CN118096982B - Construction method and system of fault inversion training platform - Google Patents
Construction method and system of fault inversion training platform Download PDFInfo
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Abstract
The invention discloses a construction method and a system of a fault inversion training platform, wherein the method comprises the following steps: dividing at least one basic material according to the type of the basic material to obtain at least one basic material set, and constructing a general material proton library based on the at least one basic material set; associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library; constructing a shared material library according to the general material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library; rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, converting the three-dimensional image into pixel flow, and transmitting the pixel flow to a preset fault inversion training platform based on a real-time flow transmission protocol. Thus, the memory space required by the model can be greatly reduced.
Description
Technical Field
The invention belongs to the technical field of fault analysis of direct current measurement equipment, and particularly relates to a construction method and a construction system of a fault inversion training platform.
Background
The traditional direct current measurement equipment has the defects of unobvious effect, low efficiency, high effect investigation difficulty and the like in fault analysis, judgment and disposal training. The traditional training mode is mainly based on unidirectional knowledge transmission, and comprises characters, pictures, videos and the like, wherein the training mode is mainly based on fault cases, the fault analysis, judgment and treatment processes in the cases are taught, operation and inspection staff cannot intuitively contact fault equipment and experience the whole fault treatment process, the training process is often tedious and boring, learning interests cannot be stimulated, and the training effect is not obvious; every new employee goes into staff or exchanges study across professionals, the same training content needs to be repeatedly developed, and various fault cases cannot be trained in detail in consideration of time problems, or a great amount of time is consumed for developing training for various fault cases each time, so that the training resource utilization efficiency is low; after training is finished, the direct current measuring equipment is expensive and does not have any relevant practical operating environment at the present stage. Therefore, a need exists for constructing a fault inversion training platform for use by training personnel.
Disclosure of Invention
The invention provides a construction method and a construction system of a fault inversion training platform, which are used for solving the technical problem of high model occupation resource in the process of constructing the fault inversion training platform.
In a first aspect, the present invention provides a method for constructing a fault inversion training platform, including: obtaining at least one basic material, dividing the at least one basic material according to the type of the basic material to obtain at least one basic material set, and constructing a general material proton library based on the at least one basic material set; the method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material; constructing a shared material library according to the general material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library; rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions; compressing the cloud three-dimension based on a preset video stream coding method, and sending the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
In a second aspect, the present invention provides a system for constructing a fault inversion training platform, comprising: the dividing module is configured to acquire at least one basic material, divide the at least one basic material according to the type of the basic material to obtain at least one basic material set, and construct a general material proton library based on the at least one basic material set; the storage module is configured to correlate the at least one basic material in the general material sub-library with a corresponding mapping set, and store the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material; the construction module is configured to construct a shared material library according to the general material sub-library and the target mapping sub-library, and construct a high-fidelity model associated with direct-current measurement equipment according to the shared material library; the rendering module is configured to render the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and convert the three-dimensional image into cloud three-dimensional; the sending module is configured to compress the cloud three-dimension based on a preset video stream coding method, and send the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of constructing a failure inversion training platform according to any of the embodiments of the invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the steps of the method for constructing a fault inversion training platform according to any of the embodiments of the present invention.
According to the method and the system for constructing the fault inversion training platform, at least one basic material is obtained, the at least one basic material is divided according to the types of the basic materials, at least one basic material set is obtained, and a general material proton library is constructed based on the at least one basic material set; the method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material; and constructing a shared material library according to the common material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library, so that the memory space required by the model can be greatly reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for constructing a failure inversion training platform according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system for constructing a failure inversion training platform according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to FIG. 1, a flow chart of a method of constructing a failure inversion training platform of the present application is shown.
As shown in FIG. 1, the construction method of the fault inversion training platform specifically comprises the following steps:
Step S101, at least one basic material is obtained, the at least one basic material is divided according to the type of the basic material, at least one basic material set is obtained, and a general material proton library is built based on the at least one basic material set.
Step S102, associating the corresponding mapping set with the at least one base material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set contains at least one mapping associated with a base material.
And step S103, constructing a shared material library according to the general material sub-library and the target mapping sub-library, and constructing a high-fidelity model associated with the direct-current measurement equipment according to the shared material library.
In this step, a general texture library is created: first, it is necessary to create a library of materials that contains a plurality of high quality base materials (e.g., metal, wood, plastic, etc.). These materials should have a high degree of reusability and versatility. Creating basic material types: basic parent material libraries are built, comprising different types of basic materials, such as metal A, metal B, wood A, wood B, stone A, stone B, etc. These parent materials should contain the basic attributes of the respective type, such as reflectance, roughness, color, etc. Optimizing map sharing: each base material should have a set of high quality maps (e.g., diffuse reflectance maps, normal maps, roughness maps, etc.), which are shared among multiple material variants. Copying and modifying the parent material: when creating the material of a specific model, the corresponding parent material is first copied. Parameters of the material, such as changing color or adjusting other visual properties, are then adjusted as desired. Masking and layering effects are applied: masks or additional maps may be superimposed to achieve specific visual effects, such as special textures, wear, erosion, etc. These masking and layering effects allow for creation of a diversified appearance without changing the original map. Reducing CPU calls to a graphical programming interface: since multiple texture variants share the same base map, this greatly reduces the CPU calls to the graphics programming interface during rendering, which is the most resource consuming part of the graphics rendering. Reducing the CPU from invoking a graphics programming interface means that the burden on the GPU is reduced, improving rendering performance, especially when rendering complex scenes of a large number of different objects.
It should be noted that, at least one base material to be used is obtained from the shared material library, and parameters of the at least one base material to be used are adjusted to obtain at least one target base material; acquiring at least one target map set corresponding to at least one base material to be used in a target map sub-library; a high-fidelity model associated with the direct current measurement device is constructed from the at least one target base material and the at least one target set of tiles.
And step S104, rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions.
In the step, in the illusion engine, in order to reduce the scene rendering burden under the visual angle, an improved octree scene management technology is adopted, and the effective visual elimination is carried out by combining an object distance clipping strategy, the importance level of the internal parts of the equipment and the projection size strategy of the object on the screen.
First, all objects in a scene are inserted into an octree data structure that divides the entire scene into multiple small regions according to the spatial locations of the objects, thereby efficiently managing and querying objects in the scene. Then, in the rendering process, objects which are too far from the camera are determined by using an object distance clipping strategy according to the view angle and the position of the camera, and the objects have small visual influence due to distance and can be removed. Clipping is performed according to the distance from the camera to the object. A distance threshold is set, and objects exceeding this threshold are not rendered. Meanwhile, considering the importance level of the components inside the device, rendering is maintained even at a longer distance for important components, while unimportant components can be rejected at a shorter distance. In addition, whether to render is further determined according to the projection size of the object on the screen, and objects which are nearly invisible on the screen can be eliminated. In this way, the illusion engine can only render important and obvious objects at the current view angle, so that the rendering calculation amount is greatly reduced, and the overall performance and efficiency are improved.
In a Physical Base Rendering (PBR) pipeline of the illusive engine, a substantially simplified unified material system is implemented for a fault inversion platform. First, the developer needs to define a set of core PBR materials that represent the basic material types common in grid applications, such as metal, plastic, and glass. The creation of each material should focus on a realistic simulation of its basic optical properties, including but not limited to reflectivity, roughness, and metallicity, while deliberately omitting some of the advanced special effects and complex textures commonly used in traditional games, film and television rendering, such as displacement mapping or subsurface scattering effects. The basic materials are designed through parameterization, so that the visual requirements of various specific objects can be adapted by adjusting a small amount of parameters (such as hue, saturation and brightness) on the premise of not sacrificing physical reality.
In terms of simplification of shader logic, emphasis is placed on reducing computational costs and avoiding complex shading effects. This includes optimizing the illumination model, retaining only the most basic diffuse and specular calculations, and removing any advanced effects that may add unnecessary rendering burden. Furthermore, for those details that do not directly affect visual perception, such as simulation of microscopic surface structures, this can be achieved by simplified textures or pre-computation methods to reduce the resource consumption in real-time rendering.
Finally, the method not only simplifies the material manufacturing and management process, but also remarkably improves the rendering efficiency.
Step S105, compressing the cloud three-dimension based on a preset video stream coding method, and sending the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
In the step, in the video coding of the fault inversion training platform, ROI (Region of Interest) coding technology is adopted to compress cloud three-dimension, identify key areas or interesting areas in the video, and use higher code rate for the areas, and use lower code rate for non-key areas. Video coding is performed by applying the ROI technology in the system, taking a transformer station or a converter station as an example, the process can be simplified into the following steps:
Video preprocessing and block division: the video frame is decomposed into a plurality of blocks for analysis of different regions. In the video of a substation or a converter station, these blocks may include buildings (non-areas of interest) and critical equipment such as transformers (areas of interest).
Static and positional feature analysis: the information entropy of each block is calculated to obtain static features, and the position features of the blocks are determined according to the positions of the blocks. For example, the block in which the transformer is located will have a high position characteristic value due to its importance.
Calculating a attention coefficient: based on the static and location features, a static attention coefficient for each block is calculated. Meanwhile, motion characteristics of the block are analyzed using a frame difference method to determine a motion attention coefficient.
Normalization and weight distribution: and normalizing the static and dynamic attention coefficients, and distributing weights to obtain the final ROI coefficient of each block.
Quadtree partitioning model application: the quadtree partitioning model is modified with ROI coefficients to focus on regions of interest, such as transformers, while simplifying non-regions of interest, such as buildings, during encoding.
It should be noted that, high quality rendering is performed using the illusion engine: at the server side, a three-dimensional scene is created by using the illusion engine, and high-quality real-time rendering is realized. Complex power facilities and equipment are modeled using the advanced graphics processing capabilities of the illusive engine. Pixel stream coding and transmission: and converting the picture rendered by the illusion engine into cloud three-dimensional. The rendered pictures are compressed using video stream coding techniques (e.g., h.264 or VP 9) to facilitate transmission. The compressed pixel stream is sent to the client in real time using a real-time streaming protocol such as WebRTC or RTP. Processing the pixel stream: a stream of pixels transmitted by a server is received and decoded in a client browser. Real-time video streams are displayed using the < video > tag of HTML5 or a related JavaScript library. Realizing real-time interaction: the JavaScript is used to monitor the user's operations (e.g., clicking, dragging, zooming, etc.) and send the instructions back to the server via WebRTC or other real-time communication technology. And the server adjusts the three-dimensional scene in the illusion engine according to the received instruction, and influences the output of the pixel stream.
In this way, the illusion engine at the server side is responsible for generating high-quality three-dimensional images and converting them into cloud three-dimensions, while the client browser is responsible for displaying these cloud three-dimensions and processing the user's interaction logic through HTML5 and JavaScript. The scheme effectively utilizes the resources of the server and the client, and realizes Gao Xiaoyun calculation methods in fault inversion and training application.
In a specific embodiment, in the process of constructing the fault inversion training platform, the whole process starts from the generation of the three-dimensional engine rendering video stream at the server side until the combination of the three-dimensional engine rendering video stream with the front-end interactive interface, and real-time communication is realized through the WebRTC technology, so that a complete system with strong interactivity is formed. Firstly, the three-dimensional engine is configured and started at a server side to generate real-time pixel stream output. This process includes setting appropriate rendering parameters to optimize the quality and performance of the video stream. The video stream is then captured and processed by the streaming service at the server side, ready for transmission to the headend.
In the front-end, HTML5 and JavaScript techniques are used to create a user interface that contains elements that can interact with the user, such as buttons, sliders, etc. Taking an interactive question and answer as an example, a question and four answer options (ABCD) are presented on the interface. When the user selects an answer, the JavaScript logic captures the interaction and sends the user's selection to the server in real time through the established WebRTC communication link.
A real-time, bi-directional communication link is established between the front-end and the server, which is responsible for transmitting the real-time video stream from the server to the user's browser, and also supports a data channel for transmitting the user's interactive data and other control instructions.
And the server receives the selection of the user and immediately processes the selection. The server-side three-dimensional engine may trigger a predefined interaction response, such as changing the color or position of a particular model in a three-dimensional scene in a pixel stream. These changes are then reflected in the pixel stream and transmitted back to the user's interface in real time via the WebRTC link described previously.
Through the process, the high-performance rendering capability of the three-dimensional engine is combined with the flexible interaction design of the HTML5 and the JavaScript, and the real-time communication capability of the WebRTC is added, so that a dynamic and interactive fault inversion training platform system is formed.
In summary, the method of the application obtains at least one basic material, divides the at least one basic material according to basic material types to obtain at least one basic material set, and constructs a general material proton library based on the at least one basic material set; the method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material; and constructing a shared material library according to the common material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library, so that the memory space required by the model can be greatly reduced.
Referring now to FIG. 2, a block diagram of a system for constructing a failure inversion training platform of the present application is shown.
As shown in FIG. 2, the construction system 200 of the failure inversion training platform includes a partitioning module 210, a storage module 220, a construction module 230, a rendering module 240, and a sending module 250.
The dividing module 210 is configured to obtain at least one base material, divide the at least one base material according to a base material type to obtain at least one base material set, and construct a general material proton library based on the at least one base material set; the storage module 220 is configured to correlate the at least one base material in the general material sub-library with a corresponding mapping set, and store the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set contains at least one mapping associated with a base material; a construction module 230 configured to construct a shared material library from the utility material sub-library and the target map sub-library, and construct a high-fidelity model associated with the direct current measurement device from the shared material library; the rendering module 240 is configured to render the high-fidelity model according to a preset illusion engine, obtain a three-dimensional image corresponding to the high-fidelity model, and convert the three-dimensional image into cloud three-dimensional; the sending module 250 is configured to compress the cloud three-dimension based on a preset video stream encoding method, and send the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol, so as to obtain a final fault inversion training platform.
It should be understood that the modules depicted in fig. 2 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 2, and are not described here again.
In other embodiments, the present invention further provides a computer readable storage medium, where a computer program is stored, where the program instructions, when executed by a processor, cause the processor to perform the method for constructing a fault inversion training platform in any of the foregoing method embodiments;
As one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
Obtaining at least one basic material, dividing the at least one basic material according to the type of the basic material to obtain at least one basic material set, and constructing a general material proton library based on the at least one basic material set;
The method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material;
constructing a shared material library according to the general material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library;
Rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions;
Compressing the cloud three-dimension based on a preset video stream coding method, and sending the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from use of the build system of the failure inversion training platform, and the like. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located with respect to the processor, which may be connected to the build system of the failure inversion training platform via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 3, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 3. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory 320, i.e., implementing the method of constructing a fault inversion training platform of the above-described method embodiment. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the build system of the failure inversion training platform. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an implementation mode, the electronic device is applied to a construction system of a fault inversion training platform and used for a client, and comprises the following components: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
Obtaining at least one basic material, dividing the at least one basic material according to the type of the basic material to obtain at least one basic material set, and constructing a general material proton library based on the at least one basic material set;
The method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material;
constructing a shared material library according to the general material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library;
Rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions;
Compressing the cloud three-dimension based on a preset video stream coding method, and sending the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The construction method of the fault inversion training platform is characterized by comprising the following steps of:
Obtaining at least one basic material, dividing the at least one basic material according to the type of the basic material to obtain at least one basic material set, and constructing a general material proton library based on the at least one basic material set;
The method comprises the steps of associating corresponding mapping sets with at least one basic material in the general material sub-library, and storing the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material;
Constructing a shared material library according to the common material proton library and the target mapping sub-library, and constructing a high-fidelity model associated with direct-current measurement equipment according to the shared material library, wherein constructing the high-fidelity model associated with the direct-current measurement equipment according to the shared material library comprises:
Acquiring at least one base material to be used from the shared material library, and performing parameter adjustment on the at least one base material to be used to obtain at least one target base material;
Acquiring at least one target map set corresponding to the at least one base material to be used from the target map sub-library;
constructing a high-fidelity model associated with the direct current measurement device according to the at least one target base material and the at least one target atlas;
Rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions;
Compressing the cloud three-dimension based on a preset video stream coding method, and sending the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
2. The method for constructing a fault inversion training platform according to claim 1, wherein the performing parameter adjustment on the at least one base material to be used to obtain at least one target base material comprises:
And adjusting the visual attribute of the at least one base material to be used to obtain at least one target base material, wherein the visual attribute comprises a color attribute.
3. The method for constructing a fault inversion training platform according to claim 1, wherein the rendering the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions comprises:
And cutting an object with a distance from the camera being larger than a preset threshold value by using an object distance cutting strategy according to the view angle and the position of the camera, rendering a high-fidelity model obtained by cutting according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and converting the three-dimensional image into cloud three-dimensions.
4. The method for constructing a fault inversion training platform according to claim 1, wherein the compressing the cloud three-dimension based on the preset video stream encoding method comprises:
video frame division: decomposing a video frame into a plurality of blocks;
Static and positional feature analysis: calculating the information entropy of each block to obtain static features, and determining the position features of each block according to the position of each block,
Calculating a attention coefficient: calculating a static attention coefficient of each block based on the static feature and the position feature, and analyzing a motion feature of each block using a frame difference method to determine a motion attention coefficient;
Normalization and weight distribution: normalizing the static attention coefficient and the motion attention coefficient, and distributing weights to obtain a final ROI coefficient of each block;
Correcting the quadtree partitioning model: correcting a pre-constructed quadtree division model by using the ROI coefficient, and compressing the cloud three-dimension according to the corrected quadtree division model.
5. A system for constructing a failure inversion training platform, comprising:
The dividing module is configured to acquire at least one basic material, divide the at least one basic material according to the type of the basic material to obtain at least one basic material set, and construct a general material proton library based on the at least one basic material set;
the storage module is configured to correlate the at least one basic material in the general material sub-library with a corresponding mapping set, and store the at least one mapping set into a preset mapping sub-library to obtain a target mapping sub-library, wherein one mapping set comprises at least one mapping associated with one basic material;
the construction module is configured to construct a shared material library according to the common material sub-library and the target mapping sub-library, and construct a high-fidelity model associated with the direct-current measurement device according to the shared material library, wherein the constructing the high-fidelity model associated with the direct-current measurement device according to the shared material library comprises:
Acquiring at least one base material to be used from the shared material library, and performing parameter adjustment on the at least one base material to be used to obtain at least one target base material;
Acquiring at least one target map set corresponding to the at least one base material to be used from the target map sub-library;
constructing a high-fidelity model associated with the direct current measurement device according to the at least one target base material and the at least one target atlas;
the rendering module is configured to render the high-fidelity model according to a preset illusion engine to obtain a three-dimensional image corresponding to the high-fidelity model, and convert the three-dimensional image into cloud three-dimensional;
the sending module is configured to compress the cloud three-dimension based on a preset video stream coding method, and send the compressed pixel stream to a preset fault inversion training platform based on a real-time stream transmission protocol to obtain a final fault inversion training platform.
6. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any of claims 1 to 4.
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