Government affair item management system

Abstract

The invention relates to the technical field of government affair management, and discloses a government affair item management system, which comprises an optimization engine module used for executing a multi-objective optimization task, including optimization of time, cost and service quality, and performing optimal configuration on resources through a multi-objective optimization algorithm; the data monitoring and feedback module is used for collecting and monitoring the execution condition of government affair items, the resource use condition and the change of constraint conditions in real time, and feeding back the collected data to the optimization engine module for dynamic adjustment; and the decision support module is used for displaying the result of the optimization engine module and supporting the user to manually adjust the optimal resource configuration scheme. By introducing dynamic resource scheduling, real-time objective function adjustment and an intelligent optimization algorithm, efficient and flexible management of government affair items is achieved, resource allocation is optimized, task execution efficiency is improved, and it is ensured that the government affair items are completed on time and the quality requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of government affairs management, specifically to a government affairs item management system. Background Art

[0002] In modern society, the efficient management of government affairs items is crucial for improving administrative efficiency and optimizing resource allocation. With the continuous development of government functions and the improvement of informatization level, how to effectively manage a large number of government affairs items and rationally allocate limited resources has become an urgent need. As an effective tool, the government affairs item management system aims to improve the work efficiency of government departments, ensure the timely completion of various tasks, optimize service quality, and avoid resource waste and uneven distribution through automated scheduling and optimized resource allocation.

[0003] Although existing government affairs item management systems can carry out resource allocation and task management to a certain extent, most systems still rely on manual intervention and static rules to guide resource allocation and task execution. Such systems usually arrange tasks according to preset goals and constraints, such as scheduling according to fixed time, resource consumption, and quality standards, in an attempt to achieve the optimal execution effect of tasks. At the same time, the prior art provides basic resource monitoring and scheduling functions, which can ensure the smooth completion of tasks under certain conditions.

[0004] However, there are still some deficiencies in the prior art. Traditional systems rely on static preset objective functions and manual intervention, and cannot adjust resource allocation in real time to cope with changes during task execution; for example, if there is a time delay or excessive resource consumption during task execution, existing systems cannot adjust the optimization plan in time, and often require manual adjustment by managers, resulting in low efficiency; in addition, existing systems lack a comprehensively integrated operation interface, and the data display and user interaction are not diverse enough, resulting in complex operations and untimely feedback. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a government affairs item management system, which solves the problems of static resource allocation, lack of dynamic adjustment in task execution, and high operation complexity in the prior art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A government affairs item management system, including: an optimization engine module for performing multi-objective optimization tasks, including the optimization of time, cost, and service quality, and optimally allocating resources through a multi-objective optimization algorithm; A data monitoring and feedback module for real-time collecting and monitoring the execution status of government affairs items, resource usage, and changes in constraint conditions, and feeding back the collected data to the optimization engine module for dynamic adjustment; A decision support module, which is used to display the results of the optimization engine module and support manual adjustment of the optimal resource allocation plan by the user; a dynamic resource scheduling module, which is used to perform dynamic scheduling and optimization of resources according to real-time data, and adopts a deep reinforcement learning algorithm to optimize resource allocation and cross-departmental task collaboration; An objective function and constraint management module, which is used to define, set and adjust in real time the objective function and constraint conditions in the government affairs matter management system, including time limit, resource upper limit and service quality requirements; A user interface module, which is used to provide a visual interface for government affairs matter management personnel to display matter processing status, optimization results, resource scheduling and decision support information.

[0007] Preferably, the optimization engine module includes: A multi-objective optimization function, which is used to minimize time consumption, minimize cost consumption and maximize service quality; A heuristic algorithm function, which globally searches for optimization objectives through a genetic algorithm or a particle swarm optimization algorithm; A Pareto optimal solution function, which is used to calculate the optimal balance among time, cost and service quality and generate a Pareto front solution set.

[0008] Preferably, the data monitoring and feedback module includes: A real-time data collection function, which is used to collect the execution time, the amount of resources consumed and the cross-departmental coordination situation in each link of the government affairs matter processing process; A constraint change monitoring function, which is used to monitor the changes in time, resources and service quality and feedback them to the optimization engine module; A real-time feedback function, which is used to feedback the monitoring data to the optimization engine module so as to dynamically adjust the resource allocation strategy.

[0009] Preferably, the decision support module includes: A result display function, which is used to visually display the optimal resource allocation plan and cross-departmental collaboration strategy information calculated by the optimization engine module; A plan recommendation and adjustment function, which is used to provide multiple plans according to the optimization results and allow the manager to manually adjust the optimization plan according to actual needs; A visual report generation function, which is used to generate reports on matter processing, resource use and service quality and perform data analysis.

[0010] Preferably, the dynamic resource scheduling module includes: A real-time resource scheduling function, which dynamically adjusts the resource allocation plan based on the real-time matter progress and resource situation through a deep reinforcement learning algorithm; The adaptive learning function optimizes the resource scheduling strategy through interaction with the actual environment, improving the processing efficiency of government affairs; the task allocation function adjusts task allocation and priorities in real time according to cross-departmental task requirements to ensure that matters are completed on time.

[0011] Preferably, the objective function and constraint management module includes: The objective function definition function is used to set each objective function in the multi-objective optimization problem, including time minimization, cost minimization, and service quality maximization; The constraint condition setting function is used to define the time limit, resource upper limit, and service quality requirements for each matter link; The dynamic adjustment function is used to adjust the objective function weights and constraint conditions according to real-time monitoring data and actual situations.

[0012] Preferably, the user interface module includes: The interface display function displays the processing status, real-time progress, and resource consumption of matters; The operation function supports users to manually adjust the optimization results, set objective weights, and reallocate resources; The visualization report function provides visualization reports and data analysis tools to help users understand the optimization results and support the decision-making process.

[0013] Preferably, the multi-objective optimization methods used by the optimization engine module include: The weight weighting method balances the relationship between time, cost, and service quality objectives by adjusting the weights of each objective function; The Pareto optimal solution calculation generates the Pareto front by calculating the optimal solution set between multiple objectives, providing the optimal resource allocation strategy.

[0014] Preferably, the deep reinforcement learning algorithms used by the dynamic resource scheduling module include: The state space definition function defines the real-time data of resource usage, matter progress, and cross-departmental collaboration as state inputs; The action space definition function defines resource scheduling, task allocation, and priority adjustment as action outputs; The reward function definition function calculates rewards by processing data on time, resource consumption, and service quality to guide the learning process.

[0015] Preferably, the objective function of the system includes the trade-off optimization among time, cost, and service quality. By weighting each objective function, a comprehensive weighted objective function is formed to maximize the global benefit.

[0016] The present invention provides a government affairs matter management system. It has the following beneficial effects: 1. Through the deep reinforcement learning algorithm, the present invention realizes the adaptive scheduling of resources. The system automatically optimizes resource allocation according to real-time task data, ensuring that each link receives appropriate support. This flexible adjustment greatly improves efficiency and avoids the limitations of manual intervention and fixed rules.

[0017] 2. Users can easily adjust the weights of optimization objectives and even modify the constraints of time, cost, and quality. Such dynamic controllability enables the system to quickly adapt to different government affairs task requirements and flexibly respond to the changing environment.

[0018] 3. The system not only displays real-time data but also provides an interactive adjustment function. Users can monitor task progress, allocate resources, and optimize objectives on the same interface. The simple and clear design improves operation efficiency and enables users to more intuitively understand data and execution status.

[0019] 4. The present invention combines an optimization engine with a feedback mechanism to automatically generate optimization solutions and provide real-time feedback on optimization effects. The report includes not only task progress but also details of resource consumption and service quality, helping management personnel keep track of task execution at any time and make accurate decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the system structure diagram of the present invention; Figure 2 is the module architecture diagram of the user interface module of the present invention; Figure 3 is the module architecture diagram of the data monitoring and feedback module of the present invention; Figure 4 is the module architecture diagram of the dynamic attitude scheduling module of the present invention; Figure 5 is the module architecture diagram of the objective function and constraint management module of the present invention; Figure 6 is the module architecture diagram of the optimization engine module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0022] Please refer to the attached Figure 1 - attached Figure 6 , the embodiments of the present invention provide a government affairs matter management system, including: Optimization Engine Module, which is used to perform multi-objective optimization tasks, including the optimization of time, cost, and service quality, and optimally allocates resources through multi-objective optimization algorithms; The Optimization Engine Module is responsible for optimizing the configuration of multiple objectives such as time, cost, and service quality. Through the optimization of each objective and resource scheduling, this module ensures that the processing of government affairs is completed efficiently and accurately under multiple constraints.

[0023] This module adopts multi-objective optimization methods, including the objectives of time, cost, and service quality, and effectively balances them through weighted methods, heuristic algorithms, and Pareto optimal solution methods. The following is a detailed description of the implementation methods of this module, including the definition of objective functions, the selection of algorithms in the optimization process, the applied formulas, and parameter definitions.

[0024] In this module, multi-objective optimization tasks are first set, corresponding to the minimization of time, cost, and maximization of service quality for government affairs. When calculating each objective function, the specific steps of matter processing and resource allocation are considered. According to the mutual influence of these objectives, comprehensive optimization is carried out through weighted methods, genetic algorithms, etc.

[0025] The time minimization objective is for the processing process of each matter, optimizing the time consumption between links to ensure that government affairs are completed within the specified time limit.

[0026] The calculation of this objective function is as follows: Where: f 1 (x) is the total time consumption objective function; t i is the time consumption coefficient of the i-th link, reflecting the time required for this link in the matter processing process; x i is the resource allocation amount of the i-th link, that is, the allocated resources (such as time, personnel, funds, etc.); n is the total number of links in matter processing.

[0027] The cost optimization objective aims to minimize the resource consumption of government affairs. The system comprehensively evaluates according to the resource consumption costs of each link to find the optimal resource configuration.

[0028] The definition of this objective function is: Where: f 2 (x) is the total cost consumption objective function; c i is the unit resource cost of the i-th link, reflecting the resource cost required for this link to execute (such as labor costs, material costs, etc.); x i is the resource allocation amount of the i-th link, determining the amount of resources used in this link; n is the total number of links in matter processing.

[0029] Service quality optimization ensures that the quality of each matter meets the standards during the processing. By maximizing service quality, the system improves the overall processing effect of government affairs matters.

[0030] The objective function is defined as: Where: f 3 (x) is the service quality objective function; q i is the service quality coefficient of the i-th link, reflecting the service quality contribution of this link; x i is the resource allocation amount of the i-th link; n is the total number of links in the matter processing.

[0031] To balance the conflicts among time, cost, and service quality, the optimization engine module adopts the weighted method. The weights w 1 , w 2 , w 3 of each objective function are respectively related to the relative importance of time, cost, and service quality. By weighted combining these objective functions, the system can adjust the priorities according to actual needs.

[0032] The weighted objective function is: f weighted (x) = w 1 ·f 1 (x) + w 2 ·f 2 (x) + w 3 ·f 3 (x) Where: f weighted (x) is the weighted objective function; w 1 , w 2 , w 3 are the weights of the time, cost, and service quality objectives, satisfying w 1 + w 2 + w 3 = 1; f 1 (x), f 2 (x), f 3 (x) are the objective functions of time, cost, and service quality.

[0033] The weights w 1 , w 2 , w 3 can be adjusted according to system requirements. For example, in urgent matters, a higher weight may be assigned to the time objective, while in the case of limited budget, the weight of the cost objective will be increased.

[0034] In the optimization engine module of the present invention, the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) are selected to achieve global search and solve the optimal resource allocation scheme. The two algorithms are suitable for different optimization problems and constraint conditions, and can effectively improve the search efficiency.

[0035] The genetic algorithm simulates the process of natural selection, generates new resource allocation schemes through operations such as selection, crossover, and mutation, and evaluates the schemes according to the fitness function (i.e., the weighted objective function). After multiple generations of evolution, the genetic algorithm can converge to the global optimal solution.

[0036] The specific steps include: Initializing the population: Generate a set of initial solutions as the population, representing different resource allocation schemes.

[0037] Selection operation: Evaluate the quality of each individual according to the fitness function, and select the individuals with higher fitness to enter the next generation.

[0038] Crossover operation: Cross the selected individuals to generate new offspring individuals.

[0039] Mutation operation: Mutate the offspring individuals to ensure the diversity of the population.

[0040] Termination condition: Terminate the algorithm by setting the maximum number of iterations or meeting the optimal solution criterion.

[0041] The particle swarm optimization algorithm simulates the process of birds foraging. Each particle represents a solution, and through the update of the position and velocity of the particles, all the particles in the group cooperate with each other to find the optimal solution.

[0042] The specific steps include: Initializing the particle swarm: Each particle represents a resource allocation scheme, and its position and velocity are randomly initialized.

[0043] Fitness evaluation: Evaluate each particle through the fitness function (i.e., the weighted objective function) to determine its quality.

[0044] Particle update: Update the position and velocity of the particles according to the historical optimal position of each particle and the optimal position of the entire group.

[0045] Termination condition: When the maximum number of iterations is reached or a particle that meets the optimal solution is found, stop the algorithm.

[0046] These two optimization algorithms can perform global search on the resource allocation problem in different ways, ensuring that the system can achieve the best balance among time, cost, and quality of service.

[0047] When solving multi-objective optimization problems, the optimization engine module adopts the calculation principle of Pareto optimal solutions. A Pareto optimal solution refers to a solution in multi-objective optimization where it is not possible to improve one objective without worsening at least one of the other objectives. By calculating the Pareto front solution set, the system provides multiple optimization schemes, and managers can select the most suitable one according to actual needs.

[0048] In multi-objective optimization problems, by calculating the Pareto front of the objective function, the optimization engine can give multiple solutions. These solutions are not superior or inferior to each other, but they perform differently on different objectives. In this way, the optimization engine provides decision-makers with multiple possible optimization paths, ensuring that the system can respond flexibly to different requirements.

[0049] The optimization engine module finally feeds back the calculated optimal resource allocation scheme and multiple Pareto optimal solutions to the decision support module. In this module, managers can further adjust the optimization scheme according to their own needs. At this time, the optimization engine module will make dynamic adjustments based on the feedback data to ensure that the final resource allocation scheme best meets the actual needs of government affairs processing.

[0050] Through the weighted method, genetic algorithm, and particle swarm optimization algorithm, the system can perform efficient resource allocation under multiple objectives and provide multiple optimization schemes through Pareto optimal solutions.

[0051] The data monitoring and feedback module is used to collect and monitor in real time the execution status of government affairs, resource usage, and changes in constraints, and feed the collected data back to the optimization engine module for dynamic adjustment; The data monitoring and feedback module is not only responsible for monitoring the execution data of each link in real time, but also needs to provide real-time feedback to the optimization engine module based on these data, so as to ensure that the system can dynamically adjust and optimize resource allocation. The efficient operation of the data monitoring and feedback module is one of the core factors to ensure the efficient completion of government affairs.

[0052] In this embodiment, the function of the data monitoring and feedback module is not limited to data collection and storage, but also includes the analysis and feedback of real-time monitoring results. Through an accurate data monitoring and feedback mechanism, this module can provide necessary support for the optimization engine so that the latter can adjust the optimization strategy and optimize the entire government affairs processing process.

[0053] The data monitoring and feedback module will first collect real-time data from each processing link in the system. These data include: Execution time: The actual execution time of each matter processing link, reflecting the speed of matter completion.

[0054] Resource consumption: The amount of resources consumed in each process, such as manpower, funds, equipment, etc., reflects the resource utilization efficiency.

[0055] Department collaboration: For processes involving multiple departments, monitor the collaboration among different departments, especially resource sharing and information flow between departments.

[0056] Constraint conditions: The changes of constraint conditions such as time limits and resource ceilings in actual execution.

[0057] To ensure the real-time and accuracy of the monitoring process, the system will design a dynamic data acquisition mechanism that can extract key data from each process in a timely manner and store it in the database. This data will serve as the basis for subsequent optimization and decision support.

[0058] In some embodiments, the system design can ensure the efficiency of data acquisition, avoid any additional delays or performance bottlenecks, and ensure the timely feedback of real-time data.

[0059] In this embodiment, the data monitoring and feedback module is not only used for data acquisition, but also can provide feedback to the optimization engine module based on real-time monitoring of data. Specifically, the data monitoring module will track the execution status of each process item, such as timeouts, excessive resource consumption, or unmet service quality standards, and feedback these problems to the optimization engine module.

[0060] For example, when the system detects that the execution time of a certain process exceeds the preset maximum time limit, or the resource consumption of a certain process exceeds the budget, the data monitoring module will immediately generate an alarm and transfer the data to the optimization engine. After receiving these feedbacks, the optimization engine module will adjust its optimization plan according to these new inputs, such as increasing resource allocation, adjusting time constraints, or changing task priorities.

[0061] Data transfer in the feedback process: The feedback data is transmitted through the data bus in the system to ensure the efficient flow of information. The content of data transmission includes: Time delay: If the execution time of a certain process exceeds the preset time limit, the system will dynamically adjust the time allocation of subsequent processes according to the feedback information.

[0062] Resource consumption: When the resource consumption of a certain process is too high, the system will adjust the resource allocation according to the feedback data to optimize the resource utilization efficiency.

[0063] Service quality: When the service quality index fails to meet the standard, the system will adjust the resource configuration to ensure that government affairs are completed according to quality requirements.

[0064] Constraints are the rules that must be followed during the processing of government affairs, including time limits for each link, upper limits on resource consumption, service quality standards, etc. These constraints may change during the actual execution process. The data monitoring and feedback module is responsible for continuously monitoring these constraints to ensure that they are always adhered to during the execution process and making adjustments according to the actual situation.

[0065] Generally, the time limit for each link and the upper limit on resource consumption are set by the system at the initial stage. However, during the actual execution process, due to unpredictable factors, the time of some links may be delayed, or the use of some resources may exceed expectations. At this time, the data monitoring and feedback module will detect these changes and make automatic adjustments.

[0066] Specifically, when the time delay of a certain link exceeds the predetermined threshold, the data monitoring and feedback module will promptly transmit this information to the optimization engine module, which will adjust the optimization strategy by appropriately extending the processing time of subsequent links or re-adjusting the task priority. Similarly, when the system detects excessive resource consumption, the monitoring module will adjust the resource allocation strategy to optimize the overall resource utilization efficiency.

[0067] In this embodiment, the formula calculation of the data monitoring and feedback module will involve changes in time, cost, and service quality. The following is an explanation of the calculation methods for time, resources, and service quality.

[0068] As described in the aforementioned optimization engine module, the time change is jointly determined by the time consumption coefficient of each link and the resource allocation amount. The formula is: t i ·x i Where: t i is the time consumption coefficient of the i-th link, indicating the time consumed when this link is executed; x i is the resource allocation amount of the i-th link, indicating the amount of resources used in this link (such as time, personnel, etc.).

[0069] When it is monitored that the time consumption exceeds the predetermined value, the system will adjust the time arrangement of subsequent links to ensure that the overall time is controlled within a reasonable range.

[0070] The resource consumption of each link will affect the resource configuration of subsequent links. The resource change formula is similar to the time change: c i ·x i Where: c i is the unit resource consumption cost of the i-th link, indicating the cost of each unit of resource consumption in this link; x i is the resource allocation amount of the i-th link.

[0071] If the resource consumption in a certain link exceeds the predetermined cost ceiling, the monitoring module will feedback to the optimization engine and reconfigure the resources.

[0072] The quality of service is jointly affected by the quality of service coefficient and resource allocation amount in each link. The calculation formula for the change in the quality of service is: q i ·x i Where: q i is the quality of service coefficient of the i-th link, reflecting the contribution of the quality of this link to the entire government affair; x i is the resource allocation amount of the i-th link.

[0073] If the quality of a certain link does not meet the predetermined standard, the data monitoring and feedback module will detect this problem and transmit the information to the optimization engine for adjustment.

[0074] In the data monitoring and feedback module, the design of the feedback mechanism is crucial. Through an efficient data transmission protocol, the monitoring module can real-time feedback the collected data to the optimization engine module. Specifically, the monitoring module transmits real-time data (such as time delay, excessive resource consumption, low quality of service, etc.) to the optimization engine through the data bus and triggers the resource adjustment function of the optimization engine.

[0075] Real-time nature of feedback data: The design of the data monitoring and feedback module ensures the real-time nature of feedback. Whenever any data change is monitored, the system will immediately feedback on these changes and prompt the optimization engine to adjust the current optimization plan. This enables the system to respond quickly when problems occur, avoiding resource waste or task delay caused by delayed adjustment.

[0076] The data monitoring and feedback module ensures the efficient transmission of the information flow through the combination of formulas and data transmission mechanisms, provides real-time feedback to the optimization engine module, and ensures the efficient operation and continuous optimization of the system.

[0077] The decision support module is used to display the results of the optimization engine module and support the user's manual adjustment of the optimal resource allocation plan; The decision support module can not only provide the optimal resource allocation plan based on the optimization engine, but also make dynamic adjustments according to the real-time feedback of the system. This module provides intuitive decision support for the managers of government affairs, helping to select and adjust the processing plan most suitable for the current task requirements. Closely connected with the optimization engine module and the data monitoring and feedback module, the decision support module can perform multi-dimensional display and adjustment based on the optimal resource allocation plan, enabling managers to effectively handle various situations that may occur in government affairs.

[0078] In this embodiment, the decision support module forms a complete set of decision support mechanisms by obtaining the resource configuration results generated by the optimization engine module and combining the real-time monitoring data provided by the data monitoring and feedback module. After the optimization engine module generates the optimal solution according to the objective function and constraint conditions set by the user, the decision support module is responsible for presenting it to the management personnel in a clear and operable manner. At the same time, the data monitoring and feedback module will provide real-time data on abnormal situations such as resource consumption and time delays, ensuring that the decision support module can respond to these abnormalities and provide corresponding adjustment suggestions.

[0079] In this embodiment, the decision support module presents the optimal resource allocation plan calculated by the optimization engine to the management personnel through a graphical interface. Specifically, information such as resource allocation, time arrangement, cost budget, and service quality assessment for each link calculated by the optimization engine module will be displayed through dashboards, charts, or other visualization tools, facilitating the management personnel to intuitively understand the optimization results.

[0080] In some embodiments, the specific content to be displayed may include: Resource allocation diagram: Displays the amount of resources allocated to each link and each department, and compares it with the preset resource budget to help the management personnel understand the rationality of resource allocation.

[0081] Time progress bar: Displays the processing progress of each link, indicating the preset time limit and actual execution time of each link.

[0082] Service quality assessment form: Lists the service quality scores of each link and compares them with the preset service quality standards to identify which links may have service quality problems.

[0083] Cross-departmental collaboration diagram: Displays the progress of matters involving multiple departments and the collaboration between departments.

[0084] Through this form of display, the decision support module provides clear and operable information for the management personnel, helping them make more flexible and reasonable adjustments during the task processing.

[0085] As an option, the decision support module not only provides the optimal resource configuration plan, but also provides multiple optimization plans for the management personnel. The system displays multiple plans based on the calculation results of the optimization engine and allows the management personnel to select the plan that best suits the current task requirements according to the real-time situation. For different government affairs, there may be different priorities and processing requirements. The decision support module allows customization of optimization plans by adjusting target weights, resource configurations, etc.

[0086] Specifically, the function of plan recommendation and adjustment includes: Multi - scenario display: The decision - making support module displays multiple optimization scenarios. Each scenario may be optimized based on different objective weights. Each scenario is optimized according to different trade - offs among time, cost, and service quality. For example, a certain scenario may prioritize minimizing time, while another scenario may focus on reducing resource consumption.

[0087] Manual adjustment function: The decision - making support module provides an intuitive operation interface that allows management personnel to manually adjust resource allocation, modify time priorities, or service quality requirements. For example, if the time for a certain link is too long, management personnel can choose to reduce the processing time of this link or adjust the required resources.

[0088] Dynamic adjustment suggestions: Based on real - time feedback data (such as time delays, resource over - consumption, etc.), the decision - making support module can give dynamic adjustment suggestions. When the system monitors resource consumption or time delays in a certain link, the module will automatically propose adjustment suggestions to help management personnel make decisions in a timely manner.

[0089] The decision - making support module also has the function of automatically generating visual reports. By converting the calculation results of the optimization engine and the real - time data of each link into charts and reports, management personnel can more clearly understand the data performance in the task processing process. Specifically, the report generation function includes: Progress report: Displays the execution progress of each government affair, including whether it is completed on time, the processing time of each link, etc.

[0090] Resource consumption report: Displays the resource consumption of each link and each department, helping management personnel to timely understand the efficiency of resource use.

[0091] Quality assessment report: Displays the service quality of each link, helps to evaluate whether it meets the expected standards, and gives further optimization suggestions.

[0092] Reports usually include graphical data displays and detailed written explanations to enable users to quickly understand the data and make corresponding adjustments.

[0093] In this embodiment, the decision - making support module allows management personnel to adjust the objective function and constraints. By modifying the weights of the objective function or adjusting the constraints, management personnel can flexibly customize the optimization scenario according to the real - time situation. For example, for some matters, time may need to be prioritized over cost. At this time, the weight of the time objective in the objective function can be adjusted to increase the priority of time optimization.

[0094] Management personnel can adjust the weights in the objective function according to specific needs.

[0095] The decision support module provides powerful decision support functions for the management of government affairs by integrating the results of the optimization engine module and the real-time data of the data monitoring and feedback module. Through result display, solution recommendation and adjustment, visualization report generation, and the configuration of objective functions and constraints, managers can flexibly adjust the optimization plan to ensure the efficient processing of government affairs.

[0096] The dynamic resource scheduling module is used to perform dynamic scheduling and optimization of resources according to real-time data, and adopts a deep reinforcement learning algorithm to optimize resource allocation and cross-departmental task collaboration; The dynamic resource scheduling module optimizes resource allocation by combining the deep reinforcement learning (DRL) algorithm and real-time data feedback. Through a continuous learning and feedback mechanism, the dynamic resource scheduling module can adaptively adjust resource configuration to ensure the smooth and efficient execution of government affairs. This embodiment further details the technical details of this module, especially the complete definition of the formulas, to ensure that those skilled in the art can clearly understand the roles and meanings of each formula.

[0097] The dynamic resource scheduling module mainly relies on the deep reinforcement learning algorithm to achieve adaptive adjustment of resources. This module learns the optimal resource scheduling strategy through interaction with the environment. Specifically, the system adjusts resource configuration based on real-time feedback data to maximize the efficiency and quality of task execution. The following are the main formulas and parameter definitions used by this module.

[0098] The state space defines all possible states of the system at a certain moment. In this embodiment, the state space includes the following dimensions: Resource Consumption: The actual amount of resources consumed in each link, such as manpower, funds, equipment, etc.; Task Progress: The execution status of each link, indicating the progress completed in this link; Service Quality: The service quality score of each link, indicating whether the execution quality of this link meets the expected standard; Cross-Department Coordination: In multi-department collaborative tasks, the coordination and resource sharing situation among departments.

[0099] The action space defines all resource scheduling operations that the system can take. In this embodiment, the action space mainly includes the following operations: Time Adjustment: Dynamically adjust the processing time of each step to ensure timely task completion or extend the task processing time according to the situation; Resource Redistribution: When resources are insufficient or excessive resources are consumed in some steps, the system adjusts resource allocation and invests more resources in critical tasks; Task Prioritization Adjustment: Adjust the priority of tasks according to the urgency or execution status of tasks to ensure that the most important tasks are processed first.

[0100] The reward function plays a core role in deep reinforcement learning and is used to evaluate the effect of each action, thus guiding the learning process. The reward function takes into account factors such as resource consumption, task completion time, and service quality. The specific definition of the reward function is as follows: R = w 4 ·Efficiency + w 5 ·Timeliness + w 6 ·Quality Where: R is the reward value, representing the effect of the current resource scheduling; Efficiency: Measures the resource utilization efficiency, calculated as the ratio between the consumed resources and the task completion volume. Its calculation formula is: Where: Resources Consumed: The total amount of resources actually consumed in this step, and the unit can be labor, time, funds, etc.; Task Completed: The task volume completed in this step, which can be quantified according to the task progress.

[0101] Timeliness: Reflects whether the task is completed on time. Its calculation formula is: Where: T actual : The actual completion time of this step; T expected : The scheduled completion time of this step; this value represents the proportion of the task completed on time, and the closer it is to 1, the more timely the task is completed.

[0102] Quality: Reflects whether the service quality meets the expectations. Its calculation formula is: q actual : The actual service quality score of this step, usually obtained from actual execution data or user feedback; q expected: The preset service quality standard for this link is usually a pre-set service standard or expectation.

[0103] w 4 , w 5 , w 6 are the weights of efficiency, time, and quality. Their sum is 1, and managers can dynamically adjust these weights according to task requirements. For example, in an urgent task, the time completion may have a higher weight, while in a situation with limited resources, efficiency may be more important.

[0104] In this embodiment, the dynamic resource scheduling module uses a feedback mechanism for learning. When the system selects an action (such as adjusting the resource allocation or priority of a certain link) based on the current state and executes it, the system will receive a feedback - the reward value R, which is used to guide the next decision. By continuously adjusting the resource allocation strategy, the system gradually learns the optimal resource scheduling strategy.

[0105] In this embodiment, the dynamic resource scheduling module adopts the following optimization strategies: Adaptive resource configuration: When the system detects that the resource consumption of a certain link is too high, the system will automatically increase the resources of this link or adjust the resource allocation of other links to optimize the resource utilization efficiency.

[0106] Dynamic adjustment of task priority: By adjusting the priority of tasks, the system ensures that the most important tasks are processed in a timely manner. The priority adjustment is based on the urgency of the tasks and the resource consumption situation.

[0107] Timeliness optimization: If the task is delayed in time, the system will automatically extend the time of this link, or allocate more resources to the time - urgent tasks to ensure timely completion.

[0108] The core advantage of the dynamic resource scheduling module lies in its adaptive ability. Through the deep reinforcement learning algorithm and the real - time feedback mechanism, the system can evaluate the effect after each adjustment and learn from the feedback. Information such as the resource consumption, time progress, and service quality of each link will be monitored in real - time and fed back to the dynamic resource scheduling module to further adjust the resource configuration strategy.

[0109] In this cycle of feedback and learning, the system can gradually optimize the resource scheduling strategy, reduce resource waste, and improve the flexibility and efficiency of resource allocation. The execution data of each link, such as resource consumption, time delay, and service quality, will affect the next round of resource scheduling decisions.

[0110] The dynamic resource scheduling module can dynamically adjust resource allocation according to changes in the process of government affairs handling, maximize resource utilization, and ensure the completion of tasks on time and with high quality. Through the design of the reward function, the module can evaluate each decision and continuously optimize the resource scheduling strategy through learning, so as to achieve the optimal resource allocation and task execution effect.

[0111] The objective function and constraint management module is used to define, set, and adjust in real time the objective function and constraint conditions in the government affairs management system, including time limits, resource ceilings, and service quality requirements; The objective function and constraint management module is used to define, adjust, and manage the optimization objectives and constraint conditions of the system, ensuring that the process of government affairs handling can maintain an optimal state in multiple dimensions such as time, resources, and quality. This module works closely with the optimization engine module, data monitoring and feedback module, and dynamic resource scheduling module to jointly achieve reasonable resource allocation, effective time control, and optimized adjustment of service quality.

[0112] Generally, the optimization engine module needs to configure resources based on the objective function, and the parameters and weights of the objective function must be dynamically adjusted according to the actual situation. The data monitoring and feedback module will provide real-time data, including task progress, resource consumption, and service quality feedback, while the dynamic resource scheduling module needs to optimize resource allocation according to the real-time adjusted constraint conditions. The main task of the objective function and constraint management module is to coordinate this information to make the optimization process meet the actual needs of the current government affairs.

[0113] In this embodiment, the objective function is mainly used to quantify the optimization objectives of government affairs, involving three core dimensions: time minimization, cost minimization, and service quality maximization. By weighing these objectives, the system can adjust the optimization direction according to actual needs.

[0114] In this embodiment, the constraint conditions are used to limit the optimization scope of the objective function, ensuring that the execution of tasks does not exceed the actual feasible resource and time range. The constraint conditions in the system mainly include time constraints, resource constraints, and service quality constraints.

[0115] The task execution time must meet the preset maximum time limit to ensure that the task is completed on time. The definition of the time constraint is as follows: t i ·x i ≤T max Where: T max is the maximum allowable time; and t i is the time consumption coefficient of the i-th link, reflecting the time required for this link in the process of matter handling; x i$x_i$ is the resource allocation amount for the $i$-th link, that is, the allocated resources (such as time, personnel, funds, etc.).

[0116] When the execution time of a certain task link exceeds the limit, the system will adjust $x$. i To optimize the time arrangement.

[0117] The resource allocation amount in the system shall not exceed the total amount of available resources. The expression of the resource constraint is as follows: Where: $R$ max is the maximum value of available resources; $x_i$ i is the resource allocation amount for the $i$-th link; $n$ is the total number of links in the matter processing.

[0118] In the case of limited resources, the system will give priority to ensuring the resource allocation of critical tasks and adjust the resource input of low-priority tasks.

[0119] The service quality must meet the minimum standard to ensure the effectiveness of government affair handling. Its constraint formula is as follows: $q_i$ i ·$x_i$ i ≥$Q$ min Where: $Q$ min is the minimum service quality standard; $q_i$ i is the service quality coefficient of the $i$-th link, reflecting the service quality contribution of this link; $x_i$ i is the resource allocation amount for the $i$-th link.

[0120] When the service quality is lower than the standard, the system will automatically adjust the resource configuration to optimize the service quality.

[0121] In this embodiment, the objective function and constraint conditions are not fixed, but are dynamically adjusted according to the real-time data during the task execution. The data monitoring and feedback module will provide the task execution situation in real time. When it is found that there is a deviation in a certain constraint condition, the system will automatically adjust the weight of the objective function or the parameter value of the constraint condition. For example: If it is found that the task execution time is too long, the system will increase $w_1$ 1 , and increase the weight of time optimization; If the resource consumption is too fast, the system will increase $w_2$ 2 , and give priority to reducing costs; If the service quality is lower than expected, the system will increase $w_3$ 3 , to ensure that the quality requirements are met.

[0122] The objective function and constraint management module can adaptively optimize in different government affair processing through the quantitative calculation of time, cost, and quality objectives, as well as the management of constraint conditions. By dynamically adjusting the weights of the objective function and the parameters of the constraint conditions, the system can achieve the optimization of resource utilization, the efficiency of task execution, and the reliability of service quality.

[0123] The user interface module is used to provide a visual interface for government affair managers, displaying the processing status of affairs, optimization results, resource scheduling, and decision-making support information. The user interface module displays various types of data through a clear and intuitive interface, allowing managers to efficiently execute tasks, adjust resource allocation, monitor the system operation status, and manually intervene in the automatic optimization process of the system as needed. The user interface module is closely connected to the aforementioned optimization engine module, data monitoring and feedback module, dynamic resource scheduling module, etc., ensuring smooth information flow and simple and efficient user operations.

[0124] In this embodiment, the user interface module not only displays key data but also provides the function of dynamically adjusting various types of information, tasks, and resource configurations in the government affair management process. Through these functions, users can monitor and operate government affairs in real time to ensure that tasks are completed smoothly and on time.

[0125] One of the core tasks of the user interface module is to clearly display real-time data and system status, helping managers quickly understand information such as task execution progress, resource usage, and optimization results. Specifically, the interface display functions include: Real-time task progress display: Present the execution progress of each task or link in real time through charts or progress bars to help managers understand the current status of tasks. The status of each task link will be updated in real time, intuitively showing which tasks have been completed, which are in progress, and which need attention.

[0126] Resource consumption display: Intuitively present the consumption of each link, each department, or each type of resource in the interface and compare it with the budget to help managers determine whether there is resource waste or improper allocation.

[0127] Optimization result display: Display the optimal resource allocation plan, time arrangement, and service quality score obtained through the optimization engine module, etc. Through these displays, managers can quickly evaluate the optimization results and make further adjustments.

[0128] Service quality analysis: Display the service quality scores of each link in the form of dashboards or pie charts to help managers determine which links have service quality below the standard and then make timely handling or resource reallocation.

[0129] The user interface module is not only a tool for data display, but also provides a series of interactive functions that allow managers to dynamically adjust task execution, resource allocation, and optimization goals based on real-time data. Specific functions include: Task priority adjustment: Managers can directly adjust the priority of tasks through the interface to ensure that critical tasks are processed first. For example, the position of a task on the timeline can be adjusted by dragging, or the task priority can be adjusted by a button.

[0130] Resource allocation adjustment: The system provides an intuitive resource allocation interface that allows users to make dynamic adjustments when resources are insufficient. Users can manually adjust the quantity or type of resource allocation (such as adding personnel, funds, or equipment, etc.), which will affect the resource configuration of task execution in real time.

[0131] Goal adjustment function: Although the adjustment of the objective function weights is involved in other modules, the user interface module provides a simple goal adjustment interface for managers to facilitate the quick modification of task goal settings. Users can choose to adjust the trade-off between time, cost, and quality, thereby affecting the optimization results.

[0132] Constraint setting: Users can modify the time limit, resource ceiling, or service quality requirements of tasks in the interface. These adjustments can be reflected in the optimization results in real time to ensure that task execution complies with the predetermined constraints.

[0133] These interactive functions enable managers to flexibly respond to changes in government affairs processing, make adjustments through quick operations, and ensure that tasks are completed on time and with high quality.

[0134] To improve the operability of the system and the comprehensibility of data, the user interface module also includes data report generation and graphical display functions. Reports can help managers deeply analyze the data during task execution, specifically including: Progress report: Provide a summary report of the execution progress of each government affair, showing the processing progress, time consumption, resource usage, etc. of each link, helping managers grasp the overall progress.

[0135] Resource consumption report: Display the resource consumption of each link, department, or resource type. The report includes a resource consumption trend chart to help managers determine whether there is excessive resource consumption or potential waste.

[0136] Service quality report: Show the service quality scores of each link and their change trends, helping managers identify which links have quality problems and make necessary adjustments.

[0137] Optimization plan comparison report: The system can generate a comparison report to show the execution effects of different optimization plans, such as the reduction amplitude of task time after optimization, cost savings, and quality improvement.

[0138] The report content is presented in the form of charts, tables, bar graphs, etc., and supports export in PDF, Excel or other formats, facilitating archiving, sharing or further analysis.

[0139] In order to ensure the fluency of user operations and the response speed of the interface, in this embodiment, the user interface module focuses on the following aspects of design: Real-time refresh: All real-time data, reports, task status, etc. ensure that the interface always displays the latest information through dynamic refresh technology. Users do not need to manually refresh the page, and the system will automatically push data updates.

[0140] Interface layout optimization: Adopting a clear modular design, with each functional block clearly divided, ensuring that users can quickly find the required functions during operation, reducing operation steps and improving efficiency.

[0141] Data response speed: The optimized design of the system ensures the efficient response of the interface. Whether it is task priority adjustment, resource allocation modification or report generation, it can be completed in a short time, avoiding operation delays.

[0142] Operation feedback: Every time the user adjusts the resource configuration, modifies the objective function or modifies the constraint conditions, the interface will immediately feedback the effect of the adjustment and display the impact on the optimization result, helping users make more accurate decisions.

[0143] Through interaction design, the user interface module enables managers to easily adjust various parameters during task execution, optimize resource allocation, and ensure that government affairs are completed on time, efficiently and in line with quality requirements. The usability and efficiency of this module not only improve the user experience of the system, but also effectively promote the optimized management of government affairs.

[0144] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Government affairs management system, characterized in that: include: The optimization engine module is used to perform multi-objective optimization tasks, including optimization of time, cost, and service quality, and to optimally configure resources through multi-objective optimization algorithms; The data monitoring and feedback module is used to collect and monitor the execution of government affairs, resource usage and changes in constraints in real time, and feed the collected data back to the optimization engine module for dynamic adjustment; The decision support module is used to display the results of the optimization engine module and support users to manually adjust the optimal resource allocation plan; Dynamic resource scheduling module, which is used to dynamically schedule and optimize resources based on real-time data, and uses deep reinforcement learning algorithms to optimize resource allocation and cross-departmental task collaboration; The objective function and constraint management module is used to define, set and adjust the objective functions and constraints in the government affairs management system in real time, including time limits, resource limits and service quality requirements; The user interface module is used to provide a visual interface for government affairs managers to display the status of affairs processing, optimization results, resource scheduling and decision support information.

2. The government affairs management system according to claim 1, characterized in that: The optimization engine module includes: Multi-objective optimization capabilities to minimize time consumption, minimize cost consumption and maximize service quality; Heuristic algorithm function, which performs global search for optimization targets through genetic algorithm or particle swarm optimization algorithm; The Pareto optimal solution function is used to calculate the optimal balance between time, cost and service quality and generate the Pareto frontier solution set.

3. The government affairs management system according to claim 1, characterized in that: The data monitoring and feedback module includes: Real-time data collection function, used to collect the execution time, resource consumption and cross-departmental coordination of each link in the process of handling government affairs; Constraint change monitoring function, which is used to monitor changes in time, resources and service quality and provide feedback to the optimization engine module; The real-time feedback function is used to feed back monitoring data to the optimization engine module in order to dynamically adjust the resource allocation strategy.

4. The government affairs management system according to claim 1, characterized in that: The decision support module includes: The result display function is used to visualize the optimal resource allocation plan and cross-departmental collaboration strategy information calculated by the optimization engine module; The solution recommendation and adjustment function is used to provide multiple solutions based on the optimization results and allow managers to manually adjust the optimization solutions according to actual needs; Visual report generation function is used to generate reports on matter processing, resource utilization and service quality, and perform data analysis.

5. The government affairs management system according to claim 1, characterized in that: The dynamic resource scheduling module includes: Real-time resource scheduling function, through deep reinforcement learning algorithm, dynamically adjusts resource allocation plan based on real-time event progress and resource conditions; Adaptive learning function, which optimizes resource scheduling strategies through interaction with the actual environment and improves the efficiency of handling government affairs; The task allocation function adjusts task allocation and priority in real time according to cross-departmental task requirements to ensure that matters are completed on time.

6. The government affairs management system according to claim 1, characterized in that: The objective function and constraint management module includes: Objective function definition function, used to set each objective function in multi-objective optimization problems, including time minimization, cost minimization and service quality maximization; Constraint setting function, used to define the time limit, resource limit and service quality requirements for each event link; Dynamic adjustment function is used to adjust the objective function weights and constraints according to real-time monitoring data and actual conditions.

7. The government affairs management system according to claim 1, characterized in that: The user interface module comprises: Interface display function, showing the processing status, real-time progress and resource consumption of matters; Operation function, which supports users to manually adjust optimization results, set target weights, and reallocate resources; Visual reporting function provides visual reports and data analysis tools to help users understand optimization results and support the decision-making process.

8. The government affairs management system according to claim 1, characterized in that: The multi-objective optimization method used by the optimization engine module includes: Weighted method, which balances the relationship between time, cost and service quality goals by adjusting the weights of each objective function; Pareto optimal solution calculation, by calculating the optimal solution set between multiple objectives, generates the Pareto frontier and provides the optimal resource allocation strategy.

9. The government affairs management system according to claim 1, characterized in that: The deep reinforcement learning algorithm used by the dynamic resource scheduling module includes: State space definition function, defining real-time data of resource usage, event progress, and cross-departmental collaboration as state input; Action space definition functions, defining resource scheduling, task allocation, and priority adjustment as action outputs; The reward function defines the functionality that guides the learning process by calculating rewards based on data on processing time, resource consumption, and quality of service.

10. The government affairs management system according to claim 8, characterized in that: The objective function of the system includes the trade-off optimization among time, cost and service quality. By weighting each objective function, a comprehensive weighted objective function is formed to maximize the global benefit.

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