Multi-platform collaborative intelligent office management system and coordination method

Abstract

The invention discloses a multi-platform cooperative intelligent office management system and a coordination method, and the method comprises the steps: a protocol unification module builds a standardized communication interface with a plurality of heterogeneous office platforms, and converts a document operation instruction of each platform into a unified intermediate protocol format; the multi-platform interactive interface module comprises a cross-platform identity verification unit and an asynchronous communication unit; the file synchronization and version control module adopts an operation conversion algorithm to realize multi-platform real-time collaborative editing; the modification content evaluation module performs agreement evaluation on the modification content of the office file by each platform to obtain an optimal modification scheme; the decision engine module is configured with a dynamic weight distribution algorithm, and an optimal version is generated by integrating user permission levels, historical behavior data and real-time evaluation results; and the feedback learning module optimizes evaluation model parameters by collecting final adoption decision data of the user. The method has the advantages that the problems of protocol isomerism, modification conflict and decision optimization in cross-platform cooperation are effectively solved.

Description

Technical Field

[0001] The present invention relates to collaborative management, and particularly to a multi-platform collaborative intelligent office management system and a coordination method. Background Art

[0002] With the continuous expansion of the scale of enterprises and institutions, the traditional office management method has gradually been unable to meet the needs of efficient management. Therefore, office management systems have emerged. By integrating modern information technologies such as cloud computing, big data, artificial intelligence, etc., they provide efficient and intelligent management solutions for various organizations. These systems usually include functions such as task assignment, document management, schedule arrangement, meeting coordination, resource scheduling, etc., and can realize information sharing and real-time communication, greatly improving work efficiency and collaborative effects.

[0003] The current office management systems on the market mainly cover multiple functions such as task assignment, project progress tracking, team collaboration, resource management, etc., helping enterprises improve office efficiency and collaborative working ability. Common office management systems include enterprise-level ERP systems, team collaboration platforms such as Trello, Asana, Monday.com, etc. These tools achieve real-time data synchronization through cloud technology, enabling team members to view project progress and communicate feedback at any time to ensure that all tasks are completed on time. In addition, there are tools focusing on time management, such as Clockify, Toggl, etc., which can help employees reasonably arrange working hours and improve work efficiency. However, traditional system methods face challenges in protocol heterogeneity, modification conflicts, and decision optimization in cross-platform collaboration, with low efficiency and difficulty in effectively coordinating workflows between different platforms. Summary of the Invention

[0004] In order to improve the existing office management system and coordination method, a multi-platform collaborative intelligent office management system and a coordination method are provided. The system improves the collaboration efficiency and data consistency between different platforms through a unified protocol interface, cross-platform authentication, and real-time file synchronization, and continuously optimizes decisions and user experience through a dynamic decision-making engine and a feedback learning module, realizing intelligent and flexible office management.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A multi-platform collaborative intelligent office management system, comprising:

[0007] A protocol unification module: The protocol unification module is mainly used to establish a standardized communication interface with multiple heterogeneous office platforms and convert the document operation instructions of each platform into a unified intermediate protocol format;

[0008] Multi-platform Interaction Interface Module: The multi-platform interaction interface module includes a cross-platform authentication unit based on OAuth2.1 and an asynchronous communication unit based on gRPC;

[0009] File Synchronization and Version Control Module: The file synchronization and version control module uses an operation transformation algorithm to achieve real-time collaborative editing across multiple platforms and includes a conflict detection unit based on a semantic tree structure;

[0010] Modified Content Evaluation Module: The modified content evaluation module is mainly used to evaluate the modified content of office files on each platform and obtain the best modification plan;

[0011] Decision Engine Module: The decision engine module is configured with a dynamic weight allocation algorithm to generate the optimal version by integrating the user permission level, historical behavior data, and real-time evaluation results;

[0012] Feedback Learning Module: The feedback learning module optimizes the evaluation model parameters by collecting user's final adopted decision data.

[0013] Preferably, the protocol unification module specifically includes:

[0014] Protocol Adapter Cluster Unit: The protocol adapter cluster unit supports three communication protocols: RESTful API, WebSocket, and MQTT;

[0015] Protocol Converter Unit: The protocol converter uses an instruction parsing method based on a finite state machine to convert the operation instructions of each platform into an intermediate representation containing a triple of <operation type, target element, parameter list>;

[0016] Data Standardization Unit: The data standardization unit is configured with an XSLT3.0 conversion engine to achieve the structural unification of documents in different formats.

[0017] Preferably, the multi-platform interaction interface module specifically includes:

[0018] Distributed API Gateway Unit: The distributed API gateway unit realizes traffic control and protocol conversion based on the Envoy proxy;

[0019] Real-time Session Management Unit: The real-time session management unit uses the CRDT (Conflict-free Replicated Data Type) algorithm to maintain the consistency of the cross-platform session state;

[0020] Permission Verification Unit: The permission verification unit integrates a federated identity authentication and an attribute access control model.

[0021] Preferably, the file synchronization and version control module specifically includes:

[0022] Conflict Detection Unit: The conflict detection unit uses an LSTM-based sequence prediction model to identify potential semantic conflicts;

[0023] Difference Merging Unit: The difference merging unit applies the Levenshtein distance algorithm and syntactic tree comparison technology to generate a merged proposal.

[0024] Preferably, the difference merging unit applying the Levenshtein distance algorithm and syntactic tree comparison technology to generate a merged proposal specifically includes:

[0025] For character-based office documents, calculate the minimum edit distance between two strings based on the Levenshtein distance algorithm. Let the two strings be A = a1, a2,..., a m and B = b1, b2,..., b m , and its recurrence formula is:

[0026]

[0027] where i and j respectively represent the character positions of strings A and B;

[0028] Based on the character distances obtained by the Levenshtein distance algorithm, obtain the Levenshtein distance matrix and generate a difference table;

[0029] For structured data office documents, parse the structured data into an abstract syntax tree and construct two versions, including version A and version B;

[0030] By comparing the structures and nodes of the trees, determine the identical subtrees and changed parts, and identify node insertions, node deletions, and node modification contents;

[0031] Based on the differences between the obtained character-based office documents and structured data office documents, perform merge insert, merge delete, and merge replace operations.

[0032] Preferably, the modification content evaluation module specifically includes:

[0033] Multimodal Feature Extraction Unit: The multimodal feature extraction unit is mainly used to integrate three types of features: text, format tags, and collaboration context;

[0034] Evaluation Model Architecture Unit: The evaluation model architecture unit includes a business relevance branch that calculates semantic association degrees based on a domain knowledge graph and a compliance verification branch that performs policy matching using a rule engine;

[0035] Evaluation Result Fusion Unit: The evaluation result fusion unit uses a gated attention mechanism to dynamically adjust the weights of each branch.

[0036] Preferably, the business relevance branch for calculating semantic association degrees based on a domain knowledge graph and the compliance verification branch for performing policy matching using a rules engine specifically include:

[0037] Based on the modification types and modification contents in the domain knowledge graph, calculate and evaluate the relevance between different modifications according to weighted average to obtain the modification contents that are more important for office documents. The formula is:

[0038]

[0039] where A i is the modification content in the office document, ω i is the importance weight of modification A i for office document T, and distance(·) is the distance between two modifications calculated through the shortest path and graph convolutional network;

[0040] Based on each modification request, the rules engine performs verification according to the rules, checks whether the file meets the compliance requirements, and generates a compliance report.

[0041] Preferably, the decision engine module specifically includes:

[0042] Dynamic weight calculation unit: The dynamic weight calculation unit generates a real-time weight matrix according to the user role, office process, and device type;

[0043] Multi-objective optimization unit: The multi-objective optimization unit uses the NSGA-II algorithm to balance the quality score, collaboration efficiency, and resource consumption;

[0044] Decision interpreter: The decision interpreter generates an interpretable report containing the confidence score and key influencing factors.

[0045] Preferably, the feedback learning module specifically includes:

[0046] Incremental training module: The incremental training module is mainly used to continuously update the evaluation model based on the elastic weight consolidation algorithm by adding a regularization term to the loss function of the model to constrain the model from deviating from the important parameters in the original evaluation process when evaluating modifications to new office documents. The loss function formula is:

[0047]

[0048] where L new is the loss function of the current modification operation, θ i is the current model parameter, is the optimal parameter in the previous modification operation, F i is the parameter θ iThe Fisher information indicates the importance of the parameter, where λ is the regularization hyperparameter that controls the weight of the regularization term.

[0049] Furthermore, a multi-platform collaborative intelligent office management and coordination method is proposed, including:

[0050] Establish a cross-platform communication channel to achieve interconnection of heterogeneous systems through a protocol adapter cluster;

[0051] Real-time capture document operations on each platform and convert them into a standardized intermediate representation;

[0052] Detect potential conflicts and generate a difference analysis report;

[0053] Start a multi-dimensional evaluation pipeline to perform syntax, business, and compliance analysis in parallel;

[0054] Dynamically calculate the decision weight matrix and generate a version recommendation with optimized sorting;

[0055] Update the evaluation model parameters and decision-making strategies to complete closed-loop optimization.

[0056] Compared with the prior art, the advantages of the present invention are as follows:

[0057] By virtue of highly integrated modules such as protocol unification, version control, and decision optimization, it effectively solves the problems of protocol heterogeneity, modification conflicts, and decision optimization in cross-platform collaboration. By establishing a standardized communication interface, it converts document operation instructions on different platforms into a unified intermediate protocol format, achieving seamless connection and interoperability between platforms. Through authentication and asynchronous communication, it ensures the consistency and security of cross-platform data transmission and operations. By adopting an operation transformation algorithm and a conflict detection unit based on a semantic tree structure, it realizes real-time collaborative editing between multiple platforms, avoiding operation conflicts and losses to the greatest extent. By comprehensively evaluating the modified content and combining a dynamic weight allocation algorithm, it generates the optimal version and conducts intelligent decision optimization based on the user's historical behavior and permissions. By collecting user decision data, it continuously optimizes the evaluation model and decision-making process. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the system proposed by the present invention;

[0059] Figure 2 It is a schematic diagram of the method proposed by the present invention;

[0060] Figure 3 It is a schematic diagram of the scheme for generating a merge recommendation proposed by the present invention;

[0061] Figure 4 It is a schematic diagram of the evaluation model architecture unit proposed by the present invention;

[0062] Figure 5It is the architecture diagram of the electronic device in this solution;

[0063] Figure 6 It is the schematic diagram of the structure of the computer-readable storage medium in this solution. Specific implementation manners

[0064] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variants.

[0065] Refer to Figure 1 As shown, a multi-platform collaborative intelligent office management system includes:

[0066] Protocol unification module: The protocol unification module is mainly used to establish a standardized communication interface with multiple heterogeneous office platforms and convert the document operation instructions of each platform into a unified intermediate protocol format;

[0067] Multi-platform interaction interface module: The multi-platform interaction interface module includes a cross-platform authentication unit based on OAuth2.1 and an asynchronous communication unit based on gRPC;

[0068] File synchronization and version control module: The file synchronization and version control module uses an operation conversion algorithm to achieve multi-platform real-time collaborative editing, and includes a conflict detection unit based on a semantic tree structure;

[0069] Modified content evaluation module: The modified content evaluation module is mainly used to evaluate the modified content of office documents on each platform and obtain the best modification plan;

[0070] Decision engine module: The decision engine module is configured with a dynamic weight distribution algorithm to generate an optimal version by integrating the user permission level, historical behavior data, and real-time evaluation results;

[0071] Feedback learning module: The feedback learning module optimizes the evaluation model parameters by collecting the user's final adopted decision data.

[0072] Refer to Figure 1 As shown, the protocol unification module specifically includes:

[0073] Protocol adapter cluster unit: The protocol adapter cluster unit supports three communication protocols, namely RESTful API, WebSocket, and MQTT;

[0074] Protocol converter unit: The protocol converter uses an instruction parsing method based on a finite state machine to convert the operation instructions of each platform into an intermediate representation including a triple <operation type, target element, parameter list>;

[0075] Data Standardization Unit: The data standardization unit is configured with an XSLT 3.0 conversion engine to achieve the structural unification of documents in different formats.

[0076] See Figure 1 As shown, the multi-platform interaction interface module specifically includes:

[0077] Distributed API Gateway Unit: The distributed API gateway unit realizes traffic control and protocol conversion based on the Envoy proxy;

[0078] Real-time Session Management Unit: The real-time session management unit uses the CRDT (Conflict-free Replicated Data Type) algorithm to maintain the consistency of cross-platform session states;

[0079] Permission Verification Unit: The permission verification unit integrates the federated identity authentication and attribute access control model.

[0080] See Figure 1 As shown, the file synchronization and version control module specifically includes:

[0081] Conflict Detection Unit: The conflict detection unit uses an LSTM-based sequence prediction model to identify potential semantic conflicts;

[0082] Difference Merging Unit: The difference merging unit applies the Levenshtein distance algorithm and syntax tree comparison technology to generate a merged proposal plan.

[0083] See Figure 3 As shown, the difference merging unit applying the Levenshtein distance algorithm and syntax tree comparison technology to generate a merged proposal plan specifically includes:

[0084] For character-based office files, calculate the minimum edit distance between two strings based on the Levenshtein distance algorithm. Let the two strings be A = a1, a2,..., a m and B = b1, b2,..., b m , and its recurrence formula is:

[0085]

[0086] where i and j respectively represent the character positions of strings A and B;

[0087] Based on the character distances obtained by the Levenshtein distance algorithm, obtain the Levenshtein distance matrix and generate a difference table;

[0088] For structured data office files, parse the structured data into an abstract syntax tree and construct two versions, including version A and version B;

[0089] By comparing the structures and nodes of the trees, determine the identical subtrees and the changed parts, and identify node insertions, node deletions, and node modification contents;

[0090] Based on the differences between the obtained character-based office documents and structured data office documents, perform merge insertions, merge deletions, and merge replacements.

[0091] It can be understood that syntax tree comparison usually requires relatively complex structured data. Especially when dealing with long texts or complex syntax structures, the computational complexity will increase sharply, resulting in a slow merge process and even performance bottlenecks. Adaptive tree comparison algorithms, such as heuristic search or divide-and-conquer strategies, can be used to reduce the comparison complexity. At the same time, parallel computing or distributed computing frameworks can be adopted to accelerate the processing speed.

[0092] Refer to Figure 1 As shown, the modification content evaluation module specifically includes:

[0093] Multimodal feature extraction unit: The multimodal feature extraction unit is mainly used to integrate three types of features: text, format markers, and collaboration context;

[0094] Evaluation model architecture unit: The evaluation model architecture unit includes a business relevance branch that calculates semantic association degrees based on a domain knowledge graph and a compliance verification branch that performs policy matching using a rule engine;

[0095] Evaluation result fusion unit: The evaluation result fusion unit uses a gated attention mechanism to dynamically adjust the weights of each branch.

[0096] Specifically, the gated attention mechanism generates an "attention" weight matrix according to the input feature information through a trained gating function, determining which branch outputs are more important in the current decision-making process and which can be ignored or have their weights reduced. This enables the evaluation result fusion unit to dynamically weight different evaluation results according to the requirements of the current task, changes in the external environment, or user preferences.

[0097] For example, in some cases, quality scores may be more important, while in other cases, collaboration efficiency or resource consumption may have a greater impact on the decision-making result. Through this adaptive adjustment, the gated attention mechanism can optimize the contributions of each branch based on real-time feedback, thereby improving the accuracy and relevance of the overall evaluation result.

[0098] Refer to Figure 4 As shown, the business relevance branch that calculates semantic association degrees based on a domain knowledge graph and the compliance verification branch that performs policy matching using a rule engine specifically include:

[0099] Based on the modification types and modification contents in the domain knowledge graph, the relevance between different modifications is evaluated according to weighted average calculation to obtain the modification contents that are more important for office documents. The formula is as follows:

[0100]

[0101] Among them, A i is the modification content in the office document, ω i is the importance weight of modification A i for office document T, and distance(·) is the distance between two modifications calculated through the shortest path and graph convolutional network;

[0102] Based on each modification request, the rule engine performs verification according to the rules, checks whether the file meets the compliance requirements, and generates a compliance report.

[0103] Refer to Figure 1 as shown, the decision engine module specifically includes:

[0104] Dynamic weight calculation unit: The dynamic weight calculation unit generates a real-time weight matrix according to the user role, office process, and device type;

[0105] Multi-objective optimization unit: The multi-objective optimization unit uses the NSGA-II algorithm to balance the quality score, collaboration efficiency, and resource consumption;

[0106] Decision interpreter: The decision interpreter generates an interpretable report containing the confidence score and key influencing factors.

[0107] Specifically, the NSGA-II algorithm ensures the diversity and quality of solutions through non-dominated sorting and crowding distance metric. During the optimization process, the system can consider multiple objectives simultaneously without sacrificing the optimization results of a certain objective, thereby ensuring the maximization of comprehensive performance.

[0108] Through the detailed analysis of the decision-making process, the report can help users understand the logic and basis behind each decision, enhancing the transparency and credibility of the system. In addition, the confidence score provided in the report can help decision-makers evaluate the reliability of the decision and then make more reasonable adjustments.

[0109] Refer to Figure 1 as shown, the feedback learning module specifically includes:

[0110] Incremental training module: The incremental training module is mainly used to continuously update the evaluation model based on the elastic weight consolidation algorithm. By adding a regularization term to the loss function of the model, it is constrained that the model does not deviate from the important parameters in the original evaluation process when evaluating the modifications of new office documents. The loss function formula is:

[0111]

[0112] Among them, L new is the loss function of the current modification operation, and θ i is the current model parameter. is the optimal parameter in the previous modification operation, and F i is the Fisher information of the parameter θ i , which represents the importance of the parameter. λ is the regularization hyperparameter that controls the weight of the regularization term.

[0113] Refer to Figure 2 As shown, a method for invoking a mathematical model based on cloud services includes:

[0114] Step 1: Establish a cross-platform communication channel to achieve interconnection of heterogeneous systems through a protocol adapter cluster;

[0115] Step 2: Capture document operations on each platform in real time and convert them into a standardized intermediate representation;

[0116] Step 3: Detect potential conflicts and generate a difference analysis report;

[0117] Step 4: Start a multi-dimensional evaluation pipeline and perform syntax, business, and compliance analysis in parallel;

[0118] Step 5: Dynamically calculate the decision weight matrix and generate an optimized and sorted version recommendation;

[0119] Step 6: Update the evaluation model parameters and decision-making strategies to complete closed-loop optimization.

[0120] Furthermore, the method according to the embodiment of the present application can also be implemented with the aid of Figure 5 the architecture of the electronic device shown. As Figure 5 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a multi-platform collaborative intelligent office management system and coordination method provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 5 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the Figure 5 electronic device may be omitted according to actual needs.

[0121] Figure 6 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 6As shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, a multi-platform collaborative intelligent office management system and a coordination method according to the embodiments of the present application described with reference to the above drawings can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0122] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized.

[0124] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-platform collaborative intelligent office management system, characterized in that, Including: Protocol Unification Module: The protocol unification module is mainly used to establish a standardized communication interface with multiple heterogeneous office platforms and convert the document operation instructions of each platform into a unified intermediate protocol format; Multi-Platform Interaction Interface Module: The multi-platform interaction interface module includes a cross-platform authentication unit based on OAuth2.1 and an asynchronous communication unit based on gRPC; File Synchronization and Version Control Module: The file synchronization and version control module uses an operation conversion algorithm to achieve real-time collaborative editing across multiple platforms and includes a conflict detection unit based on a semantic tree structure; Modified Content Evaluation Module: The modified content evaluation module is mainly used to evaluate the modified content of office files on each platform and obtain the best modification plan; Decision Engine Module: The decision engine module is configured with a dynamic weight allocation algorithm to generate an optimal version by integrating the user permission level, historical behavior data, and real-time evaluation results; Feedback Learning Module: The feedback learning module optimizes the evaluation model parameters by collecting user's final adopted decision data.

2. The multi-platform collaborative intelligent office management system according to claim 1, characterized in that, The protocol unification module specifically includes: Protocol Adapter Cluster Unit: The protocol adapter cluster unit supports three communication protocols: RESTful API, WebSocket, and MQTT; Protocol Converter Unit: The protocol converter uses an instruction parsing method based on a finite state machine to convert the operation instructions of each platform into an intermediate representation containing a triple <operation type, target element, parameter list>; Data Standardization Unit: The data standardization unit is configured with an XSLT3.0 conversion engine to achieve the structural unification of documents in different formats.

3. A multi-platform collaborative intelligent office management system according to claim 1, characterized in that, The multi-platform interaction interface module specifically includes: Distributed API Gateway Unit: The distributed API gateway unit realizes traffic control and protocol conversion based on the Envoy proxy; Real-time Session Management Unit: The real-time session management unit uses the CRDT (Conflict-Free Replicated Data Type) algorithm to maintain the consistency of cross-platform session states; Permission Verification Unit: The permission verification unit integrates a federated identity authentication and an attribute access control model.

4. A multi-platform collaborative intelligent office management system according to claim 1, characterized in that, The file synchronization and version control module specifically includes: Conflict Detection Unit: The conflict detection unit uses an LSTM-based sequence prediction model to identify potential semantic conflicts; Difference Merging Unit: The difference merging unit applies the Levenshtein distance algorithm and syntax tree comparison technology to generate a merged suggestion plan.

5. A multi-platform collaborative intelligent office management system according to claim 4, characterized in that, The difference merging unit applying the Levenshtein distance algorithm and syntax tree comparison technology to generate a merged suggestion plan specifically includes: For character-based office documents, the minimum edit distance between two strings is calculated based on the Levenshtein distance algorithm. Let the two strings be A = a1, a2,..., a m and B = b1, b2,..., b m , and its recurrence formula is: Where i and j respectively represent the character positions of strings A and B; Obtaining the Levenshtein distance matrix based on the character distances obtained by the Levenshtein distance algorithm and generating a difference table; For structured data office files, parsing the structured data into an abstract syntax tree and constructing two versions, including version A and version B; By comparing the structures and nodes of the trees, determining the same subtrees and changed parts, and identifying node insertions, node deletions, and node modification contents; Based on the differences between the obtained character-based office documents and structured data office documents, perform merge insertion, merge deletion, and merge replacement operations.

6. A multi-platform collaborative intelligent office management system according to claim 1, characterized in that, The modification content evaluation module specifically includes: Multimodal feature extraction unit: The multimodal feature extraction unit is mainly used to integrate three types of features: text, format markers, and collaboration context; Evaluation model architecture unit: The evaluation model architecture unit includes a business relevance branch that calculates semantic association degrees based on a domain knowledge graph and a compliance verification branch that performs policy matching using a rule engine; Evaluation result fusion unit: The evaluation result fusion unit uses a gated attention mechanism to dynamically adjust the weights of each branch.

7. A multi-platform collaborative intelligent office management system according to claim 6, characterized in that, The business relevance branch that calculates semantic association degrees based on a domain knowledge graph and the compliance verification branch that performs policy matching using a rule engine specifically include: Based on the modification types and modification content in the domain knowledge graph, calculate and evaluate the relevance between different modifications according to weighted average to obtain the modification content that is more important for office documents. The formula is: Among them, A i is the modified content in the office document, ω i is the importance weight of the modification A i to the office document T, and distance(·) is the distance between two modifications calculated through the shortest path and graph convolutional network; Based on each modification request, the rule engine performs verification according to the rules, checks whether the file meets the compliance requirements, and generates a compliance report.

8. A multi-platform collaborative intelligent office management system according to claim 1, characterized in that The decision engine module specifically includes: Dynamic weight calculation unit: The dynamic weight calculation unit generates a real-time weight matrix according to the user role, office process, and device type; Multi-objective optimization unit: The multi-objective optimization unit uses the NSGA-II algorithm to balance the quality score, collaboration efficiency, and resource consumption; Decision interpreter: The decision interpreter generates an interpretable report containing a confidence score and key influencing factors.

9. A multi-platform collaborative intelligent office management system according to claim 1, characterized in that, The feedback learning module specifically includes: Incremental training module: The incremental training module is mainly used to continuously update the evaluation model based on the elastic weight consolidation algorithm. By adding a regularization term to the loss function of the model, it is constrained that the model does not deviate from the important parameters in the original evaluation process when evaluating the modification of new office documents. The loss function formula is: Among them, L new is the loss function of the current modification operation, and θ i is the current model parameter, is the optimal parameter in the previous modification operation, F i is the Fisher information of the parameter θ i which represents the importance of the parameter, and λ is the regularization hyperparameter that controls the weight of the regularization term.

10. A multi-platform collaborative intelligent office management and coordination method, characterized in that, Including: Establish a cross-platform communication channel to achieve interconnection of heterogeneous systems through a protocol adapter cluster; Capture document operations on each platform in real time and convert them into a standardized intermediate representation; Detect potential conflicts and generate a difference analysis report; Start a multi-dimensional evaluation pipeline to perform syntax, business, and compliance analysis in parallel; Dynamically calculate the decision weight matrix and generate an optimized and sorted version recommendation; Update the evaluation model parameters and decision-making strategies to complete closed-loop optimization.

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