Group management and project collaboration method and system based on large model

Abstract

The invention relates to a team management and project collaboration method and system based on a large model, and belongs to the field of artificial intelligence. (1) introducing a big data model: analyzing team demands, introducing the big data model based on analysis, and providing decision support through the big data model; (2) data collection: establishing a data management system, and docking data in a team cooperation platform or a project management system; (3) data preprocessing: performing data cleaning and security processing on the data collected in the step (2) through the big data model in the step (1), then outputting a result, and performing iteration and testing of the model on a data subset; (4) model training and evaluation: performing model training by using computing resources, monitoring a training process, and evaluating model performance; and (5) model deployment and maintenance: deploying the trained model to a production environment, and carrying out continuous monitoring and maintenance. The method has the advantage that team management and project collaboration based on the large model have remarkable advantages and potential. Through application of intelligent task distribution, real-time communication, progress monitoring, risk management, decision support, team capability improvement and the like, the large model can optimize the workflow and cooperation efficiency of a team, and improve the success rate of a project and the overall performance of the team.

Description

Technical Field

[0001] The invention relates to a team management and project collaboration method based on a large model, and belongs to the field of artificial intelligence. Background Art

[0002] In current project management practices, manual management often faces a series of challenges. These problems not only affect the smooth progress of the project, but also reduce the overall efficiency and performance of the team.

[0003] Lack of trust: Trust is the cornerstone of collaboration in a team. Lack of trust will lead to suspicion and non-cooperation among team members, affecting the transparency of decision-making and the efficiency of execution.

[0004] Fear of conflict: In many teams, members tend to avoid expressing different opinions directly for fear of causing conflict.

[0005] Unclear responsibilities and goals: Project failure often stems from unclear allocation of responsibilities and unclear goal setting.

[0006] Poor communication: Effective communication is the key to teamwork. However, in many projects, poor communication often leads to untimely or inaccurate information transmission.

[0007] Uneven distribution of resources: Uneven distribution of resources affects the efficiency and fairness of the project.

[0008] Inadequate project risk management: Risk management is an important part of project management. Lack of an effective risk management mechanism will lead to a slow response to project problems, or even lead to project failure.

[0009] Lack of continuous improvement mechanism: Continuous improvement is the key to improving team and project management level.

[0010] Personalized management: Each team and project is unique, and traditional team management tools often fail to meet personalized needs. Summary of the invention

[0011] In order to overcome the defects of the prior art, the present invention provides a team management and project collaboration method based on a large model. The technical solution of the present invention is:

[0012] A team management and project collaboration method based on a big model includes the following steps:

[0013] (1) Introducing big data models: Analyze the needs of the team, introduce big data models based on the analysis, and provide decision support through big data models;

[0014] (2) Data collection: Establish a data management system to connect the data in the team collaboration platform or project management system;

[0015] (3) Data preprocessing: The data collected in step (2) is cleaned and processed securely using the big data model in step (1), and the results are output. The model is then iterated and tested on a subset of the data.

[0016] (4) Model training and evaluation: Use computing resources to train models, monitor the training process, and evaluate model performance;

[0017] (5) Model deployment and maintenance: Deploy the trained model to the production environment for continuous monitoring and maintenance.

[0018] The step (1) is specifically as follows: evaluating the current team's task allocation mechanism, identifying deficiencies in project progress monitoring, analyzing the project risk management process, and evaluating the data-driven needs in the team's decision-making process, and selecting a big data model based on the results of the needs analysis.

[0019] The step (2) is specifically as follows:

[0020] (2-1) Data source identification: Identify the data sources generated during team collaboration;

[0021] (2-2) Data format unification: Synchronize and integrate data from different sources and convert them into a unified format to facilitate subsequent processing and analysis;

[0022] (2-3) Data storage: Select a database or data storage solution to ensure data security and accessibility.

[0023] The step (3) eliminates noise and errors in the data by checking, correcting and deleting erroneous, incomplete, inconsistent or duplicate data to facilitate data analysis. At the same time, data encryption and access control strategies are used to protect sensitive information from being leaked. Specifically,

[0024] (3-1) Missing value processing: delete, fill or predict missing values ​​to ensure data integrity; when deleting, directly delete the records or columns containing missing values; when filling, use the mean, median, maximum or minimum value to fill the missing values; when predicting, use linear regression or decision tree to predict missing values;

[0025] (3-2) Duplicate data processing: Identify and delete duplicate or redundant entries in the data set; (3-3) Outlier processing: Identify and process outliers by removing, pruning, replacing or summarizing. When removing, directly remove outlier data points; when pruning, only retain a specified percentage of data and discard extreme values; when replacing, replace extreme values ​​with specified values ​​that are closer to other data points; when summarizing, replace outliers with statistical values;

[0026] (3-4) Data consistency processing: Use rule engines or natural language processing to ensure data consistency;

[0027] (3-5) Data standardization: converting data in different units into unified units;

[0028] (3-6) When encrypting data, use encryption algorithms and keys to perform image encryption, database encryption, file encryption, and key management; use the AES encryption algorithm to encrypt image data and then store the encrypted data in a secure location; encrypt sensitive information in the database to protect data privacy while ensuring the security and integrity of the database; encrypt sensitive files stored locally or on the network, and generate and securely store encryption keys when performing key management;

[0029] (3-7) When developing access control policies, it is important to identify which users or processes access which resources and what operations they perform. Specifically:

[0030] (3-7-1) Implement authentication and authorization: Ensure the authenticity of user identity through authentication, and ensure that users can only access authorized resources through authorization. (3-7-2) Monitoring and auditing: Monitor and record access activities in real time to promptly detect and respond to potential security threats.

[0031] In the step (4), when the model training is performed, specifically:

[0032] (4-1) Select computing resources and perform model training;

[0033] (4-2) Prepare training data set;

[0034] (4-3) Real-time monitoring of model training process;

[0035] (4-4) Update model parameters using optimization algorithm;

[0036] When evaluating the model, specifically:

[0037] (4-5) Use the validation dataset to evaluate the trained model;

[0038] (4-6) Fine-tune the model based on the evaluation results;

[0039] (4-7) The training, evaluation, and tuning process is iterated until the model performance is satisfactory.

[0040] The step (5) is specifically as follows:

[0041] (5-1) Deployment environment preparation: Prepare the environment for model deployment;

[0042] (5-2) Model integration: Integrate the trained model into the production environment;

[0043] (5-3) Continuous monitoring: Continuously monitor the deployed model;

[0044] (5-4) Establish a feedback mechanism: optimize and update the model based on the team’s feedback.

[0045] A team management and project collaboration system is implemented by a team management and project collaboration method based on a big model, including:

[0046] The big data model introduction module is used to analyze the needs of the team, introduce the big data model based on the analysis, provide decision support through the big data model, and help the team with task allocation, resource allocation and progress management;

[0047] Data collection module, used to establish data management system and connect data in team collaboration platform or project management system;

[0048] The data preprocessing module is used to clean the collected data and output the results after security processing through the big data model, and then iterate and test the model on the data subset to verify the correctness and accuracy of the model;

[0049] Model training and evaluation module, which is used to use computing resources to train models, monitor the training process, and evaluate model performance;

[0050] The model deployment and maintenance module is used to deploy the trained model to the production environment for continuous monitoring and maintenance.

[0051] The advantages of the present invention are:

[0052] Team management and project collaboration based on big models have significant advantages and potential. Through applications such as intelligent task allocation, real-time communication, progress monitoring, risk management, decision support, and team capacity enhancement, big models can optimize the team's workflow and collaboration efficiency, improve the project's success rate and the team's overall performance.

[0053] Optimal resource allocation: In terms of resource management, the big model can help the team optimize the allocation of human, material and financial resources. Through data analysis, the input-output ratio and efficiency level of different resources can be evaluated to maximize the utilization and conservation of resources.

[0054] Personalized management: The big model can be applied to areas such as talent selection, performance evaluation, and career development path planning. Through refined analysis of employee capabilities, behaviors, and performance data, personalized management and optimal resource allocation can be achieved, thereby improving employee satisfaction and loyalty.

[0055] Stimulate innovative thinking: The application of big model technology can stimulate the innovative thinking and creativity of team members. Through in-depth mining and analysis of data, new opportunities and challenges can be discovered, thus promoting continuous innovation and improvement of the team. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the main structure of the system of the present invention. Figure 2 It is a schematic diagram of the collaborative method flow of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0058] As shown in 2, the present invention relates to a team management and project collaboration method based on a large model, comprising the following steps:

[0059] (1) Introducing big data models: Analyze the needs of the team, introduce big data models based on the analysis, and provide decision support through big data models to help the team with task allocation, resource allocation, and progress management;

[0060] (2) Data collection: Establish a data management system to connect the data in the team collaboration platform or project management system;

[0061] (3) Data preprocessing: The data collected in step (2) is cleaned and processed securely using the big data model in step (1) and the results are output. The model is then iterated and tested on a subset of the data to verify the correctness and accuracy of the model.

[0062] (4) Model training and evaluation: Use computing resources to train models, monitor the training process, and evaluate model performance;

[0063] (5) Model deployment and maintenance: Deploy the trained model to the production environment for continuous monitoring and maintenance.

[0064] The step (1) is specifically: evaluating the current team's task allocation mechanism, identifying deficiencies in project progress monitoring, analyzing the project risk management process, and evaluating the data-driven needs in the team's decision-making process. Based on the results of the needs analysis, a big data model is selected, such as a deep learning model and a natural language processing model, to ensure the advancement and applicability of the technology. Recurrent neural networks (RNNs) have a wide range of applications in the field of natural language processing, such as speech recognition, machine translation, and text generation, and can be used to collect and organize content for video conferences.

[0065] Among them, video conferencing and remote collaboration: Computer vision technology can be applied to video conferencing to realize functions such as face recognition and gesture recognition, and enhance the interactivity and participation of the meeting. At the same time, it can also be used in remote collaboration scenarios, such as controlling virtual objects or interfaces through gesture recognition.

[0066] Image analysis and understanding: In project management, computer vision technology can analyze project-related image data, such as design drawings, product photos, etc., extract key information and assist in decision-making.

[0067] Voice assistant and automation: Through speech recognition technology, the big model can become the team's voice assistant, handling various voice commands and tasks, such as scheduling meetings, sending emails, querying information, etc. At the same time, speech synthesis technology can convert text information into voice output, making it convenient for team members to receive information through voice when they are busy.

[0068] Transformer is a deep learning model based on the self-attention mechanism. Its variants (such as BERT and GPT series) have become the core architecture in fields such as natural language processing and computer vision, and are widely used in tasks such as machine translation, text generation, and image classification.

[0069] Evaluate the current team's task allocation mechanism and determine the role of big data models in automating task allocation and optimizing resource allocation;

[0070] Progress Monitoring: Identify deficiencies in project progress monitoring and explore the use of big models for real-time progress tracking and predicting project delays.

[0071] Risk Management: Analyze the project risk management process and determine the application of big models in predicting and managing potential risks.

[0072] Decision support: Assess the data-driven needs of the team’s decision-making process and determine the application of big models in providing decision support.

[0073] The step (2) is specifically as follows:

[0074] (2-1) Data source identification: Identify the data sources generated during team collaboration; such as project management tools, communication platforms, task tracking systems, etc.

[0075] (2-2) Data format unification: Synchronize and integrate data from different sources and convert them into a unified format to facilitate subsequent processing and analysis; database synchronization refers to synchronizing and integrating data between different databases through database connection technology. This method is suitable for scenarios where data from multiple data sources need to be integrated and managed in a unified manner. Through database synchronization, data from different data sources can be synchronized and updated in real time or regularly to ensure data integrity and consistency.

[0076] You can obtain data from other systems or applications by calling the API interface. This method is suitable for scenarios where data exchange or integration with other systems is required. API interface calls have the advantages of unified data format and high transmission efficiency, but you need to comply with the usage rules and restrictions of the interface provider.

[0077] Data can also be collected through sensors, which can be achieved through wireless transmission or wired connection, with the characteristics of strong real-time performance and high data accuracy, such as through face recognition, video conferencing, etc.

[0078] (2-3) Data storage: Select a database or data storage solution to ensure data security and accessibility.

[0079] The step (3) checks, corrects and deletes erroneous, incomplete, inconsistent or duplicate data to eliminate noise and errors in the data, so as to perform data analysis and improve data quality and accuracy. At the same time, data encryption and access control strategies are used to protect sensitive information from being leaked. Specifically,

[0080] (3-1) Missing value processing: delete, fill or predict missing values ​​to ensure data integrity; when deleting, directly delete the records or columns containing missing values; when filling, use the mean, median, maximum or minimum value to fill the missing values; when predicting, use linear regression or decision tree to predict missing values;

[0081] (3-2) Duplicate data processing: Identify and delete duplicate or redundant entries in the data set to ensure the uniqueness and accuracy of the data.

[0082] (3-3) Outlier processing: Identify and process outliers by removing, pruning, replacing, or summarizing. When removing, directly remove outlier data points; when pruning, only retain a specified percentage of data and discard extreme values; when replacing, replace extreme values ​​with specified values ​​that are closer to other data points; when summarizing, replace outliers with statistical values, such as the mean or median.

[0083] (3-4) Data consistency processing: Use rule engines or natural language processing to ensure data consistency; Natural language processing (NLP) can realize text processing and generation: NLP technology can process and understand text data within the team, including meeting minutes, work reports, emails, etc., and realize functions such as automatic summarization, keyword extraction, and sentiment analysis, thereby improving the efficiency and accuracy of information processing.

[0084] Achieve semantic understanding and communication: Through NLP technology, the big model can understand the semantics and context between team members, promoting more accurate and efficient communication. For example, in cross-language or cross-departmental communication, NLP can automatically translate and convert languages ​​to reduce misunderstandings and communication barriers. (3-5) Data standardization: Convert data in different units into a unified unit to improve data consistency;

[0085] (3-6) When encrypting data, use encryption algorithms and secret keys to perform image encryption, database encryption, file encryption, and secret key management; common encryption algorithms include symmetric encryption algorithms (such as DES, 3DES, AES, RC4) and asymmetric encryption algorithms (such as RSA, ECC) and hash algorithms (such as MD5, SHA1, SHA2); use the AES encryption algorithm to encrypt image data, and then store the encrypted data in a secure location; encrypt and store sensitive information in the database to protect data privacy while ensuring the security and integrity of the database; encrypt sensitive files stored locally or on the network to ensure that even if the file is illegally obtained, the attacker cannot read the file content. Common file encryption software supports multiple encryption algorithms, such as AES, Blowfish, etc., and provides multiple encryption types such as transparent encryption, semi-transparent encryption, and smart encryption; when performing key management, generate and securely store encryption keys to ensure the confidentiality and integrity of the keys;

[0086] (3-7) When developing access control policies, it is important to identify which users or processes access which resources and what operations they perform. Specifically:

[0087] (3-7-1) Implement authentication and authorization: Ensure the authenticity of user identity through authentication, and ensure that users can only access authorized resources through authorization. (3-7-2) Monitoring and auditing: Monitor and record access activities in real time to promptly detect and respond to potential security threats.

[0088] In the step (4), when the model training is performed, specifically:

[0089] (4-1) Select computing resources, such as GPU or TPU, for model training;

[0090] (4-2) Prepare training data sets to ensure data diversity and representativeness;

[0091] (4-3) Monitor the model training process in real time to ensure the stability and convergence of the model;

[0092] (4-4) Use optimization algorithms (such as Adam, SGD, etc.) to update model parameters and improve model performance;

[0093] When evaluating the model, specifically:

[0094] (4-5) Evaluate the trained model using the validation dataset to check its accuracy, efficiency, and robustness;

[0095] (4-6) Tune the model based on the evaluation results, including adjusting hyperparameters, improving model structure, etc. Hyperparameter tuning: Adjust the model's hyperparameters, such as learning rate, number of hidden layers, etc., to optimize model performance;

[0096] (4-7) The training, evaluation, and tuning process is iterated until the model performance is satisfactory.

[0097] The step (5) is specifically as follows:

[0098] (5-1) Deployment environment preparation: Prepare the environment for model deployment to ensure compatibility with existing systems;

[0099] (5-2) Model integration: Integrate the trained model into the team management and project collaboration system;

[0100] (5-3) Continuous monitoring: Continuously monitor the deployed model to ensure its stability and reliability;

[0101] (5-4) Feedback mechanism: Establish a feedback mechanism to further optimize and update the model based on the team’s feedback.

[0102] See also Figure 1 The present invention also relates to a team management and project collaboration system, which is implemented by a team management and project collaboration method based on a large model, including:

[0103] Big data model introduction module 1 is used to analyze the needs of the team, introduce the big data model based on the analysis, provide decision support through the big data model, and help the team with task allocation, resource allocation and progress management;

[0104] Data collection module 2 is used to establish a data management system and connect data in the team collaboration platform or project management system;

[0105] Data preprocessing module 3 is used to clean the collected data and output the results after security processing through the big data model, and then iterate and test the model on the data subset to verify the correctness and accuracy of the model;

[0106] Model training and evaluation module 4, used to use computing resources to train the model, monitor the training process, and evaluate the model performance;

[0107] Model deployment and maintenance module 5 is used to deploy the trained model to the production environment for continuous monitoring and maintenance.

[0108] Based on the configuration of the present invention, the following functions are expected to be achieved:

[0109] Task allocation and planning: Tasks can be intelligently allocated based on team members’ skills, workloads, and project requirements. This helps balance workloads and ensures that tasks are assigned to the most appropriate members.

[0110] Project planning: Teams can more accurately predict project timelines and resource requirements. Big models can analyze historical data and current trends to provide valuable insights for project planning

[0111] Communication and collaboration: It can be used as a communication platform within the team, supporting real-time chat, document sharing, and collaborative editing. This helps reduce communication barriers and improve the efficiency of collaboration among team members.

[0112] Cross-domain capability collaboration: Through a unified communication platform and knowledge base, team members with different functions can communicate and collaborate more smoothly.

[0113] Knowledge sharing: Provide instant knowledge support and solutions to team members. When team members encounter problems, they can get help by interacting with the big model.

[0114] Progress monitoring and risk management: Track the progress of the project, update the task status in real time, and provide progress reports. This helps team members and management understand the latest progress of the project and adjust plans and resources in a timely manner.

[0115] Risk Management: Analyze project data, identify potential risk factors, and provide early warnings and suggestions. Through timely risk management, the team can reduce the risk of project failure.

[0116] Decision support: Processing and analyzing large amounts of data to provide the team with valuable insights and predictions. This helps the team be more objective and accurate in the decision-making process.

[0117] Smart recommendations: Provide intelligent decision-making suggestions to the team. These suggestions can be based on historical experience, market trends, team goals and other dimensions.

[0118] Improve team capabilities: Provide personalized training materials and guidance based on the skill level and needs of team members. This helps improve the professional capabilities and overall quality of team members.

[0119] Innovation incentives: Stimulate innovative thinking among team members and provide support for idea generation and solution optimization. With the assistance of large models, teams can explore new ideas and solutions more efficiently.

[0120] 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. A team management and project collaboration method based on a big model, characterized in that: The following steps are involved: (1) Introducing big data models: Analyze the needs of the team, introduce big data models based on the analysis, and provide decision support through big data models; (2) Data collection: Establish a data management system to connect the data in the team collaboration platform or project management system; (3) Data preprocessing: The data collected in step (2) is cleaned and processed securely using the big data model in step (1), and the results are output. The model is then iterated and tested on a subset of the data. (4) Model training and evaluation: Use computing resources to train models, monitor the training process, and evaluate model performance; (5) Model deployment and maintenance: Deploy the trained model to the production environment for continuous monitoring and maintenance.

2. The team management and project collaboration method based on a large model according to claim 1 is characterized in that: The step (1) is specifically as follows: evaluating the current team's task allocation mechanism, identifying deficiencies in project progress monitoring, analyzing the project risk management process, and evaluating the data-driven needs in the team's decision-making process, and selecting a big data model based on the results of the needs analysis.

3. The team management and project collaboration method based on a large model according to claim 1 or 2, characterized in that: The step (2) is specifically as follows: (2-1) Data source identification: Identify the data sources generated during team collaboration; (2-2) Data format unification: Synchronize and integrate data from different sources and convert them into a unified format to facilitate subsequent processing and analysis; (2-3) Data storage: Select a database or data storage solution to ensure data security and accessibility.

4. The team management and project collaboration method based on a large model according to claim 1 or 2, characterized in that: The step (3) eliminates noise and errors in the data by checking, correcting and deleting erroneous, incomplete, inconsistent or duplicate data to facilitate data analysis. At the same time, data encryption and access control strategies are used to protect sensitive information from being leaked. Specifically, (3-1) Missing value processing: delete, fill or predict missing values ​​to ensure data integrity; when deleting, directly delete the records or columns containing missing values; when filling, use the mean, median, maximum or minimum value to fill the missing values; when predicting, use linear regression or decision tree to predict missing values; (3-2) Duplicate data processing: Identify and delete duplicate or redundant entries in the data set; (3-3) Outlier processing: Identify and process outliers by removing, pruning, replacing or summarizing. When removing, directly remove outlier data points; when pruning, only retain a specified percentage of data and discard extreme values; when replacing, replace extreme values ​​with specified values ​​that are closer to other data points; when summarizing, replace outliers with statistical values; (3-4) Data consistency processing: Use rule engines or natural language processing to ensure data consistency; (3-5) Data standardization: converting data in different units into unified units; (3-6) When encrypting data, use encryption algorithms and keys to perform image encryption, database encryption, file encryption, and key management; use the AES encryption algorithm to encrypt image data and then store the encrypted data in a secure location; encrypt sensitive information in the database to protect data privacy while ensuring the security and integrity of the database; encrypt sensitive files stored locally or on the network, and generate and securely store encryption keys when performing key management; (3-7) When developing access control policies, it is important to identify which users or processes access which resources and what operations they perform. Specifically: (3-7-1) Implement authentication and authorization: Ensure the authenticity of the user's identity through authentication, and ensure that the user can only access the resources for which he is authorized through authorization. (3-7-2) Monitoring and Auditing: Real-time monitoring and recording of access activities to promptly detect and respond to potential security threats.

5. The team management and project collaboration method based on a large model according to claim 4 is characterized in that: In the step (4), when the model training is performed, specifically: (4-1) Select computing resources and perform model training; (4-2) Prepare training data set; (4-3) Real-time monitoring of model training process; (4-4) Update model parameters using optimization algorithm; When evaluating the model, specifically: (4-5) Use the validation dataset to evaluate the trained model; (4-6) Fine-tune the model based on the evaluation results; (4-7) The training, evaluation, and tuning process is iterated until the model performance is satisfactory.

6. The team management and project collaboration method based on a large model according to claim 5 is characterized in that: The step (5) is specifically as follows: (5-1) Deployment environment preparation: Prepare the environment for model deployment; (5-2) Model integration: Integrate the trained model into the production environment; (5-3) Continuous monitoring: Continuously monitor the deployed model; (5-4) Establish a feedback mechanism: optimize and update the model based on the team’s feedback.

7. A team management and project collaboration system, characterized in that: The method is implemented by a team management and project collaboration method based on a large model according to any one of claims 1 to 6, including: The big data model introduction module is used to analyze the needs of the team, introduce the big data model based on the analysis, provide decision support through the big data model, and help the team with task allocation, resource allocation and progress management; Data collection module, used to establish data management system and connect data in team collaboration platform or project management system; The data preprocessing module is used to clean the collected data and output the results after security processing through the big data model, and then iterate and test the model on the data subset to verify the correctness and accuracy of the model; Model training and evaluation module, which is used to use computing resources to train models, monitor the training process, and evaluate model performance; The model deployment and maintenance module is used to deploy the trained model to the production environment for continuous monitoring and maintenance.

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