Document Collaboration Method and System Based on Cloud Office

Abstract

The present application provides a document collaboration method and system based on cloud office. First, it obtains a set of operation records of target documents on the cloud office platform, including the sequence of operation events (operation types and timestamps) of user terminals. Then, based on these sequences of operation events, it identifies the characteristics of the document collaboration mode, covering the overlapping operation periods, type correlations, and frequency distributions. Next, it uses a pre-trained model to analyze and obtain a collaboration demand prediction result including a set of collaboration objects and collaboration intensity indicators, and dynamically configures the permissions for the collaboration objects according to the collaboration intensity indicators to generate a permission hierarchy structure. Finally, when responding to a collaboration request from a target user terminal, it controls the data synchronization process of the document based on its permission scope. This method effectively improves the efficiency, security, and intelligence of cloud office document collaboration.

Description

Technical Field

[0001] This application relates to the technical field of cloud office. Specifically, it relates to a document collaboration method and system based on cloud office. Background Art

[0002] With the rapid development of information technology, cloud office plays an increasingly important role in modern enterprise and team collaboration. In the cloud office environment, document collaboration is a core function.

[0003] There are many deficiencies in traditional cloud office document collaboration methods. In the early cloud office document collaboration, permission management mostly adopted a static and fixed mode. For example, the administrator pre-sets fixed permissions for users, such as editing, viewing, etc. This method lacks flexibility and cannot be dynamically adjusted according to the actual work needs and collaboration situations of users, which easily leads to unreasonable permission allocation. On the one hand, some users may be given too many unnecessary permissions, increasing the risk of the document being misoperated or maliciously tampered with; on the other hand, some users who really need more operation permissions are restricted in their permissions, affecting work efficiency. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of this application, embodiments of this application provide a document collaboration method based on cloud office, and the method includes:

[0005] Obtain a set of document operation records generated by a target document in a cloud office platform, where the set of document operation records includes a sequence of operation events performed by multiple user terminals on the target document, and the sequence of operation events includes at least one operation type and the corresponding operation timestamp;

[0006] Based on the sequences of operation events of each user terminal in the set of document operation records, identify the collaboration mode characteristics of the target document, where the collaboration mode characteristics include the operation overlap period, operation type correlation, and operation frequency distribution among multiple user terminals;

[0007] Analyze the collaboration mode characteristics through a pre-trained collaboration demand prediction model to generate a collaboration demand prediction result corresponding to the target document, where the collaboration demand prediction result includes a predicted set of collaboration objects and a collaboration intensity index, and the collaboration intensity index is used to characterize the degree of operation demand of each user terminal in the set of collaboration objects for the target document in the subsequent collaboration stage;

[0008] Dynamically configure permissions for the user terminals in the set of collaboration objects according to the collaboration intensity index to generate a permission hierarchy structure that matches the collaboration demand prediction result, where the permission hierarchy structure includes at least one permission level and the corresponding operation permission range, and the operation permission range is positively correlated with the collaboration intensity index;

[0009] In response to detecting a collaboration request from the target user terminal for the target document, based on the operation permission scope corresponding to the target user terminal in the permission hierarchy structure, control the data synchronization process of the target document between the target user terminal and the cloud office platform.

[0010] On the other hand, an embodiment of the present application further provides a cloud office service system, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0011] Based on the above aspects, by obtaining the document operation record set of the target document in the cloud office platform, the embodiment of the present application can comprehensively and accurately capture the operation situations of multiple user terminals on the document, including information such as operation types and timestamps, making the subsequent recognition of collaboration mode features more accurate and detailed, and laying a solid foundation for accurate collaboration requirement prediction. Compared with traditional document collaboration methods, it is no longer limited to simple operation record statistics, but deeply mines the pattern features behind the operations.

[0012] The collaboration mode features identified based on the operation event sequence cover multi-dimensional information such as operation overlapping periods, operation type correlations, and operation frequency distributions, which helps to more deeply understand the behavior patterns of users in document collaboration, overcomes the limitations of previous methods that can only evaluate collaboration situations from a single dimension or a simple comprehensive dimension, and thus can more accurately adapt to complex and changeable cloud office document collaboration scenarios.

[0013] Analyze the collaboration mode features by means of a pre-trained collaboration requirement prediction model to generate a collaboration requirement prediction result. Among them, the predicted collaboration object set and collaboration intensity index provide forward-looking guidance for cloud office document collaboration, and can reasonably predict subsequent collaboration requirements in advance according to the previous operation patterns of users, which is a huge breakthrough in improving document collaboration efficiency. Traditional document collaboration methods often can only passively respond to immediate operation requests and lack this effective prediction ability for future collaboration requirements, which is likely to lead to problems such as unreasonable permission allocation and untimely data synchronization.

[0014] Generate a permission hierarchy structure according to the collaboration intensity index for dynamic permission configuration, and the operation permission scope is positively correlated with the collaboration intensity index. Thus, permissions can be flexibly allocated according to the degree of operation requirements of users for the document, effectively avoiding the situations of excessive permission allocation or insufficient permissions. While ensuring document security, it maximally improves the operation flexibility of each user terminal in document collaboration, which is an advantage that cannot be compared with traditional fixed permission allocation methods.

[0015] Finally, when responding to the collaboration request of the target user terminal, the data synchronization process is controlled based on the permission hierarchy structure. This precise data synchronization control method based on dynamic permissions ensures that each user terminal efficiently conducts data interaction within its permission scope, reduces unnecessary data transmission and permission conflicts, improves the stability and efficiency of the entire cloud office document collaboration system, and further enhances the overall performance of cloud office document collaboration. Brief Description of the Drawings

[0016] Figure 1 is a schematic execution flowchart of the document collaboration method based on cloud office provided by an embodiment of the present application.

[0017] Figure 2 is a schematic hardware architecture diagram of the cloud office service system provided by an embodiment of the present application. Detailed Embodiments

[0018] The present application will be specifically described below in conjunction with the accompanying drawings of the specification. Figure 1 is a schematic flowchart of the document collaboration method based on cloud office provided by an embodiment of the present application. The document collaboration method based on cloud office will be introduced in detail below.

[0019] Step S110, obtain a set of document operation records generated by the target document in the cloud office platform. The set of document operation records includes a sequence of operation events performed by multiple user terminals on the target document, and the sequence of operation events includes at least one operation type and the corresponding operation timestamp.

[0020] In this embodiment, on the cloud office platform of a large enterprise, for example, there is a project planning document as the target document. This project involves multiple departments, including the marketing department, the R & D department, and the finance department, etc. Employees in the marketing department use user terminal A, employees in the R & D department use user terminal B, employees in the finance department use user terminal C, etc. During the project progress, these user terminals will perform various operations on the target document.

[0021] User terminal A opens the target document and views the content at 9 am. The operation type of this operation is "document viewing", and the operation timestamp is "2023 - 05 - 01 09:00:00". Subsequently, at 10 am, the market research part of the document is edited, and the operation type becomes "document editing - market research part", and the operation timestamp is "2023 - 05 - 01 10:00:00".

[0022] User terminal B also opened the target document for viewing at 10:30 am. The operation type was "document viewing", and the operation timestamp was "2023 - 05 - 01 10:30:00". Then, at 11 am, it edited the technical solution part. The operation type was "document editing - technical solution part", and the operation timestamp was "2023 - 05 - 01 11:00:00".

[0023] User terminal C opened the target document to view budget - related content at 1 pm. The operation type was "document viewing - budget part", and the operation timestamp was "2023 - 05 - 01 13:00:00". And at 2 pm, it modified the budget data. The operation type was "document editing - budget part", and the operation timestamp was "2023 - 05 - 01 14:00:00".

[0024] The cloud office platform will record the operation event sequences of these different user terminals on the target document, including the operation type and the corresponding operation timestamp, to form a document operation record set. This set comprehensively reflects the operation situations of employees in each department on the target document at different times, providing basic data for subsequent analysis.

[0025] Step S120: Based on the operation event sequences of each user terminal in the document operation record set, identify the collaboration mode features of the target document. The collaboration mode features include the operation overlap period, operation type relevance, and operation frequency distribution among multiple user terminals.

[0026] Continuing with the above - mentioned enterprise project planning document as an example, first extract the operation type sequence and the corresponding time - interval sequence from the operation event sequence. For example, the operation type sequence of user terminal A is "document viewing - document editing - market research part", and its time - interval sequence is that the interval from the viewing operation to the editing operation is 1 hour. The operation type sequence of user terminal B is "document viewing - document editing - technical solution part", and the time interval is half an hour. The operation type sequence of user terminal C is "document viewing - budget part - document editing - budget part", and the time interval is 1 hour.

[0027] Determine the operation overlap period according to the distribution density of different types of operation events in the operation type sequence in the time dimension. In this project, between 10 am and 10:30 am, user terminal A was editing the market research part, while user terminal B started viewing the document. This is an operation overlap period. At this time, employees from different departments were interacting with the target document. Although the operation types were not exactly the same, they were all in the process of processing the document.

[0028] Analyze the context dependencies between different operation types in the operation type sequence to generate the operation type correlation. For example, after the marketing department completes the editing of the market research section (user terminal A), the technical solution editing of the R & D department (user terminal B) often needs to be adjusted accordingly. Through the statistics of a large number of operation event sequences, it is found that when the editing of the market research section is completed, within the next 2 hours, the probability of the technical solution editing being adjusted is 60%, which reflects the correlation between operation types.

[0029] Statistically analyze the operation frequency distribution corresponding to different time differences in the time interval sequence, and find that the period from 9 am to 11 am every day is a period with a relatively high operation frequency. During this period, the operations of viewing and editing documents are relatively frequent, while the operation frequency is relatively low after 3 pm. Combining these operation overlap periods, operation type correlations, and operation frequency distributions, the collaboration mode characteristics of the target document are obtained. These collaboration mode characteristics reflect the collaboration rules and mutual influence relationships among various departments in the project.

[0030] Step S130, analyze the collaboration mode characteristics through a pre-trained collaboration requirement prediction model to generate a collaboration requirement prediction result corresponding to the target document. The collaboration requirement prediction result includes a predicted set of collaboration objects and a collaboration intensity index. The collaboration intensity index is used to characterize the operation requirement degree of each user terminal in the predicted set of collaboration objects for the target document in the subsequent collaboration stage.

[0031] Still taking the enterprise's project planning document as an example, input the previously identified collaboration mode characteristics, such as operation overlap periods, operation type correlations, and operation frequency distributions, into the pre-trained collaboration requirement prediction model.

[0032] During the pre-training process, the model has learned the historical collaboration data of many similar project documents. For example, there were multiple project documents before, and each document had a corresponding final set of collaboration objects (such as which departments participated in the collaboration at each stage of the project) and actual collaboration intensity indicators (the operation requirement degrees of each department for the document at different stages). Extract the historical operation records of each historical document from these historical collaboration data to generate a set of historical collaboration mode characteristics for training, and construct an initial neural network model and perform iterative optimization to obtain this pre-trained collaboration requirement prediction model.

[0033] When the collaboration mode characteristics of the current project planning document are input into the feature encoding layer of the model, a multi-dimensional feature vector will be generated. For example, the duration encoding of the operation overlap period may reflect the overlapping operation duration of the marketing department and the R & D department from 10 am to 10:30 am, the probability encoding of the operation type correlation reflects the probability that the operation of the marketing department triggers the operation of the R & D department, and the periodic encoding of the operation frequency distribution represents the periodic characteristics of frequent operations in the morning, etc.

[0034] The time series analysis layer of the model performs time-dependency modeling on the multi-dimensional feature vectors and outputs time series enhanced features. For example, the model predicts that within two hours after the Marketing Department completes the update of the new market research results (from 9:00 am to 10:00 am) in the next week, there is an 80% probability that the R & D Department will adjust the technical solution, and the Finance Department may re-evaluate the budget from 1:00 pm to 2:00 pm on the same day.

[0035] The prediction layer is used to perform pattern matching on the time series enhanced features to determine the historical collaboration contribution degree and the predicted collaboration contribution degree of each user terminal in the collaboration object set. Taking the Marketing Department as an example, its historical collaboration contribution degree is calculated based on the previous operation type weights in the document (such as a higher editing weight for the market research section) and the operation frequency. The predicted collaboration contribution degree is calculated based on the collaboration event type associated with the Marketing Department (such as future market research updates) and the trigger time interval in the time series enhanced features. A collaboration intensity index is generated based on the weighted sum of the historical collaboration contribution degree and the predicted collaboration contribution degree. For example, the collaboration intensity index of the Marketing Department is relatively high, indicating a greater demand for operating the target document in the subsequent project promotion. The collaboration object set is sorted from high to low according to the collaboration intensity index to generate a collaboration demand prediction result, which can provide a basis for subsequent operations such as permission configuration.

[0036] Step S140, perform dynamic permission configuration on the user terminals in the collaboration object set according to the collaboration intensity index, and generate a permission hierarchy structure that matches the collaboration demand prediction result. The permission hierarchy structure includes at least one permission level and the corresponding operation permission range, and the operation permission range is positively correlated with the collaboration intensity index.

[0037] Based on the collaboration intensity index in the previously calculated collaboration demand prediction result, perform dynamic permission configuration on each department (user terminal) participating in the collaboration of the project planning document. For example, due to its relatively high collaboration intensity index, the Marketing Department is classified into the high collaboration intensity level. This level is configured with a relatively wide first operation permission range, including document content editing permission (can comprehensively edit the market research section and other relevant sections), version rollback permission (if it is found that the previous edit is incorrect, it can be rolled back to the previous version), and collaboration member invitation permission (if it is considered necessary to invite other departments or personnel to participate in the collaboration of specific content, they can be invited to join).

[0038] The collaboration intensity index of the R & D Department is at a medium level and is classified into the medium collaboration intensity level. The configured second operation permission range includes document content editing permission (but may be limited to the technical solution related part) and annotation addition permission (can add annotations to the content in the document to explain one's own ideas or opinions), and the use frequency of the version rollback permission is restricted. For example, the version rollback operation can only be used once a month.

[0039] The collaboration intensity index of the Finance Department is relatively low, and is at a low collaboration intensity level. The configured third-operation permission scope only includes document content viewing permission (only the budget part and other related document contents can be viewed) and annotation viewing permission (annotations added by other departments can be viewed, but new annotations cannot be added).

[0040] The permission hierarchy structure is generated based on the mapping relationship between the permission level and the scope of operation permission. During the project progress, the changes in the collaboration intensity index are monitored in real time. For example, if the marketing department's demand for document operations suddenly decreases at a certain stage, the collaboration intensity index decreases, while the R&D department's operation demand increases and the collaboration intensity index increases, then the permission level allocation in the permission hierarchy structure will be dynamically adjusted, and some permissions of the marketing department may be adjusted to the R&D department to meet the actual collaboration needs of the project.

[0041] Step S150, in response to detecting a collaboration request of the target user terminal for the target document, based on the operation permission scope corresponding to the target user terminal in the permission hierarchy structure, controlling the data synchronization process of the target document between the target user terminal and the cloud office platform.

[0042] Assume that during the project, the marketing department staff uses the target user terminal A to initiate a collaboration request for the project planning document again. Since the marketing department is at a high collaboration intensity level, its operation permission range includes document content editing permissions. At this time, the cloud office platform will enable real-time synchronization mode. When the marketing department staff edits the target document on the target user terminal A, such as modifying the data in the market research section or adding new market analysis content, these editing operations will be uploaded to the cloud office platform in real time. In addition, because it is real-time synchronization, other online user terminals (such as user terminal B of the R&D department and user terminal C of the finance department) will receive content update notifications immediately and see the new edits of the marketing department.

[0043] If an employee of the Finance Department uses target user terminal C to initiate a collaboration request, the scope of operation permission does not include document content editing permissions due to its low collaboration intensity level. The cloud office platform will enable delayed synchronization mode and synchronize the read-only version of the target document to target user terminal C at a preset time interval (for example, every hour). In this way, employees of the Finance Department can only view the document content and cannot modify it. During the data synchronization process, the cloud office platform will record the operation logs of the target user terminal, such as the editing operation records of marketing department employees on target user terminal A or the viewing operation records of finance department employees on target user terminal C, and then merge these operation logs into the document operation record collection to update the collaboration mode features and provide new data basis for subsequent collaboration analysis and permission adjustment.

[0044] Based on the above steps, by obtaining the document operation record set of the target document on the cloud office platform in the embodiments of the present application, it is possible to comprehensively and accurately capture the operation conditions of the document by multiple user terminals, including information such as operation types and timestamps, making the subsequent recognition of collaboration mode features more accurate and detailed, and laying a solid foundation for accurate collaboration demand prediction. Compared with traditional document collaboration methods, it is no longer limited to simple operation record statistics, but deeply explores the pattern features behind the operations.

[0045] The collaboration mode features identified based on the operation event sequence cover multi-dimensional information such as operation overlap periods, operation type correlations, and operation frequency distributions, which helps to more deeply understand the behavior patterns of users in document collaboration, overcomes the limitations of previous methods that can only evaluate collaboration situations from a single dimension or a simple comprehensive dimension, and thus can more accurately adapt to complex and changeable cloud office document collaboration scenarios.

[0046] By using a pre-trained collaboration demand prediction model to analyze the collaboration mode features to generate collaboration demand prediction results, the predicted collaboration object set and collaboration intensity index provide forward-looking guidance for cloud office document collaboration, and can reasonably predict subsequent collaboration demands in advance according to the user's previous operation patterns, which is a huge breakthrough in improving document collaboration efficiency. Traditional document collaboration methods often can only passively respond to immediate operation requests and lack this effective prediction ability for future collaboration demands, which easily leads to problems such as unreasonable permission allocation and untimely data synchronization.

[0047] Generate a permission hierarchy structure according to the collaboration intensity index, and the operation permission range is positively correlated with the collaboration intensity index, so that permissions can be flexibly allocated according to the degree of the user's operation requirements for the document, effectively avoiding the situations of excessive permission allocation or insufficient permissions. While ensuring document security, it maximally improves the operation flexibility of each user terminal in document collaboration, which is an advantage that cannot be compared with traditional fixed permission allocation methods.

[0048] Finally, when responding to the collaboration request of the target user terminal, control the data synchronization process based on the permission hierarchy structure. This precise data synchronization control method based on dynamic permissions ensures that each user terminal efficiently conducts data interaction within its permission range, reduces unnecessary data transmission and permission conflicts, improves the stability and efficiency of the entire cloud office document collaboration system, and further enhances the overall performance of cloud office document collaboration.

[0049] In a possible implementation manner, step S120 includes:

[0050] Step S121: Extract the operation type sequence and the corresponding time interval sequence in the operation event sequence. The time interval sequence represents the time difference between adjacent operation events.

[0051] In this embodiment, in the scenario of the project planning document of an enterprise, for the user terminal A used by the marketing department, its operation event sequence is "document viewing" at 9:00 am and "document editing - market research part" at 10:00 am. Then the operation type sequence is "document viewing, document editing - market research part", and the time interval sequence is 1 hour from 9:00 am to 10:00 am. The operation event sequence of the user terminal B of the R & D department is "document viewing" at 10:30 am and "document editing - technical solution part" at 11:00 am. The operation type sequence is "document viewing, document editing - technical solution part", and the time interval sequence is 0.5 hour from 10:30 am to 11:00 am. The operation event sequence of the user terminal C of the finance department is "document viewing - budget part" at 1:00 pm and "document editing - budget part" at 2:00 pm. The operation type sequence is "document viewing - budget part, document editing - budget part", and the time interval sequence is 1 hour. The extraction of these operation type sequences and time interval sequences provides basic data for subsequent analysis.

[0052] Step S122: Determine the operation overlap period among the multiple user terminals according to the distribution density of different types of operation events in the operation type sequence in the time dimension. The operation overlap period is a time interval during which at least two user terminals perform the same or related operation types on the target document within a preset time window.

[0053] For example, during the period from 10:00 am to 10:30 am in the operation process of this project planning document, the user terminal A is performing the editing of the market research part, and the user terminal B starts the document viewing operation. During this period, the user terminals of different departments interact with the target document. Although the operation types are not exactly the same, they are all in the operations related to document processing. Therefore, this is an operation overlap period. Another example is from 1:00 pm to 2:00 pm. The finance department is viewing and editing the budget part. If the marketing department also views the whole document again at this time to check the correlation between the market research part and the budget, this is also an operation overlap period. These operation overlap periods reflect the collaborative attention of different departments to the document within the same time period and embody the potential collaboration relationship among departments.

[0054] Step S123: Analyze the context dependence relationship between different operation types in the operation type sequence to generate the operation type relevance. The operation type relevance is used to identify the probability that the first operation type triggers the second operation type, where the first operation type and the second operation type are executed by different user terminals.

[0055] For example, through statistics on a large number of sequences of operation events, it is found that after the marketing department completes the editing of the market research part (user terminal A), the editing of the technical solution by the R & D department (user terminal B) often adjusts accordingly. Through detailed data analysis, it is obtained that when the editing of the market research part is completed, within the next 2 hours, the probability of the adjustment of the technical solution editing is 60%. This establishes the operational type correlation between the editing operation types of the marketing department and the R & D department. Similarly, when the finance department completes the editing of the budget part, there is a 30% probability that the marketing department will review the overall document within the next 1 hour to adjust the correlation between the market research part and the budget, which also reflects the correlation between the operation types of different departments. This operational type correlation helps to deeply understand the mutual influence between the work of each department, thereby better grasping the collaboration process in the project.

[0056] Step S124: Statistically analyze the operation frequency distribution corresponding to different time differences in the time interval sequence, and determine the active operation cycle and low - activity period of the target document based on the operation frequency distribution.

[0057] For example, after statistically analyzing the time intervals of each operation throughout the project cycle, it is found that from 9:00 am to 11:00 am every day, the operation frequency of each department on the document is relatively high. For example, operations such as the marketing department viewing and editing the market research part, the R & D department viewing the document to prepare for editing the technical solution part, and the finance department viewing budget - related content occur frequently. After 3:00 pm, the operation frequency significantly decreases, and the number of operations of each department on the document reduces. From 9:00 am to 11:00 am is the active operation cycle of the target document, and after 3:00 pm is the low - activity period. The determination of this active operation cycle and low - activity period helps to reasonably arrange resources and coordinate the working rhythms of each department.

[0058] Step S125: Combine the operation overlap period, operation type correlation, and operation frequency distribution into the collaboration mode characteristics.

[0059] For example, in the collaboration mode characteristics of this project planning document, the operation overlap period reflects the collaborative operation of different departments on the document within a specific time period, the operation type correlation reflects the causal or correlative relationship between the operation types of each department, and the operation frequency distribution clarifies the active and low - activity periods of document operations. These elements combined comprehensively describe the collaboration mode characteristics of the project planning document. For example, the operation overlap period shows the overlapping operations of the marketing department and the R & D department from 10:00 am to 10:30 am, the operation type correlation indicates the probability of the R & D department's adjustment after the marketing department's editing, and the operation frequency distribution determines that from 9:00 am to 11:00 am is the active operation cycle. These combined together completely depict the working mode and mutual relationship of each department in the collaboration of the project planning document, providing an important basis for subsequent operations such as collaboration requirement prediction and permission configuration.

[0060] In a possible implementation, step S130 includes:

[0061] Step S131, input the collaboration mode feature into the feature encoding layer of the collaboration demand prediction model to generate a multi-dimensional feature vector, where the multi-dimensional feature vector includes the duration encoding of the operation overlap period, the probability encoding of the operation type relevance, and the period encoding of the operation frequency distribution.

[0062] For the duration encoding of the operation overlap period, for example, the operation overlap period of the marketing department and the R & D department from 10:00 to 10:30 in the morning determined previously will be encoded as a specific value, which reflects information such as the duration of the overlap period and its relative position in the entire project cycle. The probability encoding of the operation type relevance, such as the probability that the R & D department edits the technical solution part within 2 hours after the marketing department edits the market research part is 60%, this probability will be encoded in a form suitable for model processing. The period encoding of the operation frequency distribution, like the active operation period from 9:00 to 11:00 in the morning, will be encoded into a value containing period characteristics according to factors such as the operation frequency and operation type distribution within this period. These encodings constitute the multi-dimensional feature vector.

[0063] Step S132, perform time-dependence modeling on the multi-dimensional feature vector through the time series analysis layer of the collaboration demand prediction model, and output time series enhanced features, where the time series enhanced features include the types of collaboration events that may be triggered by the target document within a preset future time period and the corresponding trigger time intervals.

[0064] In this embodiment, the time series analysis layer will consider factors such as the order of operations, time intervals, and periodicity. For example, based on the information in the multi-dimensional feature vector, the model may output that within the next week, when the marketing department completes the update of the new market research results from 9:00 to 10:00 in the morning, there is an 80% probability that the R & D department will adjust the technical solution within the next 2 hours, and the finance department may re-evaluate the budget from 1:00 to 2:00 in the afternoon of the same day. This information about the types of collaboration events that may be triggered by the target document within a preset future time period and the corresponding trigger time intervals is the time series enhanced feature.

[0065] Step S133, use the prediction layer of the collaboration demand prediction model to perform pattern matching on the time series enhanced features, determine the historical collaboration contribution degree and the predicted collaboration contribution degree of each user terminal in the collaboration object set, where the historical collaboration contribution degree is calculated based on the operation type weight and operation frequency of the user terminal in the historical operation event sequence, and the predicted collaboration contribution degree is calculated based on the types of collaboration events associated with the user terminal and the trigger time intervals in the time series enhanced features.

[0066] For example, for the Marketing Department, its historical collaboration contribution is calculated based on the operation type weights and operation frequencies in the historical operation event sequence. For example, the editing operation weight of the market research part is relatively high, and the Marketing Department frequently performs editing operations on the market research part in the early stage of the project. Based on these operation type weights and operation frequencies, the historical collaboration contribution of the Marketing Department is calculated. The predicted collaboration contribution is calculated based on the collaboration event types associated with the Marketing Department and the trigger time intervals in the time series enhanced features. As mentioned before, after the Marketing Department updates the market research results from 9:00 to 10:00 am in the future, it may trigger relevant operations of the R & D Department and the Finance Department. Based on these associated operations and the trigger time intervals, the predicted collaboration contribution of the Marketing Department is calculated.

[0067] Step S134, generate the collaboration intensity index according to the weighted sum of the historical collaboration contribution and the predicted collaboration contribution, and sort the collaboration object set from high to low according to the collaboration intensity index to generate the collaboration demand prediction result.

[0068] For example, the historical collaboration contribution of the Marketing Department is 0.6, the predicted collaboration contribution is 0.4, and the weighted sum is 0.5 (assuming the same weight). This 0.5 is the collaboration intensity index of the Marketing Department. After calculating the collaboration intensity index of each user terminal (such as the R & D Department, the Finance Department, etc.) in this way, sort the collaboration object set from high to low according to the collaboration intensity index to generate the collaboration demand prediction result. This collaboration demand prediction result can provide an important basis for subsequent operations such as permission configuration and resource allocation, helping to manage the collaboration process of project planning documents more efficiently and improve the collaboration efficiency and quality of the entire project.

[0069] Among them, the method further includes:

[0070] Step S101, obtain the historical collaboration data of multiple historical documents in the cloud office platform, where the historical collaboration data includes the final collaboration object set of each historical document and the corresponding actual collaboration intensity index.

[0071] For example, in this enterprise, there were multiple project documents before, such as the past product promotion project documents, technology R & D project documents, etc. Each historical document has its final set of collaborative objects. For example, in the product promotion project document, the marketing department, sales department, and publicity department are the final set of collaborative objects; in the technology R & D project document, the R & D department and testing department are the final set of collaborative objects. And each historical document has a corresponding actual collaboration intensity index. For the product promotion project document, the actual collaboration intensity index of the marketing department is relatively high during the promotion stage because they frequently conduct market research, formulate promotion strategies, etc. The actual collaboration intensity index of the sales department will increase during the sales stage, and the actual collaboration intensity index of the publicity department reaches its peak during the execution of publicity activities. For the technology R & D project document, the actual collaboration intensity index of the R & D department is the highest during the development stage, and the actual collaboration intensity index of the testing department is relatively high during the testing stage.

[0072] Step S102: Extract the historical operation records of each historical document in the historical collaboration data, and generate a historical collaboration pattern feature set for training based on the historical operation records.

[0073] Taking the product promotion project document as an example, the historical operation records of the marketing department include operations such as editing the market research report section and viewing the promotion channel analysis section at different times; the historical operation records of the sales department include operations such as editing the sales data prediction section and viewing the customer feedback section; the historical operation records of the publicity department include operations such as editing the publicity copy section and viewing the publicity effect evaluation section. From the perspective of the technology R & D project document, the historical operation records of the R & D department involve records of operations such as code writing and function testing, and the historical operation records of the testing department are mainly records of operations such as test case execution and defect reporting. Based on these historical operation records, a historical collaboration pattern feature set for training is generated. These historical collaboration pattern feature sets contain feature information such as operation overlap time periods, operation type correlations, and operation frequency distributions in each historical document.

[0074] Step S103: Construct an initial neural network model, where the initial neural network model includes a feature encoding layer, a time series analysis layer, and a prediction layer.

[0075] Each layer of the initial neural network model has different functions. The feature encoding layer is responsible for encoding the input collaboration pattern features and converting them into a form suitable for subsequent analysis; the time series analysis layer focuses on modeling the time dependence of the data and mining the patterns in the time dimension of the data; the prediction layer performs pattern matching and prediction operations based on the processing results of the previous two layers.

[0076] Step S104: Input the historical collaboration mode feature set into the initial neural network model, and iteratively optimize the model parameters of the initial neural network based on the final collaboration object set and the actual collaboration intensity index until the error between the predicted collaboration intensity index output by the initial neural network model and the actual collaboration intensity index is lower than a preset threshold, thereby generating the pre-trained collaboration demand prediction model.

[0077] For example, for a product promotion project document, after inputting its historical collaboration mode feature set into the model, the model parameters are adjusted according to the final collaboration object sets of the marketing department, the sales department, and the publicity department, as well as their respective actual collaboration intensity indices. If the initially predicted collaboration intensity index of the marketing department at a certain stage has a large difference from the actual value, the encoding method of the operation type weight of the marketing department in the feature encoding layer, the analysis method of the operation time series of the marketing department in the time series analysis layer, and the matching method of the relevant mode of the marketing department in the prediction layer will be adjusted. This process continues until the error between the predicted collaboration intensity index output by the initial neural network model and the actual collaboration intensity index is lower than the preset threshold. At this time, the pre-trained collaboration demand prediction model is generated. This pre-trained model has learned the rules and patterns of collaboration for different project documents from a large amount of historical data.

[0078] In a possible implementation manner, step S123 includes:

[0079] Step S1231: Extract a set of context windows of consecutive operations from the operation type sequence. Each context window contains the target operation type and a preset number of adjacent operation types before and after it, and the adjacent operation types are executed by the same or different user terminals within a preset time difference range.

[0080] Taking the operations of the Marketing Department, R & D Department, and Finance Department on the project planning document as an example, for the operation type sequence of the Marketing Department on user terminal A, such as "document viewing, document editing - market research section, document viewing - competitor analysis", assuming the preset quantity is 1 and the time difference range is 1 hour. Then a context window may be "document viewing - document editing - market research section", where "document editing - market research section" is the target operation type and "document viewing" is its adjacent operation type before; another possible context window is "document editing - market research section - document viewing - competitor analysis", where "document editing - market research section" is the target operation type and "document viewing - competitor analysis" is its adjacent operation type after. For the operation type sequence of the R & D Department on user terminal B, "document viewing, document editing - technical solution section, document viewing - technical risk assessment", the context window is extracted according to the preset rules in the same way. The operation type sequence of the Finance Department on user terminal C, "document viewing - budget section, document editing - budget section, document viewing - cost analysis", is also extracted similarly, and thus a set of context windows containing the operations of each department is obtained.

[0081] Step S1232, generate an operation co-occurrence matrix based on the co-occurrence frequencies of the target operation type and the adjacent operation types in the context window set. The rows of the operation co-occurrence matrix represent the first operation types executed by the first user terminal, the columns represent the second operation types executed by the second user terminal, and the matrix element values represent the co-occurrence frequency distribution of the first operation type and the second operation type in the context window set.

[0082] For example, the Marketing Department executes "document editing - market research section" (as the first operation type), and the R & D Department executes "document editing - technical solution section" (as the second operation type), and their co-occurrence frequencies are counted in numerous context windows. If in 100 context windows, these two operation types appear in their respective operation sequences simultaneously 30 times, then the corresponding matrix element value (i.e., the co-occurrence frequency distribution) in the operation co-occurrence matrix is 30. Assuming the operation types of the Marketing Department are rows and those of the R & D Department are columns, the matrix elements are statistically counted and filled in sequence to construct a complete operation co-occurrence matrix. This operation co-occurrence matrix comprehensively reflects the co-occurrence situation of different operation types of different departments in the context window set.

[0083] Step S1233: Identify the conditional trigger relationship across user terminals according to the difference in the co-occurrence frequency distribution between the row direction and the column direction of the operation co-occurrence matrix. The conditional trigger relationship represents the magnitude of the probability change that the second user terminal will execute the second operation type within a subsequent preset time period when the first user terminal executes the first operation type.

[0084] For example, in the operation co-occurrence matrix, there is a difference in the co-occurrence frequency distribution between the row where the operation type "Document Editing - Market Research Section" is executed by the Marketing Department and the column where the operation type "Document Editing - Technical Solution Section" is executed by the R & D Department. Through detailed analysis, it is found that when the Marketing Department completes "Document Editing - Market Research Section", the frequency of the R & D Department executing "Document Editing - Technical Solution Section" changes significantly within the subsequent 2 hours. Specifically, before the Marketing Department performs "Document Editing - Market Research Section", the frequency of the R & D Department executing "Document Editing - Technical Solution Section" is relatively low, while within 2 hours after the Marketing Department completes this operation, the frequency of the R & D Department executing "Document Editing - Technical Solution Section" increases significantly. This change in frequency is an embodiment of the conditional trigger relationship across user terminals, which represents an influence relationship of the operation of the Marketing Department on the operation of the R & D Department.

[0085] Step S1234: Mark the operation type combinations with a probability change magnitude exceeding the preset change magnitude in the conditional trigger relationship as strongly associated operation pairs, and calculate the time sensitivity coefficient of the strongly associated operation pairs in the context window set. The time sensitivity coefficient is used to characterize the time decay characteristic of the second operation type after the execution of the first operation type.

[0086] Assume the preset change magnitude is 30%. If the probability change magnitude of the R & D Department executing "Document Editing - Technical Solution Section" after the Marketing Department executes "Document Editing - Market Research Section" reaches 40%, then this combination of the two operation types is marked as a strongly associated operation pair. For the operation type combinations marked as strongly associated operation pairs, calculate their time sensitivity coefficients in the context window set. For example, after the Marketing Department executes "Document Editing - Market Research Section", the time interval distribution of the R & D Department executing "Document Editing - Technical Solution Section" is as follows: the number of executions within 1 hour accounts for 60%, the number of executions within 2 hours accounts for 30%, and the number of executions within 3 hours accounts for 10%. As time goes by, the proportion of the R & D Department executing the corresponding operation gradually decreases, and this time decay characteristic is characterized by the time sensitivity coefficient. The calculated time sensitivity coefficient reflects the time dependence relationship of the R & D Department executing "Document Editing - Technical Solution Section" after the Marketing Department executes "Document Editing - Market Research Section".

[0087] Step S1235: Generate the operation type correlation based on the probability change amplitude and time sensitivity coefficient of the strongly correlated operation pair. The operation type correlation includes the conditional probability and effective trigger time interval for the first operation type to trigger the second operation type, where the conditional probability is positively correlated with the probability change amplitude, and the effective trigger time interval is negatively correlated with the time sensitivity coefficient.

[0088] Taking the strongly correlated operation pair of the marketing department and the R & D department as an example, the probability change amplitude is 40%. According to the positive correlation between the conditional probability and the probability change amplitude, it is obtained that the conditional probability of the first operation type (\"document editing - market research part\" of the marketing department) triggering the second operation type (\"document editing - technical solution part\" of the R & D department) is relatively high. At the same time, since the time sensitivity coefficient reflects the time decay characteristic of the R & D department after the operation of the marketing department, according to the negative correlation between the effective trigger time interval and the time sensitivity coefficient, it is obtained that the effective trigger time interval is relatively short, for example, it may be 1.5 hours. In this way, the operation type correlation includes the conditional probability of the first operation type triggering the second operation type and the effective trigger time interval. This operation type correlation accurately depicts the dependence relationship and trigger rule between different department operation types, providing an important basis for deeply understanding the collaboration mode of the project planning document. Through similar analysis of all relevant operation type combinations, the operation type correlation in the entire project planning document operation can be comprehensively constructed, thus providing strong support for subsequent collaboration requirement prediction, permission configuration and other operations.

[0089] In a possible implementation manner, step S140 includes:

[0090] Step S141: Divide the user terminals in the collaboration object set into at least three permission levels according to the collaboration intensity index. The at least three permission levels include a high collaboration intensity level, a medium collaboration intensity level, and a low collaboration intensity level.

[0091] For example, in the collaboration process of this project planning document, for example, due to the frequent operations of the marketing department on market research, competitive analysis and other contents in the early stage of the project and the continuous update of the document content according to market changes in the later stage, its collaboration intensity index is relatively high and it is divided into the high collaboration intensity level. The operations of the R & D department in the project mainly focus on the formulation and adjustment of technical solutions, and the operation frequency is slightly lower than that of the marketing department. The collaboration intensity index is at a medium level and it is divided into the medium collaboration intensity level. The finance department mainly operates on budget and cost related contents at specific stages, and the operation frequency and the impact on the overall document are relatively small. The collaboration intensity index is relatively low and it is divided into the low collaboration intensity level.

[0092] Step S142, configure a first operation permission range for the high collaboration intensity level, where the first operation permission range includes document content editing permission, version rollback permission, and collaboration member invitation permission.

[0093] For example, for the Marketing Department at the high collaboration intensity level, the first operation permission range includes document content editing permission, which enables the Marketing Department to comprehensively edit the market research section, competitor analysis section, and other parts related to market strategies in the project planning document. For example, the Marketing Department can modify the market size estimate, market trend analysis, etc. in the document based on the latest market research data. Version rollback permission is also included, which is very useful when the Marketing Department discovers errors in previous edits or needs to refer to the content of previous versions. For instance, if the Marketing Department finds a conflict with the previous market research data after modifying the market strategy section, it can use the version rollback permission to return to the previous version and re-edit. Collaboration member invitation permission is also within this range. If the Marketing Department feels that it needs other departments (such as the Public Relations Department) to participate in the discussion of the market promotion strategy or the collaboration of document content, it can invite them to join.

[0094] Step S143, configure a second operation permission range for the medium collaboration intensity level, where the second operation permission range includes document content editing permission and annotation addition permission, and restricts the usage frequency of the version rollback permission.

[0095] For example, the R & D Department is at the medium collaboration intensity level. Its second operation permission range includes document content editing permission, but mainly for editing the technical solution part. For example, the R & D Department can edit the technical architecture, technology selection, and other technical solution-related content in the document according to the development of technology or project requirements. At the same time, the R & D Department is also given the annotation addition permission, which helps the R & D Department add annotations to explain the relationship between the technical solution and market requirements or put forward technical suggestions when viewing other parts of the document (such as the market research section). However, the usage frequency of the version rollback permission is restricted. Suppose it is stipulated that the version rollback operation can only be used once a month. This is because the operations of the R & D Department are relatively stable and do not need to be adjusted frequently according to external changes like the Marketing Department. Restricting the usage frequency of the version rollback permission can ensure the relative stability of the document version.

[0096] Step S144, configure a third operation permission range for the low collaboration intensity level, where the third operation permission range only includes document content viewing permission and annotation viewing permission.

[0097] For example, the Finance Department is at a low collaboration intensity level, and its third operation permission range only includes the permission to view document content and the permission to view annotations. This means that the Finance Department can only view the budget section, cost analysis section in the document, and the annotation content added by other departments. For example, the Finance Department can view the annotations on the marketing budget added by the Marketing Department in the market research section, or the annotations on the R & D cost estimate added by the R & D Department in the technical solution section, but cannot edit the document content or add new annotations.

[0098] Step S145, generate the permission hierarchy structure according to the mapping relationship between the permission level and the operation permission range, and monitor the change of the collaboration intensity index in real time to dynamically adjust the permission level allocation in the permission hierarchy structure.

[0099] In this embodiment, this permission hierarchy structure clearly defines the operation permission range of user terminals at different collaboration intensity levels in the project planning document. During the project execution, the change of the collaboration intensity index will be monitored in real time to dynamically adjust the permission level allocation in the permission hierarchy structure. For example, as the project progresses, after the Marketing Department completes the market research and strategy formulation, the operation requirements for the document gradually decrease, and the collaboration intensity index begins to decline. While during the refinement and optimization of the technical solution, the R & D Department needs to refer more to the research results of the Marketing Department and frequently adjust the technical solution, and the collaboration intensity index gradually rises.

[0100] Among them, the method further includes:

[0101] Step S210, when it is detected that the collaboration mode feature of the target document is updated, re - execute the analysis process of the collaboration demand prediction model to generate an updated collaboration demand prediction result.

[0102] For example, in the middle of the project, the Marketing Department conducts another large - scale market research, and the research results have a significant impact on many aspects of the project's technical solution, budget, etc., which leads to the update of the collaboration mode feature. At this time, it is necessary to re - execute the analysis process of the collaboration demand prediction model to generate an updated collaboration demand prediction result. By re - analyzing the changes in factors such as operation overlap time periods, operation type correlations, and operation frequency distributions in the collaboration mode features, the collaboration demand prediction model will recalculate the collaboration intensity index of each user terminal.

[0103] Step S220, compare the differences in the collaboration intensity index before and after the update. If the change in the collaboration intensity index of a user terminal exceeds the preset range, trigger the dynamic adjustment of the permission hierarchy structure.

[0104] Assume that the preset amplitude is 30%. If the collaboration intensity index of the R & D department has increased by 40% from the previous medium level and exceeded the preset amplitude. According to the updated collaboration intensity index, the permission level is reclassified, and the permission level of the R & D department may be upgraded from the medium collaboration intensity level to the high collaboration intensity level. Then, a permission change notice is sent to the affected user terminal (here it is the R & D department), and the notice includes the changed operation permission scope (such as now having the document content editing permission, version rollback permission, and collaboration member invitation permission) and the effective time (for example, it will take effect on the next working day after the notice is issued). This mechanism for dynamically adjusting the permission hierarchy structure can ensure that each user terminal has appropriate operation permissions according to its actual collaboration needs at different stages of the project, thereby improving the collaboration efficiency of the project and the effectiveness of document management.

[0105] Step S230, reclassify the permission level according to the updated collaboration intensity index, and send a permission change notice to the affected user terminal. The permission change notice includes the changed operation permission scope and the effective time.

[0106] Throughout the life cycle of the project, this dynamic permission configuration and adjustment of the permission hierarchy structure will be continuously optimized according to the actual progress of the project and the changes in the roles of each department in document collaboration to adapt to the complexity and dynamics of the project, ensure the smooth progress of the collaboration of the project planning documents, and at the same time ensure the security and integrity of the documents, avoiding unnecessary operation risks and abuse of permissions.

[0107] In a possible implementation manner, step S150 includes:

[0108] Step S151, determine the synchronizable data content and synchronization frequency of the target document according to the operation permission scope currently held by the target user terminal.

[0109] Step S152, if the operation permission scope includes the document content editing permission, enable the real-time synchronization mode, upload the editing operations of the target user terminal on the target document to the cloud office platform in real time, and trigger the instant content update of other online user terminals.

[0110] Step S153, if the operation permission scope does not include the document content editing permission, enable the delayed synchronization mode, synchronize the read-only version of the target document to the target user terminal at a preset time interval, and restrict its modification operations on the document content.

[0111] Step S154, during the data synchronization process, record the operation log of the target user terminal, and merge the operation log into the document operation record set to update the collaboration mode characteristics.

[0112] In this embodiment, for example, the target user terminal A used by the marketing department, due to its high collaboration intensity level, the operation permission scope includes the document content editing permission. For such a situation, the cloud office platform will determine that all the content of the target document is synchronizable data content because the marketing department has the right to edit any part of the document. The synchronization frequency is real-time update in the real-time synchronization mode because the marketing department plays an important role in the collaboration of the project planning document, and its editing operations need to be promptly reflected on the cloud office platform and known to other collaborating personnel. When an employee of the marketing department performs an editing operation on the target document on the target user terminal A, such as modifying a certain data in the market research section or updating the description of the market strategy, the cloud office platform will immediately obtain these editing operations and upload them in real time. At the same time, due to the adoption of the real-time synchronization mode, other online user terminals, such as the user terminal B of the R & D department and the user terminal C of the finance department, will immediately receive the content update notification and see the new editing content of the marketing department. This can ensure that each department can promptly adjust its work content or provide feedback based on the latest information of the marketing department.

[0113] For the target user terminal C used by the finance department, due to its low collaboration intensity level, the operation permission scope does not include the document content editing permission. The cloud office platform will determine that the synchronizable data content of the target document is the read-only version, that is, all the document content except the editing operations. The synchronization frequency is carried out at a preset time interval, for example, once an hour. This means that every hour, the cloud office platform will synchronize the read-only version of the target document to the target user terminal C. And, due to the limitation of its operation permission scope, the employees of the finance department cannot modify the document content on the target user terminal C and can only view the document content. In this mode, the finance department can promptly obtain the update information of the document by other departments without interfering with the stability of the document.

[0114] During the data synchronization process, whether it is the real-time synchronization of the marketing department or the delayed synchronization of the finance department, the cloud office platform will record the operation logs of the target user terminal. For the target user terminal A of the marketing department, the operation logs will detail each editing operation, including the editing time, the specific content of the editing (such as modifying a certain data value in the market research section or adding new market trend analysis content), and the operator information, etc. For the target user terminal C of the finance department, the operation logs will record the time of viewing the document, the part of the document viewed, and other information. Then, the cloud office platform will merge these operation logs into the document operation record set to update the collaboration mode characteristics. For example, the frequent editing operations of the marketing department may affect the operation frequency distribution and further increase its operation frequency within a certain time period; the viewing operations of the finance department will also become a part of the collaboration mode characteristics, reflecting the interaction of different departments with the document under different permissions.

[0115] In a possible implementation, the method further includes:

[0116] Step S310, when it is detected that multiple user terminals simultaneously perform conflicting operations on the target document, perform conflict arbitration based on the permission levels of each user terminal in the permission hierarchy structure.

[0117] Step S320, preferentially retain the operation results of user terminals with a high collaboration intensity level, and mark the conflicting operations as pending events.

[0118] Step S330, send a conflict notification to all user terminals involved in the conflicting operations, where the conflict notification includes the type of conflicting operation, the conflicting content fragment, and a recommended solution.

[0119] Step S340, in response to receiving a conflict resolution instruction submitted by any user terminal, adjust the content of the target document according to the instruction, and update the document operation record set and the collaboration mode characteristics.

[0120] During the progress of a project, there may be a situation where multiple user terminals simultaneously perform conflicting operations on a target document. For example, an employee in the marketing department operates on a certain part of the project planning document on target user terminal A, and an employee in the R & D department operates on the same part on target user terminal B almost simultaneously. Suppose the marketing department is modifying the market size estimation data in the market research section, while the R & D department is simultaneously adjusting the technical requirement assessment of this market size according to the technical solution, and these two operations conflict.

[0121] At this time, conflict arbitration is performed based on the permission levels of each user terminal in the permission hierarchy structure. Since the marketing department is at a high collaboration intensity level and the R & D department is at a medium collaboration intensity level, according to the rule, the operation results of user terminals with a high collaboration intensity level (i.e., the marketing department) are preferentially retained. The modification operation of the market size estimation data by the marketing department is regarded as a valid operation, while the operation of the R & D department is marked as a pending event.

[0122] Then, the cloud office platform sends a conflict notification to all user terminals involved in the conflicting operations. This conflict notification includes the type of conflicting operation (such as an edit operation on market size - related content), the conflicting content fragment (such as the specific market size estimation value in the market research section and the related technical requirement assessment section), and a recommended solution. For example, the recommended solution may be that the R & D department re - adjusts the technical requirement assessment section according to the market size estimation value modified by the marketing department, or that the marketing department and the R & D department negotiate to jointly determine a market size estimation value and technical requirement assessment content that both meet market needs and conform to the technical solution.

[0123] When the cloud office platform receives a conflict resolution instruction submitted by any user terminal, it adjusts the content of the target document according to the instruction. If the R & D department re-adjusts the content of the technical requirement assessment part according to the modification of the marketing department, the cloud office platform will correspondingly update the relevant content in the target document. Moreover, in this process, the document operation record set and the collaboration mode characteristics will be updated. The document operation record set will add the record of this conflict operation, including information such as the time when the conflict occurred, the user terminals involved, and the process of conflict resolution. The collaboration mode characteristics will also be updated according to these new operation records. For example, it may affect the relevance of operation types, because this conflict operation and the resolution process may reveal new association relationships between the operations of the marketing department and the R & D department; the operation frequency distribution may also change, especially the operation frequency during the time period involving the conflict operation will be adjusted. This mechanism for handling conflict operations can ensure the accuracy and consistency of the document in the case of multi-department collaboration, and at the same time can reasonably coordinate the working relationships between different departments to ensure the smooth progress of the project.

[0124] In a possible implementation manner, the method further includes:

[0125] Step S410, generating a collaboration relationship graph based on the collaboration requirement prediction result, where the collaboration relationship graph includes collaboration links, collaboration frequencies, and collaboration content relevance among user terminals in the collaboration object set.

[0126] In this embodiment, during the collaboration process of this project planning document, the collaboration requirement prediction result includes information such as the predicted collaboration object set and the collaboration intensity index. According to this information, a collaboration relationship graph is constructed, which covers the collaboration links, collaboration frequencies, and collaboration content relevance among user terminals in the collaboration object set. For example, the marketing department, the R & D department, and the finance department are members of the collaboration object set, and there are different collaboration links among them. There is a collaboration link between the marketing department and the R & D department because the market research results of the marketing department will affect the formulation of the technical solution by the R & D department, and the technical solution of the R & D department will also give feedback on the adjustment of the marketing strategy of the marketing department. There is also a collaboration link between the marketing department and the finance department. The market promotion plan formulated by the marketing department involves budget and cost issues and needs to collaborate with the finance department to determine, and the budget adjustment of the finance department will also affect the promotion strategy of the marketing department. There is also a collaboration link between the R & D department and the finance department. The cost budget and fund allocation of the R & D project require the two to work together.

[0127] In terms of collaboration frequency, it can be determined by analyzing data such as the set of document operation records. For example, there may be multiple interactions per week between the Marketing Department and the R & D Department. Such interactions may be that after the Marketing Department provides new market research data, the R & D Department adjusts the technical solution accordingly, or after the R & D Department gives a technical feasibility assessment, the Marketing Department adjusts its market strategy. Therefore, the collaboration frequency between them is relatively high. While there may be only one or two interactions per month between the Finance Department and the R & D Department, mainly concentrated on the adjustment of project budgets and the calculation of technical costs, and the collaboration frequency is relatively low.

[0128] The relevance of collaboration content is reflected in the specific operation types. There is a relevance between the editing operations of the market research part of the Marketing Department and the editing operations of the technical solutions of the R & D Department, because information such as market demand and competitive situation in market research is one of the bases for the R & D Department to formulate technical solutions. There is a relevance between the market promotion budget part of the Marketing Department and the budget editing operations of the Finance Department, and the technical cost estimation part of the R & D Department is also related to the budget operations of the Finance Department. These collaboration links, collaboration frequencies, and the relevance of collaboration content together constitute a collaboration relationship graph, which comprehensively shows the mutual relationships among various departments in the collaboration of project planning documents.

[0129] Step S420, analyze the high-frequency collaboration links and key user nodes in the collaboration relationship graph, and generate collaboration optimization suggestions. The collaboration optimization suggestions include increasing the permission level of a specified user terminal, splitting overloaded collaboration links, or merging low-frequency collaboration links. Among them, the high-frequency collaboration links are collaboration links with a collaboration frequency greater than a first set frequency, the low-frequency collaboration links are collaboration links with a collaboration frequency less than a second set frequency, and the first set frequency is greater than the second set frequency.

[0130] Suppose the first set frequency is set to 5 times per week, and collaboration links with a frequency higher than this are regarded as high-frequency collaboration links. The collaboration link between the Marketing Department and the R & D Department may be a high-frequency collaboration link because the number of interactions per week exceeds 5 times. Key user nodes are departments with important influence in the collaboration relationship graph. For example, the Marketing Department and the R & D Department play key leading and supporting roles throughout the project, and they are key user nodes.

[0131] For the analysis of high-frequency collaboration links and key user nodes, some potential optimization points can be found. For example, due to the very frequent and important collaboration between the Marketing Department and the R & D Department, there may be situations of information overload or low collaboration efficiency. At this time, the collaboration optimization suggestions may include increasing the permission level of the designated user terminal. If the R & D Department needs to obtain the latest research information from the Marketing Department more timely during the formulation of the technical solution and needs to adjust the relevant document content more autonomously, then the permission level of the R & D Department can be considered to be increased. For example, more document content editing permissions can be given or the restrictions on the version rollback permissions can be relaxed so that the R & D Department can better collaborate with the Marketing Department.

[0132] For overloaded collaboration links, such as the frequent information interaction between the Marketing Department and the R & D Department resulting in overly frequent document updates, it may affect the need of other departments to obtain stable information. The collaboration optimization suggestion at this time may be to split the overloaded collaboration link. For example, the collaboration between the Marketing Department and the R & D Department can be split according to different project stages or content modules, and the collaboration can be carried out separately at specific stages or for specific content modules, reducing unnecessary real-time interactions and improving the overall collaboration efficiency.

[0133] For low-frequency collaboration links, assuming that the second set frequency is set to once a month, collaboration links with a frequency lower than this are regarded as low-frequency collaboration links. For example, the collaboration link between the Finance Department and the R & D Department may be a low-frequency collaboration link. In response to this situation, merging low-frequency collaboration links can be considered. For example, the sporadic interactions between the Finance Department and the R & D Department regarding budgets and costs can be merged into a fixed time period or process to reduce unnecessary communication costs and resource waste.

[0134] Step S430, push the collaboration optimization suggestion to the administrator terminal of the cloud office platform, and execute the corresponding collaboration link adjustment operation in response to the administrator's confirmation instruction.

[0135] In this embodiment, after receiving these collaboration optimization suggestions, the administrator can conduct a macro control and adjustment of the collaboration process and permission settings of the project. For example, after seeing the suggestion of increasing the permission level of the R & D Department, the administrator can evaluate the overall situation of the project and consider whether it is really necessary to give the R & D Department more permissions to improve the collaboration efficiency between the Marketing Department and the R & D Department. If the administrator confirms that the suggestion is reasonable, a confirmation instruction will be issued.

[0136] If the administrator confirms to increase the permission level of the R & D department, the cloud office platform will adjust the operation permission scope for the R & D department according to the regulations, such as adding document content editing permissions or relaxing the restrictions on version rollback permissions. If it is to split the collaboration link between the Marketing department and the R & D department, the cloud office platform will re-plan the collaboration process between the two at different stages or modules to ensure the orderly transmission of information and the stable update of documents. If it is to merge the low-frequency collaboration link between the Finance department and the R & D department, the cloud office platform will set up special processes and time periods to centrally handle the budget and cost-related matters between the two.

[0137] In a possible implementation manner, the method further includes:

[0138] Step S510, embed a collaboration visualization component in the user interface of the cloud office platform, and the collaboration visualization component dynamically displays the collaboration demand prediction result, the permission hierarchy structure, and the real-time data synchronization status.

[0139] For example, for the collaboration demand prediction result, the visualization component can display the collaboration intensity indicators of each user terminal in the form of charts or graphs, intuitively showing the collaboration demand levels of the Marketing department, the R & D department, and the Finance department in the project. In terms of the permission hierarchy structure, the visualization component can represent different permission levels through different colors or icons. For example, the Marketing department with a high collaboration intensity level is marked in red, indicating that it has a wide range of operation permissions, including document content editing permissions, version rollback permissions, and collaboration member invitation permissions; the R & D department with a medium collaboration intensity level is marked in yellow, indicating that it has document content editing permissions (with some restrictions), annotation addition permissions, and limits the usage frequency of version rollback permissions; the Finance department with a low collaboration intensity level is marked in green, indicating that it only has document content viewing permissions and annotation viewing permissions.

[0140] The real-time data synchronization status can also be displayed in the visualization component. For example, when the Marketing department performs an editing operation on a document on user terminal A and synchronizes it to the cloud office platform in real time, the visualization component can display a dynamic arrow indicating that the data flows from the user terminal of the Marketing department to the cloud office platform, and real-time update prompt messages can be seen on other online user terminals (such as user terminal B of the R & D department and user terminal C of the Finance department).

[0141] Step S520, provide a trend analysis chart of collaboration intensity indicators, a permission level distribution chart, and an operation conflict hotspot chart through the collaboration visualization component.

[0142] Step S530, in response to a filtering operation triggered by the user through the collaboration visualization component, filter and display the user terminals with specified permission levels or collaboration intensity intervals and their operation records.

[0143] The collaborative visualization component can also provide a trend analysis chart of the collaboration intensity index. This trend analysis chart can show the changes in the collaboration intensity index of each user terminal at different stages of the project. For example, at the initial stage of the project, the collaboration intensity index of the marketing department gradually increases because a large amount of market research and strategy formulation work needs to be carried out; as the project progresses to the technology R & D stage, the collaboration intensity index of the R & D department begins to increase, while the collaboration intensity index of the marketing department may slightly decrease. The permission level distribution chart intuitively shows the permission level distribution of each user terminal, enabling all personnel participating in the collaboration to clearly understand their own and other departments' permission scopes. The operation conflict hotspot chart can show the parts or time periods where conflicts are likely to occur during the document operation process. For example, if the marketing department and the R & D department often have conflicts in the market research part and the technical solution related part, the operation conflict hotspot chart will mark these two parts or the relevant operation time periods to remind the relevant personnel to pay attention.

[0144] When the user triggers a filtering operation through the collaborative visualization component, it can filter and display the user terminals with specified permission levels or collaboration intensity ranges and their operation records. For example, if the administrator wants to view the operation records of user terminals (R & D department) with medium collaboration intensity levels, they can set the filtering condition to medium collaboration intensity level on the visualization component, and then the visualization component will only display the operation records of the R & D department, including the document editing operations, annotation addition operations, and interactions with other departments by the R & D department at different times. This filtering function helps users quickly obtain relevant information according to their own needs, improving the understanding and management efficiency of the project collaboration situation. Through these functions, the collaborative visualization component provides a comprehensive, intuitive, and convenient tool for the collaborative management of the project, helping to improve the collaborative efficiency and management level of the entire project.

[0145] Figure 2 The hardware structure diagram of the cloud office service system 100 provided by the embodiment of the present application for implementing the above-mentioned document collaboration method based on cloud office is shown, as Figure 2 shown, the cloud office service system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

[0146] In one possible design, the cloud office service system 100 can be a single server or a server group. The server group can be centralized or distributed (for example, the cloud office service system 100 can be a distributed system). In some embodiments, the cloud office service system 100 can be local or remote. For example, the cloud office service system 100 can access information and / or data stored in the machine-readable storage medium 120 via a network. As another example, the cloud office service system 100 can be directly connected to the machine-readable storage medium 120 to access the stored information and / or data. In some embodiments, the cloud office service system 100 can be implemented on a server. By way of example only, the server can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc. or any combination thereof.

[0147] The machine-readable storage medium 120 can store data and / or instructions. In some embodiments, the machine-readable storage medium 120 can store data obtained from an external terminal. In some embodiments, the machine-readable storage medium 120 can store the data and / or instructions that the cloud office service system 100 uses to execute or complete the exemplary methods described in this application.

[0148] In a specific implementation process, one or more processors 110 execute the computer-executable instructions stored in the machine-readable storage medium 120, so that the processors 110 can execute the document collaboration method based on cloud office in the above method embodiments. The processors 110, the machine-readable storage medium 120, and the communication unit 140 are connected through a bus 130, and the processors 110 can be used to control the sending and receiving actions of the communication unit 140.

[0149] For the specific implementation process of the processors 110, reference can be made to the respective method embodiments executed by the above cloud office service system 100. Their implementation principles and technical effects are similar, and will not be elaborated here in this embodiment.

[0150] In addition, an embodiment of this application also provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the document collaboration method based on cloud office as described above is implemented.

[0151] It should be noted that, in order to simplify the presentation of this application disclosure and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this application, sometimes multiple features are merged into one embodiment, drawing, or description thereof.

Claims

1. A document collaboration method based on cloud office, characterized in that: The method comprises: Acquire a document operation record set generated by a target document in a cloud office platform, wherein the document operation record set includes a sequence of operation events performed by multiple user terminals on the target document, wherein the sequence of operation events includes at least one operation type and a corresponding operation timestamp; Based on the operation event sequence of each user terminal in the document operation record set, identifying the collaborative mode characteristics of the target document, the collaborative mode characteristics including the operation overlap period between multiple user terminals, the operation type correlation and the operation frequency distribution; The collaborative mode characteristics are analyzed by a pre-trained collaborative demand prediction model to generate a collaborative demand prediction result corresponding to the target document, wherein the collaborative demand prediction result includes a predicted collaborative object set and a collaborative strength index, and the collaborative strength index is used to characterize the degree of operation demand of each user terminal in the collaborative object set for the target document in a subsequent collaborative stage; Dynamically configuring permissions for user terminals in the collaboration object set according to the collaboration strength index, generating a permission hierarchy structure that matches the collaboration demand prediction result, wherein the permission hierarchy structure includes at least one permission level and a corresponding operation permission range, and the operation permission range is positively correlated with the collaboration strength index; In response to detecting a collaboration request of a target user terminal for the target document, based on the operation permission scope corresponding to the target user terminal in the permission hierarchy structure, controlling a data synchronization process of the target document between the target user terminal and the cloud office platform; The identifying the collaborative mode feature of the target document based on the operation event sequence of each user terminal in the document operation record set includes: Extracting an operation type sequence and a corresponding time interval sequence from the operation event sequence, wherein the time interval sequence represents a time difference between adjacent operation events; Determining, according to the distribution density of different types of operation events in the operation type sequence in the time dimension, an operation overlap period between the multiple user terminals, wherein the operation overlap period is a time interval in which at least two user terminals perform the same or related operation type on the target document within a preset time window; Analyzing the context dependency between different operation types in the operation type sequence to generate the operation type relevance, where the operation type relevance is used to identify a probability that a first operation type triggers a second operation type, where the first operation type and the second operation type are executed by different user terminals; Counting the operation frequency distribution corresponding to different time differences in the time interval sequence, and determining the active operation period and the low activity period of the target document based on the operation frequency distribution; combining the operation overlapping time period, operation type relevance and operation frequency distribution into the collaboration mode feature; The step of analyzing the context dependency between different operation types in the operation type sequence to generate the operation type relevance includes: Extracting a context window set of continuous operations from the operation type sequence, each context window comprising a target operation type and a preset number of adjacent operation types before and after it, wherein the adjacent operation types are executed by the same or different user terminals within a preset time difference range; Based on the co-occurrence frequency of the target operation type and the adjacent operation type in the context window set, an operation co-occurrence matrix is ​​generated, wherein the rows of the operation co-occurrence matrix represent the first operation type performed by the first user terminal, the columns represent the second operation type performed by the second user terminal, and the matrix element values ​​represent the co-occurrence frequency distribution of the first operation type and the second operation type in the context window set; identifying, according to a difference in co-occurrence frequency distribution between row directions and column directions in the operation co-occurrence matrix, a conditional trigger relationship across user terminals, the conditional trigger relationship indicating a probability change amplitude of the second user terminal performing the second operation type within a subsequent preset time period when the first user terminal performs the first operation type; Marking a combination of operation types whose probability change amplitude in the conditional trigger relationship exceeds a preset change amplitude as a strongly associated operation pair, and calculating a time sensitivity coefficient of the strongly associated operation pair in the context window set, wherein the time sensitivity coefficient is used to characterize a time attenuation characteristic of the second operation type after the first operation type is executed; The operation type association is generated according to the probability change amplitude and time sensitivity coefficient of the strongly associated operation pair, and the operation type association includes the conditional probability and effective triggering time interval of the first operation type triggering the second operation type, wherein the conditional probability is positively correlated with the probability change amplitude, and the effective triggering time interval is negatively correlated with the time sensitivity coefficient.

2. The document collaboration method based on cloud office according to claim 1, characterized in that: The analyzing the collaboration mode characteristics by the pre-trained collaboration demand prediction model to generate a collaboration demand prediction result corresponding to the target document includes: Inputting the collaboration mode feature into the feature coding layer of the collaboration demand prediction model to generate a multidimensional feature vector, wherein the multidimensional feature vector includes the duration coding of the operation overlap period, the probability coding of the operation type association, and the period coding of the operation frequency distribution; The multidimensional feature vector is modeled for time dependency through the timing analysis layer of the collaboration demand prediction model, and a timing enhancement feature is output, wherein the timing enhancement feature includes the type of collaboration event that may be triggered by the target document within a preset time period in the future and the corresponding triggering time interval; Using the prediction layer of the collaboration demand prediction model to perform pattern matching on the timing enhancement feature, determine the historical collaboration contribution and predicted collaboration contribution of each user terminal in the collaboration object set, the historical collaboration contribution is calculated based on the operation type weight and operation frequency of the user terminal in the historical operation event sequence, and the predicted collaboration contribution is calculated based on the collaboration event type and triggering time interval associated with the user terminal in the timing enhancement feature; Generating the collaboration intensity index according to the weighted sum of the historical collaboration contribution and the predicted collaboration contribution, and sorting the collaboration object set from high to low according to the collaboration intensity index to generate the collaboration demand prediction result; Wherein, the method further comprises: Acquire historical collaboration data of multiple historical documents in the cloud office platform, wherein the historical collaboration data includes a final collaboration object set of each historical document and a corresponding actual collaboration intensity index; Extracting the historical operation record of each historical document in the historical collaboration data, and generating a historical collaboration pattern feature set for training based on the historical operation record; Constructing an initial neural network model, wherein the initial neural network model comprises a feature encoding layer, a time series analysis layer and a prediction layer; The historical collaboration pattern feature set is input into the initial neural network model, and the model parameters of the initial neural network are iteratively optimized based on the final collaboration object set and the actual collaboration intensity index, until the error between the predicted collaboration intensity index output by the initial neural network model and the actual collaboration intensity index is lower than a preset threshold, thereby generating the pre-trained collaboration demand prediction model.

3. The document collaboration method based on cloud office according to claim 1, characterized in that: The dynamically configuring the permissions of the user terminals in the collaboration object set according to the collaboration strength index to generate a permission hierarchy structure matching the collaboration demand prediction result includes: Dividing the user terminals in the collaboration object set into at least three authority levels according to the collaboration strength index, wherein the at least three authority levels include a high collaboration strength level, a medium collaboration strength level, and a low collaboration strength level; Configuring a first permission scope for the high collaboration intensity level, the first permission scope including document content editing authority, version rollback authority, and collaboration member invitation authority; Configuring a second permission range for the medium collaboration intensity level, wherein the second permission range includes document content editing authority and annotation adding authority, and limits the frequency of use of version rollback authority; Configuring a third permission range for the low collaboration intensity level, wherein the third permission range only includes document content viewing authority and annotation viewing authority; Generating the permission hierarchy structure according to the mapping relationship between the permission level and the operation permission scope, and monitoring the change of the collaboration strength indicator in real time to dynamically adjust the permission level allocation in the permission hierarchy structure; Wherein, the method further comprises: When it is detected that the collaboration mode feature of the target document is updated, the analysis process of the collaboration demand prediction model is re-executed to generate an updated collaboration demand prediction result; Comparing the difference between the collaboration strength indicators before and after the update, if the collaboration strength indicator of a user terminal changes by more than a preset range, triggering a dynamic adjustment of the permission hierarchy structure; The authority levels are reclassified according to the updated collaboration strength index, and an authority change notification is sent to the affected user terminals, wherein the authority change notification includes the changed operation permission scope and effective time.

4. The document collaboration method based on cloud office according to claim 1, characterized in that: The process of controlling data synchronization of the target document between the target user terminal and the cloud office platform includes: Determining the syncable data content and synchronization frequency of the target document according to the scope of the operation permission currently held by the target user terminal; If the operation permission scope includes document content editing authority, the real-time synchronization mode is enabled to upload the editing operation of the target user terminal on the target document to the cloud office platform in real time, and trigger instant content update of other online user terminals; If the operation permission range does not include the document content editing permission, the delayed synchronization mode is enabled to synchronize the read-only version of the target document to the target user terminal at a preset time interval, and restrict the modification operation of the document content; During the data synchronization process, the operation log of the target user terminal is recorded, and the operation log is merged into the document operation record set to update the collaboration mode feature.

5. The document collaboration method based on cloud office according to claim 4, characterized in that: The method further comprises: When it is detected that multiple user terminals simultaneously perform conflicting operations on the target document, conflict arbitration is performed based on the authority level of each user terminal in the authority hierarchy structure; Prioritize the retention of operation results of user terminals with high collaboration intensity levels, and mark conflicting operations as pending events; Sending a conflict notification to all user terminals involved in the conflicting operation, wherein the conflict notification includes the conflicting operation type, the conflicting content fragment, and a suggested solution; In response to receiving a conflict resolution instruction submitted by any user terminal, adjusting the content of the target document according to the instruction, and updating the document operation record set and the collaboration mode characteristics.

6. The document collaboration method based on cloud office according to claim 1, characterized in that: The method further comprises: Generate a collaboration relationship map based on the collaboration demand prediction result, wherein the collaboration relationship map includes collaboration links, collaboration frequencies, and collaboration content relevance between user terminals in the collaboration object set; Analyze the high-frequency collaborative links and key user nodes in the collaborative relationship graph to generate collaborative optimization suggestions, wherein the collaborative optimization suggestions include increasing the authority level of a designated user terminal, splitting an overloaded collaborative link, or merging a low-frequency collaborative link, wherein the high-frequency collaborative link is a collaborative link with a collaborative frequency greater than a first set frequency, and the low-frequency collaborative link is a collaborative link with a collaborative frequency less than a second set frequency, and the first set frequency is greater than the second set frequency; The collaboration optimization suggestion is pushed to the administrator terminal of the cloud office platform, and the corresponding collaboration link adjustment operation is performed in response to the administrator confirmation instruction.

7. The document collaboration method based on cloud office according to claim 1, characterized in that: The method further comprises: Embedding a collaborative visualization component in the user interface of the cloud office platform, wherein the collaborative visualization component dynamically displays the collaborative demand prediction results, the authority hierarchy structure, and the real-time data synchronization status; Providing trend analysis diagrams of collaboration intensity indicators, permission level distribution diagrams, and operation conflict hot spots diagrams through the collaboration visualization component; In response to a screening operation triggered by a user through the collaboration visualization component, user terminals and their operation records in a specified authority level or collaboration intensity range are filtered and displayed.

8. A cloud office service system, characterized in that: The cloud office service system includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the cloud office-based document collaboration method described in any one of claims 1 to 7 above.

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