WO2023124055A1

WO2023124055A1 - Digital-twin-based artificial enhancement method and apparatus, and medium

Info

Publication number: WO2023124055A1
Application number: PCT/CN2022/108917
Authority: WO
Inventors: 黄晓庆; 王勇; 马世奎; 陈原
Original assignee: 达闼机器人股份有限公司
Priority date: 2021-12-29
Filing date: 2022-07-29
Publication date: 2023-07-06
Also published as: CN114372356B; CN114372356A

Abstract

A digital-twin-based artificial enhancement method and apparatus, and a medium, the method comprising: acquiring optimized device data obtained by converting real device data, and mapping the optimized device data to an initial digital twin spatial model by means of digital twin modeling, thus updating and obtaining a digital twin spatial model (101); obtaining set scenario artificial enhancement task data, and merging the scenario artificial enhancement task data with the digital twin spatial model so as to update the digital twin spatial model (102); when detecting a user access instruction sent by an access terminal, sending the digital twin spatial model to the access terminal (103); acquiring operation data sent by the access terminal and corresponding to the digital twin spatial model (104); and when it is determined that the operation data satisfies a trigger condition corresponding to the scenario artificial enhancement task data, sending to the access terminal feedback data corresponding to the operation data (105). The present method allows a target scenario of a cloud robot to be converted to an optimized virtual scenario, thus enhancing data processing efficiency.

Description

Artificial enhancement method, device and medium based on digital twin

cross reference

This application refers to the Chinese patent application No. 202111644024.7 entitled "Artificial Enhancement Method, Device and Medium Based on Digital Twin" submitted on December 29, 2021, which is incorporated by reference into this application in its entirety.

technical field

The embodiments of the present application relate to the technical field of digital twins, and in particular to a digital twin-based artificial enhancement method, device, and storage medium.

Background technique

The application of cloud robots is becoming more and more extensive. For example, it consists of artificial enhanced intelligence, multi-modal fusion artificial intelligence, digital twins, continuous closed-loop learning and intelligent evolution. Robots are controlled through 5G secure networks to complete various tasks (such as remote control of cloud robots to complete tasks such as picking up objects). Among them, artificial in-the-loop is an important part of cloud robot control, which is divided into online real-time or quasi-real-time artificial enhancement and offline teaching training.

However, if the robot trainer is performing online real-time artificial enhancement, quasi-real-time artificial enhancement, or offline teaching training, if the virtual model or real image of the operated object is directly displayed on the display on the side of the robot trainer (but The object to be operated is an object that is not suitable for direct display with a virtual model or a real image with a high degree of restoration), because the operation scene needs to be highly restored and the data processing efficiency will be reduced.

Contents of the invention

Embodiments of the present application provide a digital twin-based artificial enhancement method, device, and medium, which can convert the target scene of a cloud robot into an optimized virtual scene, and improve data processing efficiency.

In the first aspect, the embodiment of the present application provides a digital twin-based artificial enhancement method from a server perspective, the method comprising:

Obtain optimized equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling, so as to update the digital twin space model;

Obtain the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model;

When the user access instruction sent by the access terminal is detected, the digital twin space model is sent to the access terminal;

Obtaining the operation data corresponding to the digital twin space model sent by the access terminal;

When it is determined that the operation data satisfies the trigger condition corresponding to the scene artificial enhancement task data, the feedback data corresponding to the operation data is sent to the access terminal.

In the second aspect, the embodiment of the present application further provides a smart device, the smart device includes: a sending unit, a receiving unit, and a processing unit;

The processing unit is configured to obtain optimized equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling, so as to update and obtain the digital twin space model;

The processing unit is also used to obtain the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model;

The sending unit is configured to send the digital twin space model to the access terminal when a user access instruction sent by the access terminal is detected;

The receiving unit is configured to obtain the operation data corresponding to the digital twin space model sent by the access terminal;

The sending unit is further configured to send feedback data corresponding to the operation data to the access terminal when it is determined that the operation data satisfies a trigger condition corresponding to the scenario artificial enhancement task data.

In a third aspect, the embodiment of the present application also provides a processing device, including a processor and a memory, and a computer program is stored in the memory. When the processor invokes the computer program in the memory, it executes any digital-based Steps in a siamese artificial augmentation method.

In the fourth aspect, the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by the processor, so as to execute any one based on the method provided by the embodiment of the present application. Steps in a human-augmented approach to digital twins.

From the above content, it can be concluded that this application integrates the optimized equipment data and scene artificial enhancement task data into the initial digital twin space model to update the digital twin space model, and then when the user access instruction sent by the access terminal is detected , sending the digital twin space model to the access terminal; then obtaining the operation data corresponding to the digital twin space model sent by the access terminal; finally, when it is determined that the operation data satisfies the trigger condition corresponding to the scene artificial enhancement task data , sending the feedback data corresponding to the operation data to the access terminal. It can convert the target scene of the cloud robot into an optimized virtual scene, which improves the data processing efficiency.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

Figure 1 is a schematic flow diagram of the artificial enhancement method based on digital twins in this application;

Fig. 2 is a schematic structural diagram of a smart device in this application;

Fig. 3 is a schematic structural diagram of the processing equipment of the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of this application.

In the following description, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times to be executed by a computer, and the computer execution referred to in the embodiment of the present application includes the operation of a computer processing unit by an electronic signal representing data in a structured form. This operation transforms the data or maintains it at a location in the computer's memory system that can reconfigure or otherwise alter the operation of the computer in a manner well known to testers in the art. The data structures maintained by the data are physical locations in the memory that have certain characteristics defined by the data format. However, the principle of the present application is described in the above text, which is not meant to be a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

The principles of the present application operate with numerous other general purpose or special purpose computing, communication environments or configurations. Examples of well-known computing systems, environments, and configurations suitable for use in this application may include, but are not limited to, handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and A distributed computing environment, including any of the above systems or devices.

The terms "first", "second" and "third" in this application are used to distinguish different objects, rather than to describe a specific order. Furthermore, the terms "include" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion.

First of all, before introducing the embodiments of the present application, first introduce the relevant content of the present application about the application background.

The executor of the digital twin-based artificial enhancement method provided by this application may be the device provided by this application, or a processing device such as a server device, a physical host, or a user equipment (User Equipment, UE) that integrates the device, wherein the device It can be realized by means of hardware or software, and the UE can specifically be a terminal device such as a smart phone, a tablet computer, a notebook computer, a palmtop computer, a desktop computer, or a personal digital assistant (Personal Digital Assistant, PDA).

Next, start to introduce the digital twin-based artificial enhancement method provided by this application.

Referring to FIG. 1 , FIG. 1 shows a schematic flowchart of a digital twin-based artificial enhancement method of the present application, and the method is applied to a server. The method provided in this application may specifically include the following steps:

101. Acquire optimized equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling, so as to update the digital twin space model.

In the embodiment of this application, the initial digital twin space model is pre-stored in the server, and each sub-model in the initial digital twin space model is mapped to the original digital twin space model based on digital twin modeling, so as to realize the real world Fully restored or partially restored. More specifically, the initial digital twin space model mainly restores buildings in the real world, but does not restore other objects (such as trash cans on the road, real people, cars, etc.).

At this time, when it is necessary to add the target operation object of the cloud robot to be controlled to the initial digital twin space model through digital twin technology, it is necessary to first convert the optimized device data (generally including size data) obtained based on the real device data of the target operation object , operating parameter data, etc., the size data is the length, width, height and other parameters of the target operating object, and the operating parameter data is the average moving speed of the target operating object, etc.), and then the optimized equipment data is mapped to the initial The digital twin spatial model is updated to obtain the digital twin spatial model.

Wherein, in the process of mapping the optimized equipment data to the initial digital twin space model through digital twin modeling, the coordinates of the optimized equipment data in the physical space can be used to map to the initial digital twin space restored at 1:1 The corresponding spatial position in the model, so as to realize the accurate mapping after modeling.

In one embodiment, the step 101 includes:

Acquire real device data, and correspondingly convert the real device data into optimized device data according to a preset device conversion strategy.

In the embodiment of the present application, for example, the real equipment data of the target operation object is the corresponding waste, while the optimized equipment data of the target operation object is flowers. In this way, converting waste into flowers and displaying them in the digital twin space model can not only avoid The real display of the operation object, but also can reduce the difficulty of modeling to improve the efficiency of data processing. The solution of this application can also be applied to the following scenarios. For example, the task of sorting various garbage by cloud robots in the real world can be transformed into the task of arranging various flowers in the beautiful back garden under the digital twin space model. This solution is also applicable to offline training data collection, such as converting the collection of robot path planning training data into a racing game, where human players compete in various scenarios, and the game data will be converted into training for robots to learn path planning data.

In one embodiment, the step 101 includes:

Obtaining the dimensional data included in the optimized equipment data, and mapping the dimensional data to the initial digital twin space model through 1:1 digital twin modeling, so as to update the digital twin space model; wherein, the update obtains the digital twin space model includes an optimized equipment twin model corresponding to the optimized equipment data;

The operating parameter data included in the optimized equipment data is obtained, and the operating parameter data is fused with the optimized equipment twin model in the digital twin space model to update the digital twin space model.

In the embodiment of this application, the size data is mapped to the initial digital twin space model through 1:1 digital twin modeling, and the 1:1 restored digital twin model of the cloud robot to be operated can be mapped to the initial digital twin space In the model, there is no need to enlarge or reduce the size, and the coordinates of the optimized equipment data in the physical space are mapped to the corresponding spatial positions in the original digital twin space restored at 1:1. It can be seen that through the 1:1 digital twin modeling technology, real equipment can be quickly mapped to the digital twin space model.

102. Acquire the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model.

In the embodiment of the present application, after the digital twin modeling is completed based on the optimized equipment data, the set scene artificial enhancement task data can also be obtained, and the scene artificial enhancement task data can be fused with the digital twin space model to obtain Update the digital twin spatial model. The above operation is equivalent to selecting any digital twin model in the digital twin space model, and setting the scene artificial enhancement task data for it, where the scene artificial enhancement task data can be understood as any one or more numbers in the digital twin space model selected After the twin model, a new attribute data, that is, task data, is added to the selected digital twin model (refer to the scene in the online game where the player manipulates the game character to click on an object or game character and perform related operations to trigger the task reminder). After setting the scene artificial enhancement task data for one or more selected digital twin models, the scene artificial enhancement task data can be fused with the digital twin space model to update the digital twin space model . It can be seen that by adding scene artificially enhanced task data to the digital twin model, the data of the digital twin spatial model in the dimension of task attributes can be increased.

In one embodiment, the step 102 includes:

Acquiring the target twin model corresponding to the scene artificial enhancement task data in the digital twin space model, and fusing the scene artificial enhancement task data with the target twin model to update the digital twin space model.

In the embodiment of the present application, the target twin model corresponding to the scene artificial enhancement task data in the digital twin space model is obtained first, and the target twin model may be a digital twin model (such as the digital twin model corresponding to the optimized equipment data). Twin model), may also be a plurality of digital twin models (for example, in addition to the digital twin model corresponding to the optimized equipment data, it also includes multiple other selected digital twin models), and then the artificial enhancement task data of the scene is added to On the target twin model, as the attribute data of the target twin model, and the attribute data can be triggered by subsequent operations to display or prompt the user.

103. Send the digital twin space model to the access terminal when detecting the user access instruction sent by the access terminal.

In the embodiment of this application, when the server detects the login request of the access terminal (such as a VR device, etc.) and successfully verifies its login information, it acquires the user access instruction sent by the access terminal (which can be understood as the robot trainer operation The user access instruction generated by the access terminal) to request access to the digital twin spatial model, at this time, the digital twin spatial model corresponding to the user access instruction can be obtained and sent to the access terminal. Afterwards, the virtual model corresponding to the digital twin space model can be viewed on the access terminal based on VR technology. It can be seen that by detecting the access request of the access terminal, the completed digital twin spatial model can be quickly sent to the access terminal for display.

In one embodiment, after the step 103, it also includes:

Sending the prompt data corresponding to the scene manual enhancement task data to the access terminal; the prompt data is text prompt data or voice prompt data.

In the embodiment of the present application, after the access terminal receives the digital twin space model and displays it locally, if the operation object corresponding to the access terminal is the target twin model, the prompt data corresponding to the task data of the scene will be artificially enhanced Send it to the access terminal to prompt the user to perform corresponding operations on the target twin model through text prompt data or voice prompt data.

For example, when the scene artificially enhances the task data to pick up another target twin model for the operation target twin model, and moves the other target twin model to the specified spatial position in the digital twin space model, it can be more vividly understood as the user operating the digital twin space model The cloud robot model of the cloud picks up the model of object A, and transports the model of object A to the spatial position of the model of object B in the digital twin space model, and finally places the model of object A in the model of object B. In order to intuitively prompt the user to complete the above operations, it is necessary to send the prompt data corresponding to the scene manual enhancement task data to the access terminal. Yes, by sending text prompt data or voice prompt data corresponding to the scene artificially enhanced task data, the user can be more intuitively prompted to perform corresponding operations.

104. Acquire the operation data sent by the access terminal and corresponding to the digital twin spatial model.

In the embodiment of the present application, when the access terminal receives the digital twin space model and obtains the scene artificial enhancement task data included in it, the target twin model can be selected according to the scene artificial enhancement task data, and then the target twin model can be Corresponding operations generate corresponding operation data, and the generated operation data is sent from the access terminal to the server. It can be seen that by receiving the operation data sent by the access terminal, corresponding operations can be performed on the target twin model in the digital twin space model.

In one embodiment, after the step 104, it also includes:

Acquiring the spatial start data and spatial end data corresponding to the operation data;

Acquiring a space start point object corresponding to the space start point data, and obtaining a space end point object corresponding to the space end point data;

Obtain the current operation object set composed of the space start object and the space end object, and if the current operation object set is the same as the task object set corresponding to the scene artificial enhancement task data, determine that the operation data meets the scene artificial enhancement task data. Enhance the trigger conditions corresponding to task data.

In the embodiment of this application, after the server obtains the operation data uploaded by the access terminal, it can first obtain the spatial start data and spatial end data corresponding to the operation data, because the operation data corresponds to a specific movement track, and the At the starting point of the motion trajectory, the space starting point object will be selected (refer to the model of object A in the above example and the cloud robot model in the digital twin space model), and the space end object will be selected at the end point of the motion trajectory (refer to the object in the above example The model of B), after obtaining the space start object and the space end object corresponding to the space start data, you can judge the task corresponding to the space start object and the space end object and the scene artificial enhancement task data Whether the collection of objects is the same. If the space starting point object and the space end point object are the same as the set of task objects corresponding to the scene artificial enhancement task data, it means that the user has completed the corresponding operation according to the scene artificial enhancement task data, and the operation is determined at this time The data meets the trigger conditions corresponding to the scene artificial enhancement task data. It can be seen that by judging the operation data, it can be judged whether to send feedback data.

105. Send feedback data corresponding to the operation data to the access terminal when it is determined that the operation data satisfies the trigger condition corresponding to the scene artificial enhancement task data.

In this embodiment of the application, when it is determined that the operation data satisfies the trigger condition corresponding to the scene artificial enhancement task data, it means that the user has completed the operation corresponding to the scene artificial enhancement task data, and at this time, the corresponding reward prompt data and feedback data can be triggered and sent to the access terminal. It can be seen that by sending feedback data, the user can be reminded in time that the corresponding operation has been successfully completed according to the artificial enhancement task data of the scene

In one embodiment, the step 105 includes:

Acquiring motion track data according to the space start data and the space end data;

generating motion animation data according to the space start point object, the space end point object and the motion track data;

Acquiring task completion prompt data corresponding to the artificially enhanced task data of the scene;

Composing the motion animation data and the task completion prompt data into feedback data and sending it to the access terminal.

In the embodiment of the present application, in order to display feedback data with richer data dimensions, the operation data generated based on the scene artificial enhancement task data can be obtained to obtain the spatial starting point data and the spatial end point data, and the spatial starting point data and the spatial end point data can be obtained. Obtain motion trajectory data; then generate motion animation data based on the motion trajectory data corresponding to the space starting point object, the space end point object and the operation data for the operation data; then obtain the pre-stored task completion prompt data, and finally Composing the motion animation data and the task completion prompt data into feedback data and sending it to the access terminal. It can be seen that the feedback data based on the fusion of multi-dimensional data is sent to the access terminal for display, which can increase interactivity and increase the amount of information that users can obtain to complete tasks.

In order to better implement the method of the present application, the embodiment of the present application further provides a smart device 20 . The smart device 20 can be understood as a server, and the access terminal in subsequent solutions is a VR device or a PC device.

Please refer to FIG. 2 . FIG. 2 is a schematic structural diagram of a smart device 20 of the present application, wherein the smart device 20 may specifically include the following structures: a sending unit 201 , a receiving unit 202 and a processing unit 203 .

Wherein, the processing unit 203 is configured to obtain optimized equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling, so as to update and obtain the digital twin space model.

In one embodiment, the processing unit 203 is specifically configured to:

The processing unit 203 is further configured to obtain the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model.

In one embodiment, the processing unit 203 is further specifically configured to:

The sending unit 201 is configured to send the digital twin space model to the access terminal when a user access instruction sent by the access terminal is detected.

In an embodiment, the sending unit 201 is further configured to:

The receiving unit 202 is configured to acquire the operation data sent by the access terminal and corresponding to the digital twin space model.

In one embodiment, the step processing unit 203 is further specifically configured to:

The sending unit 201 is further configured to send feedback data corresponding to the operation data to the access terminal when it is determined that the operation data satisfies the trigger condition corresponding to the scenario artificial enhancement task data.

In one embodiment, the sending unit 201 is further specifically configured to:

The present application also provides a processing device. Referring to FIG. 3, FIG. 3 shows a schematic structural diagram of the processing device of the present application. Specifically, the processing device provided in the present application includes a processor, and the processor is used to execute the computer program stored in the memory. When implementing the steps in the embodiment corresponding to Figure 1; or, when the processor is used to execute the computer program stored in the memory, implement the functions of the modules in the embodiment corresponding to Figure 2.

Exemplarily, a computer program can be divided into one or more modules/units, and one or more modules/units are stored in a memory and executed by a processor to complete the present application. One or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

A processing device may include, but is not limited to, a processor, memory. Those skilled in the art can understand that the illustration is only an example of a processing device, and does not constitute a limitation to the processing device, and may include more or less components than those shown in the illustration, or combine some components, or different components, such as processing The device may also include an input and output device, a network access device, a bus, etc., and a processor, a memory, an input and output device, and a network access device are connected through the bus.

The processor can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the processing device, and uses various interfaces and lines to connect various parts of the entire processing device.

The memory can be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playback function, an image playback function, etc.); Data created by the use of the device (such as audio data, video data, etc.), etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, internal memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The display screen is used for displaying characters of at least one character type output by the input and output unit.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described device, processing device and corresponding modules can refer to the description in the corresponding embodiment shown in Figure 1, specifically here No longer.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by instructions controlling related hardware, and the instructions can be stored in a computer-readable storage medium, and is loaded and executed by the processor.

To this end, the embodiment of the present application provides a computer-readable storage medium, which stores a plurality of instructions that can be loaded by the processor to perform the steps in the embodiment of the present application as shown in Figure 1. The specific operations Reference may be made to the description in the embodiment corresponding to FIG. 1 , and details are not repeated here.

Wherein, the computer-readable storage medium may include: a read-only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and the like.

Because the instructions stored in the computer-readable storage medium can execute the steps in the embodiment corresponding to Figure 1 of this application, therefore, the beneficial effects that can be realized in the embodiment corresponding to Figure 1 of this application can be realized , see the previous description for details, and will not repeat them here.

A digital twin-based artificial enhancement method, device, and storage medium provided by this application have been introduced in detail above. In the embodiments of this application, specific examples have been applied to illustrate the principles and implementation methods of this application. The description of the above embodiments It is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation and application scope. In summary, this The content of the description should not be understood as limiting the application.

Claims

A digital twin-based artificial enhancement method, characterized in that the method comprises:

Obtain optimized equipment data converted based on real equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling to update the digital twin space model;

Obtain the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model;

When the user access instruction sent by the access terminal is detected, the digital twin space model is sent to the access terminal;

Obtaining the operation data corresponding to the digital twin space model sent by the access terminal;

When it is determined that the operation data satisfies the trigger condition corresponding to the scene artificial enhancement task data, the feedback data corresponding to the operation data is sent to the access terminal.
The method according to claim 1, wherein said obtaining optimized device data converted based on real device data comprises:

Acquire real device data, and correspondingly convert the real device data into optimized device data according to a preset device conversion strategy.
The method according to claim 1, wherein said obtaining optimized equipment data, mapping said optimized equipment data to an initial digital twin space model through digital twin modeling, to update and obtain a digital twin space model, comprises:

Obtaining the dimensional data included in the optimized equipment data, and mapping the dimensional data to the initial digital twin space model through 1:1 digital twin modeling, so as to update the digital twin space model; wherein, the update obtains the digital twin space model includes an optimized equipment twin model corresponding to the optimized equipment data;

The operating parameter data included in the optimized equipment data is obtained, and the operating parameter data is fused with the optimized equipment twin model in the digital twin space model to update the digital twin space model.
The method according to claim 3, wherein the acquisition of the artificially enhanced task data of the scene is performed, and the artificially enhanced task data of the scene is fused with the digital twin space model to update the digital twin space model, including :

Obtain the target twin model corresponding to the scene artificial enhancement task data in the digital twin space model, and fuse the scene artificial enhancement task data with the target twin model to update the digital twin space model.
The method according to claim 1, wherein when the user access instruction sent by the access terminal is detected, after sending the digital twin spatial model to the access terminal, further comprising:

Sending the prompt data corresponding to the scene manual enhancement task data to the access terminal; the prompt data is text prompt data or voice prompt data.
The method according to claim 1, wherein after acquiring the operation data corresponding to the digital twin space model sent by the access terminal, further comprising:

Obtaining the spatial start data and spatial end data corresponding to the operation data;

Acquiring a space start point object corresponding to the space start point data, and obtaining a space end point object corresponding to the space end point data;

Obtain the current operation object set composed of the space start object and the space end object, and if the current operation object set is the same as the task object set corresponding to the scene artificial enhancement task data, determine that the operation data meets the scene artificial enhancement task data. Enhance the trigger conditions corresponding to task data.
The method according to claim 6, wherein the sending the feedback data corresponding to the operation data to the access terminal comprises:

Acquiring motion track data according to the space start data and the space end data;

generating motion animation data according to the space start point object, the space end point object and the motion track data;

Acquiring task completion prompt data corresponding to the artificially enhanced task data of the scene;

Composing the motion animation data and the task completion prompt data into feedback data and sending it to the access terminal.
An intelligent device, characterized in that the intelligent device comprises: a sending unit, a receiving unit and a processing unit;

The processing unit is configured to obtain optimized equipment data, and map the optimized equipment data to the initial digital twin space model through digital twin modeling, so as to update and obtain the digital twin space model;

The processing unit is also used to obtain the set scene artificial enhancement task data, and fuse the scene artificial enhancement task data with the digital twin space model to update the digital twin space model;

The sending unit is configured to send the digital twin space model to the access terminal when a user access instruction sent by the access terminal is detected;

The receiving unit is configured to obtain the operation data corresponding to the digital twin space model sent by the access terminal;

The sending unit is further configured to send feedback data corresponding to the operation data to the access terminal when it is determined that the operation data satisfies a trigger condition corresponding to the scenario artificial enhancement task data.
A processing device, characterized in that it includes a processor and a memory, and a computer program is stored in the memory, and when the processor invokes the computer program in the memory, it executes the method according to any one of claims 1 to 7 method.
A computer-readable storage medium, characterized in that the computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.