Work task prompting method and system

Abstract

The invention discloses a work task prompting method and system, and relates to the technical field of task management, and the method comprises the steps: obtaining the work data of an operator in a work task management system, and constructing a task prediction model; analyzing task completion history and working habits of operators, predicting task requirements through a task prediction model, and performing priority setting of working tasks; and performing work task prompting, and comparing work task completion efficiency to adjust the task prediction model. According to the invention, the reasonability and accuracy of task allocation and the accuracy and efficiency of task allocation can be improved. It can be ensured that key tasks are processed preferentially, and the overall working efficiency and task completion quality are improved. The task demand is better predicted, more reasonable task prompt and priority setting are provided, and the efficiency and accuracy of the task management system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of task management, and in particular to a work task prompting method and system. Background Art

[0002] With the rapid development of information technology, work task management has become one of the key means to improve organizational efficiency. Traditional task management methods mainly rely on manual scheduling and supervision, which is not only inefficient but also prone to errors. In recent years, with the advancement of artificial intelligence and big data technology, automated task management methods have been widely used and developed. By collecting and analyzing a large amount of data, automated task management systems can predict task requirements and intelligently assign tasks, thereby significantly improving work efficiency and quality. However, although automated task management technology has made certain progress, there are still some challenges and limitations in practical applications.

[0003] Most current task management systems allocate tasks based on simple rules or predefined strategies, lacking in-depth analysis of operators’ work habits and task completion history. This approach often ignores the urgency of tasks and individual differences in work ability, resulting in unreasonable task allocation. In addition, existing task management systems usually do not have a dynamic adjustment mechanism, and cannot adjust task priorities and allocation strategies in real time based on task completion and employee performance. Therefore, there is still room for improvement in task management and scheduling. Especially for complex and changing work environments, a task management method that can flexibly respond to various situations is needed. Summary of the invention

[0004] In view of the problems existing in the existing work task prompting method, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a work task prompting method and system.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a work task prompt method, which includes obtaining work data of operating personnel in a work task management system and constructing a task prediction model; analyzing the task completion history and work habits of the operating personnel, predicting task requirements through the task prediction model, and setting work task priorities; performing work task prompts, comparing work task completion efficiency and adjusting the task prediction model.

[0007] As a preferred scheme of the work task prompt method described in the present invention, wherein: the work data of the operator includes the task attribute data and work habit data of the operator; the task attribute data includes task ID, task name, task description, task type, task status, task priority, task assignor, task start date, task deadline, actual completion date, task working time estimate and actual working time; the work habit data includes login time, active time period, task response time, task completion time, task completion rate, number of task delays and number of task cancellations.

[0008] As a preferred solution of the work task prompt method of the present invention, the construction of the task prediction model includes the following steps: obtaining the task attribute data and work habit data of the operator from the work task management system, and performing data cleaning and sorting, removing duplicate data and processing missing values, and constructing a task feature vector matrix; constructing the objective function of the task prediction model, the expression is:

[0009]

[0010] Where L is the task prediction objective function, x i Represents the feature vector of the i-th task, y i A binary label indicating whether the i-th task is completed, 1 for completed and 0 for uncompleted; θ is the model parameter, N is the number of tasks, α is the weight factor; p i represents the probability of completing the i-th task, g i (x i ) is the jth information filtering function, w j is the function weight, K is the number of information filtering functions; the probability of task completion is normalized and the calculation formula is:

[0011]

[0012] In the formula, is the normalized completion probability of the ith task; the task demand prediction model is obtained, and the expression is:

[0013]

[0014] In the formula, Q i is the demand forecast value of the i-th task, β j is the weight of the j-th task demand prediction function, λ j is the jth task demand prediction function in ρ i The steepness of the vicinity, ρ iis the task completion probability threshold of the j-th task demand prediction function; divide the data into training set, validation set and test set, use the training set data to train the selected model, and adjust the model parameters on the validation set; use the test set to evaluate the prediction performance of the model and calculate the evaluation index, integrate the trained model into the existing system, and output the predicted demand results of the work task.

[0015] As a preferred scheme of the work task prompt method described in the present invention, wherein: the priority setting of the work task includes the following steps: collecting the task completion history data of the operating personnel from the work task management system, and constructing a feature vector for each task based on the collected data; using the constructed task demand prediction model to predict the demand prediction value of each task, and sorting according to the task demand prediction value; obtaining a task list arranged in descending order according to the demand prediction value, and setting the prompt priority of the task in turn according to the sorted task list; integrating the prompt priority of the task into the work task management system, optimizing the prompt of the work task, and adjusting the prompt priority of the work task.

[0016] As a preferred solution of the work task prompt method described in the present invention, wherein: the adjustment of the prompt priority of the work task includes the following steps: obtaining the task prompt priority order in the work task management system, comparing the task completion rate and the number of task delays in descending order, and adjusting the prompt priority of the work task; calculating the average completion rate of all tasks completed in the past to obtain the historical average completion rate of the task, and comparing the current task completion rate with the historical average completion rate; if the current task completion rate is lower than the historical average completion rate, then lowering the priority level of the work task and lowering the prompt priority order of the work task by one; if the current task completion rate is above the historical average completion rate, then increasing the priority level of the work task and lowering the prompt priority order of the work task by one; The reminder priority level of the work task, the reminder priority order of the work task is adjusted up by one; for each task, the average number of delays of all tasks that were delayed in the past is calculated to obtain the historical average number of delays for each task, and the current number of delays is compared with the historical average number of delays; if the number of delays of the current task is above the historical average number of delays, the reminder priority level of the work task is adjusted up by one; if the number of delays of the current task is lower than the historical average number of delays, the reminder priority level of the work task is adjusted to the lowest level; according to the comparison result, the reminder order position in the task list is adjusted, and the work task prompt is performed according to the reminder order position in the adjusted task list.

[0017] As a preferred embodiment of the work task prompt method described in the present invention, the work task prompt includes the following steps: the work task prompt is performed according to the prompt sequence position in the adjusted task list; if the deadline of the work task is within the limit period from the current date, the work task prompt is directly sent to the staff; if the estimated completion time of the work task exceeds the limit time, a detailed description of the work task prompt is sent to the staff to check the completion time of the work task; the average time of the work tasks completed after the work task prompt is calculated, the historical work task completion time is collected, and the completion time is compared.

[0018] As a preferred scheme of the work task prompt method described in the present invention, wherein: the adjustment of the task prediction model includes the following steps: obtaining the average time required to complete the work task, comparing the completion time under each prompt method, and selecting the prompt method with the shortest completion time as the optimal prompt method; adjusting the task prediction model according to the work efficiency of the operating personnel under the optimal prompt method, updating the model parameters, and repeatedly evaluating the model performance.

[0019] In the second aspect, the present invention provides a work task prompt system, which includes: an acquisition module, used to obtain the work data of the operating personnel in the work task management system and construct a task prediction model; an analysis module, used to analyze the task completion history and work habits of the operating personnel, predict the task requirements through the task prediction model, and set the priority of the work task; a prompt module, used to provide work task prompts, compare the work task completion efficiency and adjust the task prediction model.

[0020] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the work task prompt method are implemented.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of a work task prompting method are implemented.

[0022] The beneficial effects of the present invention are that a comprehensive understanding of employees' work capabilities and habits can be achieved, and the rationality and accuracy of task allocation can be improved. The intelligence level of the task management system can be improved. The accuracy and efficiency of task allocation can be improved. Priority setting can ensure that key tasks are given priority, improving overall work efficiency and task completion quality. By implementing different types of prompting methods, the task prediction model can be continuously optimized to make the model more intelligent and adaptable. It can better predict task requirements and provide more reasonable task prompts and priority settings, further improving the efficiency and accuracy of the task management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 Flowchart of method prompts for work tasks. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, and provides a work task prompting method, comprising:

[0030] S1: Obtain the work data of operators in the work task management system and build a task prediction model;

[0031] Specifically, the operation personnel's work data includes the operation personnel's task attribute data and work habit data; the task attribute data includes task ID, task name, task description, task type, task status, task priority, task assignor, task start date, task deadline, actual completion date, task working time estimate and actual working time; the work habit data includes login time, active time period, task response time, task completion time, task completion rate, number of task delays and number of task cancellations.

[0032] Obtain the task attribute data and work habit data of operators from the work task management system, clean and organize the data, remove duplicate data and process missing values, and construct the task feature vector matrix;

[0033] The objective function of the task prediction model is constructed as follows:

[0034]

[0035] Where L is the task prediction objective function, x i Represents the feature vector of the i-th task, y i A binary label indicating whether the i-th task is completed, 1 for completed and 0 for uncompleted; θ is the model parameter, N is the number of tasks, α is the weight factor; p i represents the probability of completing the i-th task, g i (x i ) is the jth information filtering function, w j is the function weight, K is the number of information filtering functions;

[0036] The probability of task completion is normalized and the calculation formula is:

[0037]

[0038] In the formula, is the normalized completion probability of the i-th task;

[0039] The task demand prediction model is obtained, and the expression is:

[0040]

[0041] In the formula, Q i is the demand forecast value of the i-th task, β j is the weight of the j-th task demand prediction function, λ j is the jth task demand prediction function in ρ i The steepness of the vicinity, ρ i is the task completion probability threshold of the j-th task demand prediction function;

[0042] Divide the data into training set, validation set and test set, use the training set data to train the selected model, and adjust the model parameters on the validation set;

[0043] Use the test set to evaluate the model's predictive performance and calculate the evaluation indicators, integrate the trained model into the existing system, and output the predicted demand results of the work task.

[0044] S2: Analyze the task completion history and work habits of operators, predict task requirements through task prediction models, and set priorities for work tasks;

[0045] Specifically, the task completion history data of operators is collected from the work task management system, and the feature vector of each task is constructed based on the collected data;

[0046] Use the constructed task demand forecasting model to predict the demand forecast value of each task, and sort them according to the task demand forecast value;

[0047] Get a task list sorted in descending order according to demand forecast values, and set the prompt priority of the task in sequence according to the sorted task list;

[0048] Integrate the task reminder priority into the work task management system, optimize the work task reminder, and adjust the work task reminder priority.

[0049] Obtain the task reminder priority order in the work task management system, compare the task completion rate and the number of task delays in descending order, and adjust the reminder priority of the work task;

[0050] Calculate the average completion rate of all tasks completed in the past to obtain the historical average completion rate of the tasks, and compare the current task completion rate with the historical average completion rate;

[0051] If the current task completion rate is lower than the historical average completion rate, the priority level of the task will be lowered, and the reminder priority of the task will be lowered by one level;

[0052] If the current task completion rate is above the historical average completion rate, the reminder priority level of the work task will be increased, and the reminder priority order of the work task will be raised by one level;

[0053] For each task, calculate the average number of delays of all tasks that were delayed in the past, obtain the historical average number of delays for each task, and compare the current number of delays with the historical average number of delays;

[0054] If the delay times of the current task are higher than the historical average delay times, the reminder priority level of the task will be raised by one level;

[0055] If the delay times of the current task are lower than the historical average delay times, the reminder priority level of the work task will be adjusted to the lowest level;

[0056] The prompt sequence position in the task list is adjusted according to the comparison result, and the work task prompt is provided according to the prompt sequence position in the adjusted task list.

[0057] S3: Provide work task prompts, compare work task completion efficiency and adjust the task prediction model.

[0058] Specifically, the work task reminder is given according to the prompt sequence position in the adjusted task list. If the deadline of the work task is within the limit period from the current date, the work task reminder is directly sent to the staff member;

[0059] If the estimated completion time of a task exceeds the time limit, a reminder with detailed description of the task will be sent to the staff to check the completion time of the task;

[0060] Calculate the average time to complete work tasks after the work task prompt, collect historical work task completion times, and compare completion times.

[0061] Obtain the average time required to complete the task, compare the completion time under each prompt method, and select the prompt method with the shortest completion time as the optimal prompt method;

[0062] Adjust the task prediction model according to the operator's work efficiency under the optimal prompt method, update the model parameters, and repeatedly evaluate the model performance.

[0063] Furthermore, this embodiment also provides a work task prompt system, including: an acquisition module, used to obtain the work data of the operating personnel in the work task management system and build a task prediction model; an analysis module, used to analyze the task completion history and work habits of the operating personnel, predict the task requirements through the task prediction model, and set the priority of the work task; a prompt module, used to provide work task prompts, compare the work task completion efficiency and adjust the task prediction model.

[0064] This embodiment also provides a computer device, which is suitable for the case of a work task prompt method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0065] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0066] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0067] From the above, it can be seen that this method can achieve a comprehensive understanding of employees' work capabilities and habits by collecting the work data of operators, which helps to improve the rationality and accuracy of task allocation. The constructed task prediction model can predict task requirements based on employees' actual performance, thereby improving the intelligence level of the task management system. By analyzing employees' task completion history and work habits, the accuracy and efficiency of task allocation can be significantly improved. Priority setting can ensure that critical tasks are given priority, improve overall work efficiency and task completion quality. By implementing different types of prompts, the task prediction model can be continuously optimized to make the model more intelligent and adaptable. The adjusted model can better predict task requirements and provide more reasonable task prompts and priority settings, further improving the efficiency and accuracy of the task management system.

[0068] Example 2

[0069] Referring to Table 1 and Table 2, the second embodiment of the present invention provides a work task prompting method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0070] Table 1 Technical characteristics comparison table

[0071]

[0072]

[0073] Table 2 Test data table

[0074]

[0075] From the above, it can be seen that this method can achieve a comprehensive understanding of employees' work capabilities and habits by collecting the work data of operators, which helps to improve the rationality and accuracy of task allocation. The constructed task prediction model can predict task requirements based on employees' actual performance, thereby improving the intelligence level of the task management system. By analyzing employees' task completion history and work habits, the accuracy and efficiency of task allocation can be significantly improved. Priority setting can ensure that critical tasks are given priority, improve overall work efficiency and task completion quality. By implementing different types of prompts, the task prediction model can be continuously optimized to make the model more intelligent and adaptable. The adjusted model can better predict task requirements and provide more reasonable task prompts and priority settings, further improving the efficiency and accuracy of the task management system.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A work task prompting method, characterized in that: include, Obtain the work data of operators in the work task management system and build a task prediction model; Analyze the task completion history and work habits of operators, predict task requirements through task prediction models, and set priorities for work tasks; Provide work task prompts, compare work task completion efficiency and adjust task prediction models.

2. The work task prompting method according to claim 1, characterized in that: The operator's work data includes the operator's task attribute data and work habit data; the task attribute data includes task ID, task name, task description, task type, task status, task priority, task assignor, task start date, task deadline, actual completion date, task working time estimate and actual working time; the work habit data includes login time, active time period, task response time, task completion time, task completion rate, number of task delays and number of task cancellations.

3. The work task prompting method according to claim 2, characterized in that: The construction of the task prediction model includes the following steps: Obtain the task attribute data and work habit data of operators from the work task management system, clean and organize the data, remove duplicate data and process missing values, and construct the task feature vector matrix; The objective function of the task prediction model is constructed as follows: Where L is the task prediction objective function, x i Represents the feature vector of the i-th task, y i A binary label indicating whether the i-th task is completed, 1 for completed and 0 for uncompleted; θ is the model parameter, N is the number of tasks, α is the weight factor; p i represents the probability of completing the i-th task, g i (x i ) is the jth information filtering function, w j is the function weight, K is the number of information filtering functions; The probability of task completion is normalized and the calculation formula is: In the formula, is the normalized completion probability of the i-th task; The task demand prediction model is obtained, and the expression is: In the formula, Q i is the demand forecast value of the i-th task, β j is the weight of the j-th task demand prediction function, λ j is the jth task demand prediction function in ρ i The steepness of the vicinity, ρ i is the task completion probability threshold of the j-th task demand prediction function; Divide the data into training set, validation set and test set, use the training set data to train the selected model, and adjust the model parameters on the validation set; Use the test set to evaluate the model's predictive performance and calculate the evaluation indicators, integrate the trained model into the existing system, and output the predicted demand results of the work task.

4. The work task prompting method according to claim 3, characterized in that: The priority setting of the work task includes the following steps: Collect the task completion history data of operators from the work task management system, and construct the feature vector of each task based on the collected data; Use the constructed task demand forecasting model to predict the demand forecast value of each task, and sort them according to the task demand forecast value; Get a task list sorted in descending order according to demand forecast values, and set the prompt priority of the task in sequence according to the sorted task list; Integrate the task reminder priority into the work task management system, optimize the work task reminder, and adjust the work task reminder priority.

5. The work task prompting method according to claim 4, characterized in that: The step of adjusting the prompt priority of the work task comprises the following steps: Obtain the task reminder priority order in the work task management system, compare the task completion rate and the number of task delays in descending order, and adjust the reminder priority of the work task; Calculate the average completion rate of all tasks completed in the past to obtain the historical average completion rate of the tasks, and compare the current task completion rate with the historical average completion rate; If the current task completion rate is lower than the historical average completion rate, the priority level of the task will be lowered, and the reminder priority of the task will be lowered by one level; If the current task completion rate is above the historical average completion rate, the reminder priority level of the work task will be increased, and the reminder priority order of the work task will be raised by one level; For each task, calculate the average number of delays of all tasks that were delayed in the past, obtain the historical average number of delays for each task, and compare the current number of delays with the historical average number of delays; If the delay times of the current task are higher than the historical average delay times, the reminder priority level of the task will be raised by one level; If the delay times of the current task are lower than the historical average delay times, the reminder priority level of the work task will be adjusted to the lowest level; The prompt sequence position in the task list is adjusted according to the comparison result, and the work task prompt is provided according to the prompt sequence position in the adjusted task list.

6. The work task prompting method according to claim 5, characterized in that: The work task prompting comprises the following steps: Prompt the staff member for the work task according to the prompt sequence in the adjusted task list. If the deadline of the work task is within the limit period from the current date, the work task reminder will be sent directly to the staff member. If the estimated completion time of a task exceeds the time limit, a reminder with detailed description of the task will be sent to the staff to check the completion time of the task; Calculate the average time to complete work tasks after the work task prompt, collect historical work task completion times, and compare completion times.

7. The work task prompting method according to claim 6, characterized in that: The adjusting task prediction model comprises the following steps: Obtain the average time required to complete the work task, compare the completion time under each prompt method, and select the prompt method with the shortest completion time as the optimal prompt method; Adjust the task prediction model according to the operator's work efficiency under the optimal prompt method, update the model parameters, and repeatedly evaluate the model performance.

8. A work task reminder system, based on the work task reminder method according to any one of claims 1 to 7, characterized in that: include, The acquisition module is used to obtain the work data of operators in the work task management system and build a task prediction model; The analysis module is used to analyze the task completion history and work habits of operators, predict task requirements through task prediction models, and set the priority of work tasks; The prompt module is used to provide work task prompts, compare the work task completion efficiency and adjust the task prediction model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the work task prompting method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the work task prompting method according to any one of claims 1 to 7 are implemented.

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