CN111652403A - Feedback correction-based work platform task workload prediction method - Google Patents
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Abstract
The invention discloses a feedback correction-based work platform task workload prediction method, which comprises the following steps of: receiving a work task, issuing a work task requirement to a work platform by a packet sender, receiving various tasks by the work platform, screening the work task, primarily screening the work task, rejecting some tasks which cannot be completed, classifying the tasks, counting all the tasks, classifying the tasks, primarily classifying the tasks into three categories of general use, industrial design and software development, evaluating work content, evaluating the work content of the received tasks, and evaluating and classifying the evaluation into content evaluation. The invention can fully know the detailed information in the work task and evaluate the detailed information more accurately, and the evaluation error value of the actual construction period and the construction period of the evaluation construction period is obtained by adopting the artificial neural network through the stored data and after analog arrangement, and the feedback correction can be carried out according to the comparison of the collected information and the original data, and the work load of the task is re-evaluated.
Description
Technical Field
The invention relates to the technical field of workload assessment, in particular to a feedback correction-based work platform task workload prediction method.
Background
The work platform is an internet platform which provides various work management related services in a crowdsourcing mode. The packet sender issues work task requirements to a work platform, the platform decomposes the tasks, searches matched packet receivers from a platform talent library according to the skill requirements of each subtask, and distributes the subtasks to the proper packet receivers; and the packet receiving party starts working after receiving the assigned subtasks, the working result is submitted to the platform after the subtasks are completed, the platform needs to research a workload evaluation method of the tasks, and the workload is evaluated based on task description and related files, so that the construction period and the cost are predicted.
When a packet sender issues a task, task cost is hosted on a platform, the platform estimates workload, but when the workload is estimated, because original data cannot be updated timely, factors influencing the workload have certain changes, and a workload estimation result obtained by the platform through a unified prediction model has low accuracy, and an estimation result error under certain scenes is too large to meet requirements of platform users.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a feedback correction-based working platform task workload prediction method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a work platform task workload prediction method based on feedback correction comprises the following steps:
s1: receiving a work task, issuing a work task requirement to a work platform by a packet sender, and receiving various tasks by the work platform;
s2: screening the work tasks, performing primary screening on the work tasks, and rejecting certain tasks which cannot be completed;
s3: classifying tasks, namely classifying all the tasks after counting the tasks, and preliminarily classifying the tasks into three categories of general purpose, industrial design and software development;
s4: evaluating the work content, namely evaluating the work content of the received task, wherein the evaluation is classified into content quantity evaluation, complexity evaluation and similarity evaluation;
s5: analyzing data, namely comparing original data, comparing some variables in the existing work task with the original data to re-analyze the workload condition, and fully considering all influences brought by related influence factors under original and current different conditions;
s6: collecting feedback information, and collecting feedback information of a client on work satisfaction, task time, task completion quality and task sub-packaging condition in time after task arrangement;
s7: adjusting the distribution mode, summarizing and summarizing according to the feedback information, and revising the calculation and prediction of the task amount;
s8: and the data storage device is used for receiving and storing all submitted workload contents, working time, working processes and variables in working, counting, and making a table, a sector graph and a bar graph during counting so as to facilitate later-stage reference and analogy.
As a further scheme of the invention: in the step S3, when classifying the task, the task may be classified into six types, i.e., general purpose, mechanical drawing design, circuit drawing design, effect drawing design, general software development, and advanced software development, so as to avoid the influence on the workload prediction in the later stage due to inaccurate classification of the task caused by too few classifications.
As a further scheme of the invention: in S1, when receiving the work task, it is necessary to check whether the work information in the work task is complete, and the work information includes the technical problem to be solved, the time to use, the modification position and the modification content when solving the problem, and only the detailed information in the work task is fully known to perform more accurate evaluation.
As a further scheme of the invention: the data storage module is arranged in the S8 and stores the project period evaluation information of the original project according to the project type, so that the later-stage analogy can be conveniently carried out when similar work tasks are met, the reference of big data is provided, the data basis of workload evaluation is improved, and the evaluation accuracy is higher.
As a further scheme of the invention: in S4, a data evaluation module is required to be constructed for analyzing the submitted workload content to determine the difficulty of completing the workload content, the data analysis module includes a content evaluation module, a complexity evaluation module, and a similarity evaluation module, the content evaluation module evaluates the submitted workload content according to different workload contents in a task, an evaluation unit measures the submitted workload content according to hours, and the complexity evaluation module can be classified into four grades of simple, general, complex, and difficult during evaluation.
As a further scheme of the invention: the similarity evaluation module grades the similarity when evaluating, and the total degree is 5 grades, the similarity of the first-grade similarity representation is between 1% and 20%, the similarity of the second-grade similarity representation is between 21% and 40%, the similarity of the third-grade similarity representation is between 41% and 60%, the similarity of the fourth-grade similarity representation is between 61% and 80%, and the similarity of the fifth-grade similarity representation is between 81% and 100%.
As a further scheme of the invention: all data are stored in the S8, a basic idea of calculating the construction period can be obtained conveniently by adopting big data intelligent analysis in the later period, and the evaluation error value of the actual construction period and the construction period of the evaluation construction period can be obtained by adopting an artificial neural network through the stored data and after analog arrangement.
As a further scheme of the invention: in S5, differences between the prior art and the prior art are collected, so that comparison can be performed based on the prior art to analyze the workload in the prior task.
As a further scheme of the invention: and the S7 comprises a feedback correction module, reevaluates through collecting feedback information, performs feedback correction according to the comparison of the collected information and original data, and reevaluates the task workload.
The invention has the beneficial effects that:
1. the tasks can be classified into six types when the tasks are classified in the S3, so that the phenomenon that the task classification is not accurate due to too few classifications and the workload prediction in the later period is influenced is avoided, and the prediction accuracy is improved;
2. when the work task is received in S1, whether the work information in the work task is complete is checked, so that the detailed information in the work task is fully known and more accurate evaluation is performed;
3. the data storage module is used for storing the project period evaluation information of the original project according to the project type, so that the simulation can be conveniently carried out at the later stage when similar work tasks are met, the reference of big data is provided, the data basis of workload evaluation is improved, the evaluation accuracy is higher, and the stored data is compared and sorted by adopting an artificial neural network to obtain the project period evaluation error value of the actual project period and the evaluation project period;
4. through the arranged feedback correction module, the collected feedback information can be reevaluated, feedback correction is carried out according to the comparison of the collected information and the original data, and the task workload is reevaluated.
Drawings
Fig. 1 is a flowchart of a method for predicting workload of a work platform based on feedback correction according to the present invention.
Fig. 2 is a schematic diagram of system modules of a feedback correction-based work platform task workload prediction method according to the present invention.
Fig. 3 is a schematic diagram of a complexity evaluation module level of a feedback correction-based work platform task workload prediction method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
Referring to fig. 1-3, a method for predicting task workload of a working platform based on feedback correction includes the following steps:
s1: receiving a work task, issuing a work task requirement to a work platform by a packet sender, and receiving various tasks by the work platform;
s2: screening the work tasks, performing primary screening on the work tasks, and rejecting certain tasks which cannot be completed;
s3: classifying tasks, namely classifying all the tasks after counting the tasks, and preliminarily classifying the tasks into three categories of general purpose, industrial design and software development;
s4: evaluating the work content, namely evaluating the work content of the received task, wherein the evaluation is classified into content quantity evaluation, complexity evaluation and similarity evaluation;
s5: analyzing data, namely comparing original data, comparing some variables in the existing work task with the original data to re-analyze the workload condition, and fully considering all influences brought by related influence factors under original and current different conditions;
s6: collecting feedback information, and collecting feedback information of a client on work satisfaction, task time, task completion quality and task sub-packaging condition in time after task arrangement;
s7: adjusting the distribution mode, summarizing and summarizing according to the feedback information, and revising the calculation and prediction of the task amount;
s8: and the data storage device is used for receiving and storing all submitted workload contents, working time, working processes and variables in working, counting, and making a table, a sector graph and a bar graph during counting so as to facilitate later-stage reference and analogy.
In the invention, whether the work information in the work task is complete or not is checked when the work task is received in S1, the work information comprises the technical problem to be solved, the modification position and the modification content when the work task is used and the problem is solved, and the detailed information in the work task can be evaluated more accurately only if the detailed information is fully known, a data storage module is arranged in S8 and stores the project period evaluation information of the original project according to the project type, so that the analogy can be conveniently carried out when similar work tasks are encountered in the later period, the reference of big data is provided, the data basis of the workload evaluation is improved, the evaluation accuracy is higher, a data evaluation module needs to be constructed in S4 and is used for analyzing the submitted workload content to determine the completion difficulty of the workload content, and the data analysis module comprises a content evaluation module, a complexity evaluation module and a similarity evaluation module, the content evaluation module evaluates different work contents in tasks, an evaluation unit measures according to hours, the complexity evaluation module can be divided into four grades of simple, general, complex and difficult during evaluation, the similarity evaluation module grades the tasks during evaluation, the total grade is 5, the similarity of a first-grade similarity representative is between 1% and 20%, the similarity of a second-grade similarity representative is between 21% and 40%, the similarity of a third-grade similarity representative is between 41% and 60%, the similarity of a fourth-grade similarity representative is between 61% and 80%, the similarity of a fifth-grade similarity representative is between 81% and 100%, all data are stored in S8, a basic idea of calculating a construction period can be obtained by adopting big data intelligent analysis in the later period, an artificial neural network is adopted through the stored data, and after analog arrangement, a construction period evaluation error value of an actual construction period and an evaluation construction period is obtained, in S5, differences between the prior art and the prior art are collected, so that comparison can be performed on the basis of the prior art to analyze workload in the prior task, and in S7, a feedback correction module is included to perform re-evaluation by collecting feedback information, perform feedback correction according to comparison between the collected information and the prior data, and re-evaluate workload of the task.
Example 2
Referring to fig. 1-3, a method for predicting task workload of a working platform based on feedback correction includes the following steps:
s1: receiving a work task, issuing a work task requirement to a work platform by a packet sender, and receiving various tasks by the work platform;
s2: screening the work tasks, performing primary screening on the work tasks, and rejecting certain tasks which cannot be completed;
s3: classifying tasks, namely classifying all the tasks after counting the tasks, and preliminarily classifying the tasks into three categories of general purpose, industrial design and software development;
s4: evaluating the work content, namely evaluating the work content of the received task, wherein the evaluation is classified into content quantity evaluation, complexity evaluation and similarity evaluation;
s5: analyzing data, namely comparing original data, comparing some variables in the existing work task with the original data to re-analyze the workload condition, and fully considering all influences brought by related influence factors under original and current different conditions;
s6: collecting feedback information, and collecting feedback information of a client on work satisfaction, task time, task completion quality and task sub-packaging condition in time after task arrangement;
s7: adjusting the distribution mode, summarizing and summarizing according to the feedback information, and revising the calculation and prediction of the task amount;
s8: and the data storage device is used for receiving and storing all submitted workload contents, working time, working processes and variables in working, counting, and making a table, a sector graph and a bar graph during counting so as to facilitate later-stage reference and analogy.
In the invention, when classifying tasks in S3, the tasks can be classified into six types of general purpose, mechanical drawing design, circuit drawing design, effect diagram design, common software development and high-level software development, so as to avoid the problem that the classification of the tasks is not accurate due to too little classification and influence on the workload prediction in the later period, when receiving the work tasks in S1, whether the work information in the work tasks is complete or not is checked, the work information comprises the technical problem to be solved, the modification position and the modification content when using and solving the problem, and the detailed information in the work tasks can be more accurately evaluated only by fully knowing the detailed information, a data storage module is arranged in S8, the data storage module stores the project period evaluation information of the original project according to the project type, so that the similar work tasks can be analogized in the later period, large data reference is provided, and the data basis of workload evaluation is improved, the assessment accuracy is higher, a data assessment module is required to be constructed in S4 and used for analyzing submitted workload content to determine the completion difficulty of the workload content, the data analysis module comprises a content assessment module, a complexity assessment module and a similarity assessment module, the content assessment module assesses the submitted workload content through different workload contents in tasks, assessment units measure according to hours, the complexity assessment module can be divided into four grades of simple, general, complex and difficult during assessment, the similarity assessment module assesses the task and grades the task, the total grade is 5, the first-grade similarity representative similarity is 1% -20%, the second-grade similarity representative similarity is 21% -40%, the third-grade similarity representative similarity is 41% -60%, the fourth-grade similarity representative similarity is 61% -80%, and the similarity of the task is measured according to hours, The similarity of five-level similarity representatives is between 81% and 100%, all data are stored in S8, a basic idea of calculating a construction period can be obtained by adopting big data intelligent analysis conveniently at a later stage, an artificial neural network is adopted through the stored data, after analog sorting, a construction period evaluation error value of an actual construction period and an evaluation construction period is obtained, differences between the prior art and the prior art need to be collected in S5, comparison can be carried out on the basis of the prior art, workload in the prior task is analyzed, a feedback correction module is included in S7, re-evaluation is carried out through collection of feedback information, feedback correction is carried out according to comparison of the collected information and the prior data, and workload of the task is re-evaluated.
Example 3
Referring to fig. 1-3, a method for predicting task workload of a working platform based on feedback correction includes the following steps:
s1: receiving a work task, issuing a work task requirement to a work platform by a packet sender, and receiving various tasks by the work platform;
s2: screening the work tasks, performing primary screening on the work tasks, and rejecting certain tasks which cannot be completed;
s3: classifying tasks, namely classifying all the tasks after counting the tasks, and preliminarily classifying the tasks into three categories of general purpose, industrial design and software development;
s4: evaluating the work content, namely evaluating the work content of the received task, wherein the evaluation is classified into content quantity evaluation, complexity evaluation and similarity evaluation;
s5: analyzing data, namely comparing original data, comparing some variables in the existing work task with the original data to re-analyze the workload condition, and fully considering all influences brought by related influence factors under original and current different conditions;
s6: collecting feedback information, and collecting feedback information of a client on work satisfaction, task time, task completion quality and task sub-packaging condition in time after task arrangement;
s7: adjusting the distribution mode, summarizing and summarizing according to the feedback information, and revising the calculation and prediction of the task amount;
s8: and the data storage device is used for receiving and storing all submitted workload contents, working time, working processes and variables in working, counting, and making a table, a sector graph and a bar graph during counting so as to facilitate later-stage reference and analogy.
In the invention, when classifying tasks in S3, the tasks can be classified into six types of general purpose, mechanical drawing design, circuit drawing design, effect diagram design, common software development and high-level software development, so as to avoid the problem that the classification of the tasks is not accurate due to too little classification and influence on the workload prediction in the later period, when receiving the work tasks in S1, whether the work information in the work tasks is complete or not is checked, the work information comprises the technical problem to be solved, the modification position and the modification content when using and solving the problem, and the detailed information in the work tasks can be more accurately evaluated only by fully knowing the detailed information, a data storage module is arranged in S8, the data storage module stores the project period evaluation information of the original project according to the project type, so that the similar work tasks can be analogized in the later period, large data reference is provided, and the data basis of workload evaluation is improved, the evaluation accuracy is higher, a data evaluation module needs to be constructed in S4 and used for analyzing submitted workload content to determine the completion difficulty of the workload content, the data analysis module comprises a content evaluation module, a complexity evaluation module and a similarity evaluation module, the content evaluation module evaluates the submitted workload content through different workload contents in tasks, evaluation units measure the submitted workload content according to hours, the complexity evaluation module can be divided into three grades of simple, general and complex during evaluation, the similarity evaluation module grades the submitted workload content during evaluation, the similarity evaluation module totally comprises 4 grades, the similarity of a first-level similarity representative is 1% -25%, the similarity of a second-level similarity representative is 26% -50%, the similarity of a third-level similarity representative is 51% -75%, the similarity of a fourth-level similarity representative is 76% -100%, and all data are stored in S8, make things convenient for the later stage can adopt big data intelligent analysis to obtain the basic thinking of calculating the time limit for a project, data through the storage, adopt artificial neural network, after the analog arrangement, obtain the time limit for a project evaluation error value of actual time limit for a project and evaluation time limit for a project, the difference in original technique and the prior art will be collected in S5, thereby could compare on prior art' S basis, work load in the current task of analysis, including feedback correction module in S7, reevaluate through the information of collecting the feedback, compare according to the information of collecting and original data and carry out the feedback and correct, reevaluate task work load.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. A work platform task workload prediction method based on feedback correction comprises the following steps:
s1: receiving a work task, issuing a work task requirement to a work platform by a packet sender, and receiving various tasks by the work platform;
s2: screening the work tasks, performing primary screening on the work tasks, and rejecting certain tasks which cannot be completed;
s3: classifying tasks, namely classifying all the tasks after counting the tasks, and preliminarily classifying the tasks into three categories of general purpose, industrial design and software development;
s4: evaluating the work content, namely evaluating the work content of the received task, wherein the evaluation is classified into content quantity evaluation, complexity evaluation and similarity evaluation;
s5: analyzing data, namely comparing original data, comparing some variables in the existing work task with the original data to re-analyze the workload condition, and fully considering all influences brought by related influence factors under original and current different conditions;
s6: collecting feedback information, and collecting feedback information of a client on work satisfaction, task time, task completion quality and task sub-packaging condition in time after task arrangement;
s7: adjusting the distribution mode, summarizing and summarizing according to the feedback information, and revising the calculation and prediction of the task amount;
s8: and the data storage device is used for receiving and storing all submitted workload contents, working time, working processes and variables in working, counting, and making a table, a sector graph and a bar graph during counting so as to facilitate later-stage reference and analogy.
2. The feedback correction-based work platform task workload prediction method as claimed in claim 1, wherein in the step S3, the tasks can be classified into six types, i.e. general purpose, mechanical drawing design, circuit drawing design, effect diagram design, general software development and high-level software development, when the tasks are classified.
3. The method for predicting task workload of work platform based on feedback modification as claimed in claim 1, wherein when receiving the work task in S1, checking whether the work information in the work task is complete, wherein the work information includes technical problem to be solved, time of use, modification position and modification content when solving the problem.
4. The feedback correction-based work platform task workload prediction method according to claim 1, wherein a data storage module is arranged in S8, and the data storage module stores the project duration evaluation information of the original project according to the project type, so that an analogy can be performed later when similar work tasks are encountered, and a reference for big data is provided.
5. The feedback correction-based work platform task workload prediction method according to claim 1, wherein a data evaluation module is required to be constructed in S4, and is used for analyzing submitted workload content to determine the difficulty of completing the workload content, the data analysis module includes a content evaluation module, a complexity evaluation module, and a similarity evaluation module, the content evaluation module evaluates the task through different work contents in the task, an evaluation unit measures the task according to hours, and the complexity evaluation module can be classified into four grades of simple, general, complex, and difficult during evaluation.
6. The feedback correction-based work platform task workload prediction method according to claim 5, wherein the similarity evaluation module grades the work platform task workload when evaluating the work platform task workload, and the similarity evaluation module totally classifies the work platform task workload into 5 grades, the primary similarity representing similarity between 1% and 20%, the secondary similarity representing similarity between 21% and 40%, the tertiary similarity representing similarity between 41% and 60%, the quaternary similarity representing similarity between 61% and 80%, and the fifth similarity representing similarity between 81% and 100%.
7. The feedback correction-based work platform task workload prediction method as claimed in claim 5, wherein all data are stored in S8, so that a basic idea of a calculation period can be obtained by using big data intelligent analysis in a later period, and a period estimation error value of an actual period and an estimation period is obtained by using the stored data and an artificial neural network after analog sorting.
8. The method as claimed in claim 1, wherein the differences between the prior art and the prior art are collected in S5, so that the workload of the task can be analyzed by comparing the prior art with the prior art.
9. The method as claimed in claim 1, wherein S7 includes a feedback modification module for performing re-evaluation by collecting feedback information, performing feedback modification by comparing the collected information with original data, and re-evaluating task workload.
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| CN113822586A (en) * | 2021-09-27 | 2021-12-21 | 深圳威消保科技有限公司 | Task rewarding method and system |
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| CN112966971A (en) * | 2021-03-30 | 2021-06-15 | 建信金融科技有限责任公司 | Project workload assessment method and device |
| CN112966971B (en) * | 2021-03-30 | 2022-09-13 | 建信金融科技有限责任公司 | Project workload assessment method and device |
| CN113822586A (en) * | 2021-09-27 | 2021-12-21 | 深圳威消保科技有限公司 | Task rewarding method and system |
| CN113822586B (en) * | 2021-09-27 | 2023-11-07 | 深圳威消保科技有限公司 | Task rewarding method and system |
| CN117311805A (en) * | 2023-09-27 | 2023-12-29 | 江苏天好富兴数据技术有限公司 | Workload assessment system and method based on big data |
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