection relationship of each physical device to obtain the digital twin model corresponding to the target underground space; The state of the target underground space is predicted based on the digital twin model corresponding to the target underground space, and the state prediction result is marked on the final space model.

5. The underground space modeling method according to claim 4, characterized in that: The underground space modeling method further includes: The final annotated spatial model is sent to the display device for three-dimensional display.

6. An underground space modeling device, characterized in that: include: A data acquisition module, used to acquire a multi-source data set corresponding to a target underground space, wherein the multi-source data set includes sampled data from multiple data sources; A model building module is used to build an initial data model corresponding to the target underground space based on the sampled data of each data source, so as to obtain multiple underground space models, each of which corresponds to the sampled data of one data source; A model fusion module, used to fuse the multiple underground space models based on the data priorities corresponding to various data sources to obtain a final space model corresponding to the target underground space; The model building module is specifically used for: Record the sampling data of this data source as target sampling data, calculate the matching degree between the target sampling data and the historical sampling data in the preset database, and record the historical sampling data with the highest matching degree with the target sampling data as the target historical sampling data; If the matching degree of the target historical sampling data is greater than the preset matching threshold, each marginal surface model corresponding to the target historical sampling data is used as the target marginal surface model, and the boundary of the target marginal surface model is corrected based on the target sampling data, and the corrected target marginal surface model is spliced ​​to obtain the underground space model corresponding to the sampling data of this data source; The model fusion module is specifically used for: sorting the plurality of underground space models according to data priorities corresponding to various data sources; The two adjacent underground space models are merged in order of increasing data priority until the final space model corresponding to the target underground space is obtained; Among them, the method for fusing two adjacent underground space models is: Assigning a first weight to an underground space model with a higher data priority among two adjacent underground space models, and assigning a second weight to an underground space model with a lower data priority among two adjacent underground space models, wherein the first weight is greater than the second weight; Two adjacent underground space models are fused based on the first weight and the second weight to obtain a fused underground space model.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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