Task urging method, urging device, equipment and medium

Abstract

The invention provides a task urging method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: analyzing the current working state data, and obtaining the current working efficiency of a target employee; based on the historical working efficiency, the current working efficiency, the target task load and the corresponding execution complexity, predicting the working efficiency of the target employee in the target time period to obtain predicted working efficiency; if the target employee cannot complete the target task amount in the target time period with the predicted working efficiency, calculating the task completion efficiency of the target employee based on the target task amount and the target time period; and generating first task urging information based on the task completion efficiency and the target task load, and sending the first task urging information to the target employee. According to the invention, automatic task monitoring and task management are realized, so that the working efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a task urging method, urging device, equipment and medium. Background Art

[0002] When companies start performance-based tasks, they need to set a start time and an end time. Employees assigned to complete the task need to complete it within the specified time. Currently, most companies generally use a fixed cycle of one month or one quarter as the task execution cycle. Not only can they not be dynamically adjusted according to actual conditions, but the cycle setting itself is unreasonable and does not fully consider the actual conditions of the company and employees.

[0003] In the process of enterprise management, it is found that when the task execution cycle is long, some employees will forget the existence of the task, and some employees who are assigned to complete a task do not have the autonomous driving force, which causes some employees to delay the completion of the task until the deadline or even delay the completion of the task.

[0004] At the same time, the follow-up of existing tasks is based on HR's manual statistics of the overall completion rate. If someone has not completed the task, HR will have to follow up individually, perhaps through IM chat, or even find the employee and tell him to complete the task as soon as possible. This requires a lot of manual screening and searching in the system, and there is no intuitive and visual dashboard or operation method.

[0005] When some employees may ignore the deadline for goal setting due to busy work, managers need to remind them in time. For example, when HR is counting the completion rate of the current step, they will need to screen and check one by one in the system. For example, when screening the goal setting for the first time, it is found that 30 people have not completed it, and 20 people have not completed the goal confirmation. They need to screen and check one by one, and urge them to complete one by one. There is no visual summary tool and process, which increases the complexity of the work. In addition, due to the limitations of manual operation, omissions or misjudgments may occur. Summary of the invention

[0006] The purpose of the embodiments of the present application is to provide a task urging method, urging device, equipment and medium to solve the above-mentioned problems existing in the prior art, and to urge the completion of tasks efficiently, timely and specifically.

[0007] In a first aspect, a method for urging task completion is provided, which may include:

[0008] Obtaining the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within the target time period of the target employee who is performing the target task;

[0009] The historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time;

[0010] Analyze the current work status data to obtain the current work efficiency of the target employee;

[0011] Extracting the task amount and execution complexity of each task whose task priority is greater than the target task from the work planning data;

[0012] The unfinished workload and the extracted task workload are used as the target task workload;

[0013] Based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency;

[0014] If the target employee cannot complete the target task volume within the target time period with the predicted work efficiency, then calculating the target employee's task completion efficiency based on the target task volume and the target time period;

[0015] Based on the task completion efficiency and the target task volume, first task urging information is generated, and the first task urging information is sent to the target employee.

[0016] In an optional implementation, the task parameters also include: task amount, task limit start time and task limit end time;

[0017] The target task corresponds to a target task type; the historical work data includes: the historical task start time, historical task end time and historical task parameters of each historical task performed by the target employee in different historical time periods;

[0018] The method for obtaining the task end time includes:

[0019] Obtaining enterprise environment data of the enterprise where the employee works, as well as the total number of employees assigned to perform the target task, the historical work data of each corresponding employee, and the work planning data within a limited time period; wherein the limited time period is the time period from the task limited start time to the task limited start time;

[0020] Input the task parameters, the total number of employees corresponding to the target task, and the historical work data of each employee into a pre-trained task time prediction model to obtain the initial start prediction time and the initial end prediction time of the target task; wherein the task time prediction model is trained using the target historical task data corresponding to the target task type extracted from the configured historical task database;

[0021] The initial start prediction time and the initial end prediction time are adjusted according to the enterprise environment data and the work plan data of each employee within a limited time period to obtain the task start time and task end time of the target task.

[0022] In an optional implementation, the initial start prediction time and the initial end prediction time are adjusted according to the enterprise environment data and the work plan data of each employee within a limited time period, including:

[0023] Inputting the enterprise environment data into a pre-trained enterprise business volume prediction model to obtain the business prediction results of the enterprise within a limited time period;

[0024] The initial start prediction time, the initial end prediction time, the work planning data of each employee within a limited time period and the business prediction result are input into a pre-trained multi-objective task time optimization model to obtain the task start time and task end time of the target task; wherein, the multi-objective task time optimization model is constructed with the optimization goals of maximizing the enterprise's business volume and minimizing the burden on employees.

[0025] In an optional implementation, analyzing the current work status data to obtain the current work efficiency of the target employee includes:

[0026] Obtaining efficiency evaluation indicators for evaluating employee efficiency and weights and scoring rules corresponding to different efficiency evaluation indicators;

[0027] For any efficiency evaluation indicator, based on the scoring rule corresponding to the efficiency evaluation indicator, the current work status data of the target employee is scored to obtain the scoring result of the target employee under the efficiency evaluation indicator;

[0028] Based on the weights corresponding to different efficiency evaluation indicators, the scoring results of the target employee under different efficiency evaluation indicators are weighted and summed to obtain the first scoring result of the target employee;

[0029] Inputting the current work status data and the first scoring result into a pre-trained efficiency evaluation model to obtain a second scoring result of the target employee;

[0030] Based on the comparison result between the first scoring result and the configured efficiency evaluation threshold, and the second scoring result, the current work efficiency of the target employee is generated.

[0031] In an optional implementation, based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency, including:

[0032] From the historical work efficiency, extract historical work efficiency data in multiple consecutive time periods closest to the current time to obtain a target historical work efficiency time series;

[0033] The current work efficiency, the target historical work efficiency time series, the target task volume and the corresponding execution complexity are input into a pre-trained efficiency prediction model to obtain the predicted work efficiency.

[0034] In an optional implementation, after calculating the task completion efficiency of the target employee, the method further includes:

[0035] If the task completion efficiency meets the configured efficiency warning threshold, then based on the target task amount and the time difference between the current time and the task end time, generate second task urging information, and send the second task urging information to the target employee;

[0036] The efficiency warning threshold is determined based on the benchmark work efficiency and maximum work efficiency of the target employee; the benchmark work efficiency and maximum work efficiency are obtained by analyzing the historical work efficiency of the target employee.

[0037] In an optional implementation, the method further includes:

[0038] When the time difference between the current time and the task end time satisfies the configured task timeliness warning rule, third task urging information is generated, and the third task urging information is sent to the target employee.

[0039] In a second aspect, a task urging device is provided, which may include:

[0040] An acquisition unit is used to acquire the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within a target time period of the target employee who is executing the target task; wherein the historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority, and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time;

[0041] An analysis unit, used to analyze the current work status data to obtain the current work efficiency of the target employee;

[0042] An extraction unit, used to extract the task amount and execution complexity of each task whose task priority is greater than the target task from the work planning data; and use the unfinished workload and the extracted task amount as the target task amount;

[0043] A prediction unit, configured to predict the work efficiency of the target employee in the target time period based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, so as to obtain the predicted work efficiency;

[0044] a calculation unit, configured to calculate the task completion efficiency of the target employee based on the target task amount and the target time period if the target employee cannot complete the target task amount within the target time period with the predicted work efficiency;

[0045] The urging unit is used to generate first task urging information based on the task completion efficiency and the target task volume, and send the first task urging information to the target employee.

[0046] In a third aspect, an electronic device is provided, the electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0047] Memory, used to store computer programs;

[0048] The processor is used to implement any method step described in the first aspect when executing the program stored in the memory.

[0049] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0050] This application implements automated performance tasks or task monitoring and management to improve work efficiency and accuracy. This application uses automated reminders to promptly remind staff to handle tasks without manual intervention, thereby improving the efficiency of task completion and work progress.

[0051] This application not only realizes automated reminders, but also carries out targeted reminders based on the actual situation of employees. When an employee has not completed a task, but can complete it with the predicted work efficiency, no reminder will be made; only when an employee has not completed a task and cannot complete it with the predicted work efficiency, reminders will be made. This not only reduces the number of meaningless reminders and saves system resources; it also improves the pertinence and effectiveness of reminders, and prevents employees from missing effective reminder information due to too much invalid reminder information; and when employees receive reminder information, they can clearly understand the amount of work to be completed, the remaining time and their own predicted work efficiency, and then they can adjust their work status in a targeted manner and reasonably adjust their work plan to complete the corresponding tasks.

[0052] This application predicts employee work efficiency by not only taking into account the employee's current work status, but also combining the employee's historical work efficiency changes over time, etc., to achieve accurate work efficiency prediction, which helps employees understand their work status.

[0053] This application also overcomes the negative impact on enterprises and employees caused by the existing artificially set task completion time. It combines enterprise environmental data and employee data to optimize the time set by the enterprise with the goal of maximizing enterprise business volume and minimizing employee burden, and obtains more reasonable task start and end times that can generate greater benefits, further protecting the interests of enterprises and employees. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flowchart of a task urging method provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the structure of a task urging device provided in an embodiment of the present application;

[0057] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] The task urging method provided in the embodiment of the present application can be applied in a server or in a terminal with strong computing power. The server can be a physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a user equipment (User Equipment, UE) such as a mobile phone, a smart phone, a laptop, a digital broadcast receiver, a personal digital assistant (PDA), a tablet computer (PAD), a handheld device, a vehicle-mounted device, a wearable device, a computing device or other processing equipment connected to a wireless modem, a mobile station (Mobile Station, MS), a mobile terminal (Mobile Terminal), etc. The terminal and the server can be directly or indirectly connected by wired or wireless communication, and this application is not limited here.

[0060] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0061] Figure 1 A flowchart of a task urging method provided in an embodiment of the present application. Figure 2 As shown, the method may include:

[0062] Step S110, obtaining the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within the target time period of the target employee who is executing the target task.

[0063] In the embodiment of the present application, the target time period is the time period from the current time to the task end time; any target task corresponds to a target task type.

[0064] In an embodiment of the present application, historical work data includes: the historical work efficiency of the target employee in each historical time period, the historical task start time, historical task end time and historical task parameters of each historical task executed; work planning data includes: the task volume, task priority and execution complexity of each task to be completed by the target employee in the target time period; task parameters include: task priority, task volume, task limited start time and task limited end time.

[0065] In an embodiment of the present application, the task-limited start time and the task-limited end time are pre-set by the enterprise according to actual conditions. For example, if the enterprise expects to start the year-end performance appraisal task at the end of the year, the appraisal time can be tentatively set from January 1 to January 15, and these two dates are the task-limited start time and the task-limited end time. However, in actual applications, the task-limited start time and the task-limited end time pre-set by the enterprise may not conform to the actual conditions of the enterprise. For example, a certain enterprise happens to be at its business peak in January, so the employees do not actually have time to complete the performance appraisal tasks. Based on this, the present application adjusts the task-limited start time and the task-limited end time according to the enterprise environmental data and the actual work situation of the employees, so that the start time and the end time of the task will neither affect the company's business nor impose a heavy burden on the employees.

[0066] In an embodiment of the present application, a method for obtaining a task end time includes:

[0067] Obtain the enterprise environment data of the employee's enterprise, the total number of employees assigned to perform the target task, the corresponding historical work data of each employee, and the work plan data within a limited time period; input the task parameters, the total number of employees corresponding to the target task, and the historical work data of each employee into a pre-trained task time prediction model to obtain the initial start prediction time and initial end prediction time of the target task; adjust the initial start prediction time and initial end prediction time according to the enterprise environment data and the work plan data of each employee within a limited time period to obtain the task start time and task end time of the target task.

[0068] In an embodiment of the present application, the task time prediction model is trained using target historical task data corresponding to the target task type extracted from a configured historical task database; the target historical task data includes the task start time, task end time, task limited start time, task limited end time, historical task parameters, the total number of employees who execute each target historical task, the historical work data of each employee, and the work status data of each employee when executing each target historical task, etc.

[0069] In the embodiment of the present application, the limited time period is the time period from the task limited start time to the task limited start time;

[0070] In the embodiment of the present application, the initial start prediction time and the initial end prediction time are adjusted according to the enterprise environment data and the work planning data of each employee within a limited time period, including:

[0071] Input the enterprise environment data into the pre-trained enterprise business volume forecasting model to obtain the business forecast results of the enterprise within a limited time period; input the initial start forecast time, initial end forecast time, each employee's work planning data within a limited time period and the business forecast results into the pre-trained multi-objective task time optimization model to obtain the task start time and task end time of the target task.

[0072] In an embodiment of the present application, the enterprise environment data includes the business volume change trend data of the industry in which the enterprise is located over time, the business volume data of the enterprise in the current time in the past years, the business volume of the enterprise at the current time, and the predicted business volume within the target time period, etc.

[0073] In an embodiment of the present application, the enterprise business volume forecasting model may select any one of the time series models such as ARIMA, LSTM, Prophet, etc., so as to capture the seasonality and trend components in the time series and improve the accuracy of business volume forecasting; the training data of the enterprise business volume forecasting model selects the business volume data of the enterprise in the past 5-10 years and the impact data of major events of the enterprise or industry occurring at different historical time points, etc.

[0074] In the embodiment of the present application, the multi-objective task time optimization model is constructed with the optimization goals of maximizing the enterprise's business volume and minimizing the employee burden; that is, the optimization goals of the multi-objective task time optimization model include two. The first is to maximize the enterprise's business volume, thereby ensuring that other tasks or performance appraisal tasks are arranged in the period with the least impact on the enterprise's business, especially avoiding the arrangement of large tasks or performance appraisal tasks during business peak periods; the second is to minimize the employee burden, ensuring that the task time arrangement is as consistent as possible with the employee's work plan and reducing conflicts with personal plans.

[0075] In an embodiment of the present application, a multi-objective task time optimization model adopts a reinforcement learning framework, including: Environment: composed of the weight of business peak / low period and the employee idleness score (obtained by analyzing the employee's work plan), used to represent the current state of task scheduling; Agent: used to select actions according to the current environmental state, that is, to adjust the start and end time of the task; Action: Possible actions include advancing or postponing the start time of the task, extending or shortening the duration of the task, etc. Reward: Contains a comprehensive function that comprehensively considers business efficiency and employee idleness; for example, if the task time scheduling leads to lower business costs and higher employee satisfaction, a positive reward is given; otherwise, a negative reward is given. Policy: Q-Learning, DQN or Actor-Critic and other methods are used as learning strategies.

[0076] In an embodiment of the present application, the constraints of the multi-objective task time optimization model can be set as: the task must be completed within a continuous time period; the total duration of the task should not exceed a predetermined maximum value; the task should not be scheduled on statutory holidays.

[0077] This application can achieve scientific and reasonable adjustment of the time of performance appraisal tasks or other tasks through the above-mentioned multi-objective task time optimization model, which helps to improve the task completion effect while also taking into account the work and life balance of employees.

[0078] Step S120, analyze the current work status data to obtain the current work efficiency of the target employee; extract the task volume and execution complexity of each task whose task priority is greater than the target task from the work planning data; and use the unfinished workload and the extracted task volume as the target task volume.

[0079] In the embodiment of the present application, the current work status data is analyzed to obtain the current work efficiency of the target employee, including:

[0080] Obtain efficiency evaluation indicators for evaluating employee efficiency and weights and scoring rules corresponding to different efficiency evaluation indicators; for any efficiency evaluation indicator, score the current work status data of the target employee based on the scoring rules corresponding to the efficiency evaluation indicator to obtain the scoring result of the target employee under the efficiency evaluation indicator; based on the weights corresponding to different efficiency evaluation indicators, weighted sum the scoring results of the target employee under different efficiency evaluation indicators to obtain a first scoring result of the target employee; input the current work status data and the first scoring result into a pre-trained efficiency evaluation model to obtain a second scoring result of the target employee; based on the comparison result of the first scoring result with the configured efficiency evaluation threshold, and the second scoring result, generate the current work efficiency of the target employee.

[0081] In an embodiment of the present application, the employee's current work status data can be obtained by analyzing the employee's recent work videos, recent work logs or work reports or work conversations, including the employee's recent workload, working hours and work commitment.

[0082] In the embodiment of the present application, multiple scoring rules are pre-set. For example, the degree of work engagement can be set to a certain score, and the employee's current work status data is scored according to the scoring rules. However, the scoring output result alone is rather one-sided. Therefore, it is necessary to combine the efficiency evaluation model to evaluate the work efficiency based on the completed workload and working hours; then combine the scoring results and the evaluation results to obtain the current work efficiency determined according to the employee's current work status.

[0083] In the embodiment of the present application, each task corresponds to a task priority, and tasks with higher priority are completed first. Therefore, if the employee's work plan within the target time period also includes other tasks with higher priority than the target task, it is necessary to execute other tasks with higher priority first. Therefore, when counting the employee's workload, not only the unfinished workload of the target task should be considered, but also the workload of other tasks with higher priority.

[0084] Step S130: Based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency.

[0085] In the embodiment of the present application, based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency, including:

[0086] From the historical work efficiency, extract the historical work efficiency data in multiple consecutive time periods closest to the current time to obtain the target historical work efficiency time series;

[0087] The current work efficiency, target historical work efficiency time series, target task volume and corresponding execution complexity are input into the pre-trained efficiency prediction model to obtain the predicted work efficiency.

[0088] In an embodiment of the present application, the efficiency prediction model may adopt a time series prediction model based on random forest.

[0089] In the embodiment of the present application, the work efficiency obtained based on the employee's current work status can only reflect the work efficiency of the day or a few days, and the employee's work efficiency will change. For example, the employee's work efficiency is 100% this week, but in the next week, the employee may be physically tired due to excessive workload this week, and the work efficiency may decrease. Therefore, it is not possible to judge whether the employee can complete the target task based solely on the current work efficiency. Instead, it is necessary to comprehensively consider multiple factors and predict the work efficiency, and use the predicted work efficiency as a benchmark to judge whether the employee can complete the target task.

[0090] In practical applications, the work efficiency of employees changes periodically and is affected by the workload and the execution complexity of the corresponding tasks. Therefore, the predicted work efficiency of employees is obtained based on the efficiency prediction model trained in advance using the employee's historical work efficiency data and historical work data.

[0091] In an embodiment of the present application, the work efficiency of employees will also be affected by other environmental factors such as promotions, salary increases, and holidays. Therefore, historical environmental data can also be used to jointly train the efficiency prediction model, and the holidays in the work plan and the employee's position or salary change data within the target time period or before the target time period, together with workload and other data, can be used as inputs to the efficiency prediction model to obtain more accurate predictions of work efficiency.

[0092] Step S140: If the target employee cannot complete the target task volume within the target time period with the predicted work efficiency, the task completion efficiency of the target employee is calculated based on the target task volume and the target time period.

[0093] In the embodiment of the present application, work efficiency can be considered as the effective working time per day.

[0094] In the embodiment of the present application, the predicted work efficiency is an efficiency curve, that is, the predicted work efficiency corresponding to multiple time points within the target time period, rather than a specific value.

[0095] In an embodiment of the present application, after obtaining the predicted work efficiency, it is necessary to determine whether the predicted work efficiency can complete the target task volume within the target time period; for example, the target task volume to be completed is 100, the target time period is 30 days, and the working hours are 8 hours a day. The predicted work efficiency for each day in the next week is 90%, and the actual working hours of the employees are 8*0.9=7.2 hours per day. The employees need to spend 111.12 hours working, which is less than the target time period, indicating that the employees can complete the target task volume with the predicted work efficiency.

[0096] In the embodiment of the present application, since the employee's fatigue cycle and holidays are taken into consideration when predicting work efficiency, the predicted work efficiency is close to the employee's actual work situation. When the workload can be completed with the predicted work efficiency, the employee can actually complete it.

[0097] This application will not send reminders when an employee fails to complete the workload for a certain day. Instead, it will predict the employee's next work efficiency. If the employee can complete the corresponding target task volume with the predicted work efficiency, it means that the employee can complete the corresponding task before the task end time, and there is no need to send reminders to the employee at this time; however, if the target task volume cannot be completed before the task end time with the current work efficiency or the predicted work efficiency, a reminder message for the task will be generated to remind the employee that the predicted work efficiency cannot be completed, and the employee may need to improve work efficiency based on the predicted work efficiency.

[0098] In the embodiment of the present application, work efficiency = workload / working hours, the working day length of the target time period is the working hours, and the task completion efficiency that the target employee needs to achieve when completing the target task volume can be obtained based on the target task volume / working hours.

[0099] In an embodiment of the present application, if the task completion efficiency meets the configured efficiency warning threshold, a second task reminder information is generated based on the target task volume and the time difference between the current time and the task end time, and the second task reminder information is sent to the target employee.

[0100] In an embodiment of the present application, the efficiency warning threshold is determined based on the baseline work efficiency and maximum work efficiency of the target employee; the baseline work efficiency and the maximum work efficiency are obtained by analyzing the historical work efficiency of the target employee; the baseline work efficiency and the maximum work efficiency are respectively the employee's average work efficiency and the maximum work efficiency ever achieved.

[0101] In an embodiment of the present application, assuming that the employee's maximum work efficiency is 150% and the task completion efficiency is 130%, or the employee's baseline work efficiency is 80% and the task completion efficiency is 100%, it means that the task completion efficiency is not easy for the employee to achieve and the employee may need to significantly improve the work efficiency through other means (such as improving attention, etc.). At this time, a second task reminder message is sent to the employee to remind the employee that he must improve his work efficiency from the current time, otherwise the task cannot be completed.

[0102] In an embodiment of the present application, the second task reminder information includes: task completion efficiency, target task volume, and the time difference between the current time and the task end time; the target task volume can also be divided by the time difference to obtain the task volume to be completed each day, which is sent to employees to remind them to complete their work according to the volume every day; in order to avoid employees being unable to complete tasks on time, which would drag down the entire project.

[0103] In an embodiment of the present application, when any employee is assigned to perform a task, the daily workload can be divided for each employee based on the end time and start time of the task and the employee's work plan; if an employee sends a reminder if he / she fails to complete the corresponding workload on a certain day, multiple reminder messages need to be sent during the task execution process, and these reminder messages are of little significance, because the employee may temporarily adjust the work schedule and modify the order of task completion, resulting in the failure to complete the workload on that day.

[0104] Step S150: Generate first task urging information based on the task completion efficiency and the target task volume, and send the first task urging information to the target employee.

[0105] In an embodiment of the present application, if the task completion efficiency does not meet the configured efficiency warning threshold, a first task reminder information is generated based on the task completion efficiency, the target task volume, and the time difference between the current time and the task end time, and the first task reminder information is sent to the target employee.

[0106] In an embodiment of the present application, the enterprise or HR may also pre-set other reminder rules, including: task timeliness warning rules and completion rate reminder rules, etc.; when the time difference between the current time and the task end time meets the configured task timeliness warning rules, a third task reminder information is generated, and the third task reminder information is sent to the target employee; when the completion rate is only 3%, a reminder information is sent to all employees who are assigned to perform the task and have not completed the corresponding task amount.

[0107] For example, the company pre-sets that when there are 3 days left to the end of the task, reminders will be sent every day. Then, when there are 3 days or less left to the end of the task, reminders will be sent every day to the employees who have not completed the task.

[0108] This application can truly play the role of urging work through targeted reminder information. Starting from work efficiency, it only urges employees when they cannot complete the task. This improves the pertinence and effectiveness of the reminder and better urges employees to complete the task.

[0109] In other embodiments of the present application, completion rate urging rules may also be set, including:

[0110] After creating a performance task or project task, the enterprise can add a process monitoring tab page; on this page, there is an operation button "Create process monitoring", and the user can jump to the creation of process monitoring to-do settings after clicking it. In the settings, all to-do types of the user's enterprise will be automatically listed. For example, the user has 9 to-do types, and the performance to-do name is filled in by default according to the rules. At the same time, the start and end time are recommended according to the algorithm. The remaining fields are optional and users can make their own choices.

[0111] In addition, users can add, delete or modify the name of the to-do list. You can adjust the to-do list name according to your needs, and finally click the "OK" button to generate the "Process Monitoring-To-Do List and Completion Rate" data dashboard.

[0112] In the to-do completion rate data dashboard, the corresponding dashboard will be generated based on the settings when creating the process monitoring. For example, when the process monitoring is created, the design items include goal setting indicator confirmation, employee evaluation, multi-person evaluation, result review, result confirmation, HR review, HRD review, and viewing performance results, etc., then the corresponding process monitoring dashboard will be generated. At the same time, the corresponding number of people will be matched according to the to-do type. For example, if the number of people is set to 100, if the current step has been completed, the corresponding completion rate is 100%. At this time, for the case of a completion rate of 100%, the urging operation item will no longer be displayed. If the number of people is 100, and the current number of completed people is only 1, the corresponding completion rate is 1%, then the "Go to urging" button will appear in the current step.

[0113] After clicking on the reminder, you will enter the details page of the unfinished personnel. At this time, select all the unfinished personnel to urge them, and a second pop-up window will appear. For example, in order to reduce interference to users, the option of "no more notifications will be sent to personnel who have been urged for this link within 6 hours" is provided. If this option is selected, when sending reminders, the system will first compare to see whether a reminder has been sent within 6 hours. If it has been sent, it will be ignored this time and no reminder will be sent.

[0114] At the same time, the system will also send reminders 8 hours, 4 hours, and 30 minutes before the deadline according to the rules by default, to prevent relevant personnel from forgetting to urge the process, without leaving all of them to the system for reminders, causing excessive disturbance to users.

[0115] Finally, a "View Performance Results" dashboard will be automatically generated. When all steps are completed, HR can selectively release the performance results of some employees in batches. During the entire process, this dashboard can be used to understand the specific status of the people who have completed and those who are waiting to be done, avoiding the omission of people.

[0116] In an embodiment of the present application, after creating a performance task or project task, the enterprise also needs to design a database for the performance task monitoring process, based on the relational database MySQL, Oracle, and using the entity-relationship model (ER model) for planning. Create a performance task table (performance_activity), which contains the auto-increment primary key activity_id as a unique identifier, as well as fields such as task name, creation time and description. By setting a non-empty constraint, the task name must be filled in to ensure data integrity. At the same time, design a to-do table (todo_item), use the auto-increment primary key todo_id to identify each to-do, and associate the performance task through the foreign key activity_id, including the to-do name, start and end time, completion status and the person in charge user ID and other fields, and use the enumeration type to clarify the completion status to prevent invalid data. In addition, the user enterprise information table (enterprise_info) uses the enterprise unique identifier enterprise_id as the primary key to store enterprise-specific to-do types.

[0117] In terms of data acquisition, the main sources are database tables and user input and operations. Improve data retrieval speed by creating appropriate indexes on fields that are frequently used for querying, joining, and sorting (such as activity_id of the performance task table, activity_id and responsible_user of the to-do table).

[0118] The generation of performance to-dos is based on the to-do types and preset rules in the user's enterprise information table. After obtaining these types from the database, the performance to-do names are automatically filled in according to the rules, and the start and end times are recommended by algorithms based on historical data, business rules and other factors. The user's modification operations are updated in real time to the database. The progress statistics of the to-do nodes are calculated by comparing the completed and uncompleted quantities. The completion status of each to-do is obtained from the to-do table, and the completed quantity is divided by the total quantity to obtain the completion rate. The number of people is obtained directly from the relevant table. The deadline is calculated based on the start and end times of the to-do initially set.

[0119] Whether the reminder button is displayed or not depends on the completion rate. If the completion rate reaches 100%, the reminder button will not be displayed; if it does not reach 100%, the reminder button will be displayed. This is achieved by calculating the completion rate in real time and comparing it with the set threshold. The timing of the system sending messages is based on the deadline and the preset time interval. Record the deadline of each to-do in the to-do list, and trigger the message sending function at specific time points such as 8 hours, 4 hours, 30 minutes before the deadline through a scheduled task, and notify HR to inform how many people have not completed the current step, and ask HR to follow up in time.

[0120] The message sending function reads the to-do data, filters out the items that are about to expire but not completed, and sends a notification to the relevant person in charge. Before sending, it will also check whether the person has been urged to do the work within 6 hours to avoid repeated interruptions. The technical core idea of ​​the whole process is to realize automatic performance task monitoring and management based on accurate data storage and efficient query of the database, combined with algorithms and timed tasks, to improve work efficiency and accuracy.

[0121] Corresponding to the above method, the embodiment of the present application also provides a task urging device, such as Figure 2 As shown, the task urging device includes:

[0122] The acquisition unit 210 is used to acquire the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within the target time period of the target employee who is performing the target task; wherein the historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority, and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time;

[0123] An analysis unit 220 is used to analyze the current work status data to obtain the current work efficiency of the target employee;

[0124] An extraction unit 230 is used to extract the task amount and execution complexity of each task whose task priority is greater than the target task from the work planning data; and use the unfinished workload and the extracted task amount as the target task amount;

[0125] The prediction unit 240 is used to predict the work efficiency of the target employee in the target time period based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity to obtain the predicted work efficiency;

[0126] A calculation unit 250 is used to calculate the task completion efficiency of the target employee based on the target task amount and the target time period if the target employee cannot complete the target task amount within the target time period with the predicted work efficiency;

[0127] The urging unit 260 is used to generate first task urging information based on the task completion efficiency and the target task volume, and send the first task urging information to the target employee.

[0128] The functions of each functional unit of the task urging device provided in the above embodiments of the present application can be realized through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the task urging device provided in the embodiments of the present application will not be repeated here.

[0129] The present application also provides an electronic device, such as Figure 3 As shown, it includes a processor 310 , a communication interface 320 , a memory 330 and a communication bus 340 , wherein the processor 310 , the communication interface 320 , and the memory 330 communicate with each other via the communication bus 340 .

[0130] Memory 330, for storing computer programs;

[0131] The processor 310 is used to execute the program stored in the memory 330 to implement the following steps:

[0132] Obtain the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of unfinished workload, and work planning data within the target time period of the target employee who is executing the target task; among which, the historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority, and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time;

[0133] Analyze the current work status data to obtain the current work efficiency of the target employees;

[0134] Extract the task volume and execution complexity of each task whose task priority is greater than the target task from the work planning data; take the unfinished workload and the extracted task volume as the target task volume;

[0135] Based on historical work efficiency, current work efficiency, target task volume and corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency;

[0136] If the target employee cannot complete the target task volume within the target time period based on the predicted work efficiency, the target employee's task completion efficiency is calculated based on the target task volume and the target time period;

[0137] Based on the task completion efficiency and the target task volume, first task urging information is generated, and the first task urging information is sent to the target employee.

[0138] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0139] The communication interface is used for communication between the above electronic device and other devices.

[0140] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0141] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] The implementation methods and beneficial effects of the components of the electronic device in the above embodiments to solve the problems can be seen in Figure 1 The various steps in the illustrated embodiment are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0143] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer executes any one of the task urging methods in the above embodiments.

[0144] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the task urging methods in the above embodiments.

[0145] Those skilled in the art will appreciate that the embodiments in the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt a complete hardware embodiment, a complete software embodiment, or a form of an embodiment combining software and hardware. Moreover, the present application may adopt a form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0150] Obviously, those skilled in the art can make various changes and modifications to the embodiments in the present application without departing from the spirit and scope of the embodiments in the present application. Thus, if these modifications and variations of the embodiments in the present application fall within the scope of the claims and their equivalents in the embodiments of the present application, the embodiments of the present application are also intended to include these modifications and variations.

Claims

1. A method for urging task completion, characterized in that: The method comprises: Obtaining the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within the target time period of the target employee who is performing the target task; The historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time; Analyze the current work status data to obtain the current work efficiency of the target employee; Extracting the task amount and execution complexity of each task whose task priority is greater than the target task from the work planning data; The unfinished workload and the extracted task workload are used as the target task workload; Based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency; If the target employee cannot complete the target task volume within the target time period with the predicted work efficiency, then calculating the target employee's task completion efficiency based on the target task volume and the target time period; Based on the task completion efficiency and the target task volume, first task urging information is generated, and the first task urging information is sent to the target employee.

2. The method according to claim 1, characterized in that The task parameters also include: task amount, task limit start time and task limit end time; The target task corresponds to a target task type; the historical work data includes: the historical task start time, historical task end time and historical task parameters of each historical task performed by the target employee in different historical time periods; The method for obtaining the task end time includes: Obtaining enterprise environment data of the enterprise where the employee works, as well as the total number of employees assigned to perform the target task, the historical work data of each corresponding employee, and the work planning data within a limited time period; wherein the limited time period is the time period from the task limited start time to the task limited start time; Input the task parameters, the total number of employees corresponding to the target task, and the historical work data of each employee into a pre-trained task time prediction model to obtain the initial start prediction time and the initial end prediction time of the target task; wherein the task time prediction model is trained using the target historical task data corresponding to the target task type extracted from the configured historical task database; The initial start prediction time and the initial end prediction time are adjusted according to the enterprise environment data and the work plan data of each employee within a limited time period to obtain the task start time and task end time of the target task.

3. The method according to claim 2, characterized in that According to the enterprise environment data and the work plan data of each employee within a limited time period, the initial start prediction time and the initial end prediction time are adjusted, including: Inputting the enterprise environment data into a pre-trained enterprise business volume prediction model to obtain the business prediction results of the enterprise within a limited time period; The initial start prediction time, the initial end prediction time, the work planning data of each employee within a limited time period and the business prediction result are input into a pre-trained multi-objective task time optimization model to obtain the task start time and task end time of the target task; wherein, the multi-objective task time optimization model is constructed with the optimization goals of maximizing the enterprise's business volume and minimizing the burden on employees.

4. The method according to claim 1, characterized in that Analyze the current work status data to obtain the current work efficiency of the target employee, including: Obtaining efficiency evaluation indicators for evaluating employee efficiency and weights and scoring rules corresponding to different efficiency evaluation indicators; For any efficiency evaluation indicator, based on the scoring rule corresponding to the efficiency evaluation indicator, the current work status data of the target employee is scored to obtain the scoring result of the target employee under the efficiency evaluation indicator; Based on the weights corresponding to different efficiency evaluation indicators, the scoring results of the target employee under different efficiency evaluation indicators are weighted and summed to obtain the first scoring result of the target employee; Inputting the current work status data and the first scoring result into a pre-trained efficiency evaluation model to obtain a second scoring result of the target employee; Based on the comparison result between the first scoring result and the configured efficiency evaluation threshold, and the second scoring result, the current work efficiency of the target employee is generated.

5. The method according to claim 1, characterized in that Based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, the work efficiency of the target employee in the target time period is predicted to obtain the predicted work efficiency, including: From the historical work efficiency, extract historical work efficiency data in multiple consecutive time periods closest to the current time to obtain a target historical work efficiency time series; The current work efficiency, the target historical work efficiency time series, the target task volume and the corresponding execution complexity are input into a pre-trained efficiency prediction model to obtain the predicted work efficiency.

6. The method according to claim 1, characterized in that After calculating the task completion efficiency of the target employee, the method further includes: If the task completion efficiency meets the configured efficiency warning threshold, then based on the target task amount and the time difference between the current time and the task end time, generate second task urging information, and send the second task urging information to the target employee; The efficiency warning threshold is determined based on the benchmark work efficiency and maximum work efficiency of the target employee; the benchmark work efficiency and maximum work efficiency are obtained by analyzing the historical work efficiency of the target employee.

7. The method according to claim 1, characterized in that The method further comprises: When the time difference between the current time and the task end time satisfies the configured task timeliness warning rule, third task urging information is generated, and the third task urging information is sent to the target employee.

8. A task urging device, characterized in that: The device comprises: An acquisition unit is used to acquire the task end time and task parameters of the target task, as well as the historical work data, current work status data, unfinished workload, execution complexity of the unfinished workload, and work planning data within a target time period of the target employee who is executing the target task; wherein the historical work data includes: historical work efficiency; the work planning data includes: the task volume, task priority, and execution complexity of each task to be completed by the target employee within the target time period; the task parameters include: task priority; the target time period is the time period from the current time to the task end time; An analysis unit, used to analyze the current work status data to obtain the current work efficiency of the target employee; An extraction unit, used to extract the task amount and execution complexity of each task whose task priority is greater than the target task from the work planning data; and use the unfinished workload and the extracted task amount as the target task amount; A prediction unit, configured to predict the work efficiency of the target employee in the target time period based on the historical work efficiency, the current work efficiency, the target task volume and the corresponding execution complexity, so as to obtain the predicted work efficiency; a calculation unit, configured to calculate the task completion efficiency of the target employee based on the target task amount and the target time period if the target employee cannot complete the target task amount within the target time period with the predicted work efficiency; The urging unit is used to generate first task urging information based on the task completion efficiency and the target task volume, and send the first task urging information to the target employee.

9. An electronic device, characterized in that: The electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing any of the methods described in claims 1-7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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