Personalized work task management method and device, storage medium and server

Abstract

Embodiments of the present application disclose a personalized work task management method and device, a storage medium and a server, and relate to the field of logistics management. The steps of the present application include: obtaining a user's work task set and assigning multiple tasks; quantitatively calculating the complexity score of each to-be-processed task; analyzing user historical data to establish daily efficiency fluctuation information, dividing the working time into multiple time slices associated with the efficiency coefficient; by calculating the matching degree of the time slice efficiency coefficient and the task complexity score, dynamically mapping the task with the highest matching degree to the corresponding time slice for execution; and finally, according to the mapping result, pushing personalized work optimization suggestions to the user. The present application realizes the accurate matching of task difficulty and personal work efficiency fluctuation through a data-driven approach, effectively overcoming the randomness of task arrangement relying on human experience, and significantly improving task execution efficiency and resource utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of logistics management, and in particular to a personalized work task management method and device, a storage medium and a server. BACKGROUND

[0002] With the continuous expansion of the business scale of logistics enterprises, the number of work tasks that employees need to handle daily has increased significantly, and the complexity of work has also increased. In the current work mode, employees usually arrange the daily work order and rhythm according to personal experience and subjective habits. This task arrangement method depending on personal intuition cannot adapt to the dynamic changes of daily work state, which easily leads to large fluctuations in work efficiency and uneven task completion quality, thereby restricting the improvement of overall organizational effectiveness. SUMMARY

[0003] Embodiments of the present application provide a personalized work task management method, device, storage medium and server, which can solve the problem of low efficiency caused by employees arranging work plans by themselves in the prior art. The technical solution is as follows:

[0004] In a first aspect, embodiments of the present application provide a personalized work task management method, which comprises:

[0005] obtaining a work task set of a user;

[0006] selecting multiple work tasks in the work task set and assigning them to the user;

[0007] calculating the complexity score of each pending task of the user;

[0008] determining the daily efficiency fluctuation information of the user; the daily efficiency fluctuation information indicates that the work time period of the user is divided into multiple time slices, and each time slice is associated with an efficiency coefficient;

[0009] mapping the time slice and the pending task with the highest matching degree between the efficiency coefficient of the time slice and the complexity score of the pending task according to the matching degree;

[0010] pushing work optimization suggestions to the user according to the mapping result of the pending task and the time slice.

[0011] In a second aspect, embodiments of the present application provide a personalized work task management device, which comprises:

[0012] an obtaining unit configured to obtain a work task set of a user;

[0013] an assigning unit configured to select multiple work tasks in the work task set and assign them to the user;

[0014] a computing unit configured to calculate a complexity score of each to-be-processed task of the user;

[0015] a determining unit configured to determine day efficiency fluctuation information of the user, the day efficiency fluctuation information indicating that a working time period of the user is divided into a plurality of time slices, and each time slice is associated with an efficiency coefficient;

[0016] a mapping unit configured to map a to-be-processed task with the highest matching degree to a time slice according to the matching degree between the efficiency coefficient of the time slice and the complexity score of the to-be-processed task;

[0017] a pushing unit configured to push a working optimization suggestion to the user according to the mapping result of the to-be-processed task and the time slice.

[0018] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and performing the method steps described above.

[0019] In a fourth aspect, an embodiment of the present application provides a terminal device, which can include a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and performing the method steps described above.

[0020] The technical scheme provided by some embodiments of the present application has at least the following beneficial effects:

[0021] The present application divides the working time of a user into time slices with different efficiency coefficients by establishing a day efficiency fluctuation model based on historical data, and dynamically maps a task with a complexity score to the most suitable time slice. This matching mechanism can ensure that a high-complexity task is executed during a high-efficiency period of the user, and a low-complexity task is processed during a low-efficiency period, thereby significantly improving the efficiency and quality of task completion.

[0022] The present application converts the task arrangement from subjective experience judgment to data-driven objective decision making by quantitatively analyzing the task complexity and the user efficiency curve. This method overcomes the randomness and blindness in manual arrangement, effectively avoids the waste of resources caused by task and state mismatch, and makes the work arrangement more reasonable and predictable.

[0023] The present application not only completes the initial task mapping, but also continuously pushes personalized working optimization suggestions according to the actual execution situation. This dynamic adjustment mechanism enables the system to continuously adapt to changes in the working state of the user, promotes continuous improvement of the working mode, and thus realizes the spiral rise of individual and organizational effectiveness.

[0024] To sum up, the application effectively improves the rationality and execution efficiency of work task arrangement through intelligent and personalized task scheduling mechanism, and provides reliable technical support for optimizing human resource allocation. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0026] Figure 1 is a network architecture schematic diagram provided by the embodiment of the present application;

[0027] Figure 2 is a flowchart of the personalized work task management method provided by the embodiment of the present application;

[0028] Figure 3 is a structure schematic diagram of a personalized work task management device provided by the present application;

[0029] Figure 4 is a schematic diagram of a computer storage medium provided by the embodiment of the present application;

[0030] Figure 5 is a structure schematic diagram of a server provided by the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0032] It should be noted that the personalized work task management method provided by the present application is generally executed by a server, and correspondingly, the personalized work task management device is generally arranged in the server.

[0033] Figure 1 An exemplary system architecture that can be applied to the personalized work task management method or the personalized work task management device of the present application is shown.

[0034] As Figure 1As shown, the system architecture can include: a terminal device 101 and a server 102. The terminal device 101 and the server 102 can communicate through a network, which is a medium for providing a communication link between the above-mentioned units. The network can include various types of wired communication links or wireless communication links, for example: the wired communication link includes optical fiber, twisted pair or coaxial cable, etc., and the wireless communication link includes Bluetooth communication link, Wireless-Fidelity (Wi-Fi) communication link or microwave communication link, etc.

[0035] Among them, the employees of the enterprise log in to the server 102 through the terminal device 101, and the server 102 schedules the pending tasks of the employees, pushes personalized work optimization suggestions to the employees through short messages, AI private assistants, calls or other ways, helps the employees to grow and progress in work, and thus improves the overall operation efficiency of the enterprise.

[0036] It should be noted that the terminal device 101 and the server 102 can be hardware or software. When the terminal device 101 and the server 102 are hardware, they can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the terminal device 101 and the server 102 are software, they can be implemented as multiple software or software modules (for example, to provide distributed services), or as a single software or software module, which is not specifically limited here.

[0037] Various communication client applications can be installed on the terminal device of the present application, such as video recording applications, video playback applications, voice interaction applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0038] The terminal device can be hardware or software. When the terminal device is hardware, it can be various terminal devices with display screens, including but not limited to smartphones, tablet computers, laptop computers and desktop computers, etc. When the terminal device is software, it can be installed in the above-mentioned terminal devices. It can be implemented as multiple software or software modules (for example, to provide distributed services), or as a single software or software module, which is not specifically limited here.

[0039] When the terminal device is hardware, a display device and a camera can also be installed thereon, the display device can be various devices capable of realizing display function, and the camera is used for collecting a video stream; for example, the display device can be a cathode ray tube display (CR), a light-emitting diode display (LED), an electronic ink screen, a liquid crystal display (LCD), a plasma display panel (PDP), etc. A user can use the display device on the terminal device to view displayed text, pictures, video, etc.

[0040] It should be understood that Figure 1 The number of terminal devices, networks and servers in the above-mentioned embodiments is only illustrative. According to the needs of implementation, any number of terminal devices, networks and servers can be used.

[0041] The above-mentioned embodiments will be described in detail below with reference to the accompanying drawings. Figure 2 The personalized work task management method provided by the embodiments of the present application will be described in detail. The personalized work task management device in the embodiments of the present application can be a server as shown in the server. Figure 1

[0042] Please refer to Figure 2 A flowchart of a personalized work task management method provided by the embodiments of the present application is shown. As shown in the flowchart, the method of the embodiments of the present application can include the following steps: Figure 2

[0043] S201, obtaining a work task set of a user.

[0044] The server performs the operation of obtaining the work task set of the user. The process starts with the server receiving a request from a client or an internal scheduling module, and the request contains a unique identifier of the user, such as a user ID or a session token. The server first verifies the legality of the request and the identity of the user, and ensures that the access right conforms to the security policy.

[0045] Subsequently, the server is connected to a backend data storage system, which is usually a relational database or a distributed storage service. The server constructs a query statement, filters the relevant work task records based on the user identifier. The query can involve multiple data tables or collections, including task basic information, allocation status and user association data. The server executes the query operation to retrieve the matched task entries from the storage system.

[0046] ​​During the retrieval process, the server may apply data preprocessing steps, such as filtering invalid tasks, deduplication, or sorting, to ensure the integrity and consistency of the collection. The retrieved data includes task attributes such as task name, description, priority, due date, and current status. The server then encapsulates this data into a structured collection of work tasks, ready for subsequent processing.

[0047] Finally, the server temporarily stores the acquired set of tasks in memory or cache to improve access efficiency for subsequent steps. The entire execution process prioritizes performance and reliability; the server may implement timeout mechanisms or retry strategies to handle network latency or storage system failures.

[0048] For example: Suppose the server needs to retrieve the set of work tasks for user Zhang San. User Zhang San's unique identifier is UID_1001. After receiving the request to retrieve the task set, the server first verifies the validity of UID_1001 to confirm that the user has the necessary access permissions.

[0049] The server then connects to the configured MySQL database, which stores task data for all users. The server constructs an SQL query to filter task records from the task table where the user ID equals UID_1001 and the status is pending or in progress. After the query is executed, the database returns three task records: Task A, Task B, and Task C. Each record contains information such as task ID, name, and priority.

[0050] S202. Select multiple work tasks from the set of work tasks and assign them to the user.

[0051] In step S202, the server performs the operation of selecting a specific task from the acquired set of work tasks and assigning it to the current user. This process is the core decision-making step in task scheduling, and its goal is to select a subset of tasks that are most suitable for the user to execute within the current period from all available tasks.

[0052] The server first loads the complete set of work tasks obtained in step S201. Then, the server initiates a multi-dimensional evaluation process, comprehensively analyzing each task in the set according to a preset allocation strategy. Key attributes used in the evaluation typically include the task's urgency, preset priority tags, time remaining until the final deadline, approximate working hours required to complete the task, the degree to which the skill domain required by the task matches the user's own skill profile, and the user's current total workload.

[0053] The server can maintain a weighted scoring algorithm internally to assign appropriate weights to each evaluation dimension. For example, the urgency and priority of a task can be assigned higher weights. The server calculates a composite score for each task in the set, which quantifies the appropriateness of assigning the task to the user at this moment.

[0054] Based on the composite score, the server ranks the tasks and selects a specified number of tasks from the top of the ranked list according to the daily work capacity defined in the system configuration or user preferences, such as a maximum of five tasks. These selected tasks constitute the work task list assigned to the user this time. The server then updates the status of these tasks, marking them as assigned to the user, and persists the assignment result to the database to ensure state consistency. This assignment decision aims to optimize the balance between overall work efficiency and user work experience.

[0055] For example, assume that the server has obtained a work task set containing seven tasks for user Li Si, with task numbers from T1 to T7. The server needs to select three tasks to assign to Li Si for processing today.

[0056] The server starts the evaluation process to examine the attributes of each task. For example, task T1 has a high priority, a deadline today, but the required skills only partially match Li Si's skills. Task T2 has a medium priority, a deadline in two days, and the required skills completely match Li Si's skills. Task T3 has a low priority, with a deadline one week later. Task T4 has a high priority, but is expected to take 8 hours, far exceeding Li Si's remaining available work hours today.

[0057] After weighted scoring calculation, task T2 scores the highest due to perfect skill match and moderate deadline, task T1 scores second due to high priority and urgent deadline, and task T5 scores third due to good load balancing. Task T4 is excluded due to excessive time consumption, and task T3 is excluded due to low priority.

[0058] Therefore, the server finally selects tasks T2, T1 and T5 to form the assignment list. The server updates the status of these three tasks as assigned to user Li Si and records this assignment decision in the task management database. In this way, Li Si will clearly see these three assigned tasks in his work list for today.

[0059] S203, calculate the complexity score of each task to be processed by the user.

[0060] The server performs the operation of calculating the complexity score of each task to be processed by the user. This process aims to quantitatively evaluate the difficulty of the task, providing accurate data basis for subsequent task scheduling.

[0061] The server first extracts key attributes of each task from the assigned user's pending task list. These attributes form the basis dimensions for complexity assessment, typically including the estimated time consumption of the task, the variety and level of skills required, the inherent difficulty coefficient of the task type, the number of external factors involved, the average completion time of historical similar tasks, and the clarity of task instructions, etc.

[0062] Subsequently, the server invokes the built-in complexity calculation model. This model can be a rule-based scoring system or a lightweight machine learning model trained with historical data. The model assigns appropriate weights to each assessment dimension, for example, task time consumption or environmental uncertainty may have a larger weight. The server inputs the multi-dimensional attribute data of each task into the model, which performs comprehensive calculations through internal algorithms and outputs a quantitative complexity score. This score is usually normalized to a pre-set numerical range, such as 0 to 10 points, to facilitate subsequent comparison and matching.

[0063] After calculation, the server establishes a mapping relationship between each pending task and its corresponding complexity score, and stores these data in a cache or memory data structure to ensure efficient access in subsequent steps.

[0064] For example, assume that the user is a courier Li Si of a logistics company, and currently has two pending tasks, Task A and Task B.

[0065] The server first analyzes the attributes of Task A. Task A is to deliver a batch of high-priority packages to the city center business district, with an estimated working time of 3 hours, requiring skills familiar with complex urban routes and handling unexpected traffic conditions, and the task type is emergency delivery (with a higher built-in difficulty coefficient), requiring handling multiple recipient sign-offs and possible route adjustments, with historical data showing an average completion time of 2.5 hours for similar tasks.

[0066] Next, the server analyzes the attributes of Task B. Task B is daily package sorting and scanning in the warehouse, with an estimated working time of 1 hour, requiring basic operation skills and concentration, and the task type is routine operation (with a lower built-in difficulty coefficient), with a stable working environment and standardized processes.

[0067] The server inputs the attribute data of Task A and Task B into the complexity calculation model. The model performs comprehensive calculations according to the weights of each dimension. Finally, Task A obtains a higher complexity score, for example 8.0, due to its longer time consumption, high skill requirements, and multiple environmental variables. Task B obtains a lower complexity score, for example 2.5, due to its simplicity and repeatability. The server associates these two scores with Task A and Task B respectively, preparing for the next step of efficiency matching.

[0068] S204, determine the user's daily efficiency fluctuation information; the daily efficiency fluctuation information represents that the working time period of the user is divided into a plurality of time slices, and each time slice is associated with an efficiency coefficient.

[0069] Among them, the server performs the operation of determining the user's daily efficiency fluctuation information. This process aims to establish the model of the user's personal work efficiency within a day, and provide the basis in the time dimension for fine task scheduling.

[0070] The server first obtains the historical work efficiency data of the user. These data come from the historical records of the task management system, including the start time, end time, task type and completion quality of each task of the user in the past. The server preprocesses these raw data, including data cleaning to exclude outliers and invalid records, and formats the time stamp according to the unified standard.

[0071] Then, the server divides the user's standard working time period into several continuous time slices. The division strategy is usually based on business needs, for example, one hour or half an hour as a basic unit. After division, the server analyzes the task completion of the user in all historical working days in the corresponding time period for each time slice. The analysis indicators mainly include the average task completion speed, task accuracy and output per unit time.

[0072] Based on these analysis indicators, the server calculates an efficiency coefficient for each time slice. The calculation process may use standardization method to convert the original data to relative value between 0 and 1, where 1 represents that the user is in the highest efficiency state in the time period. The determination of efficiency coefficient may combine statistical methods, such as calculating the ratio of the index value of the time period to the average value of the whole day, or applying simple regression analysis to fit the efficiency change curve.

[0073] Finally, the server generates the user's daily efficiency fluctuation information, which is a data structure composed of time slice sequence and its corresponding efficiency coefficient. These information are stored and can be used for real-time query, forming the user's personal work efficiency portrait.

[0074] For example: take a courier of a logistics company, Mr. Zhang, as an example. The server needs to determine his daily efficiency fluctuation information on working days. Mr. Zhang's standard working time is from 8:00 am to 6:00 pm.

[0075] The server first retrieves Mr. Zhang's work data in the past three months, including the delivery records of thousands of packages, each record has the delivery start time, completion time and customer feedback score. The server divides the 10 hours of working time into 10 one-hour time slices.

[0076] Subsequently, the server analyzes the data of each time slice. The analysis finds that during the period from 9 am to 11 am (time slices 2 and 3), Master Zhang delivers the most packages, spends the least average time, and has the highest customer rating. However, during the period from 1 pm to 2 pm (time slice 6), all indicators slightly decrease due to the energy decline after lunch. By 4 pm to 5 pm (time slice 9), the efficiency indicators again show a significant decrease due to the approaching end of the workday and the evening traffic peak.

[0077] Based on this, the server assigns an efficiency coefficient to each time slice. The efficiency coefficient for the period from 9 am to 11 am is set to the highest, at 0.95 and 0.98. The efficiency coefficient for the period from 1 pm to 2 pm is 0.85. The efficiency coefficient for the period from 4 pm to 5 pm is 0.75. Other time periods are assigned coefficients between 0.8 and 0.9 according to specific data. In this way, the server successfully builds a daily efficiency fluctuation model for Master Zhang, clearly depicting the ups and downs of his work efficiency throughout the day.

[0078] S205, according to the matching degree between the efficiency coefficient of the time slice and the complexity score of the to-be-processed task, mapping the time slice and the to-be-processed task with the highest matching degree.

[0079] Among them, the server performs the operation of task mapping based on the matching degree between the time slice efficiency coefficient and the to-be-processed task complexity score. This process is a dynamic iterative scheduling loop, aiming to optimize task allocation in real time to adapt to the fluctuations in user work efficiency.

[0080] The server first initializes the scheduling process at the beginning of the user's working time. At this time, the server obtains the efficiency coefficient of the current time slice, which comes from the pre-determined daily efficiency fluctuation information. At the same time, the server loads the list of complexity scores of all to-be-processed tasks.

[0081] Next, the server calculates the matching degree between the efficiency coefficient of the current time slice and the complexity score of each to-be-processed task. The matching degree calculation usually uses a numerical comparison method, such as calculating the absolute difference between the efficiency coefficient and the complexity score. The smaller the absolute difference, the higher the degree of adaptation between the two, i.e. the better the matching degree. The server generates a matching degree value for each task.

[0082] Specifically, the process of calculating the matching degree between the to-be-processed task and the time slice includes:

[0083] The server first obtains the efficiency coefficient of the current time slot, which comes from the pre-determined daily efficiency fluctuation information, and is a value between 0 and 1, representing the relative work efficiency of the user in that time period. At the same time, the server loads the list of all to-be-processed tasks, each task associated with a complexity score, which is usually between 0 and 10, quantifying the difficulty level of the task.

[0084] The matching degree calculation is realized by numerical comparison method. The server calculates the absolute difference between the complexity score of each to-be-processed task and the efficiency coefficient of the current time slot. The calculation method of the absolute difference is the absolute value of the efficiency coefficient minus the complexity score. The smaller the value, the closer the efficiency coefficient and the complexity score, and the higher the matching degree. The server generates an absolute difference value for each task, then compares the absolute difference values of all tasks, and identifies the task with the smallest absolute value, which is the task with the highest matching degree.

[0085] Then, the server compares the matching degree values of all to-be-processed tasks, identifies the task with the optimal matching degree, i.e., the task with the smallest absolute difference. The server binds and maps this task with the current time slot, which means that the task is scheduled to be executed within this time slot. The server updates the task status to in progress and starts monitoring the execution progress of the task.

[0086] When the server detects that the task is completed, it records the specific time when the task is completed. The server queries the time slot to which the time belongs and takes it as the new current time slot. Then, the server repeats the matching degree calculation and selection process described above, but this time only for the remaining to-be-processed tasks. The server calculates the matching degree between the efficiency coefficient of the new time slot and the complexity score of the remaining tasks, and selects the task with the optimal matching degree again for mapping.

[0087] S206, according to the mapping result of the to-be-processed tasks and the time slots, and pushing work optimization suggestions to the user.

[0088] Among them, the server performs the operation of generating work optimization suggestions according to the mapping result of the to-be-processed tasks and the time slots and pushing to the user. This process is the final link of the task scheduling process, aiming to improve the user's work efficiency through data analysis.

[0089] The server first loads the task and time slot mapping records completed in step S205. These records describe in detail the tasks executed in each time slot and their corresponding complexity scores and efficiency coefficients. The server systematically analyzes these mapping data, and the analysis focuses on the matching degree quality of the mapping, the timing rationality of task execution, and the utilization of the efficiency coefficient. For example, the server evaluates whether high-complexity tasks are successfully allocated to the user's high-efficiency time slots, or whether there is a mapping deviation that leads to resource waste.

[0090] Based on the analysis results, the server initiates the suggestion generation logic. This logic may rely on a predefined rule base or statistical analysis models. The server identifies potential optimization points, such as adjustment opportunities for task order, improvement suggestions for time slicing division, or optimization tips for user work habits. The suggestion content is concretized, such as recommending to prioritize complex tasks during specific high-efficiency periods or scheduling breaks or simple transactions during low-efficiency periods. When generating suggestions, the server considers historical performance data and current mapping effects to ensure the practicality and personalization of the suggestions.

[0091] After generating the suggestions, the server sends the optimization suggestions to the user through an integrated message push service. The push channels may include enterprise internal communication systems, emails, or mobile application notifications. The server ensures that the message format is clear and easy to read, and includes necessary context information such as relevant time slices or task details. The entire push process focuses on timeliness and non-intrusiveness, avoiding interference with the user's normal work.

[0092] Finally, the server records the log of this suggestion push, including the push time, suggestion content, and user feedback mechanism, to optimize the suggestion generation algorithm for subsequent optimization. This step closes the task scheduling process, achieving continuity from execution to improvement.

[0093] For example: Take Zhao Ming, a sales staff of a logistics company, as an example. Suppose in step S205, the server has completed the mapping of Zhao Ming's daily tasks and time slices. The mapping result shows that the efficiency coefficient of the time slice from 9:00 to 10:00 is 0.9, and task A (complexity score 8 points) is assigned to this time period, i.e. contacting major customers to negotiate contracts; the efficiency coefficient of the time slice from 3:00 to 4:00 is 0.7, and task B (complexity score 4 points) is assigned to this time period, i.e. processing daily email replies.

[0094] The server analyzes the mapping result and finds that task A matches well with the high-efficiency time slice, but task B is assigned to a low-efficiency time slice, which may not fully utilize the peak period. In addition, the server compares historical data and finds that Zhao Ming's efficiency is generally lower in the afternoon, but the current mapping does not consider rest adjustments.

[0095] Based on this, the server generates work optimization suggestions. The suggestion content includes: first, recommend to arrange similar simple tasks like task B in the afternoon low-efficiency period, and suggest to increase short breaks in the afternoon to restore energy; second, prompt Zhao Ming to reserve more time for complex sales tasks in the morning high-efficiency period to improve overall output.

[0096] The server pushes the optimization suggestion to Zhao Ming's office computer and mobile phone application in the form of a text message through the company's instant messaging tool. The message clearly lists the key points of the suggestion and attaches a summary of the mapping results for the day, making it easy for Zhao Ming to refer to. In this way, Zhao Ming can adjust his work plan for the next day according to the suggestion and gradually optimize his personal work efficiency.

[0097] In some embodiments, the mapping method between the tasks to be processed and the time slices includes:

[0098] When the starting time of the working time period arrives, the matching degree between the efficiency coefficient of the first time slice and the complexity score of each task to be processed of the user is calculated, the task to be processed with the highest matching degree is determined according to the calculation result, the task to be processed is mapped to the first time slice, the task to be processed is set as a processed task when the execution of the task to be processed is detected, the ending time of the processed task is determined, the time slice corresponding to the ending time in the working time period is queried, the matching degree between the efficiency coefficient of the time slice and the complexity score of each task to be processed of the user is calculated, the task to be processed with the highest matching degree is determined according to the calculation result, the task to be processed is mapped to the queried time slice, and the task to be processed is set as a processed task when the execution of the task to be processed is detected; until the working time period ends or all tasks to be processed of the user are executed.

[0099] Specifically, the server performs a dynamic task scheduling process, which is based on the matching degree between the efficiency coefficient of the time slice and the complexity score of the task to be processed, to realize real-time mapping of tasks to time slices. The entire process runs in an iterative manner to ensure that task allocation is synchronized with fluctuations in user work efficiency.

[0100] The server first starts the scheduling cycle at the starting time of the user's working time. At this time, the server obtains the efficiency coefficient of the first time slice, which is derived from the pre-calculated daily efficiency fluctuation information. At the same time, the server loads the list of all tasks to be processed, each of which is associated with a calculated complexity score.

[0101] Next, the server calculates the matching degree between the efficiency coefficient of the first time slice and the complexity score of each task to be processed. The matching degree is determined by a numerical comparison method, such as calculating the absolute difference between the efficiency coefficient and the complexity score. The smaller the absolute difference, the higher the matching degree, i.e., the better the adaptability of the task to the time slice. The server generates a matching degree value for each task and compares these values to select the task with the highest matching degree, i.e., the task with the smallest absolute difference.

[0102] The server maps the selected task to the first time slice, meaning the task is officially scheduled to be executed within this time slice. The server updates the status of the task as in progress and initiates a monitoring mechanism to continuously detect the progress of the task execution. Monitoring can be achieved through user input or system automatic feedback.

[0103] When the server detects that the task is completed, it records the end time. The server queries the time slice to which the end time belongs within the user's working time period and takes it as the new current time slice. Then, the server repeats the matching degree calculation process, but this time only for the remaining tasks to be processed. The server calculates the matching degree of the efficiency coefficient of the new time slice and the complexity score of the remaining tasks, and selects the task with the highest matching degree again for mapping.

[0104] This iterative process continues, re-evaluating and assigning after each task is completed. The server ensures that each mapping is based on the latest time slice and task status. The termination condition of the loop is the end of the user's working time period or the completion of all tasks to be processed. In this way, the server implements an adaptive scheduling mechanism that optimizes the execution order of tasks to improve overall efficiency.

[0105] Taking a courier of a logistics company as an example, assuming his working time is from 8 am to 5 pm, the server has divided this period into 9 one-hour time slices. The daily efficiency fluctuation information shows that the efficiency coefficient of time slice 1 is 0.9, time slice 2 is 1.0, time slice 3 is 0.9, and the efficiency coefficient of subsequent time slices gradually decreases. The courier currently has 3 tasks to be processed, task 1 is to deliver a batch of urgent packages, with a complexity score of 9 points; task 2 is to arrange daily routes, with a complexity score of 5 points; task 3 is to handle customer inquiries, with a complexity score of 3 points.

[0106] At the start time of 8 am, the server starts scheduling. The current time slice is time slice 1, with an efficiency coefficient of 0.9. The server calculates the matching degree, which is the absolute difference between the efficiency coefficient and the task complexity score. The difference of task 1 is 8.1, the difference of task 2 is 4.1, and the difference of task 3 is 6.1. The difference of task 2 is the smallest, and the matching degree is the highest, so the server maps task 2 to time slice 1.

[0107] Suppose task 2 is completed at 9:30 am, and the server records the end time. This time belongs to time slice 2, with an efficiency coefficient of 1.0. The server calculates the matching degree of the efficiency coefficient of time slice 2 and the remaining tasks 1 and 3. The difference of task 1 is 8.0, and the difference of task 3 is 7.0. The difference of task 3 is smaller, and the matching degree is higher, so the server maps task 3 to time slice 2.

[0108] Task 3 is completed at 10:40, at which time time slice 3 is entered, with an efficiency coefficient of 0.9. The server calculates the matching degree with the only remaining task 1, with a difference of 8.1, and immediately maps task 1. This process continues until all tasks are completed or the workday ends, thereby achieving efficient task allocation.

[0109] In some embodiments, the selecting a plurality of work tasks from the set of work tasks for distribution to the user comprises:

[0110] According to the historical work records of the user, the daily average work amount of the user is counted;

[0111] According to the daily average work amount, a plurality of work tasks are selected from the set of work tasks, and the selected work tasks are distributed to the user.

[0112] Specifically, when the server performs work task distribution, it first needs to quantitatively evaluate the work capacity of the user. This process begins with a deep analysis of the user's historical work records. The server extracts the complete work data of the user in the past statistical period from the historical database of the task management system. These data include the number of tasks completed each day, task type, actual time consumption, and completion quality, among other key indicators.

[0113] Based on these historical data, the server starts a statistical analysis process to calculate the user's daily average work amount. This calculation is not a simple arithmetic mean, but a weighted average algorithm, with more recent data being given a higher weight to reflect the user's latest work status. At the same time, the server will perform data cleaning to exclude records of holidays or abnormal workdays, ensuring the representativeness and accuracy of the statistical results. The final daily average work amount is a comprehensive value that quantifies the total amount of work the user can typically complete in a standard workday.

[0114] After obtaining the daily average work amount, the server enters the task selection stage. The server converts each task in the user's current set of work tasks into a standard work unit, which is based on factors such as the estimated man-hours, complexity, and resource requirements of the task. Subsequently, the server performs work amount matching calculations to select a group of tasks from the task set, so that the total standard work amount of this group of tasks is basically consistent with the user's daily average work amount. The selection process also considers the priority attributes of the tasks, the urgency of the deadlines, and the logical association between tasks to ensure that the combination of allocated tasks is not only moderate in total amount but also structurally reasonable.

[0115] Finally, the server formally distributes the selected set of work tasks to the user, updates the task status to allocated, and persists the distribution results. The entire allocation decision is automatically completed by the server, forming a data-driven task allocation mechanism.

[0116] The embodiment realizes personalized task allocation in a data-driven manner, can effectively avoid excessive or insufficient user workloads, and can improve the efficiency and quality of task completion, thereby realizing optimal allocation of human resources.

[0117] In some embodiments, the task to be processed is a package delivery task.

[0118] The complexity score of each task to be processed by the user is calculated, including:

[0119] The complexity score of the package delivery task is (w_d*d + w_n*n + w_w*w + w_t*t), where d represents the delivery distance of the task, n represents the number of packages of the task, w represents the average weight of the packages of the task, and t represents the traffic factor of the task, and the value range of t is 1-10, 1 represents smooth traffic, and 10 represents traffic congestion; w_d, w_n, w_w, and w_t represent weight coefficients, respectively, for adjusting the contribution of each factor, and the sum of the weights is 1.

[0120] Specifically, when the server performs the operation of calculating the complexity score of the package delivery task, it first obtains the attribute data of each task to be processed from the task management system. These data include the delivery distance, the number of packages, the average weight of the packages, and the traffic factor. The delivery distance is usually in kilometers and is derived from a geographic information system; the number of packages and the average weight come from a warehouse database; the traffic factor is obtained through a real-time traffic API or historical data analysis, and its value range is 1 to 10, 1 representing smooth traffic and 10 representing traffic congestion.

[0121] The server is pre-configured with a set of weight coefficients, including the delivery distance weight, the number of packages weight, the average weight weight, and the traffic factor weight. The sum of these weight coefficients is 1, and their values are set based on business rules or historical optimization models to balance the contribution of each factor to the complexity. The server standardizes the attribute values of each task to ensure that data of different dimensions are comparable, such as converting distance, number, weight, and traffic factor to a unified scale.

[0122] Subsequently, the server applies a weighted summation algorithm to calculate the complexity score. Specifically, the server multiplies the delivery distance by the corresponding weight, the number of packages by the corresponding weight, the average weight of the packages by the corresponding weight, and the traffic factor by the corresponding weight, and then adds the four products to obtain the final complexity score. This score is a continuous value that quantifies the difficulty of the task.

[0123] The embodiment adopts this multi-factor weighted complexity score calculation method, which can more accurately reflect the actual difficulty of the task and improve the scientificity of task allocation, thereby optimizing resource utilization efficiency and enhancing the reliability and user satisfaction of delivery services.

[0124] In some embodiments, the server periodically updates the user's daily efficiency fluctuation information, the updating method comprising:

[0125] Periodically collecting the user's work situation data in multiple historical working days using a sliding window method, the work situation data including: task start time, task end time, task type and task quality score;

[0126] According to the preset division rule, the user's working time period is divided into multiple time slices;

[0127] For each historical working day, the total workload of the historical working day is counted, and the workload completed in each time slice is counted;

[0128] The workload completed in the time slice is divided by the total workload of the historical working day to obtain the efficiency coefficient of the time slice.

[0129] Among them, the server performs the calculation process of the daily efficiency fluctuation information, first adopts the sliding window mechanism to periodically collect the user's historical work situation data. The sliding window covers multiple historical working days, for example, the last thirty working days, and the server retrieves the task records of each working day from the task management database or the working log system. These records include task start time, task end time, task type and task quality score. The server cleans and verifies the data, excludes abnormal or invalid records, and ensures the data quality.

[0130] Next, the server divides the user's standard working time period into multiple continuous time slices according to the preset division rule. The division rule is based on business needs, for example, the working day is evenly divided into fixed length intervals from the start time to the end time, such as one time slice per hour. Each time slice represents a specific time period, and the server assigns a unique identifier to each time slice.

[0131] For each historical working day, the server counts the total workload of the day. The workload is calculated based on task attributes, which may consider the weighted combination of task quantity, task time consumption or task quality score. For example, the total workload can be defined as the sum of the quality scores of all tasks. At the same time, the server counts the workload completed in each time slice, that is, the sum of the workloads of the tasks ended in the time slice. The workload counting process involves time matching, and the server maps the end time of the task to the corresponding time slice.

[0132] Then, the server calculates the efficiency coefficient for each time slice. The efficiency coefficient is obtained by dividing the amount of work completed within a time slice by the total amount of work for that historical workday. This coefficient represents the relative efficiency contribution of that time slice in the workday. The server repeats this process for all historical workdays and calculates the average efficiency coefficient for each time slice to form the user's daily efficiency fluctuation model. The final result is stored in the server cache for subsequent task scheduling.

[0133] This embodiment can accurately quantify the daily changes in user work efficiency, providing a scientific basis for dynamic task allocation, thereby optimizing the work process and improving overall performance.

[0134] In some embodiments, the pushing of the work optimization suggestions to the user comprises:

[0135] According to the user's account login state, the corresponding message push channel is queried; the message push channel includes: the push message of the AI private assistant, the short message, or the loudspeaker of the place where the user is located;

[0136] According to the message push channel, the work optimization suggestions are pushed to the user.

[0137] Specifically, the server performs the operation of pushing work optimization suggestions to the user. This process begins with the server determining the most suitable message push channel based on the user's account login state. The server first queries the user's current login state, which is achieved by checking the authentication service or session management system to confirm whether the user is online, the type of device used, and the geographic location information.

[0138] Based on the login state query result, the server retrieves the pre-set message push channel configuration. These channels include the push message of the AI private assistant, the short message service, or the loudspeaker system in the place where the user is located. The server intelligently selects the channel based on the user's state, for example, if the user is online and active in the mobile application, the push message of the AI private assistant is preferred; if the user is offline or the device is unreachable, the short message channel is switched to; if the user is located in a fixed workplace such as a warehouse or office, the use of the place loudspeaker for audio prompts is considered.

[0139] After determining the push channel, the server formats the work optimization suggestion content. The suggestion text is generated based on the task mapping analysis results to ensure that the information is concise and clear. The server sends messages through the API interface integrated into the corresponding channel, such as calling the push service provider interface to send mobile notifications, or sending text messages through the SMS gateway, or controlling the Internet of Things device to trigger the loudspeaker broadcast.

[0140] The embodiment ensures that work optimization suggestions can be timely and accurately delivered to users, improves task execution efficiency, and enhances the usability and user experience of the system.

[0141] In some embodiments, the obtaining the work task set of the user comprises:

[0142] Obtaining the identity information of the user, querying the corresponding post type information according to the identity information, and querying the corresponding work task set according to the post type information.

[0143] Specifically, in step S201, the server performs the operation of obtaining the work task set of the user. The process starts with the server receiving a task scheduling request, which contains the identity identifier of the user, such as the user ID or login token. The server first verifies the validity and authority of the identity information to ensure that the request comes from a legal source.

[0144] Subsequently, the server connects to the user information database and uses the identity identifier as a query key to retrieve the corresponding post type information. The post type information is usually stored in a user profile table or an organizational structure table, including post name, responsibility description and associated attributes. The server performs a query operation to extract the post type of the user.

[0145] After obtaining the post type information, the server further connects to the work task management system or the task database. The server constructs a query condition based on the post type information to filter the work task records related to the post. The query may involve task allocation tables, task type mapping tables and other data sources to ensure that the returned task set meets the post responsibility range. The server integrates the query results to form a structured work task set, including basic information such as task name, priority and status.

[0146] The embodiment filters tasks by post type to ensure that task allocation is highly matched with user responsibilities, improving work relevance and efficiency while reducing irrelevant task interference.

[0147] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the method embodiments of the present application.

[0148] Please refer to Figure 3 which shows a structure diagram of a personalized work task management apparatus provided by an example embodiment of the present application, hereinafter referred to as apparatus 3. The apparatus 3 can be realized by software, hardware or a combination of both to become all or part of a server. The apparatus 3 comprises:

[0149] The obtaining unit 301 is configured to obtain a work task set of a user.

[0150] The distribution unit 302 is configured to select a plurality of work tasks from the set of work tasks for distribution to the user;

[0151] The calculation unit 303 is configured to calculate a complexity score of each to-be-processed task of the user;

[0152] The determination unit 304 is configured to determine day efficiency fluctuation information of the user; the day efficiency fluctuation information indicates that a working time period of the user is divided into a plurality of time slices, and each time slice is associated with an efficiency coefficient;

[0153] The mapping unit 305 is configured to map a to-be-processed task with the highest matching degree to a time slice according to a matching degree between the efficiency coefficient of the time slice and the complexity score of the to-be-processed task;

[0154] The pushing unit 306 is configured to push a work optimization suggestion to the user according to a mapping result of the to-be-processed task and the time slice.

[0155] In one or more possible embodiments, the mapping of the to-be-processed task with the highest matching degree to the time slice according to the matching degree between the efficiency coefficient of the time slice and the complexity score of the to-be-processed task comprises:

[0156] When a starting moment of the working time period arrives, a matching degree between an efficiency coefficient of a first time slice and the complexity score of each to-be-processed task of the user is calculated, a to-be-processed task with the highest matching degree is determined according to a calculation result, the to-be-processed task is mapped to the first time slice, the to-be-processed task is set as a processed task when the to-be-processed task is detected to be executed completely, an ending moment of the processed task is determined, a time slice corresponding to the ending moment in the working time period is queried, a matching degree between an efficiency coefficient of the time slice and the complexity score of each to-be-processed task of the user is calculated, a to-be-processed task with the highest matching degree is determined according to a calculation result, the to-be-processed task is mapped to the queried time slice, and the to-be-processed task is set as a processed task when the to-be-processed task is detected to be executed completely; until the working time period ends or all to-be-processed tasks of the user are executed completely.

[0157] In one or more possible embodiments, the selection of the plurality of work tasks from the set of work tasks for distribution to the user comprises:

[0158] The day average workload of the user is counted according to a historical work record of the user;

[0159] The plurality of work tasks are selected from the set of work tasks according to the day average workload, and the selected work tasks are distributed to the user.

[0160] In one or more possible embodiments, the to-be-handled task is a package delivery task.

[0161] The calculating the complexity score of each to-be-handled task of the user comprises:

[0162] The complexity score of the package delivery task=(w_d*d+w_n*n+w_w*w+w_t*t); wherein d represents the delivery distance of the task, n represents the number of packages of the task, w represents the average weight of the packages of the task, and t represents the traffic factor of the task, the value range of t is 1-10, 1 represents smooth traffic, and 10 represents traffic congestion; w_d, w_n, w_w, and w_t respectively represent weight coefficients, used for adjusting the contribution degree of each factor, and the sum of the weights is 1.

[0163] In one or more possible embodiments, the method further comprises:

[0164] The updating unit is configured to periodically collect work condition data of the user on a plurality of historical workdays in a sliding window manner, the work condition data comprising: task start time, task end time, task type, and task quality score;

[0165] The work time period of the user is divided into a plurality of time slices according to a preset division rule;

[0166] For each historical workday, the total workload of the historical workday is counted, and the workload completed in each time slice is counted;

[0167] The workload completed in the time slice is divided by the total workload of the historical workday to obtain an efficiency coefficient of the time slice.

[0168] In one or more possible embodiments, the pushing the work optimization suggestion to the user comprises:

[0169] According to the account login state of the user, a corresponding message pushing channel is queried; the message pushing channel comprises: a push message of an AI private assistant, a short message, or a loudspeaker in a place where the user is located;

[0170] The work optimization suggestion is pushed to the user according to the message pushing channel.

[0171] In one or more possible embodiments, the obtaining the work task set of the user comprises:

[0172] Identity information of the user is obtained, corresponding post type information is queried according to the identity information, and a corresponding work task set is queried according to the post type information.

[0173] It should be noted that the device 3 provided in the above embodiments is only illustrated by the division of the above functional modules when executing the personalized task management method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the above functions. In addition, the personalized task management device and the personalized task management method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0174] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0175] See Figure 4 The diagram shown is a schematic of a computer storage medium provided in an embodiment of this application. The computer storage medium can store multiple instructions (i.e., ... Figure 4 The computer program shown above), the instructions are adapted to be loaded and executed by a processor as described above. Figure 2 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figure 2 The specific details of the illustrated embodiments will not be elaborated here.

[0176] This application also provides a computer program product that stores at least one instruction, which is loaded and executed by the processor to implement the personalized task management method described in the above embodiments.

[0177] Please see Figure 5 This provides a schematic diagram of a server structure for an embodiment of this application. For example... Figure 5 As shown, the server 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0178] The communication bus 502 is used to enable communication between these components.

[0179] The user interface 503 may include input units such as a mouse and keyboard.

[0180] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0181] The processor 501 can include one or more processing cores. The processor 501 connects various parts within the server 500 through various interfaces and lines, performs various functions of the server 500 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Alternatively, the processor 501 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 501 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 501, but can be realized by a separate chip.

[0182] The memory 505 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 505 can alternatively be at least one storage device located away from the aforementioned processor 501. As shown in the figure, the memory 505 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program. Figure 5

[0183] In Figure 5 ​In the server 500 shown, the user interface 503 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 501 can be used to call an application stored in the memory 505, and specifically execute the method as shown in Figure 2 The specific process can refer to the method as shown in Figure 2 The specific process can refer to the method as shown in

[0184] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium, and when the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory, a random access memory, etc.

[0185] The above only describes the preferred embodiments of the present application, and of course cannot limit the scope of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope of the present application.

Claims

1. A personalized work task management method, characterized by, The method comprises: obtaining a set of work tasks of a user; selecting a plurality of work tasks from the set of work tasks to assign to the user; calculating a complexity score of each pending task of the user; determining day efficiency fluctuation information of the user, the day efficiency fluctuation information indicating that a work time period of the user is divided into a plurality of time slices, each time slice being associated with an efficiency coefficient; mapping a time slice and a pending task with the highest matching degree according to a matching degree between the efficiency coefficient of the time slice and the complexity score of the pending task, specifically comprising: when a starting time of the work time period arrives, calculating a matching degree between the efficiency coefficient of a first time slice and the complexity score of each pending task of the user, determining a pending task with the highest matching degree according to a calculation result, mapping the pending task with the first time slice, setting the pending task as a processed task when the pending task is detected to be executed, determining an ending time of the processed task, querying a time slice corresponding to the ending time in the work time period, calculating a matching degree between the efficiency coefficient of the time slice and the complexity score of each pending task of the user according to a calculation result, determining a pending task with the highest matching degree, and mapping the pending task with the queried time slice; and detecting the pending task to be executed to set the pending task as a processed task until the work time period ends or all pending tasks of the user are executed; pushing a work optimization suggestion to the user according to a mapping result of the pending task and the time slice; The method further comprises: periodically collecting work condition data of the user in a plurality of historical work days by using a sliding window mode, the work condition data comprising: a task starting time, a task ending time, a task type, and a task quality score; dividing the work time period of the user into a plurality of time slices according to a preset division rule; for each historical work day, counting a total work amount of the historical work day and counting a work amount completed in each time slice; dividing the work amount completed in the time slice by the total work amount of the historical work day to obtain the efficiency coefficient of the time slice.

2. The method of claim 1, wherein, The selecting a plurality of work tasks from the set of work tasks to assign to the user comprises: counting a daily average work amount of the user according to historical work records of the user; selecting a plurality of work tasks from the set of work tasks according to the daily average work amount, and assigning the selected work tasks to the user.

3. The method of claim 2, wherein, The pending task is a package delivery task. The calculating the complexity score of each pending task of the user comprises: A complexity score of a package delivery task is (w_d*d+w_n*n+w_w*w+w_t*t), wherein d represents a delivery distance of the task, n represents a number of packages of the task, w represents an average weight of the packages of the task, t represents a traffic factor of the task, and t is in a range of 1-10, 1 representing smooth traffic and 10 representing traffic congestion; w_d, w_n, w_w, and w_t respectively represent weight coefficients for adjusting contribution degrees of each factor, and a sum of the weight coefficients is 1.

4. The method of claim 3, wherein, The pushing of the work optimization suggestion to the user comprises: According to the user's account login state, the corresponding message push channel is queried; the message push channel includes: the push message of the AI private assistant, the short message or the loudspeaker of the place where the user is located; According to the message push channel, the work optimization suggestion is pushed to the user.

5. The method of claim 4, wherein, The acquisition of the work task set of the user comprises: Acquiring the identity information of the user, querying the corresponding post type information according to the identity information, and querying the corresponding work task set according to the post type information.

6. An individualized work task management apparatus, characterized by, Comprise: The acquisition unit is used for acquiring the work task set of the user; The distribution unit is used for selecting multiple work tasks in the work task set and distributing the multiple work tasks to the user; The calculation unit is used for calculating the complexity score of each to-be-processed task of the user; The determination unit is used for determining the day efficiency fluctuation information of the user; the day efficiency fluctuation information indicates that the work time period of the user is divided into multiple time slices, and each time slice is associated with an efficiency coefficient; The mapping unit is used for mapping the time slice and the to-be-processed task with the highest matching degree according to the matching degree between the efficiency coefficient of the time slice and the complexity score of the to-be-processed task, and specifically comprises the following steps: when the starting moment of the work time period arrives, calculating the matching degree between the efficiency coefficient of the first time slice and the complexity score of each to-be-processed task of the user, determining the to-be-processed task with the highest matching degree according to the calculation result, mapping the to-be-processed task and the first time slice, setting the to-be-processed task as a processed task when the to-be-processed task is detected to be executed, determining the ending moment of the processed task, querying the time slice corresponding to the ending moment in the work time period, calculating the matching degree between the efficiency coefficient of the time slice and the complexity score of each to-be-processed task of the user, determining the to-be-processed task with the highest matching degree according to the calculation result, mapping the to-be-processed task and the queried time slice, and setting the to-be-processed task as a processed task when the to-be-processed task is detected to be executed; until the work time period ends or all to-be-processed tasks of the user are executed; The push unit is used for pushing the work optimization suggestion to the user according to the mapping result of the to-be-processed task and the time slice; The acquisition unit is also used for periodically collecting work condition data of the user in multiple historical work days in a sliding window manner, and the work condition data comprises: task starting time, task ending time, task type, and task quality score. The acquisition unit is further configured to divide the working time period of the user into a plurality of time slices according to a preset division rule, count the total working amount of each historical working day, and count the working amount completed in each time slice, and divide the working amount completed in each time slice by the total working amount of the historical working day to obtain an efficiency coefficient of the time slice.

7. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, which are adapted to be loaded and executed by the processor to perform the method steps of any one of claims 1-5.

8. A server, characterized by Comprise: A processor and a memory; wherein the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the method steps of any one of claims 1-5.

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