Clothing cross-team design collaboration system based on cloud collaboration

Abstract

The invention relates to the technical field of informatization management in the clothing industry, and discloses a cloud collaborative clothing cross-team design collaboration system, which comprises a data acquisition and processing module for preprocessing an original design file; the version control module is used for carrying out differentiated storage on each design change and establishing a parallel evolution management mechanism; the real-time collaborative editing module is used for executing concurrent conflict detection and solution processing and pushing an editing result to all online collaborators in real time; the workflow management module is used for designing a collaborative workflow model, generating an optimization execution plan, monitoring task progress, and automatically warning and providing suggestions when risks are detected; the design auxiliary module is used for constructing a costume design field knowledge graph, executing design constraint check and carrying out multi-strategy fusion; the safety protection module is used for constructing a multi-layer safety protection system; the data analysis and optimization module is used for calculating a cooperation efficiency index, identifying a bottleneck in cooperation and generating an optimization suggestion; the costume design collaboration efficiency and quality are improved.

Description

Technical Field

[0001] This invention relates to the field of information management technology in the apparel industry, and more specifically, to a cloud-based collaborative system for cross-team apparel design. Background Technology

[0002] With the deepening of globalization and the increasing diversification of consumer demands, the design and production models of the apparel industry are undergoing profound changes. More and more apparel companies are adopting a distributed team structure, distributing design centers, pattern-making centers, fabric and accessory R&D centers, and production bases in different geographical locations to fully utilize the talent and resource advantages of each region. While this distributed structure brings flexibility and resource integration advantages, it also presents unprecedented challenges to cross-team collaboration.

[0003] Traditional apparel design collaboration primarily relies on paper documents, email communication, and instant messaging. These fragmented collaboration methods have significant limitations: information delivery is delayed, and different teams may receive inconsistent versions of information; design document version management is chaotic, making it difficult to trace historical changes and determine the latest version; conflicts easily arise when multiple people modify design schemes simultaneously, and these conflicts are difficult to identify and resolve; coordination and communication between geographically dispersed teams are costly and slow in response time. These problems severely impact product development efficiency and design quality, hindering the market competitiveness of apparel companies.

[0004] While existing cloud collaboration technologies have improved team collaboration efficiency to some extent, they still fall short of the specific needs of the apparel design field. Apparel design documents contain numerous graphic elements and are characterized by complex structures, frequent changes, and high levels of specialization. General-purpose document collaboration tools struggle to meet the demands for refined version management and conflict resolution. Furthermore, apparel design collaboration involves multiple professional fields such as design, pattern making, technology, and fabrics and accessories, requiring deep integration of domain knowledge to provide effective intelligent assistance. Therefore, a cloud-based collaborative design system specifically designed for the apparel industry is needed to address core issues such as information synchronization, version management, conflict resolution, and process coordination in cross-team collaboration. Summary of the Invention

[0005] This invention provides a cloud-based collaborative system for cross-team apparel design, which solves the technical problems of information transmission delays, chaotic design document version management, and high coordination and communication costs between cross-regional teams in related technologies.

[0006] This invention provides a cloud-based collaborative system for cross-team apparel design, comprising:

[0007] The data acquisition and processing module is used to acquire the original design files and preprocess them to obtain a standardized design dataset.

[0008] The version control module, based on a standardized design dataset, stores each design change differently, establishes a management mechanism that supports parallel evolution, and obtains a traceable design version library.

[0009] The real-time collaborative editing module is based on a traceable version control library, performs concurrent conflict detection and resolution, and pushes editing results to all online collaborators in real time, realizing a real-time synchronous push strategy.

[0010] The workflow management module designs a collaborative workflow model based on a real-time synchronous push strategy, generates optimized execution plans and monitors task progress. When a delay risk is detected, it automatically issues an alert and provides suggestions, forming an intelligent task scheduling strategy.

[0011] The design assistance module constructs a knowledge graph in the field of apparel design based on an intelligent task scheduling strategy, performs design constraint checks, and obtains intelligent design recommendation results through the fusion of multiple strategies.

[0012] The security protection module, based on the intelligent design recommendation results, constructs a multi-layered security protection system and obtains a full-process security audit strategy;

[0013] The data analysis and optimization module, based on the full-process security audit strategy, calculates collaboration efficiency indicators, identifies bottlenecks in collaboration, generates optimization suggestions, and obtains collaboration efficiency assessment and optimization recommendations.

[0014] In a preferred embodiment, the data acquisition and processing module includes:

[0015] A plug-in architecture is used to perform in-depth analysis of style drawings, structural drawings, process sheets, fabric and accessory lists, and size data, extracting design elements and converting them into editable data models, while recording the metadata information of the original files;

[0016] It performs unified mapping and conversion of design data from different systems and formats, covering field name mapping, data type conversion, unified coding standards, and unit conversion;

[0017] A comprehensive inspection of the data's integrity, consistency, and accuracy is conducted, verifying required fields, related data, and value ranges, and generating a data quality report.

[0018] In a preferred embodiment, the version control module includes:

[0019] The version repository is organized using a tree-structured directory. The main repository is deployed in a cloud data center, and clone repositories are deployed in various teams. Data consistency is maintained through a synchronization protocol, and online and offline working modes are supported.

[0020] For structured data, field-level difference comparison is used; for graphical data, layer-level difference comparison is used; for binary files, block-level difference comparison is used. Difference data is stored in the form of incremental patches. Each change generates a version snapshot and records metadata information.

[0021] Define main branch, development branch, feature branch, and fix branch; execute a three-way merge algorithm to automatically merge conflict-free changes; generate conflict flags for conflicting changes; and support branch protection rule configuration.

[0022] In a preferred embodiment, the real-time collaborative editing module includes:

[0023] The edit operation is converted into a sequence of atomic operations, sorted according to timestamp and client identifier to form a globally consistent operation order, and the operation conversion function is executed to adjust the position and parameters of the operation.

[0024] Identify the scope of concurrent operations, determine the type of conflict, including write-write conflicts, write-delete conflicts, and semantic conflicts, and calculate the severity score of the conflict.

[0025] Minor conflicts with severity scores below the first threshold are handled by automatic resolution rules, while major conflicts with severity scores at or above the first threshold are generated with a visual comparison interface.

[0026] A two-way communication channel is established using long-connection technology, and changes are transmitted using incremental push, enabling an optimistic update mechanism and real-time display of collaborator cursors.

[0027] In a preferred embodiment, the workflow management module includes:

[0028] The collaborative process is modeled as a directed graph structure using a graph-based representation. Nodes represent task nodes, and edges represent task dependencies and flow conditions. Each task node defines the task name, execution role, estimated working hours, deadline rules, preconditions, post-actions, input data, and output data.

[0029] Construct a task dependency graph and calculate the earliest start time and latest start time. Use the critical path method to identify the critical path and perform dynamic scheduling considering personnel availability constraints, skill matching constraints, and load balancing constraints.

[0030] Real-time tracking of task completion status, calculation of task completion rate, work time progress, and time progress;

[0031] By comprehensively analyzing historical delay records, current progress deviations, resource constraints, dependent task status, and external factors, a delay risk score is calculated. When the score exceeds the second threshold, an early warning notification is sent and adjustment suggestions are provided.

[0032] In a preferred embodiment, the design assistance module includes:

[0033] Knowledge is extracted from structured, semi-structured, and unstructured data sources. Entity types are defined, including fabric entities, accessory entities, process entities, and style entities. Relationship types are defined, including applicable relationships, matching relationships, inclusion relationships, and similar relationships. Entity alignment algorithms are used for identification and merging.

[0034] Perform process feasibility constraint checks, parameter rationality constraint checks, cost controllability constraint checks, and compatibility constraint checks, and generate a design constraint check report;

[0035] A multi-strategy fusion recommendation method is adopted, including recommendations based on design specifications, recommendations based on historical data, recommendations based on user preferences, and recommendations based on popular trends. Each recommendation item includes recommended content, reasons for recommendation, matching score, and relevant reference cases.

[0036] In a preferred embodiment, the security protection module includes:

[0037] Permissions are assigned to roles, and roles are then assigned to users. Permission granularity is refined to the data object dimension and operation type dimension, and dynamic permission determination based on attributes is supported.

[0038] Data in transmission is protected by a transport layer encryption protocol, sensitive data in storage is encrypted using a symmetric encryption algorithm, and the key management service adopts a hierarchical key architecture.

[0039] Invisible watermarking technology is used to embed user identification, export time, and export purpose information into the design document. The watermark embedding adopts a robust algorithm.

[0040] The system records authentication events, authorization events, data access events, data modification events, management operation events, and security anomaly events. Each audit record includes the event type, event time, operating user, operating object, operation content, operation result, client information, and context information. The audit log is stored using an append-only method.

[0041] In a preferred embodiment, the data analysis and optimization module includes:

[0042] Efficiency-related indicators are extracted from the collaborative behavior data accumulated by the system, and statistical analysis covers the dimensions of time efficiency, collaboration efficiency, communication efficiency, and resource utilization.

[0043] The critical path method and queuing theory model are used to identify process bottlenecks, resource load analysis is used to identify resource bottlenecks, social network analysis is used to identify collaboration bottlenecks, and defect tracing analysis is used to identify quality bottlenecks, generating a collaboration bottleneck analysis report.

[0044] For process bottlenecks, we recommend optimizing process design; for resource bottlenecks, we recommend adjusting resource allocation; for collaboration bottlenecks, we recommend improving collaboration mechanisms; and for quality bottlenecks, we recommend strengthening quality control. We will also refer to best practice case studies in the knowledge base and output the results in the form of an analysis report.

[0045] In a preferred embodiment, the real-time collaborative editing module further includes:

[0046] Edge service nodes are deployed at various collaborative locations. The edge nodes cache copies of hot design documents and respond to local users' read requests as close as possible.

[0047] For write operations, edge nodes forward the operations to the central server for global sorting and conflict detection, while returning optimistic acknowledgments to local users. After the central server completes the processing, it synchronizes the final result to all edge nodes.

[0048] Edge nodes also implement intelligent prefetching, predicting the design documents that users will access based on their historical access patterns and retrieving them from the central server to the local cache in advance.

[0049] This invention provides a computer-readable storage medium for storing computer-readable instructions that, when read by a computer, enable the execution of the aforementioned cloud-based collaborative apparel cross-team design system.

[0050] The beneficial effects of this invention are as follows:

[0051] The real-time collaborative editing engine enables simultaneous online editing and instant synchronization by multiple users. Combined with operation conversion algorithms and conflict detection mechanisms, it effectively solves the problems of information synchronization delays and version conflicts that are difficult to handle in traditional collaboration methods. Designers can see the modifications made by other collaborators in real time. The system automatically detects and assists in resolving editing conflicts, reducing repetitive work and design rework caused by information asynchrony, and improving the efficiency and accuracy of cross-team collaboration. Meanwhile, the distributed version control repository fully records detailed information on every design change, supports viewing and rolling back any historical version, and ensures the complete traceability of design changes.

[0052] By leveraging a knowledge graph-driven design assistance engine, the system deeply integrates professional knowledge in the field of apparel design into the collaborative system, enabling intelligent constraint checks and recommendations during the design process. The system can automatically detect issues such as process feasibility, parameter rationality, and cost controllability in design schemes, promptly issuing warnings and providing adjustment suggestions to designers, thus preventing design defects caused by insufficient professional knowledge or oversight. The intelligent recommendation function, based on multi-dimensional information such as design specifications, historical data, user preferences, and fashion trends, provides designers with recommendations on fabric and accessory combinations and process solutions, improving the scientific nature and efficiency of design decisions and shortening product development cycles. Attached Figure Description

[0053] Figure 1 This is a block diagram of a cloud-based collaborative cross-team design system for clothing, based on the present invention.

[0054] Figure 2 This is a detailed flowchart of a cloud-based collaborative cross-team design system for clothing, based on the present invention. Detailed Implementation

[0055] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0056] At least one embodiment of the present invention discloses a cloud-based collaborative system for cross-team apparel design, such as... Figures 1 to 2 As shown, it includes:

[0057] The data acquisition and processing module is used to acquire the original design files and preprocess them to obtain a standardized design dataset.

[0058] Specifically, the following steps are included:

[0059] Step 1.1: Perform multi-format file parsing;

[0060] Based on the original design files uploaded or synchronized by each team, including style drawings, structural drawings, process sheets, fabric and accessory lists, size specifications, etc., a file parsing engine is used to perform in-depth analysis of various files to obtain a set of parsed design elements.

[0061] The file parsing engine adopts a plug-in architecture, configuring corresponding parsing plug-ins for different types of design files. For style drawing files, the parsing plug-in identifies the hierarchical structure of graphic elements, extracting design elements such as silhouette outlines, component positions, color schemes, and material annotations, converting vector graphics into editable graphic object models. For structural drawing files, the parsing plug-in identifies professional elements such as structural lines, seam allowances, positioning marks, and process symbols, establishing the topological relationships between elements to form a structured structural drawing data model. For process sheet documents, the parsing plug-in uses template matching and natural language processing technology to extract structured information such as process names, process parameters, quality requirements, and time quotas from the document. For fabric and accessory list tables, the parsing plug-in identifies the table structure, extracting fields such as material codes, material names, specifications, usage data, and supplier information. For size specification data, the parsing plug-in parses according to the standard size table format, extracting data such as measurement points for each part, measurement values ​​for each size, and tolerance ranges.

[0062] During the parsing process, the file parsing engine records the original file's metadata information, including file name, file size, creation time, modification time, creator, and source system, as a basis for subsequent data tracing. The parsing results are represented in a unified internal data format to facilitate standardized operations in subsequent processing steps.

[0063] Step 1.2: Perform data mapping and format conversion;

[0064] Based on the parsed set of design elements, a data mapping rule base is used to uniformly map and transform design data from different systems and formats, resulting in a design dataset with a unified format.

[0065] The data mapping rule base predefines mapping rules from the data formats of various source systems to the target unified format. These rules cover multiple dimensions, including field name mapping, data type conversion, encoding standardization, and unit conversion. Field name mapping unifies semantically identical but differently named fields across systems into standard field names; for example, mapping "style number," "style ID," and "StyleNo" from various systems to the standard field "style_code." Data type conversion converts data in different formats to a unified data type; for example, converting the text-formatted date "December 3, 2024" to the standard date format "2024-12-03." Encoding standardization converts different encoding systems across systems to a unified enterprise encoding system; for example, converting fabric codes from various systems to the enterprise's standard fabric codes. Unit conversion converts measurement data in different units to a unified unit; for example, converting inches to centimeters.

[0066] The data mapping process is automated using a rules engine, supporting dynamic configuration and expansion of rules. When data that cannot be automatically mapped is encountered, the system logs the exception and prompts the administrator for manual configuration, gradually improving the coverage of the mapping rule base.

[0067] Step 1.3: Perform data quality checks;

[0068] Based on a standardized design dataset, a data quality inspection algorithm is used to comprehensively examine the integrity, consistency, and accuracy of the data, resulting in a standardized design dataset that passes the quality inspection.

[0069] Data integrity checks verify whether required fields are missing and whether associated data is complete. The system predefines integrity rules for various types of design data. For example, style data must include required fields such as style code, style name, designer, and creation date; structure diagram data must be associated with corresponding style data and size specification data. The verification process traverses all data records, marking and statistically analyzing records that violate integrity rules.

[0070] Data consistency checks verify whether there are contradictions or conflicts between related data. The system predefines consistency rules between various types of design data. For example, the fabric information marked in the style drawing must be consistent with the records in the fabric and accessories list, and the component dimensions in the structural drawing must match the definitions in the size specification data. The checking process cross-compares the related data and marks and statistically analyzes records that violate consistency rules.

[0071] Data accuracy verification checks whether data values ​​are within a reasonable range and whether the data format conforms to specifications. The system predefines the value range and format specifications for each field. For example, chest circumference measurements should be within a reasonable size range (chest circumference for adult clothing is typically between 70cm and 130cm, and for children's clothing, it is typically between 50cm and 80cm), and color codes should conform to a preset color coding format. The verification process validates the values ​​of each field, marking and statistically analyzing records that are out of range or do not conform to the format.

[0072] After quality inspection is completed, the system generates a data quality report, summarizing the quantity and distribution of various quality issues. For serious quality issues, the system rejects data entry and notifies the data provider to make corrections; for minor quality issues, the system records the issue details and allows data entry, pending subsequent manual verification and correction. Data that passes quality inspection is entered into the standardized design dataset, serving as the data foundation for subsequent steps.

[0073] Furthermore, since different design teams may use different versions of design software, the exported file formats may differ. Version-adaptive parsing technology can be employed to ensure correct parsing of files exported from different software versions. Specifically, this technology first identifies the software version identifier of the design file, selects the corresponding parsing rule set based on the version identifier, and for files whose version cannot be identified, uses a heuristic algorithm to try multiple parsing rules and selects the rule with the highest success rate. This technology also establishes a version compatibility mapping table to record the format differences and conversion rules between different software versions, automatically performing version conversion during the parsing process to ensure consistency of the parsing results.

[0074] The version control module, based on a standardized design dataset, stores each design change differently, establishes a management mechanism that supports parallel evolution, and obtains a traceable design version library.

[0075] Specifically, the following steps are included:

[0076] Step 2.1: Create a distributed version control repository structure;

[0077] Based on the project grouping information in the standardized design dataset, a distributed storage strategy is used to create an independent version control repository for each design project, resulting in a project-level version repository structure.

[0078] The system first parses the project grouping information in the standardized design dataset, identifying the hierarchical relationships of each design data point, such as project, season, and category. Based on the company's project organizational structure, the system organizes the version repository using a tree-like directory structure. The top level is divided by year and season, the next level by category, and the bottom level by specific styles. Each style corresponds to an independent version repository instance, containing all design data and version records related to that style.

[0079] The version control repository adopts a distributed architecture. The main repository is deployed in a cloud data center, providing centralized data storage and version management services. Clone repositories are deployed in local environments of teams in different locations, providing fast local access capabilities. The main repository and clone repositories maintain data consistency through a synchronization protocol, supporting both online and offline working modes. In online mode, local changes are pushed to the main repository in real time; in offline mode, local changes are temporarily stored in the local repository and synchronized to the main repository in batches after the network is restored.

[0080] The version repository's storage layer employs object storage technology, breaking down design files into multiple storage objects, each identified by a content hash value. Objects with identical content are stored only once, while different versions share the same objects, thus achieving efficient utilization of storage space.

[0081] Step 2.2: Perform incremental storage and change logging;

[0082] Based on the project-level version repository structure, an incremental storage algorithm is used to store each design change differently and record complete change metadata, resulting in a version snapshot sequence with complete change records.

[0083] The incremental storage algorithm first calculates the differences between the current version and the previous version. For structured data, the algorithm uses field-level difference comparison to identify newly added, modified, and deleted fields and records; for graphical data, the algorithm uses layer-level difference comparison to identify the addition, deletion, and modification of layers, as well as changes in the position and attributes of graphical elements; for binary files, the algorithm uses block-level difference comparison, dividing the file into fixed-size data blocks and identifying the changed data blocks. The difference data is stored in the form of incremental patches, occupying much less space than the storage of a complete file.

[0084] Each design change generates a version snapshot, which contains the following metadata: version identifier, using a globally unique identifier to ensure version uniqueness; parent version identifier, pointing to the previous version, forming a version evolution chain; change timestamp, recording the precise time the change occurred; changer identifier, recording the user who performed the change operation; change summary, a change description filled in by the changer; change details, an automatically generated list of change content; associated tasks, the identifier of the work tasks associated with this change.

[0085] The version snapshot sequence forms a directed acyclic graph structure, with the main version forming a trunk chain and branch versions branching off from the trunk chain. The system supports quick checkout of any version, allowing users to restore to the design state of any historical version with a single click.

[0086] Step 2.3: Establish a branch management mechanism;

[0087] Based on the version snapshot sequence, a branch management strategy is adopted to support the parallel evolution of the main version and multiple development branches, establish the relationship between branches and the merging rules, and obtain a traceable design version library that supports branch development.

[0088] The branch management mechanism defines the following branch types: main branch, which represents the officially released design version. Only design changes that have been reviewed and confirmed can be merged into the main branch; development branch, which is used for daily design and development work. Designers explore ideas and iterate solutions on the development branch; feature branch, which is used for the independent development of specific design features. Once completed, it is merged into the development branch; and fix branch, which is used to fix issues in the released version. Once the fix is ​​completed, it is merged into both the main branch and the development branch.

[0089] When a branch is created, the system records information such as the branch's creation point, the creator, and the purpose of creation, establishing the relationship between the branch and the source branch. During branch merging, the system executes a three-way merge algorithm, comparing the changes between the two branches based on their common ancestor version. It automatically merges conflict-free changes and generates conflict markers for conflicting changes, awaiting manual resolution. After the merge is complete, the system records detailed information about the merge operation, including the merge time, the merger, the source and target branches, and a list of resolved conflicts.

[0090] The branch management mechanism also supports the configuration of branch protection rules. Administrators can set protection rules for important branches, such as prohibiting forced pushes, requiring code reviews, and requiring automated checks. Protection rules ensure the stability of important branches and the controllability of changes.

[0091] Furthermore, since clothing design files typically contain numerous graphic elements, traditional text-based difference comparison algorithms struggle to accurately identify graphic changes. Therefore, semantic-based graphic difference detection technology can be employed to improve the accuracy of version comparisons for graphic design files. Specifically, this technology first parses the graphic file into a structured representation of graphic elements, including graphic type, position coordinates, size parameters, and style attributes. It then uses a graphic matching algorithm to establish a correspondence between graphic elements in two versions. Finally, it compares the attribute changes of corresponding elements to generate semantically meaningful difference descriptions, such as easily readable changes like "collar style changed from round neck to V-neck" or "sleeve length shortened by 5 cm."

[0092] The real-time collaborative editing module is based on a traceable version control library, performs concurrent conflict detection and resolution, and pushes editing results to all online collaborators in real time, realizing a real-time synchronous push strategy.

[0093] Specifically, the following steps are included:

[0094] Step 3.1: Execute the operation conversion algorithm;

[0095] Based on the current design document in the design version repository, an operation transformation algorithm is used to convert the editing operations of each collaborator into a sequence of atomic operations that can be merged, resulting in a set of edit instructions after operation transformation.

[0096] The core idea of ​​the operation transformation algorithm is to represent user editing operations as a sequence of atomic operations, and to enable the correct merging of concurrent operations through mathematical transformations. The system defines a set of atomic operation types suitable for clothing design scenarios, including: insertion operations (inserting a new design element at a specified position); deletion operations (deleting a design element at a specified position); modification operations (modifying the attribute value of a specified design element); move operations (moving a design element from one position to another); and transformation operations (performing geometric transformations such as rotation, scaling, and mirroring on design elements).

[0097] When a user performs an editing operation, the client converts the operation into a sequence of atomic operations, attaching contextual information such as the document version at the time of the operation, the location where the operation occurred, and the identifiers of the design elements involved. The client then sends the atomic operation sequence to the server's operation transformation engine.

[0098] The operation transformation engine receives operation sequences from various clients, sorts them according to the operation timestamp and client identifier, forming a globally consistent operation order. For concurrent operations, the engine executes the operation transformation function, adjusting the position and parameters of the arriving operation to ensure it is correctly applied to documents modified by earlier arriving operations. The operation transformation function defines specific transformation rules for different operation combinations. For example, when two insertion operations occur concurrently, the position offset of the later insertion is adjusted according to the order of their insertion positions; when an insertion operation and a deletion operation occur concurrently, the position and length of the affected operation are adjusted according to the overlap of their operation ranges.

[0099] Step 3.2: Perform concurrent conflict detection;

[0100] Based on the edit instruction set after operation conversion, a conflict detection algorithm is used to identify conflicting operations that act on the same design element at the same time, calculate the type and severity of the conflict, and obtain a conflict detection report.

[0101] The conflict detection algorithm first identifies the scope of concurrent operations. For editing structured data, the algorithm determines whether the scopes overlap at the data record and field level; for editing graphical elements, the algorithm determines whether the same graphical object is being targeted at the graphical object level; when the scopes of two concurrent operations overlap, the algorithm further determines the conflict type.

[0102] The system defines the following conflict types: write-write conflict, where two operations simultaneously modify the same attribute of the same design element, such as two designers simultaneously modifying the collar of the same style; write-delete conflict, where one operation modifies a design element and another operation deletes that element; and semantic conflict, where two operations, although not directly applied to the same element, create a semantic contradiction, such as one operation changing the fabric to knitted while another operation adds a process requirement that only applies to woven fabric.

[0103] The conflict detection algorithm calculates a severity score for each detected conflict. The score considers the following factors: the importance weight of the design elements involved in the conflict (conflicts involving core design elements are of higher severity); the type of operation involved in the conflict (deletion operations involve higher severity); and the user role involved in the conflict (conflicts between different roles may require a higher level of coordination).

[0104] The conflict detection report summarizes all detected conflicts, including conflict identifier, conflict type, involved operations, involved design elements, severity score, and time of occurrence. The report is sorted by severity, allowing users to prioritize the handling of important conflicts.

[0105] Step 3.3: Perform conflict resolution.

[0106] Based on the conflict detection report, a conflict resolution strategy is adopted to automatically process conflicts that can be merged, and to generate a visual comparison interface and resolution suggestions for conflicts that require manual intervention, thus obtaining a conflict solution.

[0107] The conflict resolution strategy has two modes: automatic resolution and manual resolution. For minor conflicts with a severity score below 30 (out of 100), the system uses predefined automatic resolution rules. These rules determine the final adopted operation based on the priority and timestamp of the operation. For example, for concurrent modifications to the same attribute, the operation with the newer timestamp is adopted; for write-delete conflicts, the write operation's modifications are retained and marked as pending confirmation. The results of automatic resolution are recorded in the conflict resolution log, which users can review and revert later. For significant conflicts with a severity score of 30 or higher, the system generates a visual difference comparison interface, requiring relevant users to resolve the conflict manually. The difference comparison interface uses a side-by-side display, showing one user's version on the left and another user's version on the right, with the specific location and content of the conflict highlighted in the middle. For conflicts involving graphical elements, the interface supports overlay display and animation switching to help users intuitively understand the differences between the two versions.

[0108] The system also provides users with conflict resolution suggestions based on historical conflict resolution records and knowledge graphs. These suggestions include: historical solutions to similar conflicts for user reference; recommended options based on design specifications, such as recommending fabrics that better match the style when there is a conflict in fabric selection; and communication suggestions based on collaborative relationships, such as suggesting immediate communication and confirmation with the other party in the conflict.

[0109] Once the conflict is resolved, the system generates a conflict resolution document, which records the resolution method, the person who resolved it, and the resolution time for each conflict, and updates the design document to the resolved version.

[0110] Step 3.4: Perform real-time synchronization push;

[0111] Based on the conflict resolution mechanism, a real-time synchronization protocol is adopted to push the merged editing results to all online collaborators, resulting in a real-time synchronization push strategy.

[0112] The real-time synchronization protocol employs WebSocket long-connection technology to establish a persistent, bidirectional communication channel between the server and each client. The server maintains an online user session manager, tracking all currently online users and their subscribed design documents. When a design document changes, the server immediately pushes a change notification to all online users subscribed to that document. The change notification uses an incremental push method, transmitting only the changed content rather than the entire document, reducing network traffic. Upon receiving the change notification, the client applies the incremental changes to its local document copy and updates the user interface. Incremental application uses the same mechanism as operation transformations to ensure that the document state after application is consistent with the server's.

[0113] To optimize user experience, the system implements an optimistic update mechanism. When a user performs an editing operation, the client immediately applies the operation to the local document and updates the interface, while simultaneously sending the operation to the server. If the server confirms the operation is successful, the local state remains unchanged; if the server detects a conflict and needs to roll back, the client undoes the optimistic update and applies the correct state returned by the server. The optimistic update mechanism ensures that users experience no network latency in most cases, improving the collaborative experience.

[0114] The system also features real-time display of collaborators' cursors and selection areas. When multiple users are editing the same document simultaneously, each user can see the cursor position and selection area of ​​other collaborators, with the collaborator's name or avatar displayed next to the cursor. This feature helps collaborators understand each other's work positions and avoid unnecessary conflicts in the same area.

[0115] Furthermore, since significant network latency may exist between geographically dispersed teams, impacting the real-time synchronization experience, an edge computing-enhanced synchronization strategy can be adopted to reduce the impact of network latency on the collaboration experience. Specifically, this strategy deploys edge service nodes at key collaboration locations. These edge nodes cache copies of frequently accessed design documents and respond to local user read requests based on their proximity. For write operations, edge nodes forward the operation to the central server for global sorting and conflict detection, while simultaneously returning optimistic acknowledgments to local users. After processing, the central server synchronizes the final result to all edge nodes. The edge nodes also implement intelligent prefetching, predicting upcoming design documents based on users' historical access patterns and pre-fetching them from the central server to their local cache, further reducing access latency.

[0116] The workflow management module designs a collaborative workflow model based on a real-time synchronous push strategy, generates optimized execution plans and monitors task progress. When a delay risk is detected, it automatically issues an alert and provides suggestions, forming an intelligent task scheduling strategy.

[0117] Specifically, the following steps are included:

[0118] Step 4.1, perform workflow modeling;

[0119] Based on the task decomposition structure of the design project, a workflow modeling method is used to define each task node, the dependencies between nodes, the preconditions for task execution, and the post-execution actions to obtain a design collaboration workflow model.

[0120] The workflow modeling method employs a graph-based representation, modeling collaborative processes as a directed graph structure. Nodes in the graph represent task nodes, and edges represent dependencies and transition conditions between tasks. The system supports the following types of task nodes: manual task nodes, requiring a designated executor for tasks such as a designer creating a style sketch or a pattern maker creating a structural drawing; automatic task nodes, tasks executed automatically by the system, such as automatically sending notification emails or automatically triggering quality checks; approval task nodes, tasks requiring a designated approver for review and confirmation, such as a design director approving a design proposal; branch nodes, determining different subsequent processes based on conditions; and convergence nodes, waiting for multiple preceding tasks to be completed before continuing execution.

[0121] Each task node defines the following attributes: Task Name and Description, clearly stating the content and objectives of the task; Execution Role, specifying which user roles can execute the task; Estimated Work Hours, estimating the time required to complete the task; Deadline Rules, defining how the task deadline is calculated; Preconditions, defining the conditions that must be met for the task to begin execution; Post-actions, defining the actions that will be automatically executed after the task is completed; Input Data, the input data required for task execution and its sources; Output Data, the output data generated upon task completion and its destination.

[0122] The system comes pre-loaded with commonly used workflow templates in the apparel design field, covering typical scenarios such as new product development processes, sample garment modification processes, and mass production processes. Users can directly use the pre-loaded templates, customize them based on the templates, or create entirely new workflows from scratch. The workflow modeling interface uses a visual drag-and-drop method, allowing users to complete process design simply by dragging and dropping nodes and lines, lowering the technical barrier to modeling.

[0123] Step 4.2: Execute intelligent task scheduling;

[0124] Based on the collaborative workflow model, a task scheduling algorithm is used to dynamically schedule tasks according to their priority, dependencies, and resource constraints, resulting in an optimized task execution plan.

[0125] The task scheduling algorithm first performs task dependency analysis, constructs a task dependency graph, and calculates the earliest and latest start times for each task. The critical path method is then used to identify the project's critical path. Tasks on the critical path have the greatest impact on the overall project duration and require priority resource allocation and close monitoring.

[0126] Regarding resource allocation, the scheduling algorithm considers the following constraints: personnel availability constraint, each executor can only execute a limited number of tasks at the same time; skill matching constraint, tasks need to be assigned to executors with corresponding skills; and load balancing constraint, aiming to make the workload of each executor relatively balanced. The algorithm adopts a heuristic optimization method to minimize the overall project duration or maximize resource utilization while satisfying the constraints.

[0127] Task scheduling results are presented in the form of task execution plans, including the planned start time, planned end time, assigned executor, and dependent prerequisite tasks for each task. The execution plan supports multiple view displays: a Gantt chart view intuitively displays the time schedule and dependencies of each task; a Kanban view displays tasks by status for easy tracking of task progress; and a calendar view displays the task assignments for each executor by date, facilitating individual work planning.

[0128] The scheduling algorithm supports dynamic adjustments. When changes occur during project execution, such as task delays, requirement changes, or personnel changes, the algorithm automatically recalculates the time schedule and resource allocation for the affected tasks, updates the execution plan, and notifies relevant personnel.

[0129] Step 4.3: Perform real-time progress monitoring;

[0130] Based on the task execution plan, a progress monitoring algorithm is used to track the completion status of each task in real time, calculate the overall project progress and critical path, and obtain project progress monitoring data.

[0131] The progress monitoring algorithm collects task execution status information from multiple data sources. For tasks requiring manual feedback, the executor updates the task status and completion progress through the system interface; for tasks associated with design documents, the system automatically determines the task progress based on the modification records of the design documents; for tasks with set completion conditions, the system automatically checks whether the completion conditions are met.

[0132] The algorithm calculates the following progress indicators in real time: task completion rate, which is the proportion of completed tasks to the total number of tasks; work time progress, which is the proportion of work time invested to the total planned work time; time progress, which is the proportion of time used to the total planned time; and earned value analysis indicators, including planned value, actual cost, and earned value, which are used to evaluate project performance.

[0133] Progress monitoring data is displayed through a visual dashboard. The dashboard includes a project overview area, displaying key information such as the overall project progress, key milestone status, and number of pending tasks; a task list area, displaying detailed status and progress information for each task; a trend chart area, displaying analytical charts such as progress trends, burn-down charts, and cumulative flow charts; and an alert area, displaying tasks at risk of delay and pending issues.

[0134] The progress monitoring data also supports multi-dimensional statistical analysis, including statistics on task completion status of each team, statistics on the overall progress of each project, and statistics on work efficiency trends over time periods, providing data support for management decisions.

[0135] Step 4.4: Implement a delay risk warning;

[0136] Based on project progress monitoring data, a delay warning algorithm is used to analyze the risk of task delays. When a potential delay is detected, an early warning notification is automatically sent and adjustment suggestions are provided to obtain an intelligent task scheduling strategy.

[0137] The delay warning algorithm uses a multi-factor comprehensive analysis method to predict the risk of task delays. The algorithm considers the following risk factors: historical delay records, the frequency and magnitude of historical delays for similar tasks or the same executor; current progress deviation, the degree of deviation between the actual progress of the task and the planned progress; resource tension, the current workload and available time of the executor; dependent task status, the completion status of preceding tasks and possible delays; and external factors, such as supplier delivery delays and holiday arrangements.

[0138] The algorithm calculates a delay risk score for each ongoing task, taking into account the weighted impact of the aforementioned risk factors. When the delay risk score exceeds the first warning threshold of 40 points (out of 100), the system sends a risk alert to the task executor, prompting them to monitor the task progress. When the score exceeds the second warning threshold of 60 points, the system simultaneously sends warning notifications to both the executor and their supervisor. When the score exceeds the third warning threshold of 80 points, the system triggers an escalation process, requiring project management intervention.

[0139] The warning notification includes not only risk information but also system-generated adjustment suggestions. These suggestions include: increasing resource allocation, such as assigning additional personnel to assist; adjusting task priorities, such as postponing non-critical tasks; adjusting dependencies, such as starting some subsequent tasks in parallel ahead of schedule; and adjusting planned timelines, such as negotiating with relevant parties to extend deadlines.

[0140] The intelligent task scheduling strategy integrates workflow models, task scheduling, progress monitoring, and delay warnings to form a closed-loop collaborative process management capability, ensuring efficient and orderly cross-team collaboration.

[0141] Furthermore, since design collaboration in the apparel industry often involves external suppliers and partners, internal workflow systems often struggle to cover the entire collaboration chain. Therefore, cross-organizational workflow collaboration technology can be employed to achieve integrated management of internal and external collaboration. Specifically, this technology establishes a partner access mechanism, allowing external suppliers to join the collaboration process through standardized interfaces. External partners can view relevant tasks, submit deliverables, and provide progress feedback within controlled access permissions. The system incorporates automatic checks and confirmations at internal and external handover points to ensure that external deliverables meet quality requirements. Cross-organizational task dependencies and progress information are synchronized through a secure data exchange mechanism, enabling internal managers to have a comprehensive grasp of the entire project progress, including external components.

[0142] The design assistance module constructs a knowledge graph in the field of apparel design based on an intelligent task scheduling strategy, performs design constraint checks, and obtains intelligent design recommendation results through the fusion of multiple strategies.

[0143] Specifically, the following steps are included:

[0144] Step 5.1: Construct a domain knowledge graph;

[0145] Based on knowledge entries such as fabric characteristics, process constraints, cost parameters, and fashion trends in the knowledge base of the clothing design field, a graph structure representation of entities, attributes, and relationships is established using knowledge graph construction methods to obtain a clothing design knowledge graph.

[0146] The knowledge graph construction process is divided into three stages: knowledge extraction, knowledge representation, and knowledge fusion. The knowledge extraction stage acquires domain knowledge from various sources, including structured raw material databases, process parameter tables, cost accounting tables, semi-structured technical manuals, industry standard documents, and unstructured designer experience notes, market analysis reports, etc. For structured data sources, database mapping methods are used to directly convert them into knowledge entries; for semi-structured and unstructured data sources, natural language processing techniques are used for entity recognition, relation extraction, and attribute extraction.

[0147] The knowledge representation phase defines the ontology model of the knowledge graph, including entity types, attribute definitions, and relation types. The system defines the following core entity types: Fabric entities, including attributes such as fabric code, fabric name, composition, weight, width, and price; Accessory entities, including attributes such as accessory code, accessory name, material, specifications, and price; Process entities, including attributes such as process name, process parameters, applicable fabrics, and labor quotas; Style entities, including attributes such as style code, style name, category, style, and designer. The system defines the following core relation types: Applicable relation, indicating that a certain process is applicable to a certain fabric; Matching relation, indicating that a certain accessory is suitable for matching a certain fabric; Containment relation, indicating that a certain style contains a certain design element; Similarity relation, indicating that two entities are similar in some aspect.

[0148] The knowledge fusion stage integrates and disambiguates knowledge from different sources. For different representations of the same entity in different sources, entity alignment algorithms are used for identification and merging; for conflicting information from different sources, credibility assessment and conflict resolution strategies are used to determine the final value; and for missing knowledge, knowledge completion algorithms are used for inference and supplementation.

[0149] The completed apparel design knowledge graph contains hundreds of thousands of entity nodes and millions of relationship edges, covering professional knowledge in multiple dimensions such as fabrics, accessories, processes, styles, and fashion trends, providing a knowledge foundation for subsequent knowledge reasoning and intelligent recommendation.

[0150] Step 5.2: Perform a design constraint check;

[0151] Based on the knowledge graph of clothing design and the current design context, a knowledge reasoning algorithm is used to perform constraint checks, identify problems in the design scheme that violate process constraints or cost constraints, and obtain a design constraint check report.

[0152] Design constraint checks are performed in real time during the design process. When designers perform operations such as fabric selection, process settings, and parameter adjustments, the system automatically triggers checks on relevant constraints. Constraint checks are divided into process feasibility checks, parameter rationality checks, cost controllability checks, and compatibility checks.

[0153] The process feasibility constraint check verifies whether the selected process is suitable for the selected fabric. The system queries the knowledge graph for the set of available processes for the fabric and determines whether the process selected by the designer is in the set of available processes. If the selected process is not suitable for the fabric, the system generates a process mismatch warning and recommends suitable alternative processes.

[0154] The parameter rationality constraint check verifies whether the design parameters are within a reasonable range. The system queries the knowledge graph for the reasonable value range of each design parameter to determine whether the parameter values ​​set by the designer exceed the range; if the parameter value exceeds the reasonable range, the system generates a parameter anomaly warning and suggests a reasonable value range.

[0155] The cost controllability constraint check verifies whether the cost of the current design scheme is within the budget. Based on information such as the price of the selected fabrics and accessories and the time quota of the process, the system calculates the estimated cost of the current design scheme in real time and compares it with the project budget; if the estimated cost exceeds the budget, the system generates a cost overrun warning and highlights the components with higher costs.

[0156] The compatibility constraint check verifies whether there are compatibility issues between various design elements. The system queries the knowledge graph for compatibility relationships between design elements to determine whether there are incompatible combinations of elements in the current design scheme; if incompatible combinations exist, the system generates a compatibility warning and explains the reasons and impacts of the incompatibility.

[0157] The design constraint check report summarizes all detected constraint violations, including constraint type, severity, affected design elements, scope of impact, and suggested solutions. The report is ordered by severity to help designers prioritize important issues.

[0158] Step 5.3: Implement intelligent design recommendations;

[0159] Based on the design constraint check report and current design elements, an intelligent recommendation algorithm is used to retrieve relevant fabric and accessory combinations, process schemes, and design references from the knowledge graph to obtain intelligent design recommendation results.

[0160] The intelligent recommendation algorithm adopts a multi-strategy fusion recommendation method, which comprehensively considers multiple dimensions such as design specifications, historical data, and user preferences to generate recommendation results.

[0161] Design-guideline-based recommendations start from the constraints of a knowledge graph and suggest options that conform to the design guidelines. For example, when a designer selects a certain fabric, the system automatically recommends suitable fabric processing methods, accessory pairings, seam allowance settings, etc. The recommendations are sorted according to the degree of matching, with options that match more closely appearing higher in the ranking.

[0162] Recommendations based on historical data learn patterns from successful design cases in historical enterprise design data, recommending combinations of elements that frequently appear in historical designs. The system analyzes the co-occurrence relationships of elements in historical design data and constructs a collaborative filtering model. When a designer selects certain design elements, it recommends other elements that frequently co-occur with them in historical data.

[0163] User preference-based recommendations learn a designer's design style and preferences from their historical activity records, recommending options that match their personal style. The system builds a preference profile for each designer, recording their commonly used fabrics and accessories, preferred manufacturing processes, and design style inclinations. This preference profile is used as the basis for filtering and ranking during recommendations.

[0164] The trend-based recommendation system uses trend data from a knowledge graph to suggest popular design elements and styling tips for the season. The system regularly updates trend data and integrates trend information into the recommendation algorithm, helping designers stay on top of market trends.

[0165] The intelligent design recommendations are presented in the form of a recommendation list. Each recommendation includes the recommended content, the reason for the recommendation, the matching score, and relevant reference examples. Designers can adopt the recommendations with one click, or view the details and make adjustments.

[0166] Furthermore, since the construction and maintenance of knowledge graphs require a continuous and significant investment of manpower, and knowledge may become outdated over time, automatic knowledge updates and crowdsourcing supplementation technologies can be employed to maintain the timeliness and completeness of the knowledge graph. Specifically, the automatic knowledge update technology establishes a connection mechanism with external data sources, regularly pulling the latest data from external data sources such as fabric and accessory supplier systems, industry standard publishing platforms, and trend analysis platforms. After data cleaning and format conversion, this data is updated into the knowledge graph. The crowdsourcing supplementation technology sets up a knowledge contribution entry point in the system. When designers discover knowledge gaps or errors during daily use, they can submit supplementary or corrective suggestions, which are then incorporated into the knowledge graph after review. The system also establishes a knowledge quality assessment mechanism, evaluating the quality of knowledge entries based on indicators such as usage frequency, user feedback, and verification results, and marking and cleaning up low-quality knowledge.

[0167] The security protection module, based on the intelligent design recommendation results, constructs a multi-layered security protection system and obtains a full-process security audit strategy;

[0168] Specifically, the following steps are included:

[0169] Step 6.1, define fine-grained access control policies;

[0170] Based on user role permission configuration data, a role-based access control model is adopted to define the access permissions of each role to the designed data, including operation permissions such as viewing, editing, deleting, exporting, and sharing, so as to obtain a fine-grained permission control strategy.

[0171] The access control strategy adopts a role-based access control model, which assigns permissions to roles, and then assigns roles to users, achieving centralized management and flexible configuration of permissions. The system predefines the following standard roles: System Administrator, with management permissions for all system functions; Project Manager, with full operational permissions for the projects under their jurisdiction; Design Director, with approval permissions for design schemes and viewing permissions for all design data; Designer, with editing permissions for design data of the projects they participate in; Pattern Maker, with editing permissions for structural diagram data and viewing permissions for style diagram data; Process Engineer, with editing permissions for process sheet data; Visitor, with only read-only permissions for authorized data.

[0172] Permission granularity is refined to two dimensions: data object and operation type. The data object dimension includes different granularities such as project level, style level, document level, and element level, supporting setting access permissions for individual design elements; the operation type dimension includes different operations such as viewing, editing, deleting, exporting, sharing, and approving, supporting separate authorization for different operations.

[0173] The access control policy also supports dynamic permission determination based on attributes. In addition to static role permissions, the system also supports dynamic permission rules based on data attributes, user attributes, and environment attributes. For example, a rule can be set that "only the creator can delete design documents in draft state," and the system dynamically checks the document's creator and state attributes when determining permissions.

[0174] The entire process of permission change is recorded in an audit log, including information such as the time of permission change, the person making the change, and the permission configuration before and after the change, supporting the traceability and auditing of permission changes.

[0175] Step 6.2: Implement data encryption protection;

[0176] Based on a fine-grained access control strategy, a data encryption protection mechanism is obtained by using data encryption algorithms to encrypt data in transit at the transport layer and to encrypt sensitive data in storage at the storage layer.

[0177] Transport layer encryption uses the TLS protocol to encrypt and protect all communication between the client and server. The system deploys industry-standard TLS certificates, enables TLS versions and above, and disables older versions and weak encryption suites with security vulnerabilities. All HTTP requests are forcibly redirected to HTTPS to ensure the security of the transmission channel. For mobile and desktop clients, a certificate locking mechanism is implemented to prevent man-in-the-middle attacks.

[0178] The storage layer uses the AES symmetric encryption algorithm to encrypt and store sensitive data. The system divides data into two categories: sensitive data and non-sensitive data. Sensitive data includes design file content, user passwords, access tokens, etc., while non-sensitive data includes system configurations, log records, etc. Sensitive data is encrypted with AES before being written to storage and decrypted when read. The encryption keys are managed uniformly by a key management service.

[0179] The key management service adopts a hierarchical key architecture, including multiple levels such as master key, data encryption key, and key encryption key. The master key is stored in the hardware security module, providing the highest level of security protection; the data encryption key is used for actual data encryption operations and is stored after being encrypted by the master key; the keys are rotated periodically, and data encrypted with the old key is automatically re-encrypted with the new key when accessed.

[0180] For particularly sensitive design data, the system also supports end-to-end encryption. In end-to-end encryption mode, data is encrypted on the client side before being uploaded, and the server only stores the encrypted ciphertext, making the plaintext content unknown. The decryption key is stored only on the client of authorized users, and only authorized users can decrypt and view the data.

[0181] Step 6.3: Perform digital watermark embedding;

[0182] Based on data encryption protection mechanisms, a digital watermarking algorithm is used to embed user identification watermarks in exported or shared design documents, resulting in a traceable document distribution mechanism.

[0183] The digital watermarking algorithm employs invisible watermarking technology, embedding a watermark containing information such as user identification, export time, and export purpose into the design document. The watermark information is encrypted before being embedded in the document, making it invisible to the naked eye but detectable and extractable using specialized tools. The watermark embedding uses a robust algorithm, ensuring that the watermark information can still be detected and extracted even after the document has undergone conventional editing, compression, format conversion, printing, and scanning.

[0184] For graphic design documents, watermark embedding is performed in the spatial or frequency domain of the image, achieving covert embedding by fine-tuning pixel values ​​or frequency coefficients. For text-based design documents, watermark embedding is achieved by fine-tuning character spacing, line spacing, font parameters, etc.; for structured data, watermark embedding is achieved by adding redundant information that does not affect the semantics of the data.

[0185] When a user exports a design document, the system automatically embeds the user's identity watermark. When a user shares a design document with external parties, the system embeds dual watermarks of the sharer's and recipient's identities in the shared document; if a design document leak is discovered, the source of the leak can be traced by extracting the watermarks.

[0186] The system also supports a visible watermark function, which overlays visible text or graphic watermarks on exported documents to clearly indicate the document's confidentiality level, usage restrictions, and other information, serving as a warning and deterrent.

[0187] Step 6.4, execute the security audit log;

[0188] Based on the above security mechanisms, a security audit algorithm is used to record all users' operation behaviors and data access records, resulting in a full-process security audit strategy.

[0189] The security audit system records the following types of audit events: authentication events, including user login, logout, password modification, multi-factor authentication, etc.; authorization events, including permission changes, role assignments, access control policy modifications, etc.; data access events, including data viewing, downloading, exporting, printing, sharing, etc.; data modification events, including data creation, editing, deletion, version rollback, etc.; management operation events, including system configuration changes, user management operations, etc.; and security anomaly events, including multiple failed login attempts, abnormal access behavior, and unauthorized permission attempts, etc.

[0190] Each audit log contains the following information: event type, identifying the category of the event; event time, the event occurrence time accurate to milliseconds; operating user, the user identifier who performed the operation; operation object, the identifier of the data or resource being operated on; operation content, a detailed description of the operation; operation result, whether the operation was successful and the reason for failure; client information, including IP address, device type, browser information, etc.; and context information, other contextual data related to the event.

[0191] Audit logs are stored using an append-only method, meaning they cannot be modified after being written, ensuring the integrity and non-repudiation of audit records. Audit logs are periodically archived to read-only storage media and stored long-term to meet compliance requirements.

[0192] The security audit system also provides real-time monitoring and anomaly detection capabilities. The system analyzes audit event streams in real time, using a rule engine and machine learning algorithms to detect abnormal behavior patterns, such as access at abnormal times, logins at abnormal locations, and data export at abnormal frequencies. When abnormal behavior is detected, the system immediately sends an alarm notification and can automatically take protective measures such as temporarily freezing accounts.

[0193] Furthermore, since apparel design collaborations may involve multiple countries and regions, requiring compliance with different data protection regulations, automated compliance checks and data localization technologies can be employed to ensure the system complies with the data protection regulations of each region. Specifically, the automated compliance check technology establishes a regulatory rule base, including the specific requirements of data protection regulations in various regions, such as the EU's General Data Protection Regulation (GDPR) and China's Personal Information Protection Law. The system automatically checks whether operations comply with relevant regulations during user registration, data collection, data processing, and data transmission, intercepting and alerting users to any violations. The data localization technology deploys local data centers in each major business region, storing data in the corresponding data center based on the data subject's geographical location, meeting the compliance requirement of data not leaving the country. Cross-regional data transmission requires compliance audits to ensure that transmission activities comply with relevant regulations on cross-border transmission.

[0194] The data analysis and optimization module, based on the full-process security audit strategy, calculates collaboration efficiency indicators, identifies bottlenecks in collaboration, generates optimization suggestions, and obtains collaboration efficiency assessment and optimization recommendations.

[0195] Specifically, the following steps are included:

[0196] Step 7.1: Perform collaborative efficiency statistical analysis;

[0197] Based on historical collaboration behavior data, statistical analysis methods are used to calculate collaboration efficiency indicators for each team, project, and stage, including task completion time, number of version iterations, and frequency of conflict occurrence, to obtain collaboration efficiency statistics.

[0198] Collaboration efficiency statistical analysis extracts efficiency-related indicators from the collaborative behavior data accumulated by the system. Data sources include task execution records, version commit records, collaborative editing records, conflict resolution records, and communication records. Statistical analysis covers time efficiency, collaboration efficiency, communication efficiency, and resource utilization dimensions.

[0199] The time efficiency dimension includes statistics on the average completion time of various tasks, the deviation between actual and planned completion times, and the distribution of processing time across different stages. The system calculates the end-to-end time from task creation to completion, as well as the time spent in each state, identifying stages with higher time consumption. For design tasks, it also tracks the iteration cycle and number of iterations from the initial draft to the final version of the design.

[0200] The collaboration efficiency dimension tracks efficiency metrics in multi-person collaboration scenarios, including the number of participants in collaborative editing, the duration of collaborative editing, the frequency of conflicts, and the time taken to resolve conflicts. The system calculates the percentage of effective working time during the collaboration process, which is the ratio of actual design work time to total collaboration time, reflecting the effectiveness of the collaboration.

[0201] The communication efficiency dimension tracks communication behaviors during the collaboration process, including the number of discussion messages, discussion response time, and discussion resolution rate. The system analyzes the distribution of discussion topics, identifies frequently discussed question types, and evaluates the efficiency and effectiveness of communication.

[0202] The resource utilization dimension tracks the use of personnel and system resources, including the workload of each team member, work hour utilization rate, and frequency of system function usage. The system identifies situations of uneven resource utilization, providing data support for optimal resource allocation.

[0203] Collaboration efficiency statistics are presented in the form of reports and visualizations, supporting multi-dimensional filtering and comparative analysis by time period, team, project, task type, and other dimensions.

[0204] Step 7.2: Perform collaboration bottleneck identification;

[0205] Based on collaborative efficiency statistics, a bottleneck analysis algorithm is used to identify efficiency bottlenecks and problematic steps in the collaborative process, resulting in a collaborative bottleneck analysis report.

[0206] Bottleneck analysis algorithms identify efficiency bottlenecks in collaborative processes from multiple perspectives. Process bottleneck analysis employs the critical path method and queuing theory model to analyze the processing capacity and waiting time of each step, identifying the bottleneck steps causing overall process delays. The algorithm calculates the utilization rate and queue length of each step; steps with near-saturation utilization and large queue lengths are identified as process bottlenecks.

[0207] Resource bottleneck analysis employs resource load analysis methods to statistically analyze the workload and available capacity of each resource, identifying resource nodes that are overloaded. For human resources, the algorithm analyzes the task allocation of each team member to identify personnel with excessive workloads; for system resources, the algorithm analyzes server performance metrics to identify system components with insufficient performance.

[0208] Collaboration bottleneck analysis employs social network analysis methods to construct a collaboration relationship graph and analyze the structural characteristics of the collaboration network. The algorithm identifies key nodes in the collaboration network—individuals or teams occupying crucial positions in information transmission; the efficiency of these nodes directly impacts overall collaboration efficiency. The algorithm also identifies breakpoints in the collaboration network—locations where connections are weak or information transmission is obstructed.

[0209] Quality bottleneck analysis employs a defect tracing analysis method to analyze the source distribution of design defects and problems, identifying high-risk stages. The algorithm statistically analyzes the number and severity of defects generated at each stage, as well as the timing of defect discovery and repair costs, identifying stages requiring enhanced quality control.

[0210] The collaboration bottleneck analysis report summarizes the identification results of various bottlenecks, including bottleneck type, bottleneck location, bottleneck severity, scope of impact, and supporting data. The report is sorted by the degree of impact of bottlenecks, helping managers prioritize the most critical bottleneck issues.

[0211] Step 7.3: Generate optimization and improvement suggestions;

[0212] Based on the collaboration bottleneck analysis report, an optimization suggestion generation algorithm is used in conjunction with a best practice knowledge base to generate targeted process optimization and resource allocation adjustment suggestions, resulting in collaboration efficiency assessment and optimization recommendations.

[0213] The optimization suggestion generation algorithm identifies various bottlenecks, matches corresponding optimization strategies, and generates specific optimization suggestions. For process bottlenecks, the algorithm suggests optimizing process design, such as merging redundant steps, parallelizing sequential steps, and simplifying approval processes. For resource bottlenecks, the algorithm suggests adjusting resource allocation, such as increasing staffing, upgrading system resources, and optimizing task assignment. For collaboration bottlenecks, the algorithm suggests improving collaboration mechanisms, such as establishing cross-team communication channels, assigning collaboration coordinators, and optimizing information sharing mechanisms. For quality bottlenecks, the algorithm suggests strengthening quality control, such as adding review processes, introducing automated checks, and providing training support.

[0214] The optimization suggestions are generated by referencing best practice case studies in a knowledge base. This knowledge base includes successful optimization cases from within the company and the industry, covering aspects such as the problem situation before optimization, the optimization measures taken, and the post-optimization results. The algorithm matches the currently identified bottleneck with cases in the knowledge base and recommends optimization measures from similar cases for reference.

[0215] The optimization recommendations also include a quantitative assessment of expected results. Based on historical data and optimization cases, the algorithm estimates the potential efficiency improvement after implementing the optimization recommendations, helping decision-makers evaluate the return on investment of the optimization.

[0216] Collaboration efficiency assessments and optimization recommendations are output in the form of analytical reports, including overall efficiency assessments, identification of major bottlenecks, a list of optimization suggestions, and assessments of expected results. These reports are generated regularly, supporting comparative analysis of efficiency changes before and after the implementation of optimization measures, thus forming a closed-loop mechanism for continuous improvement.

[0217] Furthermore, due to differences in collaboration models and management styles among enterprises, general optimization suggestions may not be entirely applicable to specific companies. Personalized optimization suggestion generation technology can be employed to generate suggestions that better suit the company's actual situation. Specifically, this technology first constructs a company profile, extracting organizational structure characteristics, collaboration model characteristics, and management style characteristics from historical data. During suggestion generation, the company profile is used as a constraint, filtering out suggestions that do not match the company's characteristics and prioritizing those that do. The system also supports feedback on the effectiveness of optimization suggestions, allowing company managers to evaluate the results of implemented suggestions. Based on this feedback, the system continuously optimizes its suggestion generation strategy, making the suggestions increasingly aligned with the company's actual needs.

[0218] A computer-readable storage medium for storing computer-readable instructions that, when read by a computer, enable the execution of the aforementioned cloud-based collaborative apparel cross-team design system.

[0219] In one embodiment of the present invention, a specific example is provided:

[0220] A large apparel group applied this invention's system in its spring new product development project. The group's headquarters are located in a first-tier city, its design center in a fashion capital, its pattern-making center in a city in southern China, its fabric and accessories R&D center in a textile industry cluster in eastern China, and its production base in another country. The spring new product development involved multiple categories and a total of 120 styles, requiring teams in various locations to collaborate throughout the entire process from concept design to production delivery within a 90-day timeframe.

[0221] A 90-day field test was conducted at five collaborative locations of the apparel group. During the test, a cloud-based collaborative platform server cluster was deployed, with a total of 85 local collaborative terminals configured at each location, involving 120 collaborative personnel, including designers, pattern makers, and craftspeople. The test covered the entire process of developing new spring products, including style design, pattern making, process formulation, and fabric and accessory selection, with a total collaborative data volume of 15GB.

[0222] Table 1 shows an example of the original design file data collected by the system:

[0223] Table 1: Example of original design file data;

[0224]

[0225] Table 2 shows an example of the standardized design data processed by the system.

[0226] Table 2: Examples of standardized design data;

[0227]

[0228] Through the application of the system of this invention, the apparel group has achieved efficient collaboration among teams across regions. Design changes can be synchronized to relevant teams in real time, version conflicts can be detected and resolved in a timely manner, workflows can be automated, and collaboration efficiency has been improved.

[0229] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A cloud-based collaborative system for cross-team apparel design, characterized in that, include: The data acquisition and processing module is used to acquire the original design files and preprocess them to obtain a standardized design dataset. The version control module, based on a standardized design dataset, stores each design change differently, establishes a management mechanism that supports parallel evolution, and obtains a traceable design version library. The real-time collaborative editing module is based on a traceable version control library, performs concurrent conflict detection and resolution, and pushes editing results to all online collaborators in real time, realizing a real-time synchronous push strategy. The workflow management module designs a collaborative workflow model based on a real-time synchronous push strategy, generates optimized execution plans and monitors task progress. When a delay risk is detected, it automatically issues an alert and provides suggestions, forming an intelligent task scheduling strategy. The design assistance module constructs a knowledge graph in the field of apparel design based on an intelligent task scheduling strategy, performs design constraint checks, and obtains intelligent design recommendation results through the fusion of multiple strategies. The security protection module, based on the intelligent design recommendation results, constructs a multi-layered security protection system and obtains a full-process security audit strategy; The data analysis and optimization module, based on the full-process security audit strategy, calculates collaboration efficiency indicators, identifies bottlenecks in collaboration, generates optimization suggestions, and obtains collaboration efficiency assessment and optimization recommendations.

2. The cloud-based collaborative apparel design system across teams according to claim 1, characterized in that, The data acquisition and processing module includes: A plug-in architecture is used to perform in-depth analysis of style drawings, structural drawings, process sheets, fabric and accessory lists, and size data, extracting design elements and converting them into editable data models, while recording the metadata information of the original files; It performs unified mapping and conversion of design data from different systems and formats, covering field name mapping, data type conversion, unified coding standards, and unit conversion; A comprehensive inspection of the data's integrity, consistency, and accuracy is conducted, verifying required fields, related data, and value ranges, and generating a data quality report.

3. The cloud-based collaborative apparel design system across teams according to claim 1, characterized in that, The version control module includes: The version repository is organized using a tree-structured directory structure. The main repository is deployed in a cloud data center, and clone repositories are deployed in various teams. Data consistency is maintained through a synchronization protocol, and online and offline working modes are supported. For structured data, field-level difference comparison is used; for graphical data, layer-level difference comparison is used; for binary files, block-level difference comparison is used. Difference data is stored in the form of incremental patches. Each change generates a version snapshot and records metadata information. Define main branch, development branch, feature branch, and fix branch; execute a three-way merge algorithm to automatically merge conflict-free changes; generate conflict flags for conflicting changes; and support branch protection rule configuration.

4. The cloud-based collaborative apparel design system across teams according to claim 1, characterized in that, The real-time collaborative editing module includes: The edit operation is converted into a sequence of atomic operations, sorted according to timestamp and client identifier to form a globally consistent operation order, and the operation conversion function is executed to adjust the position and parameters of the operation. Identify the scope of concurrent operations, determine the type of conflict, including write-write conflicts, write-delete conflicts, and semantic conflicts, and calculate the severity score of the conflict. Minor conflicts with severity scores below the first threshold are handled by automatic resolution rules, while major conflicts with severity scores at or above the first threshold are generated with a visual comparison interface. A two-way communication channel is established using long-connection technology, and changes are transmitted using incremental push, enabling an optimistic update mechanism and real-time display of collaborator cursors.

5. A cloud-based collaborative apparel design system for cross-teams, as described in claim 1, characterized in that, The workflow management module includes: The collaborative process is modeled as a directed graph structure using a graph-based representation. Nodes represent task nodes, and edges represent task dependencies and flow conditions. Each task node defines the task name, execution role, estimated working hours, deadline rules, preconditions, post-actions, input data, and output data. Construct a task dependency graph and calculate the earliest start time and latest start time. Use the critical path method to identify the critical path and perform dynamic scheduling considering personnel availability constraints, skill matching constraints, and load balancing constraints. Real-time tracking of task completion status, calculation of task completion rate, work time progress, and time progress; By comprehensively analyzing historical delay records, current progress deviations, resource constraints, dependent task status, and external factors, a delay risk score is calculated. When the score exceeds the second threshold, an early warning notification is sent and adjustment suggestions are provided.

6. The cloud-based collaborative apparel design system across teams according to claim 1, characterized in that, The design assistance module includes: Knowledge is extracted from structured, semi-structured, and unstructured data sources. Entity types are defined, including fabric entities, accessory entities, process entities, and style entities. Relationship types are defined, including applicable relationships, matching relationships, inclusion relationships, and similar relationships. Entity alignment algorithms are used for identification and merging. Perform process feasibility constraint checks, parameter rationality constraint checks, cost controllability constraint checks, and compatibility constraint checks, and generate a design constraint check report; A multi-strategy fusion recommendation method is adopted, including recommendations based on design specifications, recommendations based on historical data, recommendations based on user preferences, and recommendations based on popular trends. Each recommendation item includes recommended content, reasons for recommendation, matching score, and relevant reference cases.

7. A cloud-based collaborative apparel design system for cross-teams, as described in claim 1, characterized in that... The security protection module includes: Permissions are assigned to roles, and roles are then assigned to users. Permission granularity is refined to the data object dimension and operation type dimension, and dynamic permission determination based on attributes is supported. Data in transmission is protected by a transport layer encryption protocol, sensitive data in storage is encrypted using a symmetric encryption algorithm, and the key management service adopts a hierarchical key architecture. Invisible watermarking technology is used to embed user identification, export time, and export purpose information into the design document. The watermark embedding adopts a robust algorithm. The system records authentication events, authorization events, data access events, data modification events, management operation events, and security anomaly events. Each audit record includes the event type, event time, operating user, operating object, operation content, operation result, client information, and context information. The audit log is stored using an append-only method.

8. A cloud-based collaborative apparel design system for cross-teams, as described in claim 1, characterized in that: The data analysis and optimization module includes: Efficiency-related indicators are extracted from the collaborative behavior data accumulated by the system, and statistical analysis covers the dimensions of time efficiency, collaboration efficiency, communication efficiency, and resource utilization. The critical path method and queuing theory model are used to identify process bottlenecks, resource load analysis is used to identify resource bottlenecks, social network analysis is used to identify collaboration bottlenecks, and defect tracing analysis is used to identify quality bottlenecks, generating a collaboration bottleneck analysis report. For process bottlenecks, we recommend optimizing process design; for resource bottlenecks, we recommend adjusting resource allocation; for collaboration bottlenecks, we recommend improving collaboration mechanisms; and for quality bottlenecks, we recommend strengthening quality control. We will also refer to best practice case studies in the knowledge base and output the results in the form of an analysis report.

9. A cloud-based collaborative apparel design system for cross-teams, as described in claim 4, characterized in that, The real-time collaborative editing module also includes: Edge service nodes are deployed at various collaborative locations. The edge nodes cache copies of hot design documents and respond to local users' read requests as close as possible. For write operations, edge nodes forward the operations to the central server for global sorting and conflict detection, while returning optimistic acknowledgments to local users. After the central server completes the processing, it synchronizes the final result to all edge nodes. Edge nodes also implement intelligent prefetching, predicting the design documents that users will access based on their historical access patterns and retrieving them from the central server to the local cache in advance.

10. A computer-readable storage medium, characterized in that, It is used to store computer-readable instructions that, when read by a computer, enable the operation of a cloud-based collaborative apparel design system as described in any one of claims 1-9.

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