Collaboration method, device and computer equipment based on project cooperation platform

Abstract

The present application relates to the technical field of project management, and discloses a collaboration method, device and computer equipment based on a project collaboration platform. The method comprises: performing user role and user authority analysis on user registration information to obtain user role categories and corresponding authority data matrices; performing multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number; performing multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token; performing task decomposition and resource matching calculations on project requirements based on the user identity confirmation result and the access authorization token to obtain a collaboration workflow diagram; performing incremental storage and branch management to obtain a version control tree and a real-time progress report; performing content analysis on external collaboration materials to generate a multi-dimensional visual project data set. The present application improves the efficiency and security of project writing.

Description

Technical Field

[0001] The present application relates to the technical field of project management, and in particular to a collaboration method, device and computer equipment based on a project collaboration platform. Background Art

[0002] Traditional project management methods often seem powerless when faced with large-scale collaboration across regions and teams. In particular, there are many challenges in terms of information security, task allocation, progress tracking and resource integration. Although existing collaboration platforms provide basic functional support, they still have deficiencies in user role management, authority control, task decomposition and resource matching, making it difficult to meet the needs of efficient collaboration.

[0003] In addition, as the scale of the project expands and the number of participants increases, how to ensure the secure transmission of information and access control has become a prominent issue. Traditional invitation methods often have security risks, making it difficult to accurately verify user identities and unable to dynamically adjust access rights based on user roles. At the same time, during the project execution process, there are still problems of inefficiency in document version management, task progress tracking, and resource allocation optimization, which affects the overall efficiency of the project. Summary of the invention

[0004] The present application provides a collaboration method, apparatus and computer equipment based on a project collaboration platform, which are used to improve the efficiency and security of project writing.

[0005] In a first aspect, the present application provides a collaboration method based on a project collaboration platform, the collaboration method based on the project collaboration platform comprising:

[0006] Perform user role and user authority analysis on user registration information to obtain user role categories and corresponding authority data matrix;

[0007] Perform multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number;

[0008] Perform multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token;

[0009] Based on the user identity confirmation result and the access authorization token, task decomposition and resource matching calculation are performed on the project requirements to obtain a collaborative workflow diagram;

[0010] Incrementally store and manage branches of the copywriting tasks in the collaborative workflow diagram to obtain a version control tree and real-time progress report;

[0011] Content analysis is performed on external cooperation materials according to the version control tree and the real-time progress report to generate a multi-dimensional visual project data set.

[0012] In a second aspect, the present application provides a collaboration device based on a project collaboration platform, the collaboration device based on the project collaboration platform comprising:

[0013] The analysis module is used to analyze the user role and user authority of the user registration information to obtain the user role category and the corresponding authority data matrix;

[0014] An encryption module, used for performing multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number;

[0015] A verification module, used to perform multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token;

[0016] A calculation module, used to perform task decomposition and resource matching calculation on project requirements based on the user identity confirmation result and the access authorization token to obtain a collaborative workflow diagram;

[0017] A management module, used to perform incremental storage and branch management on the copywriting tasks in the collaborative workflow diagram, and obtain a version control tree and a real-time progress report;

[0018] A generation module is used to perform content analysis on external cooperation materials according to the version control tree and the real-time progress report to generate a multi-dimensional visual project data set.

[0019] The third aspect of the present application provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned collaboration method based on the project collaboration platform.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned collaboration method based on the project collaboration platform.

[0021] In the technical solution provided by this application, accurate role division and dynamic permission adjustment are achieved through in-depth analysis of user registration information and multi-dimensional permission mapping, which effectively improves the security of the platform and the flexibility of access control. The use of multi-layer encryption processing and temporary verification numbers greatly enhances the security of project invitation links and effectively prevents unauthorized access and information leakage. Through multiple decryption and information matching verification, the accuracy of user identity is ensured, and the generated access authorization token improves the security and efficiency of subsequent operations. Task decomposition and resource matching based on user identity and project requirements achieve more reasonable and efficient workflow planning and improve the efficiency of project collaboration. Incremental storage and branch management of copywriting tasks not only saves storage space, but also realizes efficient version control and conflict resolution, which facilitates team collaboration and content management. Through real-time analysis and visualization of task status, an intuitive progress report is provided, which helps project managers to keep abreast of project progress. Combined with the version control tree and progress report, content analysis of external data is performed to generate a multi-dimensional visual data set. By building a project content relationship network and knowledge graph, combined with visual layout and interactive data mining technology, the entire process of project collaboration, from user management to data visualization, is covered. Each link is interconnected and data flows smoothly, achieving a significant improvement in overall collaboration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0023] Figure 1 A schematic diagram of an embodiment of a collaboration method based on a project collaboration platform in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of an embodiment of a collaboration device based on a project collaboration platform in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiment of the present application provides a kind of collaboration method, device and computer equipment based on project cooperation platform. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of a collaboration method based on a project collaboration platform includes:

[0027] Step S101: Analyze the user role and user authority of the user registration information to obtain the user role category and the corresponding authority data matrix;

[0028] It is understandable that the execution subject of the present application can be a collaboration device based on the project collaboration platform, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0029] Specifically, the user registration information is processed to obtain the user feature vector, which represents the different attributes and characteristics of the user. The user feature vector is classified to obtain the user role category to ensure that each user is assigned to the most appropriate role category. According to the user role category obtained by classification, the user's historical behavior data is clustered and analyzed, which helps to identify and extract the commonalities and differences of user behavior and generate user role labels. These labels represent the user's behavior patterns and operating habits on the platform. The user role labels are mapped to multi-dimensional permissions to form an initial permission set. The initial permission set contains the basic permissions required for each user role, but there may be redundant and unnecessary permission items. The initial permission set is analyzed for redundancy, and repeated and unnecessary permission items are removed to obtain an optimized permission item list. Based on the optimized permission item list, a permission association graph is constructed, which shows the dependency and interaction between the permission items. By analyzing the permission association graph, the permission dependency data is extracted, and based on this data, dynamic permission adjustment rules are formulated. Dynamic permission adjustment rules allow the system to dynamically adjust permission configuration according to actual usage and user behavior, improving the flexibility and adaptability of permission management. According to the adjustment rules, the user role categories are hierarchically encoded to generate the role-authority correspondence. In order to improve the efficiency of data storage and access, the role-authority correspondence is optimized for storage. The compressed authority data structure occupies less storage space and improves the speed and efficiency of data access. The compressed authority data structure is transformed and normalized to generate the authority data matrix.

[0030] Step S102: Perform multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number;

[0031] Specifically, singular value decomposition is performed on the permission data matrix to obtain the permission feature vector. Singular value decomposition is a matrix decomposition technology that decomposes a matrix into the product of three matrices to extract important feature information. In this process, the permission data matrix is ​​decomposed into vectors representing different features and corresponding singular values. The permission feature vector is mixed with the project identifier by bit operation to obtain the initial encryption seed. Bit operation is a computer-based operation. By performing logical operations on the permission feature vector and each bit of the project identifier, the information of the two is effectively mixed to generate a new and unique initial encryption seed. A pseudo-random number sequence is generated based on the initial encryption seed to obtain a dynamic encryption key, which ensures the randomness and uniqueness of the dynamic encryption key and improves the security and unpredictability of the encryption process. Elliptic curve encryption is performed on the dynamic encryption key to obtain the first-layer encryption result. The first-layer encryption result is XORed with the current timestamp to obtain a time-sensitive intermediate ciphertext. XOR operation is a bit operation that mixes the information of the two operands by performing logical XOR on the corresponding bits of the two operands. By performing an XOR operation on the first-layer encryption result and the current timestamp, time information is embedded in the encryption process, making the intermediate ciphertext time-sensitive, that is, the encryption results generated at different times will be different. The time-sensitive intermediate ciphertext is encrypted in the second layer to obtain double-encrypted data. The digest of the double-encrypted data is calculated to obtain a temporary verification number. This number is used to verify the validity and integrity of the invitation link. The temporary verification number is embedded in the double-encrypted data to obtain a complete encrypted payload. By inserting the temporary verification number at a specific position in the double-encrypted data, it is ensured that the complete encrypted payload contains all necessary information. The complete encrypted payload is Base64-encoded and URL-safe character replacement is performed on the complete encrypted payload to obtain the encoded invitation data. Base64 encoding is an encoding method that converts binary data into an ASCII string. By encoding, the encrypted payload is ensured not to be destroyed during transmission. URL-safe character replacement is to replace certain characters in the Base64 encoding with URL-safe characters to ensure that the encoded data can be safely included in the URL. The encoded invitation data is concatenated with a predefined URL prefix to obtain a unique invitation link containing a temporary verification number.

[0032] Step S103: Perform multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token;

[0033] Specifically, the unique invitation link is URL parsed and Base64 decoded to obtain the original encrypted payload, and the embedded temporary verification number is extracted from the original encrypted payload to obtain the temporary number to be verified. URL parsing decomposes the unique invitation link into basic components and extracts the encrypted data segment from it. Through Base64 decoding, the encrypted data segment is converted back to its original binary form to obtain the original encrypted payload. The original encrypted payload is decrypted to obtain a time-sensitive intermediate ciphertext. The decryption process usually uses an algorithm and key opposite to the encryption process. The initial decryption restores the original encrypted payload to a time-sensitive intermediate ciphertext containing timestamp information. The time-sensitive intermediate ciphertext is XORed to obtain the first-level decryption result. The XOR operation removes the timestamp information from the ciphertext and restores the initial encrypted information. The first-level decryption result is elliptic curve decrypted to obtain a decrypted permission feature vector and a project identifier. Based on the decrypted permission feature vector, the permission data matrix is ​​reconstructed to obtain user permission information. The user permission information is matched with the pre-stored user data to obtain an initial confirmation result. Compare the decrypted and reconstructed permission information with the existing user data in the platform to confirm the correctness of the user's identity and permissions. Perform a consistency check on the initial confirmation result and the temporary number to be verified to obtain the user identity confirmation result. By comparing the temporary verification number extracted during the decryption process with the number in the initial verification process, ensure that the data has not been tampered with during transmission and processing. Generate a session key based on the user identity confirmation result and the project identifier to obtain a temporary session identifier. The session key is a temporary key used to ensure the security of the session. It is generated based on the user identity confirmation result and the project identifier to ensure the uniqueness and security of the session. Construct a digital signature based on the temporary session identifier and user permission information to obtain an access authorization token. The digital signature signs the session identifier and user permission information through an encryption algorithm. The generated access authorization token serves as the user's credential for accessing the project cooperation platform, ensuring that the user's operations on the platform are authorized and verified.

[0034] Step S104: Based on the user identity confirmation result and the access authorization token, perform task decomposition and resource matching calculation on the project requirements to obtain a collaborative workflow diagram;

[0035] Specifically, the user identity confirmation result is parsed by permission level to obtain a list of project resources that the user can access, ensuring that the user can access and use all project resources within his or her permission. The project requirement document is extracted based on the access authorization token to obtain a structured requirement description. By structuring the requirement document, the requirement description is converted into a format that is easy to analyze and process. The structured requirement description is task-decomposed to decompose complex project requirements into a series of smaller and more specific tasks to obtain an initial task tree structure. The task tree structure is analyzed for critical paths to obtain dependencies and priority rankings between tasks. Critical path analysis is used to determine the dependencies and completion times of tasks in a project. By identifying and analyzing these critical paths, it is clear which tasks are key factors for the timely completion of the project, and then the priorities and dependencies of the tasks are determined. Based on the list of project resources that the user can access, a capability assessment is performed to obtain a resource capability matrix. The resource capability matrix is ​​a quantitative representation of the capabilities of users and project resources, describing the specific capabilities and roles of each user and resource in the project. By evaluating resource capabilities, the strengths and limitations of each user and resource are understood. The task tree structure and the resource capability matrix are matched and calculated to obtain an initial resource allocation plan. Generate multiple possible workflow solutions based on the initial resource allocation plan, and evaluate and sort the multiple possible workflow solutions to obtain the optimal workflow solution. Multiple factors will be considered during the evaluation process, including task dependencies, resource availability and capabilities, and overall project progress and quality. Convert the optimal workflow solution into graph structure data to obtain the initial workflow diagram. Perform visual optimization and interactive design on the initial workflow diagram to obtain a collaborative workflow diagram. Visual optimization and interactive design aim to improve the readability and user experience of the workflow diagram, so that users can understand and operate the workflow diagram more intuitively.

[0036] Step S105: incrementally store and manage branches of the copywriting tasks in the collaborative workflow diagram to obtain a version control tree and a real-time progress report;

[0037] Specifically, the task type is identified on the collaborative workflow diagram to obtain a copywriting task subset. The content changes in the copywriting task subset are calculated to obtain an incremental data packet. The incremental data packet records the changes between different versions of the copywriting task, thereby avoiding repeated storage of the complete content and improving storage efficiency. The incremental data packet is converted into a reversible compression format to obtain a compressed incremental storage unit. By compressing the incremental data packet, the storage space occupied is reduced. The compressed incremental storage unit is distributedly stored to obtain incremental data backed up by multiple nodes. Distributed storage improves data availability and fault tolerance by storing data on multiple nodes, and enhances system reliability. The incremental data backed up by multiple nodes is organized to obtain an initial control tree. The initial control tree is a hierarchical organization of incremental data, reflecting the relationship and evolution path between different versions. Conflict detection is performed on the initial control tree to obtain a list of potential conflict points. Conflict detection is used to identify possible conflicts between incremental data of different versions, which may be caused by different modifications made to the same copywriting task by multiple users. A branch merge strategy is generated based on the list of potential conflict points to obtain a version control tree. The branch merge strategy defines how to handle and resolve conflicts between different versions. Through a reasonable merge strategy, a unified version control tree is generated to reflect the latest version and status of all copywriting tasks. Traverse the version control tree to get the latest status of each task. Traversing the version control tree helps to confirm the current version and status of each copywriting task. Compare the latest status of each task with the preset progress indicator to obtain task completion data. Perform data visualization on the task completion data to obtain a real-time progress report. Data visualization uses charts and graphs to intuitively display task completion and progress.

[0038] Step S106: Perform content analysis on external cooperation materials based on the version control tree and the real-time progress report to generate a multi-dimensional visualized project data set.

[0039] Specifically, the version control tree is topologically sorted to obtain the document version evolution sequence. Topological sorting is a method for sorting directed acyclic graphs, which can determine the order relationship between document versions and form a linear evolution sequence. According to the document version evolution sequence, the external cooperative materials are time-aligned to obtain a material-version mapping table. The text content in the material-version mapping table is subjected to topic extraction. Topic extraction is a processing technology that extracts the main topics from the text content by analyzing the text content and represents these topics as vectors. The multi-dimensional topic vector is reduced in dimension to obtain a two-dimensional topic distribution map. Dimensionality reduction techniques such as principal component analysis or t-SNE compress high-dimensional data into two-dimensional space for easy visualization and analysis. In the process of dimensionality reduction, the main information of the topic vector is kept unchanged so that the relationship and distribution between different topics can be intuitively displayed in the two-dimensional space. Task completion data is extracted according to the real-time progress report to obtain a progress trend curve. The progress trend curve shows the completion status and progress speed of each task in the project, providing a dynamic view of the project progress. The progress trend curve is merged with the two-dimensional topic distribution map to obtain a topic-progress association matrix. Show the role and impact of different topics in the project progress, thus forming a multi-dimensional association matrix. Group the topic-progress association matrix to obtain a set of key project nodes. Group analysis helps identify key nodes in the project, that is, those tasks and topics that play a decisive role in the project progress and success. Construct a knowledge graph based on the set of key project nodes to obtain a project content relationship network. A knowledge graph is a graphical structure that represents the relationship between different concepts and entities. Through the knowledge graph, the association and dependency between tasks, topics and materials in the project are displayed. Perform a graphical layout analysis on the project content relationship network to obtain an initial visualization layout scheme. The graphical layout analysis aims to determine the optimal arrangement of nodes and edges so that the entire network structure is clear and easy to read. By optimizing the layout, ensure that the information in the knowledge graph is displayed in the most intuitive way. Combine the initial visualization layout scheme with interactive data to obtain a multi-dimensional visualization project data set. Interactive data includes user operations and feedback. By combining with the visualization layout, dynamic and interactive data display is achieved to enhance user experience and data understanding.

[0040] In the embodiment of the present application, by performing in-depth analysis and multi-dimensional permission mapping of user registration information, accurate role division and dynamic permission adjustment are achieved, effectively improving the security of the platform and the flexibility of access control. The use of multi-layer encryption processing and temporary verification numbers greatly enhances the security of project invitation links and effectively prevents unauthorized access and information leakage. Through multiple decryption and information matching verification, the accuracy of user identity is ensured, and the generated access authorization token improves the security and efficiency of subsequent operations. Task decomposition and resource matching based on user identity and project requirements achieve more reasonable and efficient workflow planning and improve the efficiency of project collaboration. Incremental storage and branch management of copywriting tasks not only saves storage space, but also realizes efficient version control and conflict resolution, which is convenient for team collaboration and content management. Through real-time analysis and visualization of task status, an intuitive progress report is provided, which helps project managers to keep abreast of project progress. Combined with the version control tree and progress report, content analysis of external data is performed to generate a multi-dimensional visual data set. By building a project content relationship network and knowledge graph, combined with visual layout and interactive data mining technology, the entire process of project collaboration, from user management to data visualization, is covered. Each link is interconnected and data flows smoothly, achieving a significant improvement in overall collaboration efficiency.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Process the user registration information to obtain the user feature vector, and classify the user feature vector to obtain the user role category;

[0043] (2) Perform cluster analysis on user historical behavior data according to user role categories to obtain user role labels, and perform multi-dimensional permission mapping on user role labels to obtain the initial permission set;

[0044] (3) Perform redundancy analysis on the initial permission set to obtain an optimized permission item list, and construct a permission association graph based on the optimized permission item list to obtain permission dependency relationship data;

[0045] (4) Process the permission dependency data to obtain dynamic permission adjustment rules, and hierarchically encode user role categories according to the dynamic permission adjustment rules to obtain the role-permission correspondence relationship;

[0046] (5) Optimize the storage of the role-authority correspondence to obtain a compressed permission data structure, and perform matrix transformation and normalization on the compressed permission data structure to obtain a permission data matrix.

[0047] Specifically, the user registration information is processed to extract the user's feature vector. Processing techniques such as word segmentation, part-of-speech tagging, named entity recognition, etc. are used to convert the text data in the registration information into structured data. Assuming that the user registration information contains fields such as "name", "position", "department", "skill description", etc., the keywords and related features of each field can be extracted through processing. The user feature vectors are classified to obtain user role categories. Clustering algorithms such as K-means, hierarchical clustering or DBSCAN are used to cluster similar user feature vectors together to form different user role categories. For example, through K-means clustering, users are divided into categories, each category represents a user role category. The goal of the clustering algorithm is to minimize the distance between each user feature vector and the center of its category, as follows:

[0048] ;

[0049] in, Indicates clusters, It is The center of each cluster. Perform cluster analysis on the user's historical behavior data according to the user role category to obtain the user role label. The historical behavior data may include the user's system operation record, access frequency, usage function, etc. By clustering these behavior data, the behavior patterns of users of different role categories are identified, and corresponding role labels are assigned to each user role category. Perform multi-dimensional permission mapping on the user role labels to form an initial permission set. According to different role labels, determine the operations that each role can perform and the resources that can be accessed in the system. For example, a user with the role label "Project Manager" can have permissions such as "Project Creation", "Task Assignment", and "Progress Tracking". Perform redundancy analysis on the initial permission set to identify and eliminate redundant items in the permission set and optimize permission allocation. For example, if the "Project View" permission is implicit in the "Project Edit" permission, the former can be removed to simplify the permission set. Through redundancy analysis, an optimized list of permission items is obtained. Based on the optimized permission list, a permission association diagram is constructed to reflect the dependency relationship between different permissions. For example, the "task assignment" permission may depend on the "project creation" permission. Permission association diagram middle, Indicates a permission item. Represents the dependency between permissions. Process the permission association graph to generate dynamic permission adjustment rules. Dynamic permission adjustment rules allow the system to dynamically adjust permissions based on actual usage and user behavior. For example, if a user frequently uses a certain permission and another permission is rarely used, their permission configuration can be adjusted appropriately to ensure the flexibility and dynamic adaptability of permission allocation. According to the dynamic permission adjustment rules, the user role categories are hierarchically encoded to obtain the role-permission correspondence. Encoding permissions according to the hierarchical structure makes permission management more systematic and clear. For example, basic permissions are at a lower level and advanced permissions are at a higher level. Storage optimization is performed on the role-permission correspondence to obtain a compressed permission data structure. Storage optimization can use compression algorithms, such as prefix tree structures or other data compression technologies, to reduce storage space usage and improve query efficiency. The compressed permission data structure is matrix transformed and normalized to obtain a permission data matrix. Matrix transformation converts the role-permission correspondence into a matrix form, and normalization ensures that the scale of the matrix data is consistent, which is convenient for further calculation and analysis. Permission data matrix It is expressed as:

[0050] ;

[0051] in, Indicates Role categories for The normalized permission value of the item permission.

[0052] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0053] (1) Perform singular value decomposition on the permission data matrix to obtain the permission feature vector, and then perform bitwise operation on the permission feature vector and the project identifier to obtain the initial encryption seed;

[0054] (2) Generate a pseudo-random number sequence based on the initial encryption seed to obtain a dynamic encryption key, and perform elliptic curve encryption on the dynamic encryption key to obtain the first layer of encryption result;

[0055] (3) Perform an XOR operation on the first-layer encryption result and the current timestamp to obtain a time-sensitive intermediate ciphertext, and perform a second-layer encryption on the time-sensitive intermediate ciphertext to obtain double-encrypted data;

[0056] (4) Perform a digest calculation on the double encrypted data to obtain a temporary verification number, and embed the temporary verification number into the double encrypted data to obtain a complete encrypted payload;

[0057] (5) Perform Base64 encoding and URL-safe character replacement on the complete encrypted payload to obtain the encoded invitation data;

[0058] (6) Concatenate the encoded invitation data with the predefined URL prefix to obtain a unique invitation link containing a temporary verification number.

[0059] Specifically, perform singular value decomposition on the permission data matrix to obtain the permission eigenvector. Assume that the permission data matrix is , decomposed into the product of three matrices through SVD: .in, and is an orthogonal matrix, is a diagonal matrix whose diagonal elements are singular values. The authority eigenvector can be obtained from or Extract it, usually choose The row vector of is used as the permission feature vector. For each permission vector , which represents the permission features in different dimensions. Mix it with the project identifier by bitwise operation to get the initial encryption seed. Assume that the project identifier is , combine the two through bit operations (such as bitwise XOR), the formula is as follows:

[0060] ;

[0061] in, is the initial encryption seed, Represents a bitwise XOR operation to ensure that the initial encryption seed contains both permission feature information and the project's unique identification information, thereby enhancing encryption security. Generate a pseudo-random number sequence to obtain a dynamic encryption key. A pseudo-random number sequence generator (such as a linear congruential generator or Mersenne Twister) can use the initial encryption seed as the seed value to generate a highly random pseudo-random number sequence through a series of algorithms. Assume that the generated pseudo-random number sequence is , which will be used as the dynamic encryption key. Perform elliptic curve encryption on the dynamic encryption key to obtain the first layer of encryption result. Elliptic curve encryption is a public key encryption technology with high security and small key length. Perform an XOR operation on the first layer of encryption result and the current timestamp to obtain a time-sensitive intermediate ciphertext. Perform a second layer of encryption on the time-sensitive intermediate ciphertext to obtain double encrypted data. The second layer of encryption can use a symmetric encryption algorithm (such as AES) to ensure the further security of the ciphertext. Perform a digest calculation on the double encrypted data to obtain a temporary verification number. The digest calculation usually uses a hash function (such as SHA-256). Embed the temporary verification number into the double encrypted data to obtain a complete encrypted payload. Perform Base64 encoding and URL-safe character replacement on the complete encrypted payload to obtain the encoded invitation data. Base64 encoding converts binary data into a text string and performs URL-safe character replacement to ensure that there will be no errors when the data is transmitted in the URL. Concatenate the encoded invitation data with the predefined URL prefix to obtain a unique invitation link containing the temporary verification number. For example, assuming the permission data matrix for:

[0062] ;

[0063] Through singular value decomposition, we get The permission feature vector of a row in is Assuming the project identifier is [9, 8, 7], then the initial encryption seed based on Generate a pseudo-random number sequence, assuming that the dynamic encryption key is obtained , and then use elliptic curve encryption to get the first layer of encryption result . Assume that the current timestamp is , the time-sensitive intermediate ciphertext is Through the second layer of AES encryption, assuming symmetric key =[21, 31, 41], we get double encrypted data . Using SHA-256 Perform summary calculation to obtain a temporary verification number and compare it with Combined to get the complete encrypted payload Through Base64 encoding and URL safe character replacement, the encoded invitation data is obtained, and the URL prefix is ​​concatenated to generate the final unique invitation link.

[0064] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0065] (1) Perform URL parsing and Base64 decoding on the unique invitation link to obtain the original encrypted payload, and extract the embedded temporary verification number from the original encrypted payload to obtain the temporary number to be verified;

[0066] (2) Decrypt the original encrypted payload to obtain the time-sensitive intermediate ciphertext, and perform an XOR operation on the time-sensitive intermediate ciphertext to obtain the first-layer decryption result;

[0067] (3) Perform elliptic curve decryption on the first-layer decryption result to obtain the decrypted permission feature vector and project identifier;

[0068] (4) Reconstruct the permission data matrix based on the decrypted permission feature vector to obtain user permission information, and match the user permission information with the pre-stored user data to obtain an initial confirmation result;

[0069] (5) Perform a consistency check on the initial confirmation result and the temporary number to be verified to obtain the user identity confirmation result;

[0070] (6) Generate a session key based on the user identity confirmation result and the project identifier to obtain a temporary session identifier, and construct a digital signature based on the temporary session identifier and user authority information to obtain an access authorization token.

[0071] Specifically, the unique invitation link is URL parsed and Base64 decoded. The unique invitation link usually consists of a URL prefix and the encoded invitation data. The two parts can be separated by URL parsing. The encoded invitation data is Base64 decoded to obtain the original encrypted payload. The original encrypted payload contains all encrypted data and the embedded temporary verification number. By parsing the original encrypted payload, the embedded temporary verification number is extracted to obtain the temporary number to be verified. The original encrypted payload is decrypted to obtain the time-sensitive intermediate ciphertext. The decryption process usually uses the same symmetric key or algorithm as the encryption process. The time-sensitive intermediate ciphertext contains the initial first-layer encryption result and timestamp information. By performing an XOR operation on the time-sensitive intermediate ciphertext, the timestamp information is removed and the first-layer decryption result is restored. The first-layer decryption result is decrypted by elliptic curve to obtain the decrypted permission feature vector and project identifier. Elliptic curve decryption is a secure and efficient decryption method. By using the elliptic curve private key, the permission feature vector and project identifier are restored. Based on the decrypted permission feature vector, the permission data matrix is ​​reconstructed to obtain the user permission information. The permission data matrix is ​​a detailed description of user permissions. The relevant parts of the permission data matrix are reconstructed through the permission feature vector. The user permission information is matched with the pre-stored user data to obtain the initial confirmation result. The pre-stored user data contains the permissions and identity information of all users in the system. Through matching, it is confirmed whether the decrypted permission information is consistent with the records in the system. The initial confirmation result and the temporary number to be verified are checked for consistency to ensure that the data has not been tampered with during transmission and processing. The temporary verification number is generated during the encryption process and should be consistent with the initial temporary number after decryption. Through consistency check, the integrity and authenticity of the data are verified to obtain the user identity confirmation result. A session key is generated based on the user identity confirmation result and the project identifier to obtain a temporary session identifier. The session key is a temporary encryption key used to ensure data security during the session. The temporary session identifier is generated based on the user identity confirmation result and the project identifier to ensure that each session is unique. A digital signature is constructed based on the temporary session identifier and the user permission information to obtain an access authorization token. The digital signature signs the temporary session identifier and the user permission information through an encryption algorithm. The generated access authorization token is used as the user's credential to access the system to ensure that the user's operations on the platform are authorized and verified.

[0072] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0073] (1) Analyze the user identity confirmation result according to the permission level to obtain the list of project resources that the user can access;

[0074] (2) Extract the project requirement document based on the access authorization token to obtain a structured requirement description, and perform task decomposition on the structured requirement description to obtain an initial task tree structure;

[0075] (3) Perform critical path analysis on the task tree structure to obtain the dependencies and priority rankings between tasks;

[0076] (4) Perform capacity assessment based on the user-accessible project resource list to obtain a resource capacity matrix, and perform matching calculations on the task tree structure and the resource capacity matrix to obtain an initial resource allocation plan;

[0077] (5) Generate multiple possible workflow solutions based on the initial resource allocation plan, and evaluate and sort the multiple possible workflow solutions to obtain the optimal workflow solution;

[0078] (6) The optimal workflow solution is converted into graph structure data to obtain the initial workflow diagram, and the initial workflow diagram is visualized and interactively designed to obtain a collaborative workflow diagram.

[0079] Specifically, the user identity confirmation result is parsed by permission level to obtain a list of project resources that the user can access. The user identity confirmation result contains the user's role and permission information. By parsing this information, the resources and operation permissions that the user can access in the project are determined. For example, the user role is project manager, and its permission level includes accessing project documents, assigning tasks, and viewing progress. The parsed result will generate a resource list that lists all project resources that the user can access. Extract the project requirement document based on the access authorization token to obtain a structured requirement description. After the access authorization token verifies the user's identity, extract the project requirement document that the user has permission to access, and convert the document content into a structured requirement description. For example, through processing technology, unstructured text is converted into structured data. Task decomposition is performed on the structured requirement description to obtain an initial task tree structure. Complex project requirements are decomposed into a series of smaller and more manageable tasks to form a task tree structure. Each task node represents a specific task, and the hierarchical relationship and dependency relationship between tasks are clearly displayed in the task tree. Task decomposition helps project managers understand and manage various tasks and ensure that all requirements are properly handled. Perform critical path analysis on the task tree structure to obtain the dependency relationship and priority ranking between tasks. Critical path analysis is a project management technique used to determine the longest time path required to complete tasks in a project. By identifying and analyzing these critical paths, it is clear which tasks are critical factors for the timely completion of the project. Tasks on the critical path have the highest priority. Critical path analysis helps project teams determine the order and dependencies of tasks to ensure that the project proceeds as planned. Capacity assessment is performed based on the user-accessible project resource list to obtain a resource capacity matrix. The resource capacity matrix is ​​a quantitative representation of the capacity and availability of each resource in the project, describing the capabilities and limitations of each resource in different tasks. During the resource assessment process, the skills, experience and available time of each resource are considered. Capacity assessment helps ensure that resources are reasonably allocated to the most suitable tasks and improve project efficiency. The task tree structure and the resource capacity matrix are matched and calculated to obtain the initial resource allocation plan. Assign tasks to the most suitable resources to ensure that tasks are completed on time and meet the expected quality. The initial resource allocation plan is a preliminary resource allocation plan that takes into account the dependencies of tasks and the capabilities of resources. Through the optimization algorithm, the best combination of resource allocation is found to meet project requirements. Based on the initial resource allocation plan, multiple possible workflow plans are generated, and multiple possible workflow plans are evaluated and sorted to obtain the optimal workflow plan. During the evaluation process, multiple factors are considered, such as task completion time, resource utilization, and project cost. Through comprehensive evaluation, the optimal workflow solution is found to ensure that the project is completed within the budget and time frame. The optimal workflow solution is converted into graph structure data to obtain the initial workflow diagram. In the workflow diagram, each node represents a task or resource, and the edge represents the dependency relationship between tasks.The initial workflow diagram is optimized visually and interactively designed to obtain a collaborative workflow diagram. Visual optimization aims to improve the readability and user experience of the workflow diagram, so that the project team can understand and operate the workflow diagram more intuitively. Interactive design adds the user-to-workflow diagram interaction function, allowing users to dynamically view and adjust task and resource allocation, thereby managing projects more effectively.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] (1) Identify the task type of the collaborative workflow diagram to obtain a copywriting task subset, and calculate the content changes in the copywriting task subset to obtain an incremental data package;

[0082] (2) Convert the incremental data packet into a reversible compression format to obtain a compressed incremental storage unit, and perform distributed storage on the compressed incremental storage unit to obtain incremental data backed up by multiple nodes;

[0083] (3) Organize the incremental data of multi-node backup to obtain the initial control tree, and perform conflict detection on the initial control tree to obtain a list of potential conflict points;

[0084] (4) Generate a branch merge strategy based on the list of potential conflict points, obtain the version control tree, and traverse the version control tree to obtain the latest status of each task;

[0085] (5) Compare the latest status of each task with the preset progress indicator to obtain the task completion data, and perform data visualization on the task completion data to obtain a real-time progress report.

[0086] Specifically, the task type is identified for the collaborative workflow diagram. The workflow diagram usually contains multiple different types of tasks, such as development tasks, testing tasks, and copywriting tasks. By identifying the task type, these tasks are classified to obtain a copywriting task subset. Copywriting tasks usually involve document writing, editing, and reviewing. Through text analysis and task label identification, these tasks are extracted from the overall workflow diagram to form an independent subset. The content changes in the copywriting task subset are calculated to obtain an incremental data packet. The content changes of the copywriting task usually involve the modification, addition, or deletion of the text. By comparing the copywriting content of the current version and the previous version, the specific changed parts are identified and an incremental data packet is generated. The incremental data packet records all the change information, including the modified text fragment, the change time, and the identifier of the related task. The incremental data packet is converted into a reversible compression format to obtain a compressed incremental storage unit. The reversible compression format is a lossless compression technology that can completely restore the original data when needed. By compressing the incremental data packet, the storage space is reduced. The compressed incremental storage unit is distributedly stored to obtain incremental data for multi-node backup. Distributed storage stores data on multiple nodes, which not only improves the availability and fault tolerance of data, but also enhances the reliability of the system. Each node stores a backup of incremental data to ensure that when a node fails, the data can still be restored from other nodes. Through multi-node backup, high availability and high reliability of data are achieved. The incremental data of multi-node backup is organized to obtain the initial control tree. The initial control tree is a hierarchical organization of incremental data, reflecting the relationship and evolution path between different versions. By constructing the initial control tree, the evolution process of incremental data over time is displayed, which facilitates subsequent version management and conflict detection. The nodes of the control tree represent each version, and the edges represent the relationship between versions. After obtaining the initial control tree, conflict detection is performed on it to obtain a list of potential conflict points. Conflict detection is used to identify possible conflicts between incremental data of different versions. These conflicts may be caused by different modifications made by multiple users to the same copywriting task. Through conflict detection, potential conflict points are identified and recorded in the conflict point list for subsequent processing. According to the list of potential conflict points, a branch merge strategy is generated to obtain a version control tree. The branch merge strategy defines how to handle and resolve conflicts between different versions. Through a reasonable merge strategy, a unified version control tree is generated to reflect the latest version and status of all copywriting tasks. The version control tree shows the version history and merge relationship of all tasks, ensuring that all changes to copywriting tasks are properly handled and recorded. Traverse the version control tree to get the latest status of each task. Traversing the version control tree can confirm the current version and status of each copywriting task, providing an accurate data basis for subsequent progress reports. During the traversal process, the latest status and version number of each task can be determined by comparing the relationship between nodes.Compare the latest status of each task with the preset progress indicators to obtain the task completion data. Progress indicators are pre-set goals and standards. By comparing the latest status with these indicators, the completion of the task can be quantified and evaluated. The completion data reflects the progress and current status of each task. Perform data visualization on the task completion data to obtain a real-time progress report. Data visualization uses charts and graphs to intuitively display the task completion and progress, allowing project managers and team members to quickly understand the current status and progress of the project, so as to make timely adjustments and decisions. The real-time progress report reflects the latest progress of the project through dynamic updates, helping the team to identify problems in a timely manner and take appropriate measures.

[0087] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0088] (1) Topologically sort the version control tree to obtain the document version evolution sequence, and align the external collaborative data in time according to the document version evolution sequence to obtain a data-version mapping table;

[0089] (2) Extract topics from the text content in the document-version mapping table to obtain a multidimensional topic vector, and then reduce the dimensionality of the multidimensional topic vector to obtain a two-dimensional topic distribution map;

[0090] (3) Extract task completion data based on the real-time progress report to obtain a progress trend curve, and then merge the progress trend curve with the two-dimensional topic distribution map to obtain a topic-progress correlation matrix;

[0091] (4) Group the topic-progress association matrix to obtain a set of key project nodes, and build a knowledge graph based on the set of key project nodes to obtain a project content relationship network;

[0092] (5) Perform a graphical layout analysis on the project content relationship network to obtain an initial visualization layout scheme, and then combine the initial visualization layout scheme with the interactive data to obtain a multi-dimensional visualization project dataset.

[0093] Specifically, the version control tree is topologically sorted to obtain the document version evolution sequence. The version control tree is a directed acyclic graph, in which nodes represent different document versions and edges represent the evolution relationship between versions. Through topological sorting, all versions are arranged linearly to form an evolution sequence from the initial version to the latest version. The result of topological sorting is an ordered version list, which represents the evolution path of the document. Assume that the version in the version control tree is , the result of topological sorting is ,in exist The order in represents the order in the document evolution. According to the document version evolution sequence, the external cooperation data are time-sequenced to obtain the data-version mapping table. External cooperation data are usually associated with document versions. By aligning the data with the corresponding document version, it is ensured that each data corresponds to the correct document version. The timestamp of each data is matched with the timestamp of the document version to construct the data-version mapping table. The mapping table is in the form of ,in Indicates , Indicates the corresponding document version. Perform topic extraction on the text content in the document-version mapping table to obtain a multi-dimensional topic vector. Topic extraction is a processing technique that extracts the main topics in the text by analyzing the text content and represents these topics as vectors. Assume that each document The text content is , through the topic extraction algorithm (such as LDA), get the topic vector of each document The dimensions of the topic vector represent different topics, and the value of each element represents the weight of the topic in the text. Dimensionality reduction is performed on the multidimensional topic vector to obtain a two-dimensional topic distribution map. Dimensionality reduction techniques (such as PCA or t-SNE) can compress high-dimensional data into a two-dimensional space, which is convenient for visualization and analysis. Assume that the multidimensional topic vector is , through the dimensionality reduction algorithm, we can get the two-dimensional topic vector , expressed as reduction The two-dimensional topic distribution graph shows the topic distribution of each data. The position of different data in the graph indicates the similarity of their topics. The task completion data is extracted according to the real-time progress report to obtain the progress trend curve. The progress report usually records the completion status of each task in the project. By analyzing this data, the task completion curve can be generated. Assume that the task completion data is ,in Indicates The completion degree of each task, the progress trend curve can be expressed as a function of time , reflecting the progress of the task over time. The progress trend curve is integrated with the two-dimensional topic distribution map to obtain the topic-progress association matrix. By combining the task completion data with the topic vector, an association matrix is ​​constructed to reflect the relationship between different topics and task progress. Assume that the association matrix is , whose elements Indicates Theme and The correlation matrix is ​​in the form of:

[0094] ;

[0095] in Indicates the subject With the task The correlation between the subject-progress correlation matrix is ​​grouped to obtain the key node set of the project. Grouping analysis helps to identify the key nodes in the project, that is, the tasks and topics that play a decisive role in the progress and success of the project. Through the clustering algorithm, the elements of the correlation matrix are grouped to identify the key node set of the project. The key node set is expressed as , where each Represents a key node. Based on the key node set of the project, a knowledge graph is constructed to obtain the project content relationship network. The knowledge graph is a graphical structure that represents the relationship between different concepts and entities. Through the knowledge graph, the association and dependency between the tasks, topics and materials in the project can be clearly displayed. The nodes of the knowledge graph represent the entities in the project, and the edges represent the relationship between the entities. The elements in the key node set are used as nodes, and the edges are constructed according to the relationships in the association matrix. The project content relationship network is analyzed by graphical layout to obtain the initial visualization layout scheme. The graphical layout analysis aims to determine the optimal arrangement of nodes and edges so that the entire network structure is clear and easy to read. By optimizing the layout, the information in the knowledge graph is displayed in the most intuitive way. The initial visualization layout scheme is combined with interactive data to obtain a multi-dimensional visualization project data set. Interactive data includes user operations and feedback. By combining with the visualization layout, dynamic and interactive data display is achieved to enhance user experience and data understanding. For example, users can view detailed information by clicking on a node, or adjust the layout by dragging a node, so as to better understand the content and progress of the project.

[0096] The above describes the collaboration method based on the project collaboration platform in the embodiment of the present application. The following describes the collaboration device based on the project collaboration platform in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a collaboration device based on a project collaboration platform includes:

[0097] The analysis module 201 is used to analyze the user role and user authority of the user registration information to obtain the user role category and the corresponding authority data matrix;

[0098] An encryption module 202 is used to perform multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number;

[0099] Verification module 203, used to perform multiple decryption and information matching verification on the unique invitation link to obtain a user identity confirmation result and an access authorization token;

[0100] The calculation module 204 is used to perform task decomposition and resource matching calculation on the project requirements based on the user identity confirmation result and the access authorization token to obtain a collaborative workflow diagram;

[0101] Management module 205, used to perform incremental storage and branch management on the copywriting tasks in the collaborative workflow diagram, and obtain a version control tree and real-time progress report;

[0102] The generation module 206 is used to perform content analysis on the external cooperation materials according to the version control tree and the real-time progress report to generate a multi-dimensional visualized project data set.

[0103] Through the collaboration of the above components, through in-depth analysis of user registration information and multi-dimensional permission mapping, accurate role division and dynamic permission adjustment are achieved, effectively improving the security of the platform and the flexibility of access control. The use of multi-layer encryption processing and temporary verification numbers greatly enhances the security of project invitation links and effectively prevents unauthorized access and information leakage. Through multiple decryption and information matching verification, the accuracy of user identity is ensured, and the generated access authorization token improves the security and efficiency of subsequent operations. Task decomposition and resource matching based on user identity and project requirements achieve more reasonable and efficient workflow planning and improve the efficiency of project collaboration. Incremental storage and branch management of copywriting tasks not only saves storage space, but also realizes efficient version control and conflict resolution, facilitating team collaboration and content management. Through real-time analysis and visualization of task status, intuitive progress reports are provided, which helps project managers to grasp project progress in a timely manner. Combined with the version control tree and progress report, content analysis of external data is performed to generate a multi-dimensional visual data set. By building a project content relationship network and knowledge graph, combined with visual layout and interactive data mining technology, the entire process of project collaboration, from user management to data visualization, is covered. Each link is interconnected and data flows smoothly, achieving a significant improvement in overall collaboration efficiency.

[0104] The present application also provides a computer device, which includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the collaboration method based on the project collaboration platform in the above-mentioned embodiments.

[0105] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the collaboration method based on the project collaboration platform.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0108] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A collaboration method based on a project collaboration platform, characterized in that: The method comprises: Perform user role and user authority analysis on user registration information to obtain user role categories and corresponding authority data matrix; Perform multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number; Perform multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token; Based on the user identity confirmation result and the access authorization token, task decomposition and resource matching calculation are performed on the project requirements to obtain a collaborative workflow diagram; Incrementally store and manage branches of the copywriting tasks in the collaborative workflow diagram to obtain a version control tree and real-time progress report; Content analysis is performed on external cooperation materials according to the version control tree and the real-time progress report to generate a multi-dimensional visual project data set.

2. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The user registration information is analyzed for user roles and user permissions to obtain a user role category and a corresponding permission data matrix, including: Processing user registration information to obtain a user feature vector, and classifying the user feature vector to obtain a user role category; Performing cluster analysis on the user's historical behavior data according to the user role category to obtain a user role tag, and performing multi-dimensional permission mapping on the user role tag to obtain an initial permission set; Performing redundancy analysis on the initial permission set to obtain an optimized permission item list, and constructing a permission association graph based on the optimized permission item list to obtain permission dependency relationship data; Processing the permission dependency data to obtain dynamic permission adjustment rules, and hierarchically encoding the user role categories according to the dynamic permission adjustment rules to obtain role-permission correspondence; The role-authority correspondence is optimized for storage to obtain a compressed authority data structure, and the compressed authority data structure is subjected to matrix transformation and normalization processing to obtain an authority data matrix.

3. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The multi-layer encryption process is performed according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number, including: Performing singular value decomposition on the authority data matrix to obtain an authority feature vector, and performing bitwise mixing of the authority feature vector and the project identifier to obtain an initial encryption seed; Generate a pseudo-random number sequence based on the initial encryption seed to obtain a dynamic encryption key, and perform elliptic curve encryption on the dynamic encryption key to obtain a first-layer encryption result; Performing an XOR operation on the first-layer encryption result and the current timestamp to obtain a time-sensitive intermediate ciphertext, and performing a second-layer encryption on the time-sensitive intermediate ciphertext to obtain double-encrypted data; Performing digest calculation on the double encrypted data to obtain a temporary verification number, and embedding the temporary verification number into the double encrypted data to obtain a complete encrypted payload; Perform Base64 encoding and URL-safe character replacement on the complete encrypted payload to obtain encoded invitation data; The encoded invitation data is concatenated with a predefined URL prefix to obtain a unique invitation link containing a temporary verification number.

4. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The method of performing multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token includes: Performing URL parsing and Base64 decoding on the unique invitation link to obtain an original encrypted payload, and extracting the embedded temporary verification number from the original encrypted payload to obtain a temporary number to be verified; Decrypting the original encrypted payload to obtain a time-sensitive intermediate ciphertext, and performing an XOR operation on the time-sensitive intermediate ciphertext to obtain a first-layer decryption result; Perform elliptic curve decryption on the first-layer decryption result to obtain a decrypted authority feature vector and project identifier; Reconstructing the permission data matrix based on the decrypted permission feature vector to obtain user permission information, and matching the user permission information with pre-stored user data to obtain an initial confirmation result; Performing a consistency check on the initial confirmation result and the temporary number to be verified to obtain a user identity confirmation result; A session key is generated according to the user identity confirmation result and the project identifier to obtain a temporary session identifier, and a digital signature is constructed based on the temporary session identifier and the user authority information to obtain an access authorization token.

5. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The step of performing task decomposition and resource matching calculation on project requirements based on the user identity confirmation result and the access authorization token to obtain a collaborative workflow diagram includes: Performing permission level analysis on the user identity confirmation result to obtain a list of project resources accessible to the user; Extracting the project requirement document according to the access authorization token to obtain a structured requirement description, and performing task decomposition on the structured requirement description to obtain an initial task tree structure; Performing a critical path analysis on the task tree structure to obtain dependencies and priority rankings between tasks; Performing a capability assessment based on the project resource list accessible to the user to obtain a resource capability matrix, and performing a matching calculation on the task tree structure and the resource capability matrix to obtain an initial resource allocation plan; Generate multiple workflow solutions according to the initial resource allocation solution, and evaluate and sort the multiple workflow solutions to obtain the optimal workflow solution; The optimal workflow solution is converted into graph structure data to obtain an initial workflow diagram, and the initial workflow diagram is visually optimized and interactively designed to obtain a collaborative workflow diagram.

6. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The incremental storage and branch management of the copywriting tasks in the collaborative workflow diagram to obtain a version control tree and a real-time progress report includes: Performing task type identification on the collaborative workflow diagram to obtain a copywriting task subset, and calculating content changes in the copywriting task subset to obtain an incremental data packet; Convert the incremental data packet into a reversible compression format to obtain a compressed incremental storage unit, and perform distributed storage on the compressed incremental storage unit to obtain incremental data backed up by multiple nodes; Organizing the incremental data of the multi-node backup to obtain an initial control tree, and performing conflict detection on the initial control tree to obtain a list of potential conflict points; Generate a branch merge strategy according to the potential conflict point list, obtain a version control tree, and traverse the version control tree to obtain the latest status of each task; The latest status of each task is compared with the preset progress index to obtain task completion data, and the task completion data is visualized to obtain a real-time progress report.

7. The collaboration method based on the project collaboration platform according to claim 1, characterized in that: The content analysis of the external cooperation data is performed according to the version control tree and the real-time progress report to generate a multi-dimensional visualized project data set, including: Topologically sorting the version control tree to obtain a document version evolution sequence, and performing time alignment on the external cooperative data according to the document version evolution sequence to obtain a data-version mapping table; Performing topic extraction on the text content in the document-version mapping table to obtain a multi-dimensional topic vector, and performing dimensionality reduction on the multi-dimensional topic vector to obtain a two-dimensional topic distribution map; Extracting task completion data according to the real-time progress report to obtain a progress trend curve, and fusing the progress trend curve with the two-dimensional topic distribution map to obtain a topic-progress association matrix; Grouping the subject-progress association matrix to obtain a set of key project nodes, and constructing a knowledge graph based on the set of key project nodes to obtain a project content relationship network; A graphical layout analysis is performed on the project content relationship network to obtain an initial visualization layout scheme, and the initial visualization layout scheme is combined with interactive data to obtain a multi-dimensional visualization project data set.

8. A collaboration device based on a project collaboration platform, characterized in that: The device is used to execute the collaboration method based on the project collaboration platform as described in any one of claims 1 to 7, comprising: The analysis module is used to analyze the user role and user authority of the user registration information to obtain the user role category and the corresponding authority data matrix; An encryption module, used for performing multi-layer encryption processing according to the authority data matrix and the project identifier to obtain a unique invitation link containing a temporary verification number; A verification module, used to perform multiple decryptions and information matching verifications on the unique invitation link to obtain a user identity confirmation result and an access authorization token; A calculation module, used to perform task decomposition and resource matching calculation on project requirements based on the user identity confirmation result and the access authorization token to obtain a collaborative workflow diagram; A management module, used to perform incremental storage and branch management on the copywriting tasks in the collaborative workflow diagram, and obtain a version control tree and a real-time progress report; A generation module is used to perform content analysis on external cooperation materials according to the version control tree and the real-time progress report to generate a multi-dimensional visual project data set.

9. A computer device, characterized in that: The computer device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the computer device to execute the collaboration method based on the project collaboration platform according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, a collaboration method based on a project collaboration platform as described in any one of claims 1 to 7 is implemented.

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