Tidal play material collection method and system based on big data

Abstract

The invention relates to the technical field of data acquisition, in particular to a tidal play material acquisition method and system based on big data, and the method comprises the following steps: obtaining an augmented reality creation data stream uploaded by a user terminal through an authorization API interface; performing cross-device feature alignment on the creation data stream to generate a standardized culture gene unit, the culture gene unit comprising a visual expression vector, an interactive semantic tag and a group contribution coefficient; the connection strength between the network nodes of the dynamic culture topology network is jointly determined by the collaborative editing association degree of the user and the culture propagation entropy; and according to node betweenness centrality distribution of the dynamic culture topology network, activating the high-potential culture gene unit and generating a decentralized material acquisition queue. According to the method and the device, the timeliness of material acquisition and the system scheduling efficiency are improved, and the problems of response lag, resource waste, content redundancy and the like in a traditional material acquisition scheme are solved.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition technology, and in particular to a method and system for acquiring trendy toy materials based on big data. Background Art

[0002] With the development of augmented reality (AR), blockchain and creator economy, the creation of trendy toys (trendy plays) has gradually shown the content ecological characteristics of cross-terminal collaboration, real-time interaction and high-frequency adaptation. A large number of users use AR tools to perform three-dimensional painting, material combination and cultural derivative creation, forming a distributed content flow with "cultural genes" as the basic unit. The collection of trendy play materials is not only related to new product development, IP operation and content asset management, but also becomes a key link in cultural evolution and user value participation.

[0003] However, existing methods for collecting trendy toy materials mainly rely on targeted screening by platforms or user-uploaded materials, which have the following technical limitations:

[0004] Coarse content recognition granularity and delayed response: Traditional methods lack fine-grained awareness of the entire creative process and are unable to promptly capture creative units with high dissemination potential, resulting in material updates lagging behind changes in user interests.

[0005] The lack of network structure perception and the inability to model dissemination characteristics make it difficult to identify the dissemination paths, evolutionary trends, and group co-creation relationships of creative content in collaborative networks, making it impossible to effectively build a content evaluation model oriented towards cultural dissemination.

[0006] Collection task scheduling is rigid and difficult to adapt to multi-source constraints: Traditional material collection tasks are mostly executed based on static rules or centralized processes. They lack dynamic adaptation mechanisms to multiple constraints such as geographic location, cultural policies, and equipment capabilities, resulting in low collection efficiency and high compliance risks. Summary of the Invention

[0007] The present invention provides a method and system for collecting trendy toy materials based on big data, which integrates an intelligent trendy toy material collection method that integrates multi-source creative behavior, dissemination structure and collection compliance, thereby improving the timeliness, accuracy and strategic flexibility of material acquisition.

[0008] The method for collecting trendy toy materials based on big data includes the following steps:

[0009] S1: Obtaining the augmented reality creation data stream uploaded by the user terminal through the authorized API interface, wherein the creation data stream includes a 3D brush stroke trajectory, a material selection sequence, and a multi-person collaborative editing log;

[0010] S2: performing cross-device feature alignment on the creative data stream to generate a standardized cultural gene unit, wherein the cultural gene unit includes a visual expression vector, an interactive semantic label, and a group contribution coefficient;

[0011] S3: Input cultural gene units into the spatiotemporal evolution prediction model and output a dynamic cultural topological network. The connection strength between network nodes in the dynamic cultural topological network is determined by the user collaborative editing correlation and cultural communication entropy.

[0012] S4: Based on the node betweenness centrality distribution of the dynamic cultural topology network, high-potential cultural gene units are activated and a decentralized material collection queue is generated.

[0013] Optionally, the S1 specifically includes:

[0014] S11: When the user launches the augmented reality creation tool, an authorization request is initiated based on the OAuth 2.0 protocol to obtain access rights to the user's device sensors and collaborative editing data sharing permissions;

[0015] S12: Capturing three-dimensional pen stroke trajectory data in real time through an encrypted data pipeline, the three-dimensional pen stroke trajectory data including a spatial coordinate sequence, a pen stroke pressure value, and a device gyroscope attitude parameter, and encapsulating the data into a streaming data packet marked with a timestamp;

[0016] S13: parsing the operation event chain in the multi-person collaborative editing log, wherein the multi-person collaborative editing log records the editing operation type, material selection time sequence, and version conflict resolution mark of each participating user;

[0017] S14: The streaming data packet is spatiotemporally aligned with the collaborative editing log to generate a creative data stream with a user anonymous identifier, and is differentially privately processed through an edge node before being uploaded to a central server.

[0018] Optionally, the S2 specifically includes:

[0019] S21: Construct a device-independent reference coordinate system to map the 3D pen stroke trajectories of different terminals to a unified spatial grid, and eliminate the differences in motion data collection between devices through an adaptive sampling rate compensation algorithm;

[0020] S22: Using a multi-head attention mechanism to parse the collaborative editing log and extract the interaction semantic tag set, which includes the operation intention classification code, material combination pattern code, and conflict resolution strategy code;

[0021] S23: extracting a visual expression vector based on a spatiotemporal graph convolutional network, wherein the visual expression vector integrates geometric topological features of the brush stroke trajectory, material reflection characteristics, and light and shadow change parameters;

[0022] S24: Calculate the group contribution coefficient, which is determined by the influence diffusion weight of the user's editing operation, the contribution transmission rate in the version inheritance chain, and the efficiency of reaching consensus in the collaborative session;

[0023] S25: Encapsulating the visual expression vector, interactive semantic tag set and group contribution coefficient into a cultural gene unit with a time and space stamp, and storing it in a distributed cultural gene library.

[0024] Optionally, the group contribution coefficient is defined as:

[0025] γ j =α·w j +β·μ j +δ·η j , where γ j represents the group contribution coefficient of the jth user, w j represents the influence diffusion weight of the jth user editing operation, μ j represents the contribution transmission rate of the jth user's editing operation in the version inheritance chain, η j represents the efficiency of consensus in the j-th user collaborative session, and α, β, and δ represent weight coefficients.

[0026] Optionally, the influence diffusion weight w j Calculated as: Among them, C j is the total number of times user j’s content is cited or modified by others, T j represents the total number of editing operations of user j (including unreferenced ones);

[0027] The contribution transfer rate μ j Calculated as: Among them, R j Indicates the number of times user j’s content is retained in subsequent versions, N j The total number of content units created for user j;

[0028] The consensus reaching efficiency η j Calculated as: Among them, A j The number of valid edit suggestions submitted by user j (adopted or merged), I j represents the total number of collaborative interaction rounds in which user j participates.

[0029] Optionally, the S3 specifically includes:

[0030] S31: Deconstructing the meme unit into a time evolution feature matrix and a spatial correlation tensor, wherein the time evolution feature matrix includes the meme unit evolution trend parameters corresponding to the time and space stamps, and the spatial correlation tensor encodes the collaborative creation relationship between different user terminals;

[0031] S32: A spatiotemporal graph neural network is used to jointly model feature matrices and tensors. In the temporal dimension, gated recurrent units are used to capture the mutation inflection points of cultural elements, while in the spatial dimension, a graph attention mechanism is used to calculate the influence propagation path of user creative behavior.

[0032] S33: Constructing a user association subgraph based on the collaborative editing log and calculating the user collaborative editing association degree, where the association degree is determined by the number of design element inheritances between users, conflict resolution response speed, and collaborative session depth;

[0033] S34: Quantify the efficiency of element diffusion through the cultural communication entropy algorithm. The entropy value is calculated using the Shannon information entropy formula. The input parameters include the number of element replication and mutation, the cross-community propagation delay, and the length of the user's secondary creation derivative chain.

[0034] S35: Perform nonlinear fusion of user collaborative editing correlation and cultural communication entropy to generate a dynamic connection strength coefficient. When the dynamic connection strength coefficient exceeds a preset threshold, a bidirectional enhanced connection is established in the cultural topology network and the cultural gene mutation channel is activated.

[0035] Optionally, the user association subgraph is represented as: in, is the user collaborative association subgraph, which represents the interactive relationship network between users formed in the collaborative creation process. V is the node set in the graph, which represents all users participating in the collaborative creation. Each node corresponds to a user anonymous identifier. E is the edge set in the graph, which represents the directed or undirected connection between users with collaborative editing relationships. Each edge has an edge weight, which represents the degree of user collaborative editing association.

[0036] Optionally, the edge weights in the user association subgraph are expressed as: κ i,j represents the number of design element inheritances between users i and j, τ i,j represents the average response time required to resolve conflicts between two users, σ i,j represents the collaborative session depth, i.e., the number of collaborative editing steps, and λ1, λ2, and λ3 represent weighting coefficients.

[0037] Optionally, the S4 specifically includes:

[0038] S41: Based on the improved Brandes algorithm, the real-time betweenness centrality of each node is calculated, and the cultural communication entropy is introduced as the edge weight correction factor to generate a dynamic betweenness distribution heat map;

[0039] S42: Set an activation threshold. When the betweenness centrality value of a node exceeds the activation threshold and the associated cultural transmission entropy is on an upward trend, the high-potential gene unit activation event is triggered.

[0040] S43: Construct a cross-chain index for the activated gene units and generate a collection task package including time and space constraints between distributed nodes through smart contracts. The task package encapsulates the following parameters:

[0041] Gene unit traceability hash;

[0042] A whitelist of terminal device fingerprints that are allowed to be collected;

[0043] Maximum propagation delay tolerance threshold.

[0044] S44: Using the PBFT (Practical Byzantine Fault Tolerance) consensus mechanism, edge nodes verify the legitimacy of collection tasks and generate a decentralized collection queue. The priority of tasks in the queue is determined by the centrality gradient of the gene unit betweenness and the expected value of secondary creation.

[0045] The trendy toy material collection system based on big data is used to implement the above collection method and includes the following modules:

[0046] The data acquisition module obtains the augmented reality creation data stream uploaded by the user terminal and extracts the 3D brush stroke trajectory, material selection sequence and multi-person collaborative editing log;

[0047] A feature alignment and gene generation module, configured to perform cross-device feature alignment on the creative data stream and generate standardized cultural gene units including visual expression vectors, interactive semantic labels, and group contribution coefficients;

[0048] A topological modeling module is used to construct a spatiotemporal evolution prediction model based on the cultural gene unit and output a dynamic cultural topological network, whose network node connection strength is determined by the user collaborative editing correlation and cultural communication entropy;

[0049] The task scheduling module is used to activate high-potential cultural gene units and generate a decentralized material collection queue based on the node betweenness centrality distribution of the topological network.

[0050] Beneficial effects of the present invention:

[0051] The present invention integrates the trendy toy material collection method of cultural communication feature perception, dynamic network evolution modeling and decentralized collection and scheduling, which can realize the accurate identification, compliant activation and intelligent collection of high-potential cultural gene units under multiple constraints, improve the timeliness of material acquisition and system scheduling efficiency, and solve the problems of response lag, resource waste and content redundancy in traditional material collection solutions, and has broad application and promotion value.

[0052] The present invention introduces cultural communication entropy as an edge weight correction factor and integrates the Brandes betweenness centrality algorithm to realize dynamic node heat calculation for communication efficiency. By constructing a time-attenuated weighted heat map, the system can identify high-potential cultural gene units in active communication cycles in real time. This mechanism effectively avoids the dependence of traditional graph centrality indicators on static structures, has stronger time sensitivity and communication orientation, and provides a data-driven basis for subsequent collection decisions.

[0053] After activating high-potential genes, this method constructs a task package based on a cross-chain index. This package encapsulates multiple collection constraints, including a whitelist of terminal device fingerprints, maximum propagation delay, geo-fencing, and cultural taboo time windows. It then schedules collection tasks in a distributed manner using a practical Byzantine Fault Tolerant (PBFT) consensus mechanism, embedding a cultural compliance verification layer to automatically filter requests that do not comply with regional cultural policies. This mechanism ensures the legality, compliance, and precise control of material collection.

[0054] This method prioritizes collection tasks based solely on the rate of change of dynamic betweenness centrality, simplifying the scheduling model structure and improving response computational efficiency. Compared to scheduling strategies that rely on complex predictive models, this method prioritizes tasks based on the real-time evolution of node influence within the network. This allows for rapid adaptation to changes in communication hotspots, dynamically adjusting the order of collection resource allocation, and effectively improving the processing throughput and decision-making agility of edge nodes in material collection tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of a collection method according to an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the functional modules of the acquisition system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0059] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0060] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0061] like Figure 1 As shown in FIG, the method for collecting trendy toy materials based on big data includes the following steps:

[0062] S1: Obtain the augmented reality creation data stream uploaded by the user terminal through the authorized API interface. The creation data stream includes 3D brush stroke trajectories, material selection sequences, and multi-person collaborative editing logs;

[0063] S2: Align the creative data stream across devices to generate standardized cultural gene units. Cultural gene units include visual expression vectors, interaction semantic labels, and group contribution coefficients.

[0064] S3: Input cultural gene units into the spatiotemporal evolution prediction model and output a dynamic cultural topological network. The connection strength between network nodes in the dynamic cultural topological network is determined by the user collaborative editing correlation and cultural communication entropy.

[0065] S4: Based on the node betweenness centrality distribution of the dynamic cultural topology network, high-potential cultural gene units are activated and a decentralized material collection queue is generated.

[0066] S1 specifically includes:

[0067] S11: When the user starts the augmented reality creation tool, an authorization request is initiated based on the OAuth2.0 protocol to obtain sensor access rights of the user device and collaborative editing data sharing permission.

[0068] S12: Capture 3D pen stroke trajectory data in real time through the encrypted data pipeline. The 3D pen stroke trajectory data is expressed as: T(t)={(x i ,y i ,z i,p i ,θ i ,φ i ,ψ i )|i=1,2,...,n}, where t represents the current timestamp, (x i ,y i ,z i ) represents the three-dimensional space coordinates of the i-th pen touch point, p i Indicates the pen pressure value of the pen touch point, (θ i ,φ i ,ψ i ) represents the Euler angle attitude parameters provided by the device's gyroscope, corresponding to roll, pitch, and yaw angles, and n is the number of trajectory points captured in the current time window;

[0069] The above data is encapsulated into a streaming data packet with a timestamp, represented as follows: Where P represents the set of brush flow data packets, t k is the acquisition time of the kth time slice, and m is the number of time slices.

[0070] S13: Analyze the operation event chain in the multi-person collaborative editing log. Suppose the operation log is:

[0071] L={(u j ,a j ,s j (t),r j )|j=1,2,...,q}, where u j represents the anonymous identifier of participating user j, a j Indicates the editing operation type of the participating user j (such as adding strokes, modifying materials, undoing operations, etc.), s j (t) represents the material selection time series of the jth user at time t, r j represents the version conflict resolution marker corresponding to the jth user, and q is the total number of collaborative editing users (or number of records) recorded in the log.

[0072] S14: Perform spatiotemporal alignment on the streaming data packet P and the collaborative editing log L to obtain the user-level authoring data stream D: T j (t) represents the three-dimensional pen stroke trajectory data of the h-th user at time t. Before uploading, the differential privacy processing function is executed on the edge computing node: in, is the uploaded data after disturbance, Denotes a differential privacy mechanism with privacy budget ∈.

[0073] S2 specifically includes:

[0074] S21: Construct a device-independent reference coordinate system and assume that the unified spatial grid Q is:

[0075] Q={q b |b=1,2,...,N q}; Combine the three-dimensional stroke trajectory T from different terminals i (t) = {(x i ,y i ,z i ,p i ,θ i ,φ i ,ψ i )}, mapped to the node set in the grid Q, and the adaptive sampling rate compensation algorithm S adapt Unify the trajectory sampling rate and output a standardized trajectory set: Where Δt i represents the original sampling interval of the i-th device, The trajectory data is normalized.

[0076] S22: Use multi-head attention mechanism to collaboratively edit log L = {(u j ,a j ,s j (t),r j )} to encode and extract the interaction semantic label set: E j =MultiHeadAttn(L j );

[0077] Output label vector: in, Indicates the classification code of the operation intention (such as create, modify, delete, etc.), Indicates the material combination mode encoding, Indicates the version conflict resolution strategy encoding.

[0078] S23: Input the normalized trajectory into the spatiotemporal graph convolutional network (ST-GCN) to extract the visual expression vector:

[0079] Among them, V j represents the visual expression vector of the jth user, M j (t) represents the time-varying sequence of the material reflection parameters selected by the user, L j Representing the context of dynamic light and shadow changes in which the user participates (such as light source direction, light intensity changes, etc.), the network integrates the geometric topological features of the brushstrokes, the reflective properties of the material, and the dynamic light and shadow changes.

[0080] S24: Calculate the group contribution coefficient γ j , defined as a weighted combination of the following three factors:

[0081] γ j =α·w j +β·μ j +δ·η j , where w j represents the influence diffusion weight of the editing operation (i.e., the frequency of its content being cited by subsequent editors), μ j represents the contribution transfer rate in the version inheritance chain (i.e., the degree to which the user content continues in subsequent versions), η j It represents the efficiency of consensus in collaborative sessions (such as average response delay and number of effective interaction rounds), and α, β, and δ represent weight coefficients, which are used to balance the influence of the three contribution factors.

[0082] α=0.4: The influence diffusion weight is slightly higher, emphasizing the spreadability of the content;

[0083] β=0.35: Version inheritance stability has a medium weight;

[0084] δ=0.25: Synergistic efficiency is a supplementary factor with a slightly lower weight.

[0085] Influence diffusion weight w j Calculated as: Among them, C j is the total number of times user j’s content is cited or modified by others, T j represents the total number of editing operations of user j (including unreferenced ones);

[0086] Contribution transfer rate μ j Calculated as: Among them, R j Indicates the number of times user j’s content is retained in subsequent versions, N j The total number of content units created for user j;

[0087] Consensus reaching efficiency η j Calculated as: Among them, A j The number of valid edit suggestions submitted by user j (adopted or merged), I j represents the total number of collaborative interaction rounds in which user j participates.

[0088] S25: Encapsulate the above-extracted results into a cultural gene unit: G=(V j ,E j ,γ j ,t j ,loc j ), where G is the cultural gene unit, t j Timestamp generated for the meme unit, loc jThe spatial identifier generated for the unit (such as the user's location or AR creation scene location information).

[0089] 1. Use the multi-head attention mechanism to encode the collaborative editing log and extract the interaction semantic label set as follows:

[0090] The collaborative editing log contains the operational behaviors of multiple users in different time periods, such as material selection, brush stroke undo, version conflict resolution, etc. This data carries behavioral intentions, operational logic, and collaborative relationships.

[0091]

Processing Flow

[0092] Log-structured encoding: Each operation event in the collaborative editing log is organized into a vector sequence in a unified format in chronological order. Each event contains fields such as operation type, material ID, timestamp, conflict flag, etc., and is mapped into a learnable embedding representation.

[0093] Multi-head attention mechanism processing: Construct a multi-head attention structure to focus on different event subsequences from different semantic perspectives (such as operation frequency, material relevance, and conflict context), forming multiple sets of attention weight matrices.

[0094] Semantic interaction modeling: Each attention head focuses on a certain type of behavioral intention (such as "operation intention", "material matching preference", and "conflict resolution strategy"), and guides the model to extract semantic clues in the event sequence through different attention weights.

[0095] Label generation and fusion: The behavior representations extracted by each attention head are concatenated and compressed into three types of label vectors, representing:

[0096] The behavioral intent of the current operation sequence (e.g., drawing vs. modifying vs. deleting);

[0097] Material usage patterns (e.g., whether certain material combinations frequently co-occur);

[0098] Conflict resolution preferences (such as priority for undoing, overwriting, or merging).

[0099] The final output label set is a standardized semantic vector, which serves as the semantic dimension input of the cultural gene unit.

[0100] Second, the normalized trajectory is input into the spatiotemporal graph convolutional network (ST-GCN) to extract the visual expression vector as follows:

[0101] User-generated 3D brushstrokes contain rich geometric structures and dynamic variations, exhibiting continuity and dependencies across time and space. To extract discernible visual features, a spatiotemporal graph neural network is required to model their structure and evolution.

[0102]

Processing Flow

[0103] Trajectory graph construction: Each pen touch point in the standardized pen stroke trajectory (device alignment and sampling compensation have been performed) is regarded as a graph node. Adjacent pen touch points are connected in chronological order to form a dynamic trajectory graph. Each node in the graph is accompanied by attributes such as coordinates, pressure, material code, etc.

[0104] Spatial feature extraction: In the graph structure, the spatial graph convolution module is first used to process the geometric information of the stroke nodes (such as bends, curvatures, pressure gradients, etc.) to capture the local structural features of the stroke lines.

[0105] Temporal dependency modeling: Introducing a temporal convolutional layer or temporal gating mechanism to capture the changing trends of trajectories over time, such as dynamic performance such as stroke speed, direction changes, and repeated paths.

[0106] Material and light and shadow fusion: The material parameters bound to each stroke node (such as reflection coefficient and texture ID) are combined with external scene lighting parameters (such as light source direction and brightness and darkness changes) into a feature channel to enhance the realism and recognition of visual expression.

[0107] Global expression generation: Through global pooling or feature aggregation mechanisms, the entire trajectory graph is encoded into a high-dimensional vector of fixed length as the visual expression result of the creative behavior.

[0108] Outputting a visual expression vector: The resulting visual expression vector not only reflects the graphic characteristics of the brushstroke itself, but also incorporates the material usage style and dynamic change pattern, serving as the "visual dimension" of the cultural meme unit.

[0109] S3 specifically includes:

[0110] S31, deconstruct the cultural gene unit into a time evolution feature matrix and a spatial correlation tensor, where:

[0111] Time evolution feature matrix: The evolution trend of cultural gene units is extracted and stored, including the evolution parameters of cultural gene units at different timestamps, reflecting the spread speed of cultural elements and the fluctuation characteristics of their life cycle;

[0112] Spatial correlation tensor: The spatial correlation tensor is used to describe the collaborative creative relationship between different user terminals. By constructing the spatial correlation tensor, it expresses the relationship network between users, including spatial overlap, device type similarity, and synchronous editing frequency. The spatial correlation tensor captures how users influence the spread and change of cultural meme units in the spatial dimension.

[0113] S32, Spatiotemporal Graph Neural Network Joint Modeling: The extracted time evolution feature matrix and spatial correlation tensor are input into the spatiotemporal graph neural network for joint modeling, including:

[0114] Temporal dimension modeling: In spatiotemporal graph neural networks, the temporal dimension is modeled using gated recurrent units (GRUs). GRUs capture the mutation points or inflection points of cultural elements along the temporal dimension, identify dramatic changes in cultural meme units over different periods, track the evolution of cultural memes, and respond at key time points.

[0115] Spatial dimension modeling: The spatial dimension is modeled using the Graph Attention Mechanism (GAT). The GAT calculates the connection strength between nodes, representing the influence propagation path of user creative behavior in the cultural meme network. It updates the connection strength in the network and captures the spatial correlation and propagation pattern of user behavior.

[0116] S33: Calculation of user collaborative editing association: Build a user association subgraph by analyzing collaborative editing logs and calculate the collaborative editing association between users, including:

[0117] Constructing a user association subgraph: The user association subgraph consists of the collaborative editing relationships between users. The nodes of the graph represent different users, and the edges represent the collaborative relationships between users. The collaborative editing strength between users, i.e., the collaborative editing association degree, is calculated.

[0118] Calculating the collaborative editing relevance includes:

[0119] The degree of collaborative editing relevance is composed of three main factors:

[0120] Number of design element inheritances between users: This indicates how many times two users have inherited or modified each other's design elements;

[0121] Conflict resolution response time between users: This reflects the user's response speed during the collaboration process and can reflect the user's collaboration efficiency;

[0122] Collaborative session depth: indicates the depth of user interaction in collaborative editing, measuring the number of interactions or the richness of interactions during the creation process;

[0123] These factors work together to determine the strength of the collaborative editing relationship between users.

[0124] The user association subgraph is represented as: in, is the user collaborative association subgraph, which represents the interactive relationship network between users formed in the collaborative creation process. V is the node set in the graph, which represents all users participating in the collaborative creation. Each node corresponds to a user anonymous identifier. E is the edge set in the graph, which represents the directed or undirected connection between users in the collaborative editing relationship. Each edge has an edge weight, which represents the degree of user collaborative editing association: κi,j represents the number of design element inheritances between users i and j, τ i,j represents the average response time required to resolve conflicts between two users, σ i,j represents the depth of collaborative conversation, i.e., the number of interaction rounds or collaborative editing steps, λ1, λ2, and λ3 represent weighting coefficients (the sum is 1), which are set to 0.4, 0.3, and 0.3.

[0125] Based on the Shannon entropy formula, calculate the propagation entropy H of cultural element ζ ζ :

[0126] Among them, p k represents the normalized weight of the kth propagation path, The original propagation weight ω of the kth path k Expressed as: θ k Indicates the number of replication and mutation of the cultural gene on path k; δ k represents the propagation delay on path k, represents the length of the secondary creation derivative chain of users on path k (i.e., the depth of subsequent adaptation), and n1 represents the total number of propagation paths identified for the cultural meme in the propagation network;

[0127] S34: Calculation of Cultural Diffusion Entropy: We use the cultural diffusion entropy algorithm to quantify the diffusion efficiency of cultural elements. By calculating the diversity and uncertainty of the diffusion paths, we can evaluate the diffusion effect of cultural genes. Specifically, we include:

[0128] Calculation of propagation path weights: The weight of each propagation path is calculated by considering the number of replication mutations, propagation delay, and the length of the user-created derivative chain. The weight of the propagation path determines the importance of each path in the overall cultural dissemination;

[0129] Calculation of cultural communication entropy: Based on the Shannon information entropy formula, the communication entropy of a cultural meme unit is calculated. The value of the communication entropy reflects the information uncertainty of the cultural meme unit during the communication process. The higher the diversity of the communication path, the greater the entropy value, indicating that the element has greater influence and communication potential in the community;

[0130] S35: Dynamic connection strength fusion and topological activation: By nonlinearly fusing user collaborative editing correlation and cultural communication entropy, the dynamic connection strength coefficient between cultural gene units is generated, including:

[0131] Generation of connection strength: By combining the degree of collaborative editing between users and the propagation entropy of cultural memes, the influence of each cultural meme unit on other units is obtained. If this strength exceeds the set adaptive threshold, a two-way reinforcement connection is established in the cultural topology network, indicating that the cultural exchange between the two becomes closer.

[0132] Activation of meme mutation channels: When the connection strength coefficient exceeds a preset threshold, the system activates the meme mutation channels. These mutation channels promote more changes and interactions among meme units within the network, driving the further evolution and diffusion of cultural elements. The preset threshold is the average of all dynamic connection strength coefficients plus their standard deviation.

[0133] S4 specifically includes:

[0134] S41, dynamic betweenness centrality calculation and heat map construction: introduce the propagation entropy correction factor and calculate the dynamic betweenness centrality of node v based on the Brandes algorithm:

[0135] BC′(v)=BC(v)×log(1+E p (v)), where node v∈V, BC(v) represents the standard betweenness centrality of node v in the original topological graph, and E p (v) represents the cultural gene propagation entropy associated with node v, BC′(v) represents the modified dynamic betweenness centrality, which has the ability to perceive cultural diffusion. The BC′(v) value is mapped to the node heat value, combined with the time decay coefficient λ t , construct a heat map, expressed as:

[0136] H(v,t) represents the heat value of node v at time t, which is used to indicate its propagation activity at the current moment. v is the timestamp of the last update of node v, T now is the current time, λ t is the time decay coefficient, which is used to reduce the weight of outdated cultural nodes.

[0137] S42, Gene activation mechanism triggering: setting the activation threshold θ bc ,When the following two conditions are met, the high potential cultural gene unit activation event is triggered:

[0138] Condition 1: BC′(v)>θ bc ;

[0139] Condition 2: Propagation entropy E p (v) In an upward trend, satisfying: It means that the influence of the node in the network is increasing and its cultural diffusion continues to improve.

[0140] is the mean of the dynamic betweenness centrality of all nodes, σ BC′ is the standard deviation of dynamic betweenness centrality, ω is the adjustment coefficient, and its value range is 0.5 to 0.8.

[0141] S43, Collection Task Package Construction and Parameter Encapsulation: For cultural gene units that meet the activation conditions, build a cross-chain index structure, generate a multi-chain verifiable traceability structure, and construct a collection task package. The collection task package structure is as follows: h ζ Represents cultural gene unit The traceability hash value, W ζ Indicates the whitelist of device fingerprints allowed to be collected, Φ spatial is the geo-fence constraint parameter, Φ temporal is the cultural taboo time window parameter. The task package is published by the smart contract among distributed nodes and bound to the collection permission logic. The maximum propagation delay tolerance threshold is 6-12 hours for fast propagation (such as hotspot IP joint name), and 1-2 days for slow propagation (such as creative adaptation).

[0142] S44, decentralized collection queue generation and consensus mechanism execution:

[0143] The Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is adopted, and the edge nodes verify the legitimacy of the collection tasks and generate a decentralized collection queue.

[0144] The priority of each collection task is based on Sort, where It represents the node betweenness centrality gradient, indicating the speed of change of the influence of the cultural gene unit in the topology. If the value is large, it means that the node is rapidly becoming a key intermediary for information dissemination and is in a stage of rapid diffusion or detonation. Therefore, this type of cultural gene unit has a higher value for material collection and should be prioritized for collection tasks.

[0145] like Figure 2 As shown, the trendy toy material collection system based on big data is used to implement the above method, including the following modules:

[0146] The data acquisition module obtains the augmented reality creation data stream uploaded by the user terminal and extracts the 3D brush stroke trajectory, material selection sequence and multi-person collaborative editing log;

[0147] A feature alignment and gene generation module, configured to perform cross-device feature alignment on the creative data stream and generate standardized cultural gene units including visual expression vectors, interactive semantic labels, and group contribution coefficients;

[0148] A topological modeling module is used to construct a spatiotemporal evolution prediction model based on the cultural gene unit and output a dynamic cultural topological network, whose network node connection strength is determined by the user collaborative editing correlation and cultural communication entropy;

[0149] The task scheduling module is used to activate high-potential cultural gene units and generate a decentralized material collection queue based on the node betweenness centrality distribution of the topological network.

[0150] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0151] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for collecting trendy toy materials based on big data, characterized in that: The following steps are involved: S1: Obtaining the augmented reality creation data stream uploaded by the user terminal through the authorized API interface, wherein the creation data stream includes a 3D brush stroke trajectory, a material selection sequence, and a multi-person collaborative editing log; S2: performing cross-device feature alignment on the creative data stream to generate a standardized cultural gene unit, wherein the cultural gene unit includes a visual expression vector, an interactive semantic label, and a group contribution coefficient; S3: Input cultural gene units into the spatiotemporal evolution prediction model and output a dynamic cultural topological network. The connection strength between network nodes in the dynamic cultural topological network is determined by the user collaborative editing correlation and cultural communication entropy. S4: Based on the node betweenness centrality distribution of the dynamic cultural topology network, high-potential cultural gene units are activated and a decentralized material collection queue is generated.

2. The method for collecting trendy toy materials based on big data according to claim 1, characterized in that: Said S1 specifically includes: S11: When the user launches the augmented reality creation tool, an authorization request is initiated based on the OAuth 2.0 protocol to obtain access rights to the user's device sensors and collaborative editing data sharing permissions; S12: Capturing three-dimensional pen stroke trajectory data in real time through an encrypted data pipeline, the three-dimensional pen stroke trajectory data including a spatial coordinate sequence, a pen stroke pressure value, and a device gyroscope attitude parameter, and encapsulating the data into a streaming data packet marked with a timestamp; S13: parsing the operation event chain in the multi-person collaborative editing log, wherein the multi-person collaborative editing log records the editing operation type, material selection time sequence, and version conflict resolution mark of each participating user; S14: The streaming data packet is spatiotemporally aligned with the collaborative editing log to generate a creative data stream with a user anonymous identifier, and is differentially privately processed through an edge node before being uploaded to a central server.

3. The method for collecting trendy toy materials based on big data according to claim 1, characterized in that: The S2 specifically includes: S21: Construct a device-independent reference coordinate system to map the 3D pen stroke trajectories of different terminals to a unified spatial grid, and eliminate the differences in motion data collection between devices through an adaptive sampling rate compensation algorithm; S22: Using a multi-head attention mechanism to parse the collaborative editing log and extract the interaction semantic tag set, which includes the operation intention classification code, material combination pattern code, and conflict resolution strategy code; S23: extracting a visual expression vector based on a spatiotemporal graph convolutional network, wherein the visual expression vector integrates geometric topological features of the brush stroke trajectory, material reflection characteristics, and light and shadow change parameters; S24: Calculate the group contribution coefficient, which is determined by the influence diffusion weight of the user's editing operation, the contribution transmission rate in the version inheritance chain, and the efficiency of reaching consensus in the collaborative session; S25: Encapsulating the visual expression vector, interactive semantic tag set and group contribution coefficient into a cultural gene unit with a time and space stamp, and storing it in a distributed cultural gene library.

4. The method for collecting trendy toy materials based on big data according to claim 3 is characterized in that: The group contribution coefficient is defined as: γ j =α·w j +β·μ j +δ·η j , where γ j represents the group contribution coefficient of the jth user, w j represents the influence diffusion weight of the jth user editing operation, μ j represents the contribution transmission rate of the jth user's editing operation in the version inheritance chain, η j represents the efficiency of consensus in the j-th user collaborative session, and α, β, and δ represent weight coefficients.

5. The method for collecting trendy toy materials based on big data according to claim 4 is characterized in that: The influence diffusion weight w j Calculated as: Among them, C j is the total number of times user j’s content is cited or modified by others, T j represents the total number of editing operations of user j; The contribution transfer rate μ j Calculated as: Among them, R j Indicates the number of times user j’s content is retained in subsequent versions, N j The total number of content units created for user j; The consensus reaching efficiency η j Calculated as: Among them, A j The number of valid edit suggestions submitted by user j, I j represents the total number of collaborative interaction rounds in which user j participates.

6. The method for collecting trendy toy materials based on big data according to claim 1, characterized in that: The S3 specifically includes: S31: Deconstructing the meme unit into a time evolution feature matrix and a spatial correlation tensor, wherein the time evolution feature matrix includes the meme unit evolution trend parameters corresponding to the time and space stamps, and the spatial correlation tensor encodes the collaborative creation relationship between different user terminals; S32: A spatiotemporal graph neural network is used to jointly model feature matrices and tensors. In the temporal dimension, gated recurrent units are used to capture the mutation inflection points of cultural elements, while in the spatial dimension, a graph attention mechanism is used to calculate the influence propagation path of user creative behavior. S33: Constructing a user association subgraph based on the collaborative editing log and calculating the user collaborative editing association degree, where the association degree is determined by the number of design element inheritances between users, conflict resolution response speed, and collaborative session depth; S34: Quantify the efficiency of element diffusion through the cultural communication entropy algorithm. The entropy value is calculated using the Shannon information entropy formula. The input parameters include the number of element replication and mutation, the cross-community propagation delay, and the length of the user's secondary creation derivative chain. S35: Perform nonlinear fusion of user collaborative editing correlation and cultural communication entropy to generate a dynamic connection strength coefficient. When the dynamic connection strength coefficient exceeds a preset threshold, a bidirectional enhanced connection is established in the cultural topology network and the cultural gene mutation channel is activated.

7. The method for collecting trendy toy materials based on big data according to claim 6, characterized in that: The user association subgraph is represented as: in, is the user collaborative association subgraph, which represents the interactive relationship network between users formed in the collaborative creation process. V is the node set in the graph, which represents all users participating in the collaborative creation. Each node corresponds to a user anonymous identifier. E is the edge set in the graph, which represents the directed or undirected connection between users with collaborative editing relationships. Each edge has an edge weight, which represents the degree of user collaborative editing association.

8. The method for collecting trendy toy materials based on big data according to claim 7, characterized in that: The edge weights in the user association subgraph are expressed as: κ i,j represents the number of design element inheritances between users i and j, τ i,j represents the average response time required to resolve conflicts between two users, σ i,j represents the collaborative session depth, i.e., the number of collaborative editing steps, and λ1, λ2, and λ3 represent weighting coefficients.

9. The method for collecting trendy toy materials based on big data according to claim 8, characterized in that: The S4 specifically includes: S41: Based on the improved Brandes algorithm, the real-time betweenness centrality of each node is calculated, and the cultural communication entropy is introduced as the edge weight correction factor to generate a dynamic betweenness distribution heat map; S42: Set an activation threshold. When the betweenness centrality value of a node exceeds the activation threshold and the associated cultural transmission entropy is on an upward trend, the high-potential gene unit activation event is triggered. S43: Construct a cross-chain index for the activated gene units and generate a collection task package including time and space constraints between distributed nodes through smart contracts. The task package encapsulates the following parameters: Gene unit traceability hash; A whitelist of terminal device fingerprints that are allowed to be collected; Maximum propagation delay tolerance threshold; S44: Using the PBFT consensus mechanism, edge nodes verify the legitimacy of collection tasks and generate a decentralized collection queue. The priority of tasks in the queue is determined by the centrality gradient of the gene unit betweenness and the expected value of secondary creation.

10. A trendy toy material collection system based on big data, used to implement the trendy toy material collection method based on big data as described in any one of claims 1 to 9, characterized in that: Includes the following modules: The data acquisition module obtains the augmented reality creation data stream uploaded by the user terminal and extracts the 3D brush stroke trajectory, material selection sequence and multi-person collaborative editing log; A feature alignment and gene generation module, configured to perform cross-device feature alignment on the creative data stream and generate standardized cultural gene units including visual expression vectors, interactive semantic labels, and group contribution coefficients; A topological modeling module is used to construct a spatiotemporal evolution prediction model based on the cultural gene unit and output a dynamic cultural topological network, whose network node connection strength is determined by the user collaborative editing correlation and cultural communication entropy; The task scheduling module is used to activate high-potential cultural gene units and generate a decentralized material collection queue based on the node betweenness centrality distribution of the topological network.

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