CN117974923A - Method and device for determining three-dimensional modeling data, storage medium and electronic equipment - Google Patents
Method and device for determining three-dimensional modeling data, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN117974923A CN117974923A CN202410168851.0A CN202410168851A CN117974923A CN 117974923 A CN117974923 A CN 117974923A CN 202410168851 A CN202410168851 A CN 202410168851A CN 117974923 A CN117974923 A CN 117974923A
- Authority
- CN
- China
- Prior art keywords
- data
- map
- dimensional modeling
- modeling data
- dimensional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000003860 storage Methods 0.000 title claims abstract description 20
- 238000012216 screening Methods 0.000 claims abstract description 39
- 238000013500 data storage Methods 0.000 claims abstract description 35
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 31
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 8
- 238000000547 structure data Methods 0.000 claims description 58
- 238000006243 chemical reaction Methods 0.000 claims description 38
- 238000010845 search algorithm Methods 0.000 claims description 26
- 238000000605 extraction Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 13
- 238000010586 diagram Methods 0.000 description 16
- 238000004590 computer program Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000013135 deep learning Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Remote Sensing (AREA)
- Geometry (AREA)
- Computer Graphics (AREA)
- Processing Or Creating Images (AREA)
- Image Analysis (AREA)
Abstract
The application discloses a method and a device for determining three-dimensional modeling data, a storage medium and electronic equipment. Relates to the field of information technology or other related fields, and the method comprises the following steps: acquiring a map acquisition request sent by a client, and identifying the map acquisition request to obtain target feature information, wherein the map acquisition request is used for indicating the region information of a target map; searching three-dimensional modeling data associated with target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, and the three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model; and sending the target three-dimensional modeling data to the client. The application solves the problems of low screening efficiency and low screening accuracy when screening three-dimensional modeling data from a platform in the related technology.
Description
Technical Field
The present application relates to the field of information technology or other related fields, and in particular, to a method and apparatus for determining three-dimensional modeling data, a storage medium, and an electronic device.
Background
Due to the increase of the number of street community cells facing each provincial and urban area, along with the current increasingly mature unmanned aerial vehicle technology, more and more clients select three-dimensional modeling maps as business auxiliary tools, the three-dimensional modeling technology can simulate objects and scenes in the real world more truly, can visualize abstract data information into images with visual feeling, has the advantage of describing the shape, size, position and direction of the objects with high precision, and after a plurality of three-dimensional modeling maps are drawn based on the technology, the three-dimensional modeling maps are stored in a platform for management, and when the platform manages the multi-level regional information, how to quickly find out three-dimensional modeling data needed by the clients and smoothly present the three-dimensional modeling data in front of the clients becomes an important means for improving the service satisfaction degree of the platform at present.
In the related art, by relying on manual searching or simple keyword matching, under the condition of excessive modeling data or complex query authority, the query efficiency is low, and problems such as jamming and the like can occur when the three-dimensional modeling data is queried and rendered by the front end, so that the response is too slow, and the use of a user is influenced.
Aiming at the problems of low screening efficiency and low screening accuracy when three-dimensional modeling data are screened from a platform in the related technology, no effective solution is proposed at present.
Disclosure of Invention
The application mainly aims to provide a method and a device for determining three-dimensional modeling data, a storage medium and electronic equipment, so as to solve the problems of low screening efficiency and low screening accuracy when three-dimensional modeling data are screened from a platform in the related technology.
In order to achieve the above object, according to one aspect of the present application, there is provided a method of determining three-dimensional modeling data. The method comprises the following steps: acquiring a map acquisition request sent by a client, and identifying the map acquisition request to obtain target feature information, wherein the map acquisition request is used for indicating the region information of a target map; searching three-dimensional modeling data associated with target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer; and sending the target three-dimensional modeling data to the client.
Further, before searching the three-dimensional modeling data associated with the target feature information from the modeling data store via a search algorithm, the method further comprises: acquiring three-dimensional map data associated with M three-dimensional maps to obtain M groups of three-dimensional map data, and acquiring map names of the M three-dimensional maps to obtain M three-dimensional map names, wherein each group of three-dimensional map data at least comprises one of the following: geographic structure data and picture file data; performing format conversion processing on M groups of three-dimensional map data to obtain M groups of initial map data, inputting the M groups of initial map data into a convolutional neural network model, and outputting map feature values associated with the M groups of initial map data to obtain M groups of map feature values; and matching the M groups of map feature values with the M three-dimensional map names to obtain M three-dimensional modeling data, and storing the M three-dimensional modeling data into a modeling data storage library.
Further, searching three-dimensional modeling data associated with the target feature information from the modeling data repository through a search algorithm, the obtaining target three-dimensional modeling data comprising: extracting geographic structure data in the M three-dimensional modeling data to obtain M geographic structure data, and arranging the M three-dimensional modeling data based on the M geographic structure data to obtain a map feature queue, wherein each geographic structure data is used for representing geographic information of each three-dimensional map data, and each geographic structure data at least comprises one of the following: longitude and latitude data and geological data; and screening from the map feature queue according to the target feature information to obtain target three-dimensional modeling data.
Further, ranking the M three-dimensional modeling data based on the M geographic structure data, the obtaining a map feature queue includes: judging whether an association relationship exists between every two three-dimensional modeling data according to M geographic structure data; under the condition that N groups of three-dimensional modeling data with association relations are contained in the M groups of three-dimensional modeling data, acquiring target geographic information of the N groups of three-dimensional modeling data to obtain N groups of target geographic information, wherein the N groups of target geographic information are used for representing common characteristic information in the N groups of three-dimensional modeling data, N is smaller than or equal to M, and N is a positive integer; determining expansion nodes according to the N groups of target geographic information to obtain N expansion nodes; and acquiring Y three-dimensional modeling data, and randomly arranging the three-dimensional modeling data associated with the N expansion nodes and the Y three-dimensional modeling data to obtain a map feature queue, wherein the Y three-dimensional modeling data are three-dimensional modeling data without association relation, and Y=M-N, and Y is a positive integer.
Further, performing format conversion processing on the M sets of three-dimensional map data to obtain M sets of initial map data, where the obtaining includes: performing first format conversion processing on geographic structure data in the M groups of three-dimensional map data to obtain M conversion structure data, and performing second format conversion processing on picture file data in the M groups of three-dimensional map data to obtain M conversion file data; and combining the M conversion structure data and the M conversion file data to obtain M groups of initial map data.
Further, inputting the M sets of initial map data into the convolutional neural network model, outputting map feature values associated with the M sets of initial map data, and obtaining the M sets of map feature values includes: acquiring a preset strategy packet, and carrying out vectorization processing on M groups of initial map data according to the preset strategy packet to obtain M groups of processed map data; and acquiring a preset configuration code, and performing feature extraction processing on the M groups of processed map data according to the preset configuration code to obtain M groups of map feature values, wherein the preset configuration code is used for extracting the feature values in each group of processed map data.
Further, the step of screening the map feature queue to obtain the target three-dimensional modeling data according to the target feature information comprises the following steps: performing similarity calculation on M three-dimensional modeling data of a map feature queue according to the target feature information to obtain M similarity data, wherein each similarity data is used for representing similarity conditions of the target feature information and each three-dimensional modeling data; and performing descending order sequencing on the M similarity data to obtain a similarity queue, acquiring similarity data of a preset bit sequence in the similarity queue, and determining three-dimensional modeling data associated with the similarity data of the preset bit sequence as target three-dimensional modeling data.
In order to achieve the above object, according to another aspect of the present application, there is provided a determination apparatus of three-dimensional modeling data. The device comprises: the first acquisition unit is used for acquiring a map acquisition request sent by the client, identifying the map acquisition request and obtaining target feature information, wherein the map acquisition request is used for indicating the region information of a target map; the system comprises a search unit, a modeling data storage library and a storage unit, wherein the search unit is used for searching three-dimensional modeling data associated with target feature information from the modeling data storage library through a search algorithm to obtain target three-dimensional modeling data, the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer; and the sending unit is used for sending the target three-dimensional modeling data to the client.
According to another aspect of the embodiment of the present invention, there is also provided a computer storage medium for storing a program, where the program is executed to control a device in which the computer storage medium is located to perform a method for determining three-dimensional modeling data.
According to another aspect of embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory; the memory has stored therein computer readable instructions for executing the computer readable instructions, wherein the computer readable instructions execute a method of determining three-dimensional modeling data.
According to the application, the following steps are adopted: acquiring a map acquisition request sent by a client, and identifying the map acquisition request to obtain target feature information, wherein the map acquisition request is used for indicating the region information of a target map; searching three-dimensional modeling data associated with target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer; the method comprises the steps of sending target three-dimensional modeling data to a client, solving the problems of low screening efficiency and low screening accuracy when three-dimensional modeling data are screened from a platform in the related technology, identifying a map acquisition request sent by the client to obtain target feature information by acquiring the map acquisition request, searching three-dimensional modeling data associated with the target feature information from a modeling data storage library by utilizing a search algorithm to obtain target three-dimensional modeling data, and sending the target three-dimensional modeling data to the client, so that the effect of improving the efficiency and accuracy of screening the three-dimensional modeling data is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a method of determining three-dimensional modeling data provided in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative method of determining three-dimensional modeling data provided in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of a feature extraction method for a convolutional neural network model provided in accordance with an embodiment of the present application;
FIG. 4 is a schematic diagram of a screening method of breadth-first search algorithm provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic diagram of a determination apparatus of three-dimensional modeling data provided according to an embodiment of the present application;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, related information (including, but not limited to, user equipment information, user personal information, etc.) and data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by a user or sufficiently authorized by each party.
The present application will be described with reference to preferred implementation steps, and fig. 1 is a flowchart of a method for determining three-dimensional modeling data according to an embodiment of the present application, as shown in fig. 1, and the method includes the following steps:
step S101, a map acquisition request sent by a client is acquired, the map acquisition request is identified, and target feature information is obtained, wherein the map acquisition request is used for indicating the region information of a target map.
Specifically, when a client needs to acquire a three-dimensional map associated with certain three-dimensional modeling data from a platform, the client may first send a map acquisition request to the platform through the client, and after receiving the map acquisition request, the platform identifies the request to determine target feature information characterizing the target map area information, for example, the target feature information may include feature information such as geographic coordinates, geographic names, and the like of the target map. The platform may be a SaaS platform, where the SaaS platform (Software AS A SERVICE) refers to a Software as a service platform, and is a Software delivery mode platform based on a cloud computing mode.
Step S102, three-dimensional modeling data associated with target feature information is searched from a modeling data storage library through a search algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer.
Specifically, the search algorithm may be a breadth-first search algorithm, after determining the target feature information by identifying the map acquisition request, the breadth-first search algorithm may be used to acquire the required target three-dimensional modeling data from the plurality of three-dimensional modeling data, the breadth-first search algorithm (Breadth-FIRST SEARCH, BFS) uses a queue to store the nodes to be accessed, ensuring that the access is performed sequentially.
When the target three-dimensional modeling data is obtained from the plurality of three-dimensional modeling data based on the breadth-first search algorithm, feature recognition can be performed on the plurality of three-dimensional maps by using a convolutional neural network model, so as to obtain the plurality of three-dimensional modeling data, wherein the convolutional neural network (Convolutional Neural Networks, CNN) model is a deep learning algorithm for image recognition and feature recognition.
Step S103, the target three-dimensional modeling data is sent to the client.
Specifically, after target three-dimensional modeling data is obtained from a modeling data repository using a breadth-first search algorithm, it can be presented to a customer via a client with an associated three-dimensional map.
According to the method for determining the three-dimensional modeling data, provided by the embodiment of the application, the map acquisition request sent by the client is acquired, and the map acquisition request is identified to obtain target feature information, wherein the map acquisition request is used for indicating the region information of the target map; searching three-dimensional modeling data associated with target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer; the method comprises the steps of sending target three-dimensional modeling data to a client, solving the problems of low screening efficiency and low screening accuracy when three-dimensional modeling data are screened from a platform in the related technology, identifying a map acquisition request sent by the client to obtain target feature information by acquiring the map acquisition request, searching three-dimensional modeling data associated with the target feature information from a modeling data storage library by utilizing a search algorithm to obtain target three-dimensional modeling data, and sending the target three-dimensional modeling data to the client, so that the effect of improving the efficiency and accuracy of screening the three-dimensional modeling data is achieved.
In the method for determining three-dimensional modeling data provided by the embodiment of the application, before searching the three-dimensional modeling data associated with the target feature information from the modeling data storage library through the search algorithm, the method further comprises: acquiring three-dimensional map data associated with M three-dimensional maps to obtain M groups of three-dimensional map data, and acquiring map names of the M three-dimensional maps to obtain M three-dimensional map names, wherein each group of three-dimensional map data at least comprises one of the following: geographic structure data and picture file data; performing format conversion processing on M groups of three-dimensional map data to obtain M groups of initial map data, inputting the M groups of initial map data into a convolutional neural network model, and outputting map feature values associated with the M groups of initial map data to obtain M groups of map feature values; and matching the M groups of map feature values with the M three-dimensional map names to obtain M three-dimensional modeling data, and storing the M three-dimensional modeling data into a modeling data storage library.
Specifically, before acquiring target three-dimensional modeling data from a modeling data storage library based on a breadth-first search algorithm, firstly, feature extraction is required to be performed on all candidate three-dimensional maps, the extracted features are combined into three-dimensional modeling data to be stored in the modeling data storage library, firstly, related interfaces are required to be compiled on a platform, and data related to the three-dimensional maps uploaded by different clients are acquired by using the interfaces to obtain multiple groups of three-dimensional map data, wherein the three-dimensional map data can comprise geographic structure data and picture file data.
Because the three-dimensional map data acquired by the platform is data uploaded by different clients, there may be a format difference between each group of three-dimensional map data, and thus, steps such as data preprocessing and format conversion need to be performed on each group of three-dimensional map data acquired, for example, preprocessing operations such as data cleaning may be performed on the three-dimensional map data, and format conversion processing may be performed on the preprocessed data to obtain corresponding initial map data.
And further, inputting the initial map data into a convolutional neural network model capable of performing feature extraction operation, performing feature extraction operation on each group of initial map data by using the model, so as to obtain map feature values associated with each group of initial map data, and further matching the map feature values with three-dimensional map names of corresponding three-dimensional maps, so as to obtain three-dimensional modeling data stored in a modeling data storage library, and providing important data support for subsequent map extraction operation. According to the embodiment, the three-dimensional modeling data can be obtained by carrying out data processing on the three-dimensional map, and a foundation is laid for selecting target three-dimensional modeling data.
When the breadth-first search algorithm is used to screen the target three-dimensional modeling data from the modeling data storage, the determination may be performed based on the target feature information and the related information in each three-dimensional modeling data, optionally, in the method for determining three-dimensional modeling data provided in the embodiment of the present application, searching the three-dimensional modeling data associated with the target feature information from the modeling data storage through the search algorithm, to obtain the target three-dimensional modeling data includes: extracting geographic structure data in the M three-dimensional modeling data to obtain M geographic structure data, and arranging the M three-dimensional modeling data based on the M geographic structure data to obtain a map feature queue, wherein each geographic structure data is used for representing geographic information of each three-dimensional map data, and each geographic structure data at least comprises one of the following: longitude and latitude data and geological data; and screening from the map feature queue according to the target feature information to obtain target three-dimensional modeling data.
Specifically, when the identified target feature information is geographic information of the target map, geographic structure data representing the three-dimensional map may be obtained from each three-dimensional modeling data, and then all three-dimensional modeling data in the modeling data repository may be ranked by using the geographic structure data, for example, the geographic structure data may be attribution information of each three-dimensional map, and further, a map feature queue may be determined based on the attribution, for example, the three-dimensional modeling data a is map data of province a, the three-dimensional modeling data B is map data of province B, and when the data are ranked, random ranking may be performed, otherwise, if the three-dimensional modeling data a is map data of province a, the three-dimensional modeling data B is map data of province a, and when the map feature queue is determined, ranking may be performed in order A, B.
After the three-dimensional modeling data are arranged by the geographic structure data and the map feature queue is obtained, each node in the map feature queue can be traversed through the target feature information in sequence, and then the target three-dimensional modeling data which accords with the target feature information are screened out. According to the method, the three-dimensional modeling data are arranged by utilizing the geographic structure data, and the map feature queue is obtained, so that the screening efficiency and the accuracy can be improved.
In order to improve screening efficiency, optionally, in the method for determining three-dimensional modeling data provided by the embodiment of the present application, the step of arranging M three-dimensional modeling data based on M geographic structure data to obtain a map feature queue includes: judging whether an association relationship exists between every two three-dimensional modeling data according to M geographic structure data; under the condition that N groups of three-dimensional modeling data with association relations are contained in the M groups of three-dimensional modeling data, acquiring target geographic information of the N groups of three-dimensional modeling data to obtain N groups of target geographic information, wherein the N groups of target geographic information are used for representing common characteristic information in the N groups of three-dimensional modeling data, N is smaller than or equal to M, and N is a positive integer; determining expansion nodes according to the N groups of target geographic information to obtain N expansion nodes; and acquiring Y three-dimensional modeling data, and randomly arranging the three-dimensional modeling data associated with the N expansion nodes and the Y three-dimensional modeling data to obtain a map feature queue, wherein the Y three-dimensional modeling data are three-dimensional modeling data without association relation, and Y=M-N, and Y is a positive integer.
Specifically, when the geographic structure data is utilized to arrange the plurality of three-dimensional modeling data, whether an association relationship exists between every two three-dimensional modeling data or not can be judged through the geographic structure data, for example, the A three-dimensional modeling data is map data of province A, the B three-dimensional modeling data is map data of province A and the B three-dimensional modeling data is map data of province B, and when the two three-dimensional modeling data are judged based on the geographic structure data, the association relationship exists between A, B two three-dimensional modeling data can be obtained.
Further, after the association relation exists among the plurality of sets of three-dimensional modeling data based on the geographic structure data, the target geographic information of each set of three-dimensional modeling data can be obtained by acquiring the geographic information of the same data in each set of three-dimensional modeling data, and the target geographic information is used as an expansion node in the map feature queue, so that after the map feature queue is determined, the B three-dimensional modeling data can be used as the extension data of the A three-dimensional modeling data by the expansion node, and the tree-shaped map feature queue can be obtained. According to the method, the ' tree ' -shaped ' map feature queue is generated by utilizing the geographic structure data, so that a foundation is laid for acquiring target three-dimensional modeling data faster in the follow-up process.
Before feature extraction is performed by using the convolutional neural network model, data needs to be processed, optionally, in the method for determining three-dimensional modeling data provided by the embodiment of the application, format conversion processing is performed on M groups of three-dimensional map data, and obtaining M groups of initial map data includes: performing first format conversion processing on geographic structure data in the M groups of three-dimensional map data to obtain M conversion structure data, and performing second format conversion processing on picture file data in the M groups of three-dimensional map data to obtain M conversion file data; and combining the M conversion structure data and the M conversion file data to obtain M groups of initial map data.
Specifically, after preprocessing each set of three-dimensional map data, format conversion processing is required to be performed on the preprocessed data, for example, when each set of three-dimensional map data includes geographic structure data for representing the geographic condition of the three-dimensional map and picture file data for displaying each map picture, when format conversion is performed on the three-dimensional map data, vectorization processing is required to be performed on the geographic structure data, format conversion processing is required to be performed on the picture file data, finally, the converted data are combined into initial map data, and the initial map data are stored in a modeling data storage library. The embodiment provides a data basis for determining the map feature queue by performing format conversion processing on the three-bit map data.
Optionally, in the method for determining three-dimensional modeling data provided by the embodiment of the present application, inputting M sets of initial map data into a convolutional neural network model, outputting map feature values associated with the M sets of initial map data, and obtaining the M sets of map feature values includes: acquiring a preset strategy packet, and carrying out vectorization processing on M groups of initial map data according to the preset strategy packet to obtain M groups of processed map data; and acquiring a preset configuration code, and performing feature extraction processing on the M groups of processed map data according to the preset configuration code to obtain M groups of map feature values, wherein the preset configuration code is used for extracting the feature values in each group of processed map data.
Specifically, when extracting the characteristics of each initial map data based on the convolutional neural network model, the relevant jar package such as nd4J-native-platform, deeplearning4J-core can be obtained as a preset strategy package, when the preset strategy package is DEEPLEARNING J-core package, DEEPLEARNING J codes in the DEEPLEARNING J-core jar package and datavec library can be used for vectorizing the initial map data, and then an initial size method in the DEEPLEARNING J-core package is called to obtain a plurality of processed map data.
Further, after processing the initial map data based on the preset policy package, a preset configuration code may be obtained, for example, a DataSet Iterator construction method and a next () method may be called, each set of processed map data is obtained, a MultiLayerConfiguration object is generated through a neurolnetconfiguration. According to the embodiment, the characteristic extraction is carried out by utilizing the convolutional neural network model, the preset strategy package and the preset configuration code, so that the map characteristic value with higher degree of distinction can be extracted, and a foundation is laid for the follow-up accurate screening of the target three-dimensional modeling data.
When three-dimensional modeling data is screened from a map feature queue by utilizing target feature information, determination of the target three-dimensional modeling data can be performed based on a mode of calculating similarity, and optionally, in the method for determining three-dimensional modeling data provided by the embodiment of the application, screening the target three-dimensional modeling data from the map feature queue according to the target feature information includes: performing similarity calculation on M three-dimensional modeling data of a map feature queue according to the target feature information to obtain M similarity data, wherein each similarity data is used for representing similarity conditions of the target feature information and each three-dimensional modeling data; and performing descending order sequencing on the M similarity data to obtain a similarity queue, acquiring similarity data of a preset bit sequence in the similarity queue, and determining three-dimensional modeling data associated with the similarity data of the preset bit sequence as target three-dimensional modeling data.
Specifically, each three-dimensional modeling data in the map feature queue is traversed by utilizing the target feature information, similarity calculation is further carried out on the map feature value of each three-dimensional modeling data and the target feature information, so that a plurality of similarity data are obtained, finally, the similarity data are ordered in a descending order, and the three-dimensional modeling data associated with the first phase similarity data in the arrangement are determined to be the target three-dimensional modeling data. According to the method, the device and the system, the target three-dimensional modeling data are determined in a similarity matching mode, so that the screening efficiency can be improved, and meanwhile, the screening accuracy can be improved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application also provides a method for determining three-dimensional modeling data, and fig. 2 is a schematic diagram of an alternative method for determining three-dimensional modeling data according to an embodiment of the application, as shown in fig. 2, where the method includes:
Firstly, compiling related interfaces on a platform, acquiring three-dimensional map related data uploaded by different clients by utilizing the interfaces to obtain a plurality of groups of three-dimensional map data, carrying out data preprocessing, format conversion and other steps on each group of acquired three-dimensional map data to obtain corresponding initial map data, inputting the initial map data into a convolutional neural network model capable of carrying out feature extraction operation, and carrying out feature extraction operation on each group of initial map data by utilizing the model to obtain three-dimensional modeling data stored in a modeling data storage library.
When a client needs to acquire a three-dimensional map associated with certain three-dimensional modeling data from a platform, the client can send a map acquisition request to the platform through a client, after receiving the map acquisition request, the platform identifies the request to determine target feature information representing target map area information, acquires the required target three-dimensional modeling data from a plurality of three-dimensional modeling data by utilizing a breadth-first search algorithm, and finally sends the target three-dimensional modeling data to the client used by the client.
It should be noted that, fig. 3 is a schematic diagram of a feature extraction method of a convolutional neural network model according to an embodiment of the present application, as shown in fig. 3, when feature values of each set of initial map data are extracted by using the convolutional neural network model, a data set composed of three-dimensional map data associated with a plurality of three-dimensional maps needs to be read first, and the defined initial convolutional neural network model is trained, and then feature values are extracted by using the trained convolutional neural network model, so as to obtain a plurality of three-dimensional modeling data stored in a modeling data repository.
In addition, fig. 4 is a schematic diagram of a screening method of a breadth-first search algorithm according to an embodiment of the present application, as shown in fig. 4, when screening target three-dimensional modeling data by using the breadth-first search algorithm, geographic structure data in a plurality of three-dimensional modeling data needs to be acquired, and the three-dimensional modeling data is ranked by using the geographic structure data, so as to obtain a map feature queue composed of different nodes.
When screening is carried out based on the map feature queue, a node is firstly taken out from the head of the queue, whether three-dimensional modeling data associated with the node exists or not is judged, if the node exists, whether the three-dimensional modeling data associated with the node is target three-dimensional modeling data or not is judged, if the three-dimensional modeling data associated with the node is the target three-dimensional modeling data, the data can be returned to the front end and displayed to a client, otherwise, if the three-dimensional modeling data associated with the node is not the target three-dimensional modeling data, the screening needs to be traversed again.
According to the method, the device and the system, the map acquisition request sent by the client is acquired, the map acquisition request is identified, the target feature information is obtained, the three-dimensional modeling data associated with the target feature information is searched from the modeling data storage library by using the search algorithm, the target three-dimensional modeling data is obtained, the target three-dimensional modeling data is sent to the client, and the effect of improving the efficiency and the accuracy of screening the three-dimensional modeling data is achieved.
The embodiment of the application also provides a device for determining the three-dimensional modeling data, and the device for determining the three-dimensional modeling data can be used for executing the method for determining the three-dimensional modeling data provided by the embodiment of the application. The following describes a device for determining three-dimensional modeling data provided by an embodiment of the present application.
FIG. 5 is a schematic diagram of a device for determining three-dimensional modeling data provided according to an embodiment of the present application, as shown in FIG. 5, the device includes: a first acquisition unit 50, a search unit 51, a transmission unit 52.
A first obtaining unit 50, configured to obtain a map obtaining request sent by a client, identify the map obtaining request, and obtain target feature information, where the map obtaining request is used to indicate area information of a target map;
A search unit 51, configured to search, through a search algorithm, three-dimensional modeling data associated with target feature information from a modeling data repository, to obtain target three-dimensional modeling data, where the target three-dimensional modeling data is used to characterize modeling information of a target map, M three-dimensional modeling data are stored in the modeling data repository, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps by a convolutional neural network model, and M is a positive integer;
and a transmitting unit 52 for transmitting the target three-dimensional modeling data to the client.
Optionally, in the apparatus for determining three-dimensional modeling data provided by the embodiment of the present application, the apparatus further includes: a second obtaining unit, configured to obtain three-dimensional map data associated with M three-dimensional maps before searching three-dimensional modeling data associated with target feature information from a modeling data repository through a search algorithm, to obtain M sets of three-dimensional map data, and obtain map names of the M three-dimensional maps, to obtain M three-dimensional map names, where each set of three-dimensional map data includes at least one of: geographic structure data and picture file data; the processing unit is used for carrying out format conversion processing on the M groups of three-dimensional map data to obtain M groups of initial map data, inputting the M groups of initial map data into the convolutional neural network model, and outputting map feature values associated with the M groups of initial map data to obtain M groups of map feature values; and the matching unit is used for matching the M groups of map feature values with the M three-dimensional map names to obtain M three-dimensional modeling data, and storing the M three-dimensional modeling data into the modeling data storage library.
Optionally, in the apparatus for determining three-dimensional modeling data provided in the embodiment of the present application, the search unit 51 includes: the extraction module is used for extracting geographic structure data in the M three-dimensional modeling data to obtain M geographic structure data, and ranking the M three-dimensional modeling data based on the M geographic structure data to obtain a map feature queue, wherein each geographic structure data is used for representing geographic information of each three-dimensional map data, and each geographic structure data at least comprises one of the following: longitude and latitude data and geological data; and the screening module is used for screening and obtaining target three-dimensional modeling data from the map feature queue according to the target feature information.
Optionally, in the apparatus for determining three-dimensional modeling data provided in the embodiment of the present application, the search unit 51 includes: the judging module is used for respectively judging whether an association relationship exists between every two three-dimensional modeling data according to the M geographic structure data; the first acquisition module is used for acquiring target geographic information of N groups of three-dimensional modeling data under the condition that the M groups of three-dimensional modeling data contain N groups of three-dimensional modeling data with association relation to obtain N groups of target geographic information, wherein the N groups of target geographic information are used for representing common characteristic information in the N groups of three-dimensional modeling data, N is smaller than or equal to M, and N is a positive integer; the determining module is used for determining the expansion nodes according to the N groups of target geographic information to obtain N expansion nodes; the second acquisition module is used for acquiring Y three-dimensional modeling data, and randomly arranging the three-dimensional modeling data associated with the N expansion nodes and the Y three-dimensional modeling data to obtain a map feature queue, wherein the Y three-dimensional modeling data refer to three-dimensional modeling data without association relation, and Y=M-N, and Y is a positive integer.
Optionally, in the apparatus for determining three-dimensional modeling data provided in the embodiment of the present application, the search unit 51 includes: the searching module is used for carrying out first format conversion processing on the geographic structure data in the M groups of three-dimensional map data to obtain M conversion structure data, and carrying out second format conversion processing on the picture file data in the M groups of three-dimensional map data to obtain M conversion file data; and the combination module is used for combining the M conversion structure data and the M conversion file data to obtain M groups of initial map data.
Optionally, in the apparatus for determining three-dimensional modeling data provided in the embodiment of the present application, the search unit 51 includes: the third acquisition module is used for acquiring a preset strategy packet, and carrying out vectorization processing on M groups of initial map data according to the preset strategy packet to obtain M groups of processed map data; and the fourth acquisition module is used for acquiring preset configuration codes, and carrying out feature extraction processing on the M groups of processed map data according to the preset configuration codes to obtain M groups of map feature values, wherein the preset configuration codes are used for extracting the feature values in each group of processed map data.
Optionally, in the apparatus for determining three-dimensional modeling data provided in the embodiment of the present application, the search unit 51 includes: the computing module is used for carrying out similarity computation on M three-dimensional modeling data of the map feature queue according to the target feature information to obtain M similarity data, wherein each similarity data is used for representing the similarity condition of the target feature information and each three-dimensional modeling data; the sorting module is used for sorting the M similarity data in a descending order to obtain a similarity queue, obtaining similarity data of a preset order in the similarity queue, and determining three-dimensional modeling data associated with the similarity data of the preset order as target three-dimensional modeling data.
According to the determining device for the three-dimensional modeling data, provided by the embodiment of the application, a map acquisition request sent by a client is acquired through the first acquiring unit 50, the map acquisition request is identified, and target characteristic information is obtained, wherein the map acquisition request is used for indicating the region information of a target map; the searching unit 51 searches three-dimensional modeling data associated with the target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data is used for representing modeling information of a target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer; the sending unit 52 sends the target three-dimensional modeling data to the client, solves the problems of low screening efficiency and low screening accuracy when screening the three-dimensional modeling data from the platform in the related art, and obtains the target feature information by acquiring the map acquisition request sent by the client and identifying the map acquisition request, and then searches the three-dimensional modeling data associated with the target feature information from the modeling data storage library by using a search algorithm to obtain the target three-dimensional modeling data, and sends the target three-dimensional modeling data to the client, thereby achieving the effect of improving the efficiency and accuracy of screening the three-dimensional modeling data.
The determination device of the three-dimensional modeling data includes a processor and a memory, and the first acquisition unit 50, the search unit 51, the transmission unit 52, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, and the problems of low screening efficiency and low screening accuracy in screening three-dimensional modeling data from a platform in the related technology are solved by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the invention provides a computer storage medium which is used for storing a program, wherein the program is used for controlling equipment where the computer storage medium is located to execute a method for determining three-dimensional modeling data when running.
Fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 6, an electronic device 60 is provided, where the electronic device 60 includes a processor, a memory, and a program stored on the memory and executable on the processor, and the processor is configured to execute computer readable instructions, where the computer readable instructions execute a method for determining three-dimensional modeling data when executed. The device herein may be a server, PC, PAD, cell phone, etc.
The application also provides a computer program product adapted to perform a method of determining three-dimensional modeling data when executed on a data processing apparatus.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method for determining three-dimensional modeling data, comprising:
acquiring a map acquisition request sent by a client, and identifying the map acquisition request to obtain target feature information, wherein the map acquisition request is used for indicating area information of a target map;
searching three-dimensional modeling data associated with the target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of the target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer;
And sending the target three-dimensional modeling data to the client.
2. The method of claim 1, wherein prior to searching the three-dimensional modeling data associated with the target feature information from a modeling data store via a search algorithm, the method further comprises:
acquiring three-dimensional map data associated with the M three-dimensional maps to obtain M groups of three-dimensional map data, and acquiring map names of the M three-dimensional maps to obtain M three-dimensional map names, wherein each group of three-dimensional map data at least comprises one of the following: geographic structure data and picture file data;
Performing format conversion processing on the M groups of three-dimensional map data to obtain M groups of initial map data, inputting the M groups of initial map data into the convolutional neural network model, and outputting map feature values associated with the M groups of initial map data to obtain M groups of map feature values;
And matching the M groups of map feature values with the M three-dimensional map names to obtain M three-dimensional modeling data, and storing the M three-dimensional modeling data into the modeling data storage library.
3. The method of claim 1, wherein searching three-dimensional modeling data associated with the target feature information from a modeling data store via a search algorithm to obtain target three-dimensional modeling data comprises:
Extracting geographic structure data in the M three-dimensional modeling data to obtain M geographic structure data, and arranging the M three-dimensional modeling data based on the M geographic structure data to obtain a map feature queue, wherein each geographic structure data is used for representing geographic information of each three-dimensional map data, and each geographic structure data at least comprises one of the following: longitude and latitude data and geological data;
And screening the map feature queue according to the target feature information to obtain the target three-dimensional modeling data.
4. The method of claim 3, wherein ranking the M three-dimensional modeling data based on the M geographic structure data to obtain a map feature queue comprises:
Judging whether an association relationship exists between every two three-dimensional modeling data according to the M geographic structure data;
Acquiring target geographic information of the N groups of three-dimensional modeling data under the condition that the M groups of three-dimensional modeling data contain N groups of three-dimensional modeling data with the association relation, and obtaining N groups of target geographic information, wherein the N groups of target geographic information are used for representing common characteristic information in the N groups of three-dimensional modeling data, N is smaller than or equal to M, and N is a positive integer;
determining expansion nodes according to the N groups of target geographic information to obtain N expansion nodes;
And acquiring Y three-dimensional modeling data, and randomly arranging the three-dimensional modeling data associated with the N expansion nodes and the Y three-dimensional modeling data to obtain the map feature queue, wherein the Y three-dimensional modeling data are three-dimensional modeling data without the association relationship, and Y=M-N, and Y is a positive integer.
5. The method of claim 2, wherein performing format conversion processing on the M sets of three-dimensional map data to obtain M sets of initial map data comprises:
Performing first format conversion processing on the geographic structure data in the M groups of three-dimensional map data to obtain M conversion structure data, and performing second format conversion processing on the picture file data in the M groups of three-dimensional map data to obtain M conversion file data;
and combining the M conversion structure data with the M conversion file data to obtain M groups of initial map data.
6. The method of claim 2, wherein inputting the M sets of initial map data into the convolutional neural network model, outputting map feature values associated with the M sets of initial map data, and obtaining the M sets of map feature values comprises:
Acquiring a preset strategy packet, and carrying out vectorization processing on the M groups of initial map data according to the preset strategy packet to obtain M groups of processed map data;
And acquiring a preset configuration code, and performing feature extraction processing on the M groups of processed map data according to the preset configuration code to obtain M groups of map feature values, wherein the preset configuration code is used for extracting feature values in each group of processed map data.
7. The method of claim 3, wherein screening the map feature queue for the target three-dimensional modeling data based on the target feature information comprises:
performing similarity calculation on the M three-dimensional modeling data of the map feature queue according to the target feature information to obtain M similarity data, wherein each similarity data is used for representing the similarity condition of the target feature information and each three-dimensional modeling data;
And performing descending order sequencing on the M similarity data to obtain a similarity queue, acquiring similarity data of a preset bit sequence in the similarity queue, and determining three-dimensional modeling data associated with the similarity data of the preset bit sequence as the target three-dimensional modeling data.
8. A determination apparatus for three-dimensional modeling data, comprising:
The first acquisition unit is used for acquiring a map acquisition request sent by a client, identifying the map acquisition request and obtaining target feature information, wherein the map acquisition request is used for indicating the region information of a target map;
The searching unit is used for searching three-dimensional modeling data associated with the target feature information from a modeling data storage library through a searching algorithm to obtain target three-dimensional modeling data, wherein the target three-dimensional modeling data are used for representing modeling information of the target map, M three-dimensional modeling data are stored in the modeling data storage library, the M three-dimensional modeling data are obtained by processing three-dimensional map data associated with M three-dimensional maps through a convolutional neural network model, and M is a positive integer;
and the sending unit is used for sending the target three-dimensional modeling data to the client.
9. A computer storage medium for storing a program, wherein the program when run controls a device in which the computer storage medium is located to perform the method of determining three-dimensional modeling data according to any one of claims 1 to 7.
10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of determining three-dimensional modeling data of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410168851.0A CN117974923A (en) | 2024-02-05 | 2024-02-05 | Method and device for determining three-dimensional modeling data, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410168851.0A CN117974923A (en) | 2024-02-05 | 2024-02-05 | Method and device for determining three-dimensional modeling data, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117974923A true CN117974923A (en) | 2024-05-03 |
Family
ID=90863036
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410168851.0A Pending CN117974923A (en) | 2024-02-05 | 2024-02-05 | Method and device for determining three-dimensional modeling data, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117974923A (en) |
-
2024
- 2024-02-05 CN CN202410168851.0A patent/CN117974923A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9880012B2 (en) | Hybrid road network and grid based spatial-temporal indexing under missing road links | |
| CN110275983B (en) | Retrieval method and device of traffic monitoring data | |
| CN110597804B (en) | Facilitating spatial indexing on a distributed key value store | |
| KR101266358B1 (en) | A distributed index system based on multi-length signature files and method thereof | |
| CN111143685B (en) | Commodity recommendation method and device | |
| CN110020144B (en) | Recommendation model building method and equipment, storage medium and server thereof | |
| CN110796204A (en) | Video tag determination method and device and server | |
| CN109002499B (en) | Discipline correlation knowledge point base construction method and system | |
| CN113821657A (en) | Artificial intelligence-based image processing model training method and image processing method | |
| CN111159563A (en) | Method, device and equipment for determining user interest point information and storage medium | |
| CN117351368B (en) | Natural village boundary acquisition method and device, electronic equipment and storage medium | |
| CN111427976B (en) | Road freshness obtaining method and device | |
| CN114461853A (en) | Training sample generation method, device and equipment of video scene classification model | |
| CN115730507A (en) | Model engine construction method, kernel function processing method, device and storage medium | |
| CN104376000A (en) | Webpage attribute determination method and webpage attribute determination device | |
| CN117974923A (en) | Method and device for determining three-dimensional modeling data, storage medium and electronic equipment | |
| CN116796083A (en) | Space data partitioning method and system | |
| CN111291904B (en) | Preference prediction method and device and computer equipment | |
| CN115455426A (en) | Business error analysis method based on vulnerability analysis model development and cloud AI system | |
| CN110856253B (en) | Positioning method, positioning device, server and storage medium | |
| CN118261247B (en) | Identification analysis recommendation method, equipment and storage medium based on knowledge graph | |
| CN116612421B (en) | Method and equipment for identifying built environment by fusing multi-source space data | |
| CN114491307B (en) | Equipment aggregation method and device | |
| CN110889042B (en) | Resource recommendation method and device, computer equipment and storage medium | |
| CN114780779A (en) | Image searching method, device, system, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |