Cloud-based garment 3D design method, system and device supporting multi-person online collaboration

Abstract

The application relates to a cloud clothing 3D design method, system and device supporting online collaboration of multiple people, and belongs to the technical field of clothing design. The method comprises the following steps: receiving an encapsulated event sent by a role client, and extracting modified parameters and target elements in the encapsulated event, wherein the encapsulated event is formed by the role client structuring and encapsulating captured user role operation information; loading design rules related to the modified parameters from a constraint library, and verifying whether the modified parameters meet all constraint conditions; if not, generating a correction suggestion; if yes, adding the encapsulated event as a new node to a constructed intent directed acyclic graph, and analyzing whether target elements of all nodes in the graph conflict, wherein the attributes of the nodes include operation ID, user role, target element and intent context; according to the analysis result, determining a risk level, and according to the risk level, executing a corresponding conflict resolution strategy. The application has the beneficial effects of improving design quality and collaboration efficiency.

Description

Technical Field

[0001] This application relates to the technical field of clothing design, and in particular to a cloud-based 3D clothing design method, system, and device that supports multi-user online collaboration. Background Technology

[0002] In today's digital and globalized era, the apparel industry faces increasingly fierce market competition and diverse consumer demands. To meet consumers' pursuit of personalized and fashionable clothing, apparel companies need to continuously improve design efficiency and quality, and shorten product development cycles.

[0003] Currently, traditional 3D clothing design methods primarily rely on individual design using local software. Designers use specialized 3D design software on their own computers to model, render, and modify clothing. Collaboration among multiple people typically relies on file sharing and version control tools. Designers upload their design files to a shared folder, and other members download, modify, and provide feedback. While this approach achieves some degree of collaboration, it suffers from several problems, such as low file transfer efficiency, disorganized version management, and difficulties in real-time collaboration. Furthermore, the verification of design parameters and conflict detection often require manual intervention by the designer, which is prone to omissions and errors, leading to a decline in design quality. Summary of the Invention

[0004] To improve design quality and collaboration efficiency, this application provides a cloud-based 3D clothing design method, system, and device that supports multi-user online collaboration.

[0005] Firstly, this application provides a cloud-based 3D clothing design method that supports multi-user online collaboration, employing the following technical solution:

[0006] A cloud-based 3D clothing design method supporting multi-user online collaboration includes:

[0007] Receive the encapsulated event sent by the role client, and extract the modified parameters and target elements from the encapsulated event. The encapsulated event is formed by the role client through structured encapsulation of the captured user role operation information.

[0008] Load the design rules related to the modified parameters from the predefined constraint library and verify whether the modified parameters satisfy all constraints.

[0009] If not, then generate a correction suggestion;

[0010] If so, the encapsulated event is added as a new node to the constructed intention directed acyclic graph, and the target elements of all nodes in the graph are analyzed to see if they conflict. The attributes of the nodes include operation ID, user role, target element, and intention context.

[0011] Based on the analysis results, the risk level is determined, and the corresponding conflict resolution strategy is implemented according to the risk level.

[0012] By adopting the above technical solution, and by loading design rules related to the modified parameters from a predefined constraint library and verifying whether these parameters meet all constraints, it is possible to ensure that the parameters input by designers conform to established design specifications and requirements. When the modified parameters do not meet the constraints, the system generates correction suggestions, providing designers with clear directions for improvement. Adding events as new nodes to the constructed intention-directed acyclic graph clearly records each designer's operations and intentions. By analyzing whether the target elements of all nodes in the graph conflict, potential conflicts during multi-person collaboration can be identified in a timely manner, avoiding duplication of work and conflicts, and improving collaboration efficiency. Determining the risk level based on the analysis results and implementing corresponding conflict resolution strategies can effectively resolve conflicts that arise during multi-person collaboration.

[0013] Optionally, the steps for determining the risk level based on the analysis results include:

[0014] If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are mutually exclusive, then the risk level is determined to be high risk.

[0015] If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are dependent, then the risk level is determined to be medium risk.

[0016] If the target element corresponding to the new operation node has no overlap with the target elements of other user operation nodes in progress, the risk level is determined to be low risk.

[0017] By adopting the above technical solutions, when a situation is deemed high-risk, it means that the target element of the current node intersects with the target elements of other user operation nodes, and the operation types are mutually exclusive. For example, in 3D clothing design, one designer is modifying the neckline of a garment to a circular shape, while another designer is simultaneously modifying the same neckline to a square shape. These two operations cannot coexist and are mutually exclusive. The system can promptly identify this as high-risk, preventing further deterioration of design conflicts, ensuring the accuracy and consistency of clothing design, and guaranteeing the final design quality. Medium-risk assessment addresses situations where target elements intersect and operation types are dependent. For example, when designing clothing, one designer is adjusting the length of the garment, while another designer is designing the skirt style based on this length. The two operations are dependent. Timely identification of this medium-risk situation allows team members to recognize the connections between operations, enabling them to communicate and coordinate in advance and avoid design deviations caused by inconsistent operation sequences or parameters.

[0018] Optionally, the steps of implementing the corresponding conflict resolution strategy based on the risk level include:

[0019] When the risk level is determined to be high risk: the operation of the user with high role privileges automatically overrides the conflicting operation of the user with low role privileges; push the operation conflict notification to the role client of the overridden user, mark the overriding node relationship in the directed acyclic graph of the intent, and generate a conflict resolution log.

[0020] When the risk level is determined to be medium risk: lock the conflicting target element, send a collaboration request to the relevant user's role client, and start the real-time shared screen parameter adjustment collaboration interface; in the collaboration interface, map the operation parameters of both parties to the linked sliders, and generate the allowable value range based on the design rules;

[0021] When the risk level is determined to be low: add new nodes in parallel to the intention directed acyclic graph, manage parallel design through version branches; highlight overlapping target elements in the 3D model preview interface and label the operation IDs of different users.

[0022] By adopting the above technical solutions, and through the automatic overriding of high-level user operations, the efficiency of design decisions is ensured while maintaining team transparency through operation logs and notification mechanisms, avoiding resource waste caused by invalid operations by low-privilege users. Traditional "conflicts" are transformed into collaborative opportunities: the dual-slider linkage mechanism visualizes parameter dependencies, allowing designers to verify the effects of design combinations in real time within constraints, upgrading conflict resolution to design co-creation. Based on node association analysis of the intention-directed acyclic graph, the path of conflict can be traced back, providing data support for subsequent design rule optimization.

[0023] Optionally, the cloud-based 3D clothing design method further includes:

[0024] After conflict resolution, commutative operations are directly merged, and the final value of mutually exclusive operations is determined according to the resolution result.

[0025] A global version snapshot is generated every n seconds and saved to the database;

[0026] The final parameters of the event are sent to the character clients of all relevant users, and the 3D interface of the character clients is updated in real time.

[0027] By adopting the above technical solutions, after conflict resolution, exchangeable operations are directly merged, avoiding redundant operations and unnecessary communication costs. For example, when multiple designers collaborate on clothing design, different designers may adjust the colors of different parts of the garment. If these operations are exchangeable, direct merging can quickly integrate the design results, reducing waiting and coordination time, making the design process smoother, and thus significantly improving design efficiency. For mutually exclusive operations, the final value is determined based on the resolution result, ensuring the consistency and accuracy of the design. For example, when designing the neckline style of clothing, different designers may propose different solutions. Conflict resolution determines the final neckline style, avoiding contradictions and confusion in the design, and ensuring that the final design conforms to the overall style and requirements. A global version snapshot is generated every n seconds and saved to the database, providing a complete historical record of the design process. The final parameters of events are sent to all relevant client roles, enabling the 3D interface of the client roles to update in real time. This allows team members to understand the design progress and modifications of other members in real time. For example, when a designer completes a modification to a part of the garment, other members' clients can immediately display the modified effect, achieving true real-time collaboration.

[0028] Optionally, the step of generating the correction suggestion includes:

[0029] Receive procurement data and marketing data sent by the procurement client and marketing client respectively;

[0030] Based on the procurement data and the marketing data, calculate the optimal parameter value of the target element corresponding to the encapsulation event;

[0031] Based on the optimal parameter values ​​and the modified parameters, a structured correction suggestion is created and presented to the role client through a visual prompt. The structured correction suggestion includes specific modification values ​​and explanations.

[0032] By adopting the above technical solution, receiving procurement and marketing data sent by the procurement and marketing clients respectively, it is possible to comprehensively consider factors such as procurement costs and market demand. Integrating this data to calculate the optimal parameter values ​​for the target elements corresponding to the encapsulated events makes the design more aligned with actual business needs. This helps designers achieve cost control and improved market competitiveness while ensuring design quality, making design decisions more rational and accurate. Based on the optimal parameter values ​​and the modified parameters, structured correction suggestions are created. Designers no longer need to spend a lot of time analyzing and guessing how to modify; they only need to follow the specific modification values ​​in the suggestions and understand the reasons for the modifications to quickly and accurately correct the design parameters, improving the efficiency of design correction.

[0033] Optionally, the step of calculating the optimal parameter value of the target element corresponding to the encapsulation event based on the procurement data and the marketing data includes:

[0034] The procurement data and marketing data are standardized, and a feature library relating target elements to the standardized procurement and marketing data is established.

[0035] Based on the fabric unit price, process complexity, and supply chain constraints in the procurement data, and combined with the aforementioned associated feature library, a cost function is constructed.

[0036] Based on marketing data, including trends, user preferences, and competitor differentiation, and combined with the aforementioned associated feature library, a market value function is constructed.

[0037] By combining the physical rules of 3D clothing design, parameter boundary conditions are added, and dynamic weights are assigned to the cost function and the market value function.

[0038] Solve for the optimal parameter vector under constraints so that the comprehensive objective function V( )=w1*M( )-w2*C( )maximize, Let M be the parameter vector of the target element. C( ) is the market value function. ) is the cost function, and w1 and w2 are the assigned dynamic weights.

[0039] By adopting the above technical solutions, the supply chain constraints on the procurement side and the market demands on the marketing side are transformed into calculable parameter optimization targets, avoiding the problems of "good-looking designs but unable to be mass-produced" or "cost-effective but lacking market competitiveness" caused by information silos for designers.

[0040] Optionally, the cloud-based 3D clothing design method further includes:

[0041] When the risk level is determined to be high risk and the role permissions are equivalent, the real-time social collaboration layer embedded in the 3D interface is automatically activated, the online status and operation ID of the relevant user are displayed in the side bar of the interface, and text / voice chat, screen annotation and voting functions are provided.

[0042] When a user initiates a discussion or feedback request through the social collaboration layer, the chat content and annotation information are bound to the corresponding node in the directed acyclic graph of intent in real time to form an association log.

[0043] Once the conflict is resolved or the design is complete, a collaboration report is automatically generated, summarizing the key points of the discussion, the basis for the decisions, and the tasks to be completed, and then pushed to all relevant client roles.

[0044] By adopting the above technical solution, when the risk level is determined to be high and the roles have equivalent permissions, the real-time social collaboration layer in the 3D interface is automatically activated, allowing users to directly engage in activities such as text / voice chat, screen annotation, and voting within the design interface. This instant messaging and collaboration method significantly reduces communication costs among team members, improves communication efficiency, and enables the team to reach consensus and resolve design conflicts more quickly. Chat content and annotation information are bound in real-time to corresponding nodes in the directed acyclic graph of intent, forming a relational log. This not only records the entire process of discussion and feedback but also links this information to specific design operations, facilitating subsequent review and analysis. Collaboration reports are pushed to all relevant role clients, ensuring that everyone can promptly obtain the latest design progress and decision-making information, helping to maintain information synchronization among team members and avoiding work disconnects or duplication of effort due to information asymmetry.

[0045] Secondly, this application provides a cloud-based 3D clothing design system that supports multi-user online collaboration, employing the following technical solution:

[0046] A cloud-based 3D clothing design system supporting multi-user online collaboration includes:

[0047] The information receiving module is used to receive encapsulated events sent by the role client. The encapsulated events are formed by the role client performing structured encapsulation of captured user role operation information.

[0048] The parameter extraction module is used to extract the modified parameters and target elements from the encapsulated event;

[0049] The parameter verification module is used to load design rules related to the modified parameters from a predefined constraint library and verify whether the modified parameters meet all constraints.

[0050] A suggestion generation module is used to generate correction suggestions when all constraints are not met;

[0051] The operation conflict analysis module is used to add the encapsulated event as a new node to the constructed intention directed acyclic graph after all constraints are met, and analyze whether the target elements of all nodes in the graph conflict. The attributes of the nodes include operation ID, user role, target element and intention context.

[0052] The conflict resolution module is used to determine the risk level based on the analysis results, and to execute the corresponding conflict resolution strategy according to the risk level.

[0053] Thirdly, this application provides a computer device that adopts the following technical solution:

[0054] A computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the cloud-based 3D clothing design method supporting multi-user online collaboration as described in the first aspect.

[0055] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0056] A computer-readable storage medium storing a computer program capable of being loaded by a processor and executed as described in the first aspect, which supports a cloud-based 3D clothing design method for multi-user online collaboration.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] By loading design rules related to the modified parameters from a predefined constraint library and verifying that these parameters meet all constraints, the system ensures that the parameters input by designers conform to established design specifications and requirements. When modified parameters fail to meet constraints, the system generates correction suggestions, providing designers with clear directions for improvement. Adding events as new nodes to the constructed directed acyclic graph of intent clearly records each designer's actions and intents. By analyzing whether the target elements of all nodes in the graph conflict, potential conflicts during multi-person collaboration can be identified in a timely manner, avoiding duplication of work and conflicts, and improving collaboration efficiency. Determining the risk level based on the analysis results and implementing corresponding conflict resolution strategies can effectively resolve conflicts that arise during multi-person collaboration. Attached Figure Description

[0059] Figure 1 This is a first flowchart of an embodiment of the method of this application;

[0060] Figure 2 This is a second flowchart of an embodiment of the method of this application;

[0061] Figure 3 This is a third flowchart of an embodiment of the method of this application;

[0062] Figure 4 This is the fourth flowchart of an embodiment of the method of this application. Detailed Implementation

[0063] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-4 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0064] The first embodiment of this application discloses a cloud-based 3D clothing design method that supports multi-user online collaboration. (Refer to...) Figure 1The cloud-based 3D clothing design method includes S110-S150:

[0065] S110, Receive the encapsulation event sent by the role client, and extract the modified parameters and target elements from the encapsulation event. The encapsulation event is formed by the role client through structured encapsulation of the captured user role operation information.

[0066] S120: Load the design rules related to the modified parameters from the predefined constraint library and verify whether the modified parameters meet all constraints.

[0067] S130, if not, generate a correction suggestion;

[0068] S140, if so, add the encapsulated event as a new node to the constructed intent directed acyclic graph, and analyze whether the target elements of all nodes in the graph conflict. The attributes of the nodes include operation ID, user role, target element and intent context.

[0069] S150. Based on the analysis results, determine the risk level and implement the corresponding conflict resolution strategy according to the risk level.

[0070] Specifically, in step S110, the system uses the WebSocket protocol to receive JSON-encapsulated events sent by the role client, distinguishes the design operation type by parsing the operation ID field, identifies the designer or pattern maker from the user role field, and extracts the target element (such as "sleeve-cuff") and the corresponding parameters before and after modification (such as changing the cuff diameter from 10cm to 15cm). At the same time, the intent context (such as "increase the cuff looseness to improve comfort") is stored in a temporary cache.

[0071] In step S120, the system calls the RESTful API of the predefined constraint library and dynamically loads association rules based on the target element and modified parameters (such as the cuff diameter needing to meet the requirements of fabric stretch rate ≤30% and sleeve length ratio 1:5, etc.). The system matches parameter values ​​with rule thresholds using JavaScript regular expressions. For example, if the cuff diameter is detected to exceed the limit, the S130 process is triggered.

[0072] Reference Figure 2 S130, the steps for generating correction suggestions include S210-S230:

[0073] S210 receives procurement data and marketing data sent by the procurement client and marketing client respectively;

[0074] S220, calculates the optimal parameter values ​​for the target element corresponding to the encapsulated event based on procurement and marketing data;

[0075] S230: Based on the optimal parameter values ​​and the modified parameters, a structured correction suggestion is created and presented to the role client through a visual prompt. The structured correction suggestion includes specific modified values ​​and explanations.

[0076] Reference Figure 3 The steps for calculating the optimal parameter values ​​for the target element corresponding to the encapsulation event, based on procurement and marketing data, include S310-S350:

[0077] S310 standardizes procurement and marketing data and establishes a feature library linking target elements with standardized procurement and marketing data.

[0078] S320 constructs a cost function based on fabric unit price, process complexity, and supply chain constraints in procurement data, combined with an associated feature library.

[0079] S330 constructs a market value function based on popular trends, user preferences, and competitor differentiation in marketing data, combined with a related feature library.

[0080] S340 incorporates the physical rules of 3D clothing design, adds parametric boundary conditions, and assigns dynamic weights to the cost function and market value function.

[0081] S350, under constraints, solve for the optimal parameter vector to maximize the comprehensive objective function value, where the comprehensive objective function = w1 * market value function - w2 * cost function.

[0082] Specifically, the comprehensive objective function V( )=w1*M( )-w2*C( ), Let M be the parameter vector of the target element. C( ) is the market value function. ) is the cost function, and w1 and w2 are the assigned dynamic weights.

[0083] The system receives XML-formatted procurement data (including fabric number, unit price, and supplier capacity) from procurement clients and CSV-formatted marketing data (including the percentage of popular elements in the past three months, user profile preference tags, and competitor style differences) from marketing clients via a RESTful API. The data is then standardized using ETL tools: for example, fabric unit prices are uniformly converted to "yuan / square meter," trend data is quantified into a trend index of 0-10, user preference keywords are extracted using the TF-IDF algorithm, and a feature library is established linking target elements (such as "dress_neckline") with standardized data. For example, "V-neck" is associated with "chiffon fabric (unit price 80 yuan / ㎡)," "2024 summer trend index 8.5," and "92% preference rate among women aged 25-35."

[0084] The system constructs cost and market value functions separately. The cost function is based on the fabric unit price (80 yuan / ㎡), process complexity (V-neck requires 0.3 more labor hours than round neck, with a labor cost of 50 yuan / hour), and supply chain constraints (minimum order quantity for chiffon fabric is 500 meters) from the procurement data, combined with fabric utilization parameters from the associated feature library (V-neck style utilization rate is 85%), and is formulated as follows: Cost = (Fabric usage × Unit price) / Utilization rate + Process complexity × Labor cost + Minimum order quantity penalty. The market value function integrates the trend index (8.5), user preference (92%), and competitor differentiation score (differentiation from competitor A is 75%) from the marketing data, and is constructed through weighted summation: Value = 0.4 × Trend Index + 0.3 × Preference + 0.3 × Differentiation Score, with weights determined using the Analytic Hierarchy Process (AHP).

[0085] The system integrates the ClothWorks physics engine to simulate fabric drape, setting parameter boundary conditions (such as the neckline depth needing to meet ergonomic requirements and avoid the risk of exposure, setting the boundary condition "depth ≤ 12cm"), and dynamically allocating weights according to the current product lifecycle stage: for example, in the early design stage (concept stage), the market value function is allocated w1=0.7 and the cost function w2=0.3; when entering the mass production stage, it is automatically adjusted to w1=0.4 and w2=0.6.

[0086] A genetic algorithm is used to iteratively solve for the optimal parameter vector within a constrained space. The comprehensive objective function (0.7 × market value - 0.3 × cost) is used as the fitness function. After 50 generations of evolution, the optimal parameters "V-neck depth 7cm, angle 65°" are output. Then, based on the difference between the optimal parameters (depth 7cm) and the modified parameters (8cm), the system creates a structured correction suggestion: "It is recommended to adjust the V-neck depth from 8cm to 7cm (reason: the current depth exceeds the optimal range for process cost; adjustment can reduce fabric waste by 12% and meet ergonomic standards)". This suggestion is visualized in the sidebar of the 3D interface of the character client as a yellow warning box, along with a comparison of the parameter adjustment effect.

[0087] In step S140, if the verification passes, the encapsulated event is added as a new node to the Directed Acyclic Graph (DAG) of intents. The node attributes in the graph include operation ID, user role, target element, and intent context. The system analyzes the target element conflicts of all nodes using a graph traversal algorithm. For example, it detects that a pattern maker is modifying "dress_cuffs" (target elements have no intersection), while a designer is simultaneously modifying "dress_neckline dart amount" (target elements have intersection).

[0088] Based on the analysis results, the steps to determine the risk level include:

[0089] If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are mutually exclusive, then the risk level is determined to be high risk.

[0090] If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are dependent, then the risk level is determined to be medium risk.

[0091] If the target element corresponding to the new operation node has no overlap with the target elements of other user operation nodes in progress, the risk level is determined to be low risk.

[0092] Based on the risk level, the steps for implementing the corresponding conflict resolution strategy include:

[0093] When the risk level is determined to be high: the actions of users with higher role privileges automatically override conflicting actions of users with lower role privileges; an action conflict notification is pushed to the role client of the overridden user, and the relationship between the overriding nodes is marked in the directed acyclic graph of intent, generating a conflict resolution log; in addition, when the role privileges are equivalent, a real-time social collaboration layer embedded in the 3D interface is automatically activated, displaying the online status and action ID of the relevant users in the sidebar of the interface, and providing text / voice chat, screen annotation, and voting functions; when users initiate discussions or feedback requests through the social collaboration layer, the chat content and annotation information are bound to the corresponding nodes in the directed acyclic graph of intent in real time, forming an association log.

[0094] When the risk level is determined to be medium risk: lock the conflicting target element, send a collaboration request to the relevant user's role client, and start the real-time shared screen parameter adjustment collaboration interface; in the collaboration interface, map the operation parameters of both parties to the linked sliders, and generate the allowable value range based on the design rules;

[0095] When the risk level is determined to be low: add new nodes in parallel to the intention directed acyclic graph, manage parallel design through version branches; highlight overlapping target elements in the 3D model preview interface and label the operation IDs of different users.

[0096] Specifically, the system uses the Neo4j graph database to construct a directed acyclic graph of intent. New node attributes include an operation ID such as "OP20231001", a user role such as "Designer A", a target element such as "sleeve-cuff", and intent context. Target element conflicts are checked using Cypher queries. In risk assessment, the system defines an operation type dictionary (mutually exclusive: cutting / sewing; dependent: pattern making / grading). If Designer A's "modify cuff" and Pattern Maker B's "delete cuff" operations have overlapping target elements and are mutually exclusive, the system is classified as high-risk using the rule engine. If Pattern Maker B's operation is "adjust sleeve length" (which has a dependency on the cuff), it is classified as medium-risk. Operations with no overlap are marked as low-risk. For high-risk scenarios, the system invokes the RBAC permission management module. When Designer A's permission level (e.g., Lv.5) is higher than Pattern Maker B's (e.g., Lv.3), it automatically executes SQL statements to update records in the directed acyclic graph of intents and pushes a conflict notification to Pattern Maker B's client via the WebSocket protocol, containing the message "Operation overridden by Designer A: Cuff structure modification has higher priority than deletion." When permission levels are the same, the real-time social collaboration layer developed using the Electron framework is activated, displaying online user avatars and operation IDs on the right side of the interface. WebRTC is integrated for voice calls, and a screen annotation tool is provided via Fabric.js. Voting results are synchronized to the MongoDB database in real time. In medium-risk scenarios, the system calls a Redis distributed lock to lock the "sleeve" element, sends collaboration requests to both clients, loads the shared-screen collaboration interface developed using React, maps the cuff diameter and sleeve length parameters to linked sliders, dynamically generates slider ranges using constraint library rules (e.g., 5-15cm), and renders a 3D preview of the parameter changes in real time. In low-risk scenarios, the Git version control concept is adopted, creating parallel branch nodes in the directed acyclic graph of intent. Different user operation areas are marked on the 3D model using Three.js highlighting materials (red for designer A, blue for pattern maker B), and the branch version number is displayed in the upper left corner.

[0097] Reference Figure 4Cloud-based 3D clothing design methods also include S410-S430:

[0098] S410: After conflict resolution, commutative operations are directly merged, and the final value of mutually exclusive operations is determined according to the resolution result.

[0099] S420 generates a global version snapshot every n seconds and saves it to the database;

[0100] S430 sends the final parameters of the event to the role clients of all relevant users, and the 3D interface of the role clients is updated in real time.

[0101] In addition, after conflict resolution or design completion, a collaboration report is automatically generated, summarizing the key points of the discussion, the basis for the decision, and the to-do items, and is pushed to all relevant client roles.

[0102] Specifically, after conflict resolution, the system uses the Apache Camel integration framework to implement a merging strategy for exchangeable operations (such as different users changing different button colors), and to determine the final value for mutually exclusive operations (such as cuff diameter conflicts) based on voting results or permission levels, and to update the node attributes of the intended directed acyclic graph. A global version snapshot is triggered every 30 seconds via a Spring Scheduler task, employing an incremental storage strategy to save only changed parameters. The snapshot data is compressed into ZIP format and stored in a MySQL database, with the snapshot naming convention being "YYYYMMDD_HHMMSS_version number".

[0103] The system broadcasts the final parameter JSON to relevant clients via the MQTT protocol. The front-end uses Vue3's reactive mechanism to update the 3D interface and calls WebGL to implement smooth transition animations of model parameters (such as the cuff diameter gradually changing from 15cm to 13cm). Finally, the system uses the Python-docx library to automatically generate a collaboration report, extracting discussion points (such as "3 users support 13cm cuff diameter"), decision-making basis (such as cost reduction of 12%), and to-do items (such as "verify the feasibility of 13cm cuff technology") from the directed acyclic graph of intent. The report is then pushed to the role clients via email and other messaging tools, and a report preview card is displayed on the client's homepage.

[0104] Based on the above method embodiments, the second embodiment of this application discloses a cloud-based 3D clothing design system supporting multi-user online collaboration. This cloud-based 3D clothing design system can implement any of the above-described methods for supporting multi-user online collaboration in cloud-based 3D clothing design, and the specific working processes of each module in the system can be referred to the corresponding processes in the above method embodiments.

[0105] For ease of understanding, an example is given below: A cloud-based 3D clothing design system that supports multi-user online collaboration includes:

[0106] The information receiving module is used to receive encapsulated events sent by the role client. The encapsulated events are formed by the role client through structured encapsulation of the captured user role operation information.

[0107] The parameter extraction module is used to extract the modified parameters and target elements from the encapsulated event;

[0108] The parameter verification module is used to load design rules related to the modified parameters from a predefined constraint library and verify whether the modified parameters meet all constraints.

[0109] A suggestion generation module is used to generate correction suggestions when all constraints are not met;

[0110] The operation conflict analysis module is used to add the encapsulated event as a new node to the constructed intention directed acyclic graph after all constraints are met, and analyze whether the target elements of all nodes in the graph conflict. The attributes of the nodes include operation ID, user role, target element and intention context.

[0111] The conflict resolution module is used to determine the risk level based on the analysis results and to execute the corresponding conflict resolution strategy according to the risk level.

[0112] The third embodiment of this application provides a computer device, which may include a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement a cloud-based 3D clothing design method that supports multi-user online collaboration.

[0113] The memory can communicate with the processor via a communication bus, which can be an address bus, a data bus, a control bus, etc.

[0114] Additionally, the memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0115] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0116] The fourth embodiment of this application provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to support a cloud-based 3D clothing design method for multi-user online collaboration.

[0117] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0118] It should be noted that the computer device and storage medium in the embodiments of this application are respectively electronic devices and storage media for applying the above-described cloud-based 3D clothing design method supporting multi-user online collaboration. Therefore, all embodiments of the above-described cloud-based 3D clothing design method supporting multi-user online collaboration are applicable to the computer device and storage medium, and can achieve the same or similar beneficial effects. For the computer device / storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple; relevant details can be found in the descriptions of the method embodiments.

[0119] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0120] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A cloud-based 3D clothing design method supporting multi-user online collaboration, characterized in that, include: Receive the encapsulated event sent by the role client, and extract the modified parameters and target elements from the encapsulated event. The encapsulated event is formed by the role client through structured encapsulation of the captured user role operation information. Load the design rules related to the modified parameters from the predefined constraint library and verify whether the modified parameters satisfy all constraints. If not, then generate a correction suggestion; If so, the encapsulated event is added as a new node to the constructed intention directed acyclic graph, and the target elements of all nodes in the graph are analyzed to see if they conflict. The attributes of the nodes include operation ID, user role, target element, and intention context. Based on the analysis results, the risk level is determined, and the corresponding conflict resolution strategy is executed according to the risk level. Based on the analysis results, the steps to determine the risk level include: If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are mutually exclusive, then the risk level is determined to be high risk. If the target element corresponding to the new operation node intersects with the target element of other user operation nodes in progress, and the operation types are dependent, then the risk level is determined to be medium risk. If the target element corresponding to the new operation node has no intersection with the target elements of other user operation nodes in progress, the risk level is determined to be low risk. Based on the risk level, the steps for implementing the corresponding conflict resolution strategy include: When the risk level is determined to be high risk: the operation of the user with high role privileges automatically overrides the conflicting operation of the user with low role privileges; push the operation conflict notification to the role client of the overridden user, mark the overriding node relationship in the directed acyclic graph of the intent, and generate a conflict resolution log. When the risk level is determined to be medium risk: lock the conflicting target element, send a collaboration request to the relevant user's role client, and start the real-time shared screen parameter adjustment collaboration interface; in the collaboration interface, map the operation parameters of both parties to the linked sliders, and generate the allowable value range based on the design rules; When the risk level is determined to be low: add new nodes in parallel to the intention directed acyclic graph, manage parallel design through version branches; highlight overlapping target elements in the 3D model preview interface and label the operation IDs of different users.

2. The cloud-based 3D clothing design method supporting multi-user online collaboration as described in claim 1, characterized in that, The cloud-based 3D clothing design method also includes: After conflict resolution, commutative operations are directly merged, and the final value of mutually exclusive operations is determined according to the resolution result. A global version snapshot is generated every n seconds and saved to the database; The final parameters of the event are sent to the character clients of all relevant users, and the 3D interface of the character clients is updated in real time.

3. The cloud-based 3D clothing design method supporting multi-user online collaboration as described in claim 1, characterized in that, The steps for generating correction suggestions include: Receive procurement data and marketing data sent by the procurement client and marketing client respectively; Based on the procurement data and the marketing data, calculate the optimal parameter value of the target element corresponding to the encapsulation event; Based on the optimal parameter values ​​and the modified parameters, a structured correction suggestion is created and presented to the role client through a visual prompt. The structured correction suggestion includes specific modification values ​​and explanations.

4. The cloud-based 3D clothing design method supporting multi-user online collaboration as described in claim 3, characterized in that, The step of calculating the optimal parameter value of the target element corresponding to the encapsulation event based on the procurement data and the marketing data includes: The procurement data and marketing data are standardized, and a feature library relating target elements to the standardized procurement and marketing data is established. Based on the fabric unit price, process complexity, and supply chain constraints in the procurement data, and combined with the aforementioned associated feature library, a cost function is constructed. Based on marketing data, including trends, user preferences, and competitor differentiation, and combined with the aforementioned associated feature library, a market value function is constructed. By combining the physical rules of 3D clothing design, parameter boundary conditions are added, and dynamic weights are assigned to the cost function and the market value function. Solve for the optimal parameter vector under constraints so that the comprehensive objective function V( )=w1*M( )-w2*C( )maximize, Let M be the parameter vector of the target element. C( ) is the market value function. ) is the cost function, and w1 and w2 are the assigned dynamic weights.

5. The cloud-based 3D clothing design method supporting multi-user online collaboration according to claim 1, characterized in that, The cloud-based 3D clothing design method also includes: When the risk level is determined to be high risk and the role permissions are equivalent, the real-time social collaboration layer embedded in the 3D interface is automatically activated, the online status and operation ID of the relevant user are displayed in the side bar of the interface, and text / voice chat, screen annotation and voting functions are provided. When a user initiates a discussion or feedback request through the social collaboration layer, the chat content and annotation information are bound to the corresponding node in the directed acyclic graph of intent in real time to form an association log. Once the conflict is resolved or the design is complete, a collaboration report is automatically generated, summarizing the key points of the discussion, the basis for the decisions, and the tasks to be completed, and then pushed to all relevant client roles.

6. A cloud-based 3D clothing design system supporting multi-user online collaboration, characterized in that, Performing the cloud-based 3D clothing design method supporting multi-user online collaboration as described in any one of claims 1 to 5 includes: The information receiving module is used to receive encapsulated events sent by the role client. The encapsulated events are formed by the role client performing structured encapsulation of captured user role operation information. The parameter extraction module is used to extract the modified parameters and target elements from the encapsulated event; The parameter verification module is used to load design rules related to the modified parameters from a predefined constraint library and verify whether the modified parameters meet all constraints. A suggestion generation module is used to generate correction suggestions when all constraints are not met; The operation conflict analysis module is used to add the encapsulated event as a new node to the constructed intention directed acyclic graph after all constraints are met, and analyze whether the target elements of all nodes in the graph conflict. The attributes of the nodes include operation ID, user role, target element and intention context. The conflict resolution module is used to determine the risk level based on the analysis results, and to execute the corresponding conflict resolution strategy according to the risk level.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the cloud-based 3D clothing design method supporting multi-user online collaboration as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1 to 5, which supports a cloud-based 3D clothing design method for multi-user online collaboration.

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