AI mobilization method of assistance team
By introducing a multi-module AI mobilization system in team work, the problem of low task release and collaboration efficiency in team work is solved. The AI algorithm is used to optimize the collaboration process, and the rapid task release and efficient collaboration are achieved, which improves the team's work efficiency and Cohesion.
Patent Information
- Application Number
- CN202411272837.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-11
- Publication Date
- 2024-12-17
AI Technical Summary
It is not convenient to quickly release tasks during the team work, and it is impossible to cooperate efficiently among the members of the group.
A method of AI mobilization to help the team is designed, including mobilizing multiple modules of the system, such as login, editing, uploading, task management, collaboration, resource sharing, recommendation, data collection and analysis, etc., through these modules, task release and collaboration are achieved. Work, resource sharing, data analysis and risk management, optimize team collaboration processes using AI learning algorithms such as linear regression, logistic regression, Transformer and EMA algorithms.
It realizes rapid release of tasks and efficient collaboration among team members, improves work efficiency and team cohesion. Through the use of AI mobilization systems, teams can more effectively share resources, learn dynamics, adjust strategies, and manage risks.
Smart Images
Figure CN119151490A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of assisting teams, and in particular to an AI mobilization method for assisting teams. Background Art
[0002] A team is a community composed of grassroots and management personnel. It rationally utilizes the knowledge and skills of each member to work together, solve problems, and achieve common goals. The specific goal of mobilizing the team is to improve the work enthusiasm and work efficiency of employees, enhance team cohesion, and promote the development and growth of the company's business.
[0003] In the existing technology, it is not convenient to quickly release tasks during team work, and team members cannot collaborate efficiently. For this reason, we propose an AI mobilization method to help teams solve the above problems. Summary of the invention
[0004] The purpose of the present invention is to solve the shortcomings of the prior art that it is not convenient to quickly release task work and team members cannot collaborate efficiently during team work, and to propose an AI mobilization method to assist the team.
[0005] This application provides an AI mobilization method for assisting a team using the following technical solutions:
[0006] An AI mobilization method for assisting a team includes the following steps:
[0007] S1: Prepare a mobilization system, the mobilization system comprising a login module, the login module is connected to an editing module, the editing module is connected to an uploading module, the uploading module is connected to a task management module, the task management module is connected to a publishing module, the publishing module is connected to an incentive module, a query module and a collaboration module, the incentive module is connected to a setting module, the collaboration module is connected to a resource sharing module and a recommendation module, the recommendation module is connected to a learning module, the publishing module is connected to a query module, the query module is connected to a data collection module, the data collection module is connected to a data analysis module, the data collection module and the data analysis module are connected to an encryption module, the data analysis module is connected to an evaluation module, the data analysis module is connected to a suggestion module, the suggestion module is connected to a risk management module and a feedback module, and the feedback module is connected to an optimization module;
[0008] S2: First, log in to the editing module through the login module, edit the task through the editing module, and upload the edited task to the task management module through the upload module. Publish the task through the publishing module. The incentive module provides rewards and incentives based on the difficulty of the task type, and the incentive mechanism is designed through the setting module;
[0009] S3: Then, the team works together through the collaboration module to complete the released tasks. Team members share personal and team resources through the resource sharing module, provide methods and resources through the recommendation module, and the learning module continuously learns the new dynamics of the team and adjusts its recommendations and strategies to adapt to team changes;
[0010] S4: Finally, the task status is queried through the query module, the data collection module collects the task data, the data analysis module analyzes the collected data, the evaluation module evaluates the task completion according to the analysis results, the suggestion module provides corresponding opinions according to the analysis results, the risk management module analyzes potential risks and provides avoidance strategies, team members provide feedback through the feedback module, and the optimization module performs targeted optimization based on the feedback.
[0011] Furthermore, the resource sharing module includes a document sharing unit, a knowledge base sharing unit and a tool sharing unit, the document sharing unit is connected to the knowledge base sharing unit, and the knowledge base sharing unit is connected to the tool sharing unit.
[0012] Furthermore, the recommendation module includes a learning resource unit, a practice method unit and a personalized suggestion unit, wherein the learning resource unit is connected to the practice method unit, and the practice method unit is connected to the personalized suggestion unit.
[0013] Furthermore, the collaboration module is used for team members to work together to complete the published tasks, the resource sharing module is used to share personal and team resources, and the recommendation module is used to provide methods and resources.
[0014] Furthermore, the data collection module is used to collect task data, and the data analysis module is used to analyze the collected data.
[0015] Furthermore, the evaluation module is used to evaluate the task completion according to the analysis results, the suggestion module is used to provide corresponding opinions according to the analysis results, and the risk management module is used to analyze potential risks and provide avoidance strategies.
[0016] Furthermore, the learning module is used to continuously learn new dynamics of the team and adjust its recommendations and strategies to adapt to team changes.
[0017] Furthermore, the incentive module is used to reward and motivate team members according to the difficulty of the task type, and the setting module is used to design an incentive mechanism according to the difficulty of the task type.
[0018] Furthermore, the collaboration module includes an internal communication unit, a video conferencing unit and a collaboration tool unit, wherein the internal communication unit is connected to the video conferencing unit, and the video conferencing unit is connected to the collaboration tool unit.
[0019] Furthermore, the task management module includes a task allocation unit, a progress tracking unit and a reminder unit, wherein the task allocation unit is connected to the progress tracking unit, and the progress tracking unit is connected to the reminder unit.
[0020] In summary, the present application includes at least one of the following beneficial technical effects:
[0021] 1. This solution logs in to the editing module through the login module, edits tasks through the editing module, and uploads the edited tasks to the task management module through the upload module. Tasks can be quickly published through the publishing module;
[0022] 2. This solution can enable members to work together and complete the published tasks through the collaboration module. Team members can share personal and team resources through the resource sharing module, and methods and resources can be provided through the recommendation module.
[0023] The present invention can facilitate the rapid release of task work during use, and team members can collaborate efficiently. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 This is a structural block diagram of a mobilization system in an AI mobilization method for assisting a team proposed by the present invention;
[0025] Figure 2 This is a structural block diagram of a task management module in an AI mobilization method for assisting a team proposed by the present invention;
[0026] Figure 3 This is a structural block diagram of a resource sharing module in an AI mobilization method for assisting a team proposed by the present invention;
[0027] Figure 4 This is a structural block diagram of a collaboration module in an AI mobilization method for assisting a team proposed by the present invention;
[0028] Figure 5 This is a structural block diagram of a recommendation module in an AI mobilization method for assisting a team proposed in the present invention. DETAILED DESCRIPTION
[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0030] Embodiment 1
[0031] Reference Figure 1 , an AI mobilization method to assist teams, including the following steps:
[0032] S1: Prepare a mobilization system. The mobilization system includes a login module. The login module is connected to an editing module. The editing module is connected to an uploading module. The uploading module is connected to a task management module. The task management module is connected to a publishing module. The publishing module is connected to an incentive module, a query module and a collaboration module. The incentive module is connected to a setting module. The collaboration module is connected to a resource sharing module and a recommendation module. The recommendation module is connected to a learning module. The publishing module is connected to a query module. The query module is connected to a data collection module. The data collection module is connected to a data analysis module. The data collection module and the data analysis module are connected to an encryption module. The data analysis module is connected to an evaluation module. The data analysis module is connected to a suggestion module. The suggestion module is connected to a risk management module and a feedback module. The feedback module is connected to an optimization module. The incentive module is used to reward incentives according to the difficulty of the task type. Reward team members. The setting module is used to design incentive mechanisms according to the difficulty of task types. The collaboration module is used for team members to work together to complete the released tasks. The resource sharing module is used to share personal and team resources. The recommendation module is used to provide methods and resources. The learning module is used to continuously learn new dynamics of the team and adjust its recommendations and strategies to adapt to team changes. The data collection module is used to collect task data. The data analysis module is used to analyze the collected data. The evaluation module is used to evaluate the task completion based on the analysis results. The suggestion module is used to provide corresponding opinions based on the analysis results. The risk management module is used to analyze potential risks and provide avoidance strategies. The learning module uses AI learning algorithms, which include: linear regression model, logistic regression algorithm, Transformer algorithm and EMA algorithm.
[0033] 1. Linear regression model: Linear regression is one of the most basic and commonly used algorithms in machine learning. Its basic idea is to find the best fit line by minimizing the difference between the predicted value and the actual value. The linear regression model can be expressed as y=wx+by+b, where y is the predicted value, x is the input variable, and w and b are model parameters. By minimizing the loss function L(w,b), better parameter values can be obtained;
[0034] 2. Logistic regression algorithm: Logistic regression is often used for binary classification problems. Its basic principle is to linearly combine features through a linear regression model, and then map the result of the linear combination to the (0,1) interval through the Sigmoid function as the probability of classification. The formula for logistic regression is: Where P is the probability that the sample belongs to the positive class, z is the result of the linear combination, and e is the base of the natural logarithm;
[0035] 3. Transformer algorithm: Transformer is an intelligent writing algorithm based on deep learning, developed by Google. It uses a self-attention mechanism to deeply understand and analyze the input text, thereby generating high-quality text content. The core formula of the Transformer algorithm is the self-attention mechanism, which is expressed as:
[0036]
[0037] Where Q, K, and V are query, key, and value vectors respectively, k is the dimension of the key vector;
[0038] 4. EMA algorithm: EMA (exponential moving average) algorithm is a statistical method used to process time series data. In writing algorithms, EMA algorithm can be used to analyze text data to extract features and patterns. The calculation formula of EMA involves parameters such as closing price, opening price, higher price and lower price.
[0039] S2: First, log in to the editing module through the login module, edit the task through the editing module, and upload the edited task to the task management module through the upload module. Publish the task through the publishing module. The incentive module provides rewards and incentives based on the difficulty of the task type, and the incentive mechanism is designed through the setting module;
[0040] S3: Then, the team works together through the collaboration module to complete the released tasks. Team members share personal and team resources through the resource sharing module, provide methods and resources through the recommendation module, and the learning module continuously learns the new dynamics of the team and adjusts its recommendations and strategies to adapt to team changes;
[0041] S4: Finally, the task status is queried through the query module, the data collection module collects the task data, the data analysis module analyzes the collected data, the evaluation module evaluates the task completion according to the analysis results, the suggestion module provides corresponding opinions according to the analysis results, the risk management module analyzes potential risks and provides avoidance strategies, team members provide feedback through the feedback module, and the optimization module performs targeted optimization based on the feedback.
[0042] Reference Figure 2 The task management module includes a task allocation unit, a progress tracking unit and a reminder unit. The task allocation unit is connected to the progress tracking unit, and the progress tracking unit is connected to the reminder unit.
[0043] Reference Figure 3 ,The resource sharing module includes a document sharing unit, a knowledge base sharing unit and a tool sharing unit. ,The document sharing unit is connected with the knowledge base sharing unit, and the knowledge base sharing unit is connected with the tool sharing unit.
[0044] Reference Figure 4 The collaboration module includes an internal communication unit, a video conferencing unit and a collaboration tool unit. The internal communication unit is connected to the video conferencing unit, and the video conferencing unit is connected to the collaboration tool unit.
[0045] Reference Figure 5 ,The recommendation module includes a learning resource unit, a practice method unit and a personalized ,suggestion unit. The learning resource unit is connected with the practice method unit, and the practice ,method unit is connected with the personalized suggestion unit.
[0046] The implementation principle in this embodiment is: when in use, first log in to the editing module through the login module, edit the task through the editing module, and upload the edited task to the task management module through the upload module, and publish the task through the publishing module. The incentive module rewards and incentivizes according to the difficulty of the task type, and designs the incentive mechanism through the setting module. Then, the team works together through the collaboration module to complete the published tasks, and the team members share personal and team resources through the resource sharing module, and provide methods and resources through the recommendation module. The learning module continuously learns the new dynamics of the team and adjusts its recommendations and strategies to adapt to team changes. Finally, the task status is queried through the query module, the data collection module collects the task data, the data analysis module analyzes the collected data, the evaluation module evaluates the task completion according to the analysis results, the suggestion module provides corresponding opinions according to the analysis results, the risk management module analyzes potential risks and provides avoidance strategies, and the team members provide feedback through the feedback module, and the optimization module performs targeted optimization according to the feedback.
[0047] Embodiment 2
[0048] The difference between this embodiment and the first embodiment is that the editing module is connected to the intelligent generation module, and the intelligent generation module can generate coherent and logical paragraphs according to the provided keywords, themes or brief descriptions.
[0049] Embodiment 3
[0050] The difference between this embodiment and the first embodiment is that the editing module is connected to a language optimization module, which uses an advanced language model to automatically check and correct grammatical errors, provide fluent and natural language expressions, and can adjust vocabulary selection according to the context to enhance the accuracy and richness of the text.
[0051] Embodiment 4
[0052] The difference between this embodiment and the first embodiment is that the editing module is connected to the input selection module, and the input selection module can select different input modes and types, including text, voice and image, to meet the needs of different users.
[0053] Embodiment 5
[0054] The difference between this embodiment and the first embodiment is that the editing module is connected to the protection module, and the protection module can encrypt and protect the edited content to ensure privacy security.
[0055] Experimental example
[0056] By comparing the AI mobilization scheme of the support team of the entity archive proposed in Examples 1 to 5 with the conventional AI mobilization scheme of the support team, the experimental data is as follows:
[0057]
[0058] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
Claims
1. An AI mobilization method for assisting a team, characterized by: The following steps are involved: S1: Prepare a mobilization system, the mobilization system comprising a login module, the login module is connected to an editing module, the editing module is connected to an uploading module, the uploading module is connected to a task management module, the task management module is connected to a publishing module, the publishing module is connected to an incentive module, a query module and a collaboration module, the incentive module is connected to a setting module, the collaboration module is connected to a resource sharing module and a recommendation module, the recommendation module is connected to a learning module, the publishing module is connected to a query module, the query module is connected to a data collection module, the data collection module is connected to a data analysis module, the data collection module and the data analysis module are connected to an encryption module, the data analysis module is connected to an evaluation module, the data analysis module is connected to a suggestion module, the suggestion module is connected to a risk management module and a feedback module, and the feedback module is connected to an optimization module; S2: First, log in to the editing module through the login module, edit the task through the editing module, and upload the edited task to the task management module through the upload module. Publish the task through the publishing module. The incentive module provides rewards and incentives based on the difficulty of the task type, and the incentive mechanism is designed through the setting module; S3: Then, the team works together through the collaboration module to complete the released tasks. Team members share personal and team resources through the resource sharing module, provide methods and resources through the recommendation module, and the learning module continuously learns the new dynamics of the team and adjusts its recommendations and strategies to adapt to team changes; S4: Finally, the task status is queried through the query module, the data collection module collects the task data, the data analysis module analyzes the collected data, the evaluation module evaluates the task completion according to the analysis results, the suggestion module provides corresponding opinions according to the analysis results, the risk management module analyzes potential risks and provides avoidance strategies, team members provide feedback through the feedback module, and the optimization module performs targeted optimization based on the feedback.
2. The AI mobilization method for assisting a team according to claim 1, characterized in that: The task management module includes a task allocation unit, a progress tracking unit and a reminder unit. The task allocation unit is connected to the progress tracking unit, and the progress tracking unit is connected to the reminder unit.
3. The AI mobilization method for assisting a team according to claim 2, characterized in that: The resource sharing module includes a document sharing unit, a knowledge base sharing unit and a tool sharing unit. The document sharing unit is connected to the knowledge base sharing unit, and the knowledge base sharing unit is connected to the tool sharing unit.
4. The AI mobilization method for assisting a team according to claim 3, characterized in that: The collaboration module includes an internal communication unit, a video conferencing unit and a collaboration tool unit. The internal communication unit is connected to the video conferencing unit, and the video conferencing unit is connected to the collaboration tool unit.
5. The AI mobilization method for assisting a team according to claim 4, characterized in that: The recommendation module includes a learning resource unit, a practice method unit and a personalized suggestion unit. The learning resource unit is connected to the practice method unit, and the practice method unit is connected to the personalized suggestion unit.
6. The AI mobilization method for assisting a team according to claim 5, characterized in that: The incentive module is used to reward and motivate team members according to the difficulty of the task type, and the setting module is used to design an incentive mechanism according to the difficulty of the task type.
7. The AI mobilization method for assisting a team according to claim 6, characterized in that: The collaboration module is used for team members to work together to complete the published tasks, the resource sharing module is used to share personal and team resources, and the recommendation module is used to provide methods and resources.
8. The AI mobilization method for assisting a team according to claim 7, characterized in that: The learning module is used to continuously learn new dynamics of the team and adjust its recommendations and strategies to adapt to team changes.
9. The AI mobilization method for assisting a team according to claim 8, characterized in that: The data collection module is used to collect task data, and the data analysis module is used to analyze the collected data.
10. The AI mobilization method for assisting a team according to claim 9, characterized in that: The evaluation module is used to evaluate the task completion according to the analysis results, the suggestion module is used to provide corresponding opinions according to the analysis results, and the risk management module is used to analyze potential risks and provide avoidance strategies.