Multi-system collaborative work platform integrated with business generator

Abstract

The present invention relates to the field of collaborative work technology, specifically a multi-system collaborative work platform integrated with a business generator. The platform includes an identity authentication module, a task scheduling module, an application configuration module, and a data analysis module. The identity authentication module performs permission verification based on identity information received from the user, matches it with information stored in a database, compares the user-provided key with the stored key, and generates an identity authentication token. The task scheduling module screens task data accessible under the user's authority based on the identity authentication token. The present invention uses a deep Q network to dynamically adjust resource and permission configuration, increasing the adaptability of the platform and allowing flexible adjustments based on real-time business needs, thereby improving resource utilization efficiency. The gradient boosting tree algorithm is used to predict workloads, accurately manage resource allocation, reduce resource waste, ensure the continuity and efficiency of business operations, and guarantee the optimal allocation of resources between different applications.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative work, and in particular to a multi-system collaborative work platform integrating a business generator. Background Art

[0002] The field of collaborative work technology focuses on using information technology to improve interaction and work efficiency among teams. It covers everything from data sharing and task coordination to the integrated use of communication tools to support effective collaboration among team members in different geographical locations, enabling organizations to better manage complex projects and ensure that information and resources are efficiently allocated and used within the team.

[0003] Among them, the multi-system collaborative work platform integrated by the business generator refers to a platform that integrates multiple general software and professional applications, aiming to provide standardized and efficient business operation support. The business middle platform has built-in standard applications such as office OA, HR system, elevator maintenance system, contract management system, project management system and order management system, and supports large-screen reports and data statistical analysis functions. Such a platform can realize resource sharing and unified management, making collaboration between different departments within the organization smoother, greatly improving the speed and quality of project execution.

[0004] Traditional platforms lack the ability to respond quickly to emergencies and real-time changes, resulting in reliance on outdated information in the decision-making process and the inability to adjust resources immediately, which affects the effectiveness of decisions and the timely delivery of projects. In terms of resource management, the existing system cannot flexibly adjust resource allocation and lacks the ability to handle emergencies. This leads to delays or failures in critical tasks when resource demand is high, resulting in waste of resources and inefficient allocation, especially in a volatile environment, affecting team collaboration efficiency and project management flexibility. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a multi-system collaborative work platform with integrated service generators.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-system collaborative work platform integrated with a service generator, the platform comprising:

[0007] The authentication module performs permission verification based on the identity information received from the user, matches it with the information stored in the database, compares the key provided by the user with the stored key, and generates an authentication token;

[0008] The task scheduling module filters the task data accessible under the user's authority based on the authentication token, and prioritizes the tasks by evaluating the task attributes and criticality ratings to generate a priority task list;

[0009] The application configuration module analyzes the degree of match between the current application status and the required permissions based on the priority task list, adjusts the operating permissions and access levels of multiple applications, and dynamically adjusts the application permission configuration based on business needs using the Deep Q network to generate a configuration dynamic adjustment table;

[0010] The data analysis module uses the parameters in the configuration dynamic adjustment table to collect and aggregate business data, perform resource classification efficiency analysis on the business data, and adopts the gradient boosting tree algorithm to predict the workload of the business data, adjust the resource load between multiple applications, and obtain the optimal resource allocation measures.

[0011] The present invention is improved in that the steps of obtaining the identity authentication token are specifically as follows:

[0012] Based on the identity information received from the user, a preliminary match is performed with the stored information to verify the user's identity and obtain a preliminary verification result;

[0013] Based on the preliminary verification results, the key is compared with the key provided by the user and key verification is performed using the formula:

[0014]

[0015] Calculate key consistency DV k , generate the key verification result, where k ui represents the i-th key element provided by the user, k si Represents the i-th key element in the stored data, n d is the number of elements in the key;

[0016] Based on the key verification result, if the verification passes, an authentication token is generated.

[0017] The present invention is improved in that the evaluation steps of the criticality classification are specifically as follows:

[0018] Collect basic task information, including deadlines, required resources, and departments. Convert the collected task information into quantifiable values. Convert the deadline into the number of days from the current date. Calculate the resource requirements of the task based on the task size and expected completion time.

[0019] Grade the criticality of each task based on its resource requirements using the formula:

[0020]

[0021] Calculate the mission criticality score SV, where wv D and wv R are the weight factors of deadline and resource requirements, D VRepresents the number of days from the task deadline. V Represents the resource requirements of the task.

[0022] The present invention is improved in that the step of obtaining the priority task list is specifically as follows:

[0023] Determine the identity and corresponding permission level based on the authentication token provided by the user, and filter the task data accessible under the target user's permission;

[0024] Perform attribute assessment on the screened task data, including assessment of urgency, deadlines, and resource requirements;

[0025] Prioritize tasks using a sorting algorithm, using the formula:

[0026]

[0027] Get a priority task list, where n q Indicates the number of task attributes, xq i represents the score of the i-th task attribute, wq i is the weight coefficient of the corresponding attribute.

[0028] The present invention is improved in that the steps for adjusting the operating authority and access level of the application are specifically as follows:

[0029] Analyze the attributes of each task in the priority task list and compare the task attributes with business requirements, including required completion time and safety level;

[0030] Use the formula to calculate the operation permission score of each application:

[0031]

[0032] Where PS(a) is the operation permission score of application a, vs i is the weight of the attribute associated with the i-th task, ts i is the value of the i-th task attribute associated with application a, us j is the weight of the j-th business requirement associated attribute, bs j is the value of the jth attribute of the business requirement, n S and m s are the total number of task attributes and business requirement attributes respectively.

[0033] The present invention is improved in that the steps of obtaining the configuration dynamic adjustment table are specifically as follows:

[0034] Analyze the obtained priority task list, including the priority and associated attributes of each task, determine the operation permissions and access levels of multiple applications based on the priority and business needs, and match the task priorities with the application needs;

[0035] Apply the Deep Q network to dynamically adjust application permission configuration using the formula:

[0036]

[0037] Get the optimal application configuration, where su i is the i-th element in the state vector, au i is the i-th element in the action vector, wu i and bu i is the corresponding weight coefficient, s u represents the state vector, a u represents the action vector, n u is the number of elements.

[0038] The present invention is improved in that the analysis steps of the resource classification efficiency are specifically as follows:

[0039] Collect business data and aggregate corresponding resource usage information, including CPU usage, memory usage, and network bandwidth usage;

[0040] Calculate the utilization rate of each type of resource during peak and valley periods. The utilization rate is defined as the ratio of resource usage to total available capacity. The peak utilization rate is U peak , the valley utilization rate is U valley , and dynamically adjust parameters according to business needs;

[0041] By comparing U peak and U valley The difference between the two values ​​is used to evaluate the efficiency, using the formula:

[0042]

[0043] Among them, n m represents the number of resource types, EM is the resource classification efficiency, U peak,i and U valley,i They represent the utilization rates of the i-th resource during peak and valley periods, respectively.

[0044] The present invention is improved in that the steps for obtaining the optimal resource allocation measures are specifically as follows:

[0045] Using the gradient boosting tree algorithm, we predict the resource requirements of each application in the future time period based on historical data, and collect and evaluate the amount of resources currently used by each application;

[0046] Use the formula to optimize the amount of resources for each application:

[0047]

[0048] Determine the amount of resources HC that each application should receive, where n c Indicates the number of applications, wc i represents the weight of the i-th application, Dc i represents the predicted demand of the i-th application, Tc i Indicates the amount of resources currently allocated to the i-th application.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, the security of sensitive data and the accuracy of access control are ensured through user identity authentication and permission matching. Resource optimization and allocation ensure the rational allocation of resources during demand peaks and troughs through dynamic analysis and prediction, reducing waste and improving operational efficiency. The in-depth integration of data analysis provides real-time data support for decision-making, enhances the data-driven nature of decision-making, enables project adjustments to immediately reflect changes in the operating environment, and enables organizations to optimize overall resource allocation and project management processes while ensuring the success of key projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of a multi-system collaborative work platform integrating a business generator is proposed for the present invention;

[0052] Figure 2 This is a flow chart of obtaining an identity authentication token in the present invention;

[0053] Figure 3 This is a flow chart for evaluating the criticality classification in the present invention;

[0054] Figure 4 A flowchart for obtaining a priority task list in the present invention;

[0055] Figure 5 A flowchart for adjusting the operating permissions and access levels of an application program in the present invention;

[0056] Figure 6 A flowchart for obtaining a dynamic adjustment table configured in the present invention;

[0057] Figure 7 This is a flow chart for analyzing resource classification efficiency in the present invention;

[0058] Figure 8 This is a flow chart for obtaining the optimal resource allocation measures in the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0061] Example

[0062] See also Figure 1 The present invention provides a technical solution: a multi-system collaborative work platform integrated with a business generator includes:

[0063] The authentication module performs permission verification based on the identity information received from the user, matches it with the information stored in the database, compares the key provided by the user with the stored key, and generates an authentication token;

[0064] The task scheduling module filters the task data accessible to the user based on the authentication token, and prioritizes the tasks by evaluating the task attributes and criticality ratings to generate a priority task list;

[0065] The application configuration module analyzes the degree of match between the current application status and the required permissions based on the priority task list, adjusts the operating permissions and access levels of multiple applications, and dynamically adjusts the application permission configuration based on business needs using the Deep Q network to generate a dynamic configuration adjustment table.

[0066] The data analysis module uses the parameters in the configuration dynamic adjustment table to collect and aggregate business data, conduct resource classification efficiency analysis on the business data, pay attention to the data of resource usage peak and valley periods, and use the gradient boosting tree algorithm to predict the workload of business data, adjust the resource load between multiple applications, and obtain the optimal resource allocation measures.

[0067] The authentication token includes the encryption key, user identity, and validity period. The priority task list specifically includes the task number, urgency level, and estimated completion time. The configuration dynamic adjustment table includes the permission level, configuration status, and last update time.

[0068] See also Figure 2 , the steps to obtain the authentication token are as follows:

[0069] Based on the identity information received from the user, a preliminary match is performed with the stored information to verify the user's identity and obtain a preliminary verification result;

[0070] During identity authentication, it is necessary to collect the user's identity information, such as username and password. This information will be used to match the user information pre-stored in the database to verify whether the submitted information is consistent with the known user data. Before being processed, the received user information needs to undergo a security check to prevent data tampering or leakage. Through this operation, it can be ensured that the collected user information is accurate and secure before key comparison, which is the first step in identity authentication.

[0071] Based on the preliminary verification results, the key is compared with the key provided by the user and key verification is performed using the formula:

[0072]

[0073] Calculate key consistency DV k , generate the key verification result, where k ui represents the i-th key element provided by the user, k si Represents the i-th key element in the stored data, n d is the number of elements in the key;

[0074] Based on the key verification result, if the verification is successful, an authentication token is generated. The token is used for user session management and service access;

[0075] If there is a user-provided key element [k ui ] and the corresponding key element in the database [k si ], the following data were collected, n d =3,k ui =[110,115,105], k si =[100,120,100], substitute into the formula:

[0076]

[0077] The result shows that the key consistency is 0.0142, indicating that there is a high consistency between the keys, which is used to verify the legitimacy of the user identity.

[0078] After the key verification process is completed, the decision on whether to grant the user an authentication token will be based on the key consistency evaluation results. The token is a confirmation of the legitimacy of the user's identity. The token contains the user's identity information and permission level. After encryption, it can be used for the user's subsequent operations to ensure the legitimacy and security of the user's operations. It is the key to connecting user authentication and service access, and can effectively prevent unauthorized access attempts.

[0079] See also Figure 3 The specific steps for evaluating criticality are as follows:

[0080] Collect basic task information, including deadlines, required resources, and departments. Convert the collected task information into quantifiable values. Convert the deadline into the number of days from the current date. Calculate the resource requirements of the task based on the task size and expected completion time.

[0081] Basic task information such as deadlines, required resources, and departments is systematically collected and recorded. This information is the basis for conversion into quantifiable values. Deadlines are converted into the number of days from the current date through date difference calculation, while resource requirements are evaluated based on the scale and expected completion time of each task. This is crucial for subsequent task planning and resource allocation to accurately assess and predict upcoming demands, ensure the rational allocation and efficient use of resources, and thus optimize the overall process and efficiency of task execution.

[0082] Grade the criticality of each task based on its resource requirements using the formula:

[0083]

[0084] Calculate the mission criticality score SV, where wv D and wv R are the weight factors of deadline and resource requirements, D V +1 for making sure even D V 0 (i.e. today's deadline) will not cause calculation abnormalities. V Represents the number of days from the task deadline. V Indicates the resource requirements of the task;

[0085] Assume that a project team manages three different tasks, and the weight factors are wv D = 0.7 (deadline weight) and wv R = 0.3 (resource demand weight). Assume that the deadlines of the three tasks are 5, 2, and 10 days from today, and their resource demand scores are 50, 75, and 20, respectively. Calculate the criticality score of each task:

[0086]

[0087] The calculation results show that the second task has the highest criticality score, indicating that it is the most urgent and resource-intensive task and should be prioritized. This calculation method makes task prioritization more scientific and practical, and helps to manage resources more efficiently.

[0088] See also Figure 4 , the specific steps for obtaining the priority task list are:

[0089] Determine the identity and corresponding permission level based on the authentication token provided by the user, and filter the task data accessible under the target user's permissions;

[0090] Based on the authentication token provided by the user, the user's identity information is obtained by parsing the OAuth token or JWT and other authentication mechanisms. After parsing, the identity is confirmed by comparing it with the user data in the server-side database. After successful authentication, the user's permission level will be further matched. According to the user's permission settings in the system, the permission management will be automatically called to determine whether the user is an administrator, advanced user or ordinary user. After determining the permission, the task data that the user has access to will be filtered. By calling the query rules in the permission model, the tasks that the user can access will be filtered. For example, administrators can access all task data, while ordinary users can only access tasks related to themselves. The system will call SQL query statements based on the permission level to filter the data, and apply conditional filtering in the relevant fields of each task (such as task ownership, priority, etc.) to ensure that the task data finally returned meets the user's permission range.

[0091] Perform attribute assessment on the screened task data, including assessment of urgency, deadlines, and resource requirements;

[0092] After the task data is filtered, the tasks are evaluated for their attributes. The main attributes evaluated include urgency, deadline, and resource requirements. First, the task deadline field is extracted and combined with the current date to calculate the task's urgency. The smaller the time difference, the higher the urgency. The conventional classification standard may be more than 7 days for low urgency and less than 2 days for high urgency. Next, the resource requirements are queried from the resource table associated with the task data to see how much human, material, and other resources each task requires. The data will be quantified into numerical values ​​for use in the subsequent sorting process. After the task evaluation is completed, these attribute data are passed to the sorting algorithm, and each task is scored according to its urgency, deadline, and resource requirements to generate a priority task list.

[0093] Prioritize tasks using a sorting algorithm, using the formula:

[0094]

[0095] Get a priority task list, where n q Indicates the number of task attributes, xq i represents the score of the i-th task attribute, wq i is the weight coefficient of the corresponding attribute;

[0096] For example, the attributes considered include urgency, deadline and resource requirements, a total of 3 attributes, n q =3; let the weight of urgency be w q1 =0.5, the weights of deadline and resource requirements are w q2 =0.3 and w q3 = 0.2; The urgency score can be scored on a scale of 1 to 10. For example, if the urgency of a task is 8, the number of days remaining to the deadline is scored as 6 in the inverse proportion, and the resource requirement score is 7, substitute the values ​​into the formula:

[0097] sort(x q )=(0.5·8)+(0.3·6)+(0.2·7)=4+1.8+1.4=7.2;

[0098] This result indicates that the task has a priority score of 7.2 and can be compared with other tasks to sort out the priority list.

[0099] See also Figure 5 The specific steps for adjusting the application's operating permissions and access levels are as follows:

[0100] Analyze the attributes of each task in the prioritized task list and compare the task attributes with business requirements, including the required completion time and safety level;

[0101] Before scoring the application's operational permissions, we first analyze the attributes of each task in the priority task list and compare them with business needs. This involves a detailed comparison of the time required to complete the task and the security level. This comparison can help determine which tasks are critical and which should be postponed. This approach can optimize resource allocation, ensure that important tasks are prioritized, and ensure security, thereby significantly improving the application's operational efficiency and security management level.

[0102] Use the formula to calculate the operation permission score of each application:

[0103]

[0104] Where PS(a) is the operation permission score of application a, vs i is the weight of the attribute associated with the i-th task, ts i is the value of the i-th task attribute associated with application a, usj is the weight of the j-th business requirement associated attribute, bs j is the value of the jth attribute of the business requirement, n S and m s are the total number of task attributes and business requirement attributes, respectively;

[0105] Assume that application a is associated with three task attributes (n S =3), each task attribute has a weight vs i and attribute value ts i , the weights are vs1=0.5, vs2=0.3, vs3=0.2, the attribute values ​​are ts1=100, ts2=80, ts3=60, and similarly, there are two business requirement attributes (m s =2), weight us j and attribute value bs j , the weights are us1=0.6, us2=0.4, the attribute values ​​are bs1=50, bs2=70, and the formula is used to calculate:

[0106] PS(a)=(0.5×100)+(0.3×80)+(0.2×60)+(0.6×50)+(0.4×70);

[0107] PS(a)=50+24+12+30+28=144;

[0108] The result shows that the operation permission score of application a is 144, indicating that the application has high operation permissions and involves high-risk or high-security level operations, and further security measures need to be adjusted according to business policies.

[0109] See also Figure 6 , the steps for obtaining the configuration dynamic adjustment table are as follows:

[0110] Analyze the obtained priority task list, including the priority and associated attributes of each task, determine the operation permissions and access levels of multiple applications based on priority and business needs, and match task priorities with application needs;

[0111] When analyzing the priority task list, the first thing to consider is the priority of each task and its associated attributes. This involves a careful comparison of the time required to complete the task and the security level. By comparing these attributes, the relative importance of tasks can be determined. This priority analysis provides a basis for setting the application's operational permissions, ensuring that tasks with high security risks or urgency are prioritized, thereby achieving effective resource allocation and meeting business needs, which is crucial for maintaining the overall security and efficiency of the system.

[0112] Apply the Deep Q network to dynamically adjust application permission configuration using the formula:

[0113]

[0114] Get the optimal application configuration, where su i is the i-th element in the state vector, representing the current state (task type and user requirements), au i is the i-th element in the action vector, indicating the action taken (authority extension and restriction), wu i and bu i is the corresponding weight coefficient, which is used to adjust the sensitivity of the model to state and action, s u represents the state vector, a u represents the action vector, n u is the number of elements;

[0115] Let the state vector s u and action vector a u Contains 3 elements each, where n u =3, and set weights wu = [0.5, 0.3, 0.2], bu = [0.4, 0.4, 0.2], state vector su = [1, 0, 1] to represent the urgency and safety requirements of the task, and action vector au = [1, 1, 0] to represent the authorized extension and restriction operations. Substitute them into the formula for calculation:

[0116] QR(a u ,s u )=(0.5×1+0.4×1)+(0.3×0+0.4×1)+(0.2×1+0.2×0);

[0117] =0.9+0.4+0.2=1.5;

[0118] The result shows that the application configuration score is 1.5, indicating that based on the current task urgency and security needs, as well as the authorized operations, the permission settings of the application are relatively appropriate and meet business needs and security requirements.

[0119] See also Figure 7 ,The analysis steps of resource classification efficiency are as follows:

[0120] Collect business data and aggregate corresponding resource usage information, including CPU usage, memory usage, and network bandwidth usage;

[0121] Focus on collecting various business-related data, especially resource usage information, including CPU usage, memory usage, and network bandwidth usage. Data is crucial for understanding and optimizing system performance. Aggregated data provides a comprehensive view of system operation status, allowing administrators to see the consumption of various resources, providing a basis for subsequent resource allocation and performance adjustment. This process ensures the effective use of resources, reduces resource waste, enhances the system's responsiveness to changing business needs, and makes resource management more efficient and accurate.

[0122] Calculate the utilization rate of each type of resource during peak and valley periods. The utilization rate is defined as the ratio of resource usage to total available capacity. The peak utilization rate is U peak , the valley utilization rate is U valley , and dynamically adjust parameters according to business needs;

[0123] For each resource, calculate its peak and valley utilization rates during daily operations. The indicators reflect resource usage and load changes at different times. Calculating these utilization rates involves monitoring the ratio between resource consumption and total available capacity. This not only helps identify peaks and valleys in resource usage, but also provides data support for resource scheduling. By dynamically adjusting relevant parameters, we can better match business needs and resource supply, reduce resource redundancy and shortages, and thus improve overall operational efficiency.

[0124] By comparing U peak and U valley The difference between the two values ​​is used to evaluate the efficiency, using the formula:

[0125]

[0126] Among them, n m represents the number of resource types, EM is the resource classification efficiency, U peak,i and U valley,i Represent the utilization rate of the i-th resource during peak and valley periods respectively;

[0127] There are three types of resources, namely n m =3, the collected data include peak usage rate U peak =[0.90,0.85,0.80] and valley utilization rate U valley =[0.50,0.45,0.40], substitute into the formula for calculation:

[0128]

[0129] The result shows that the calculated result of resource allocation efficiency is 0.16, indicating that the difference in resource usage between peaks and valleys is small, indicating that the resource allocation is relatively reasonable, and parameters should continue to be adjusted to further optimize resource utilization efficiency.

[0130] See also Figure 8 , the specific steps for obtaining the optimal resource allocation measures are:

[0131] Using the gradient boosting tree algorithm, we predict the resource requirements of each application in the future time period based on historical data, and collect and evaluate the amount of resources currently used by each application;

[0132] The gradient boosting tree algorithm is used to predict the resource requirements of each application in the future time period. The process includes collecting current resource usage data, including CPU utilization, memory usage, and network bandwidth. Through in-depth analysis of historical data, it can effectively predict the possible future resource requirements of each application. This helps to understand and predict resource allocation needs, thereby optimizing the overall resource utilization efficiency. This is key to ensuring that resources are not over- or under-allocated.

[0133] Use the formula to optimize the amount of resources for each application:

[0134]

[0135] Determine the amount of resources HC that each application should receive, where n c Indicates the number of applications, wc i represents the weight of the i-th application, Dc i represents the predicted demand of the i-th application, Tc i Indicates the amount of resources currently allocated to the i-th application;

[0136] There are 3 applications, namely n c =3, weight wc = [0.5, 0.3, 0.2], predicted demand Dc = [100, 150, 120], current resource Tc = [50, 100, 90], the calculation process is as follows:

[0137]

[0138]

[0139] The results show that the optimal amount of resources is calculated with a score of 0.15, which means that given its weight and resource usage, the first application should receive the largest share of resources to ensure that its performance and response time are optimized.

[0140] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of protection of the technical solution of the present invention.

Claims

1. The multi-system collaborative work platform integrated with the business generator is characterized by: The platform includes: The authentication module performs permission verification based on the identity information received from the user, matches it with the information stored in the database, compares the key provided by the user with the stored key, and generates an authentication token; The task scheduling module filters the task data accessible under the user's authority based on the authentication token, and prioritizes the tasks by evaluating the task attributes and criticality ratings to generate a priority task list; The evaluation steps for the criticality classification are as follows: Collect basic task information, including deadlines, required resources, and departments. Convert the collected task information into quantifiable values. Convert the deadline into the number of days from the current date. Calculate the resource requirements of the task based on the task size and expected completion time. Grade the criticality of each task based on its resource requirements using the formula: ； Calculating mission criticality score ,in, and are the weighting factors for deadline and resource requirements, respectively, Represents the number of days from the task deadline. Indicates the resource requirements of the task; The steps for obtaining the priority task list are specifically as follows: Determine the identity and corresponding permission level based on the authentication token provided by the user, and filter the task data accessible under the target user's permission; Perform attribute assessment on the screened task data, including assessment of urgency, deadlines, and resource requirements; Prioritize tasks using a sorting algorithm, using the formula: ； Get a priority task list, where Indicates the priority of the task, Indicates the number of task attributes, Indicates the The rating of each task attribute, is the weight coefficient of the corresponding attribute; The application configuration module analyzes the degree of match between the current application status and the required permissions based on the priority task list, adjusts the operating permissions and access levels of multiple applications, and dynamically adjusts the application permission configuration based on business needs using the Deep Q network to generate a configuration dynamic adjustment table; The data analysis module uses the parameters in the configuration dynamic adjustment table to collect and aggregate business data, perform resource classification efficiency analysis on the business data, and use the gradient boosting tree algorithm to predict the workload of the business data, adjust the resource load between multiple applications, and obtain the optimal resource allocation measures; The analysis steps of the resource classification efficiency are specifically as follows: Collect business data and aggregate corresponding resource usage information, including CPU usage, memory usage, and network bandwidth usage; Calculate the utilization rate of each type of resource during peak and valley periods. The utilization rate is defined as the ratio of resource usage to total available capacity. The peak utilization rate is , the valley utilization rate is , and dynamically adjust parameters according to business needs; By comparison and The difference between the two values ​​is used to evaluate the efficiency, using the formula: ； in, Represents the number of resource types. For resource classification efficiency, and Representing the The utilization rate of class resources during peak and valley periods; The steps for obtaining the optimal resource allocation measures are specifically as follows: Using the gradient boosting tree algorithm, we predict the resource requirements of each application in the future time period based on historical data, and collect and evaluate the amount of resources currently used by each application; Use the formula to optimize the amount of resources for each application: ； Determine the amount of resources each application should receive ,in, Indicates the number of applications, Indicates the The weight of the application, Indicates the The demand forecast for each application, Indicates the The amount of resources currently allocated to an application.

2. The multi-system collaborative work platform integrated with the business generator according to claim 1, characterized in that: The steps for obtaining the authentication token are as follows: Based on the identity information received from the user, a preliminary match is performed with the stored information to verify the user's identity and obtain a preliminary verification result; Based on the preliminary verification results, the key is compared with the key provided by the user and key verification is performed using the formula: ； Calculating key consistency , generate the key verification result, where, The first key elements, Represents the first key elements, is the number of elements in the key; Based on the key verification result, if the verification passes, an authentication token is generated.

3. The multi-system collaborative work platform integrated with the business generator according to claim 1, characterized in that: The specific steps for adjusting the operating permissions and accessibility levels of the application are as follows: Analyze the attributes of each task in the priority task list and compare the task attributes with business requirements, including required completion time and safety level; Use the formula to calculate the operation permission score of each application: ； in, It's an application Operation authority score, It is The weight of the attribute associated with each task, It's an application The associated The value of a task attribute, It is The weight of the attributes associated with each business requirement, Is the first business requirement The value of an attribute, and are the total number of task attributes and business requirement attributes respectively.

4. The multi-system collaborative work platform integrated with the business generator according to claim 1, characterized in that: The steps for obtaining the configuration dynamic adjustment table are specifically as follows: Analyze the obtained priority task list, including the priority and associated attributes of each task, determine the operation permissions and access levels of multiple applications based on the priority and business needs, and match the task priorities with the application needs; Apply the Deep Q network to dynamically adjust application permission configuration using the formula: ； Get the optimal application configuration, where is the first elements, is the first elements, and is the corresponding weight coefficient, represents the state vector, represents the action vector, is the number of elements.

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