Method and System for Creating Virtual Mapping of Intelligent Internet of Things Devices in the Metaverse

Abstract

The present invention provides a method and system for creating virtual mapping of intelligent Internet of Things devices in the metaverse, relating to the technical field of the metaverse, including determining an initial position in the virtual space of the metaverse and selecting a matching virtual mapping model by receiving device identification information; constructing a multi-level virtual mapping adaptive system, including a physical layer adaptation module and an interaction layer synchronization module, to achieve consistent mapping of physical attributes and real-time synchronization of interaction states; and generating interaction behaviors of virtual mapping objects by using a sphere bounding box algorithm. The present invention improves the virtual mapping accuracy and interaction synchronization, enhances the user experience, and realizes the seamless connection between the physical world and the virtual world.

Description

Technical Field

[0001] The present invention relates to the technical field of the metaverse, and particularly to a method and system for creating virtual mappings of intelligent Internet of Things devices in the metaverse. Background Art

[0002] With the rapid development of metaverse technology, mapping intelligent Internet of Things devices in the real world to the virtual space has become an important way to achieve digital twins and immersive experiences. As a bridge connecting the real world and the digital world, the virtual mapping technology of intelligent Internet of Things devices has received extensive attention. These devices include, but are not limited to, various Internet of Things terminals such as smart home devices, industrial control devices, and wearable devices. At present, the integration of Internet of Things technology and the metaverse has been applied in multiple fields, such as smart cities, intelligent manufacturing, and telemedicine. By mapping the functions, states, and interaction capabilities of physical devices to the virtual space, users can perceive and control real devices in the metaverse environment, breaking the limitations of space and time and providing new possibilities for human-computer interaction.

[0003] Existing virtual mapping technologies mainly adopt the methods of static model matching and simple state synchronization, generating corresponding virtual objects by collecting basic device information and according to preset templates. In the metaverse platform, these virtual objects can display the basic states of the devices and support limited interaction functions. With the improvement of computing power and network transmission technology, real-time data transmission and complex interaction simulation have become possible, but there is still a large room for improvement in the accuracy, real-time performance, and interaction naturalness of existing virtual mapping technologies.

[0004] There is a problem of inaccurate mapping of physical attributes in the existing virtual mapping technology of intelligent Internet of Things devices in the metaverse. Traditional mapping methods often only focus on the basic functions and simple states of devices, and the mapping of physical properties such as deformation, mass, inertia, etc. of devices is not accurate enough, resulting in obvious differences in physical behaviors between virtual objects in the metaverse and real devices, making it difficult to achieve a real physical interaction experience. Especially in application scenarios involving complex physical environments, this difference will significantly reduce the user's immersion.

[0005] There are obvious lags in real-time synchronization and interaction state consistency in the existing technology. Due to factors such as network latency and data processing time, there is often a time difference in the state updates between virtual mapping objects and actual devices, and there is a lack of an effective delay compensation mechanism, resulting in that when users operate virtual objects in the metaverse environment, it cannot be timely reflected on physical devices, or the state changes of physical devices cannot be updated to the virtual space in time, seriously affecting the coherence and reliability of the interaction experience.

[0006] The existing virtual mapping technology lacks a systematic solution in terms of spatial constraints and interaction behavior generation. In the virtual space of the metaverse, the interaction behavior of devices needs to consider the particularities of the virtual environment, such as spatial layout, object collision, environmental rules, etc. However, most of the existing technologies adopt simple state transition models, ignoring the complexity and diversity of the virtual space, and are unable to effectively handle the problems of multi-device collaborative interaction and behavior adaptation in complex environments. As a result, the interaction behavior of virtual mapping objects in the metaverse is not natural and intelligent enough, restricting the depth and breadth of user-device interaction. Summary of the Invention

[0007] The embodiments of the present invention provide a method and system for creating virtual mapping of intelligent IoT devices in the metaverse, which can solve the problems in the existing technology.

[0008] In the first aspect of the embodiments of the present invention, a method for creating virtual mapping of intelligent IoT devices in the metaverse is provided, including:

[0009] Receiving device identification information sent by the intelligent IoT device, where the device identification information includes device type information, device location information, and device function parameter information;

[0010] Based on the device identification information, determining the initial position of the virtual mapping object in the metaverse virtual space, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between the device type and the virtual mapping model, to generate the virtual mapping object of the intelligent IoT device;

[0011] Constructing a multi-level virtual mapping adaptive system, the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module, where the physical layer adaptation module is used to implement the consistent mapping of the physical attributes of the virtual mapping object and the real device, and the interaction layer synchronization module is used to ensure the real-time synchronization of the interaction state between the virtual mapping object and the real device through an adaptive compensation algorithm;

[0012] Generating an action sequence according to the output result of the multi-level virtual mapping adaptive system combined with the metaverse space constraints, and adopting a sphere bounding box algorithm to generate the interaction behavior of the virtual mapping object in the metaverse virtual space.

[0013] Based on the device identification information, determining the initial position of the virtual mapping object in the metaverse virtual space, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between the device type and the virtual mapping model, to generate the virtual mapping object of the intelligent IoT device includes:

[0014] The device location information of the intelligent Internet of Things device is converted into an initial position coordinate in the metaverse virtual space by using a spatial coordinate transformation matrix, where the spatial coordinate transformation matrix includes a rotation matrix parameter and a translation vector parameter;

[0015] The device type information and the device function parameter information are converted into a device feature vector;

[0016] Based on the device feature vector, the matching degree between each virtual mapping model in the preset virtual mapping model library and the intelligent Internet of Things device is calculated, and the calculation of the matching degree includes the calculation of the cosine similarity of the feature vectors;

[0017] The virtual mapping model with the highest matching degree is used as the optimal virtual mapping model to generate a virtual mapping object of the intelligent Internet of Things device.

[0018] The method further includes:

[0019] An attribute set of the virtual mapping object is determined according to the optimal virtual mapping model, and the attribute set includes basic attributes, functional attributes, and interaction attributes. The basic attributes include geometric feature parameters and material property parameters, the functional attributes correspond to the device function parameter information, and the interaction attributes define the interaction mode and interaction ability of the virtual mapping object;

[0020] A state vector of the virtual mapping object is generated according to the attribute set, the device feature vector, and the initial position coordinate, and a mapping relationship between the state vector and the intelligent Internet of Things device is established.

[0021] The physical layer adaptation module is used to realize the consistency mapping of the physical attributes of the virtual mapping object and the real device, including:

[0022] The physical attributes are processed in layers, and a geometric feature mapping relationship and a material property mapping relationship are respectively established to generate initial physical feature data of the virtual mapping object;

[0023] The physical feature consistency evaluation data between the virtual mapping object and the real device is calculated, and the physical feature consistency evaluation data is obtained by weighted calculation of the feature differences between the virtual mapping object and the real device in each physical feature dimension;

[0024] When the physical feature consistency evaluation data exceeds a preset threshold, physical feature compensation data is calculated based on the gradient descent method, and the physical feature data of the virtual mapping object is dynamically corrected by combining historical compensation data;

[0025] The physical feature data of the virtual mapping object is updated in real time. When the geometric feature error data and the material property error data are both less than the corresponding error thresholds, the physical layer adaptation of the virtual mapping object is completed.

[0026] The interaction layer synchronization module is used to ensure the real-time synchronization of the interaction state between the virtual mapping object and the real device through an adaptive compensation algorithm, including:

[0027] Calculating the interaction state deviation between the virtual mapping object and the real device in real time. When the interaction state deviation exceeds the preset deviation threshold, the interaction parameters of the virtual mapping object are dynamically adjusted using the adaptive compensation algorithm;

[0028] Updating the interaction state parameters of the virtual mapping object in real time to achieve the real-time synchronization of the interaction state between the virtual mapping object and the real device.

[0029] Generating an action sequence based on the output result of the multi-level virtual mapping adaptive system and the metaverse space constraints, and generating the interaction behavior of the virtual mapping object in the metaverse virtual space using the sphere bounding box algorithm, including:

[0030] Generating an action sequence based on the output result of the multi-level virtual mapping adaptive system and the metaverse space constraints;

[0031] Calculating the minimum distance between the virtual mapping object and the surrounding environment using the sphere bounding box method. When the minimum distance is less than the preset safety distance, obstacle avoidance processing is triggered, and the interaction feedback force is calculated based on the displacement deviation, speed deviation, and contact energy.

[0032] In the second aspect of the embodiments of the present invention, a virtual mapping creation system of intelligent IoT devices in the metaverse is provided, including:

[0033] A first unit for receiving the device identification information sent by the intelligent IoT device, where the device identification information includes device type information, device location information, and device function parameter information;

[0034] A second unit for determining the initial position of the virtual mapping object in the metaverse virtual space based on the device identification information, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between the device type and the virtual mapping model to generate the virtual mapping object of the intelligent IoT device;

[0035] A third unit for constructing a multi-level virtual mapping adaptive system, where the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module. The physical layer adaptation module is used to achieve the consistent mapping of the physical attributes of the virtual mapping object and the real device, and the interaction layer synchronization module is used to ensure the real-time synchronization of the interaction state between the virtual mapping object and the real device through an adaptive compensation algorithm;

[0036] The fourth unit is used to generate an action sequence according to the output result of the multi-level virtual mapping adaptive system in combination with the metaverse space constraints, and adopts a sphere bounding box algorithm to generate the interaction behavior of the virtual mapping object in the metaverse virtual space.

[0037] In the third aspect of the embodiments of the present invention, there is provided an electronic device, including:

[0038] A processor;

[0039] A memory for storing instructions executable by the processor;

[0040] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0041] In the fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0042] The beneficial effects of this application are as follows:

[0043] By constructing a multi-level virtual mapping adaptive system, high-precision mapping of intelligent IoT devices in the metaverse is achieved, ensuring the consistency of physical properties and real-time synchronization of interaction states between virtual objects and real devices, and improving the realism and reliability of users' manipulation of intelligent IoT devices in the metaverse.

[0044] Adopting a sphere bounding box algorithm to generate the interaction behavior of virtual mapping objects effectively solves the collision detection and dynamic response problems of different types of intelligent IoT devices in the metaverse virtual space, optimizes the utilization efficiency of system resources, and ensures a smooth interaction experience in complex scenarios.

[0045] By automatically matching a suitable virtual model through the preset correspondence between device types and virtual mapping models, the mapping process of intelligent IoT devices in the metaverse is simplified, the technical threshold is reduced, enabling various intelligent IoT devices to quickly access the metaverse platform, realizing seamless connection between the physical world and the virtual world, and providing users with a more convenient smart home management experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the method for creating virtual mapping of intelligent IoT devices in the metaverse in the embodiments of the present invention;

[0047] Figure 2 It is a schematic diagram for comparing the matching accuracy of feature vectors under different environmental conditions in the embodiments of the present invention;

[0048] Figure 3 It is a complete flowchart of the optimal virtual mapping model in the embodiments of the present invention;

[0049] Figure 4 This is a schematic diagram for comparing the calculation accuracy of interaction feedback force in different scenarios of the embodiments of the present invention. Specific implementation manners

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in 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. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0052] Figure 1 This is a schematic flowchart of a method for creating a virtual mapping of an intelligent Internet of Things device in the metaverse in the embodiments of the present invention. As Figure 1 shown, the method includes:

[0053] Receiving device identification information sent by the intelligent Internet of Things device, where the device identification information includes device type information, device location information, and device function parameter information;

[0054] Based on the device identification information, determining the initial position of the virtual mapping object in the metaverse virtual space, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between the device type and the virtual mapping model, and generating a virtual mapping object of the intelligent Internet of Things device;

[0055] Constructing a multi-level virtual mapping adaptive system, where the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module. The physical layer adaptation module is used to implement the consistent mapping of the physical attributes of the virtual mapping object and the real device, and the interaction layer synchronization module is used to ensure the real-time synchronization of the interaction state between the virtual mapping object and the real device through an adaptive compensation algorithm;

[0056] Generating an action sequence according to the output result of the multi-level virtual mapping adaptive system in combination with the metaverse space constraints, and generating the interaction behavior of the virtual mapping object in the metaverse virtual space by using the sphere bounding box algorithm.

[0057] In an alternative embodiment, based on the device identification information, an initialization position of the virtual mapping object is determined in the metaverse virtual space, and according to the correspondence between the preset device type and the virtual mapping model, a virtual mapping model matching the device type information is selected, and generating the virtual mapping object of the intelligent IoT device includes:

[0058] Using a spatial coordinate transformation matrix to convert the device position information of the intelligent IoT device into an initialization position coordinate in the metaverse virtual space, where the spatial coordinate transformation matrix includes a rotation matrix parameter and a translation vector parameter;

[0059] Converting the device type information and the device function parameter information into a device feature vector;

[0060] Based on the device feature vector, calculating the matching degree between each virtual mapping model in the preset virtual mapping model library and the intelligent IoT device, and the calculation of the matching degree includes calculating the cosine similarity of the feature vectors;

[0061] Taking the virtual mapping model with the highest matching degree as the optimal virtual mapping model, and generating the virtual mapping object of the intelligent IoT device.

[0062] According to the obtained intelligent IoT device identification information, it is first necessary to determine the initialization position of the device in the metaverse virtual space. The real physical position of the device is usually represented by three-dimensional coordinates (x, y, z), and the coordinate system in the metaverse virtual space may be different from that in the physical world, so spatial coordinate conversion is required.

[0063] Using a spatial coordinate transformation matrix to convert the physical position information of the device into an initialization position coordinate in the virtual space. The spatial coordinate transformation matrix includes a rotation matrix parameter and a translation vector parameter, which are used to realize the mapping between coordinate systems.

[0064] The rotation matrix parameter is used to adjust the orientation of the device to ensure that the orientation of the virtual mapping object in the virtual space is consistent with that of the real device. For example, if the orientation of the intelligent IoT device in the physical space is the northeast direction, then through the setting of the rotation matrix parameter, the virtual mapping object in the virtual space is also oriented in the corresponding direction. When specifically implemented, Euler angles or quaternions can be used to represent the rotation.

[0065] The translation vector parameter is used to correspond to the actual position of the device in the physical space. For example, if the physical coordinates of the intelligent IoT device are (10, 20, 0) meters, and there is an offset of (100, 100, 10) meters between the origin of the metaverse virtual space and the origin of the physical space, then through the translation vector parameter, the device position is mapped to (110, 120, 10) in the virtual space.

[0066] Suppose an intelligent lighting device in the physical world is located at coordinates (5.2, 3.7, 1.8) meters with an orientation angle of 30 degrees. Given that there is an offset of (50, 30, 0) between the virtual space and the physical space, and the coordinate axes are rotated by 45 degrees, through the calculation of the space coordinate transformation matrix, the initial position of the device in the virtual space can be obtained as (53.6, 35.4, 1.8), and the orientation angle is 75 degrees.

[0067] To accurately select a virtual mapping model that matches the device type, it is necessary to convert the device type information and functional parameter information into a device feature vector. The device feature vector is a multi-dimensional numerical representation that describes the device's attributes and functions and is used for subsequent model matching calculations.

[0068] Device type information usually exists in text form, such as "intelligent lighting", "intelligent door lock", "intelligent speaker", etc. During the conversion process, the one-hot encoding method can be used to convert the text type into a vector representation. For example, if there are 10 preset device types in total, and a certain device's type is "intelligent lighting" with a corresponding type number of 3, it can be represented as a 10-dimensional vector, where the 3rd element is 1 and the rest are 0, that is, [0,0,1,0,0,0,0,0,0,0].

[0069] Device functional parameter information includes the specific functional characteristics of the device, such as the brightness adjustment range, color temperature adjustment ability, remote control function, etc. These parameters can be directly converted into vector form. For example, an intelligent lighting device has functions such as brightness adjustment (0 - 100%), color temperature adjustment (2700K - 6500K), timing switch, and remote control, then it can be represented as a vector [100,3800,1,-1], where 100 represents the maximum brightness adjustment range, 3800 represents the color temperature adjustment range, 1 represents the presence of a timing switch function, and -1 represents the presence of a remote control function.

[0070] Concatenate the device type vector and the functional parameter vector to form a complete device feature vector. Taking the above intelligent lighting device as an example, its complete feature vector may be:

[0071] [0,0,1,0,0,0,0,0,0,0,100,3800,1,1].

[0072] After determining the device's feature vector, it is necessary to select the most suitable virtual mapping model for the device from the preset virtual mapping model library. This process is achieved by calculating the matching degree between the device feature vector and each virtual mapping model in the model library.

[0073] The matching degree is calculated using the cosine similarity method of feature vectors. Cosine similarity is an index to measure the similarity of the directions of two vectors. The closer its value is to 1, the more similar the two vectors are; the closer it is to 0, the less similar the two vectors are. In this embodiment, the cosine similarity between the device feature vector and the feature vectors of each virtual mapping model in the model library is calculated to determine the most matching virtual mapping model.

[0074] A virtual mapping model library is established in advance, and each model has a corresponding feature vector. For example, there is a "standard intelligent lamp" model in the model library, and its feature vector is [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 100, 4000, 1, 1]; an "advanced intelligent lamp" model, and its feature vector is [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 150, 5000, 1, 1, 1], where the last 1 indicates the function of having music rhythm.

[0075] For the above intelligent lighting device, calculate the cosine similarity between its feature vector [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 100, 3800, 1, 1] and the feature vector of the "standard intelligent lamp" model:

[0076] [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 100, 4000, 1, 1], and the result is approximately 0.999;

[0077] And the feature vector of the "advanced intelligent lamp" model:

[0078] [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 150, 5000, 1, 1, 1], and the result is approximately 0.95.

[0079] By comparison, the "standard intelligent lamp" model has a higher matching degree with the device. Therefore, the "standard intelligent lamp" model is selected as the virtual mapping model of the device.

[0080] After determining the initialization position and the optimal virtual mapping model of the device, the last step is to generate the virtual mapping object of the device. The virtual mapping object is the digital twin of the device in the metaverse virtual space, which not only has a three-dimensional model similar to the appearance of the device, but also includes the virtual implementation of the device functions.

[0081] Load the selected virtual mapping model, which contains the basic 3D model and functional interfaces of the device; configure the functional interfaces of the model according to the functional parameter information of the device so that it can accurately reflect the actual functions of the device; place the model at the calculated initial position coordinates and adjust the direction of the model according to the orientation information of the device; establish a data communication mechanism between the device and its virtual mapping object so that changes in the device state can be reflected on the virtual mapping object in real time, and operations on the virtual mapping object can also be converted into control instructions for the actual device.

[0082] For the aforementioned intelligent lighting device, its virtual mapping object will be a 3D model similar to the appearance of the actual lamp, located at the position of (53.6, 35.4, 1.8) in the virtual space, with an orientation angle of 75 degrees. This virtual mapping object can display the current state of the lamp, such as the switch state, brightness, color temperature, etc. Users can adjust these parameters interactively in the virtual space, and the corresponding control instructions will be sent to the actual device to change its state according to the virtual operation.

[0083] Figure 2 Schematic diagram for comparing the matching accuracy of feature vectors under different environmental conditions in the embodiments of the present invention:

[0084] This figure shows the comparison of the recognition accuracies of three different technical solutions under various environmental conditions. It is presented in the form of a line chart, where the horizontal axis represents six different environmental conditions (from left to right are standard indoor, strong light environment, low light environment, noisy background, multi-device interference, and large open space), and the vertical axis represents the percentage of recognition accuracy (70% - 100%).

[0085] The three technical solutions are represented by a dotted line (this technical solution), a diamond line (vector dot product method), and a square line (normalized vector matching) respectively. From the data trend, it can be seen that under all environmental conditions, this technical solution always maintains the highest recognition accuracy, gradually decreasing from about 97% in the standard indoor environment to about 90% in the large open space. The vector dot product method ranks second, with the accuracy decreasing from about 92% in the standard indoor environment to about 80% in the large open space. The performance of the normalized vector matching is the worst, decreasing from about 89% in the standard indoor environment to about 74% in the large open space.

[0086] All three curves show a downward trend as the environmental conditions change, but the decline amplitudes are different. The curve of this technical solution is the flattest, indicating that it has the strongest adaptability to various environmental conditions and the most stable performance; while the curve of the normalized vector matching drops the steepest, indicating that this method is greatly affected by environmental factors and has poor stability. This visual comparison clearly shows the performance differences of the three technical solutions in different application scenarios, which is helpful for the decision-making of technology selection.

[0087] In an alternative embodiment, the method further includes:

[0088] Determine the attribute set of the virtual mapping object according to the optimal virtual mapping model, where the attribute set includes basic attributes, functional attributes, and interaction attributes. The basic attributes include geometric feature parameters and material property parameters, the functional attributes correspond to the device function parameter information, and the interaction attributes define the interaction method and interaction ability of the virtual mapping object;

[0089] Generate the state vector of the virtual mapping object according to the attribute set, the device feature vector, and the initial position coordinates, and establish the mapping relationship between the state vector and the intelligent IoT device.

[0090] After obtaining the optimal virtual mapping model, it is necessary to determine the attribute set of the virtual mapping object. This attribute set includes three main parts: basic attributes, functional attributes, and interaction attributes.

[0091] The basic attributes mainly include geometric feature parameters and material property parameters, which determine the basic appearance and physical characteristics of the virtual mapping object in the virtual space.

[0092] 1. Determination of geometric feature parameters:

[0093] Determine the geometric parameters such as the length, width, and height of the virtual mapping object according to the real size data of the intelligent IoT device. For example, for an intelligent lighting device, its geometric feature parameters may include: length of 300 mm, width of 200 mm, and height of 100 mm.

[0094] Determine the shape parameters of the virtual mapping object according to the shape characteristics of the device. For example, the device may be cylindrical, cubic, or irregular in shape, and needs to be accurately described in a parametric way.

[0095] For complex devices with multiple components, it is necessary to determine the relative positions and connection relationships between the components to ensure that the virtual mapping object can accurately reflect the overall structure of the device.

[0096] 2. Determination of material property parameters:

[0097] Determine the surface material properties of the virtual mapping object according to the actual material information of the device, including parameters such as color, texture, gloss, and transparency.

[0098] For the intelligent lighting, its material properties may include: the housing material is matte plastic, the color is pure white (RGB value: 255, 255, 255), the transparency is 0%, the lampshade material is semi-transparent material, and the transparency is 60%.

[0099] For devices with a display screen, it is also necessary to determine the light-emitting characteristics and display effect parameters of the screen area.

[0100] The functional attributes directly correspond to the device function parameter information, reflecting the various functional characteristics and operation capabilities of the intelligent Internet of Things devices.

[0101] 1. Functional status attribute:

[0102] Based on the device function parameter information, determine the functional status attributes that the virtual mapping object needs to display. For example, for intelligent lamps, its functional status attributes include: switch status (Boolean value: on / off), brightness level (integer value: 0 - 100), color temperature value (integer value: 2700K - 6500K), RGB color value (triple of RGB values: 0 - 255).

[0103] For multi-functional devices, it is necessary to determine the status attributes of all functional modules. For example, the functional status attributes of an intelligent air conditioner may include: working mode (enumeration value: cooling / heating / dehumidifying / air supply), set temperature (integer value: 16 - 30 °C), wind speed level (enumeration value: low / medium / high / automatic).

[0104] 2. Functional operation attribute:

[0105] Determine the functional operation attributes that the virtual mapping object can execute, and these attributes correspond to the control instructions that the device can respond to. For example, the functional operation attributes of intelligent lamps include: switch control (operation type: on / off), brightness adjustment (operation type: brightness value setting, parameter range: 0 - 100), color temperature adjustment (operation type: color temperature value setting, parameter range: 2700K - 6500K).

[0106] For functions with multi-level operations, it is necessary to define the hierarchical structure and parameter limits of the operations. For example, the channel switching function of an intelligent TV may have two operation methods: directly inputting the channel number and switching up and down.

[0107] The interaction attribute defines how the virtual mapping object responds to user operations and the way it interacts with other virtual objects or system components.

[0108] 1. User interaction method:

[0109] Determine the interaction methods supported by the virtual mapping object, such as operation types like click, long press, slide, drag, etc. For intelligent lamps, its user interaction methods may include: clicking on the lamp body to switch the switch status, long pressing on the lamp body to bring up the brightness controller, and sliding on the brightness controller to adjust the brightness value.

[0110] Define the response behavior and visual feedback effect for each interaction method. For example, after clicking the switch, the color of the virtual lamp will change accordingly, accompanied by a short transition animation.

[0111] 2. Interaction ability parameters:

[0112] Determine the interaction ability parameters of the virtual mapping object, including the interaction sensitive area, interaction response time, interaction feedback intensity, etc. For example, the interaction sensitive area of the switch button of the intelligent lamp is a circular area with a diameter of 50 pixels; the interaction response time is 100 milliseconds, and a slight vibration feedback is generated after the interaction is successful.

[0113] For complex devices with multi-level interaction interfaces, it is necessary to define the interface hierarchy structure and the switching conditions between levels.

[0114] After determining the attribute set, it is necessary to generate the state vector of the virtual mapping object according to the attribute set, device feature vector and initial position coordinates, and establish the mapping relationship between the state vector and the intelligent Internet of Things device.

[0115] 1. State vector structure design:

[0116] Design a structured state vector, including basic state components, functional state components and interaction state components.

[0117] The basic state components include: position coordinates (x, y, z), rotation angles (pitch, yaw, roll), scaling ratios (sx, sy, sz), and basic attribute parameters.

[0118] The functional state components include: the current values of all functional attributes, such as switch state, brightness value, temperature value, etc.

[0119] The interaction state components include: the current interaction state identifier, interaction history records, etc.

[0120] 2. Initial state assignment:

[0121] Set the position information in the state vector according to the initial position coordinates. For example, the initial position coordinates of the intelligent lamp are (120, 85, 200), indicating its position in the virtual space.

[0122] Initialize the functional state components according to the current state information of the device in the device feature vector. For example, if the device feature vector indicates that the intelligent lamp is currently in the on state, the brightness is 75%, and the color temperature is 5000K, then assign these values to the corresponding functional state components in the state vector.

[0123] Initialize the interaction state components, usually set to the default non-interaction state.

[0124] 3. State vector update mechanism:

[0125] Design a dynamic update mechanism for the state vector. When the device state changes, the corresponding components in the state vector are updated accordingly.

[0126] When the user turns on the smart lamp through the physical switch, the device sends a state change message to the system. After receiving this message, the system updates the switch state component in the state vector from 0 (off) to 1 (on).

[0127] When the user operates the virtual mapping object in the virtual environment, the system updates the corresponding components in the state vector and sends control instructions to the actual device.

[0128] 1. Unique identification mapping:

[0129] Assign a unique identifier to each virtual mapping object and establish a one-to-one correspondence between this identifier and the device ID of the corresponding intelligent IoT device. For example, if the device ID of the smart lamp is "LIGHT_001", the identifier of its corresponding virtual mapping object can be set as "VM_LIGHT_001".

[0130] Record these corresponding relationships in the system's mapping relationship table to ensure that the system can accurately transfer messages and synchronize states between virtual objects and actual devices.

[0131] 2. State synchronization mapping:

[0132] Establish a synchronization mapping relationship between the components in the state vector and the actual parameters of the device. For example, the brightness component value 75 in the state vector is mapped to the brightness parameter value 75% of the device.

[0133] Design the trigger conditions and synchronization frequencies for state synchronization to ensure that the virtual mapping object can promptly reflect the state changes of the device. For important state changes, such as the switch state change, use the real-time push method for synchronization; for non-critical states, such as small temperature changes, the periodic polling method can be used for synchronization.

[0134] 3. Control instruction mapping:

[0135] Establish a mapping relationship between the operation behaviors of the virtual mapping object and the device control instructions. For example, the dragging operation of the brightness slider of the smart lamp in the virtual environment is mapped to sending a brightness adjustment instruction to the device.

[0136] Define parameter conversion rules in the mapping relationship to ensure that virtual operations can be accurately converted into the control instruction format recognizable by the device. For example, the brightness scale of 0 - 100 in the virtual environment needs to be converted into the brightness parameter format of 0% - 100% accepted by the device API.

[0137] Figure 3Complete flowchart of the optimal virtual mapping model in the embodiments of the present invention:

[0138] This figure shows the complete flowchart of an optimal virtual mapping model, which details the entire process from model establishment to the formation of the final mapping relationship. The process starts with the "optimal virtual mapping model" and then decomposes by determining the attribute sets of the virtual mapping object. These attribute sets are divided into three main categories: basic attributes (including geometric feature parameters and material property parameters), functional attributes (corresponding to device function parameter information), and interaction attributes (including interaction methods and interaction capabilities). These three categories of attributes act together in subsequent processing steps.

[0139] The middle part of the flowchart shows that the system needs to integrate and process two input parameters, the "device feature vector" and the "initial position coordinates", with the aforementioned three categories of attributes to finally generate the state vector of the virtual mapping object. This state vector is the key link connecting the virtual and the real. In the final stage of the process, the system will establish the mapping relationship between the state vector and the intelligent IoT device to achieve the effective docking of the virtual space and the real world.

[0140] The entire flowchart adopts a top-down hierarchical structure, clearly showing the logical relationship and data flow between each processing step through blue boxes and connection lines. This systematic mapping model design effectively solves the interaction mapping problem between virtual objects and actual devices, providing a theoretical basis and implementation framework for the realization of the combination of virtual and real in the intelligent IoT system. Each step in the figure is an indispensable component, jointly constituting a complete virtual mapping system.

[0141] In an alternative embodiment, the physical layer adaptation module is used to achieve the consistent mapping of the physical attributes of the virtual mapping object and the real device, including:

[0142] Perform hierarchical processing on the physical attributes, respectively establish geometric feature mapping relationships and material property mapping relationships, and generate the initial physical feature data of the virtual mapping object;

[0143] Calculate the physical feature consistency evaluation data between the virtual mapping object and the real device, and the physical feature consistency evaluation data is obtained by weighted calculation of the feature differences between the virtual mapping object and the real device in each physical feature dimension;

[0144] When the physical feature consistency evaluation data exceeds the preset threshold, calculate the physical feature compensation data based on the gradient descent method, and dynamically correct the physical feature data of the virtual mapping object in combination with the historical compensation data;

[0145] Update the physical characteristic data of the virtual mapping object in real time. When the geometric feature error data and the material property error data are both less than the corresponding error thresholds, complete the physical layer adaptation of the virtual mapping object.

[0146] The physical layer adaptation module first performs a hierarchical processing on the physical properties, divides the physical properties into two levels of geometric features and material properties, and establishes mapping relationships respectively.

[0147] Geometric features include, but are not limited to, attributes such as the size, shape, mass, center of gravity position, and inertia matrix of the real device. For example, for an industrial robotic arm, its length is 1200mm, width is 300mm, weight is 35kg, and the center of gravity position is 250mm directly above the base of the robotic arm. The system will obtain the surface point cloud data of the device through a high-precision 3D scanner. The accuracy of the point cloud data reaches 0.1mm, about 500,000 points are collected in total, and each point contains spatial three-dimensional coordinates and normal vector information. After the point cloud data is processed, a 3D mesh model is generated, and the number of mesh triangular patches is about 100,000. The system uses this mesh model as the basic representation of geometric features.

[0148] Material properties include elastic coefficient, friction coefficient, density distribution, surface roughness, thermal conductivity, etc. For example, the friction coefficient of each joint part of the robotic arm is 0.25 - 0.35, the Ra value of the surface roughness is 3.2μm, the main body material is aluminum alloy, and the thermal conductivity is 237W / (m·K). The system collects the above parameters in real time through a material sensor array, and the sampling frequency is 100Hz.

[0149] The system combines the above physical characteristic data to generate an initial physical characteristic data set of the virtual mapping object. This data set contains a feature vector of 89 dimensions, covering all key physical properties.

[0150] The physical layer adaptation module calculates the physical characteristic consistency evaluation data between the virtual mapping object and the real device. In the evaluation process, first standardize the physical characteristics of each dimension, convert the features with different dimensions into dimensionless values, and the value range is limited between 0 and 1.

[0151] Calculate the feature differences between the virtual mapping object and the real device in each physical characteristic dimension. For example, for geometric dimensions, if a certain dimension of the real device is 120mm and the corresponding dimension of the virtual object is 121.5mm, then the difference value of this dimension is 1.5mm, and after standardization, it is 0.0125.

[0152] Adopt a weighted calculation method to synthesize the differences of each dimension, and different weights are assigned to different dimensions. For example, for an industrial robotic arm, the weight of the joint position accuracy is 0.3, the weight of the surface shape accuracy is 0.15, the weight of the mass distribution is 0.25, and the weight of the material property is 0.3. The system can dynamically adjust each weight value according to specific application scenarios.

[0153] After the consistency evaluation data calculation is completed, the system compares this value with a preset threshold. The preset threshold is usually set to 0.15, indicating that when the comprehensive physical feature difference exceeds 15%, the compensation mechanism needs to be activated.

[0154] When the physical feature consistency evaluation data exceeds the preset threshold, the physical layer adaptation module calculates the physical feature compensation data based on the gradient descent method.

[0155] The compensation calculation process first identifies the feature dimension with the largest difference. For example, if it is found that the contribution of the surface friction coefficient difference is the largest, this parameter is adjusted first. The system uses a prediction model trained with historical data to infer the parameter adjustment direction and step size. The initial value of the step size is set to 50% of the difference value. For example, if the current difference is 0.08, the initial step size is 0.04.

[0156] An iterative optimization strategy is adopted. Each iteration adjusts the physical feature parameters once and re-evaluates the consistency. If the consistency improves, continue to adjust in the current direction; if the consistency decreases, reduce the step size and try to adjust in the opposite direction. During the iteration process, the step size gradually shrinks, successively decreasing from the initial value to 80%, 64%, 51%, etc., until the step size is less than the preset minimum value of 0.001 or the maximum number of iterations of 30 times is reached.

[0157] The physical feature compensation data not only considers the current real-time calculation results but also combines historical compensation data. The system maintains a historical database containing the last 50 compensation records and integrates the historical compensation data through time-weighted averaging, with more recent data having a higher weight. For example, the weight of the most recent compensation is 0.2, the previous one is 0.15, and the one before that is 0.1, and so on.

[0158] Based on the calculated compensation data, the physical layer adaptation module dynamically corrects the physical feature data of the virtual mapping object. The correction process is divided into two modes: batch correction and incremental correction.

[0159] Batch correction is executed during the system initialization phase, adjusting all the physical parameters of the virtual object at once, including the feature values of 89 dimensions. Incremental correction is continuously performed during operation, updating once every 100 milliseconds, and each time only adjusting a subset of the parameters with obvious differences, usually 5 - 10 parameters.

[0160] After the correction is completed, the physical feature data of the virtual mapping object is updated in real time. The update frequency is adaptively adjusted, increasing to 200Hz during the critical stage of interaction and decreasing to 50Hz during the non-critical stage to save computing resources.

[0161] Continuously monitor the geometric feature error data and material property error data. When the geometric feature error is less than 3% and the material property error is less than 5%, the system considers that the physical layer adaptation of the virtual mapping object is completed. At this time, the physical behavior of the virtual object is basically the same as that of the real device, and it can accurately simulate the physical response of the real device under various environmental conditions.

[0162] In a practical application, when the system initially adapts the robotic arm model, the geometric error is 7.8% and the material property error is 9.2%. After 12 iterations of adjustment, the geometric error drops to 2.1% and the material property error drops to 3.5%, meeting the preset threshold requirements and completing the physical layer adaptation. When the virtual robotic arm after adaptation is subjected to an external force, the error in its deformation behavior from the real robotic arm does not exceed 2.5 mm, and the error in the response time does not exceed 15 milliseconds, meeting the requirements of industrial applications.

[0163] In an alternative embodiment, the interaction layer synchronization module is used to ensure the real-time synchronization of the interaction states of the virtual mapping object and the real device through an adaptive compensation algorithm, including:

[0164] Real-time calculate the deviation of the interaction state between the virtual mapping object and the real device. When the interaction state deviation exceeds the preset deviation threshold, use the adaptive compensation algorithm to dynamically adjust the interaction parameters of the virtual mapping object;

[0165] Real-time update the interaction state parameters of the virtual mapping object to achieve the real-time synchronization of the interaction states of the virtual mapping object and the real device.

[0166] The interaction layer synchronization module receives the state information from the real device and the state information of the virtual mapping object. By comparing the differences between the two, it calculates the interaction state deviation, and when the deviation exceeds the preset threshold, it uses the adaptive compensation algorithm to dynamically adjust the interaction parameters of the virtual mapping object to achieve the synchronization of their interaction states.

[0167] The interaction layer synchronization module first obtains the interaction state parameters of the real device and the virtual mapping object. These parameters include, but are not limited to, physical parameters such as position coordinates, rotation angles, motion speeds, accelerations, etc., and device-specific working state parameters, such as the joint angles of a robotic arm, the motor speeds of a robot, etc.

[0168] Taking an industrial robot as an example, the interaction state parameters include:

[0169] The angular values of each joint of the robot (J1, J2, J3, J4, J5, J6);

[0170] The position coordinates (X, Y, Z) of the end effector of the robot;

[0171] The attitude angles (A, B, C) of the robot end effector;

[0172] The movement speeds and accelerations of each joint of the robot;

[0173] The calculation method of the interaction state deviation is as follows:

[0174] 1. Obtain the real-time state parameter set R_state from the real device, including the current values of all relevant parameters;

[0175] 2. Obtain the corresponding state parameter set V_state from the virtual mapping object;

[0176] 3. Calculate the relative deviation of each parameter:

[0177] For position-type parameters: Calculate the Euclidean distance as the deviation value;

[0178] For angle-type parameters: Calculate the absolute value of the angle difference as the deviation value;

[0179] For speed-type parameters: Calculate the modulus of the speed vector difference as the deviation value;

[0180] 4. Assign weights (W_i) according to the importance of each parameter, and calculate the comprehensive deviation value;

[0181] For example, for a six-axis industrial robot, the deviation calculation can be specific as follows:

[0182] Position deviation = ;

[0183] Attitude deviation = |A_r - A_v| + |B_r - B_v| + |C_r - C_v|;

[0184] Joint angle deviation = Σ|J_i_r - J_i_v| (i ranges from 1 to 6);

[0185] Comprehensive deviation = W_pos × Position deviation + W_att × Attitude deviation + W_joint × Joint angle deviation;

[0186] W_pos, W_att, and W_joint are the weight coefficients of position, attitude, and joint angle respectively, which are usually set according to the application scenario. For example, W_pos = 0.5, W_att = 0.3, W_joint = 0.2.

[0187] The preset deviation threshold is set according to different types of devices and application scenarios, and usually the following factors are considered:

[0188] The accuracy requirements of the device;

[0189] The real-time requirements;

[0190] Network latency situation;

[0191] Computing resource limitations;

[0192] High-precision processing equipment may need to set a lower deviation threshold (such as a position deviation of 0.01 mm), while general transportation robots may be able to accept a higher deviation threshold (such as a position deviation of 10 mm).

[0193] In practical applications, the system can set multiple levels of deviation thresholds, for example:

[0194] Slight deviation threshold: 0.5 mm (position) / 0.5° (angle);

[0195] Moderate deviation threshold: 2 mm (position) / 2° (angle);

[0196] Severe deviation threshold: 5 mm (position) / 5° (angle);

[0197] Deviations at different levels will trigger compensation adjustments of different intensities.

[0198] When it is detected that the interaction state deviation exceeds the preset threshold, the interaction layer synchronization module starts an adaptive compensation algorithm to dynamically adjust the interaction parameters of the virtual mapping object. The adaptive compensation algorithm dynamically adjusts the compensation parameters according to the magnitude, change trend of the deviation, and historical synchronization data.

[0199] The implementation steps of the adaptive compensation algorithm are as follows:

[0200] 1. Calculate the deviation change rate: By comparing the difference between the current deviation and the previous N deviations, calculate the change trend of the deviation.

[0201] Deviation change rate = (current deviation - average value of previous N deviations) / time interval;

[0202] 2. Determine the basic compensation coefficient: Determine the basic compensation coefficient according to the ratio of the current deviation value to the preset threshold.

[0203] Basic compensation coefficient = Min(Deviation value / Preset threshold × Maximum compensation coefficient, Maximum compensation coefficient);

[0204] For example, if the current position deviation is 3 mm, the preset threshold is 2 mm, and the maximum compensation coefficient is 1.5, then the basic compensation coefficient = Min(3 / 2 × 1.5, 1.5) = 1.5;

[0205] 3. Adaptively adjust the compensation coefficient: Adjust the compensation coefficient according to the deviation change rate, increasing the compensation intensity when the deviation increases and decreasing the compensation intensity when the deviation decreases.

[0206] If the deviation change rate > 0, it means the deviation is increasing, and the compensation is enhanced:

[0207] The adjusted compensation coefficient = base compensation coefficient × (1 + rate coefficient × deviation change rate);

[0208] If the deviation change rate < 0, it means the deviation is decreasing, and the compensation is weakened:

[0209] The adjusted compensation coefficient = base compensation coefficient × (1 - rate suppression coefficient × |deviation change rate|);

[0210] For example, assume the base compensation coefficient is 1.2, the rate coefficient is 0.5, and the current deviation change rate is 0.2. Then the adjusted compensation coefficient = 1.2 × (1 + 0.5 × 0.2) = 1.32;

[0211] 4. Consider historical compensation effects: Analyze the effects of historical compensation operations and adjust the compensation strategy.

[0212] Calculate the effectiveness index of the most recent N compensations = degree of deviation reduction after compensation / compensation intensity;

[0213] Adjust the current compensation strategy according to the effectiveness index. If the effectiveness is high, maintain the strategy; if the effectiveness is low, adjust the strategy;

[0214] 5. Perform the compensation operation: Calculate the compensation value according to the finally determined compensation coefficient and apply it to the interaction parameters of the virtual mapping object.

[0215] Compensation value = (real device parameter - virtual object parameter) × adjusted compensation coefficient;

[0216] The updated virtual object parameter = current virtual object parameter + compensation value;

[0217] Taking an industrial robot as an example, if the position of the end effector of the real robot is (100, 150, 200) mm and the position of the virtual mapping object is (98, 152, 197) mm, the calculated position deviation is 5.1 mm, which exceeds the preset threshold of 4 mm and compensation is required. Assume the adjusted compensation coefficient is 1.2, then:

[0218] Compensation value in the X direction = (100 - 98) × 1.2 = 2.4 mm;

[0219] Compensation value in the Y direction = (150 - 152) × 1.2 = -2.4 mm;

[0220] Z - direction compensation value = (200 - 197) × 1.2 = 3.6mm;

[0221] The position of the updated virtual mapping object is (100.4, 149.6, 200.6)mm, and at this time it is closer to the position of the real device.

[0222] After completing the compensation calculation, the interaction layer synchronization module applies the updated parameters to the virtual mapping object to achieve the synchronization of the interaction state between the virtual mapping object and the real device. The update process includes:

[0223] 1. Write the compensated parameters obtained from the calculation into the state cache of the virtual object;

[0224] 2. Trigger the state update event of the virtual object;

[0225] 3. The virtual environment engine re - renders the virtual object according to the updated parameters;

[0226] 4. Record the timestamp, deviation value, and compensation value of this synchronization for subsequent compensation strategy optimization;

[0227] To ensure the smoothness of synchronization, the interaction layer synchronization module can dynamically adjust the synchronization frequency according to network latency and computing resources. When the network is good, the synchronization frequency can be increased (such as 50Hz), and when the network is congested, the synchronization frequency can be appropriately reduced (such as 10Hz) but the single - time compensation intensity is increased.

[0228] Through the above implementation method, the interaction layer synchronization module can effectively ensure the real - time synchronization of the interaction state between the virtual mapping object and the real device. In actual application tests, this method can control the position deviation within the millimeter level (usually less than 2mm) and the angle deviation within 1 degree, and can maintain a good synchronization effect even when the network latency reaches 200ms.

[0229] This method is applicable to various human - machine interaction systems that require real - time synchronization, such as industrial robot remote control, surgical robot remote guidance, virtual reality interaction and other scenarios, and can effectively improve the synchronization accuracy between virtual and real and the user experience.

[0230] In an optional implementation manner, an action sequence is generated according to the output result of the multi - level virtual mapping adaptive system combined with the meta - universe space constraint, and the interaction behavior of the virtual mapping object in the meta - universe virtual space is generated by using the sphere bounding box algorithm, including:

[0231] Generate an action sequence according to the output result of the multi - level virtual mapping adaptive system combined with the meta - universe space constraint;

[0232] The sphere bounding box method is used to calculate the minimum distance between the virtual mapped object and the surrounding environment. When the minimum distance is less than the preset safety distance, obstacle avoidance processing is triggered, and the interaction feedback force is calculated based on the displacement deviation, speed deviation, and contact energy.

[0233] The multi-level virtual mapping adaptive system generates initial output results, which need to be combined with the metaverse space constraints to generate an action sequence that conforms to physical rules and space limitations. The metaverse space constraints mainly include three categories: physical constraints, geometric constraints, and interaction constraints.

[0234] Physical constraints include physical rules such as gravity, friction, and collision detection. Geometric constraints include space limitations such as space boundaries, obstacles, and passable areas. Interaction constraints include interaction rules with other virtual objects and user input responses.

[0235] The action sequence generation process adopts a hierarchical planning strategy, which is divided into two levels: global path planning and local action optimization. Global path planning is based on the A* algorithm to search for the optimal path from the starting point to the target point in the metaverse space. The path evaluation function comprehensively considers path length, energy consumption, and smoothness. For example, for the task of moving from coordinates (10, 20, 30) to coordinates (50, 60, 70), the system will search in the three-dimensional grid space to obtain the best path point sequence that avoids obstacles {(10,20,30), (15,25,35), (25,35,45), (40,50,60), (50,60,70)}.

[0236] Local action optimization adopts a sampling optimization method to generate smooth actions that conform to the object's motion characteristics under the guidance of the global path. For example, for a humanoid virtual mapped object, the system will generate a complete action sequence including gait parameters, torso posture, and arm movements. The action sequence is stored in the form of key frames, and each key frame contains information such as position, rotation, and speed. Continuous actions are generated between adjacent key frames through interpolation algorithms. The typical data structure of action key frames contains attributes such as timestamp, joint position, joint rotation, linear velocity, and angular velocity.

[0237] To ensure that the generated action sequence conforms to the metaverse space constraints, the system adopts an iterative optimization strategy. After each generation of the action sequence, it checks whether there are any violations of the constraints. If there are violations, the optimization parameters are adjusted and regenerated until all constraints are met. For example, when it is detected that there is a wall-passing behavior in the action sequence, the system will increase the obstacle avoidance weight and re-optimize the action sequence.

[0238] After the action sequence generation is completed, the system uses the sphere bounding box algorithm for collision detection and interactive feedback force calculation. This algorithm first simplifies the virtual mapped object into a combination of multiple spheres to improve the calculation efficiency. For example, a humanoid character can be simplified into a model composed of multiple spheres such as the head, torso, and limbs, and each sphere is defined by the center coordinates and radius. Specifically, the head can be represented as a sphere with the center at (0, 175, 0) and a radius of 15 units, the torso is represented as a sphere with the center at (0, 140, 0) and a radius of 25 units, and so on.

[0239] For static objects and other dynamic objects in the environment, the sphere bounding box or sphere bounding box hierarchy is also used for representation. The system regularly calculates the minimum distance between the virtual mapped object and all objects in the surrounding environment. The minimum distance calculation method is to calculate the distance between the centers of the two spheres and then subtract the sum of the radii of the two spheres. If the calculation result is negative, it means that the two spheres have penetrated; if it is positive, it means the minimum distance between the two spheres.

[0240] When it is detected that the minimum distance is less than the preset safety distance, the system will trigger obstacle avoidance processing. The preset safety distance is dynamically adjusted according to the moving speed of the virtual mapped object. The faster the moving speed, the greater the safety distance. Taking a humanoid virtual character as an example, when the walking speed is 1.5 m / s, the preset safety distance is 0.5 m; when the running speed reaches 5 m / s, the preset safety distance increases to 1.5 m.

[0241] The obstacle avoidance processing uses a potential field-based method, regarding the obstacle as a repulsive potential field source and the target point as an attractive potential field source. The virtual mapped object adjusts its movement direction and speed under the action of the combined potential field. For example, when an obstacle is detected 0.3 m to the right, the system will generate an obstacle avoidance displacement to the left to make the object bypass the obstacle and continue to move forward.

[0242] The calculation of the interactive feedback force is based on three key parameters: displacement deviation, velocity deviation, and contact energy. Displacement deviation refers to the difference between the actual position of the virtual object after being blocked and the expected position, velocity deviation refers to the difference between the actual velocity and the expected velocity, and contact energy is related to the contact area, contact depth, and relative velocity.

[0243] The feedback force calculation uses a spring-damper model. The magnitude of the feedback force is proportional to the displacement deviation and the direction is opposite to the direction of the displacement deviation, while considering the damping effect generated by the velocity deviation. For example, when a virtual character hits a wall at a speed of 3 m / s and generates a displacement deviation of 0.1 m, the system will calculate a rebound force of approximately 300 Newtons to make the character bounce back and decelerate.

[0244] For interactive objects of different materials, the system sets different stiffness and damping parameters. For example, the stiffness parameter of a metal material is 10,000 Newtons / meter, and the damping parameter is 1,000 Newton-seconds / meter; while the stiffness parameter of a cork material is 3,000 Newtons / meter, and the damping parameter is 500 Newton-seconds / meter. The interactive feedback force not only affects the motion state of the virtual mapped object but also triggers visual, auditory, and tactile feedback, enhancing the user's immersion. For example, a strong collision will produce vibration effects, collision sounds, and particle effects, etc.

[0245] The feedback strategy under different interaction intensities is also implemented. A slight contact (contact energy less than 10 joules) only produces minor deformation and tactile feedback; a medium collision (contact energy between 10 and 100 joules) produces an obvious change in the motion state and sound effects; a strong collision (contact energy greater than 100 joules) may cause a change in the state of the virtual object, such as falling, damage, or functional failure, etc.

[0246] Figure 4 Schematic diagram for comparing the calculation accuracy of the interactive feedback force in different scenarios of the embodiments of the present invention:

[0247] This figure shows the comparison of the recognition accuracies of three different technical solutions in eight interactive scenarios. The chart is in the form of a line chart. The horizontal axis represents different interactive scenarios (light touch scenario, fast sliding, continuous pressure, multi-point contact, vibration environment, high-speed collision, deformable object, low-friction surface), and the vertical axis represents the recognition accuracy percentage (60% - 100%).

[0248] From the data performance, the technical solution of the present invention (blue dot line) maintains high stability in various scenarios, and the accuracy rate generally remains above 90%. Among them, it reaches the highest value of about 95% in the continuous pressure scenario, and even remains at about 88% in the most challenging deformable object scenario. The solution based on the spring model (red square line) has the second-best overall performance, and the accuracy rate fluctuates between 70% - 90%. It reaches the peak value of about 90% in the continuous pressure scenario, but performs poorly in scenarios such as the vibration environment and low-friction surface, dropping to about 72%. The linear feedback model (green triangle line) has the most unstable performance, and the accuracy rate fluctuates in the range of 65% - 85%, dropping to the lowest of about 66% in the deformable object scenario.

[0249] The performance curves of the three solutions all show a certain degree of volatility, but the volatility amplitudes are different. The curve of the technical solution of the present invention is the smoothest, indicating that it has the best scene adaptability and stability; while the performances of the other two solutions vary greatly in different scenarios, especially in some complex interactive scenarios, the performance drops significantly. This comparative analysis effectively demonstrates the advantages and disadvantages of the three technical solutions in practical applications and provides an important reference basis for technology selection.

[0250] In the second aspect of the embodiments of the present invention, a virtual mapping creation system for intelligent Internet of Things devices in the metaverse is provided, including:

[0251] A first unit, configured to receive device identification information sent by an intelligent Internet of Things device, where the device identification information includes device type information, device location information, and device function parameter information;

[0252] A second unit, configured to determine an initial position of a virtual mapping object in the metaverse virtual space based on the device identification information, and select a virtual mapping model matching the device type information according to the corresponding relationship between the preset device type and the virtual mapping model, and generate a virtual mapping object of the intelligent Internet of Things device;

[0253] A third unit, configured to construct a multi-level virtual mapping adaptive system, where the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module, where the physical layer adaptation module is configured to implement a consistent mapping of the physical attributes of the virtual mapping object and the real device, and the interaction layer synchronization module is configured to ensure real-time synchronization of the interaction state between the virtual mapping object and the real device through an adaptive compensation algorithm;

[0254] A fourth unit, configured to generate an action sequence according to the output result of the multi-level virtual mapping adaptive system combined with the metaverse space constraint, and generate an interaction behavior of the virtual mapping object in the metaverse virtual space by using a sphere bounding box algorithm.

[0255] In the third aspect of the embodiments of the present invention, an electronic device is provided, including:

[0256] A processor;

[0257] A memory for storing processor-executable instructions;

[0258] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0259] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0260] The present invention may be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for performing various aspects of the present invention are uploaded.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for creating virtual mapping of intelligent Internet of Things devices in the metaverse, characterized in that Including: Receiving device identification information sent by an intelligent Internet of Things device, where the device identification information includes device type information, device location information, and device function parameter information; Based on the device identification information, determining an initial position of a virtual mapping object in the metaverse virtual space, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between device types and virtual mapping models, to generate a virtual mapping object of the intelligent Internet of Things device; Constructing a multi-level virtual mapping adaptive system, where the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module. The physical layer adaptation module is used to achieve a consistent mapping of the physical attributes of the virtual mapping object and the real device, and the interaction layer synchronization module is used to ensure real-time synchronization of the interaction states of the virtual mapping object and the real device through an adaptive compensation algorithm; Generating an action sequence based on the output result of the multi-level virtual mapping adaptive system in combination with metaverse space constraints, and using a sphere bounding box algorithm to generate the interaction behavior of the virtual mapping object in the metaverse virtual space; The physical layer adaptation module is used to achieve a consistent mapping of the physical attributes of the virtual mapping object and the real device, including: Performing hierarchical processing on the physical attributes, respectively establishing geometric feature mapping relationships and material property mapping relationships, to generate initial physical feature data of the virtual mapping object; Calculating physical feature consistency evaluation data between the virtual mapping object and the real device, where the physical feature consistency evaluation data is obtained by weighted calculation of the feature differences between the virtual mapping object and the real device in each physical feature dimension; When the physical feature consistency evaluation data exceeds a preset threshold, calculating physical feature compensation data based on the gradient descent method, and dynamically correcting the physical feature data of the virtual mapping object in combination with historical compensation data; Updating the physical feature data of the virtual mapping object in real time. When the geometric feature error data and the material property error data are both less than the corresponding error thresholds, the physical layer adaptation of the virtual mapping object is completed.

2. The method according to claim 1, characterized in that, Based on the device identification information, determining an initial position of a virtual mapping object in the metaverse virtual space, and selecting a virtual mapping model that matches the device type information according to the preset correspondence between device types and virtual mapping models, to generate a virtual mapping object of the intelligent Internet of Things device, including: Using a spatial coordinate transformation matrix to convert the device location information of the intelligent Internet of Things device into an initial position coordinate in the metaverse virtual space, where the spatial coordinate transformation matrix includes rotation matrix parameters and translation vector parameters; Converting the device type information and the device function parameter information into a device feature vector; Based on the device feature vector, calculating the matching degree between each virtual mapping model in a preset virtual mapping model library and the intelligent Internet of Things device, and the calculation of the matching degree includes calculating the cosine similarity of the feature vectors; Taking the virtual mapping model with the highest matching degree as the optimal virtual mapping model, and generating a virtual mapping object of the intelligent Internet of Things device.

3. The method according to claim 2, wherein The method further includes: Determine the attribute set of the virtual mapping object according to the optimal virtual mapping model. The attribute set includes basic attributes, functional attributes, and interaction attributes. Among them, the basic attributes include geometric feature parameters and material property parameters. The functional attributes correspond to the device function parameter information, and the interaction attributes define the interaction method and interaction ability of the virtual mapping object; Generate the state vector of the virtual mapping object according to the attribute set, the device feature vector, and the initial position coordinates, and establish the mapping relationship between the state vector and the intelligent IoT device.

4. The method according to claim 1, wherein The physical layer adaptation module is used to realize the consistent mapping of the physical attributes of the virtual mapping object and the real device, including: Perform hierarchical processing on the physical attributes, establish geometric feature mapping relationships and material property mapping relationships respectively, and generate the initial physical feature data of the virtual mapping object; Calculate the physical feature consistency evaluation data between the virtual mapping object and the real device. The physical feature consistency evaluation data is obtained by weighted calculation of the feature differences between the virtual mapping object and the real device in each physical feature dimension; When the physical feature consistency evaluation data exceeds the preset threshold, calculate the physical feature compensation data based on the gradient descent method, and dynamically correct the physical feature data of the virtual mapping object in combination with the historical compensation data; Update the physical feature data of the virtual mapping object in real time. When the geometric feature error data and the material property error data are both less than the corresponding error thresholds, complete the physical layer adaptation of the virtual mapping object.

5. The method according to claim 1, characterized in that The interaction layer synchronization module is used to ensure the real-time synchronization of the interaction states of the virtual mapping object and the real device through an adaptive compensation algorithm, including: Calculate the interaction state deviation between the virtual mapping object and the real device in real time. When the interaction state deviation exceeds the preset deviation threshold, use the adaptive compensation algorithm to dynamically adjust the interaction parameters of the virtual mapping object; Update the interaction state parameters of the virtual mapping object in real time to realize the real-time synchronization of the interaction states of the virtual mapping object and the real device.

6. The method according to claim 1, wherein Generate an action sequence according to the output result of the multi-level virtual mapping adaptive system combined with the metaverse space constraints, and generate the interaction behavior of the virtual mapping object in the metaverse virtual space by using the sphere bounding box algorithm, including: Generate an action sequence according to the output result of the multi-level virtual mapping adaptive system combined with the metaverse space constraints; Use the sphere bounding box method to calculate the minimum distance between the virtual mapping object and the surrounding environment. When the minimum distance is less than the preset safety distance, trigger obstacle avoidance processing, and calculate the interaction feedback force according to the displacement deviation, speed deviation, and contact energy.

7. A virtual mapping creation system for intelligent Internet of Things devices in the metaverse, which is used to implement the method described in any one of claims 1-6, characterized in that, Include: The first unit is used to receive the device identification information sent by the intelligent IoT device, where the device identification information includes device type information, device location information, and device function parameter information; The second unit is used to determine the initial position of the virtual mapping object in the metaverse virtual space based on the device identification information, and select a virtual mapping model that matches the device type information according to the preset correspondence between the device type and the virtual mapping model, and generate the virtual mapping object of the intelligent IoT device; The third unit is used to construct a multi-level virtual mapping adaptive system, and the multi-level virtual mapping adaptive system includes a physical layer adaptation module and an interaction layer synchronization module. The physical layer adaptation module is used to realize the consistent mapping of the physical attributes of virtual mapping objects and real devices, and the interaction layer synchronization module is used to ensure the real-time synchronization of the interaction states of virtual mapping objects and real devices through an adaptive compensation algorithm; The fourth unit is used to generate an action sequence according to the output result of the multi-level virtual mapping adaptive system in combination with the metaverse space constraints, and generate the interaction behavior of virtual mapping objects in the metaverse virtual space by using the sphere bounding box algorithm.

8. An electronic device, characterized in that, Comprising: A processor; A memory for storing processor-executable instructions; Wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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