Cloud platform real scene modeling control method and system

Abstract

The invention provides a cloud platform live-action modeling control method and system, and the method comprises the steps: collecting live-action data according to a preset shooting mode based on an unmanned plane, and uploading the live-action data to a cloud platform; extracting composition features of the live-action data based on the cloud platform, and splitting the live-action data based on the composition features to obtain a modeling task set; issuing the modeling task set to each modeling node in a distributed manner, performing dynamic interaction analysis on each modeling task based on the modeling nodes, performing coupling coordination on analysis results, and constructing a live-action model; and performing multi-terminal visual configuration on the live-action model to complete live-action modeling. The real scene modeling efficiency is improved, accurate and effective construction of the real scene model is realized, effective operation of the real scene model on a plurality of different terminals is ensured, and the real scene modeling effect and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a cloud platform real-scene modeling control method and system. Background Art

[0002] With the development of information technology, the demand for reality modeling is growing in many fields, such as urban planning, geographic information, and architectural design;

[0003] In traditional reality modeling, there may be problems such as unreasonable allocation of computing resources, low data processing efficiency, slow model building speed, etc. At the same time, different users and projects have different needs and requirements for reality modeling. In addition, when collecting reality data, the collection of reality data is not comprehensive and accurate, which greatly reduces the effect and accuracy of reality modeling.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides a cloud platform real-scene modeling control method and system. Summary of the invention

[0005] The present invention provides a cloud platform real-scene modeling control method and system, which are used to collect real-scene data according to a preset shooting mode by controlling a drone, so as to achieve comprehensive and effective acquisition of the real-scene data, and upload the collected real-scene data to the cloud platform, so as to facilitate the cloud platform to process the real-scene data, and provide reliable data support for real-scene modeling. Secondly, the real-scene data is split through the cloud platform to achieve accurate and effective determination of the modeling task set, and the modeling tasks are distributed and sent to each modeling node, so as to facilitate each modeling node to synchronously execute the modeling tasks, thereby improving the efficiency of real-scene modeling. When performing real-scene modeling, dynamic interaction analysis is performed between each modeling node to ensure the accuracy and reliability of the model finally obtained by each modeling node. Finally, the results obtained by each modeling node are coupled and coordinated to achieve accurate and effective construction of the real-scene model, and the real-scene model is visually configured for multiple terminals to ensure that the real-scene model can be effectively operated on multiple different terminals, thereby improving the effect and accuracy of real-scene modeling.

[0006] The present invention provides a cloud platform real scene modeling control method, comprising:

[0007] Step 1: Collect real scene data based on the drone according to the preset shooting mode, and upload the real scene data to the cloud platform;

[0008] Step 2: Extract the constituent features of the real scene data based on the cloud platform, and split the real scene data based on the constituent features to obtain a set of modeling tasks;

[0009] Step 3: Distribute the modeling task set to each modeling node in a distributed manner, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model;

[0010] Step 4: Perform multi-terminal visualization configuration on the real-scene model to complete the real-scene modeling.

[0011] Preferably, a cloud platform real scene modeling control method, in step 1, collecting real scene data based on a drone according to a preset shooting mode, includes:

[0012] Obtain the real scene data collection requirements, and analyze the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone;

[0013] At the same time, the scene distribution characteristics of the area to be collected are obtained, and the flight route of the UAV over the area to be collected is determined based on the scene distribution characteristics and the collection accuracy;

[0014] The preset shooting mode of the UAV is obtained based on the acquisition change angle and the flight route, and the UAV is controlled to collect real-scene data based on the preset shooting mode.

[0015] Preferably, a cloud platform real scene modeling control method controls a drone to collect real scene data based on a preset shooting mode, comprising:

[0016] Acquire the collected real scene data, and perform edge contour detection on the real scene image of the real scene data to obtain the edge contour enclosing rectangle of the real scene image at each collection angle;

[0017] Determine adjacent image groups of real scene images of real scene data based on the flight path of the UAV over the area to be collected, and overlap the real scene images in the adjacent image groups in opposite directions;

[0018] Determine the overlap degree of the edge contour enclosing rectangles of adjacent real scene images based on the opposite overlap, and re-collect the real scene data when the overlap degree does not meet a preset threshold;

[0019] Otherwise, the real scene images in the adjacent image group are retained, and position tags are added to the real scene images based on the retained results.

[0020] Preferably, a cloud platform real scene modeling control method, in step 1, uploading the real scene data to the cloud platform, comprises:

[0021] Obtain the communication parameters of the local device, and connect the local device and the drone based on the communication parameters;

[0022] Based on the docking results, the real scene data collected by the drone is cached in the local device, and a communication link between the local device and the cloud platform is established based on the cached results;

[0023] Compressing the cached real scene data, and splitting the compressed real scene data to obtain compressed data blocks;

[0024] The compressed data blocks are uploaded to the cloud platform in sequence based on the communication link.

[0025] Preferably, a cloud platform real scene modeling control method, in step 2, extracting constituent features of real scene data based on the cloud platform, and splitting the real scene data based on the constituent features to obtain a modeling task set, including:

[0026] Acquire the obtained real scene data, and parse the real scene data based on the scene modeling knowledge system to obtain the semantic information of each monomer data in the real scene data;

[0027] Determine the spatial position relationship between the real scene objects corresponding to each monomer data based on the semantic information, and annotate the monomer data based on the spatial position relationship to obtain the composition features of the real scene data;

[0028] The real scene data is split based on the composition features to obtain multiple groups of real scene data, and attribute information of each group of real scene data is extracted based on the annotation results;

[0029] The scene classification name corresponding to each group of real scene data is obtained based on the attribute information, and the corresponding real scene data group is labeled based on the scene classification name to obtain a modeling task set.

[0030] Preferably, a cloud platform reality modeling control method, in step 3, distributes the modeling task set to each modeling node, and dynamically interacts and analyzes each modeling task based on the modeling node, and couples and coordinates the analysis results to build a reality model, including:

[0031] Obtaining the system composition of the real scene modeling, and dividing the system composition into nodes based on functional attributes to obtain a modeling node set;

[0032] Determine the mapping relationship between modeling tasks and modeling nodes based on the functional attributes of the modeling nodes and the task requirements of each modeling task in the modeling task set, build a distributed delivery link based on the mapping relationship, and deliver each modeling task to each modeling node in parallel based on the distributed delivery link;

[0033] Based on the parallel delivery results, each modeling node is controlled to perform task analysis on the corresponding modeling task, the framework structure corresponding to each modeling task is obtained, and the modeling task is verified based on the framework structure;

[0034] Based on the interactive mechanism of modeling nodes, the modeling task is partially modified according to the task verification result, and the structure of the partially modified modeling task is analyzed based on the modeling nodes to obtain the local structural features and three-dimensional point cloud data of the real scene;

[0035] Obtaining edge contours of the real scene based on local structural features, and constructing a model framework of the real scene based on the edge contours; at the same time, determining a hierarchical structure of the real scene based on the edge contours, and sequentially constructing a local three-dimensional spatial structure in the model framework according to the three-dimensional point cloud data based on the hierarchical structure;

[0036] Directed association is performed on adjacent local three-dimensional spatial structures, and based on the directed association result, the local three-dimensional spatial structure is nested and associated with the model framework of the real scene;

[0037] Performing texture mapping on the nested association results based on the real scene data to obtain an initial real scene model of the real scene;

[0038] Determine the spatial position of each initial real scene model based on the real scene data, and determine the structural splicing boundary of adjacent initial real scene models based on the spatial position;

[0039] The initial real-scene models of each modeling node are structurally spliced ​​based on the structural splicing boundary, and the structural splicing results are coupled and coordinated to obtain the final real-scene model.

[0040] Preferably, a cloud platform reality modeling control method is provided, which sends each modeling task to each modeling node in parallel based on a distributed sending link, including:

[0041] Based on the parallel delivery results, the start time of each modeling node is recorded. At the same time, the background record log is built and the record log is divided into regions;

[0042] Based on the regional division results, the start time of each modeling node is entered as initial record data, and based on the entry results, the real-time modeling data of each modeling node is synchronously recorded;

[0043] Perform periodic traversal on the input results in the record log based on a preset time interval, and obtain the progress index of each modeling node under the start time limit based on the periodic traversal results;

[0044] Determine the modeling status of each modeling node based on the relative size relationship between the progress indicator and the preset progress indicator, and lock the abnormal modeling node based on the modeling status;

[0045] Adjust the parameters of the abnormal modeling nodes until the preset progress indicator requirements are met.

[0046] Preferably, a cloud platform reality modeling control method, in step 4, performs multi-terminal visualization configuration on the reality model to complete the reality modeling, including:

[0047] Acquire the obtained real scene model, determine the available terminals of the real scene model, and extract the operating environment of the available terminals;

[0048] Determine the development framework of each available terminal based on the operating environment, and convert model parameters of the real scene model based on the development framework;

[0049] Determine the interface layout of the real scene model on each available terminal based on the model parameter conversion result, and configure the resolution of the interface layout of each available terminal based on the basic configuration of each available terminal;

[0050] Based on the configuration results, the real scene model is tested on each available terminal, and based on the test results, the real scene model is optimized and rendered on each available terminal to complete the real scene modeling.

[0051] The present invention provides a cloud platform real scene modeling control system, comprising:

[0052] A data acquisition module is used to collect real-scene data based on the drone according to a preset shooting mode and upload the real-scene data to the cloud platform;

[0053] A task determination module is used to extract the composition features of the real scene data based on the cloud platform, and split the real scene data based on the composition features to obtain a modeling task set;

[0054] The real scene model building module is used to distribute the modeling task set to each modeling node, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model;

[0055] The visualization configuration module is used to perform multi-terminal visualization configuration of the real-scene model to complete the real-scene modeling.

[0056] Preferably, a cloud platform real scene modeling control system, a data acquisition module, comprises:

[0057] The parameter configuration unit is used to obtain the real scene data collection requirements, and analyze the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone;

[0058] A route determination unit is used to simultaneously obtain the scene distribution characteristics of the area to be collected, and determine the flight route of the UAV over the area to be collected based on the scene distribution characteristics and the collection accuracy;

[0059] The data acquisition unit is used to obtain a preset shooting mode of the drone based on the acquired change angle and flight path, and control the drone to collect real-scene data based on the preset shooting mode.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. By controlling the drone to collect real-scene data according to the preset shooting mode, the real-scene data can be fully and effectively acquired, and the collected real-scene data can be uploaded to the cloud platform, which is convenient for the cloud platform to process the real-scene data and provide reliable data support for real-scene modeling. Secondly, the real-scene data is split through the cloud platform to accurately and effectively determine the modeling task set, and the modeling tasks are distributed to each modeling node, which is convenient for each modeling node to synchronously execute the modeling tasks, thereby improving the efficiency of real-scene modeling. When performing real-scene modeling, the dynamic interaction analysis between the modeling nodes ensures the accuracy and reliability of the model finally obtained by each modeling node. Finally, the results obtained by each modeling node are coupled and coordinated to achieve accurate and effective construction of the real-scene model, and the real-scene model is visualized on multiple terminals to ensure that the real-scene model can be effectively operated on multiple different terminals, thereby improving the effect and accuracy of real-scene modeling.

[0062] 2. By analyzing the requirements for real-scene data collection, the collection change angle and collection accuracy of the tilt camera on the drone can be accurately and effectively determined. Secondly, the scene distribution characteristics of the area to be collected are determined, and the flight path of the drone over the area to be collected is locked according to the scene distribution characteristics, so that the drone can conduct comprehensive and effective real-scene data collection. Finally, the collection change angle and flight path are summarized to effectively determine the preset shooting mode of the drone, which provides a reliable guarantee for controlling the drone to collect real-scene data and ensures the accuracy and reliability of the collected real-scene data.

[0063] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0064] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0066] Figure 1This is a flow chart of a cloud platform real scene modeling control method according to an embodiment of the present invention;

[0067] Figure 2 This is a flowchart of step 1 in a cloud platform real scene modeling control method in an embodiment of the present invention;

[0068] Figure 3 It is a structural diagram of a cloud platform real-scene modeling control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0070] Embodiment 1:

[0071] This embodiment provides a cloud platform real scene modeling control method, such as Figure 1 As shown, including:

[0072] Step 1: Collect real scene data based on the drone according to the preset shooting mode, and upload the real scene data to the cloud platform;

[0073] Step 2: Extract the constituent features of the real scene data based on the cloud platform, and split the real scene data based on the constituent features to obtain a set of modeling tasks;

[0074] Step 3: Distribute the modeling task set to each modeling node in a distributed manner, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model;

[0075] Step 4: Perform multi-terminal visualization configuration on the real-scene model to complete the real-scene modeling.

[0076] In this embodiment, the preset shooting modes are set in advance, including zigzag flight, circular flight, and the shooting angle of the tilted camera carried by the drone.

[0077] In this embodiment, the real scene data refers to parameters or images that can characterize the on-site scene conditions and are obtained by collecting specific on-site conditions through a drone.

[0078] In this embodiment, the constituent features refer to the types of data contained in the real scene data, for example, real scene data about roads, real scene data about buildings, and real scene data about forests.

[0079] In this embodiment, the modeling task set refers to different types of modeling data obtained after splitting the real scene data according to the constituent features of the real scene data, including modeling tasks corresponding to buildings, modeling tasks for roads, and modeling tasks for rivers.

[0080] In this embodiment, the modeling node is set in advance, is an execution entity for constructing different parts of the scene model, and is a part of the entire modeling system.

[0081] In this embodiment, the dynamic interactive analysis refers to the modeling node verifying each modeling task and promptly correcting the modeling task when there are differences in the modeling tasks.

[0082] In this embodiment, coupling coordination refers to associating and splicing the local models finally obtained by different modeling nodes, and finally obtaining the real scene model.

[0083] In this embodiment, the multi-terminal visualization configuration refers to the configuration of the format, resolution and interaction mode of the real scene model, with the purpose of ensuring that the real scene model can be effectively run on different terminals.

[0084] The working principle and beneficial effects of the above technical solution are: by controlling the drone to collect real-scene data according to the preset shooting mode, the real-scene data can be fully and effectively acquired, and the collected real-scene data can be uploaded to the cloud platform, so that the cloud platform can process the real-scene data and provide reliable data support for real-scene modeling. Secondly, the real-scene data is split through the cloud platform to accurately and effectively determine the modeling task set, and the modeling tasks are distributed to each modeling node, so that each modeling node can synchronously execute the modeling tasks, thereby improving the efficiency of real-scene modeling. When performing real-scene modeling, the modeling nodes dynamically interact and analyze each other to ensure the accuracy and reliability of the model finally obtained by each modeling node. Finally, the results obtained by each modeling node are coupled and coordinated to achieve accurate and effective construction of the real-scene model, and the real-scene model is visualized in multiple terminals to ensure that the real-scene model can be effectively operated on multiple different terminals, thereby improving the effect and accuracy of real-scene modeling.

[0085] Embodiment 2:

[0086] Based on Example 1, this embodiment provides a cloud platform real scene modeling control method, such as Figure 2 As shown, in step 1, real scene data is collected based on the drone according to the preset shooting mode, including:

[0087] Step 101: obtaining real scene data collection requirements, and parsing the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone;

[0088] Step 102: At the same time, the scene distribution characteristics of the area to be collected are obtained, and the flight path of the UAV over the area to be collected is determined based on the scene distribution characteristics and the collection accuracy;

[0089] Step 103: obtaining a preset shooting mode of the drone based on the acquisition change angle and the flight path, and controlling the drone to collect real scene data based on the preset shooting mode.

[0090] In this embodiment, the requirements for real-scene data collection are set in advance, including the collection angle and collection accuracy.

[0091] In this embodiment, the acquisition change angle refers to the change in the angle of each acquisition by the tilt camera on the drone, the purpose of which is to collect real scene data at different angles.

[0092] In this embodiment, the scene distribution feature refers to the area distribution of the area to be collected, so as to facilitate the determination of the flight route of the drone.

[0093] The working principle and beneficial effects of the above technical solution are: by analyzing the requirements for real-scene data collection, the collection change angle and collection accuracy of the tilt camera on the drone can be accurately and effectively determined; secondly, the scene distribution characteristics of the area to be collected are determined, and the flight route of the drone over the area to be collected is locked according to the scene distribution characteristics, so that the drone can conduct comprehensive and effective real-scene data collection; finally, the collection change angle and flight route are summarized to effectively determine the preset shooting mode of the drone, which provides a reliable guarantee for controlling the drone to collect real-scene data and ensures the accuracy and reliability of the collected real-scene data.

[0094] Embodiment 3:

[0095] Based on Example 2, this embodiment provides a cloud platform real scene modeling control method, which controls the drone to collect real scene data based on a preset shooting mode, including:

[0096] Acquire the collected real scene data, and perform edge contour detection on the real scene image of the real scene data to obtain the edge contour enclosing rectangle of the real scene image at each collection angle;

[0097] Determine adjacent image groups of real scene images of real scene data based on the flight path of the UAV over the area to be collected, and overlap the real scene images in the adjacent image groups in opposite directions;

[0098] Determine the overlap degree of the edge contour enclosing rectangles of adjacent real scene images based on the opposite overlap, and re-collect the real scene data when the overlap degree does not meet a preset threshold;

[0099] Otherwise, the real scene images in the adjacent image group are retained, and position tags are added to the real scene images based on the retained results.

[0100] In this embodiment, edge contour detection refers to detecting the edge distribution state of the real scene image, including edge structure and shape.

[0101] In this embodiment, the edge contour enclosing rectangle refers to a frame that can cover the edge of the real scene image.

[0102] In this embodiment, the adjacent image group refers to the real scene images corresponding to the adjacent flight routes when the drone is performing image acquisition, that is, the real scene images having the same boundary area.

[0103] In this embodiment, the overlapping refers to matching the degree of overlap of adjacent real scene images.

[0104] In this embodiment, the preset threshold is set in advance and can be adjusted.

[0105] In this embodiment, the location tag refers to a marking symbol that can distinguish the location of the scene recorded by the real scene image.

[0106] The working principle and beneficial effects of the above technical solution are: by performing edge contour detection on the real-scene image of the collected real-scene data, the edge contour enclosing rectangle of the real-scene image is determined according to the edge contour detection result, which provides convenience for determining the overlap degree. Secondly, the adjacent image groups of the real-scene image are determined according to the flight route, and the overlap degree of the adjacent image groups is matched, so as to facilitate the effective judgment of the collection qualification of the real-scene data according to the overlap degree. Finally, when the overlap degree does not meet the preset threshold, the real-scene data is re-collected, otherwise, the collected real-scene data is retained, providing reliable data support for real-scene modeling.

[0107] Embodiment 4:

[0108] Based on Example 1, this embodiment provides a cloud platform real scene modeling control method. In step 1, uploading real scene data to the cloud platform includes:

[0109] Obtain the communication parameters of the local device, and connect the local device and the drone based on the communication parameters;

[0110] Based on the docking results, the real scene data collected by the drone is cached in the local device, and a communication link between the local device and the cloud platform is established based on the cached results;

[0111] Compressing the cached real scene data, and splitting the compressed real scene data to obtain compressed data blocks;

[0112] The compressed data blocks are uploaded to the cloud platform in sequence based on the communication link.

[0113] In this embodiment, the communication parameters refer to the communication parameters of the local device, including communication bandwidth and communication format requirements.

[0114] In this embodiment, the local device refers to a device that can communicate and connect with the drone.

[0115] In this embodiment, the compressed data block refers to a result obtained by splitting the data compression result of the real scene data.

[0116] The working principle and beneficial effects of the above technical solution are: by building a communication link between the local device and the drone and a communication link between the local device and the cloud platform, the real-scene data collected by the drone through the local device can be effectively uploaded to the cloud platform, thereby facilitating the analysis of the real-scene data through the cloud platform and providing convenience for real-scene modeling.

[0117] Embodiment 5:

[0118] Based on Example 1, this embodiment provides a cloud platform real scene modeling control method. In step 2, the composition features of the real scene data are extracted based on the cloud platform, and the real scene data are split based on the composition features to obtain a modeling task set, including:

[0119] Acquire the obtained real scene data, and parse the real scene data based on the scene modeling knowledge system to obtain the semantic information of each monomer data in the real scene data;

[0120] Determine the spatial position relationship between the real scene objects corresponding to each monomer data based on the semantic information, and annotate the monomer data based on the spatial position relationship to obtain the composition features of the real scene data;

[0121] The real scene data is split based on the composition features to obtain multiple groups of real scene data, and attribute information of each group of real scene data is extracted based on the annotation results;

[0122] The scene classification name corresponding to each group of real scene data is obtained based on the attribute information, and the corresponding real scene data group is labeled based on the scene classification name to obtain a modeling task set.

[0123] In this embodiment, the scene modeling knowledge system is pre-set, including the data type corresponding to the real scene data and the object type represented.

[0124] In this embodiment, the monomer data refers to the specific data information corresponding to each moment in the real scene data.

[0125] In this embodiment, semantic information refers to the specific meaning content corresponding to each monomer data, including the location information of the real scene object corresponding to the monomer data.

[0126] In this embodiment, labeling the monomer data according to the spatial position relationship refers to determining the corresponding monomer data according to the idle position relationship of each real scene object, so as to facilitate the splitting of the real scene data.

[0127] In this embodiment, the multiple groups of real scene data refer to the results obtained after splitting the real scene data.

[0128] In this embodiment, the attribute information refers to the type and location information of the real scene object corresponding to each group of real scene data.

[0129] In this embodiment, the scene classification names include "house", "road" and "river" and the like.

[0130] In this embodiment, the subsidiary marking refers to marking the corresponding real scene data group accordingly through the obtained scene classification name.

[0131] The working principle and beneficial effects of the above technical solution are: by parsing the real-scene data according to the scene modeling knowledge system, the constituent features of the real-scene data can be accurately and effectively determined; secondly, the real-scene data is split according to the constituent features, and the attribute information of each group of real-scene data obtained by the split is determined; finally, the scene classification name of each group of real-scene data is determined according to the attribute information, and the real-scene data group is attached according to the scene classification name, so as to achieve accurate and effective determination of the modeling task set, thereby facilitating the real-scene data to be sent to the corresponding modeling nodes respectively, thereby improving the efficiency of scene modeling.

[0132] Embodiment 6:

[0133] Based on Example 1, this embodiment provides a cloud platform real scene modeling control method. In step 3, the modeling task set is distributed to each modeling node, and each modeling task is dynamically interactively analyzed based on the modeling node, and the analysis results are coupled and coordinated to build a real scene model, including:

[0134] Obtaining the system composition of the real scene modeling, and dividing the system composition into nodes based on functional attributes to obtain a modeling node set;

[0135] Determine the mapping relationship between modeling tasks and modeling nodes based on the functional attributes of the modeling nodes and the task requirements of each modeling task in the modeling task set, build a distributed delivery link based on the mapping relationship, and deliver each modeling task to each modeling node in parallel based on the distributed delivery link;

[0136] Based on the parallel delivery results, each modeling node is controlled to perform task analysis on the corresponding modeling task, the framework structure corresponding to each modeling task is obtained, and the modeling task is verified based on the framework structure;

[0137] Based on the interactive mechanism of modeling nodes, the modeling task is partially modified according to the task verification result, and the structure of the partially modified modeling task is analyzed based on the modeling nodes to obtain the local structural features and three-dimensional point cloud data of the real scene;

[0138] Obtaining edge contours of the real scene based on local structural features, and constructing a model framework of the real scene based on the edge contours; at the same time, determining a hierarchical structure of the real scene based on the edge contours, and sequentially constructing a local three-dimensional spatial structure in the model framework according to the three-dimensional point cloud data based on the hierarchical structure;

[0139] Directed association is performed on adjacent local three-dimensional spatial structures, and based on the directed association result, the local three-dimensional spatial structure is nested and associated with the model framework of the real scene;

[0140] Performing texture mapping on the nested association results based on the real scene data to obtain an initial real scene model of the real scene;

[0141] Determine the spatial position of each initial real scene model based on the real scene data, and determine the structural splicing boundary of adjacent initial real scene models based on the spatial position;

[0142] The initial real-scene models of each modeling node are structurally spliced ​​based on the structural splicing boundary, and the structural splicing results are coupled and coordinated to obtain the final real-scene model.

[0143] In this embodiment, the functional attributes refer to the functions or effects that can be achieved in different links of the reality modeling system.

[0144] In this embodiment, the modeling node set refers to the links that can respectively execute corresponding modeling requirements after dividing the real scene modeling according to functional attributes.

[0145] In this embodiment, the task requirements refer to the purpose that each modeling task needs to achieve and the type of modeling business that needs to be performed.

[0146] In this embodiment, the mapping relationship refers to the corresponding relationship between the modeling tasks and the modeling nodes, so as to facilitate the distribution of the modeling tasks to the corresponding modeling nodes and perform the corresponding real-scene modeling operations.

[0147] In this embodiment, the framework structure refers to the basic framework of each real scene object obtained after each modeling node analyzes the modeling task.

[0148] In this embodiment, task verification refers to checking the framework structures determined by different modeling nodes, so as to facilitate determining that there are no duplications or conflicts in the real-scene modeling tasks performed by each modeling node.

[0149] In this embodiment, the interaction mechanism is set in advance and is used to represent the method of communication or interaction between the modeling nodes when performing modeling operations.

[0150] In this embodiment, local correction refers to adjusting the abnormal modeling task when there is an abnormality in the modeling task.

[0151] In this embodiment, the local structural feature refers to the specific structural conditions of the model part that needs to be constructed by each modeling node.

[0152] In this embodiment, the model framework is a framework structure constructed by edge contours of the real scene and capable of representing the specific appearance of the current part.

[0153] In this embodiment, the hierarchical structure refers to the distribution of levels corresponding to different heights in the real scene, so as to facilitate the construction of a three-dimensional model consistent with the real scene.

[0154] In this embodiment, the local three-dimensional spatial structure refers to the specific position distribution corresponding to the detail points in the interior and exterior of the real scene determined based on the three-dimensional point cloud data.

[0155] In this embodiment, directed association refers to splicing local three-dimensional spatial structures with positional association, thereby ensuring accurate filling of the interior of the model.

[0156] In this embodiment, the nested association refers to placing the local three-dimensional space structure obtained by the directed association in the model framework of the real scene, thereby realizing the effective construction of the scene model.

[0157] In this embodiment, texture mapping refers to filling the surface of the constructed model with texture and color to ensure that the final model is consistent with the real scene.

[0158] In this embodiment, the initial real scene model refers to a part of the model in the real scene finally obtained by each modeling node.

[0159] In this embodiment, the structural splicing boundary refers to a specific position where adjacent initial real scene models are spliced.

[0160] In this embodiment, coupling coordination refers to adjusting and optimizing the splicing result to ensure the integrity and completeness of the final real scene model.

[0161] The working principle and beneficial effects of the above technical solution are: by dividing the real-scene modeling system into nodes according to functional attributes, the modeling node set can be accurately and effectively determined, and the mapping relationship between the modeling task and each modeling node can be determined, so that each modeling task can be accurately and effectively sent to the corresponding modeling node according to the mapping relationship, thereby improving the scene modeling efficiency; secondly, by parsing the received modeling tasks through each modeling node, the initial real-scene model of each part in the real-scene scene can be accurately and effectively constructed, ensuring the accuracy and reliability of the initial real-scene model of each part; finally, the spatial position of each initial real-scene model is determined according to the real-scene data, and the structural splicing boundary of the initial real-scene model corresponding to the adjacent real-scene scene part is determined according to the spatial position, so as to facilitate the structural splicing of the initial real-scene model according to the structural splicing boundary, thereby ensuring the reliability of the final real-scene model and improving the effect and accuracy of real-scene modeling.

[0162] Embodiment 7:

[0163] On the basis of Example 6, this embodiment provides a cloud platform real scene modeling control method, which sends each modeling task to each modeling node in parallel based on a distributed sending link, including:

[0164] Based on the parallel delivery results, the start time of each modeling node is recorded. At the same time, the background record log is built and the record log is divided into regions;

[0165] Based on the regional division results, the start time of each modeling node is entered as initial record data, and based on the entry results, the real-time modeling data of each modeling node is synchronously recorded;

[0166] Perform periodic traversal on the input results in the record log based on a preset time interval, and obtain the progress index of each modeling node under the start time limit based on the periodic traversal results;

[0167] Determine the modeling status of each modeling node based on the relative size relationship between the progress indicator and the preset progress indicator, and lock the abnormal modeling node based on the modeling status;

[0168] Adjust the parameters of the abnormal modeling nodes until the preset progress indicator requirements are met.

[0169] In this embodiment, the background log is used to record the operation data of different modeling nodes when performing modeling tasks.

[0170] In this embodiment, the area division refers to dividing the background record log into areas according to the number of modeling nodes, so as to ensure a one-to-one correspondence between the modeling nodes and the record areas.

[0171] In this embodiment, the initial recorded data refers to the time information when each modeling node starts to execute the modeling task, that is, the corresponding start time.

[0172] In this embodiment, the real-time modeling data refers to the specific execution data corresponding to each modeling node during the modeling process.

[0173] In this embodiment, the preset time interval is set in advance.

[0174] In this embodiment, the progress indicator refers to the progress of modeling performed by each modeling node under different cycle traversals, including the modeling rate and the like.

[0175] In this embodiment, the preset progress indicator is set in advance and is a reference for measuring whether the current progress indicator meets the requirements and can be adjusted.

[0176] In this embodiment, locking the abnormal modeling node based on the modeling status refers to determining the modeling node whose progress index does not meet the preset progress index requirement as the abnormal modeling node.

[0177] The working principle and beneficial effects of the above technical solution are: by constructing a background record log and dividing the background record log into regions, it is convenient to effectively record the start time and real-time modeling data corresponding to different modeling nodes; secondly, the record data in the background record log is traversed at preset time intervals, and the modeling progress of different modeling nodes is determined according to the traversal records, so as to effectively determine the working status of different modeling nodes; finally, the abnormal modeling nodes are locked according to the modeling status of each modeling node, and the parameters of the abnormal modeling nodes are adjusted according to the locking results, thereby ensuring the reliability and accuracy of modeling of real scenes.

[0178] Embodiment 8:

[0179] On the basis of Example 1, this embodiment provides a cloud platform reality modeling control method. In step 4, multi-terminal visualization configuration is performed on the reality model to complete the reality modeling, including:

[0180] Acquire the obtained real scene model, determine the available terminals of the real scene model, and extract the operating environment of the available terminals;

[0181] Determine the development framework of each available terminal based on the operating environment, and convert model parameters of the real scene model based on the development framework;

[0182] Determine the interface layout of the real scene model on each available terminal based on the model parameter conversion result, and configure the resolution of the interface layout of each available terminal based on the basic configuration of each available terminal;

[0183] Based on the configuration results, the real scene model is tested on each available terminal, and based on the test results, the real scene model is optimized and rendered on each available terminal to complete the real scene modeling.

[0184] In this embodiment, the available terminal refers to a device on which the real scene model can be successfully displayed, including a computer, a mobile phone, etc.

[0185] In this embodiment, the operating environment refers to the operating conditions and operating requirements of the available terminals.

[0186] In this embodiment, the development framework refers to the coding format or template corresponding to each available terminal when it is running or building.

[0187] In this embodiment, the basic configuration refers to the performance parameters of each available terminal.

[0188] The working principle and beneficial effects of the above technical solution are: by determining the available terminals of the real-scene model and extracting the operating environment of the available terminals, the parameter conversion of the real-scene model is realized according to the operating environment, ensuring that the real-scene model can be effectively visualized on different terminals; secondly, the resolution of the interface layout of each available terminal is configured according to the basic configuration of each available terminal, ensuring the display effect of the real-scene model on each available terminal; finally, the real-scene model is tested on different available terminals to ensure the operating effect of the real-scene model.

[0189] Embodiment 9:

[0190] This embodiment provides a cloud platform real scene modeling control system, such as Figure 3 As shown, including:

[0191] A data acquisition module is used to collect real-scene data based on the drone according to a preset shooting mode and upload the real-scene data to the cloud platform;

[0192] A task determination module is used to extract the composition features of the real scene data based on the cloud platform, and split the real scene data based on the composition features to obtain a modeling task set;

[0193] The real scene model building module is used to distribute the modeling task set to each modeling node, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model;

[0194] The visualization configuration module is used to perform multi-terminal visualization configuration of the real-scene model to complete the real-scene modeling.

[0195] The working principle and beneficial effects of the above technical solution are: by controlling the drone to collect real-scene data according to the preset shooting mode, the real-scene data can be fully and effectively acquired, and the collected real-scene data can be uploaded to the cloud platform, so that the cloud platform can process the real-scene data and provide reliable data support for real-scene modeling. Secondly, the real-scene data is split through the cloud platform to accurately and effectively determine the modeling task set, and the modeling tasks are distributed to each modeling node, so that each modeling node can synchronously execute the modeling tasks, thereby improving the efficiency of real-scene modeling. When performing real-scene modeling, the modeling nodes dynamically interact and analyze each other to ensure the accuracy and reliability of the model finally obtained by each modeling node. Finally, the results obtained by each modeling node are coupled and coordinated to achieve accurate and effective construction of the real-scene model, and the real-scene model is visualized in multiple terminals to ensure that the real-scene model can be effectively operated on multiple different terminals, thereby improving the effect and accuracy of real-scene modeling.

[0196] Embodiment 10:

[0197] Based on Example 9, this embodiment provides a cloud platform real scene modeling control system, a data acquisition module, including:

[0198] The parameter configuration unit is used to obtain the real scene data collection requirements, and analyze the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone;

[0199] A route determination unit is used to simultaneously obtain the scene distribution characteristics of the area to be collected, and determine the flight route of the UAV over the area to be collected based on the scene distribution characteristics and the collection accuracy;

[0200] The data acquisition unit is used to obtain a preset shooting mode of the drone based on the acquired change angle and flight path, and control the drone to collect real-scene data based on the preset shooting mode.

[0201] The working principle and beneficial effects of the above technical solution are: by analyzing the requirements for real-scene data collection, the collection change angle and collection accuracy of the tilt camera on the drone can be accurately and effectively determined; secondly, the scene distribution characteristics of the area to be collected are determined, and the flight route of the drone over the area to be collected is locked according to the scene distribution characteristics, so that the drone can conduct comprehensive and effective real-scene data collection; finally, the collection change angle and flight route are summarized to effectively determine the preset shooting mode of the drone, which provides a reliable guarantee for controlling the drone to collect real-scene data and ensures the accuracy and reliability of the collected real-scene data.

[0202] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A cloud platform real scene modeling control method, characterized in that: include: Step 1: Collect real scene data based on the drone according to the preset shooting mode, and upload the real scene data to the cloud platform; Step 2: Extract the constituent features of the real scene data based on the cloud platform, and split the real scene data based on the constituent features to obtain a set of modeling tasks; Step 3: Distribute the modeling task set to each modeling node in a distributed manner, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model; Step 4: Perform multi-terminal visualization configuration on the real-scene model to complete the real-scene modeling.

2. A cloud platform reality modeling control method according to claim 1, characterized in that: In step 1, real scene data is collected based on the drone according to the preset shooting mode, including: Obtain the real scene data collection requirements, and analyze the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone; At the same time, the scene distribution characteristics of the area to be collected are obtained, and the flight route of the UAV over the area to be collected is determined based on the scene distribution characteristics and the collection accuracy; The preset shooting mode of the UAV is obtained based on the acquisition change angle and the flight route, and the UAV is controlled to collect real-scene data based on the preset shooting mode.

3. A cloud platform reality modeling control method according to claim 2, characterized in that: Control the drone to collect real-scene data based on preset shooting modes, including: Acquire the collected real scene data, and perform edge contour detection on the real scene image of the real scene data to obtain the edge contour enclosing rectangle of the real scene image at each collection angle; Determine adjacent image groups of real scene images of real scene data based on the flight path of the UAV over the area to be collected, and overlap the real scene images in the adjacent image groups in opposite directions; Determine the overlap degree of the edge contour enclosing rectangles of adjacent real scene images based on the opposite overlap, and re-collect the real scene data when the overlap degree does not meet a preset threshold; Otherwise, the real scene images in the adjacent image group are retained, and position tags are added to the real scene images based on the retained results.

4. A cloud platform reality modeling control method according to claim 1, characterized in that: In step 1, the real scene data is uploaded to the cloud platform, including: Obtain the communication parameters of the local device, and connect the local device and the drone based on the communication parameters; Based on the docking results, the real scene data collected by the drone is cached in the local device, and a communication link between the local device and the cloud platform is established based on the cached results; Compressing the cached real scene data, and splitting the compressed real scene data to obtain compressed data blocks; The compressed data blocks are uploaded to the cloud platform in sequence based on the communication link.

5. A cloud platform reality modeling control method according to claim 1, characterized in that: In step 2, the constituent features of the real scene data are extracted based on the cloud platform, and the real scene data is split based on the constituent features to obtain a set of modeling tasks, including: Acquire the obtained real scene data, and parse the real scene data based on the scene modeling knowledge system to obtain the semantic information of each monomer data in the real scene data; Determine the spatial position relationship between the real scene objects corresponding to each monomer data based on the semantic information, and annotate the monomer data based on the spatial position relationship to obtain the composition features of the real scene data; The real scene data is split based on the composition features to obtain multiple groups of real scene data, and attribute information of each group of real scene data is extracted based on the annotation results; The scene classification name corresponding to each group of real scene data is obtained based on the attribute information, and the corresponding real scene data group is labeled based on the scene classification name to obtain a modeling task set.

6. A cloud platform reality modeling control method according to claim 1, characterized in that: In step 3, the modeling task set is distributed to each modeling node, and each modeling task is dynamically interactively analyzed based on the modeling node, and the analysis results are coupled and coordinated to build a real scene model, including: Obtaining the system composition of the real scene modeling, and dividing the system composition into nodes based on functional attributes to obtain a modeling node set; Determine the mapping relationship between modeling tasks and modeling nodes based on the functional attributes of the modeling nodes and the task requirements of each modeling task in the modeling task set, build a distributed delivery link based on the mapping relationship, and deliver each modeling task to each modeling node in parallel based on the distributed delivery link; Based on the parallel delivery results, each modeling node is controlled to perform task analysis on the corresponding modeling task, the framework structure corresponding to each modeling task is obtained, and the modeling task is verified based on the framework structure; Based on the interactive mechanism of modeling nodes, the modeling task is partially modified according to the task verification result, and the structure of the partially modified modeling task is analyzed based on the modeling nodes to obtain the local structural features and three-dimensional point cloud data of the real scene; Obtaining edge contours of the real scene based on local structural features, and constructing a model framework of the real scene based on the edge contours; at the same time, determining a hierarchical structure of the real scene based on the edge contours, and sequentially constructing a local three-dimensional spatial structure in the model framework according to the three-dimensional point cloud data based on the hierarchical structure; Directed association is performed on adjacent local three-dimensional spatial structures, and based on the directed association result, the local three-dimensional spatial structure is nested and associated with the model framework of the real scene; Performing texture mapping on the nested association results based on the real scene data to obtain an initial real scene model of the real scene; Determine the spatial position of each initial real scene model based on the real scene data, and determine the structural splicing boundary of adjacent initial real scene models based on the spatial position; The initial real-scene models of each modeling node are structurally spliced ​​based on the structural splicing boundary, and the structural splicing results are coupled and coordinated to obtain the final real-scene model.

7. A cloud platform reality modeling control method according to claim 6, characterized in that: Based on the distributed delivery link, each modeling task is delivered to each modeling node in parallel, including: Based on the parallel delivery results, the start time of each modeling node is recorded. At the same time, the background record log is built and the record log is divided into regions; Based on the regional division results, the start time of each modeling node is entered as initial record data, and based on the entry results, the real-time modeling data of each modeling node is synchronously recorded; Perform periodic traversal on the input results in the record log based on a preset time interval, and obtain the progress index of each modeling node under the start time limit based on the periodic traversal results; Determine the modeling status of each modeling node based on the relative size relationship between the progress indicator and the preset progress indicator, and lock the abnormal modeling node based on the modeling status; Adjust the parameters of the abnormal modeling nodes until the preset progress indicator requirements are met.

8. A cloud platform reality modeling control method according to claim 1, characterized in that: In step 4, the real scene model is configured for multi-terminal visualization to complete the real scene modeling, including: Acquire the obtained real scene model, determine the available terminals of the real scene model, and extract the operating environment of the available terminals; Determine the development framework of each available terminal based on the operating environment, and convert model parameters of the real scene model based on the development framework; Determine the interface layout of the real scene model on each available terminal based on the model parameter conversion result, and configure the resolution of the interface layout of each available terminal based on the basic configuration of each available terminal; Based on the configuration results, the real scene model is tested on each available terminal, and based on the test results, the real scene model is optimized and rendered on each available terminal to complete the real scene modeling.

9. A cloud platform real scene modeling control system, characterized in that: include: A data acquisition module is used to collect real-scene data based on the drone according to a preset shooting mode and upload the real-scene data to the cloud platform; A task determination module is used to extract the composition features of the real scene data based on the cloud platform, and split the real scene data based on the composition features to obtain a modeling task set; The real scene model building module is used to distribute the modeling task set to each modeling node, and dynamically interact and analyze each modeling task based on the modeling node, and couple and coordinate the analysis results to build a real scene model; The visualization configuration module is used to perform multi-terminal visualization configuration of the real-scene model to complete the real-scene modeling.

10. A cloud platform reality modeling control system according to claim 9, characterized in that: Data acquisition module, including: The parameter configuration unit is used to obtain the real scene data collection requirements, and analyze the real scene data collection requirements to obtain the collection transformation angle and collection accuracy of the tilt camera on the drone; A route determination unit is used to simultaneously obtain the scene distribution characteristics of the area to be collected, and determine the flight route of the UAV over the area to be collected based on the scene distribution characteristics and the collection accuracy; The data acquisition unit is used to obtain a preset shooting mode of the drone based on the acquired change angle and flight path, and control the drone to collect real-scene data based on the preset shooting mode.

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