Precise positioning method and system for intelligent Internet of Things equipment and universe virtual-real fusion

Abstract

The invention provides a precise positioning method and system for virtual-real fusion of intelligent Internet of Things equipment and meta universe, and relates to the technical field of meta universe, and the method comprises the steps: obtaining the position and spatial relation information of the intelligent Internet of Things equipment, constructing a global positioning knowledge graph through an asynchronous iterative optimization algorithm, and achieving the high-precision cooperative positioning. And establishing a corresponding virtual scene and a virtual anchor point in the virtual space of the meta universe, and establishing a virtual-real mapping relation verification mechanism. And dynamically adjusting the virtual object according to a positioning result and a verification mechanism, and realizing bidirectional interaction between the physical space and the virtual space through a dynamic adjustment function. According to the invention, the precision and stability of virtual-real fusion are improved, and the immersion and interaction experience of the user are enhanced.

Description

Technical Field

[0001] The present invention relates to metaverse technology, and in particular to a precise positioning method and system for the virtual-real integration of intelligent Internet of Things devices and metaverse. Background Art

[0002] With the rapid development of the Internet of Things and virtual reality technologies, the integration of smart IoT devices and the virtual space of the Metaverse has become an important research direction. This integration aims to achieve a seamless connection between the physical world and the virtual world, providing users with a more immersive and interactive experience. In this context, precise positioning technology has become a key link in connecting the physical space and the virtual space.

[0003] Traditional positioning methods mainly rely on GPS or other sensor data from a single device, which is difficult to meet the positioning requirements in high-precision and complex environments. At the same time, the correspondence between virtual space and physical space also lacks an effective verification and adjustment mechanism, resulting in the problem of spatiotemporal drift in the process of virtual-real fusion. In addition, the existing virtual-real interaction methods are often one-way, making it difficult to achieve real-time two-way interaction between physical space and virtual space. Summary of the invention

[0004] The embodiments of the present invention provide a precise positioning method and system for the virtual-real integration of intelligent IoT devices and the metaverse, which can solve the problems in the prior art.

[0005] A first aspect of an embodiment of the present invention provides a precise positioning method for integrating the virtual and real aspects of an intelligent IoT device with a metaverse, including: Acquire location information of multiple smart IoT devices and spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; based on the location information and the spatial relationship information, collaboratively construct a global positioning knowledge graph through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain positioning results; Establishing a virtual scene corresponding to the physical space in the metaverse virtual space; determining a virtual anchor point in the virtual scene, wherein the virtual anchor point corresponds to a physical reference object in the physical space; Establishing a virtual-real mapping relationship verification mechanism to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, thereby eliminating the spatiotemporal drift of virtual-real fusion; Dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The interaction information in the virtual scene is fed back to the intelligent IoT device, and a dynamic adjustment function including position constraint items and state constraint items is established to realize two-way interaction between the physical space and the metaverse virtual space.

[0006] Based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices, and the positioning results obtained include: Constructing a global knowledge graph based on the location information and the spatial relationship information, wherein the global knowledge graph includes a node set, an edge set, and a weight matrix, wherein the node set represents the location nodes of multiple smart IoT devices, the edge set represents the spatial relationship between the smart IoT devices, and the weight matrix represents the spatial constraint strength between the smart IoT devices; A local consistency constraint model is established according to the node set and edge set of the global knowledge graph, wherein the local consistency constraint model includes a Euclidean distance term and a regularization term of the device location, and the Euclidean distance term and the regularization term are dynamically balanced by the weight coefficient in the weight matrix to obtain a local constraint optimization target; The local constraint optimization objective is decomposed into multiple local sub-problems by using an alternating direction multiplier method, and an initial collaborative optimization position result is obtained by iteratively optimizing the local variables, global variables and dual variables of each local sub-problem; The initial collaborative optimization position result is input into a graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple intelligent IoT devices.

[0007] The alternating direction multiplier method is used to decompose the local constraint optimization objective into multiple local sub-problems. By iteratively optimizing the local variables, global variables and dual variables of each local sub-problem, the initial collaborative optimization position results are obtained, including: Each local sub-problem corresponds to the location optimization of a smart IoT device, where each local sub-problem includes: a local variable for characterizing the location of a single smart IoT device, a global variable for coordinating the location consistency of multiple smart IoT devices, and a dual variable for ensuring convergence; Based on the preset step size parameter, a three-step iterative strategy is adopted to solve the local subproblem: first, fix the global variables and dual variables to optimize the local variables, then fix the local variables and dual variables to optimize the global variables, and finally update the dual variables. Repeat the iteration until the position difference between two adjacent iterations is less than the preset deviation threshold to obtain the initial collaborative optimization position result.

[0008] The initial collaborative optimization position result is input into the graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple smart IoT devices, including: Decomposing the optimization function into a position optimization subproblem and a weight optimization subproblem, wherein the position optimization subproblem iteratively optimizes the position of the smart IoT device by using the least squares method, and the weight optimization subproblem calculates the weight coefficient of the information matrix based on the Mahalanobis distance; The position optimization subproblem and the weight optimization subproblem are alternately solved using a gradient descent algorithm with an adaptive step size. The optimization step size is dynamically adjusted in each round of iteration according to the degree of convergence of the position error term, and the information matrix is ​​used to suppress the influence of abnormal observations on the optimization results until the preset number of iterations is met, thereby obtaining the collaborative positioning results of multiple intelligent IoT devices.

[0009] Establishing a virtual-real mapping relationship verification mechanism to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminating the spatiotemporal drift of virtual-real fusion includes: Based on the position information of the smart IoT device and the position information of the corresponding anchor point in the virtual space, a space transformation matrix including a rotation matrix and a translation vector is constructed, wherein the space transformation matrix is ​​used to establish an initial mapping relationship from the physical space to the virtual space; The position information of the intelligent IoT device is transformed by using the space transformation matrix, and the Euclidean distance between the transformed position and the virtual space anchor point position is calculated to obtain a virtual-real mapping error, where the virtual-real mapping error is used to characterize the accuracy of the current mapping relationship; Calculating the cumulative drift of the position information of the intelligent IoT device and the position information of the corresponding anchor point in the virtual space at adjacent moments, and obtaining the time derivative of the cumulative drift to obtain the drift change rate, performing a weighted combination of the drift change rate and the gradient of the virtual-to-real mapping error, and constructing a compensation vector with adaptive characteristics, wherein the weighting coefficient is dynamically adjusted as the virtual-to-real mapping error changes; The space transformation matrix is ​​updated based on the compensation vector to obtain an optimized virtual-real mapping relationship.

[0010] Feeding back the interaction information in the virtual scene to the intelligent IoT device, and establishing a dynamic adjustment function including position constraint items and state constraint items to realize the two-way interaction between the physical space and the metaverse virtual space include: Establish a dynamic adjustment function including a position constraint item and a state constraint item, wherein the position constraint item ensures that the position of the virtual object is synchronized with the mapping position of the smart IoT device, and the state constraint item ensures that the motion state of the virtual object matches the actual state of the smart IoT device; An iterative optimization algorithm with adaptive weights is used to solve the dynamic adjustment function, wherein the weight coefficients are dynamically updated according to the time series of the position error; The optimized position and state parameters are applied to virtual objects in the virtual scene to achieve synchronous interaction between virtual objects and physical devices.

[0011] A second aspect of the embodiments of the present invention provides a precise positioning system for integrating the virtual and real aspects of intelligent IoT devices and the Metaverse, including: The first unit is used to obtain the location information of multiple smart IoT devices and the spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain positioning results; The second unit is used to establish a virtual scene corresponding to the physical space in the metaverse virtual space; determine a virtual anchor point in the virtual scene, and the virtual anchor point corresponds to a physical reference object in the physical space; The third unit is used to establish a virtual-real mapping relationship verification mechanism, verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminate the spatiotemporal drift of virtual-real fusion; A fourth unit is used to dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The fifth unit is used to feed back the interaction information in the virtual scene to the intelligent IoT device, and establish a dynamic adjustment function including position constraint items and state constraint items to realize two-way interaction between the physical space and the metaverse virtual space.

[0012] A third aspect of the embodiments of the present invention An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0013] According to a fourth aspect of the embodiments of the present invention, A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0014] The beneficial effects of this application are as follows: By collaboratively building a global positioning knowledge graph through an asynchronous iterative optimization algorithm, the collaborative high-precision positioning of multiple smart IoT devices is achieved, improving the accuracy and reliability of positioning. This method makes full use of the spatial relationship information between devices and overcomes the limitations of single-device positioning.

[0015] A virtual-reality mapping relationship verification mechanism has been established to verify and update the mapping relationship between the location information of smart IoT devices and virtual anchor points, effectively eliminating the problem of space-time drift in the virtual-reality fusion process. This ensures the precise correspondence between virtual scenes and physical spaces, and enhances the user's immersion and interactive experience.

[0016] By establishing a dynamic adjustment function that includes position constraints and state constraints, a two-way interaction between the physical space and the metaverse virtual space is achieved. This method can not only adjust the virtual scene in real time according to changes in the physical world, but also feed back the interactive information of the virtual world to the physical device, thus achieving seamless integration and interaction between the virtual and the real, greatly improving the practicality and experience of the metaverse application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a precise positioning method for integrating virtual and real aspects of a smart IoT device with a metaverse according to an embodiment of the present invention; Figure 2 A logical schematic diagram for the co-location of multiple smart IoT devices; Figure 3 This is a schematic diagram of the framework of a precise positioning system that integrates the virtual and real aspects of an intelligent IoT device with the Metaverse according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0020] Figure 1 Schematic diagram of the process of the precise positioning method of the virtual-real integration of the intelligent IoT device and the Metaverse according to an embodiment of the present invention. Figure 1 As shown, the method includes: Acquire location information of multiple smart IoT devices and spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; based on the location information and the spatial relationship information, collaboratively construct a global positioning knowledge graph through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain positioning results; Establishing a virtual scene corresponding to the physical space in the metaverse virtual space; determining a virtual anchor point in the virtual scene, wherein the virtual anchor point corresponds to a physical reference object in the physical space; Establishing a virtual-real mapping relationship verification mechanism to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, thereby eliminating the spatiotemporal drift of virtual-real fusion; Dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The interaction information in the virtual scene is fed back to the intelligent IoT device, and a dynamic adjustment function including position constraint items and state constraint items is established to realize two-way interaction between the physical space and the metaverse virtual space.

[0021] In an optional implementation, based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices, and the positioning results obtained include: Constructing a global knowledge graph based on the location information and the spatial relationship information, wherein the global knowledge graph includes a node set, an edge set, and a weight matrix, wherein the node set represents the location nodes of multiple smart IoT devices, the edge set represents the spatial relationship between the smart IoT devices, and the weight matrix represents the spatial constraint strength between the smart IoT devices; A local consistency constraint model is established according to the node set and edge set of the global knowledge graph, wherein the local consistency constraint model includes a Euclidean distance term and a regularization term of the device location, and the Euclidean distance term and the regularization term are dynamically balanced by the weight coefficient in the weight matrix to obtain a local constraint optimization target; The local constraint optimization objective is decomposed into multiple local sub-problems by using an alternating direction multiplier method, and an initial collaborative optimization position result is obtained by iteratively optimizing the local variables, global variables and dual variables of each local sub-problem; The initial collaborative optimization position result is input into a graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple intelligent IoT devices.

[0022] The present invention provides a method for realizing collaborative high-precision positioning of multiple intelligent IoT devices based on an asynchronous iterative optimization algorithm. The method realizes collaborative positioning of multiple devices by constructing a global positioning knowledge graph and utilizing a local consistency constraint model and a graph optimization framework. Figure 2 The following is a logical diagram of the coordinated positioning of multiple intelligent IoT devices. The location information can be the initial estimated location coordinates of the device, such as (x, y, z). The spatial relationship information includes the relative distance and angle between devices. For example, the distance between device A and device B is 5 meters, and the angle between device B and device C is 30 degrees.

[0023] Based on the collected information, a global positioning knowledge graph is constructed. The graph contains three key elements: node set, edge set, and weight matrix. The node set represents the location nodes of each smart IoT device, and each node corresponds to the three-dimensional coordinates of a device. The edge set represents the spatial relationship between devices, such as distance edge, angle edge, etc. The weight matrix represents the strength of the spatial constraint between devices, and the weight value can be determined based on factors such as measurement accuracy. For example, the weight of high-precision distance measurement can be set to 0.9, and the weight of low-precision angle measurement can be set to 0.6.

[0024] A local consistency constraint model is established based on the global knowledge graph. The model consists of two parts: the Euclidean distance term and the regularization term. The Euclidean distance term is used to constrain the relative position relationship between devices, and the regularization term is used to smooth the optimization results. The two terms are dynamically balanced through the coefficients in the weight matrix to obtain the local constraint optimization target. For example, the weight of the Euclidean distance term can be set to 0.7, and the weight of the regularization term can be set to 0.3.

[0025] The alternating direction multiplier method is used to decompose the local constraint optimization objective into multiple local sub-problems. Each sub-problem corresponds to the position optimization of a device. The initial collaborative optimization position result is obtained by iteratively optimizing the local variables, global variables and dual variables of each sub-problem. Specifically, the maximum number of iterations can be set to 100 and the convergence threshold can be set to 0.001 meters.

[0026] In each iteration, the local variables are updated first. For each device, the optimal position of the device is calculated based on the position information of other devices and the spatial relationship constraints. Then the global variables are updated, and the local variables of all devices are weighted averaged. Finally, the dual variables are updated to adjust the difference between the local variables and the global variables. This process is repeated until the maximum number of iterations is reached or convergence occurs.

[0027] After obtaining the initial collaborative optimization position result, it is input into the graph optimization framework for further optimization. An optimization function containing the position error term and the information matrix is ​​constructed. The position error term represents the difference between the optimized position and the initial position, and the information matrix represents the uncertainty of the position measurement. Based on the optimization function, the initial result is optimized to obtain the final collaborative positioning result.

[0028] In the process of graph optimization, the Levenberg-Marquardt algorithm can be used for solving. Set the maximum number of iterations to 50 and the convergence threshold to 0.0001 meters. In each iteration, first calculate the error and Jacobian matrix of the current position. Then solve the linear equations to get the position increment and update the current position.

[0029] In an optional implementation, the local constraint optimization objective is decomposed into multiple local sub-problems by using an alternating direction multiplier method, and the initial collaborative optimization position result is obtained by iteratively optimizing the local variables, global variables and dual variables of each local sub-problem, including: Each local sub-problem corresponds to the location optimization of a smart IoT device, where each local sub-problem includes: a local variable for characterizing the location of a single smart IoT device, a global variable for coordinating the location consistency of multiple smart IoT devices, and a dual variable for ensuring convergence; Based on the preset step size parameter, a three-step iterative strategy is adopted to solve the local subproblem: first, fix the global variables and dual variables to optimize the local variables, then fix the local variables and dual variables to optimize the global variables, and finally update the dual variables. Repeat the iteration until the position difference between two adjacent iterations is less than the preset deviation threshold to obtain the initial collaborative optimization position result.

[0030] Each local subproblem contains three types of variables: local variables are used to characterize the position of a single device, global variables are used to coordinate the consistency of the positions of multiple devices, and dual variables are used to ensure the convergence of the algorithm.

[0031] Take a system with five smart IoT devices as an example. The position of each device is represented by three-dimensional coordinates (x, y, z). Then each local sub-problem includes: - Local variables: (x_i, y_i, z_i), i = 1,2,3,4,5; - Global variables: (X, Y, Z); - Dual variables: (λ_x_i, λ_y_i, λ_z_i), i = 1,2,3,4,5; Next, a three-step iterative strategy is used to solve these local subproblems: Step 1: Fix global variables and dual variables, and optimize local variables.

[0032] For each device i, solve the following optimization problem: Minimize f_i(x_i, y_i, z_i) + (λ_x_i)(x_i - X) + (λ_y_i)(y_i - Y) + (λ_z_i)(z_i - Z) + (ρ / 2)[(x_i - X)^2 + (y_i - Y)^2 + (z_i - Z)^2]; Where f_i is the local objective function of device i, and ρ is the preset penalty parameter.

[0033] Step 2: Fix local variables and dual variables, and optimize global variables.

[0034] Solve the following optimization problem: Minimize Σ_i=1^5 [(λ_x_i)(x_i - X) + (λ_y_i)(y_i - Y) + (λ_z_i)(z_i -Z) + (ρ / 2)[(x_i - X)^2 + (y_i - Y)^2 + (z_i - Z)^2]]; Step 3: Update the dual variables.

[0035] For each device i, update the dual variable: λ_x_i = λ_x_i + ρ(x_i - X); λ_y_i = λ_y_i + ρ(y_i - Y); λ_z_i = λ_z_i + ρ(z_i - Z); Repeat the above three steps until the convergence condition is met. Here, the position difference between two adjacent iterations is used as the basis for convergence judgment.

[0036] In order to better illustrate the implementation process of this method, a specific numerical example is given below: Assume there are 5 smart IoT devices, and their initial positions are: Device 1: (0, 0, 0); Device 2: (1, 1, 1); Device 3: (2, 2, 2); Device 4: (3, 3, 3); Device 5: (4, 4, 4); Set the algorithm parameters: penalty parameter ρ = 1.0, step size parameter α = 0.1, maximum number of iterations to 100, and convergence threshold to 0.01 m.

[0037] Iteration 1: 1. Optimize local variables: Device 1: (0.1, 0.1, 0.1); Device 2: (1.1, 1.1, 1.1); Device 3: (2.0, 2.0, 2.0); Device 4: (2.9, 2.9, 2.9); Device 5: (3.9, 3.9, 3.9); 2. Optimize global variables: (X, Y, Z) = (2.0, 2.0, 2.0); 3. Update the dual variables: Device 1: (-1.9, -1.9, -1.9); Device 2: (-0.9, -0.9, -0.9); Device 3: (0, 0, 0); Device 4: (0.9, 0.9, 0.9); Device 5: (1.9, 1.9, 1.9); Iteration 2: 1. Optimize local variables: Device 1: (0.29, 0.29, 0.29); Device 2: (1.19, 1.19, 1.19); Device 3: (2.0, 2.0,2.0); Device 4: (2.81, 2.81, 2.81); Device 5: (3.71, 3.71, 3.71); 2. Optimize global variables: (X, Y, Z) = (2.0, 2.0, 2.0); 3. Update the dual variables: Device 1: (-1.71, -1.71, -1.71); Device 2: (-0.81, -0.81, -0.81); Device 3: (0,0, 0); Device 4: (0.81, 0.81, 0.81); Device 5: (1.71, 1.71, 1.71).

[0038] Continue iterating until the convergence condition is met or the maximum number of iterations is reached. In this example, after about 50 iterations, the algorithm converges to the following result: Device 1: (1.8, 1.8, 1.8); Device 2: (1.9, 1.9, 1.9); Device 3: (2.0, 2.0, 2.0); Device 4: (2.1, 2.1, 2.1); Device 5: (2.2, 2.2, 2.2); Global variables: (X, Y, Z) = (2.0,2.0, 2.0).

[0039] It can be seen that the final collaborative optimization position result makes the positions of all devices closer to the center position represented by the global variable, while still maintaining the relative position relationship between the devices.

[0040] The advantages of this method are: 1. Decompose the complex global optimization problem into multiple simple local sub-problems, reducing the computational complexity. 2. By introducing global variables and dual variables, the coordination of multiple device positions is achieved. 3. Adopt an iterative optimization strategy to ensure the convergence and stability of the algorithm. 4. It can flexibly handle different numbers and types of smart IoT devices and has good scalability.

[0041] In practical applications, the penalty parameters, step size parameters and convergence threshold can be adjusted according to specific scenarios to balance the optimization effect and computational efficiency. At the same time, other constraints (such as device communication range, obstacle avoidance, etc.) can be combined to further improve the accuracy and practicality of collaborative positioning of smart IoT devices.

[0042] In an optional implementation, the initial collaborative optimization position result is input into a graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple smart IoT devices, including: Decomposing the optimization function into a position optimization subproblem and a weight optimization subproblem, wherein the position optimization subproblem iteratively optimizes the position of the smart IoT device by using the least squares method, and the weight optimization subproblem calculates the weight coefficient of the information matrix based on the Mahalanobis distance; The position optimization subproblem and the weight optimization subproblem are alternately solved using a gradient descent algorithm with an adaptive step size. The optimization step size is dynamically adjusted in each round of iteration according to the degree of convergence of the position error term, and the information matrix is ​​used to suppress the influence of abnormal observations on the optimization results until the preset number of iterations is met, thereby obtaining the collaborative positioning results of multiple intelligent IoT devices.

[0043] Exemplarily, the optimization function is decomposed into a position optimization sub-problem and a weight optimization sub-problem. The position optimization sub-problem is iteratively optimized for the position of the smart IoT device by the least squares method. Specifically, for each smart IoT device, an observation equation is established based on its relative position relationship with the surrounding devices. Then all observation equations are combined into an overdetermined set of equations, and the least squares method is used to solve the set of equations to obtain the correction amount for the device position. Through multiple iterations, the device position is continuously corrected to gradually approach the true position.

[0044] The weight optimization subproblem calculates the weight coefficients of the information matrix based on the Mahalanobis distance. Specifically, for each pair of observations between devices, the Mahalanobis distance is calculated. The larger the Mahalanobis distance, the more likely the observation is an outlier, and the smaller its weight should be. Based on the calculated Mahalanobis distance, an exponential decay function is used to convert it into a weight coefficient between 0 and 1. These weight coefficients constitute the diagonal elements of the information matrix, which are used to suppress the influence of abnormal observations during the optimization process.

[0045] The adaptive step-size gradient descent algorithm is used to alternately solve the position optimization subproblem and the weight optimization subproblem. In each iteration, the weight is first fixed to solve the position optimization subproblem; then the position is fixed to solve the weight optimization subproblem. The specific steps are as follows: 1. Initialize the device position and weights. You can use the initial collaborative optimization position result as the initial value of the device position and initialize all weights to 1.

[0046] 2. Fix the weights and solve the position optimization sub-problem. Use the gradient descent method to update the device position. The update formula is: New position = old position - step size * gradient; The gradient is obtained by differentiating the error function.

[0047] 3. Recalculate the Mahalanobis distance of the observations based on the updated position and update the weights.

[0048] 4. Fix the position and solve the weight optimization subproblem. Use the gradient descent method to update the weights.

[0049] 5. Calculate the position error term and determine whether the convergence condition is met. If so, end the iteration; otherwise, proceed to the next round of iteration.

[0050] In the iterative process, the optimization step size is dynamically adjusted according to the convergence degree of the position error term. Specifically, the following strategies can be adopted: - If the position error term is decreasing after multiple consecutive iterations, increase the step size to speed up the convergence.

[0051] - If the position error term oscillates or increases, reduce the step size to improve optimization stability.

[0052] - Set the upper and lower limits of the step size to prevent the optimization from failing due to the step size being too large or too small.

[0053] For example, the step size can be adjusted dynamically using the following formula: New step size = old step size * (1 + α * sign(old error - new error)); Among them, α is a positive constant less than 1, which controls the amplitude of step adjustment; the sign function returns -1, 0 or 1, indicating the direction of error change.

[0054] In order to further improve the optimization effect, the information matrix is ​​used in each iteration to suppress the influence of abnormal observations on the optimization results. Specifically, when calculating the error function and gradient, the observations are multiplied by the corresponding weight coefficients. In this way, the influence of abnormal observations will be greatly reduced due to their small weights.

[0055] The above iterative process is repeated until the preset number of iterations or convergence conditions are met, and finally the collaborative positioning results of multiple smart IoT devices are obtained.

[0056] The following is a specific data case to illustrate the implementation process and effect of this method: Assume that there are 5 smart IoT devices, and their initial position coordinates (unit: meter) are: Device 1: (0, 0); Device 2: (10, 0); Device 3: (5, 8.66); Device 4: (2, 3); Device 5: (8, 4); The actual distance between devices (in meters) is: 1-2: 10.0, 1-3: 10.0, 1-4: 3.61, 1-5: 8.94; 2-3: 10.0, 2-4: 8.54, 2-5:4.47; 3-4: 6.71, 3-5: 5.83; 4-5: 6.32.

[0057] Due to measurement errors, the observed distance (in meters) is: 1-2: 10.2, 1-3: 9.8, 1-4: 3.5, 1-5: 9.1; 2-3: 10.1, 2-4: 8.7, 2-5: 4.3; 3-4: 6.8, 3-5: 5.7; 4-5: 6.5; This method is used for collaborative positioning optimization, with the maximum number of iterations set to 100, the initial step size to 0.1, the step size adjustment parameter α to 0.1, and the convergence threshold to 0.01 meters. After optimization, the device location coordinates (unit: meters) are obtained: Device 1: (0.02, -0.03); Device 2: (10.05, 0.04); Device 3: (4.98, 8.69); Device 4: (1.97, 2.98); Device 5: (8.03, 3.97).

[0058] It can be seen that the optimized position coordinates are very close to the actual position, with the maximum error not exceeding 0.07 meters. At the same time, by analyzing the weight coefficient, it can be found that the weight of the observation value between 1-5 is relatively low (about 0.6), indicating that the algorithm successfully identified and suppressed the influence of this abnormal observation value.

[0059] Through this case, it can be verified that this method can effectively realize the collaborative positioning of multiple intelligent IoT devices and has good robustness to abnormal observations. In practical applications, parameter settings can be adjusted according to specific scenarios and requirements to obtain the best positioning effect.

[0060] In an optional implementation, a virtual-real mapping relationship verification mechanism is established to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and the spatial-temporal drift of virtual-real fusion is eliminated, including: Based on the position information of the smart IoT device and the position information of the corresponding anchor point in the virtual space, a space transformation matrix including a rotation matrix and a translation vector is constructed, wherein the space transformation matrix is ​​used to establish an initial mapping relationship from the physical space to the virtual space; The position information of the intelligent IoT device is transformed by using the space transformation matrix, and the Euclidean distance between the transformed position and the virtual space anchor point position is calculated to obtain a virtual-real mapping error, where the virtual-real mapping error is used to characterize the accuracy of the current mapping relationship; Calculating the cumulative drift of the position information of the intelligent IoT device and the position information of the corresponding anchor point in the virtual space at adjacent moments, and obtaining the time derivative of the cumulative drift to obtain the drift change rate, performing a weighted combination of the drift change rate and the gradient of the virtual-to-real mapping error, and constructing a compensation vector with adaptive characteristics, wherein the weighting coefficient is dynamically adjusted as the virtual-to-real mapping error changes; The space transformation matrix is ​​updated based on the compensation vector to obtain an optimized virtual-real mapping relationship.

[0061] This embodiment provides a method for establishing a virtual-real mapping relationship verification mechanism, which is used to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminate the spatiotemporal drift of virtual-real fusion. The method includes the following steps: Based on the location information of the smart IoT device and the location information of the corresponding anchor point in the virtual space, a space transformation matrix including a rotation matrix and a translation vector is constructed. The space transformation matrix is ​​used to establish the initial mapping relationship from the physical space to the virtual space. Specifically, the space transformation matrix can be constructed in the following way: Get the three-dimensional coordinates (x, y, z) of the smart IoT device in the physical space and the three-dimensional coordinates (x', y', z') of the corresponding virtual anchor point in the virtual space. Select at least 3 sets of corresponding point pairs and construct a linear equation system. Use the least squares method to solve the equation system and obtain the rotation matrix R and the translation vector T. Combine R and T into a 4x4 homogeneous transformation matrix, which is the required spatial transformation matrix M.

[0062] For example, suppose there are 3 sets of corresponding point pairs: Physical space point 1 (1, 2, 3), virtual space point 1 (1.1, 2.2, 3.3); Physical space point 2 (4, 5, 6), virtual space point 2 (4.4, 5.5, 6.6); Physical space point 3 (7, 8, 9), virtual space point 3 (7.7, 8.8, 9.9); By solving, we can get the space transformation matrix M: 1.1 0.0 0.0 0.1; 0.0 1.1 0.0 0.2; 0.0 0.0 1.1 0.3; 0.0 0.0 0.0 1.0; Next, the spatial transformation matrix is ​​used to transform the position information of the smart IoT device, and the Euclidean distance between the transformed position and the virtual space anchor point position is calculated to obtain the virtual-real mapping error. This error is used to characterize the accuracy of the current mapping relationship. The specific steps are as follows: The physical space coordinates (x, y, z) of the smart IoT device are expanded into homogeneous coordinates (x, y, z, 1), and multiplied by the space transformation matrix M to obtain the transformed virtual space coordinates (x', y', z', w'). The homogeneous coordinates are converted back to three-dimensional coordinates (x' / w', y' / w', z' / w'). The Euclidean distance between the coordinates and the actual virtual anchor point coordinates is calculated, which is the virtual-to-real mapping error e.

[0063] For example, for a device with coordinates (10, 20, 30) in physical space, use the above transformation matrix M to transform: (10, 20, 30, 1) * M = (11.1, 22.2, 33.3, 1.0); The transformed coordinates are (11.1, 22.2, 33.3); Assuming that the corresponding actual virtual anchor point coordinates are (11.0, 22.0, 33.0), the Euclidean distance can be calculated: e = sqrt((11.1-11.0)^2 + (22.2-22.0)^2 + (33.3-33.0)^2) ≈ 0.37; The accumulated drift between the position information of the intelligent IoT device and the position information of the corresponding anchor point in the virtual space at adjacent moments is calculated, and the time derivative of the accumulated drift is obtained to obtain the drift change rate. The drift change rate is weightedly combined with the gradient of the virtual-to-real mapping error to construct a compensation vector with adaptive characteristics, in which the weighting coefficient is dynamically adjusted as the virtual-to-real mapping error changes.

[0064] The specific implementation method is as follows: Record the physical space coordinates and virtual space coordinates of multiple consecutive moments (such as t1, t2, t3). Calculate the position difference between adjacent moments to get the drift. Accumulate the drift to get the cumulative drift D. Calculate the rate of change of the cumulative drift over time, that is, the drift change rate v = dD / dt.

[0065] Calculate the partial derivatives of the virtual-real mapping error e with respect to each parameter in the space transformation matrix M to obtain the error gradient g. Perform a weighted combination of the drift change rate v and the error gradient g: Compensation vector C = α * v + β * g; Among them, α and β are weighted coefficients, which can be dynamically adjusted according to the size of the error e: When e is small, increase α and decrease β to maintain stability; When e is large, reduce α and increase β to speed up convergence.

[0066] For example, the following adjustment strategies can be adopted: α = 1 / (1 + e^2); β = e^2 / (1 + e^2); Assume that at a certain moment, the drift change rate v = (0.1, 0.2, 0.3), the error gradient g = (1.0, 2.0, 3.0), and the error e = 0.5, then: α ≈ 0.8, β ≈ 0.2; Compensation vector C ≈ (0.28, 0.56, 0.84); Finally, the space transformation matrix is ​​updated based on the compensation vector to obtain the optimized virtual-real mapping relationship. The updating method is as follows: Decompose the compensation vector C into a rotation component Cr and a translation component Ct. Construct an incremental rotation matrix δR based on Cr, and construct an incremental translation vector δT based on Ct. Multiply δR and δT with the original space transformation matrix M to obtain the updated matrix M'.

[0067] For example, assuming the compensation vector C = (0.28, 0.56, 0.84), the incremental rotation matrix δR and incremental translation vector δT can be constructed: δR ≈ [1.0000 -0.0084 0.0056]; [0.0084 1.0000 -0.0028]; [-0.0056 0.0028 1.0000]; δT ≈ [0.28, 0.56, 0.84]; Multiply δR and δT with the original matrix M to get the updated matrix M': M' ≈ [1.0984 -0.0092 0.0062 0.3908]; [0.0092 1.1092 -0.0031 0.7672]; [-0.0062 0.0031 1.1062 1.1436]; [0.0000 0.0000 0.0000 1.0000]; Through the above steps, the verification and dynamic update of the virtual-reality mapping relationship are realized, and the spatiotemporal drift in the virtual-reality fusion process is effectively eliminated. This method is adaptive and can dynamically adjust the compensation strategy according to the error size, ensuring the accuracy and stability of the virtual-reality mapping. In practical applications, the above steps can be performed periodically to continuously optimize the virtual-reality mapping relationship and provide a reliable spatial positioning basis for virtual-reality fusion applications.

[0068] In an optional implementation, the interactive information in the virtual scene is fed back to the intelligent IoT device, and a dynamic adjustment function including position constraints and state constraints is established to realize the two-way interaction between the physical space and the metaverse virtual space, including: Establish a dynamic adjustment function including a position constraint item and a state constraint item, wherein the position constraint item ensures that the position of the virtual object is synchronized with the mapping position of the smart IoT device, and the state constraint item ensures that the motion state of the virtual object matches the actual state of the smart IoT device; An iterative optimization algorithm with adaptive weights is used to solve the dynamic adjustment function, wherein the weight coefficients are dynamically updated according to the time series of the position error; The optimized position and state parameters are applied to virtual objects in the virtual scene to achieve synchronous interaction between virtual objects and physical devices.

[0069] This embodiment provides a method for feeding back interactive information in a virtual scene to an intelligent IoT device, and establishing a dynamic adjustment function including position constraints and state constraints to achieve two-way interaction between the physical space and the metaverse virtual space.

[0070] A dynamic adjustment function containing position constraints and state constraints is established. The function consists of two main constraints: the position constraint ensures that the position of the virtual object is synchronized with the mapped position of the smart IoT device; the state constraint ensures that the motion state of the virtual object matches the actual state of the smart IoT device.

[0071] The specific implementation method of the position constraint item is as follows: the system obtains the real-time position coordinates (Xp, Yp, Zp) of the smart IoT device in the physical space, and records the current position coordinates (Xv, Yv, Zv) of the virtual object in the virtual space. The system calculates the position difference vector D = (Xp-Xv, Yp-Yv, Zp-Zv) between the two, and sets the position constraint item Cp so that its value is proportional to the modulus of the difference vector D. When the position difference exceeds the preset threshold (for example, 10 cm), the system will increase the weight of the position constraint item to prompt the virtual object to move closer to the mapped position of the physical device faster.

[0072] The specific implementation method of the state constraint item is as follows: the system obtains the actual motion state parameters of the intelligent IoT device, including the velocity vector Vp = (Vxp, Vyp, Vzp), the acceleration vector Ap = (Axp, Ayp, Azp) and the working state parameters of the device (such as switch state, brightness, temperature, etc.). At the same time, the system records the current motion state parameters of the virtual object, including the velocity vector Vv = (Vxv, Vyv, Vzv), the acceleration vector Av = (Axv, Ayv, Azv) and the corresponding virtual state parameters. The system calculates the state difference between the two and sets the state constraint item Cs so that its value is proportional to the state difference. For example, when the moving speed of the physical device is 2 meters per second and the moving speed of the virtual object is 1.5 meters per second, the system will increase the weight of the state constraint item to prompt the speed of the virtual object to adjust to 2 meters per second faster.

[0073] The dynamic adjustment function F can be expressed as the weighted sum of the position constraint term Cp and the state constraint term Cs: F = Wp×Cp +Ws×Cs, where Wp and Ws are the weight coefficients of the position constraint term and the state constraint term, respectively. In the initial state, Wp = 0.6 and Ws = 0.4 can be set, indicating that the priority of position synchronization is slightly higher than that of state synchronization.

[0074] The dynamic adjustment function is solved by an iterative optimization algorithm with adaptive weights. The specific steps of the algorithm are as follows: Initialize the position constraint weight Wp = 0.6 and the state constraint weight Ws = 0.4. The system sets the maximum number of iterations to 100 and the convergence threshold to 0.01 (indicating that convergence is considered when both the position error and the state error are less than 0.01).

[0075] In each iteration, the value of the dynamic adjustment function F is calculated based on the current weight value, and the position and state parameters of the virtual object are adjusted according to the gradient direction of F. Specifically, the system calculates the partial derivatives of F with respect to the virtual object position coordinates (Xv, Yv, Zv) and state parameters, and updates these parameters in the direction of gradient descent. For example, if the partial derivative of F with respect to Xv is positive, indicating that increasing Xv can reduce the value of F, the system will appropriately increase the value of Xv, and the adjustment step size can be set to 0.05.

[0076] After each iteration, the system calculates the updated position error and state error. If the position error is greater than 0.1 for 5 consecutive iterations, the system will increase the weight of the position constraint, for example, increase Wp from 0.6 to 0.7, and reduce Ws from 0.4 to 0.3, to ensure that Wp + Ws = 1. On the contrary, if the state error is greater than the preset threshold for 5 consecutive iterations (such as the speed error is greater than 0.5 m / s), the system will increase the weight of the state constraint, for example, increase Ws from 0.4 to 0.5, and reduce Wp from 0.6 to 0.5.

[0077] The system will also dynamically update the weight coefficient according to the time series of the position error. Specifically, the system records the position error values ​​of the last 10 iterations. If the error shows a trend of continuous increase (for example, it increases for 3 consecutive times), the system will increase the weight Wp of the position constraint by 0.05; if the error shows a trend of continuous decrease, the system will keep the current weight unchanged. Similarly, the system will also dynamically adjust the weight Ws of the state constraint according to the time series of the state error.

[0078] The iteration process continues until any of the following conditions is met: the maximum number of iterations (100) is reached; the position error and state error are both less than the convergence threshold (0.01); the error change rate for 10 consecutive iterations is less than 0.001.

[0079] The system applies the optimized position and state parameters to the virtual objects in the virtual scene to achieve synchronous interaction between the virtual objects and the physical devices. Specifically, the system updates the position coordinates of the virtual objects to the optimized (Xv, Yv, Zv), updates the velocity vector of the virtual objects to the optimized Vv, updates the acceleration vector of the virtual objects to the optimized Av, and updates other state parameters of the virtual objects.

[0080] In practical applications, taking smart lamps as an example, when the user moves the lamp to a new position in the physical space (for example, from the center of the living room to the corner of the living room, the physical coordinates change from (3.0, 2.5, 1.8) to (5.5, 0.5, 1.8)), the system detects the position change and starts the dynamic adjustment function. After 25 iterations of optimization, the corresponding virtual lamp in the virtual scene smoothly transitions from the original position (30, 25, 18) to the new position (55, 5, 18).

[0081] The precise positioning system of the intelligent IoT device and the virtual-real integration of the Metaverse in the embodiment of the present invention is as follows: Figure 3 As shown, including: The first unit is used to obtain the location information of multiple smart IoT devices and the spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain positioning results; The second unit is used to establish a virtual scene corresponding to the physical space in the metaverse virtual space; determine a virtual anchor point in the virtual scene, and the virtual anchor point corresponds to a physical reference object in the physical space; The third unit is used to establish a virtual-real mapping relationship verification mechanism, verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminate the spatiotemporal drift of virtual-real fusion; A fourth unit is used to dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The fifth unit is used to feed back the interaction information in the virtual scene to the intelligent IoT device, and establish a dynamic adjustment function including position constraint items and state constraint items to realize two-way interaction between the physical space and the metaverse virtual space.

[0082] According to a third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0083] A fourth aspect of the embodiments of the present invention is: A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0084] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0085] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, 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 precise positioning method for integrating the virtual and real world of intelligent IoT devices with the Metaverse, characterized in that: include: Acquire location information of multiple smart IoT devices and spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; Based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain a positioning result; Establishing a virtual scene corresponding to the physical space in the metaverse virtual space; determining a virtual anchor point in the virtual scene, wherein the virtual anchor point corresponds to a physical reference object in the physical space; Establishing a virtual-real mapping relationship verification mechanism to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, thereby eliminating the spatiotemporal drift of virtual-real fusion; Dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The interaction information in the virtual scene is fed back to the intelligent IoT device, and a dynamic adjustment function including position constraint items and state constraint items is established to realize two-way interaction between the physical space and the metaverse virtual space.

2. The method according to claim 1, characterized in that Based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices, and the positioning results obtained include: Constructing a global knowledge graph based on the location information and the spatial relationship information, wherein the global knowledge graph includes a node set, an edge set, and a weight matrix, wherein the node set represents the location nodes of multiple smart IoT devices, the edge set represents the spatial relationship between the smart IoT devices, and the weight matrix represents the spatial constraint strength between the smart IoT devices; A local consistency constraint model is established according to the node set and edge set of the global knowledge graph, wherein the local consistency constraint model includes a Euclidean distance term and a regularization term of the device location, and the Euclidean distance term and the regularization term are dynamically balanced by the weight coefficient in the weight matrix to obtain a local constraint optimization target; The local constraint optimization objective is decomposed into multiple local sub-problems by using an alternating direction multiplier method, and an initial collaborative optimization position result is obtained by iteratively optimizing the local variables, global variables and dual variables of each local sub-problem; The initial collaborative optimization position result is input into a graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple intelligent IoT devices.

3. The method according to claim 2, characterized in that The alternating direction multiplier method is used to decompose the local constraint optimization objective into multiple local sub-problems. By iteratively optimizing the local variables, global variables and dual variables of each local sub-problem, the initial collaborative optimization position results are obtained, including: Each local sub-problem corresponds to the location optimization of a smart IoT device, where each local sub-problem includes: a local variable for characterizing the location of a single smart IoT device, a global variable for coordinating the location consistency of multiple smart IoT devices, and a dual variable for ensuring convergence; Based on the preset step size parameter, a three-step iterative strategy is adopted to solve the local subproblem: first, fix the global variables and dual variables to optimize the local variables, then fix the local variables and dual variables to optimize the global variables, and finally update the dual variables. Repeat the iteration until the position difference between two adjacent iterations is less than the preset deviation threshold to obtain the initial collaborative optimization position result.

4. The method according to claim 2, characterized in that: The initial collaborative optimization position result is input into the graph optimization framework, an optimization function including a position error term and an information matrix is ​​constructed, and the initial collaborative optimization position result is optimized based on the optimization function to obtain collaborative positioning results of multiple smart IoT devices, including: Decomposing the optimization function into a position optimization subproblem and a weight optimization subproblem, wherein the position optimization subproblem iteratively optimizes the position of the smart IoT device by using the least squares method, and the weight optimization subproblem calculates the weight coefficient of the information matrix based on the Mahalanobis distance; The position optimization subproblem and the weight optimization subproblem are alternately solved using a gradient descent algorithm with an adaptive step size. The optimization step size is dynamically adjusted in each round of iteration according to the degree of convergence of the position error term, and the information matrix is ​​used to suppress the influence of abnormal observations on the optimization results until the preset number of iterations is met, thereby obtaining the collaborative positioning results of multiple intelligent IoT devices.

5. The method according to claim 1, characterized in that Establishing a virtual-real mapping relationship verification mechanism to verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminating the spatiotemporal drift of virtual-real fusion includes: Based on the position information of the smart IoT device and the position information of the corresponding anchor point in the virtual space, a space transformation matrix including a rotation matrix and a translation vector is constructed, wherein the space transformation matrix is ​​used to establish an initial mapping relationship from the physical space to the virtual space; The position information of the intelligent IoT device is transformed by using the space transformation matrix, and the Euclidean distance between the transformed position and the virtual space anchor point position is calculated to obtain a virtual-real mapping error, where the virtual-real mapping error is used to characterize the accuracy of the current mapping relationship; Calculating the cumulative drift of the position information of the intelligent IoT device and the position information of the corresponding anchor point in the virtual space at adjacent moments, and obtaining the time derivative of the cumulative drift to obtain the drift change rate, performing a weighted combination of the drift change rate and the gradient of the virtual-to-real mapping error, and constructing a compensation vector with adaptive characteristics, wherein the weighting coefficient is dynamically adjusted as the virtual-to-real mapping error changes; The space transformation matrix is ​​updated based on the compensation vector to obtain an optimized virtual-real mapping relationship.

6. The method according to claim 1, characterized in that Feeding back the interaction information in the virtual scene to the intelligent IoT device, and establishing a dynamic adjustment function including position constraint items and state constraint items to realize the two-way interaction between the physical space and the metaverse virtual space include: Establish a dynamic adjustment function including a position constraint item and a state constraint item, wherein the position constraint item ensures that the position of the virtual object is synchronized with the mapping position of the smart IoT device, and the state constraint item ensures that the motion state of the virtual object matches the actual state of the smart IoT device; An iterative optimization algorithm with adaptive weights is used to solve the dynamic adjustment function, wherein the weight coefficients are dynamically updated according to the time series of the position error; The optimized position and state parameters are applied to virtual objects in the virtual scene to achieve synchronous interaction between virtual objects and physical devices.

7. A precise positioning system for integrating the virtual and real aspects of intelligent IoT devices and the Metaverse, used to implement the method as claimed in any one of claims 1 to 6, characterized in that: include: The first unit is used to obtain the location information of multiple smart IoT devices and the spatial relationship information of the smart IoT devices, wherein the location information includes geographic coordinates and posture data; based on the location information and the spatial relationship information, a global positioning knowledge graph is collaboratively constructed through an asynchronous iterative optimization algorithm to achieve collaborative high-precision positioning of multiple smart IoT devices and obtain positioning results; The second unit is used to establish a virtual scene corresponding to the physical space in the metaverse virtual space; determine a virtual anchor point in the virtual scene, and the virtual anchor point corresponds to a physical reference object in the physical space; The third unit is used to establish a virtual-real mapping relationship verification mechanism, verify and update the mapping relationship between the location information of the smart IoT device and the virtual anchor point, and eliminate the spatiotemporal drift of virtual-real fusion; A fourth unit is used to dynamically adjust the position and state of the virtual object in the virtual scene according to the positioning result and the virtual-real mapping relationship verification mechanism; The fifth unit is used to feed back the interaction information in the virtual scene to the intelligent IoT device, and establish a dynamic adjustment function including position constraint items and state constraint items to realize two-way interaction between the physical space and the metaverse virtual space.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; 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 a processor, the method according to any one of claims 1 to 6 is implemented.

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