A method and device for intelligent event scheduling based on scheduling strategies

Abstract

This invention relates to the field of resource allocation and task scheduling technology, and provides a method and device for intelligent event scheduling based on scheduling strategies. The method includes: configuring capability weights for each grid member; calculating their task allocation ratio within their respective grid based on these capability weights; storing the task allocation ratio, the total number of tasks in the grid within a period, and the number of tasks already allocated to each grid member as strategy data; triggering a scheduling process in response to a new event entering the system and being associated with a specific grid, and obtaining the strategy data corresponding to that grid; calculating the real-time availability index of each grid member within the specific grid based on the obtained strategy data, and selecting the grid member with the highest real-time availability index value as the target grid member; allocating the new event to the target grid member, and updating the number of tasks already allocated to that grid member in the strategy data. This invention can improve the rationality and execution efficiency of task allocation, and effectively ensure the compliance of long-term allocation strategies and the balance of the system.

Description

Technical Field

[0001] This invention relates to the field of resource allocation and task scheduling technology, and in particular to a method and device for intelligent event scheduling based on scheduling strategies. Background Technology

[0002] In modern urban management and enterprise operations, the rapid and reasonable allocation of various events (such as municipal facility repair requests, citizen complaints, internal work orders, and logistics orders) is the core of ensuring service efficiency and quality.

[0003] Currently, technological development in this field mainly revolves around the following three typical solutions, but each has its own limitations and shortcomings:

[0004] 1. Static Strategy Allocation: Tasks are assigned to personnel based on preset, fixed rules (e.g., a simple polling order or an average ratio manually set by the administrator). Its drawback is a lack of adaptability; it cannot perceive or respond to real-time changes in personnel capabilities or dynamic fluctuations in workload. It is a typical open-loop, rigid allocation model, which can easily lead to resource misallocation in the long run.

[0005] 2. Priority-based dynamic scheduling: This approach sets priorities based on the urgency and type of events, allocating higher-priority tasks first. However, this solution focuses on the "task" rather than the "person," failing to link scheduling decisions to the quantifiable processing capabilities of the personnel. This can lead to assigning a highly complex and urgent task to someone already at full capacity or not skilled in such tasks, compromising optimal task execution and potentially exacerbating problems due to mishandling.

[0006] 3. Load Balancing Algorithms: These algorithms (such as Least Tasks First Allocation) focus on balancing the instantaneous workload of each executor in real time, enabling rapid response to short-term task backlogs. However, their drawback is the lack of a long-term allocation perspective and strategic constraints. They only pursue instantaneous load balancing without considering a long-term, reasonable task allocation ratio based on individual capabilities. This leads to a phenomenon of "short-term balance, long-term imbalance." In the long run, highly capable personnel may not be assigned a workload commensurate with their abilities, while less capable personnel may continue to be assigned tasks due to the algorithm's short-term balancing operations, deviating from the fair principle of contribution based on ability.

[0007] In summary, existing technologies have the following shortcomings in achieving refined and intelligent scheduling:

[0008] 1. Lack of individual capability quantification mechanism: The multidimensional capability differences of executors (such as historical efficiency, professional skills, and current saturation) are not transformed into configurable and calculable quantitative parameters (such as weight allocation), which makes scheduling decisions lack a scientific basis and seriously disconnected from actual execution efficiency.

[0009] 2. Mismatch between static configuration and dynamic environment: Strategies that rely on fixed rules or outdated historical data are difficult to adapt to real-time fluctuations in workload and changes in personnel status, and are prone to structural contradictions where "high-capacity personnel are idle" and "low-capacity personnel are overloaded".

[0010] 3. Neglect of long-term allocation ratio constraints: Most dynamic scheduling algorithms (such as greedy algorithms) only focus on the "optimal" allocation at the current moment and do not set a ratio upper limit constraint based on ability weight for the total amount of task allocation within a period (such as daily or weekly), which may cause the long-term cumulative allocation results to deviate significantly from the preset fair or optimal strategy.

[0011] 4. Gap in closed-loop feedback optimization: The lack of a periodic calibration mechanism makes it impossible to dynamically correct scheduling decisions by comparing the "actual allocation amount" with the "planned configuration ratio". This causes initial configuration errors or temporary adjustment deviations to accumulate and amplify over long-term system operation, ultimately leading to scheduling failure.

[0012] Chinese patent CN113361836A discloses a resource allocation method, apparatus, computer-readable storage medium, and electronic device. This solution provides an effective method for periodic performance evaluation and resource quota allocation. However, its technical nature determines that it is not suitable for "event-based intelligent scheduling" scenarios that require real-time response, dynamic adjustment, continuous balancing, and consideration of immediate task-executor matching. Its "post-evaluation, hierarchical allocation" mode has fundamental drawbacks such as slow response, rigid mechanism, and neglect of real-time load, and cannot solve the problem of timely, accurate, and fair task allocation in dynamic environments.

[0013] Therefore, how to fundamentally solve the key problems of insufficient flexibility, poor long-term fairness, and weak adaptability of existing technologies, and provide a quantifiable, traceable, and sustainably optimized technical path for intelligent event scheduling, has become an urgent technical problem to be solved. Summary of the Invention

[0014] In view of this, in order to overcome the shortcomings of the prior art, the present invention aims to provide a method and device for intelligent event scheduling based on scheduling strategies.

[0015] According to a first aspect of the present invention, a method for implementing intelligent event scheduling based on a scheduling strategy is provided, the method comprising:

[0016] Step S1: Configure a capability weight for each grid member, calculate their task allocation ratio in their respective grid based on the capability weight, and store the task allocation ratio, the total number of tasks in the grid within the period, and the number of tasks already allocated to each grid member as strategy data.

[0017] Step S2: In response to a new event entering the system and being associated with a specific grid, trigger the scheduling process and obtain the policy data corresponding to that grid;

[0018] Step S3: Calculate the real-time availability index of each grid member within the specific grid based on the acquired strategy data, and select the grid member with the highest real-time availability index value as the target grid member;

[0019] Step S4: Assign the new event to the target grid worker and update the assigned task quantity of that grid worker in the policy data.

[0020] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, in step S1, the ability weights of each grid member are configured in the following manner: the ability weights are determined by multi-dimensional analysis of the grid members' historical performance data. The multi-dimensional analysis includes at least the evaluation and quantification of historical task processing efficiency, the type and level of professional skills mastered, and real-time workload saturation.

[0021] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, step S1, calculating the task allocation ratio of the capability weight in its respective grid, includes:

[0022] The ratio of a single grid member's ability weight to the sum of the ability weights of all grid members in its grid is used to calculate an original ratio value.

[0023] The original proportion values ​​calculated by all grid members within the grid are normalized to ensure that the sum of the preset task allocation proportions of all grid members is 100%.

[0024] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, step S2, in response to a new event entering the system and being associated with a specific grid, includes: parsing the geographic location metadata carried by the new event, and determining the physical management grid to which the new event belongs and its corresponding unique grid identifier by matching the geographic location metadata with a predefined grid electronic map.

[0025] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, step S3, calculating the real-time availability index of each grid member in the specific grid according to the acquired strategy data, includes: subtracting the percentage of the currently assigned task volume of each grid member from the task allocation ratio of each grid member to the percentage of the total task volume of the grid in the current scheduling period, and using the difference as the real-time availability index of the grid member, wherein the assigned task volume is the sum of the number of all event tasks that have been assigned to the grid member in the current scheduling period and have not yet been marked as completed.

[0026] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, step S3, calculating the real-time availability index of each grid member in the specific grid according to the acquired strategy data, further includes: introducing one or more additional decision factors and corresponding adjustment coefficients. The additional decision factors include an emergency priority factor generated based on the urgency label of the new event and a balance factor used to measure the degree of workload balance among grid members. By comprehensively calculating the real-time availability index and each weighted additional decision factor, a composite decision index integrating multiple scheduling objectives is formed, which is used to select grid members in complex scenarios.

[0027] Optionally, in the method for intelligent event scheduling based on scheduling strategy of the present invention, step S4, updating the assigned task quantity of the grid worker in the strategy data includes: performing an incremental update on the assigned task quantity field of the target grid worker in an atomic operation manner in a database transaction.

[0028] Optionally, the method for intelligent event scheduling based on scheduling strategy of the present invention further includes dynamically optimizing the capability weights in the following manner:

[0029] Periodically collect performance feedback data from grid workers after they have completed their assigned tasks. This performance feedback data includes user evaluation scores after the task is completed and the deviation between the actual task processing time and the standard processing time.

[0030] Based on the performance feedback data, a preset weight update algorithm is used to dynamically adjust the original ability weights of grid workers, and the adjusted new weight values ​​are updated to the strategy database for subsequent recalculation of task allocation ratios.

[0031] Optionally, the method for intelligent event scheduling based on scheduling strategy of the present invention further includes performing periodic calibration after step S4 in the following manner: setting a periodic task volume threshold, continuously monitoring the cumulative value of the total task volume within the grid period, and automatically triggering a reset operation when the cumulative value reaches the periodic task volume threshold, clearing the total grid task volume within the current period and the allocated task volume of all grid members within the grid stored in the database to zero, ending the current scheduling period and starting a new period.

[0032] According to a second aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0033] The method and apparatus for intelligent event scheduling based on scheduling strategies of the present invention have the following beneficial technical effects:

[0034] First, improve the rationality of task allocation and execution efficiency.

[0035] This invention not only optimizes human resource allocation and reduces the risk of task backlog and processing delays caused by mismatch between capabilities and tasks, but also ensures at the system level that tasks are handled by suitable personnel who are more likely to complete them efficiently, thereby improving the overall response speed and completion quality of incident handling. Empirical evidence shows that the method of this invention can significantly reduce the deviation between the actual allocation result and the preset capability ratio, far exceeding traditional polling or simple balanced allocation schemes.

[0036] Second, it effectively ensures the compliance of long-term allocation strategies and the balance of the system.

[0037] This system can strictly ensure that within a scheduling cycle (such as a natural week), the actual cumulative workload undertaken by each grid member will not exceed their pre-set allocation ratio limit based on their capabilities, thus eliminating the "short-term equilibrium, long-term imbalance" problem commonly found in traditional dynamic scheduling. Simultaneously, the system's built-in cycle reset function can periodically clear accumulated data, eliminating error accumulation and ensuring the long-term stability and robustness of the allocation system. Even in the event of unforeseen circumstances such as temporary absences of individual grid members, the remaining tasks can be automatically and reasonably redistributed based on the real-time indicators of other on-duty personnel, enhancing the system's anti-interference capabilities.

[0038] Third, reduce the cost and complexity of manual intervention in system operation and maintenance.

[0039] Improve scheduling response speed; the allocation decision for new events can be completed automatically in a very short time (such as milliseconds), solving the problem of time-consuming traditional manual or semi-automatic allocation; freeing managers from heavy and repetitive daily allocation tasks, allowing them to focus more on handling abnormal situations and optimizing strategies; the system can automatically handle the accurate scheduling of most routine events, reducing the manpower cost and management complexity of operation and maintenance.

[0040] Fourth, it has good scalability and scenario adaptability, and supports multi-objective collaborative optimization.

[0041] It can flexibly adapt to the specific needs of various complex scenarios, from municipal grid management to enterprise work order dispatch and logistics distribution scheduling. The system can not only pursue fair allocation based on capabilities, but also take into account multiple management objectives such as task urgency and global load balancing, achieving a leap from single-objective optimization to multi-objective collaborative optimization and broadening the application boundaries of the technology. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating a method for implementing intelligent event scheduling based on a scheduling strategy according to an exemplary embodiment 1 of the present invention;

[0044] Figure 2 This is a flowchart illustrating the definition and storage of a grid worker allocation strategy for a method of implementing intelligent event scheduling based on a scheduling strategy according to an exemplary embodiment 2 of the present invention.

[0045] Figure 3 This is a flowchart illustrating the intelligent scheduling process according to an exemplary embodiment 2 of the present invention, which describes a method for implementing intelligent event scheduling based on a scheduling strategy.

[0046] Figure 4 This is a schematic diagram of the structure of the device provided by the present invention. Detailed Implementation

[0047] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0049] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0050] Example 1

[0051] Exemplary embodiment 1 of the present invention provides a method for intelligent event scheduling based on a scheduling strategy. Figure 1 This is a flowchart illustrating a method for intelligent event scheduling based on a scheduling strategy according to an exemplary embodiment 1 of the present invention; as follows: Figure 1 As shown, the method for intelligent event scheduling based on scheduling strategy in this embodiment is implemented in the following manner:

[0052] Step S1: Configure a capability weight for each grid member, calculate their task allocation ratio in their respective grid based on the capability weight, and store the task allocation ratio, the total number of tasks in the grid within the period, and the number of tasks already allocated to each grid member as strategy data.

[0053] In this embodiment, the ability weights are determined by multi-dimensional analysis of the grid workers' historical performance data. This multi-dimensional analysis includes at least the assessment and quantification of historical task processing efficiency, the types and levels of professional skills mastered, and real-time workload saturation.

[0054] In this embodiment, the task allocation ratio of a grid member in its grid is calculated based on the ability weight in the following manner: the ratio of the ability weight of a single grid member to the sum of the ability weights of all grid members in its grid is calculated to obtain an original ratio value; the original ratio values ​​calculated for all grid members in the grid are normalized to ensure that the sum of the preset task allocation ratios of all grid members is 100%.

[0055] Step S2: In response to a new event entering the system and being associated with a specific grid, the scheduling process is triggered to obtain the policy data corresponding to that grid.

[0056] In this embodiment, in response to a new event entering the system and being associated with a specific grid, the process includes: parsing the geographic location metadata carried by the new event, and determining the physical management grid to which the new event belongs and its corresponding unique grid identifier by matching the geographic location metadata with a predefined grid electronic map.

[0057] Step S3: Calculate the real-time availability index of each grid member within the specific grid based on the acquired strategy data, and select the grid member with the highest real-time availability index value as the target grid member.

[0058] In this embodiment, the task allocation ratio of each grid member is subtracted from the percentage of their currently allocated tasks relative to the total number of grid tasks in the current scheduling period. The resulting difference is used as the real-time availability index of the grid member. The allocated task quantity is the sum of all event tasks that have been allocated to the grid member in the current scheduling period and have not yet been marked as completed.

[0059] It should be noted that, in practical applications, the method of this embodiment can also introduce one or more additional decision factors and corresponding adjustment coefficients. The additional decision factors include an emergency priority factor generated based on the urgency label of the new event and an equilibrium factor used to measure the degree of workload balance among grid workers. By comprehensively calculating the real-time availability index and each weighted additional decision factor, a composite decision index integrating multiple scheduling objectives is formed, which is used to select grid workers in complex scenarios.

[0060] Step S4: Assign the new event to the target grid worker and update the assigned task quantity of that grid worker in the policy data.

[0061] In this embodiment, updating the assigned task quantity of the grid worker in the strategy data includes: performing an incremental update on the assigned task quantity field of the target grid worker in an atomic operation within a database transaction.

[0062] This embodiment also includes dynamically optimizing the capability weights in the following manner:

[0063] Periodically collect performance feedback data from grid workers after they have completed their assigned tasks. This performance feedback data includes user evaluation scores after the task is completed and the deviation between the actual task processing time and the standard processing time.

[0064] Based on the performance feedback data, a preset weight update algorithm is used to dynamically adjust the original ability weights of grid workers, and the adjusted new weight values ​​are updated to the strategy database for subsequent recalculation of task allocation ratios.

[0065] In practical applications, after step S4, periodic calibration is performed as follows: a periodic task volume threshold is set, the cumulative value of the total task volume within the grid cycle is continuously monitored, and when the cumulative value reaches the periodic task volume threshold, a reset operation is automatically triggered, the total grid task volume within the current cycle stored in the database and the assigned task volume of all grid members within the grid are cleared to zero, the current scheduling cycle ends and a new cycle begins.

[0066] Example 2

[0067] Exemplary Example 2 of the present invention provides a method for intelligent event scheduling based on a scheduling strategy. This method for intelligent event scheduling based on a scheduling strategy is implemented as follows:

[0068] Step 1: Define and store the grid worker allocation strategy

[0069] Figure 2 This is a flowchart illustrating the definition and storage of a grid worker allocation strategy for a method of implementing intelligent event scheduling based on a scheduling strategy according to an exemplary embodiment 2 of the present invention. Figure 2 As shown, the method of this embodiment defines and stores the grid worker allocation strategy in the following manner.

[0070] 1. Grid member attribute parameter configuration:

[0071] Grid worker competency weights ( ): Quantitative calculations are performed based on dimensions such as historical task completion efficiency, professional skills scores, and workload. 0 < ≤1.

[0072] Grid worker job type tags ( ): Mark the types of tasks that grid workers are good at (such as urban facility maintenance, dispute mediation, etc.).

[0073] Allocation ratio calculation:

[0074] Within the same grid, grid workers Distribution ratio Defined by the following formula:

[0075]

[0076] in, The total number of grid members in the current grid, and satisfying the following conditions: .

[0077] 2. Policy storage and updates

[0078] The configured policy data is stored in the database, and the fields include:

[0079] Grid ID, Grid Member ID, Allocation Ratio Total workload within the period Assigned task volume In practical applications, administrators can dynamically adjust settings through the interface. The system updates automatically. And synchronize it to the scheduling module.

[0080] Step 2: Dynamic Event Allocation and Proportion Calibration

[0081] Figure 3 This is a flowchart illustrating the intelligent scheduling process of a method for implementing intelligent event scheduling based on a scheduling strategy according to an exemplary embodiment 2 of the present invention. Figure 3 As shown, the method of this embodiment performs intelligent scheduling in the following manner.

[0082] 1. Scheduling trigger conditions

[0083] When new events Upon entering the system, the scheduling process is triggered when the following conditions are met:

[0084] New events Associating a grid with a specific geographic location; there are incomplete tasks within the grid, i.e. .

[0085] 2. Allocation Decision:

[0086] First, for each grid member within the grid... Calculate real-time availability metrics as follows: :

[0087]

[0088] in, For grid workers already assigned during the current cycle The workload.

[0089] Then, select within the same grid. The grid worker with the highest value ,Right now

[0090]

[0091] If multiple grid workers If they are the same, then follow the rules. Sort and allocate from highest to lowest.

[0092] Next, the new event Assigned to grid workers Update its assigned task quantity as follows:

[0093] =

[0094] If the total number of tasks within the grid cycle Automatic reset when the preset threshold (based on a natural week) is reached. and Entering a new cycle.

[0095] In this embodiment, if the grid worker's ability weight Dynamic updates are needed, and the following feedback mechanism should be used to weight the grid worker's capabilities based on the quality of task completion (such as user ratings and completion time). Perform periodic dynamic updates:

[0096]

[0097] in, The rating coefficient is 0 ≤ ≤1, This is the adjustment factor (default is 0.1).

[0098] In this embodiment, in calculation Furthermore, multi-objective optimization scheduling can be performed by superimposing multi-objective constraints (such as prioritizing urgent tasks and balancing work duration) in the following ways:

[0099]

[0100] in, , , These are the weighting coefficients. This represents the current load deviation value for the grid worker.

[0101] The method of this embodiment will be further described below in a specific scenario.

[0102] In this scenario, there are 3 grid members within the first grid, with ability weights as follows: =0.6, =0.3, =0.1, allocation ratio =60%, =30%, =10%.

[0103] Total workload within the period Current allocated amount =50, =20, =5.

[0104] When a new event arrives, calculate the performance of each grid worker. : , , ,choose The largest grid member 1 or grid member 2, press The priority is assigned to grid member 1.

[0105] Example 3

[0106] Exemplary Example 3 of the present invention provides a method for intelligent event scheduling based on a scheduling strategy. This method for intelligent event scheduling based on a scheduling strategy is implemented as follows:

[0107] New events This event, generated within the system, carries key metadata such as the associated grid ID. Its occurrence serves as the initial trigger condition for initiating the intelligent scheduling process.

[0108] The intelligent scheduling module monitors the event queue in real time, and when a new event is detected... At that time, the grid ID information is parsed out. Based on the extracted grid ID, a precise query command is sent to the configuration database through a predetermined database access interface to request the information containing the grid worker allocation ratio. Assigned task volume Total workload within the period Key configuration data, etc.

[0109] After receiving a query request, the configuration database uses an efficient data retrieval algorithm to accurately locate and extract the configuration data for the corresponding grid from massive amounts of data. This data is then returned to the intelligent scheduling module via a data transmission channel. Upon receiving the configuration data, the intelligent scheduling module performs its pre-defined calculations according to the following logic:

[0110]

[0111] Real-time availability metrics for each grid member within the grid Perform enumeration calculations one by one.

[0112] The intelligent scheduling module uses a sorting algorithm to calculate the number of grid members. Sort the values ​​in descending order and select the top of the sequence. The grid worker with the highest value If there are multiple identical maximum values In the case of values, the allocation ratio shall apply. A second round of screening is conducted from high to low.

[0113] The intelligent scheduling module is based on the selected grid worker. The allocated task volume is updated through data update logic. Perform an atomic increment operation to ensure the accuracy and consistency of task counting.

[0114] The intelligent scheduling module encapsulates the updated assigned task volume data into a standard update request and sends it to the configuration database. Upon receiving the request, the configuration database performs a data persistence update operation and returns a confirmation message of successful update to the intelligent scheduling module. After confirming the successful update of the assigned task volume, the intelligent scheduling module uses a message queue or direct communication mechanism to send the new event... Assigned to selected grid worker The task processing queue to which it belongs. The grid worker group detects a new event in the task processing queue. Upon entry, the system performs event reception confirmation processing and then sends an allocation confirmation receipt containing information such as task reception time and reception status to the intelligent scheduling module.

[0115] The method for intelligent event scheduling based on scheduling strategies in this invention has the following beneficial technical effects by introducing a two-layer collaborative mechanism of "quantitative configuration ratio constraint" and "real-time dynamic allocation calibration":

[0116] First, improve the rationality of task allocation and execution efficiency.

[0117] This invention quantifies the individual ability differences of grid workers (such as processing speed, professional skills, and workload) into configurable ability weights, and automatically generates a preset task allocation ratio for each worker within their assigned grid. It transforms scheduling decisions from the traditional "task-centric" or "simple equilibrium" approach to a more refined "person-centric" matching. This ensures that high-ability grid workers receive a higher proportion of tasks matching their abilities, while low-ability workers are prevented from being assigned workloads beyond their capacity. This not only optimizes human resource allocation and reduces the risk of task backlog and processing delays due to mismatches between abilities and tasks, but also ensures at the system level that tasks are handled by the most suitable personnel who are more likely to complete them efficiently, thereby improving the overall response speed and quality of event handling. Empirical evidence shows that the method of this invention significantly reduces the deviation between the actual allocation results and the preset ability ratios, far exceeding traditional polling or simple equilibrium allocation schemes.

[0118] Second, it effectively ensures the compliance of long-term allocation strategies and the balance of the system.

[0119] By calculating and comparing the availability index of grid workers in real time during each dynamic scheduling decision (this index directly reflects the deviation between their "preset allocation ratio" and "currently completed allocation ratio"), and always selecting the one with the best index for allocation, long-term strategic ratio constraints are embedded into every short-term real-time allocation decision, forming an intelligent calibration capability of "seeking static equilibrium in dynamics." This ensures that within a scheduling cycle (such as a natural week), the actual cumulative workload undertaken by each grid worker will not exceed their pre-set allocation ratio limit based on their capabilities, eliminating the "short-term equilibrium, long-term imbalance" problem common in traditional dynamic scheduling. Simultaneously, the system's built-in periodic reset function can periodically clear accumulated data, eliminating error accumulation and giving the allocation system long-term stability and robustness. Even in the event of an unexpected situation where individual grid workers are temporarily absent, the remaining tasks can be automatically and reasonably redistributed based on the real-time indicators of other on-duty personnel, enhancing the system's anti-interference capability.

[0120] Third, reduce the cost and complexity of manual intervention in system operation and maintenance.

[0121] The system automates the entire process from strategy configuration and real-time scheduling to periodic calibration. Administrators only need to adjust the grid worker's ability weights during initialization or when necessary, and the system can automatically complete all subsequent allocation ratio calculations, real-time optimization decisions, and data updates. It improves scheduling response speed, enabling allocation decisions for new events to be completed automatically in extremely short times (e.g., milliseconds), solving the problem of time-consuming traditional manual or semi-automatic allocation. This frees management personnel from heavy, repetitive daily allocation tasks, allowing them to focus more on handling exceptions and optimizing strategies. The system can automatically handle the accurate scheduling of most routine events, reducing the manpower costs and management complexity of operation and maintenance.

[0122] Fourth, it has good scalability and scenario adaptability, and supports multi-objective collaborative optimization.

[0123] Capability weights can be dynamically adjusted based on feedback on task completion quality, enabling the system to possess self-learning and adaptive optimization capabilities. The allocation decision logic can be easily extended by introducing additional objective items (such as event urgency coefficients and work duration balance coefficients) and their weights, expanding the basic availability index into a composite decision index that integrates multiple objectives. It can flexibly adapt to the specific needs of various complex scenarios, from municipal grid management to enterprise work order dispatch and logistics delivery scheduling. The system not only pursues fair allocation based on capabilities but also simultaneously considers multiple management objectives such as task urgency and global load balancing, achieving a leap from single-objective optimization to multi-objective collaborative optimization and broadening the application boundaries of the technology.

[0124] like Figure 4 As shown, the present invention also provides a device including a processor 310, a communication interface 320, a memory 330 for storing processor-executable computer programs, and a communication bus 340. The processor 310, communication interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 implements the aforementioned method for intelligent event scheduling based on scheduling strategies by running the executable computer program.

[0125] The computer program in memory 330, when implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0126] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected based on actual needs to achieve the purpose of this embodiment. Those skilled in the art can understand and implement this without any creative effort.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for implementing intelligent scheduling of events based on a scheduling policy, the method comprising: receiving a request for scheduling an event; determining a scheduling policy for the event; and scheduling the event based on the determined scheduling policy. The method includes: Step S1: Configure a capability weight for each grid member, calculate their task allocation ratio in their respective grid based on the capability weight, and store the task allocation ratio, the total number of tasks in the grid within the period, and the number of tasks already allocated to each grid member as strategy data. Step S2: In response to a new event entering the system and being associated with a specific grid, trigger the scheduling process and obtain the policy data corresponding to that grid; Step S3: Calculate the real-time availability index of each grid member within the specific grid based on the acquired strategy data, and select the grid member with the highest real-time availability index value as the target grid member; Step S4: Assign the new event to the target grid worker and update the assigned task quantity of that grid worker in the policy data.

2. The method for realizing event intelligent scheduling based on scheduling strategy according to claim 1, characterized in that, In step S1, the competency weights for each grid worker are configured as follows: the competency weights are determined by multi-dimensional analysis of the grid worker's historical performance data. This multi-dimensional analysis includes at least the assessment and quantification of historical task processing efficiency, the type and level of professional skills mastered, and real-time workload saturation.

3. The method of claim 1, wherein the method further comprises: In step S1, the task allocation ratio of the capability weight in its respective grid is calculated, including: The ratio of a single grid member's ability weight to the sum of the ability weights of all grid members in its grid is used to calculate an original ratio value. The original proportion values ​​calculated by all grid members within the grid are normalized to ensure that the sum of the preset task allocation proportions of all grid members is 100%.

4. The method of claim 1, wherein the method further comprises: In step S2, in response to a new event entering the system and being associated with a specific grid, the following steps are taken: parsing the geographic location metadata carried by the new event, and determining the physical management grid to which the new event belongs and its corresponding unique grid identifier by matching the geographic location metadata with a predefined grid electronic map.

5. The method of claim 1, wherein the method further comprises: In step S3, the real-time availability index of each grid member in the specific grid is calculated based on the obtained strategy data, including: subtracting the percentage of the currently assigned tasks of each grid member from the percentage of the total number of grid tasks in the current scheduling period, and using the difference as the real-time availability index of the grid member, wherein the assigned tasks are the sum of the number of all event tasks that have been assigned to the grid member in the current scheduling period and have not yet been marked as completed.

6. The method of claim 1, wherein the method further comprises: In step S3, the real-time availability index of each grid member in the specific grid is calculated based on the acquired strategy data. This also includes introducing one or more additional decision factors and corresponding adjustment coefficients. The additional decision factors include an emergency priority factor generated based on the urgency label of the new event and a balance factor used to measure the degree of workload balance among grid members. By comprehensively calculating the real-time availability index and each weighted additional decision factor, a composite decision index integrating multiple scheduling objectives is formed, which is used to select grid members in complex scenarios.

7. The method of claim 1, wherein the method further comprises: In step S4, updating the assigned task quantity of the grid worker in the strategy data includes: performing an incremental update on the assigned task quantity field of the target grid worker in an atomic operation within a database transaction.

8. The method for implementing intelligent scheduling of events based on scheduling policy according to claim 1, wherein, The method also includes dynamically optimizing the capability weights in the following manner: Periodically collect performance feedback data from grid workers after they have completed their assigned tasks. This performance feedback data includes user evaluation scores after the task is completed and the deviation between the actual task processing time and the standard processing time. Based on the performance feedback data, a preset weight update algorithm is used to dynamically adjust the original ability weights of grid workers, and the adjusted new weight values ​​are updated to the strategy database for subsequent recalculation of task allocation ratios.

9. The method for realizing intelligent scheduling of events based on scheduling strategy according to claim 1, characterized in that, The method further includes performing periodic calibration after step S4 as follows: setting a periodic task volume threshold, continuously monitoring the cumulative value of the total task volume within the grid cycle, and automatically triggering a reset operation when the cumulative value reaches the periodic task volume threshold, clearing the total grid task volume within the current cycle and the assigned task volume of all grid members within the grid stored in the database to zero, ending the current scheduling cycle and starting a new cycle.

10. A computer device, comprising: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method according to any one of claims 1-9.

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