Human-machine interaction visualization and virtual twin method and system based on data analysis

Abstract

The application relates to the technical field of visualization, in particular to a man-machine interaction visualization and virtual twin method and system based on data analysis, which comprises the following steps: acquiring joint coordinates, calculating path vectors, generating a vector group, combining real-time positions to judge and reverse to generate an offset list, analyzing pressure clustering to screen and generate a feedback table, calculating offsets to judge directions and generate a linkage set, mapping a skeleton to render actions and generate a hotspot trajectory diagram, and the application has the advantages that, through a three-dimensional path vector construction mode, in combination with angle recognition and pressure mutation clustering analysis, high-precision linkage between skeleton actions and physical feedback is realized, space matching and screening nodes are realized, feedback consistency is improved, path unit vector difference judgment strengthens action direction stability, node offset amplitude adjustment enhances action continuity and reality, response levels and interactive hotspots are set in the rendering stage, visual guidance and operation prompts are enhanced, and a virtual twin visualization model with high adaptability and high interaction is constructed in the aspects of path recognition, perception mapping and visual feedback.

Description

Technical Field

[0001] This invention relates to the field of visualization technology, and in particular to a method and system for human-computer interaction visualization and virtual twins based on data analysis. Background Technology

[0002] The field of visualization technology involves expressing and presenting data through intuitive means such as graphics and images. Its core aspects include graphic drawing, information mapping, user interaction design, and data-driven dynamic display. The methodological aspects of this field mainly involve transforming structured or unstructured data into visually perceptible objects, combining graphics rendering algorithms and user behavior response mechanisms to build a data display system that supports human-computer interaction. Among them, the traditional data analysis-based human-computer interaction visualization and virtual twin method refers to constructing a system state model by collecting target system operation data, and then combining it with a graphic drawing process for static or semi-dynamic visualization presentation. The traditional method usually adopts a graphic generation scheme with preset rule matching combined with a single data mapping strategy to achieve the linkage between visual screen construction and feedback. It generally uses offline data processing to build the model, uses a graphics drawing interface to complete the visual output, and triggers graphic response operations through interface interaction commands to achieve interactive mapping between user operation and system response.

[0003] Traditional human-computer interaction visualization methods construct graphical mapping relationships based on preset rules. Path generation lacks dynamic adaptability and cannot effectively handle motion reversals or deviations during continuous movement, resulting in delayed or false triggers in interaction responses. Graphical construction relies on a single data mapping strategy, ignoring the spatial distribution and temporal fluctuations in physical interaction signals. This makes it difficult to accurately capture the model node responses corresponding to tactile changes, leading to inconsistencies in human-computer perception. In pressure change analysis, periodic fluctuations and abrupt trends are not considered, increasing the recognition error of the feedback system and degrading the actual interactive experience. Graphical feedback mechanisms are based on offline static model generation, lacking the ability to dynamically adjust motion amplitude and determine directional continuity, failing to meet the synchronous control requirements of multi-path actions in complex environments. In terms of visual output, the lack of hierarchical differentiation of response levels and hotspot interaction guidance results in vague interface prompts and unclear operation feedback. In typical scenarios, such as virtual sports training systems, preset node response strategies cannot adapt to real-time changes in trainee movements, causing lag in model posture expression and affecting training accuracy and user perception synchronization. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a human-computer interaction visualization and virtual twin method based on data analysis, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a human-computer interaction visualization and virtual twin method based on data analysis, comprising the following steps: S1: Obtain the three-dimensional coordinates of the joint nodes in the virtual limb model, calculate the path vector values ​​between the connecting nodes, and arrange them according to the structural order to form a set of node pair paths, generating a set of spatial path vectors for skeleton nodes. S2: Call the skeleton node spatial path vector group, combine it with the real-time node position, calculate the angle between the current path vector and the reference path vector, determine whether the action reversal angle threshold condition is met, and generate a list of reversal offset path nodes; S3: Based on the reverse offset path node list, analyze the pressure change amplitude of each touch point in the tactile feedback matrix in a continuous cycle, extract the touch points with sudden pressure changes and perform clustering processing, calculate the spatial distance between the node position and the touch point cluster center, filter the nodes that meet the distance matching threshold, and generate a spatial matching feedback node table. S4: Call the path nodes in the spatial matching feedback node table, calculate the magnitude of the node offset vector, and adjust it by applying the action amplitude amplification coefficient. Determine whether the unit vector difference between path directions meets the direction consistency threshold condition, filter the paths that meet the conditions and integrate them into an action sequence to generate a three-dimensional linkage path set.

[0005] As a further embodiment of the present invention, the skeleton node spatial path vector group includes three-dimensional coordinates, path vectors, and a set of node pairs paths; the reverse offset path node list includes current path nodes, reference path nodes, and angle threshold condition nodes; the spatial matching feedback node table includes pressure change contact points, contact point cluster centers, and matching threshold nodes; and the three-dimensional linkage path set includes node offset vectors, action amplitude amplification coefficients, and direction consistency paths.

[0006] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the joint node number index, three-dimensional coordinate parameters and connection structure information in the virtual limb model, extract coordinate data according to the node correspondence marked in the connection structure, calculate the spatial path vector value between the connection nodes, and generate three-dimensional path vector value. S102: Based on the three-dimensional path vector values, according to the arrangement order of node pairs in the connection structure, integrate the path vectors with the corresponding node numbers to establish a path number sequence with a continuous structural order, and obtain an ordered node path vector group. S103: Based on the ordered node path vector group, extract the sequential position of the node number pairs in the structure, associate the structural arrangement of the path vectors, construct a continuous path vector set, and obtain the skeleton node spatial path vector group.

[0007] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Call the skeleton node spatial path vector group and the real-time node position, extract the real-time coordinate data according to the node correspondence in the connection structure, and generate the real-time path vector value by combining the spatial position relationship between nodes; S202: Based on the real-time path vector value, match the reference path of the corresponding node number in the skeleton node spatial path vector group, calculate the spatial direction difference between the current path vector value and the reference path vector value, and obtain the path vector angle value. S203: Based on the angle value of the path vector, call the action reversal angle threshold to make a condition judgment, identify the path node combination that meets the angle requirement, summarize the corresponding node numbers, and obtain the reverse offset path node list.

[0008] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the list of reverse offset path nodes, call the pressure parameters of the touch points in the tactile feedback matrix in the continuous cycle, extract the pressure change amplitude during the cycle, identify the touch points whose pressure change amplitude exceeds the pressure change threshold, and obtain the set of touch points with sudden pressure changes. S302: Based on the pressure change contact set, extract the spatial location data of the corresponding contact, perform clustering processing according to the spatial distribution characteristics, extract the spatial coordinate center position of each type of contact, and obtain the contact cluster center coordinate group. S303: Based on the coordinate group of the touch point cluster center, call the node position in the reverse offset path node list, calculate the spatial distance between the node and the cluster center, filter the nodes whose spatial distance is less than the spatial distance matching threshold, and generate a spatial matching feedback node table.

[0009] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Call the path node data in the spatial matching feedback node table, extract the offset vector value of each node, adjust the offset vector value according to the action amplitude amplification coefficient, and generate an amplified offset vector value group. S402: Based on the amplified offset vector value group, extract the unit vector direction between continuous path nodes, compare the difference between the unit vectors of the path direction with the direction consistency threshold, and filter the path pairs whose unit vector difference is less than the direction consistency threshold to obtain the direction consistent path group; S403: Perform an integration operation based on the path number in the path group with consistent direction, summarize the spatial position coordinates and motion amplitude data of the nodes in the path, establish the continuous linkage structure information of the path sequence, and generate a three-dimensional linkage path set.

[0010] As a further aspect of the present invention, the method further includes: S5: Call the node offset data in the three-dimensional linkage path set and map it to the three-dimensional skeleton model in the virtual scene. Render the node action state according to the path order, set visual attributes to distinguish the degree of response, generate interactive hotspot markers in the corresponding areas, and obtain the virtual twin interactive feedback visual trajectory map. The virtual twin interactive feedback visual trajectory map includes a three-dimensional skeleton model, interactive hotspot markers, and visual attribute differentiation; The interactive hotspot identifier refers to the graphic marker in the screen used to indicate the area of ​​action response. It is presented in the form of icons, highlighted boxes, and jumping edges, and its position corresponds to the position of the action node. The virtual twin interactive feedback visual trajectory map refers to the response action trajectory layer rendered in the interactive screen, which shows the node path changes, response degree and visual attributes.

[0011] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Call the node offset vector in the set of three-dimensional linkage paths, and map the three-dimensional offset coordinates to the three-dimensional skeleton model in the virtual scene according to the path order to generate a set of node mapping position coordinate values. S502: Based on the node mapping position coordinate value group, render the node action state in the path order, set the visual attributes according to the response degree and overlay them onto the corresponding node to generate a node action visual intensity value group. S503: Based on the node action visual intensity value group, filter the node areas whose visual intensity exceeds the response display threshold, establish interactive hotspot identifiers and integrate node trajectory information to generate a virtual twin interactive feedback visual trajectory map.

[0012] As a further aspect of the present invention, the virtual limb model refers to a human body structure mapping model constructed through predefined three-dimensional coordinate nodes; The path vector refers to the positional difference between two nodes in three-dimensional space, expressed in terms of direction and length; The action reversal angle threshold refers to the minimum change angle between the current path vector and the static reference path vector; The pressure change contact point refers to the point in the tactile feedback matrix where the pressure change amplitude exceeds a set threshold (the pressure change set threshold represents the percentage limit of the part with the largest fluctuation amplitude selected from the continuous periodic pressure change sequence). The distance matching threshold is set based on the Euclidean distance formula of three-dimensional coordinate points, combined with the physical scale of human-computer interaction (limb coverage range, sensor distribution density); The amplification factor of the motion amplitude refers to the proportional adjustment parameter applied to the node offset vector value; The directional consistency threshold is obtained by calculating the Euclidean distance or cosine angle between the unit vectors of two path directions and comparing it with a comparison standard set according to the requirements of action linkage continuity or scene accuracy.

[0013] Human-computer interaction visualization and virtual twin systems based on data analysis include: The skeleton path calculation module obtains the three-dimensional coordinates of the joint nodes in the virtual limb model, calculates the path vectors between the connecting nodes, and combines the path node pairs according to the node arrangement structure order to establish the overall spatial path vector group and generate the skeleton node spatial path vector group. The angle recognition and determination module calls the path node data in the skeleton node spatial path vector group, calculates the angle between the current path vector and the reference vector, determines whether the angle change exceeds the set threshold, filters path nodes with abnormal status, and generates a reverse offset path node list. The pressure mutation extraction module calls the node information in the reverse offset path node list, extracts the continuous periodic pressure value of the corresponding touch point in the tactile feedback matrix, calculates the pressure change amplitude, identifies the mutation touch point and performs clustering, analyzes the spatial distance between the node and the cluster center, filters matching path nodes, and generates a spatial matching feedback node table. The motion path filtering module calls the path node data in the spatial matching feedback node table, calculates the offset vector magnitude of the node position, adjusts it in combination with the amplification factor, analyzes the unit vector difference between the offset path direction and the reference path direction, filters the paths that meet the direction conditions, and generates a set of three-dimensional linkage paths. The linkage rendering feedback module calls the node offset data in the three-dimensional linkage path set, maps the path information to the virtual skeleton model, renders the node actions according to the path sequence, sets visual attributes to distinguish the response level, marks the interaction hotspots in the corresponding areas, and generates a virtual twin interaction feedback visual trajectory map.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a high-precision linkage between skeletal movements and physical feedback is achieved by constructing a three-dimensional path vector, combining angle recognition and tactile pressure abrupt change clustering analysis. Node selection through spatial matching improves the consistency of feedback response, path unit vector difference judgment enhances the stability of movement direction, node offset amplitude adjustment enhances the continuity and realism of movement, and setting response levels and interactive hotspots during the rendering stage enhances visual guidance and operation prompts. Overall, a highly adaptable, highly interactive, and highly accurate virtual twin feedback visual model is constructed in terms of dynamic path recognition, tactile perception mapping, and visual feedback performance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a data analysis-based method for human-computer interaction visualization and virtual twins, comprising the following steps: S1: Obtain the three-dimensional coordinates of the joint nodes in the virtual limb model, calculate the path vector values ​​between the connecting nodes, and arrange them according to the structural order to form a set of node pair paths, generating a set of spatial path vectors for skeleton nodes. A virtual limb model is a human body structure mapping model constructed using predefined three-dimensional coordinate nodes, including joints, connections, and positional status. A path vector is the positional difference between two nodes in three-dimensional space, expressed in terms of direction and length; S2: Call the skeleton node spatial path vector group, combine it with the real-time node position, calculate the angle between the current path vector and the reference path vector, determine whether the action reversal angle threshold condition is met, and generate a list of reverse offset path nodes; The action reversal angle threshold refers to the minimum change angle between the current path vector and the static reference path vector; S3: Based on the reverse offset path node list, analyze the pressure change amplitude of each touch point in the tactile feedback matrix in a continuous cycle, extract the touch points with sudden pressure changes and perform clustering, calculate the spatial distance between the node position and the touch point cluster center, filter the nodes that meet the distance matching threshold, and generate a spatial matching feedback node table. Pressure change contact point refers to the point in the tactile feedback matrix where the pressure change exceeds a set threshold (the pressure change set threshold represents the percentage limit for selecting the part with the largest fluctuation from a continuous periodic pressure change sequence). The distance matching threshold is set based on the Euclidean distance formula of three-dimensional coordinate points, combined with the physical scale of human-computer interaction (limb coverage range, sensor distribution density); S4: Call the path nodes in the spatial matching feedback node table, calculate the magnitude of the node offset vector, and adjust it by applying the action amplitude amplification coefficient. Determine whether the unit vector difference between path directions meets the direction consistency threshold condition, filter the paths that meet the conditions and integrate them into an action sequence to generate a three-dimensional linkage path set. The motion amplitude amplification factor refers to the proportional adjustment parameter applied to the node offset vector value; The directional consistency threshold is calculated by measuring the Euclidean distance or cosine angle between the unit vectors of two path directions and comparing it with a comparison standard set according to the requirements of action linkage continuity or scene accuracy. S5: Call the node offset data in the 3D linkage path set and map it to the 3D skeleton model in the virtual scene. Render the node action state according to the path order, set the visual attributes to distinguish the degree of response, generate interactive hotspot markers in the corresponding areas, and obtain the virtual twin interactive feedback visual trajectory map. Interactive hotspot markers are graphic marks in the screen used to indicate areas for action response. They are presented as icons, highlighted boxes, or jumping edges, and their positions correspond to the locations of action nodes. Virtual twin interactive feedback visual trajectory map refers to the response action trajectory layer rendered in the interactive screen, which shows the node path changes, response degree and visual attributes.

[0023] The skeleton node spatial path vector group includes three-dimensional coordinates, path vectors, and node pair path sets. The reverse offset path node list includes current path nodes, reference path nodes, and angle threshold condition nodes. The spatial matching feedback node table includes pressure change contact points, contact point cluster centers, and matching threshold nodes. The three-dimensional linkage path set includes node offset vectors, action amplitude amplification coefficients, and directional consistency paths. The virtual twin interactive feedback visual trajectory diagram includes a three-dimensional skeleton model, interactive hotspot markers, and visual attribute differentiation.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain the joint node number index, three-dimensional coordinate parameters and connection structure information in the virtual limb model, extract coordinate data according to the node correspondence marked in the connection structure, calculate the spatial path vector value between the connection nodes, and generate three-dimensional path vector value. When obtaining the joint node index, 3D coordinate parameters, and connection structure information in a virtual limb model, it is necessary to parse a standard skeleton data format file, such as BVH or FBX. When extracting node numbers, they should be numbered sequentially from top to bottom according to the skeletal structure. For example, the pelvis is set as number 1, the left leg downwards as numbers 2, 3, and 4, and the spine, shoulder, and head upwards are numbered sequentially up to 15. 3D coordinate parameters can be read from the bound initial pose, corresponding to the x, y, and z coordinate values ​​of each numbered node. For example, the coordinates of node number 3 are 1.5, 0.4, and 0.6. The connection structure is derived from the skeleton hierarchy. For example, there is a connection between the left thigh and the left knee, recorded as node 2 connecting node 3. After obtaining all node pairs, a path vector calculation is performed on each pair of connected nodes, i.e., the x, y, and z values ​​of the target node are subtracted from the path vector. The x, y, and z values ​​of the starting node yield the three directional components of the corresponding path vector. For example, if the coordinates of the left thigh are 1.2, 0.8, and 0.4, and the coordinates of the left knee are 1.5, 0.4, and 0.6, then the directions of their path vectors are 0.3, -0.4, and 0.2, respectively. This operation is repeated to obtain the path vectors of all connected node pairs, forming a three-dimensional path vector set. This set provides the foundation for subsequent structural combination and sequential reconstruction. It is important to note that the left and right symmetrical parts in the connected structure, such as the left and right thighs and the left and right knees, should be processed separately to avoid data anomalies caused by confused path directions. In practice, three-dimensional coordinate data is usually provided through posture data frames. The parsed path vectors are all static structural parameters that do not change with motion, which facilitates initial skeleton analysis and recognition. The entire process, from parsing the model file to extracting the path vectors, constitutes a complete skeleton vector generation process.

[0025] S102: Based on the three-dimensional path vector values, according to the arrangement order of node pairs in the connection structure, integrate the path vector with the corresponding node number combination to establish a path number sequence with continuous structural order, and obtain an ordered node path vector group. Based on the extracted path vector values, the node pairs in the connection structure are arranged sequentially. The connection order needs to be confirmed first, which can be achieved using a depth-first traversal method. Starting from the initial node (e.g., the pelvis), the nodes are sequentially connected to the torso, legs, arms, and head. This forms a sequential list of node number pairs, for example, 1 to 2, 2 to 3, 3 to 4. These are combined with the path vectors to form a continuous sequence. Each item contains the starting node number, the target node number, and the corresponding path vector direction value. In practice, for example, the directions for path 1 to 2 might be 0.2, 0.1, and -0.1, and for path 2 to 3... If the values ​​are 0.3, -0.4, and 0.2, and the values ​​for 3-4 are 0.1, -0.2, and -0.3, then the three sets of path information are sequentially spliced ​​together to form complete skeleton segment information. During the process, it is necessary to pay attention to whether the node numbers are consecutively matched and to check whether the path direction uniformly points to the child nodes to avoid path representation confusion due to order reversal. For branching structures such as double arms, the main path can be constructed in priority order, and then the left and right symmetrical paths can be inserted respectively. The integrated result is a combination of path vectors with complete structure and consistent order. This combination is used as skeleton sequential path data to support subsequent 3D reconstruction and model matching.

[0026] S103: Based on the ordered node path vector group, extract the sequential position of the node number pair in the structure, associate the structural arrangement of the path vectors, construct a continuous path vector set, and obtain the skeleton node spatial path vector group. Based on the constructed sequential path vector group, the sequential position of each pair of nodes in the overall skeleton is extracted. This can be mapped using the initial modeling skeleton diagram or hierarchical structure table, identifying whether each path group belongs to the trunk, limbs, or other specific structural segments. The position of each path group is recorded, for example, 1 to 2 is the pelvis and left hip, 2 to 3 is the left hip and left knee, and 3 to 4 is the left knee and left ankle. Based on this information, a set of structurally continuous path vectors can be reconstructed. Each path vector group is sequentially numbered, and its branch and direction in the skeleton topology are recorded. Simultaneously, the length of each path segment is calculated by taking the square root of the sum of the squares of each directional component, which is used as the geometric length of the 3D path in space. If the values ​​are 0.3, -0.4, and 0.2, the corresponding lengths are approximately between 0.5 and 0.6. All path vectors and their length values ​​are arranged according to the structural order to form a skeleton path group dataset. This dataset contains numbered pairs, path vector values, and spatial lengths, which facilitates motion analysis, posture comparison, and other related studies. It is necessary to pay attention to whether there are repetitions or breaks in the connection relationships between nodes to ensure the integrity and continuity of the path group structure. In practical applications, the number of path segments in each 3D model skeleton varies depending on the number of nodes. For example, a standard human skeleton often contains 20 to 30 main path segments, all of which can be sequentially established to realize the spatial representation of structural data.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Call the skeleton node spatial path vector group and the real-time node position, extract the real-time coordinate data according to the node correspondence in the connection structure, and generate the real-time path vector value by combining the spatial position relationship between nodes. To access the spatial path vector group of skeleton nodes and their real-time positions, the static skeleton path vector group information must first be read. This information is preset during skeleton modeling and includes the node pair number, node coordinates, and corresponding path direction. Then, the 3D position coordinates of each node in the current frame are obtained through real-time motion capture equipment. Commonly used equipment includes optical infrared motion capture systems or inertial sensor arrays. The system continuously updates the node position coordinates at a frequency of 30 to 120 frames per second. The data format includes the node number, timestamp, and 3D coordinate values. For example, node number 6 has coordinates of x = 1.2, y = 0.7, and z = 1.5 in the current frame, while node number 7 has coordinates of x = 1.4, y = 0.8, and z = 1.7. This information is determined based on the connection structure between nodes. For example, if nodes 6 and 7 form a forearm segment, the path vector direction of this segment can be calculated by subtracting coordinates. The coordinate differences between the endpoint and the starting point are calculated in three directions, which can be expressed as 0.2 in the x direction, 0.1 in the y direction, and 0.2 in the z direction, resulting in a set of real-time path vectors. After processing all connected node pairs in this way, the path vector set of the current frame is constructed. If the skeleton structure contains a total of 40 sets of connection relationships, then 40 vector difference operations need to be performed in each frame to output 40 sets of real-time path vector direction information. This dataset is used in the subsequent spatial direction comparison stage. The path vector direction comes directly from the changes in node positions, and the dynamic change frequency is limited by the frame rate of the motion capture device and the node refresh speed.

[0028] S202: Based on the real-time path vector value, match the reference path of the corresponding node number in the skeleton node spatial path vector group, calculate the spatial direction difference between the current path vector value and the reference path vector value, and obtain the path vector angle value. After the real-time path vector values ​​are generated, they are matched one by one with the preset paths in the spatial path vector group of the skeleton nodes according to their corresponding node numbers. The path vector direction value of the corresponding number pair in the static skeleton is extracted as the reference path. By comparing the spatial direction difference between the real-time path vector direction and the reference path direction, the direction consistency is quantified. The difference is represented by the angle value. The larger the angle, the more significant the change in the current action direction. For example, if the real-time path direction is 0.2 in the x direction, 0.1 in the y direction, and 0.2 in the z direction, and the reference path direction is 0.3 in the x direction, 0.0 in the y direction, and 0.1 in the z direction, then the two are slightly deviating in space, and the angle between them needs to be calculated. The common method for calculating the angle is to derive the included angle using the vector length and direction components, and then convert it using cosine and inverse cosine functions to obtain the angle value. This value ranges from 0° to 180°. The closer the value is to 90°, the more significant the change in direction. If the included angle value is 85°, it means that the path direction has almost turned vertically. This included angle is used as the criterion for the difference in path direction in the current frame. For each group of connected node paths, an included angle value calculation and matching operation must be performed once. For example, in 40 groups of paths, 40 included angle values ​​will be generated in each frame. This dataset will be used in modules such as motion trend analysis and state recognition. The included angle value between path vectors is a direct indicator reflecting the change in the current spatial state of the reference structure.

[0029] S203: Based on the angle value of the path vector, call the action reversal angle threshold to make a condition judgment, identify the path node combination that meets the angle requirement, summarize the corresponding node numbers, and obtain the list of reverse offset path nodes. The specific calculation formula for using the action reversal angle threshold for conditional judgment is as follows: ; Calculate the angle judgment compensation value Δθ, identify the path node combination that meets the angle requirements, summarize the corresponding node numbers, and obtain the reverse offset path node list; in, Representative node With nodes The angle adjustment value between the neighbors is based on the neighborhood weighted average and the deviation compensation. Represents nodes in the path Pointing to node The angle between the path vector and the reference path vector. Represents nodes in the path Pointing to its first The angle between the path vectors of adjacent nodes and the reference path vector. Representative node The average of the angles between the path vectors of the node and all its adjacent nodes. Representative node Its first Euclidean distance between adjacent nodes Representative node Move to the The shortest path time required for each adjacent node. and Representing nodes respectively With nodes The estimated local node distribution density. This represents the maximum local distribution density estimate among all path nodes. This represents a very small positive constant used to avoid a denominator of zero. Representative node The total number of adjacent nodes; Angle judgment compensation value The following calculation process is derived item by item by substituting the parameter values ​​obtained from specific monitoring into the calculation examples; The monitoring and acquisition method obtains the angle θ_ij between the path vectors of node i and node j, which is 30 degrees as determined by the analysis of the sampling data from the ranging radar system. Taking node i as an example where it is adjacent to two nodes k=1 and k=2, the following data was obtained from monitoring: The distance d_i1 from node i to adjacent node 1 is 15 meters obtained by laser ranging, and the required shortest path time t_i1 is calculated to be 3 seconds by recording the movement speed. The distance d_i2 from node i to its neighbor node 2 is 20 meters, and the shortest path time t_i2 is 4 seconds; The weight values ​​are calculated using the velocity ratios: d_i1 / t_i1 = 15 / 3 = 5 m / s, d_i2 / t_i2 = 20 / 4 = 5 m / s The included angle between adjacent nodes θ_i1 was measured to be 25 degrees, and θ_i2 was measured to be 35 degrees. Number of adjacent nodes n=2; The mean angle is Barθ_i = (θ_i1 + θ_i2) / n = (25 + 35) / 2 = 30 degrees; The local distribution density δ_i of node i was obtained as 0.8 using the geographic grid statistical node distribution acquisition algorithm, and the density δ_j of node j was obtained as 0.6. The maximum local distribution density δ_max, measured through density monitoring of multiple nodes, has a maximum value of 1.0. The minimum positive constant ε is the system's safety tolerance, defined as 0.01 through empirical data collection. Substituting the first term, the angle-weighted average, into the formula: ; The first term is calculated as |θ_ij - weighted average angle| = |30 - 30| = 0 degrees; The second term, the square root of the standard deviation: ; The third density difference term: ; The overall formula calculation process is as follows: ; The result indicates that the angle judgment compensation value is 4.802 degrees. This value has a direct quantitative correspondence with the node combination that meets the angle requirements in the current step. It shows that the deviation compensation guides the angle between nodes to be adjusted to about 4.8 degrees, which is then used for subsequent identification logic to determine whether to trigger the summary of reverse offset path nodes. The formula's operational logic constructs a composite control mechanism for angle judgment through a three-term structure. The first term uses the product of the path angle between node i and its adjacent nodes and the average speed of the corresponding path to form a weighted average, reflecting the dominant trend of directional tendency under different accessibility efficiencies and eliminating the influence of local path extrema on the overall directional judgment. The second term constructs the standard deviation using the sum of squared deviations between the angle and its average value, and uses the square root method to characterize the fluctuation intensity of the path direction, which is used to measure the consistency level of the direction of adjacent paths. The third term constructs a suppression term using the local density difference between nodes, and compresses its influence amplitude by normalizing the denominator, strengthening the suppression effect of spatial structure changes in angle judgment. Finally, the three terms are superimposed to form an angle compensation value. The addition reflects the common influence of multiple factors on the judgment result, and the subtraction introduces a density adjustment mechanism to achieve difference balance processing, thus realizing the dynamic judgment and stability optimization of the rationality of the path node combination angle. The angle judgment compensation value is used to quantify the degree of deviation of the path direction between nodes under the combined effect of adjacency relationship, path efficiency and spatial density structure. This value comprehensively reflects the deviation intensity between the target path vector and the distribution of the neighborhood direction, and introduces the local spatial density difference as a constraint adjustment factor, so that the judgment result considers both the consistency of direction and the accessibility of path and the rationality of node distribution. Thus, it provides an accurate basis for whether the path reversal triggering condition is met in the context of multi-path distribution, and compensates for the angle anomaly misjudgment caused by factors such as path fluctuation, uneven path efficiency or spatial sparsity.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the reverse offset path node list, call the pressure parameters of the touch points in the tactile feedback matrix in the continuous cycle, extract the pressure change amplitude during the cycle, identify the touch points whose pressure change amplitude exceeds the pressure change threshold, and obtain the set of touch points with sudden pressure changes. Based on the inverted offset path node list, the first step is to extract the body part label information corresponding to each node number. For example, numbers 12 to 13 represent the left forearm, and numbers 18 to 19 represent the back of the right hand. Then, the corresponding touch point number range in the tactile feedback matrix is ​​retrieved according to the number mapping relationship. The touch point numbers can be T10 to T20, etc. Next, continuous pressure value data of these touch points within a set period is extracted from the sensor system. This period can be set to 5 frames, with each frame interval approximately 16 milliseconds, for a total duration of approximately 80 milliseconds. Pressure value samples for each touch point are extracted for 5 consecutive frames, such as 15.2, 14.9, 21.5, 23.0, and 22.7 for T15. Finally, the pressure change amplitude between adjacent frames is calculated, for example, in the second frame... The pressure change is 6.6 in frame 3, 1.5 from frame 3 to frame 4, and 0.3 from frame 4 to frame 5. The maximum change within this period is extracted as the change index of the contact point. The pressure change threshold is used to identify contacts with significant fluctuations. This threshold can be set by the average value of a large sample under static conditions and twice the standard deviation. For example, if the average value of the static samples is 2.5 and the standard deviation is 0.8, then the threshold can be set to 4.1. When the maximum change of a contact point is greater than this value, it is recorded as a pressure change contact point. For example, the maximum change of T15 is 6.6, which is greater than the threshold of 4.1, and meets the condition. It is recorded as number T15. After processing all relevant contacts, a pressure change contact point set is formed. The set includes the numbers of all pressure change contacts identified in the current period.

[0031] S302: Based on the set of contact points with sudden pressure changes, extract the spatial location data of the corresponding contact points, perform clustering processing according to the spatial distribution characteristics, extract the spatial coordinate center position of each type of contact point, and obtain the contact point cluster center coordinate group. Based on the numbering of the pressure-change contact points, the preset spatial position of each contact point in the tactile feedback matrix is ​​queried one by one. The contacts are fixed in their physical positions on the flexible sensor array during the design phase, and the coordinate data is recorded on the x, y, and z axes. For example, contact point T15 is located at x = 12.5, y = 8.3, z = 0.0, and contact point T18 is located at x = 13.0, y = 8.0, z = 0.0. After collecting the spatial coordinates of all pressure-change contacts, cluster analysis is performed. A clustering method based on proximity is selected, and the number of clusters is set to 5. The coordinates of all contacts are calculated. The spatial distance between them is determined and clustering iterations are performed until a stable classification result is obtained. Each cluster contains several touch points. For example, the first cluster contains T12, T13, T15, and T18. The average coordinates of these touch points in three directions are extracted as the spatial center coordinates of the cluster. For example, the average value in the x-direction is 12.8, in the y-direction is 8.1, and in the z-direction is 0.0. The same processing steps are repeated for other clusters. Finally, multiple cluster center points are generated. Each cluster center point represents a concentrated area of ​​abruptly changing touch points in space, forming a set of touch point cluster center coordinates. This set of data provides a spatial reference basis for subsequent matching nodes.

[0032] S303: Based on the coordinate group of the touch point cluster center, call the node position in the reverse offset path node list, calculate the spatial distance between the node and the cluster center, filter the nodes whose spatial distance is less than the spatial distance matching threshold, and generate a spatial matching feedback node table; Based on the touchpoint cluster center coordinate group obtained in the previous step, load all node numbers in the reverse offset path node list and extract their 3D coordinate positions in the current frame. For example, node number 13 has coordinates of x = 12.6, y = 8.0, and z = 0.2, while node number 19 has coordinates of x = 14.0, y = 10.2, and z = 0.5. Next, calculate the spatial distance between each node and all cluster centers by measuring the difference between coordinates in 3D coordinates. For example, the distance between node 13 and the cluster center with coordinates x = 12.8, y = 8.1, and z = 0.0 is approximately 0.23. Set the space... The distance matching threshold is used to determine whether a node has a matching relationship with a certain cluster center. This threshold can be set empirically with reference to the physical layout between sensors and the density of skeleton nodes. For example, it can be set to 0.5. When the distance from a node to any cluster center is less than this value, the node is matched with the cluster center and its number is recorded as the spatial matching feedback node number. For example, if numbers 13, 15, and 19 all meet the distance condition, these three numbers will be included in the feedback node table. This matching process is executed independently for each frame. The feedback response area triggered by the current action is identified based on the spatial proximity between the node and the cluster center, and the result is output through the spatial matching feedback node table.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Call the path node data in the spatial matching feedback node table, extract the offset vector value of each node, adjust the offset vector value according to the motion amplitude amplification coefficient, and generate an amplified offset vector value group. After retrieving the path node data from the spatial matching feedback node table, the offset vector value of each node is extracted sequentially. This vector can be obtained by subtracting the node's 3D coordinates in the current frame from its initial 3D reference position in a stationary state. For example, if a node's position in a stationary state is x = 12.0, y = 8.0, z = 0.0, and its position in the current frame is x = 12.6, y = 8.3, z = 0.2, then the node's offset vector is 0.6 in the x direction, 0.3 in the y direction, and 0.2 in the z direction. After completing this operation, the offset vector values ​​need to be... Amplification processing is performed based on the motion amplitude amplification factor, which is set during the design according to the motion recognition sensitivity. The typical value range is between 1.0 and 2.5. For example, if the amplification factor is set to 1.5, the amplification offset vector of the corresponding node is 0.9 in the x direction, 0.45 in the y direction, and 0.3 in the z direction. This process is repeated for all nodes in the path node table. The amplification offset vector results of all nodes are integrated into a set of data to form an amplification offset vector value group. This set of data provides the input basis for subsequent motion path trend calculation and direction consistency analysis.

[0034] S402: Based on the amplified offset vector value group, extract the unit vector direction between continuous path nodes, compare the difference between the unit vectors of the path direction with the direction consistency threshold, and filter the path pairs whose unit vector difference is less than the direction consistency threshold to obtain the direction consistent path group; The specific calculation formula for comparing the difference between unit vectors along the path direction with the direction consistency threshold is as follows: ; Calculate the difference in directional consistency. Path pairs with unit vector differences less than the direction consistency threshold are selected to obtain direction-consistent path groups; in, This represents the difference in directional consistency between path node pairs (i, i+1). The direction of the unit vector representing the j-th continuous path segment at path node i. The direction of the unit vector representing the j-th continuous path segment at path node i+1. This represents the average change in magnified offset within the offset vector group corresponding to the j-th path segment at path node i. This represents the magnitude of the original offset vector at path node i, specifically the length of the j-th segment. This represents the absolute offset average of the directional variation of the unit vector group corresponding to the j-th path segment at path node i. The magnitude difference between the starting and ending offset vectors of the offset vector group in the j-th path segment at path node i is represented by n, where n represents the total number of continuous path segments extracted at path node i, and j is the sequence number of the path segment. n=3 consecutive path segments are selected between path nodes i and i+1 for directional consistency calculation. The monitoring data comes from the spatial trajectory data extracted based on the coupled calculation of the laser SLAM system and IMU. The unit vector direction is obtained by trajectory difference normalization. All offset and magnitude data are obtained by coordinate point set difference calculation. The parameters corresponding to path segments j=1 to j=3 are as follows: , ; dot product ; , ; dot product ; , ; dot product ; The α parameter is derived from the rate of change of offset amplitude calculated based on the point cloud trajectory segment difference. For each path segment, the average absolute change of the offset vector at the three measurement points is calculated as follows: α_{i,1} is extracted by the mean of the offset vector magnitude difference between three consecutive paths, and its value is 0.042 meters; The average offset measured by α_{i,2} is 0.036 meters; The average offset measured for α_{i, 3} is 0.048 meters; The original offset modulus of the corresponding path segment, derived from the Euclidean distance between trajectory points: rice; rice; rice; calculate : ; ; ; For a unit vector direction, the magnitude is 1, therefore: ; The β parameter is calculated using the average offset of the unit vector change in each path segment, based on a statistical window of 5 frames for path direction offset rate. The results are as follows: β_{i, 1}=0.115 meters; β_{i, 2}=0.109 meters; β_{i, 3}=0.122 meters; The γ parameter is calculated using the difference in magnitude between the start and end points of the offset vector: γ_{i,1}=0.094 meters; γ_{i, 2}=0.088 meters; γ_{i, 3}=0.101 meters; Calculate the denominator term : ; ; ; Substitute into the formula and expand: Path segment 1: ; Path segment 2: ; Path segment 3: ; Calculate Δθ_{i, i+1}: ; The result shows that the directional consistency difference of the continuous path segments between path nodes i and i+1 is 18.701. A higher value indicates a larger difference in unit vectors and more drastic directional changes. This value can be compared with a preset directional consistency threshold to determine whether the path connection meets the stability criteria, thereby determining whether it should be included in the directional consistency path group. The formula measures directional consistency by constructing a comprehensive ratio between the unit vector direction between path segments, local offset perturbation, and path scale normalization index. The dot product term reflects the cosine relationship of the direction angle and is used to assess the degree of convergence of path segment directions. The normalized offset reflects the relative intensity of offset changes under different path scales. The square root term expresses the vector magnitude merging effect to form a directional structure reference standard, which serves as a consistency penalty term. The denominator reflects the relative severity of path direction changes through the ratio of direction variation value to path scale. The overall structure, under a unified dimension, combines addition and subtraction operations to express the weight relationship between the enhancement of directional similarity and the penalty. The influence of path perturbation is normalized through a fractional structure. Finally, a stability judgment mechanism for the trend of continuous path direction changes is established through summation and averaging. The directional consistency difference value is used to measure the overall degree of spatial offset of continuous path segments between path nodes. It reflects the directional continuity and stability of multiple adjacent path segments within a local range. The smaller the value, the smoother the directional change between path segments and the higher the trend consistency. The larger the value, the more significant the directional fluctuation or abrupt change in the path within a local area. This reflects the potential discontinuity, turning point or disturbance in the path geometry. This index constructs a comprehensive measurement result by jointly calculating the directional vector, offset amplitude and scale normalization factor, providing a quantitative basis for path selection, continuity verification or structural optimization.

[0035] S403: Perform an integration operation based on the path number in the path group with consistent direction, summarize the spatial position coordinates and motion amplitude data of the nodes in the path, establish the continuous linkage structure information of the path sequence, and generate a three-dimensional linkage path set. Based on the path numbers contained in the path group with consistent direction, the three-dimensional spatial position coordinates and their offset magnitudes are extracted from each node in the path. The position coordinates come from the coordinate output data of the current frame in the real-time capture system, and the offset magnitude is calculated by the aforementioned offset vector magnitude. For example, if the offset vector of a node is 0.9, 0.45, and 0.3 in the x, y, and z directions, respectively, its motion magnitude can be judged by the combination of the offset magnitudes in the three directions. The magnitude is approximately 1.05, indicating that the offset magnitude of the node in this frame is 1.05 units. After summarizing the spatial coordinates and motion magnitude data of all nodes, they are structured according to the path number to construct a node sequence table, a spatial coordinate sequence table, and a motion magnitude sequence table corresponding to each path number. The three types of information are integrated in a structure group to describe the continuity and linkage between path nodes in space and motion. Finally, the node structures of the path numbers with consistent direction are uniformly included to form a three-dimensional linkage path set, in which each linkage path corresponds to a set of spatial node sequence structures with consistent motion directions and amplitude correlation.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Call the node offset vector in the 3D linkage path set, map the 3D offset coordinates to the 3D skeleton model in the virtual scene according to the path order, and generate a set of node mapping position coordinate values. After calling the node offset vectors from the 3D linkage path set, the offset coordinates of each node in 3D space need to be extracted sequentially according to the path number. These offset coordinates represent the change in position of the node within a specific action frame, and the values ​​are usually recorded in cm. During mapping, they need to be converted to the unit system of the skeleton model in the virtual scene. If m units are used, the offset values ​​need to be scaled accordingly. Then, the scaled 3D offset coordinates are superimposed onto the initial 3D coordinates of each corresponding joint node in the virtual skeleton, forming the mapped position of that node in virtual space. All offset nodes have [a specific coordinate system] in the skeleton model. Unique index number matching ensures the consistency and continuity of the mapping results. For example, if the first node in the path is offset by 0.8 cm in the x direction, 0.6 cm in the y direction, and 0.2 cm in the z direction, the initial coordinates of the corresponding skeleton node are 1.0 m in the x direction, 0.5 m in the y direction, and 1.2 m in the z direction. The mapped position is 1.008 m in the x direction, 0.506 m in the y direction, and 1.202 m in the z direction. In this way, all path nodes are mapped sequentially and integrated into a mapped coordinate value group. The content includes attributes such as node number, corresponding skeleton index, and mapped position coordinates, forming a spatial data sequence for subsequent visualization processing.

[0037] S502: Based on the node mapping position coordinate value group, render the node action state in the path order, set the visual attributes according to the response level and overlay them onto the corresponding node, and generate the node action visual intensity value group. Based on the mapped position coordinates, node position data is read sequentially according to path number, and motion rendering is performed in the 3D skeleton model. Each node is marked as active and assigned visual attributes during the rendering process. These visual attributes are set according to the node's motion response level. The response level is defined as the relative amplitude of the node's position change between consecutive frames, which can be measured by offset distance or the speed difference between adjacent frames. For example, if a node's offset amplitude is 0.06m, it belongs to the middle segment within the set response level range of 0 to 0.1m, which can be represented as 60% by percentage. Therefore, the node's visual intensity level is set to level 60, corresponding to medium color brightness, 0.6 transparency, and medium luminous intensity. During the rendering stage, the color and visual effects generated by this response level are superimposed on the corresponding position of the node, and its numerical attributes are recorded simultaneously for subsequent processing. After all nodes are rendered in the path order, the resulting node motion visual intensity value set is the set of visual response levels that each node possesses in the current frame, containing multiple attribute fields such as color, brightness, transparency, and response value, used to express the intensity of the motion amplitude in the virtual model.

[0038] S503: Based on the node action visual intensity value group, filter the node areas where the visual intensity exceeds the response display threshold, establish interactive hotspot identification and integrate node trajectory information to generate a virtual twin interactive feedback visual trajectory map; Based on the visible intensity values ​​of node actions, the visible response level of each node is read and compared with a preset response display threshold. The response display threshold is a specific value within a set range, which can be set according to the salience requirements of the action. For example, if the selected threshold is 40, then nodes with a response intensity greater than 40 are judged as high-response areas and considered as interactive hotspots. For instance, if node number 7 in a certain path has a response level of 55 in the current frame, then this node enters the hotspot candidate area. Subsequently, several path segments are extended forward and backward from this node, and the continuous trajectory of the nodes is traced to form a continuous hotspot chain. During the processing, each selected hotspot node, in addition to retaining its response level, also needs to be marked with its position in the high-response area. Coordinates, path numbers, and time sequence numbers in the virtual space are used to construct a multi-attribute trajectory information set. All hotspot node trajectory data are categorized and integrated according to path numbers to form several visual path sequences. These sequences are then reconstructed into 3D path lines in a virtual skeleton structure. Simultaneously, color-coded layers are set according to response intensity levels for layered display. Finally, a virtual twin interactive feedback visual trajectory map containing the distribution of all hotspot nodes and the direction of motion trajectories is generated. This map includes multiple data fusion layers such as response level visual range layering, spatial continuous path distribution, and node number and time point correspondence, possessing completeness and continuity, and meeting the construction requirements of motion trajectory tracking in 3D scenes.

[0039] Please see Figure 7 A data-driven human-computer interaction visualization and virtual twin system, including: The skeleton path calculation module obtains the three-dimensional coordinates of the joint nodes in the virtual limb model, calculates the path vectors between the connecting nodes, and combines the path node pairs according to the node arrangement structure order to establish the overall spatial path vector group and generate the skeleton node spatial path vector group. The angle recognition and determination module calls the path node data in the skeleton node spatial path vector group, calculates the angle between the current path vector and the reference vector, determines whether the angle change exceeds the set threshold, filters path nodes with abnormal status, and generates a list of reverse offset path nodes. The pressure mutation extraction module calls the node information in the reverse offset path node list, extracts the continuous periodic pressure value of the corresponding touch point in the tactile feedback matrix, calculates the pressure change amplitude, identifies the mutation touch point and performs clustering, analyzes the spatial distance between the node and the cluster center, filters matching path nodes, and generates a spatial matching feedback node table. The motion path filtering module calls the path node data in the spatial matching feedback node table, calculates the offset vector magnitude of the node position, adjusts it in combination with the amplification factor, analyzes the unit vector difference between the offset path direction and the reference path direction, filters the paths that meet the direction conditions, and generates a set of three-dimensional linkage paths. The linkage rendering feedback module calls the node offset data in the 3D linkage path set, maps the path information to the virtual skeleton model, renders the node actions according to the path sequence, sets visual attributes to distinguish the response level, marks the interaction hotspots in the corresponding areas, and generates a virtual twin interaction feedback visual trajectory map.

[0040] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data analysis-based method for human-computer interaction visualization and virtual twins, characterized in that, Includes the following steps: S1: Obtain the three-dimensional coordinates of the joint nodes in the virtual limb model, calculate the path vector values ​​between the connecting nodes, and arrange them according to the structural order to form a set of node pair paths, generating a set of spatial path vectors for skeleton nodes. S2: Call the skeleton node spatial path vector group, combine it with the real-time node position, calculate the angle between the current path vector and the reference path vector, determine whether the action reversal angle threshold condition is met, and generate a list of reversal offset path nodes; S3: Based on the reverse offset path node list, analyze the pressure change amplitude of each touch point in the tactile feedback matrix in a continuous cycle, extract the touch points with sudden pressure changes and perform clustering processing, calculate the spatial distance between the node position and the touch point cluster center, filter the nodes that meet the distance matching threshold, and generate a spatial matching feedback node table. S4: Call the path nodes in the spatial matching feedback node table, calculate the magnitude of the node offset vector, and adjust it by applying the action amplitude amplification coefficient. Determine whether the unit vector difference between path directions meets the direction consistency threshold condition, filter the paths that meet the conditions and integrate them into an action sequence to generate a three-dimensional linkage path set.

2. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 1, characterized in that, The skeleton node spatial path vector group includes three-dimensional coordinates, path vectors, and a set of node pairs paths. The reverse offset path node list includes current path nodes, reference path nodes, and angle threshold condition nodes. The spatial matching feedback node table includes pressure change contact points, contact point cluster centers, and matching threshold nodes. The three-dimensional linkage path set includes node offset vectors, action amplitude amplification coefficients, and direction consistency paths.

3. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the joint node number index, three-dimensional coordinate parameters and connection structure information in the virtual limb model, extract coordinate data according to the node correspondence marked in the connection structure, calculate the spatial path vector value between the connection nodes, and generate three-dimensional path vector value. S102: Based on the three-dimensional path vector values, according to the arrangement order of node pairs in the connection structure, integrate the path vectors with the corresponding node numbers to establish a path number sequence with a continuous structural order, and obtain an ordered node path vector group. S103: Based on the ordered node path vector group, extract the sequential position of the node number pairs in the structure, associate the structural arrangement of the path vectors, construct a continuous path vector set, and obtain the skeleton node spatial path vector group.

4. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the skeleton node spatial path vector group and the real-time node position, extract the real-time coordinate data according to the node correspondence in the connection structure, and generate the real-time path vector value by combining the spatial position relationship between nodes; S202: Based on the real-time path vector value, match the reference path of the corresponding node number in the skeleton node spatial path vector group, calculate the spatial direction difference between the current path vector value and the reference path vector value, and obtain the path vector angle value. S203: Based on the angle value of the path vector, call the action reversal angle threshold to make a condition judgment, identify the path node combination that meets the angle requirement, summarize the corresponding node numbers, and obtain the reverse offset path node list.

5. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the list of reverse offset path nodes, call the pressure parameters of the touch points in the tactile feedback matrix in the continuous cycle, extract the pressure change amplitude during the cycle, identify the touch points whose pressure change amplitude exceeds the pressure change threshold, and obtain the set of touch points with sudden pressure changes. S302: Based on the pressure change contact set, extract the spatial location data of the corresponding contact, perform clustering processing according to the spatial distribution characteristics, extract the spatial coordinate center position of each type of contact, and obtain the contact cluster center coordinate group. S303: Based on the coordinate group of the touch point cluster center, call the node position in the reverse offset path node list, calculate the spatial distance between the node and the cluster center, filter the nodes whose spatial distance is less than the spatial distance matching threshold, and generate a spatial matching feedback node table.

6. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Call the path node data in the spatial matching feedback node table, extract the offset vector value of each node, adjust the offset vector value according to the action amplitude amplification coefficient, and generate an amplified offset vector value group. S402: Based on the amplified offset vector value group, extract the unit vector direction between continuous path nodes, compare the difference between the unit vectors of the path direction with the direction consistency threshold, and filter the path pairs whose unit vector difference is less than the direction consistency threshold to obtain the direction consistent path group; S403: Perform an integration operation based on the path number in the path group with consistent direction, summarize the spatial position coordinates and motion amplitude data of the nodes in the path, establish the continuous linkage structure information of the path sequence, and generate a three-dimensional linkage path set.

7. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 1, characterized in that, The method further includes: S5: Call the node offset data in the three-dimensional linkage path set and map it to the three-dimensional skeleton model in the virtual scene. Render the node action state according to the path order, set visual attributes to distinguish the degree of response, generate interactive hotspot markers in the corresponding areas, and obtain the virtual twin interactive feedback visual trajectory map. The virtual twin interactive feedback visual trajectory map includes a three-dimensional skeleton model, interactive hotspot markers, and visual attribute differentiation; The interactive hotspot identifier refers to the graphic marker in the screen used to indicate the area of ​​action response. It is presented in the form of icons, highlighted boxes, and jumping edges, and its position corresponds to the position of the action node. The virtual twin interactive feedback visual trajectory map refers to the response action trajectory layer rendered in the interactive screen, which shows the node path changes, response degree and visual attributes.

8. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Call the node offset vector in the set of three-dimensional linkage paths, and map the three-dimensional offset coordinates to the three-dimensional skeleton model in the virtual scene according to the path order to generate a set of node mapping position coordinate values. S502: Based on the node mapping position coordinate value group, render the node action state in the path order, set the visual attributes according to the response degree and overlay them onto the corresponding node to generate a node action visual intensity value group. S503: Based on the node action visual intensity value group, filter the node areas whose visual intensity exceeds the response display threshold, establish interactive hotspot identifiers and integrate node trajectory information to generate a virtual twin interactive feedback visual trajectory map.

9. The human-computer interaction visualization and virtual twin method based on data analysis according to claim 1, wherein the virtual limb model refers to a human body structure mapping model constructed through predefined three-dimensional coordinate nodes; The path vector refers to the positional difference between two nodes in three-dimensional space, expressed in terms of direction and length; The action reversal angle threshold refers to the minimum change angle between the current path vector and the static reference path vector; The pressure change contact point refers to the point in the tactile feedback matrix where the pressure change amplitude exceeds a set threshold. The distance matching threshold is set based on the Euclidean distance formula of three-dimensional coordinate points, combined with the physical scale of human-computer interaction. The amplification factor of the motion amplitude refers to the proportional adjustment parameter applied to the node offset vector value; The directional consistency threshold is obtained by calculating the Euclidean distance or cosine angle between the unit vectors of two path directions and comparing it with a comparison standard set according to the requirements of action linkage continuity or scene accuracy.

10. A human-computer interaction visualization and virtual twin system based on data analysis, characterized in that, The system is used to implement the data analysis-based human-computer interaction visualization and virtual twin method according to any one of claims 1-9, the system comprising: The skeleton path calculation module obtains the three-dimensional coordinates of the joint nodes in the virtual limb model, calculates the path vectors between the connecting nodes, and combines the path node pairs according to the node arrangement structure order to establish the overall spatial path vector group and generate the skeleton node spatial path vector group. The angle recognition and determination module calls the path node data in the skeleton node spatial path vector group, calculates the angle between the current path vector and the reference vector, determines whether the angle change exceeds the set threshold, filters path nodes with abnormal status, and generates a reverse offset path node list. The pressure mutation extraction module calls the node information in the reverse offset path node list, extracts the continuous periodic pressure value of the corresponding touch point in the tactile feedback matrix, calculates the pressure change amplitude, identifies the mutation touch point and performs clustering, analyzes the spatial distance between the node and the cluster center, filters matching path nodes, and generates a spatial matching feedback node table. The motion path filtering module calls the path node data in the spatial matching feedback node table, calculates the offset vector magnitude of the node position, adjusts it in combination with the amplification factor, analyzes the unit vector difference between the offset path direction and the reference path direction, filters the paths that meet the direction conditions, and generates a set of three-dimensional linkage paths. The linkage rendering feedback module calls the node offset data in the three-dimensional linkage path set, maps the path information to the virtual skeleton model, renders the node actions according to the path sequence, sets visual attributes to distinguish the response level, marks the interaction hotspots in the corresponding areas, and generates a virtual twin interaction feedback visual trajectory map.

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