CN120106800B

CN120106800B - Service generator integrated multi-system cooperative work platform

Info

Publication number: CN120106800B
Application number: CN202510191987.8A
Authority: CN
Inventors: 黄涛; 孙宏; 孟晨; 吴冰
Original assignee: Tianjin Yitian Digital Service Co ltd
Current assignee: Tianjin Yitian Digital Service Co ltd
Priority date: 2025-02-21
Filing date: 2025-02-21
Publication date: 2025-09-26
Anticipated expiration: 2045-02-21
Also published as: CN120106800A

Abstract

The invention relates to the technical field of collaborative work, in particular to a multi-system collaborative work platform integrated by a service generator, which comprises an identity verification module, a task scheduling module, an application configuration module and a data analysis module, wherein the identity verification module performs authority verification based on receiving identity information input by a user, matches information stored in a database, compares a key provided by the user with a storage key to generate an identity verification token, and the task scheduling module screens task data which can be contacted under the authority of the user based on the identity verification token. The invention adopts the deep Q network to dynamically adjust the resource and the authority configuration, increases the adaptability of the platform, allows flexible adjustment according to the real-time service demand, thereby improving the resource utilization efficiency, predicts the workload through the gradient lifting tree algorithm, accurately manages the resource allocation, reduces the resource waste, ensures the continuity and the efficiency of service operation, and ensures the optimal configuration of the resource among different application programs.

Description

Service generator integrated multi-system cooperative work platform

Technical Field

The invention relates to the technical field of cooperative work, in particular to a multi-system cooperative work platform integrated by a service generator.

Background

The technical field of collaborative work is focused on improving interaction and working efficiency among teams by utilizing information technology, and covers the integration and use from data sharing, task coordination to communication tools so as to support team members in different geographic positions to effectively cooperate, so that organizations can better manage complex projects, and information and resources are distributed and used in teams efficiently.

The service generator integrated multi-system cooperative work platform refers to a platform integrating a plurality of general software and professional applications, aims to provide standardized and efficient service operation support, embeds standard applications such as office OA, HR systems, elevator maintenance systems, contract management systems, project management systems and order management systems, supports a report large screen and a data statistics analysis function, can realize resource sharing, and is unified to manage, so that cooperation among different departments in an organization is smoother, and project execution speed and quality are greatly improved.

The traditional platform lacks the quick response capability to sudden events and real-time changes, so that outdated information is relied on in the decision making process, resources cannot be adjusted in time, the effectiveness of decision making and the timely delivery of projects are affected, in the aspect of resource management, the conventional system cannot flexibly adjust resource allocation, and for the insufficient processing capability of sudden events, delay or failure of key tasks is caused when the resources are in high demand, resource waste and low allocation efficiency are caused, and especially in a changeable environment, the cooperation efficiency of a team and the flexibility of project management are affected.

Disclosure of Invention

The invention aims to solve the defects in the prior art, and provides a multi-system cooperative work platform integrated by a service generator.

In order to achieve the above purpose, the invention adopts the following technical scheme that the service generator integrated multi-system cooperative work platform comprises:

The identity verification module performs authority verification based on the received identity information input by the user, matches the information stored in the database, compares a key provided by the user with a stored key, and generates an identity verification token;

The task scheduling module screens the task data which can be contacted under the user permission based on the identity verification token, and prioritizes the tasks by evaluating the task attribute and the criticality classification to generate a priority task list;

The application configuration module analyzes the matching degree of the current application state and the required permission according to the priority task list, adjusts the operation permission and the accessible level of a plurality of application programs, dynamically adjusts the application permission configuration by the application depth Q network according to the service requirement, and generates a configuration dynamic adjustment table;

And the data analysis module collects and aggregates the service data by utilizing the parameters in the configuration dynamic adjustment table, analyzes the resource classification efficiency of the service data, adopts a gradient lifting tree algorithm to predict the workload of the service data, and adjusts the resource load among multiple application programs to obtain resource optimal allocation measures.

The invention improves that the acquisition steps of the authentication token are as follows:

based on the received identity information input by the user, performing preliminary matching with the stored information, and verifying the identity of the user to obtain a preliminary verification result;

based on the preliminary verification result, comparing with the key provided by the user, executing the key verification, and adopting the formula:

Calculating key consistency DV _k to generate a key verification result, wherein k _ui represents an ith key element provided by a user, k _si represents the ith key element in stored data, and n _d is the number of elements in a key;

and generating an identity authentication token according to the key verification result if the authentication is passed.

The invention improves, the evaluation step of the criticality grading specifically comprises:

collecting basic information of a task, including an expiration date, required resources and departments, converting the collected task information into quantifiable numerical values, converting the expiration date into days from the current date, calculating the resource requirements of the task according to the task scale and the expected completion time;

the criticality of each task is ranked according to the resource requirements of the task, using the formula:

Task criticality scores SV are calculated, where wv _D and wv _R are weight factors for the expiration date and the resource demand, respectively, D _V represents the number of days from the task expiration date, and R _V represents the resource demand of the task.

The invention improves that the acquisition steps of the priority task list are as follows:

According to the identity verification token provided by the user, determining the identity and the corresponding authority level, and screening the task data accessible under the authority of the target user;

Performing attribute evaluation on the screened task data, wherein the evaluation comprises the evaluation of the emergency degree, the expiration date and the resource requirement;

the tasks are prioritized by a sequencing algorithm, and the formula is adopted:

A prioritized task list is obtained, where n _q represents the number of task attributes, xq _i represents the score of the ith task attribute, and wq _i is the weight coefficient of the corresponding attribute.

The invention improves that the operation authority and the accessible level of the application program are specifically:

analyzing the attribute of each task in the priority task list, and comparing the task attribute with the service requirement, wherein the task attribute comprises the required completion time and the security level;

Calculating the operation authority score of each application program by adopting a formula:

Where PS (a) is the operation authority score of application a, vs _i is the weight of the ith task-associated attribute, ts _i is the value of the ith task attribute associated with application a, us _j is the weight of the jth business requirement-associated attribute, bs _j is the value of the jth attribute of the business requirement, and n _S and m _s are the total number of task attributes and business requirement attributes, respectively.

The invention improves that the acquisition steps of the configuration dynamic adjustment table specifically comprise:

The obtained priority task list is analyzed, the priority and the associated attribute of each task are included, the operation authority and the accessible level of a plurality of application programs are determined according to the priority and the service requirement, and the task priority is matched with the application requirement;

The application depth Q network dynamically adjusts the application authority configuration by adopting the formula:

The optimal application configuration is obtained, 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 are the corresponding weight coefficients, s _u represents the state vector, a _u represents the action vector, and n _u is the number of elements.

The invention improves, the analysis step of the resource classification efficiency specifically comprises:

Collecting service data, and aggregating corresponding resource use information, including CPU use rate, memory use amount and network bandwidth use condition;

Calculating the utilization rate of each type of resource in the peak period and the valley period, defining the utilization rate as the ratio of the utilization amount of the resource to the total available amount, recording the utilization rate in the peak period as U _peak and the utilization rate in the valley period as U _valley, and dynamically adjusting parameters according to service requirements;

By comparing the difference between U _peak and U _valley, an efficiency assessment is made, using the formula:

Where n _m represents the number of kinds of resources, EM is the efficiency of resource classification, and U _peak,i and U _valley,i represent the usage of the i-th kind of resources in peak and valley periods, respectively.

The invention improves that the acquisition steps of the resource optimal allocation measure are as follows:

Predicting the resource demand of each application program in a future time period according to historical data by utilizing a gradient lifting tree algorithm, and collecting and evaluating the current used resource quantity of each application program;

and optimizing and calculating the resource quantity of each application program by adopting a formula:

An amount of resources HC that should be received by each application is determined, where n _c represents the number of applications, wc _i represents the weight of the ith application, dc _i represents the predicted amount of demand by the ith application, and Tc _i represents the amount of resources currently allocated by the ith application.

Compared with the prior art, the invention has the advantages and positive effects that:

according to the invention, through user identity verification and authority matching, the security of sensitive data and the accuracy of access control are ensured, resource optimization allocation is dynamically analyzed and predicted, reasonable allocation of resources in the periods of high and low demands is ensured, waste is reduced, operation efficiency is improved, deep integration of data analysis provides real-time data support for decision making, data driving performance of decision making is enhanced, project adjustment can reflect the change of an operation environment in time, and an organization can optimize the whole resource configuration and project management flow while ensuring success of key projects.

Drawings

FIG. 1 is a flow chart of a multi-system collaborative work platform for business generator integration in accordance with the present invention;

FIG. 2 is a flow chart of the acquisition of an authentication token according to the present invention;

FIG. 3 is a flow chart of the evaluation of criticality ratings in the present invention;

FIG. 4 is a flow chart of the acquisition of a priority task list in the present invention;

FIG. 5 is a flowchart illustrating the adjustment of the operation authority and the accessibility level of an application program according to the present invention;

FIG. 6 is a flow chart of the acquisition of a configuration dynamic adjustment table in the present invention;

FIG. 7 is a flow chart of an analysis of the efficiency of resource classification in the present invention;

Fig. 8 is a flowchart of the resource optimization measure acquisition in the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," etc. indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the invention and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Examples

Referring to fig. 1, the present invention provides a technical solution, in which a service generator integrated multi-system cooperative work platform includes:

the application configuration module analyzes the matching degree of the current application state and the required permission according to the priority task list, adjusts the operation permission and the accessible level of a plurality of application programs, dynamically adjusts the application permission configuration according to the service requirement, and generates a configuration dynamic adjustment table;

The data analysis module collects and aggregates the service data by utilizing the parameters in the configuration dynamic adjustment table, analyzes the resource classification efficiency of the service data, pays attention to the data of the peak period and the valley period of the resource use, predicts the workload of the service data by adopting a gradient lifting tree algorithm, adjusts the resource load among multiple application programs and obtains the resource optimal allocation measure.

The authentication token comprises an encryption key, a user identity and a valid period, the priority task list comprises a task number, an emergency level and expected completion time, and the configuration dynamic adjustment table comprises a permission level, a configuration state and last update time.

Referring to fig. 2, the steps for obtaining the authentication token specifically include:

In authentication, user identity information, such as a user name and a password, needs to be collected, and this information is used to match user data stored in advance in a database to verify whether submitted information matches known user data, and the received user information needs to be subjected to security check before being processed to prevent data from being tampered or leaked.

according to the key verification result, if verification is passed, an identity verification token is generated, and the token is used for session management and service access of the user;

If there is a key element [ k _ui ] provided by a user and a corresponding key element [ k _si ] in the database, the following data is collected, n _d＝3,k_ui＝[110,115,105],k_si = [100,120,100], substituted into the formula:

The result shows that the consistency of the keys is 0.0142, and the keys have higher consistency and are used for verifying the legitimacy of the identity of the user.

After the key verification process is finished, whether to grant the user identity verification token is determined based on the evaluation result of the key consistency, the token is used for confirming the identity legitimacy of the user, the token contains the identity information and the authority level of the user, the encrypted token can be used for subsequent operation of the user, the legitimacy and the safety of the operation of the user are ensured, the key for connecting the authentication of the user and the access of the service is provided, and the unauthorized access attempt can be effectively prevented.

Referring to fig. 3, the key classification evaluation steps specifically include:

Basic information of tasks such as expiration dates, required resources and departments are systematically collected and recorded, the information is the basis for conversion into quantifiable values, the expiration dates are converted into days from the current date, the days are calculated through date difference, the resource requirements are evaluated according to the scale and expected completion time of each task, the resource requirements are vital to follow-up task plans and resource allocation so as to accurately evaluate and predict the upcoming requirements, reasonable allocation and efficient use of the resources are ensured, and therefore the overall flow and efficiency of task execution are optimized.

Calculating a task criticality score SV, wherein wv _D and wv _R are weight factors of the expiration date and the resource requirement, respectively, D _V +1 ensures that even if D _V is 0 (i.e. is blocked today), no calculation abnormality is caused, D _V represents the number of days from the expiration date of the task, and R _V represents the resource requirement of the task;

Let a project team manage three different tasks, the weighting factors be wv _D =0.7 (deadline weight) and wv _R =0.3 (resource demand weight), let the deadline of the three tasks be 5, 2 and 10 days from today, the resource demand scores be 50, 75 and 20, respectively, calculate the criticality score of each task:

Based on the calculation, the second task has the highest criticality score, indicating the most urgent and most resource demanding task, should be preferentially processed. The calculation mode enables the task priority to be set more scientifically and practically, and is beneficial to efficient management of resources.

Referring to fig. 4, the step of acquiring the priority task list specifically includes:

According to an identity verification token provided by a user, determining an identity and a corresponding authority level, and screening task data accessible under the authority of a target user;

According to an identity verification token provided by a user, acquiring identity information of the user by analyzing an OAuth token or a JWT (joint verification wt) and other identity verification mechanisms, carrying out identity verification on user data in a database of a server side after analysis, further matching authority levels of the user after successful identity verification, automatically invoking authority management according to authority settings of the user in a system, determining whether the user is an administrator, an advanced user or a common user, after determining the authority, starting screening task data which the user has authority to access, screening tasks which the user can access, for example, the administrator can access all task data, and the common user can only access tasks related to the administrator, invoking SQL query sentences according to the authority levels by the system, filtering data, and applying condition screening in related fields (such as task attribution, priority and the like) of each task to ensure that finally returned task data accords with the authority range of the user.

after task data is filtered, attribute evaluation is performed on the tasks. The main attributes of evaluation comprise urgency, expiration date and resource requirement, firstly, the expiration date field of a task is extracted, the urgency of the task is calculated in combination with the current date, the urgency is higher when the time difference is smaller, the conventional division standard can be that the urgency is low in more than 7 days and the urgency is high in less than 2 days, then, the resource requirement is queried from a task data association resource table, how many resources such as manpower and material resources are needed by each task are checked, the data are quantized into numerical values so as to be used in the subsequent sorting process, after the task evaluation is finished, the attribute data are transmitted into a sorting algorithm, and a priority task list is generated according to the urgency, the expiration date and the resource requirement of each task.

Obtaining a priority task list, wherein n _q represents the number of task attributes, xq _i represents the score of the ith task attribute, and wq _i is the weight coefficient of the corresponding attribute;

For example, the attributes considered include urgency, expiration date and resource requirement, total 3 attributes, n _q =3, setting the urgency weight to w _q1 =0.5, expiration date and resource requirement weight to w _q2 =0.3 and w _q3 =0.2, respectively, the urgency score may be scored in the interval of 1 to 10, for example, urgency of a task is 8, remaining days of expiration date are scored in the opposite proportion as 6, score of resource requirement is 7, and the numerical value is substituted into the formula:

sort(x_q)=(0.5·8)+(0.3·6)+(0.2·7)=4+1.8+1.4=7.2;

The result shows that the task has a priority of 7.2 points and can be compared with other tasks to discharge the priority list.

Referring to fig. 5, the steps of adjusting the operation authority and the accessible level of the application program specifically include:

Before scoring the operation authority of the application program, each task attribute in the priority task list is analyzed first and compared with the service requirement, the detailed comparison of the time and the security level required to be completed by the tasks is related, the comparison can help to determine which tasks are critical tasks and which are executed later, by the method, the resource allocation can be optimized, the priority treatment of the important tasks is ensured, and meanwhile, the security is ensured, so that the running efficiency and the security management level of the application program are obviously improved.

Wherein PS (a) is the operation authority score of the application program a, vs _i is the weight of the ith task-related attribute, ts _i is the value of the ith task attribute associated with the application program a, us _j is the weight of the jth business requirement-related attribute, bs _j is the value of the jth attribute of the business requirement, and n _S and m _s are the total number of task attributes and business requirement attributes, respectively;

Let the application a be associated with 3 task attributes (n _S =3), each task attribute having a weight vs _i and an attribute value ts _i, the weight being vs ₁＝0.5,vs₂＝0.3,vs₃ =0.2, the attribute value ts ₁＝100,ts₂＝80,ts₃ =60, and likewise, 2 business requirement attributes (m _s =2), a weight us _j and an attribute value bs _j, a weight us ₁＝0.6,us₂ =0.4, the attribute value bs ₁＝50,bs₂ =70, and the formula is substituted for the calculation:

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

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

The result shows that the operation authority score of the application program a is 144, which indicates that the application program has higher operation authority, and relates to high risk or high security level operation, and further security measures need to be adjusted according to the business strategy.

Referring to fig. 6, the steps for obtaining the configuration dynamic adjustment table specifically include:

Analyzing the obtained priority task list, including the priority and the associated attribute of each task, determining the operation authorities and the accessible level of a plurality of application programs according to the priority and the service requirements, and matching the task priority with the application requirements;

In the analysis of the priority task list, the priority of each task and the associated attribute thereof are firstly considered, the careful comparison of the time and the security level of the task to be completed is related, the relative importance among the tasks can be determined by comparing the attributes, the priority analysis provides a basis for the operation authority setting of the application program, and the priority processing of the tasks with high security risk or urgency is ensured, so that the effective allocation of resources and the satisfaction of service requirements are realized, and the importance is important for the overall security and efficiency of a maintenance system.

Obtaining an optimal application configuration, wherein su _i is the ith element in the state vector, representing the current state (task type and user requirement), au _i is the ith element in the action vector, representing the actions taken (rights expansion and restriction), wu _i and bu _i are corresponding weight coefficients for adjusting the sensitivity of the model to the state and actions, s _u represents the state vector, a _u represents the action vector, and n _u is the number of elements;

let the state vector s _u and the action vector a _u contain 3 elements each, where n _u =3, and let the weights wu= [0.5,0.3,0.2], bu= [0.4,0.4,0.2], the state vector su= [1,0,1] represent the urgency and security requirement of the task, and the action vector au= [1,0] represents the operation of authorizing expansion and restriction, and the substitution formula is used for calculation:

QR(a_u,s_u)=(0.5×1+0.4×1)+(0.3×0+0.4×1)+(0.2×1+0.2×0);

=0.9+0.4+0.2=1.5;

the result shows that the configuration score of the application program is 1.5, the permission setting of the application program is proper based on the current task urgency and security requirements and the authorized operation, and the application program meets the business requirements and the security requirements.

Referring to fig. 7, the steps of analyzing the resource classification efficiency specifically include:

The method and the system pay attention to collecting related data of each service, particularly resource utilization information, including CPU utilization rate, memory utilization amount and network bandwidth utilization condition, wherein the data is critical to understanding and optimizing system performance, aggregate data provides a comprehensive view of system operation conditions, allows an administrator to see consumption conditions of each resource, provides basis for subsequent resource allocation and performance adjustment, ensures effective utilization of the resource, reduces resource waste, enhances response capability of the system to changing service requirements, and enables resource management to be more efficient and accurate.

For each resource, the peak period and valley period utilization rate of the resource in daily operation is calculated, the indexes reflect the utilization condition and load change of the resource at different times, the calculation of the utilization rates involves monitoring the ratio between the consumption amount and the total available amount of the resource, the peak and valley of the resource utilization are identified, the data support is provided for resource scheduling, the service demand and the resource supply can be matched better through dynamic adjustment of related parameters, the resource redundancy and the shortage condition are reduced, and the overall operation efficiency is improved.

Wherein n _m represents the kinds and numbers of resources, EM is the resource classification efficiency, and U _peak,i and U _valley,i respectively represent the utilization rate of the ith resource in the peak period and the valley period;

There are 3 resource types, namely, n _m =3, the collected data are that the peak period utilization rate U _peak = [0.90,0.85,0.80] and the valley period utilization rate U _valley = [0.50,0.45,0.40], and the data are calculated by substituting the formula:

The result shows that the calculation result of the resource allocation efficiency is 0.16, which shows that the resource use difference of the system between peaks and valleys is smaller, which means that the resource allocation is more reasonable, and the parameter should be continuously adjusted to further optimize the resource use efficiency.

Referring to fig. 8, the steps for obtaining the resource optimal allocation measure specifically include:

The gradient lifting tree algorithm is used for predicting the resource demand of each application program in the future time period, the process comprises the steps of collecting current resource usage data, the data cover the conditions of CPU usage rate, memory usage amount and network bandwidth, and through deep analysis of historical data, the possible resource demand of each application program in the future can be effectively predicted so as to understand and predict the resource allocation demand, so that the overall utilization efficiency of resources is optimized, and the key for ensuring that the resources cannot be excessively or inadequately configured is provided.

Determining the amount of resources HC that each application should receive, wherein n _c represents the number of applications, wc _i represents the weight of the ith application, dc _i represents the predicted required amount of the ith application, and Tc _i represents the current allocated amount of resources of the ith application;

There are 3 applications, i.e., n _c =3, weight wc= [0.5,0.3,0.2], predicted demand dc= [100,150,120], current resource tc= [50,100,90], and the calculation process is as follows:

The result shows that the calculated score for the optimal amount of resources is 0.15, which means that the first application should receive the largest share of resources given the weight and the use of resources, ensuring that its performance and response time are optimized.

In the foregoing, the present invention is not limited to the preferred embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure of the present invention.

Claims

1. A multi-system collaborative work platform integrated with a service generator, characterized in that 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, conducts resource classification efficiency analysis on the business data, and uses 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 a business generator according to claim 1, wherein the step of obtaining the identity authentication token is specifically:

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 a business generator according to claim 1, wherein the step of adjusting the operating authority and access level of the application program is specifically 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 a service generator according to claim 1, wherein the step of obtaining the dynamic configuration adjustment table is specifically:

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.