A Drag-and-Drop Layout Interaction Method and System for Live Streaming Rooms Based on Dynamic Gesture Recognition

Abstract

This invention relates to the field of feature recognition technology, and discloses a draggable layout interaction method and system for live streaming rooms based on dynamic gesture recognition. The method includes: constructing a layout adjustment controller using the drag permissions and operation boundaries of interactive elements; extracting the gesture interaction response features of the target user during the dragging process of interactive elements by analyzing the layout change rate and effective operation range of the interactive elements; extracting the spatial trajectory features and movement rhythm patterns corresponding to the interactive elements to identify the target user's dragging interaction intention; generating personalized layout adjustment instructions by combining the dragging interaction intention and gesture interaction response features; creating a visual feedback view of the live streaming room based on adaptive layout constraints and the layout adjustment controller; and generating a draggable interactive layout of the live streaming room based on the dragging interaction intention, through the personalized layout adjustment instructions and the visual feedback view. This invention can improve the convenience and accuracy of live streaming room layout adjustments.

Description

Technical Field

[0001] This invention relates to a draggable layout interaction method and system for live streaming rooms based on dynamic gesture recognition, belonging to the field of feature recognition technology. Background Technology

[0002] Drag-and-drop layout interaction in live streaming rooms refers to the process of scaling, reorganizing, and adjusting the layout of live streaming rooms by capturing real-time drag-and-drop operation data (such as drag trajectory, stopping position, and operation force) and combining it with the display characteristics of live streaming room elements. This can effectively improve the user's interactive experience and interface utilization. However, the types of elements in live streaming rooms are complex and diverse, and users' operating habits vary greatly. These factors make it difficult for the smoothness and adaptability of live streaming room layout adjustments to fully meet actual needs, thus limiting further improvement in the interactive experience and interface utilization of live streaming rooms. Therefore, optimizing and improving drag-and-drop layout interaction technology in live streaming rooms is particularly crucial.

[0003] Existing methods mostly rely on a single operation mode with a fixed trigger area, such as dragging a button with a mouse or sliding a border on the touch screen, to adjust the layout of the live broadcast room. Although this method is intuitive, it cannot achieve real-time dynamic recognition of user operation intentions and intelligent adaptation of layout schemes, resulting in insufficient convenience and accuracy of live broadcast room layout adjustments.

[0004] Therefore, there is an urgent need for a solution that can improve the convenience and accuracy of adjusting the layout of live streaming rooms. Summary of the Invention

[0005] This invention provides a draggable layout interaction method and system for live streaming rooms based on dynamic gesture recognition, the main purpose of which is to improve the convenience and accuracy of adjusting the layout of live streaming rooms.

[0006] To achieve the above objectives, the present invention provides a draggable layout interaction method for live streaming rooms based on dynamic gesture recognition, comprising:

[0007] Obtain the interactive elements of the target user in the live broadcast room, determine the drag permissions and operation boundaries of the interactive elements, and construct the layout adjustment controller of the interactive elements based on the drag permissions and operation boundaries.

[0008] Based on the layout adjustment controller, the gesture-element association features of the live broadcast room are extracted. Based on the gesture-element association features, the layout change rate and effective operation range of the interactive elements are determined. Based on the layout change rate and the effective operation range, the interface layout adaptability of the interactive elements is calculated.

[0009] Based on the interactive elements, real-time gesture data of the target user is collected. Based on the real-time gesture data, spatial trajectory features and movement rhythm patterns corresponding to the interactive elements are extracted. Based on the spatial trajectory features and movement rhythm patterns, the drag interaction intention of the target user is identified.

[0010] Based on the interface layout adaptability, the gesture interaction response features of the target user during the dragging process of the interactive element are extracted, and personalized layout adjustment instructions for the interactive element are generated according to the dragging interaction intention and the gesture interaction response features.

[0011] The interface operation status of the live room is monitored in real time. Based on the personalized layout adjustment command and the interface operation status, adaptive layout constraints of the interactive elements are set. According to the adaptive layout constraints and the layout adjustment controller, a visual feedback view of the live room is created.

[0012] Based on the drag-and-drop interaction intent, the draggable interactive layout of the live streaming room is generated through the personalized layout adjustment command and the visual feedback view.

[0013] Optionally, the step of constructing the layout adjustment controller for the interactive element based on the drag-and-drop permission and the operation boundary includes:

[0014] Obtain the drag-and-drop permission and the operation permission conditions corresponding to the operation boundary;

[0015] Extract the key restriction parameters from the operation permission conditions;

[0016] Identify the spatial topological relationships between the interactive elements;

[0017] Based on the spatial topological relationship, calculate the association coefficient of the interactive elements;

[0018] Based on the key constraint parameters and the correlation coefficient, a control priority queue for the interactive elements is generated.

[0019] Based on the control priority queue, set the hierarchical adjustment rules for the interactive elements, and establish a rule conflict arbitration mechanism for the hierarchical adjustment rules;

[0020] By combining the control priority queue, the hierarchical adjustment rules, and the rule conflict arbitration mechanism, a layout adjustment controller for the interactive elements is constructed.

[0021] Optionally, the step of extracting the gesture-element association features of the live stream based on the layout adjustment controller includes:

[0022] Based on the layout adjustment controller, the gesture input sequence and element response sequence of the live broadcast room are collected;

[0023] The gesture input sequence and the element response sequence are synchronized and aligned using a dynamic time warping algorithm to obtain a time-matching pair.

[0024] Based on the time-series matching pairs, construct the gesture-element linkage probability distribution map of the live broadcast room;

[0025] The gesture-element association features of the live broadcast room are extracted using the gesture-element linkage probability distribution map.

[0026] Optionally, determining the layout change rate and effective operation range of the interactive element based on the gesture-element association features includes:

[0027] Based on the gesture-element association features, obtain the spatiotemporal sequence data and spatial coordinate data of the interactive element;

[0028] Based on the spatiotemporal sequence data, extract the gesture dynamics features corresponding to the interactive elements;

[0029] Based on the gesture dynamics features, a set of dynamic response parameters for the interactive elements is generated;

[0030] The layout change rate of the interactive elements is determined by the dynamic response parameter set.

[0031] Based on the spatial coordinate data, establish the spatial coordinate curve of the interactive element;

[0032] Identify the key coordinate points of the spatial coordinate curve, and determine the gesture convex hull boundary corresponding to the interactive element through the key coordinate points;

[0033] Based on the gesture convex hull boundary, construct the initial operation range constraint set of the interactive element;

[0034] By combining the dynamic response parameter set and the initial operation range constraint set, the effective operation range of the interactive element is determined.

[0035] Optionally, identifying the target user's drag-and-drop interaction intent based on the spatial trajectory features and the motion rhythm pattern includes:

[0036] The spatial trajectory features and the motion rhythm patterns are normalized to generate a standardized feature set;

[0037] Extract the spatiotemporal correlation features of the standardized feature set, and identify the drag-and-drop operation pattern of the target user based on the spatiotemporal correlation features;

[0038] Analyze the state evolution pattern of the drag-and-drop operation mode;

[0039] Based on the state evolution law, generate the drag state transition matrix of the target user;

[0040] Based on the drag state transition matrix, a drag intent recognition network for the target user is constructed.

[0041] The drag-and-drop intent recognition network identifies the drag-and-drop interaction intent of the target user.

[0042] Optionally, the step of extracting the gesture interaction response features of the target user during the dragging process of the interactive element based on the interface layout adaptability includes:

[0043] The target user's drag speed during the dragging process of the interactive element is obtained, and the rate of change of the drag speed is calculated.

[0044] Based on the rate of change of speed, the dragging process is segmented to obtain the dragging operation stage;

[0045] Based on the drag-and-drop operation phase, identify the target user's current drag-and-drop operation intent and determine the current interface element type corresponding to the interactive element;

[0046] Define the adaptive threshold of the interface layout adaptability based on the current drag operation intent and the current interface element type;

[0047] Based on the adaptive threshold, the dragging behavior pattern of the target user during the dragging operation phase is divided.

[0048] Based on the drag-and-drop behavior pattern, calculate the cross-pattern feature offset of the target user;

[0049] Based on the cross-mode feature offset, the drag-and-drop operations of the target user are clustered to obtain a classification operation style group;

[0050] Collect the gesture dynamic data of the classified operation style groups, and filter out key indicators that are strongly correlated with the interface layout adaptability from the gesture dynamic data;

[0051] Based on the aforementioned key indicators, the gesture interaction response features of the target user are extracted.

[0052] Optionally, generating personalized layout adjustment instructions for the interactive elements based on the drag-and-drop interaction intent and the gesture interaction response characteristics includes:

[0053] Based on the drag-and-drop interaction intent and the gesture interaction response characteristics, reconstruct the motion trajectory of the interactive element;

[0054] Calculate the motion coherence index of the motion trajectory;

[0055] Based on the motion trajectory and the gesture interaction response features, identify the dragging behavior corresponding to the interactive element;

[0056] Extract the fine-tuning stability index from the dragging behavior;

[0057] The gesture performance index of the interactive element is quantified by combining the motion coherence index and the fine-tuning stability index.

[0058] Analyze the layout adaptation requirements corresponding to the gesture performance metrics;

[0059] Based on the gesture performance metrics and the layout adaptation requirements, construct layout adjustment decision rules for the interactive elements.

[0060] Based on the layout adjustment decision rules, identify typical drag-and-drop scenarios for the interactive elements;

[0061] Based on the typical drag-and-drop scenario, personalized layout adjustment instructions for the interactive elements are generated.

[0062] Optionally, setting adaptive layout constraints for the interactive elements based on the personalized layout adjustment instructions and the interface running state includes:

[0063] Based on the personalized layout adjustment instructions, the user operation preferences corresponding to the interactive elements are identified;

[0064] Based on the interface's operating status, determine the system's real-time performance indicators corresponding to the interactive elements.

[0065] Based on the user operation preferences and the system's real-time performance indicators, a dynamic layout constraint evaluation system for the interactive elements is constructed.

[0066] Using the dynamic layout constraint evaluation system, the layout adaptability score of the interactive elements under different constraint conditions is calculated:

[0067] Based on the layout adaptability score, a set of candidate layout constraints for the interactive elements is selected.

[0068] The feasibility of the candidate layout constraint set is verified to obtain the target layout constraint.

[0069] Based on the dynamic layout constraint evaluation system, key indicators of the target layout constraints are monitored in real time to establish a dynamic baseline comparison mechanism.

[0070] Based on the dynamic baseline comparison mechanism, a dual-channel feedback node is set for the target layout constraints;

[0071] By combining the dynamic layout constraint evaluation system, the dynamic baseline comparison mechanism, and the dual-channel feedback node, adaptive layout constraints are set for the interactive elements.

[0072] Optionally, creating the visual feedback view of the live streaming room based on the adaptive layout constraints and the layout adjustment controller includes:

[0073] The dynamic relaxation parameters of the adaptive layout constraints and the instruction conflict flag bits of the layout adjustment controller are collected synchronously.

[0074] The stretchable area of ​​the live streaming room is determined based on the dynamic relaxation parameter.

[0075] Perform decoding of the instruction conflict flag bit to determine the conflict type;

[0076] Based on the stretchable area, generate the elastic grid layer of the live broadcast room;

[0077] Based on the conflict type, set the fault diagnosis flag for the live broadcast room;

[0078] Identify the physical screen boundaries and key element lock areas of the live streaming room;

[0079] By combining the dynamic relaxation parameter, the physical screen boundary, and the key element locking area, an adjustable threshold for the elastic mesh layer is set;

[0080] Based on the adjustable threshold, the layout adjustment guide lines of the live broadcast room are generated;

[0081] The fault diagnosis identifier is associated with the conflict resolution of the layout adjustment controller to obtain fault operation guidance.

[0082] By integrating the elastic grid layer, the fault diagnosis indicator, the layout adjustment guide line, and the fault operation guide, a visual feedback view of the live broadcast room is created.

[0083] To address the aforementioned problems, the present invention also provides a draggable layout interaction system for live streaming rooms based on dynamic gesture recognition, the system comprising:

[0084] The condition recognition module is used to obtain the interactive elements of the target user in the live broadcast room, determine the drag permission and operation boundary of the interactive elements, and construct the layout adjustment controller of the interactive elements based on the drag permission and the operation boundary.

[0085] The adaptation analysis module is used to extract the gesture-element association features of the live broadcast room based on the layout adjustment controller, determine the layout change rate and effective operation range of the interactive elements according to the gesture-element association features, and calculate the interface layout adaptation degree of the interactive elements based on the layout change rate and the effective operation range.

[0086] The intent recognition module is used to collect real-time gesture data of the target user based on the interactive element, extract spatial trajectory features and motion rhythm patterns corresponding to the interactive element based on the real-time gesture data, and identify the drag interaction intent of the target user based on the spatial trajectory features and the motion rhythm patterns.

[0087] The instruction generation module is used to extract the gesture interaction response features of the target user during the dragging process of the interactive element based on the interface layout adaptability, and generate personalized layout adjustment instructions for the interactive element according to the drag interaction intention and the gesture interaction response features.

[0088] The visual feedback module is used to monitor the interface operation status of the live broadcast room in real time, set adaptive layout constraints for the interactive elements based on the personalized layout adjustment instructions and the interface operation status, and create a visual feedback view of the live broadcast room according to the adaptive layout constraints and the layout adjustment controller.

[0089] The layout output module is used to generate a draggable interactive layout for the live streaming room based on the drag-and-drop interaction intent, the personalized layout adjustment instructions, and the visual feedback view.

[0090] Compared to the problems described in the background art, the embodiments of the present invention, by determining the drag permissions and operation boundaries of the interactive elements, can ensure that drag operations in the live broadcast room are conducted within a reasonable and controllable range. Furthermore, by constructing a layout adjustment controller for the interactive elements based on the drag permissions and operation boundaries, the embodiments of the present invention can integrate scattered permission rules and boundary restriction data to form a multi-dimensional, collaborative layout control system. This provides a structured framework for accurately matching user interaction intentions with element adjustment logic, ensuring orderly control of drag operations in complex live broadcast scenarios and improving the overall adaptability of interactive element layout adjustments. The present invention, by extracting the gesture-element association features of the live streaming room based on the layout adjustment controller, can significantly enhance the naturalness and intuitiveness of draggable interaction in the live streaming room, and improve the accuracy and flexibility of layout adjustment. Furthermore, by determining the layout change rate and effective operation range of the interactive elements based on the gesture-element association features, the present invention can transform abstract gesture interaction rules into executable dynamic adjustment indicators, providing a quantitative benchmark for achieving precise matching of gesture operations and element responses. This significantly enhances the ability to ensure operational smoothness and boundary controllability in complex interactive scenarios within a live streaming room. The present invention, by calculating the interface layout adaptability of the interactive elements based on the layout change rate and the effective operation range, can optimize the adjustment accuracy of dynamic gestures on the live broadcast room interface layout, while ensuring the matching degree between the layout changes of interface elements and user gesture operations, improving the smoothness of live broadcast room layout interaction and user operation experience. Furthermore, the present invention, by extracting the spatial trajectory features and movement rhythm patterns corresponding to the interactive elements based on the real-time gesture action data, can accurately identify the user's interaction trajectory patterns and operation rhythm features for specific interface elements, ensuring that subsequent interface layout adjustments are highly consistent with the user's interaction habits and operation intentions. The present invention, by identifying the target user's drag interaction intention based on the spatial trajectory features and movement rhythm patterns, can provide a basis for distinguishing between the user's effective operation and accidental touch behavior, while avoiding operation interruption or experience fragmentation caused by the mismatch between interaction feedback and actual operation intention. Furthermore, the present invention, by extracting the target user's gesture interaction response features during the dragging process of the interactive elements based on the interface layout adaptability, can dynamically quantify the real-time matching deviation between user operation and interface layout, improving the intelligence of live broadcast room layout interaction and the smoothness of user operation.This invention, through generating personalized layout adjustment instructions for interactive elements based on the drag-and-drop interaction intent and the gesture interaction response characteristics, can enhance the accuracy of the interface layout's adaptation to user operating habits, while also improving the smoothness and user experience of live streaming interactions. Furthermore, by setting adaptive layout constraints for interactive elements based on the personalized layout adjustment instructions and the interface's running state, this invention can accurately match user operating habits with real-time interface performance, thereby improving the accuracy of layout adjustment adaptation. Simultaneously, by dynamically optimizing the constraint strength, it can improve the stability of interface interactions. This invention, through creating a layout adjustment controller based on the adaptive layout constraints, can further enhance the accuracy of the layout adjustment adaptation. The visual feedback view of the live streaming room can map the adaptation relationship between gesture operations and layout constraints in real time, enhancing the user's perception of the interaction boundaries and improving the accuracy and efficiency of live streaming room layout adjustments. Finally, this embodiment of the invention generates a draggable interactive layout for the live streaming room based on the drag-and-drop interaction intent, the personalized layout adjustment command, and the visual feedback view. This ensures that the live streaming room layout accurately matches the operating habits of different users, increases the user's freedom of control over the layout, and achieves dynamic adaptation of layout forms in different live streaming scenarios. Simultaneously, it provides intuitive user behavior data support for iterative innovation in live streaming room interaction design, fundamentally enhancing the content presentation quality and user engagement in complex interactive scenarios. Therefore, the draggable layout interaction method and system for live streaming rooms based on dynamic gesture recognition provided by this embodiment of the invention can improve the convenience and accuracy of live streaming room layout adjustments. Attached Figure Description

[0091] Figure 1 This is a flowchart illustrating a draggable layout interaction method for a live streaming room based on dynamic gesture recognition, provided in an embodiment of the present invention.

[0092] Figure 2 A schematic diagram of the gesture convex hull boundary of a draggable layout interaction method for a live streaming room based on dynamic gesture recognition, provided in an embodiment of the present invention.

[0093] Figure 3 This is a functional module diagram of a draggable layout interactive system for a live streaming room based on dynamic gesture recognition, provided as an embodiment of the present invention.

[0094] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0095] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0096] This application provides a draggable layout interaction method for live streaming rooms based on dynamic gesture recognition. The executing entity of this draggable layout interaction method for live streaming rooms based on dynamic gesture recognition includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, this draggable layout interaction method for live streaming rooms based on dynamic gesture recognition can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0097] Reference Figure 1 The diagram shown is a flowchart illustrating a draggable layout interaction method for a live streaming room based on dynamic gesture recognition, according to an embodiment of the present invention. In this embodiment, the draggable layout interaction method for a live streaming room based on dynamic gesture recognition includes:

[0098] S1. Obtain the interactive elements of the target user in the live broadcast room, determine the drag permissions and operation boundaries of the interactive elements, and construct the layout adjustment controller of the interactive elements based on the drag permissions and operation boundaries.

[0099] This invention, by acquiring the interactive elements of a target user in a live stream, can clearly define the specific target and scope of interactive operations. The target user refers to a specific user group that watches and interacts in the live stream, such as consumers and streamers. The live stream refers to a digital virtual space used to conduct live streaming activities, including multiple functional modules such as a live stream display area and interactive function areas (such as a bullet screen area, comment area, and gift panel). The interactive elements refer to interface objects in the live stream that can be moved, scaled, or hidden by users or streamers through gestures, clicks, or other operations, such as the main live stream window, a floating gift effect frame, a bullet screen display area, and interactive buttons.

[0100] Furthermore, by determining the drag permissions and operation boundaries of the interactive elements, this embodiment of the invention can ensure that drag operations in the live broadcast room are conducted within a reasonable and controllable range. The drag permissions refer to the division of operation permissions set for interactive elements in the live broadcast room, used to clarify which interactive elements are allowed to be dragged by the target user, which are only allowed to be operated by the host, or different levels of users have different drag permission ranges. For example, the host can drag, move, and resize core content areas such as the main live broadcast screen, the guest interaction window, and the product explanation PPT panel. In addition to having the drag permissions of ordinary users, paid members can also drag exclusive gift effect display frames. The operation boundaries refer to the spatial and logical restrictions imposed on interactive elements during the dragging process, including the draggable area of ​​the element, the size restrictions during dragging, and the interaction restrictions between elements. For example, the main live broadcast screen can be limited to dragging within 80% of the central area of ​​the screen, but cannot be dragged beyond the 20% boundary of the screen edge.

[0101] Optionally, the drag-and-drop permissions of the interactive elements can be determined by the user role hierarchy and the element's functional attributes, and the operation boundaries can be determined by the live broadcast room interface layout specifications and user interaction experience requirements.

[0102] This invention, through the construction of a layout adjustment controller for interactive elements based on drag-and-drop permissions and operation boundaries, integrates scattered permission rules and boundary restriction data to form a multi-dimensional, collaborative layout control system. This provides a structured framework for accurately matching user interaction intentions with element adjustment logic, ensuring orderly control of drag-and-drop operations in complex live streaming scenarios, and improving the overall adaptability of interactive element layout adjustments. The layout adjustment controller is a core control module used to manage and constrain user operations such as dragging, scaling, and rotating interactive elements (such as video windows, bullet screen areas, and product cards) within the live streaming room.

[0103] As an embodiment of the present invention, the step of constructing the layout adjustment controller for the interactive element based on the drag-and-drop permission and the operation boundary includes:

[0104] Obtain the drag-and-drop permission and the operation permission conditions corresponding to the operation boundary;

[0105] Extract the key restriction parameters from the operation permission conditions;

[0106] Identify the spatial topological relationships between the interactive elements;

[0107] Based on the spatial topological relationship, calculate the association coefficient of the interactive elements;

[0108] Based on the key constraint parameters and the correlation coefficient, a control priority queue for the interactive elements is generated.

[0109] Based on the control priority queue, set the hierarchical adjustment rules for the interactive elements, and establish a rule conflict arbitration mechanism for the hierarchical adjustment rules;

[0110] By combining the control priority queue, the hierarchical adjustment rules, and the rule conflict arbitration mechanism, a layout adjustment controller for the interactive elements is constructed.

[0111] The operation permission conditions refer to a comprehensive set of conditions that standardize the eligibility and scope of interactive element operations, based on drag-and-drop permissions and operation boundaries. The key restriction parameters are core parameters extracted from the operation permission conditions that play a decisive role in adjusting the layout of interactive elements, such as the drag-and-drop permission level value corresponding to the user role and the maximum / minimum draggable size threshold of an element. The spatial topology relationship refers to the relative positional relationship of multiple interactive elements in the spatial layout of the live stream, including relationships such as inclusion, adjacency, overlap, and separation between elements. For example, the main live stream screen and the bullet screen area are adjacent but do not overlap, while the gift panel and the interaction button are separate. The correlation coefficient refers to a matrix representation of the degree of correlation between interactive elements. The numerical values ​​refer to the priority queue, which is a queue formed by sorting the layout adjustment order of interactive elements based on key constraint parameters and correlation coefficients. The hierarchical adjustment rules refer to the differentiated layout adjustment rules for interactive elements at different levels, formulated according to the priority queue. For example, high-priority elements can be adjusted in a larger space and are not restricted by low-priority elements. Low-priority elements must avoid high-priority elements when adjusting. Elements at the same level follow the principle of equal avoidance. The rule conflict arbitration mechanism refers to a set of decision-making logic used to determine the conflict priority, select the applicable rule, and provide a reasonable solution when different hierarchical adjustment rules contradict or restrict each other (i.e., rule conflict) during the hierarchical adjustment of interactive elements.

[0112] Optionally, based on the spatial topological relationship, the correlation coefficient of the interactive elements can be calculated using the Pearson correlation coefficient; based on the key constraint parameters and the correlation coefficient, the control priority queue of the interactive elements can be generated using a weighted scoring function; and the rule conflict arbitration mechanism of the hierarchical adjustment rules can be established using fuzzy logic algorithms. For example, the importance score of the anchor window and the interaction demand intensity of the bullet screen area can be used as inputs to divide the fuzzy set; then, a fuzzy rule base is defined. For example, if the anchor window has high importance and the bullet screen interaction demand is medium, then rule A is executed first; if the anchor window has medium importance and the bullet screen interaction demand is high, then rule B is executed first; then, the input variables are processed through fuzzy reasoning (such as the Mamdani method) to obtain the fuzzy output of the conflict arbitration, such as rule A having a priority of 0.8 and rule B having a priority of 0.3; finally, after defuzzification (such as the centroid method), the fuzzy output is transformed into a clear decision result—rule A is executed first.

[0113] S2. Based on the layout adjustment controller, extract the gesture-element association features of the live broadcast room, determine the layout change rate and effective operation range of the interactive elements according to the gesture-element association features, and calculate the interface layout adaptability of the interactive elements based on the layout change rate and the effective operation range.

[0114] This invention, through the extraction of gesture-element association features of the live stream based on the layout adjustment controller, can significantly enhance the naturalness and intuitiveness of draggable interaction in the live stream, and improve the accuracy and flexibility of layout adjustment. The gesture-element association features refer to the association features between the user's dynamic gesture operation and the layout adjustment response of interactive elements captured by the layout adjustment controller during the draggable layout interaction in the live stream. For example, when two fingers make an opening gesture on the product description panel, the panel will be enlarged proportionally according to the opening degree of the gesture.

[0115] As an embodiment of the present invention, the step of extracting the gesture-element association features of the live broadcast room based on the layout adjustment controller includes:

[0116] Based on the layout adjustment controller, the gesture input sequence and element response sequence of the live broadcast room are collected;

[0117] The gesture input sequence and the element response sequence are synchronized and aligned using a dynamic time warping algorithm to obtain a time-matching pair.

[0118] Based on the time-series matching pairs, construct the gesture-element linkage probability distribution map of the live broadcast room;

[0119] The gesture-element association features of the live broadcast room are extracted using the gesture-element linkage probability distribution map.

[0120] The gesture input sequence refers to an ordered set of continuous gesture data collected by the layout adjustment controller when a user performs interactive operations such as dragging within the live stream. This includes information such as the gesture type (e.g., single-finger drag, two-finger zoom), trajectory coordinates, operation duration, and force changes, arranged chronologically. The element response sequence refers to an ordered set of continuous response data showing changes in the layout state (e.g., position movement, size scaling, show / hide switching) of interactive elements after receiving user gestures. The dynamic time warping algorithm is an algorithm for aligning time-series data, capable of calculating... The optimal matching path between the gesture input sequence and the element response sequence on the time axis is calculated. The time-series matching pair refers to the pair of data combinations formed by the gesture data at a certain moment in the gesture input sequence and the element response data at the corresponding moment in the element response sequence after processing by the dynamic time warping algorithm. Each matching pair contains a set of time-synchronized gesture operation information and element response information. The gesture-element linkage probability distribution map is a distribution map that is constructed based on the time-series matching pair and presents the distribution of the association strength between gestures and elements in a visual way. The horizontal axis in the map can represent the gesture type, the vertical axis can represent the interactive element type, and the value or color depth of the coordinate point represents the probability of a certain gesture and a certain element producing a linkage response.

[0121] Optionally, based on the temporal matching pair, the gesture-element linkage probability distribution map of the live broadcast room can be constructed by kernel density estimation (KDE) and three-dimensional spatial discretization. For example, a Gaussian kernel function can be used to perform kernel density estimation on the gesture type, element type, and linkage probability data in the temporal matching pair to obtain a continuous probability distribution model. Then, the three-dimensional space is discretized at a probability interval of 0.1 to generate a grid-like gesture-element linkage probability distribution map. The gesture-element association features of the live broadcast room can be extracted through the gesture-element linkage probability distribution map using a non-maximum suppression algorithm. For example, local extremum detection is performed on the probability values ​​of each grid point in the distribution map, and local maxima points with probability values ​​higher than the surrounding 8 neighboring grid points are retained to remove redundant non-maximum points, thereby extracting gesture-element association feature points with significant association strength.

[0122] Furthermore, by determining the layout change rate and effective operating range of the interactive elements based on the gesture-element association features, this embodiment of the invention can transform abstract gesture interaction rules into executable dynamic adjustment indicators. This provides a quantitative benchmark for achieving accurate matching between gesture operations and element responses, significantly enhancing the ability to ensure smooth operation and boundary controllability in complex interactive scenarios in live streaming rooms. The layout change rate refers to the speed at which the position, size, and other layout attributes of interactive elements change with user gesture operations in draggable layout interactions in live streaming rooms based on dynamic gesture recognition. For example, when a user drags the product display window in a live streaming room at a speed of 5 centimeters per second, the window moves with the gesture at a speed of 100 pixels per second. Here, 100 pixels per second is the layout change rate of the product display window. The effective operating range refers to the spatial boundary range within which interactive elements can respond to user dynamic gesture operations and make layout adjustments in a live streaming room scenario. For example, the draggable area of ​​the chat interaction panel in a live streaming room is limited to 30% of the screen area on the right side of the main live streaming screen. This range is the effective operating range within which the panel itself can move.

[0123] As an embodiment of the present invention, determining the layout change rate and effective operation range of the interactive element based on the gesture-element association features includes:

[0124] Based on the gesture-element association features, obtain the spatiotemporal sequence data and spatial coordinate data of the interactive element;

[0125] Based on the spatiotemporal sequence data, extract the gesture dynamics features corresponding to the interactive elements;

[0126] Based on the gesture dynamics features, a set of dynamic response parameters for the interactive elements is generated;

[0127] The layout change rate of the interactive elements is determined by the dynamic response parameter set.

[0128] Based on the spatial coordinate data, establish the spatial coordinate curve of the interactive element;

[0129] Identify the key coordinate points of the spatial coordinate curve, and determine the gesture convex hull boundary corresponding to the interactive element through the key coordinate points;

[0130] Based on the gesture convex hull boundary, construct the initial operation range constraint set of the interactive element;

[0131] By combining the dynamic response parameter set and the initial operation range constraint set, the effective operation range of the interactive element is determined.

[0132] The spatiotemporal sequence data refers to a sequence of spatial location information of interactive elements that changes over time, including the association data of timestamps and corresponding coordinates. The spatial coordinate data refers to the position coordinate information of interactive elements in two-dimensional or three-dimensional space. For example, on a touch screen, the (x,y) coordinate sequence of the user's finger touch position, or the set of three-dimensional coordinates (x,y,z) of the gesture trajectory. The gesture dynamics features refer to the features related to the motion state extracted from the gesture movement process, reflecting the motion law and physical characteristics of the gesture, such as the speed and acceleration of the gesture movement. The dynamic response parameter set refers to the set of parameters used to describe the dynamic changes of interactive elements with gestures. The parameter set includes sensitivity coefficients, damping coefficients, and inertia parameters for element layout changes. The spatial coordinate curve refers to a continuous curve formed by fitting discrete spatial coordinate data through an interpolation algorithm, used to describe the motion trajectory of interactive elements. The key coordinate points refer to points with significant characteristics in the spatial coordinate curve, usually points with obvious curvature changes or that play a decisive role in the curve shape. The gesture convex hull boundary refers to the smallest convex polygon boundary containing all key coordinate points, used to describe the spatial range contour of gesture operations. The initial operation range constraint set refers to the set of constraints that limit the effective operation area of ​​interactive elements, including parameters such as boundary range and interaction threshold.

[0133] To more clearly understand how the convex hull boundary of a gesture supports the determination of the effective operating range, refer to [reference needed]. Figure 2 The diagram shows a gesture convex hull boundary of a draggable layout interaction method for a live streaming room based on dynamic gesture recognition, according to an embodiment of the present invention. The diagram clearly marks the convex hull polygon formed by key coordinate points (AH), whose vertices correspond to the extreme positions of the gesture action. The area inside the polygon is the initially defined range of the gesture action. Specifically, after determining the gesture convex hull boundary through the key coordinate points of the spatial coordinate curve, the gesture convex hull boundary diagram can intuitively present the geometric shape of the boundary, such as the number of vertices contained in the boundary, the coordinate distribution of each vertex, and the spatial positional relationship between the boundary and interactive elements. This provides an intuitive geometric reference for the subsequent construction of the constraint set and the final determination of the effective operation range.

[0134] Optionally, the layout change rate of the interactive element can be determined by a second-order dynamic equation, such as a second-order equation based on a spring-damped model. This equation, combined with parameters like damping coefficient and sensitivity from the dynamic response parameter set, calculates the layout change rate of the interactive element over time. The equation can be expressed as m·a + c·v + k·x = F, where m represents the mass coefficient, c represents the damping coefficient, k represents the stiffness coefficient, and F represents the gesture force. Based on the spatial coordinate data, the spatial coordinate curve of the interactive element can be established using a B-spline interpolation algorithm. The key coordinate points of the spatial coordinate curve can be identified using the Douglas-Peucker algorithm. For example, by setting a distance threshold, points on the curve that deviate from the approximate straight line by more than the threshold are recursively retained, ultimately obtaining key coordinate points that represent the curve shape. The gesture convex hull boundary corresponding to the interactive element can be implemented using the Delaunay triangulation algorithm based on these key coordinate points.

[0135] This invention, through calculating the interface layout adaptability of interactive elements based on the layout change rate and the effective operating range, can optimize the adjustment accuracy of dynamic gestures on the live broadcast room interface layout. At the same time, it can ensure the matching degree between the layout changes of interface elements and user gesture operations, improve the smoothness of live broadcast room layout interaction and user operation experience. The interface layout adaptability refers to the degree of matching between the layout change state of interactive elements (such as the bullet screen area, gift panel, anchor window, etc.) in the live broadcast room and the layout change rate and effective operating range corresponding to the user's dynamic gestures.

[0136] Furthermore, as another embodiment of the present invention, the interface layout adaptability of the interactive element can be calculated using the following formula:

[0137]

[0138] in, This indicates the degree of UI layout adaptation for interactive elements. This represents the layout change rate of the i-th interactive element, in pixels per second (px / s). This represents the maximum rate of change of the layout of interactive elements. This represents the effective operation range of the i-th interactive element, in pixels (px). This represents the actual user interaction range of the i-th interactive element, in pixels (px). This represents the length of the layout vector of the i-th interactive element in the interface, in pixels (px). This represents the maximum length of the layout vector for interactive elements, in pixels (px). represents the weight adjustment coefficient, n represents the number of interactive elements, and i represents the index of the interactive element.

[0139] It should be noted that in this application, This represents the effective operational area of ​​the i-th interactive element, in pixels (px). Specifically, it represents the clickable area of ​​an interactive element on mobile / web interfaces, defined by pixel coordinates. For example, the effective operational area of ​​a button can be a rectangular area of ​​80px × 40px. (Formula) This ratio reflects the degree of dynamic adjustment of interactive elements; the closer the ratio is to 1, the more frequently the elements change. This is used to measure the degree of fit between design and user behavior; the closer the difference is to 1, the better the fit. The normalized layout vector length can suppress the dominant influence of large or distant elements on the result. It is worth noting that α∈[0,1], and the range of A is usually [0,2]. The larger the value of A, the higher the layout fit.

[0140] For example, suppose the live stream interface has 3 buttons: Button 1: =10px / s, =50px, =45px, =100px; Button 2: =5px / s, =30px, =35px, =150px; Button 3: =20px / s ), =80px, =80px, =200px, take =0.5, so A The results show that the interface adaptability is good.

[0141] S3. Based on the interactive element, collect the real-time gesture data of the target user, extract the spatial trajectory features and movement rhythm patterns corresponding to the interactive element based on the real-time gesture data, and identify the drag interaction intention of the target user based on the spatial trajectory features and the movement rhythm patterns.

[0142] This invention, through collecting real-time gesture data of the target user based on the interactive elements, can accurately capture the user's interactive intent towards specific interface elements, ensuring a precise match between the live stream's interactive response and the user's operational needs. The real-time gesture data refers to the raw data set that reflects the dynamic changes of gestures, collected in real time by sensors (such as cameras, depth sensors, motion sensing devices, etc.) during the target user's interaction with interactive elements in the live stream. Examples include the spatial coordinates of the gesture moving over time, the motion angles of each joint of the gesture, and the speed and acceleration of the gesture.

[0143] Furthermore, this embodiment of the invention extracts the spatial trajectory features and movement rhythm patterns corresponding to the interactive elements based on the real-time gesture data. This allows for accurate identification of the user's interaction trajectory patterns and operational rhythm features for specific interface elements, ensuring that subsequent interface layout adjustments are highly consistent with the user's interaction habits and operational intentions. The spatial trajectory features refer to the geometric features extracted from the user's real-time gesture data that describe the movement path of the gesture in three-dimensional or two-dimensional space. These features include the coordinates of the starting and ending points of the gesture movement, the curvature of the trajectory, changes in the direction of movement, and the spatial range covered by the trajectory. For example, when a user drags a gesture... The path of a finger swiping across the screen from right to center when viewing the panel, or the fan-shaped diffusion trajectory of two fingers from together to apart when zooming in and out of the broadcast window, are all spatial trajectory features. The so-called motion rhythm pattern refers to the periodicity, speed change pattern, or time distribution characteristics of the gesture action in the time dimension, reflecting the speed rhythm and temporal rhyme of the gesture operation, including the speed change trend of the gesture movement, the duration of the action, the pause interval, the cycle of repeated operation, etc. For example, the high-speed continuous movement gesture of a user quickly swiping the bullet screen area in a short period of time, or the low-speed smooth drag gesture of slowly adjusting the position of the chat window, correspond to different motion rhythm patterns.

[0144] Optionally, based on the real-time gesture data, the spatial trajectory features corresponding to the interactive elements can be extracted using a multi-scale feature fusion method. For example, the basic geometric parameters of the trajectory can be calculated using coordinates; the main direction of the trajectory can be calculated using principal component analysis (PCA); the degree of curvature can be described using the control point distribution of B-spline curves, etc. The motion rhythm patterns corresponding to the interactive elements can be extracted using a time-frequency domain analysis method. For example, the average velocity, velocity variance (reflecting stability), and frequency of acceleration peak occurrences can be calculated using a sliding window (such as a 50ms window); and the main frequency components of the interactive elements can be extracted by performing a Fourier transform on the velocity sequence.

[0145] This invention identifies the drag-and-drop interaction intent of the target user based on the spatial trajectory characteristics and the rhythmic patterns of the movement. This provides a basis for distinguishing between the user's effective operations and accidental touches, and can also avoid operation interruptions or a disconnect in experience caused by a mismatch between the interactive feedback and the actual operation intent. The drag-and-drop interaction intent refers to the specific goal or expected result that the user wants to achieve through the drag-and-drop action, inferred by analyzing the spatial trajectory characteristics and rhythmic patterns of the user's gestures. This includes intentions such as position adjustment, fine calibration, range filtering, and rapid transfer. For example, a user might want to quickly drag the chat interaction panel from the left side of the screen to the right side.

[0146] As an embodiment of the present invention, the step of identifying the drag interaction intent of the target user based on the spatial trajectory features and the motion rhythm pattern includes:

[0147] The spatial trajectory features and the motion rhythm patterns are normalized to generate a standardized feature set;

[0148] Extract the spatiotemporal correlation features of the standardized feature set, and identify the drag-and-drop operation pattern of the target user based on the spatiotemporal correlation features;

[0149] Analyze the state evolution pattern of the drag-and-drop operation mode;

[0150] Based on the state evolution law, generate the drag state transition matrix of the target user;

[0151] Based on the drag state transition matrix, a drag intent recognition network for the target user is constructed.

[0152] The drag-and-drop intent recognition network identifies the drag-and-drop interaction intent of the target user.

[0153] The standardized feature set refers to the feature set formed after standardizing the spatial trajectory features (such as trajectory length and curvature) and the motion rhythm (such as speed and frequency). For example, by using min-max standardization to convert the trajectory length (unit: pixels) and motion speed (unit: pixels / second) to the [0,1] interval, the differences in the numerical range of different features can be eliminated, resulting in a set containing parameters such as standardized trajectory curvature and standardized motion frequency. The spatiotemporal correlation feature refers to the comprehensive feature that reflects the intrinsic relationship between the user's gesture in space (spatial feature) and its motion rhythm (temporal feature) that changes over time in the drag-and-drop interaction scenario. This feature combines spatial features and temporal features. Features and spatiotemporal coupling characteristics, such as the trajectory changing from a straight line to an arc and the speed increasing by 30% within 0.5 seconds; the drag operation mode refers to the typical drag behavior type expressed by the user through body movements (such as gestures and body movements), for example, by feature clustering to identify fast long-distance straight drag (high speed, low curvature, long trajectory) and fine short-distance adjustment (low speed, high curvature, short trajectory); the state evolution law refers to the transformation law of drag operation mode over time; the drag state transition matrix refers to the matrix describing the transformation probability between different drag operation modes; the drag intention recognition network refers to the model used to map drag operation mode and interaction intention, which can output the user's intention category, such as moving to edge-aligned elements.

[0154] Optionally, the state evolution law of the drag operation mode can be analyzed using a Hidden Markov Model. Based on the state evolution law, the drag state transition matrix of the target user can be generated by the Baum-Welch algorithm. For example, based on a large amount of historical drag data, the state transition probability of the HMM can be estimated iteratively using the Baum-Welch algorithm to obtain a transition probability matrix such as 0.2 for fine adjustment → fast drag and 0.7 for fast drag → stop. Based on the drag state transition matrix, the drag intention recognition network of the target user can be constructed using a hybrid HMM + neural network model. For example, the state probability distribution of the current operation mode can be output using an HMM first, and then the distribution can be input into a neural network. The probability of intentions such as adjusting position, scaling, etc., can be output through a fully connected layer and a softmax activation function. The category corresponding to the highest probability value is the recognition result.

[0155] S4. Based on the interface layout adaptability, extract the gesture interaction response features of the target user during the dragging process of the interactive element, and generate personalized layout adjustment instructions for the interactive element according to the dragging interaction intention and the gesture interaction response features.

[0156] This invention, through its embodiment, extracts the gesture interaction response features of the target user during the dragging process of interactive elements based on the interface layout adaptability. This dynamically quantifies the real-time matching deviation between user operations and the interface layout, improving the intelligence of the live streaming room layout interaction and the smoothness of user operations. The gesture interaction response features refer to the set of features extracted during the target user's dragging operation of interactive elements, through correlation analysis of their gesture actions and interface layout adaptability, which can reflect the operation state, interaction feedback effect, and user operation habits. These features include operation dynamic features, feedback response features, and interaction matching features. For example, when the element alignment logic in the interface layout is clear (high adaptability), the frequency of the user's dragging deceleration and fine adjustment will increase (the feature value will become larger), indicating that the user is using layout feedback for precise operation.

[0157] As an embodiment of the present invention, the step of extracting the gesture interaction response features of the target user during the dragging process of the interactive element based on the interface layout adaptability includes:

[0158] The target user's drag speed during the dragging process of the interactive element is obtained, and the rate of change of the drag speed is calculated.

[0159] Based on the rate of change of speed, the dragging process is segmented to obtain the dragging operation stage;

[0160] Based on the drag-and-drop operation phase, identify the target user's current drag-and-drop operation intent and determine the current interface element type corresponding to the interactive element;

[0161] Define the adaptive threshold of the interface layout adaptability based on the current drag operation intent and the current interface element type;

[0162] Based on the adaptive threshold, the dragging behavior pattern of the target user during the dragging operation phase is divided.

[0163] Based on the drag-and-drop behavior pattern, calculate the cross-pattern feature offset of the target user;

[0164] Based on the cross-mode feature offset, the drag-and-drop operations of the target user are clustered to obtain a classification operation style group;

[0165] Collect the gesture dynamic data of the classified operation style groups, and filter out key indicators that are strongly correlated with the interface layout adaptability from the gesture dynamic data;

[0166] Based on the aforementioned key indicators, the gesture interaction response features of the target user are extracted.

[0167] The dragging speed refers to the displacement of the user's gesture (or interactive element) per unit time during the dragging process, usually measured in pixels per second. It reflects the speed of the dragging operation; for example, a finger dragging a gift panel on the screen moves 500 pixels per second. The rate of change of speed refers to the degree of change in dragging speed over time, i.e., the increase or decrease in speed per unit time (unit: pixels per second²). The dragging operation phase refers to dividing the complete dragging process into continuous time periods with clear operational intentions based on the characteristics of the rate of change of speed (such as whether it is zero, positive or negative, and its range). For example, the initiation phase (rate of change of speed > 0, speed increases from 0 to a threshold); the constant speed phase (…). The speed change rate is approximately 0, and the speed stabilizes within a certain range; during the deceleration phase (speed change rate < 0, speed drops from its peak to 0), the current drag operation intent refers to the specific operational purpose of the target user in the current stage, inferred from the drag operation stage and speed change characteristics, such as the intention to move quickly during the startup phase. The current interface element type refers to the functional attribute classification of the draggable interactive element in the live broadcast interface, such as the barrage switch, the host window, the gift animation panel, etc. The adaptive threshold refers to the interface layout adaptation threshold dynamically adjusted based on the current drag operation intent and the current interface element type, with a value range of 0-1. A higher value indicates a stricter adaptation requirement, for example, for the precise positioning intent + host... For windows (heavyweight elements), the adaptive threshold can be set to 0.8; for quick movement intent + bullet screen switch (lightweight elements), the adaptive threshold can be set to 0.4. The drag-and-drop behavior mode refers to the behavioral characteristic type of the target user during the current drag-and-drop operation, defined based on the comparison between real-time interface layout adaptation and the adaptive threshold. This includes free drag-and-drop mode and constrained adaptation mode. The cross-mode feature offset refers to the quantified change in core gesture features (such as trajectory curvature and speed fluctuation value) of the target user under different drag-and-drop behavior modes. For example, if the trajectory curvature is 0.3 in free mode and 0.05 in constrained mode, then the offset is 0.25. The drag-and-drop operation refers to the target user using gestures (such as finger swipes)... The specific actions (including motion sensing) that change the position, size, or state of interactive elements in the interface; the classification of operation style groups refers to the clustering results based on cross-modal feature offsets, dividing target users with similar dragging habits into groups, such as sensitive adaptive type and inertial retention type; the dynamic data refers to the raw data set reflecting the real-time state of gestures collected by sensors (such as cameras and touch screens) during dragging operations, including but not limited to the three-dimensional coordinate sequence of gestures, velocity / acceleration time series curves, touch pressure change values, operation duration, etc.; the key indicators refer to those selected from the dynamic data of gestures that have a statistically significant correlation with the interface layout adaptability (e.g., through correlation analysis, p < 0).05) It can also distinguish feature parameters of different operational style groups, such as constraint response latency and mode switching compliance.

[0168] Optionally, based on the drag-and-drop behavior pattern, the cross-mode feature offset of the target user can be calculated using a temporal feature alignment algorithm. For example, if the mean curvature of the drag trajectory of the target user is 0.3 in touch screen mode and 0.7 in mouse mode, the initial offset of 0.4 can be obtained through the temporal feature alignment algorithm. The clustering of the target user's drag-and-drop operations can be achieved using the Ward method. The key indicators strongly correlated with the interface layout adaptation can be selected from the gesture dynamic data by combining correlation analysis and principal component analysis. For example, gesture features are first extracted from the gesture dynamic data, then the Spearman rank correlation coefficient between each gesture feature and the interface layout adaptation is calculated, and then the significance test is performed on the strongly correlated gesture features. Features with p < 0.05 and |ρ| ≥ 0.3 are retained. Finally, PCA is used to reduce the dimensionality of the selected features and extract the key indicators with the strongest explanatory power.

[0169] Furthermore, this embodiment of the invention generates personalized layout adjustment instructions for the interactive elements based on the drag-and-drop interaction intent and the gesture interaction response characteristics. This can enhance the accuracy of the interface layout adaptation to user operating habits, and also improve the smoothness of live streaming interaction and user experience. The personalized layout adjustment instructions refer to layout adjustment commands automatically generated by the system for interactive elements in the interface based on the user's drag-and-drop operation intent in the live streaming interface and the response characteristics recognized by the system after the user's gesture interaction. For example, if a user drags the bullet screen area to the top of the screen multiple times, the system-generated instruction could be to fix the bullet screen display area at the top of the screen by default and adjust its width to 80% of the screen width commonly used by the user.

[0170] As an embodiment of the present invention, generating personalized layout adjustment instructions for the interactive elements based on the drag-and-drop interaction intent and the gesture interaction response characteristics includes:

[0171] Based on the drag-and-drop interaction intent and the gesture interaction response characteristics, reconstruct the motion trajectory of the interactive element;

[0172] Calculate the motion coherence index of the motion trajectory;

[0173] Based on the motion trajectory and the gesture interaction response features, identify the dragging behavior corresponding to the interactive element;

[0174] Extract the fine-tuning stability index from the dragging behavior;

[0175] The gesture performance index of the interactive element is quantified by combining the motion coherence index and the fine-tuning stability index.

[0176] Analyze the layout adaptation requirements corresponding to the gesture performance metrics;

[0177] Based on the gesture performance metrics and the layout adaptation requirements, construct layout adjustment decision rules for the interactive elements.

[0178] Based on the layout adjustment decision rules, identify typical drag-and-drop scenarios for the interactive elements;

[0179] Based on the typical drag-and-drop scenario, personalized layout adjustment instructions for the interactive elements are generated.

[0180] The motion trajectory refers to the spatial position change path of an interactive element over time in the interface coordinate system during dragging by the target user. The motion coherence index is a numerical indicator used to quantify the continuity of the motion trajectory. It evaluates the smoothness of element movement during dragging (e.g., the degree of no obvious stuttering or jumping) by calculating parameters such as the displacement change rate and direction angle deviation of adjacent coordinate points in the trajectory. The higher the value, the more coherent the dragging action. It can be calculated using the mean cosine similarity. The dragging behavior refers to a specific description of the target user's dragging operation method, combining the shape of the motion trajectory and the gesture interaction response characteristics. For example, fast straight dragging (the trajectory is a straight line, the speed is fast, and there is no pause), reciprocating fine-tuning dragging (the trajectory is short-distance reciprocating, the speed is slow, and there are frequent pauses). The fine-tuning stability index is an indicator that measures the operational stability of the target user when making small position adjustments during dragging (e.g., moving within ±5 pixels before final positioning). It is determined by calculating the deviation fluctuation range and the number of adjustments between the trajectory and the target position during the fine-tuning stage. The gesture performance index is a combination of the motion coherence index and the fine-tuning stability index, calculated by pre- The layout adaptation requirement, calculated using weights such as 60% for consistency and 40% for fine-tuning stability, refers to the specific requirements that the interface layout must meet to match user drag-and-drop operations, derived from gesture performance index analysis. For example, if the gesture performance index shows low user consistency (easy to lag), the layout adaptation requirement is to reduce element occlusion on the drag path; if the fine-tuning stability is high (precise operation), the requirement is to enhance the stickiness of the target position. The layout adjustment decision rule refers to the condition-action type established based on gesture performance index and layout adaptation requirements. Rules are used to clarify under what circumstances the interface should perform what layout adjustments. For example, when the fine-tuning stability index in the gesture performance index is ≤0.3 (i.e., the fine-tuning fluctuation is large), the element snapping range is automatically expanded to ±10 pixels. The typical drag-and-drop scenario refers to the representative drag-and-drop scenarios extracted from actual applications based on the layout adjustment decision rules. It includes clear user operation goals and interface environment characteristics. For example, in an area where multiple elements overlap, the user accurately locates and drags a small button across the entire screen, or the user quickly moves a large panel from the left to the right.

[0181] Optionally, based on the drag interaction intent and the gesture interaction response features, the motion trajectory of the interactive element can be reconstructed using a cubic spline interpolation algorithm; based on the motion trajectory and the gesture interaction response features, the drag behavior corresponding to the interactive element can be identified using a lightweight RandomForest model; and based on the gesture performance indicators and the layout adaptation requirements, the layout adjustment decision rules corresponding to the interactive element can be constructed using the Drools rule engine.

[0182] S5. Monitor the running status of the live room interface in real time, set adaptive layout constraints for the interactive elements based on the personalized layout adjustment instructions and the running status of the interface, and create a visual feedback view for the live room according to the adaptive layout constraints and the layout adjustment controller.

[0183] This invention, through real-time monitoring of the live stream interface's operational status, can accurately capture user feedback on the interface. This feedback is then used to adjust the interface layout or functional configuration, improving the ease of interaction between the user and the live stream. The interface operational status refers to the comprehensive state of the live stream interface during operation, including its technical performance, functional availability, and user interaction response, such as interface loading speed and the availability of features like the like button, comment box, and gift panel.

[0184] Optionally, the running status of the live streaming room interface can be monitored through front-end tracking. For example, monitoring code can be embedded in key components of the live streaming room interface. When the user operates these components or the component status changes, the monitoring code will record relevant events and data, such as the component loading completion time, click response time, status change logs, etc.

[0185] Furthermore, by setting adaptive layout constraints for the interactive elements based on the personalized layout adjustment instructions and the interface running state, this embodiment of the invention can accurately match user operating habits with real-time interface performance, thereby improving the adaptation accuracy of layout adjustment. At the same time, by dynamically optimizing the constraint strength, the stability and smoothness of interface interaction can be improved. The adaptive layout constraints refer to the parameter standards that can automatically adapt to the layout of interactive elements (such as buttons, input boxes, pop-ups, etc.) according to real-time changes in the scene during the dynamic adjustment of the interface layout.

[0186] As an embodiment of the present invention, setting adaptive layout constraints for the interactive elements based on the personalized layout adjustment command and the interface running state includes:

[0187] Based on the personalized layout adjustment instructions, the user operation preferences corresponding to the interactive elements are identified;

[0188] Based on the interface's operating status, determine the system's real-time performance indicators corresponding to the interactive elements.

[0189] Based on the user operation preferences and the system's real-time performance indicators, a dynamic layout constraint evaluation system for the interactive elements is constructed.

[0190] Using the dynamic layout constraint evaluation system, the layout adaptability score of the interactive elements under different constraint conditions is calculated:

[0191] Based on the layout adaptability score, a set of candidate layout constraints for the interactive elements is selected.

[0192] The feasibility of the candidate layout constraint set is verified to obtain the target layout constraint.

[0193] Based on the dynamic layout constraint evaluation system, key indicators of the target layout constraints are monitored in real time to establish a dynamic baseline comparison mechanism.

[0194] Based on the dynamic baseline comparison mechanism, a dual-channel feedback node is set for the target layout constraints;

[0195] By combining the dynamic layout constraint evaluation system, the dynamic baseline comparison mechanism, and the dual-channel feedback node, adaptive layout constraints are set for the interactive elements.

[0196] The user operation preferences refer to the stable operating habits and tendencies exhibited by users during interaction with interactive elements. For example, some users are accustomed to quickly dragging elements for large-scale movement, while others prefer slow dragging for precise positioning. The system real-time performance indicators refer to the quantitative parameters of system performance during the operation of interactive elements, including interface rendering frame rate, element response latency, and memory usage. The dynamic layout constraint evaluation system is a comprehensive analytical framework that integrates user operation preferences and system real-time performance indicators to evaluate the adaptability of different layout constraints. The layout adaptability score is the result of quantitatively evaluating layout constraints through the dynamic layout constraint evaluation system. It can be weighted from three dimensions: visual salience, ease of operation, and system load, with preference weighting ≥60%. The candidate layout constraint set refers to a combination of multiple layout constraints with a certain degree of adaptability selected based on the layout adaptability score. For example, this combination could include an element snapping range of 5px and an element maximum movement speed limit of 200px / ... The candidate conditions include an element snapping range of 10px and a maximum element movement speed limit of 300px / s. The feasibility verification process refers to the actual operation test and verification of each condition in the candidate layout constraint set to determine whether it can be effectively executed in the current interface environment. The key indicators refer to the core parameters used to measure the running effect of the target layout constraints, including element click accuracy, accidental touch rate, rendering time, etc. The dynamic baseline comparison mechanism refers to the mechanism of establishing a benchmark value for the key indicators of the target layout constraints based on the dynamic layout constraint evaluation system and comparing the real-time monitored indicators with the benchmark value. For example, the benchmark value for element click accuracy is set to 95%. When the real-time monitored value is lower than the benchmark, an early warning will be issued. The dual-channel feedback node refers to two ways to collect feedback on the running of layout constraints, including explicit feedback channels (such as users rating the interface operation experience through pop-up windows) and implicit feedback channels (such as the system automatically recording the smoothness of the user operation flow. If the user frequently cancels the operation, it indicates a poor experience).

[0197] Optionally, the user operation preferences corresponding to the interactive elements can be quantitatively determined by identifying the high-frequency operation areas of the interactive elements through behavioral trajectory clustering analysis. For example, by analyzing the trajectory of users dragging elements over the past week, it can be found that 70% of the operations are concentrated in the right side of the interface, thus determining that users prefer to place elements on the right side. The dynamic layout constraint evaluation system of the interactive elements can be constructed using multi-objective optimization algorithms, such as genetic algorithms. The feasibility verification of the candidate layout constraint set can be performed through A / B testing in a sandbox environment. For example, two candidate constraints can be run simultaneously in the sandbox, and the user operation efficiency and system operation status can be statistically analyzed to select the condition with better performance.

[0198] This invention creates a visual feedback view for the live streaming room based on the adaptive layout constraints and the layout adjustment controller. This view can map the adaptation relationship between gesture operations and layout constraints in real time, enhancing the user's perception of the interaction boundaries and improving the accuracy and efficiency of live streaming room layout adjustments. The visual feedback view refers to a visual interface that is presented to the user after integrating multiple information during the live streaming room layout adjustment process. It can intuitively reflect the layout adjustment status and related guidance. For example, when the user drags the gift panel in the live streaming room, the visual feedback view will display an elastic grid containing the area where the gift panel is located. If the gift panel and the bullet screen area conflict, a red exclamation mark will be displayed at the conflict point, and a dotted box will indicate the reasonable position that the gift panel can be moved to. The interface will also pop up a text prompt: "Please move the gift panel 2 cm to the left to avoid conflict."

[0199] As an embodiment of the present invention, the step of creating the visual feedback view of the live broadcast room based on the adaptive layout constraints and the layout adjustment controller includes:

[0200] The dynamic relaxation parameters of the adaptive layout constraints and the instruction conflict flag bits of the layout adjustment controller are collected synchronously.

[0201] The stretchable area of ​​the live streaming room is determined based on the dynamic relaxation parameter.

[0202] Perform decoding of the instruction conflict flag bit to determine the conflict type;

[0203] Based on the stretchable area, generate the elastic grid layer of the live broadcast room;

[0204] Based on the conflict type, set the fault diagnosis flag for the live broadcast room;

[0205] Identify the physical screen boundaries and key element lock areas of the live streaming room;

[0206] By combining the dynamic relaxation parameter, the physical screen boundary, and the key element locking area, an adjustable threshold for the elastic mesh layer is set;

[0207] Based on the adjustable threshold, the layout adjustment guide lines of the live broadcast room are generated;

[0208] The fault diagnosis identifier is associated with the conflict resolution of the layout adjustment controller to obtain fault operation guidance.

[0209] By integrating the elastic grid layer, the fault diagnosis indicator, the layout adjustment guide line, and the fault operation guide, a visual feedback view of the live broadcast room is created.

[0210] The dynamic relaxation parameter refers to the allowable range of deviation of layout elements from their initial position or size under adaptive layout constraints, such as element spacing and the variable ratio of size. The instruction conflict flag is an identifier used to mark whether there is a contradiction between multiple instructions received by the layout adjustment controller; for example, if the same element is simultaneously instructed to be enlarged and reduced, it can be considered an instruction conflict. The stretchable area refers to the area in the live broadcast interface that can be stretched or compressed according to screen size or layout requirements without affecting the display of key content, such as the background area and non-core information bars. The decoding process refers to parsing the encoded information of the instruction conflict flag to transform the abstract flag into specific conflict content, such as parsing a size adjustment conflict from flag 101. The conflict type refers to the specific category of instruction conflict, such as size conflict (simultaneous enlargement and reduction), position conflict (simultaneous left and right shift), and hierarchy conflict (contradictory element display priorities). The elastic grid layer refers to a virtual grid structure generated based on the stretchable area, used to constrain the adjustment range of layout elements. The fault diagnosis identifier is a visual marker used to intuitively indicate the location and type of conflict during layout adjustment, such as in the conflicting elements... A red exclamation mark icon is displayed next to the conflict area, or the conflict area is marked with a border of different colors. The physical screen boundary refers to the actual screen edge range of the display device (such as mobile phone, computer, tablet) where the live broadcast room is located. The key element locking area refers to the fixed area in the live broadcast room interface where the core content (such as the anchor window, interactive buttons) is located. This area is not allowed to be stretched, obscured or moved during layout adjustment. The adjustable threshold refers to the maximum allowable stretching / compression range of the elastic grid layer. For example, horizontal stretching should not exceed 20% of the original size. The layout adjustment guide line refers to the auxiliary lines displayed in the visual feedback view, used to indicate the adjustable direction, range or target position of the elastic grid layer. For example, a dashed box is used to mark the path where the element can be moved. The conflict solution refers to the preset strategy in the layout adjustment controller used to resolve different types of instruction conflicts. For example, when a size conflict occurs, the instruction to keep the original size is executed first. The association mapping processing refers to the process of establishing a one-to-one correspondence between the fault diagnosis identifier and the corresponding conflict solution. The fault operation guidance refers to the specific steps generated based on the association mapping processing results to guide the user or system to resolve layout conflicts. For example, clicking the unlock button can unlock the element and resolve the position conflict.

[0211] Optionally, the decoding of the instruction conflict flag can be implemented using Python's int(mark,2), and the association mapping between the fault diagnosis identifier and the conflict solution of the layout adjustment controller can be achieved by constructing a conflict type-solution mapping table. For example, all possible conflict types in the layout adjustment controller can be pre-classified and encoded (e.g., using numbers "1-5" to represent different types); and a corresponding conflict solution can be bound to each conflict type to form a basic mapping table.

[0212] S6. Based on the drag-and-drop interaction intent, the draggable interactive layout of the live streaming room is generated through the personalized layout adjustment command and the visual feedback view.

[0213] This invention generates a draggable interactive layout for the live streaming room based on the drag-and-drop interaction intent, the personalized layout adjustment instructions, and the visual feedback view. This ensures that the live streaming room layout accurately matches the operating habits of different users, improves the user's freedom of control over the layout, and achieves dynamic adaptation of the layout form in different live streaming scenarios. At the same time, it provides intuitive user behavior data support for the iterative innovation of live streaming room interaction design, fundamentally enhancing the content presentation quality and user participation of the live streaming room in complex interactive scenarios. The draggable interactive layout refers to a live streaming room layout form that supports free drag-and-drop adjustment by the system by capturing the user's drag-and-drop operation on elements in the live streaming room interface in real time, combining the personalized layout adjustment instructions of live streaming room elements, and the real-time generated visual feedback view.

[0214] Compared to the problems described in the background art, the embodiments of the present invention, by determining the drag permissions and operation boundaries of the interactive elements, can ensure that drag operations in the live broadcast room are conducted within a reasonable and controllable range. Furthermore, by constructing a layout adjustment controller for the interactive elements based on the drag permissions and operation boundaries, the embodiments of the present invention can integrate scattered permission rules and boundary restriction data to form a multi-dimensional, collaborative layout control system. This provides a structured framework for accurately matching user interaction intentions with element adjustment logic, ensuring orderly control of drag operations in complex live broadcast scenarios and improving the overall adaptability of interactive element layout adjustments. The present invention, by extracting the gesture-element association features of the live streaming room based on the layout adjustment controller, can significantly enhance the naturalness and intuitiveness of draggable interaction in the live streaming room, and improve the accuracy and flexibility of layout adjustment. Furthermore, by determining the layout change rate and effective operation range of the interactive elements based on the gesture-element association features, the present invention can transform abstract gesture interaction rules into executable dynamic adjustment indicators, providing a quantitative benchmark for achieving precise matching of gesture operations and element responses. This significantly enhances the ability to ensure operational smoothness and boundary controllability in complex interactive scenarios within a live streaming room. The present invention, by calculating the interface layout adaptability of the interactive elements based on the layout change rate and the effective operation range, can optimize the adjustment accuracy of dynamic gestures on the live broadcast room interface layout, while ensuring the matching degree between the layout changes of interface elements and user gesture operations, improving the smoothness of live broadcast room layout interaction and user operation experience. Furthermore, the present invention, by extracting the spatial trajectory features and movement rhythm patterns corresponding to the interactive elements based on the real-time gesture action data, can accurately identify the user's interaction trajectory patterns and operation rhythm features for specific interface elements, ensuring that subsequent interface layout adjustments are highly consistent with the user's interaction habits and operation intentions. The present invention, by identifying the target user's drag interaction intention based on the spatial trajectory features and movement rhythm patterns, can provide a basis for distinguishing between the user's effective operation and accidental touch behavior, while avoiding operation interruption or experience fragmentation caused by the mismatch between interaction feedback and actual operation intention. Furthermore, the present invention, by extracting the target user's gesture interaction response features during the dragging process of the interactive elements based on the interface layout adaptability, can dynamically quantify the real-time matching deviation between user operation and interface layout, improving the intelligence of live broadcast room layout interaction and the smoothness of user operation.This invention, through generating personalized layout adjustment instructions for interactive elements based on the drag-and-drop interaction intent and the gesture interaction response characteristics, can enhance the accuracy of the interface layout's adaptation to user operating habits, while also improving the smoothness and user experience of live streaming interactions. Furthermore, by setting adaptive layout constraints for interactive elements based on the personalized layout adjustment instructions and the interface's running state, this invention can accurately match user operating habits with real-time interface performance, thereby improving the accuracy of layout adjustment adaptation. Simultaneously, by dynamically optimizing the constraint strength, it can improve the stability of interface interactions. This invention, through creating a layout adjustment controller based on the adaptive layout constraints, can further enhance the accuracy of the layout adjustment adaptation. The visual feedback view of the live streaming room can map the adaptation relationship between gesture operations and layout constraints in real time, enhancing the user's perception of the interaction boundaries and improving the accuracy and efficiency of live streaming room layout adjustments. Finally, this embodiment of the invention generates a draggable interactive layout for the live streaming room based on the drag-and-drop interaction intent, the personalized layout adjustment command, and the visual feedback view. This ensures that the live streaming room layout accurately matches the operating habits of different users, increases the user's freedom of control over the layout, and achieves dynamic adaptation of layout forms in different live streaming scenarios. Simultaneously, it provides intuitive user behavior data support for iterative innovation in live streaming room interaction design, fundamentally enhancing the content presentation quality and user engagement in complex interactive scenarios. Therefore, the draggable layout interaction method and system for live streaming rooms based on dynamic gesture recognition provided by this embodiment of the invention can improve the convenience and accuracy of live streaming room layout adjustments.

[0215] like Figure 3 The diagram shown is a functional block diagram of a draggable layout interactive system for live streaming rooms based on dynamic gesture recognition, according to the present invention.

[0216] The draggable layout interaction system 200 for live streaming rooms based on dynamic gesture recognition, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the draggable layout interaction system for live streaming rooms based on dynamic gesture recognition may include a condition recognition module 201, an adaptation analysis module 202, an intent recognition module 203, an instruction generation module 204, a visual feedback module 205, and a layout output module 206. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0217] In this embodiment of the invention, the functions of each module / unit are as follows:

[0218] The condition recognition module 201 is used to obtain the interactive elements of the target user in the live broadcast room, determine the drag permission and operation boundary of the interactive elements, and construct the layout adjustment controller of the interactive elements based on the drag permission and the operation boundary.

[0219] The adaptation analysis module 202 is used to extract the gesture-element association features of the live broadcast room based on the layout adjustment controller, determine the layout change rate and effective operation range of the interactive elements according to the gesture-element association features, and calculate the interface layout adaptation degree of the interactive elements based on the layout change rate and the effective operation range.

[0220] The intent recognition module 203 is used to collect real-time gesture data of the target user based on the interactive element, extract spatial trajectory features and motion rhythm patterns corresponding to the interactive element based on the real-time gesture data, and identify the drag interaction intent of the target user based on the spatial trajectory features and the motion rhythm patterns.

[0221] The instruction generation module 204 is used to extract the gesture interaction response features of the target user during the dragging process of the interactive element based on the interface layout adaptability, and generate personalized layout adjustment instructions for the interactive element according to the drag interaction intention and the gesture interaction response features.

[0222] The visual feedback module 205 is used to monitor the interface operation status of the live room in real time, set adaptive layout constraints for the interactive elements based on the personalized layout adjustment instructions and the interface operation status, and create a visual feedback view of the live room according to the adaptive layout constraints and the layout adjustment controller.

[0223] The layout output module 206 is used to generate a draggable interactive layout for the live streaming room based on the drag-and-drop interaction intent, through the personalized layout adjustment command and the visual feedback view.

[0224] In detail, the modules in the draggable layout interactive system 200 for live streaming rooms based on dynamic gesture recognition described in this embodiment of the invention employ the same methods as described above. Figure 1 This method uses the same technique as the one described above for a draggable layout interaction method for a live streaming room based on dynamic gesture recognition, and can produce the same technical effect, so it will not be repeated here.

[0225] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0226] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A live room dragable layout interaction method based on dynamic gesture recognition, characterized in that, The method comprises: acquiring an interactive element of a target user in a live room, determining a drag permission and an operation boundary of the interactive element, and constructing a layout adjustment controller of the interactive element according to the drag permission and the operation boundary; based on the layout adjustment controller, extracting gesture-element association features of the live room, determining a layout change rate and an effective operation range of the interactive element according to the gesture-element association features, and calculating an interface layout adaptation degree of the interactive element based on the layout change rate and the effective operation range; according to the interactive element, collecting real-time gesture action data of the target user, extracting spatial trajectory features and motion rhythm rules corresponding to the interactive element based on the real-time gesture action data, and identifying a drag interaction intention of the target user according to the spatial trajectory features and the motion rhythm rules; based on the interface layout adaptation degree, extracting gesture interaction response features of the target user in the drag process of the interactive element, and generating a personalized layout adjustment instruction of the interactive element according to the drag interaction intention and the gesture interaction response features; real-time monitoring of the interface running state of the live room, setting adaptive layout constraint conditions of the interactive element based on the personalized layout adjustment instruction and the interface running state, and creating a visual feedback view of the live room according to the adaptive layout constraint conditions and the layout adjustment controller; based on the drag interaction intention, generating a draggable interactive layout of the live room through the personalized layout adjustment instruction and the visual feedback view; according to the drag permission and the operation boundary, constructing a layout adjustment controller of the interactive element, comprising: acquiring operation permission conditions corresponding to the drag permission and the operation boundary; extracting key restriction parameters in the operation permission conditions; identifying the spatial topological relationship between the interactive elements; calculating the correlation coefficient of the interactive elements according to the spatial topological relationship; based on the key restriction parameters and the correlation coefficient, generating a control priority queue of the interactive elements; according to the control priority queue, setting a hierarchical adjustment rule of the interactive elements, and establishing a rule conflict arbitration mechanism of the hierarchical adjustment rule; combining the control priority queue, the hierarchical adjustment rule and the rule conflict arbitration mechanism, constructing the layout adjustment controller of the interactive elements; based on the layout adjustment controller, extracting gesture-element association features of the live room, comprising: based on the layout adjustment controller, collecting gesture input sequences and element response sequences of the live room; using a dynamic time warping algorithm to perform synchronous alignment processing on the gesture input sequences and the element response sequences to obtain a time sequence matching pair; based on the time sequence matching pair, constructing a gesture-element linkage probability distribution graph of the live room; extracting gesture-element association features of the live room through the gesture-element linkage probability distribution graph; according to the gesture-element association features, determining a layout change rate and an effective operation range of the interactive element, comprising: according to the gesture-element association features, acquiring spatio-temporal sequence data and spatial coordinate data of the interactive element; based on the spatio-temporal sequence data, extracting gesture dynamics features corresponding to the interactive element; according to the gesture dynamics features, generating a dynamic response parameter set of the interactive element; determining the layout change rate of the interactive element through the dynamic response parameter set; Based on the spatial coordinate data, a spatial coordinate curve of the interactive element is established; Key coordinate points of the spatial coordinate curve are identified, and a gesture convex hull boundary corresponding to the interactive element is determined through the key coordinate points; According to the gesture convex hull boundary, an initial operation range constraint set of the interactive element is constructed; An effective operation range of the interactive element is determined in combination with a dynamic response parameter set and the initial operation range constraint set; Based on the personalized layout adjustment instruction and the interface running state, adaptive layout constraint conditions of the interactive element are set, including: According to the personalized layout adjustment instruction, a user operation preference corresponding to the interactive element is identified; Based on the interface running state, a system real-time performance index corresponding to the interactive element is determined; According to the user operation preference and the system real-time performance index, a dynamic layout constraint evaluation system of the interactive element is constructed; Through the dynamic layout constraint evaluation system, a layout adaptation degree score of the interactive element under different constraint conditions is calculated: According to the layout adaptation degree score, a candidate layout constraint condition set of the interactive element is screened out; The candidate layout constraint condition set is subjected to feasibility verification processing to obtain a target layout constraint condition; Based on the dynamic layout constraint evaluation system, key indicators of the target layout constraint condition are monitored in real time to establish a dynamic baseline comparison mechanism; According to the dynamic baseline comparison mechanism, a double-channel feedback node of the target layout constraint condition is set; In combination with the dynamic layout constraint evaluation system, the dynamic baseline comparison mechanism and the double-channel feedback node, adaptive layout constraint conditions of the interactive element are set; According to the adaptive layout constraint conditions and the layout adjustment controller, a visual feedback view of the live room is created, including: Synchronously collecting a dynamic relaxation degree parameter of the adaptive layout constraint condition and an instruction conflict marker bit of the layout adjustment controller; According to the dynamic relaxation degree parameter, a stretchable area of the live room is determined; Decoding processing of the instruction conflict marker bit is performed to determine a conflict type; Based on the stretchable area, an elastic grid layer of the live room is generated; According to the conflict type, a fault diagnosis identifier of the live room is set; Physical screen boundaries and key element lock areas of the live room are identified; In combination with the dynamic relaxation degree parameter, the physical screen boundaries and the key element lock areas, an adjustable threshold of the elastic grid layer is set; Based on the adjustable threshold, a layout adjustment guide line of the live room is generated; The fault diagnosis identifier is associated with a conflict resolution scheme of the layout adjustment controller for mapping processing to obtain a fault operation guide; The elastic grid layer, the fault diagnosis identifier, the layout adjustment guide line and the fault operation guide are integrated to create the visual feedback view of the live room.

2. The live room dragable layout interaction method based on dynamic gesture recognition according to claim 1, wherein, According to the spatial trajectory features and the motion rhythm rules, the target user's drag interaction intention is identified, including: The spatial trajectory features and the motion rhythm rules are normalized to generate a standardized feature set; The spatio-temporal correlation features of the standardized feature set are extracted, and based on the spatio-temporal correlation features, a drag operation mode of the target user is identified; The state evolution rule of the drag operation mode is analyzed; According to the state evolution rule, a drag state transition matrix of the target user is generated; Based on the drag state transition matrix, a drag intention recognition network of the target user is constructed; The dragging interaction intention of the target user is recognized through a dragging intention recognition network.

3. The live room dragable layout interaction method based on dynamic gesture recognition according to claim 1, wherein, Based on the interface layout adaptation degree, gesture interaction response features of the target user in the dragging process of the interactive element are extracted, including: The dragging speed of the target user in the dragging process of the interactive element is obtained, and the speed change rate of the dragging speed is calculated; According to the speed change rate, segmented processing of the dragging process is performed to obtain a dragging operation stage; Based on the dragging operation stage, the current dragging operation intention of the target user is recognized, and the current interface element type corresponding to the interactive element is determined; Through the current dragging operation intention and the current interface element type, an adaptive threshold of the interface layout adaptation degree is defined; According to the adaptive threshold, the dragging behavior mode corresponding to the target user in the dragging operation stage is divided; Based on the dragging behavior mode, a cross-mode feature offset of the target user is calculated; According to the cross-mode feature offset, the dragging operation of the target user is clustered to obtain a classified operation style group; Gesture dynamic data of the classified operation style group are collected, and key indicators strongly associated with the interface layout adaptation degree are screened from the gesture dynamic data; Based on the key indicators, gesture interaction response features of the target user are extracted.

4. The live room dragable layout interaction method based on dynamic gesture recognition according to claim 1, wherein, According to the dragging interaction intention and the gesture interaction response features, a personalized layout adjustment instruction of the interactive element is generated, including: According to the dragging interaction intention and the gesture interaction response features, the motion trajectory of the interactive element is reconstructed; The motion continuity index of the motion trajectory is calculated; Based on the motion trajectory and the gesture interaction response features, a dragging behavior corresponding to the interactive element is recognized; A fine-tuning stability indicator in the dragging behavior is extracted; Combining the motion continuity index and the fine-tuning stability indicator, a gesture performance indicator of the interactive element is quantified; The layout adaptation demand corresponding to the gesture performance indicator is analyzed; Based on the gesture performance indicator and the layout adaptation demand, a layout adjustment decision rule corresponding to the interactive element is constructed; According to the layout adjustment decision rule, a typical dragging scenario of the interactive element is recognized; Based on the typical dragging scenario, a personalized layout adjustment instruction of the interactive element is generated.

5. A live room draggable layout interaction system based on dynamic gesture recognition, the system implements the method of claim 1, characterized in that, The system comprises: A condition recognition module is configured to obtain an interactive element of a target user in a live room, determine a dragging permission and an operation boundary of the interactive element, and construct a layout adjustment controller of the interactive element according to the dragging permission and the operation boundary; An adaptation analysis module is configured to extract a gesture-element association feature of the live room based on the layout adjustment controller, determine a layout change rate and an effective operation range of the interactive element according to the gesture-element association feature, and calculate an interface layout adaptation degree of the interactive element based on the layout change rate and the effective operation range; An intention recognition module is configured to collect real-time gesture action data of the target user according to the interactive element, extract spatial trajectory features and motion rhythm rules corresponding to the interactive element based on the real-time gesture action data, and recognize a dragging interaction intention of the target user according to the spatial trajectory features and the motion rhythm rules. The instruction generation module is configured to extract gesture interaction response characteristics of the target user in a process of dragging the interactive element based on the interface layout adaptation degree, and generate a personalized layout adjustment instruction of the interactive element according to the drag interaction intention and the gesture interaction response characteristics. The visual feedback module is configured to monitor a running state of the interface of the live broadcast room in real time, set adaptive layout constraint conditions of the interactive element based on the personalized layout adjustment instruction and the running state of the interface, and create a visual feedback view of the live broadcast room according to the adaptive layout constraint conditions and the layout adjustment controller. The layout output module is configured to generate a draggable interactive layout of the live broadcast room based on the drag interaction intention, the personalized layout adjustment instruction and the visual feedback view.

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