Job task allocation method and system, electronic equipment and storage medium
By acquiring employee operation logs through IoT devices and calculating evaluation indicators to determine employee skill levels, the problem of unreasonable task allocation caused by differences in employee skill levels has been solved, thereby improving the rationality of task allocation and production efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-02
- Publication Date
- 2026-03-10
AI Technical Summary
In existing technologies, the differences in employees' skill levels lead to unreasonable task allocation, resulting in poor product quality and low efficiency.
By acquiring operational logs of employees' historical tasks through IoT devices, including operational data, quality inspection data, and capability data, evaluation indicators such as operational efficiency, operational experience, operational quality, equipment operation capability, and process capability are calculated using this data. This determines the employee's skill level and assigns tasks based on that skill level.
This enabled accurate evaluation of employee skill levels, improved the rationality of task allocation, and enhanced production efficiency and product quality.
Smart Images
Figure CN121638705A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, in particular to a job task allocation method and system, an electronic device and a storage medium. BACKGROUND
[0002] In the clothing, logistics, mechanical manufacturing and other industries, employees need to operate equipment to perform corresponding operations of corresponding job tasks. However, there are various different job tasks to be processed in actual production. In related technologies, job tasks are usually allocated according to the business pressure of employees, but the technical level of different employees has certain differences, and the above-mentioned allocation of job tasks is easy to cause problems such as poor product quality and low efficiency.
[0003] Therefore, how to accurately evaluate the skill level of employees and improve the rationality of job task allocation is a technical problem to be solved by those skilled in the art at present. SUMMARY
[0004] The purpose of the present application is to provide a job task allocation method, system, electronic device and storage medium, which can accurately evaluate the skill level of employees and improve the rationality of job task allocation.
[0005] To solve the above technical problems, the present application provides a job task allocation method, comprising: obtaining an operation log of an employee performing a historical job task by using an Internet of Things device; wherein the operation log comprises job data, quality inspection data and ability data; the job data comprises time consumption and number of times of the employee operating each process, the quality inspection data comprises product rework number of each process, and the ability data comprises device type and process level that the employee can operate; determining a value corresponding to an evaluation index according to the operation log; wherein the evaluation index comprises a job efficiency index, a job experience index, a job quality index, a device operation ability index and a process ability index; determining a skill level of the employee according to the values corresponding to all the evaluation indexes, and allocating a new job task to the employee according to the skill level.
[0006] Optionally, determining a value corresponding to an evaluation index according to the operation log comprises: calculating average time consumption of the employee operating each process according to the job data in the operation log; calculating a process job efficiency of each process according to a ratio of standard working hours to average time consumption of the process; calculating an efficiency evaluation value corresponding to the process job efficiency of each process; performing weighted calculation on the efficiency evaluation values of all processes to obtain a value corresponding to the job efficiency index.
[0007] Optionally, the method for determining the value corresponding to the evaluation index according to the operation log comprises: determining the effective operation times of each process operated by the employee according to the job data and the quality inspection data in the operation log; taking the ratio of the effective operation times of each process to the corresponding standard times as the proficiency of each process; calculating the proficiency evaluation value corresponding to the proficiency of each process; performing weighted calculation on the proficiency evaluation values of all processes to obtain the value corresponding to the job experience index.
[0008] Optionally, the method for determining the value corresponding to the evaluation index according to the operation log comprises: calculating the rework rate of each process according to the job data and the quality inspection data in the operation log; calculating the rework rate evaluation value corresponding to the rework rate of each process; performing weighted calculation on the rework rate evaluation values of all processes to obtain the value corresponding to the job quality index.
[0009] Optionally, the method for determining the value corresponding to the evaluation index according to the operation log comprises: determining the highest process level of each device operable by the employee according to the ability data in the operation log; calculating the device evaluation value of each device operated by the employee according to the highest process level; adding the device evaluation values of all devices to obtain the value corresponding to the device operation ability index.
[0010] Optionally, the method for determining the value corresponding to the evaluation index according to the operation log comprises: determining the process type operable by the employee according to the ability data in the operation log; wherein the process type is a combination of the device type and the process level; querying the process difficulty evaluation value corresponding to each process type; adding the process difficulty evaluation values corresponding to all process types to obtain the value of the process ability index.
[0011] Optionally, the operation log of the employee performing the historical job task is obtained by using the Internet of Things device, comprising: capturing video data of the employee performing the historical job task by using the Internet of Things device; obtaining the job data by recognizing the target operation action in the video data; wherein the target operation action includes the action of the employee operating each process; The target operation action in the video data is compared with the corresponding standard action, and the quality inspection data is determined based on the comparison result; The capability data is determined based on the equipment type and process level corresponding to the target operation action in the video data.
[0012] This application also provides a job assignment system, the system comprising: The data acquisition module is used to acquire operation logs of employees performing historical tasks using IoT devices; wherein, the operation logs include operation data, quality inspection data, and capability data; the operation data includes the time and number of times the employee performs each process, the quality inspection data includes the number of products reworked for each process, and the capability data includes the types of equipment and process levels that the employee can operate; The indicator value calculation module is used to determine the value corresponding to the evaluation indicator based on the operation log; wherein, the evaluation indicator includes operation efficiency indicator, operation experience indicator, operation quality indicator, equipment operation capability indicator and process capability indicator; The task allocation module is used to determine the employee's skill level based on the values corresponding to all the evaluation indicators, and to assign new work tasks to the employee based on the skill level.
[0013] This application also provides a storage medium storing a computer program thereon, which, when executed, implements the steps of the above-described job task allocation method.
[0014] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor invokes the computer program in the memory to implement the steps of the above-described job task allocation method.
[0015] This application provides a job task allocation method. This method utilizes an IoT device to obtain operation logs of employees performing historical job tasks. These logs record operational details of employees performing different processes, including work data, quality inspection data, and capability data. Based on these operation logs, this embodiment can determine the employee's values for work efficiency, work experience, work quality, equipment operation capability, and process capability indicators, and then comprehensively evaluate the employee's skill level based on these evaluation indicators. This application matches the task content of the job to be assigned with the employee's skill level, and then assigns new job tasks to the employee based on the degree of matching. Therefore, this application can accurately evaluate the employee's skill level and improve the rationality of job task allocation. This application also provides a job task allocation system, a storage medium, and an electronic device, which have the above-mentioned beneficial effects, and will not be elaborated further here. Attached Figure Description
[0016] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A flowchart illustrating a job task allocation method provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a sewing employee skills evaluation system based on Internet of Things technology provided in this application embodiment; Figure 3 A radar chart of employee skills provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a job assignment system provided in an embodiment of this application. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0019] Please see below. Figure 1 , Figure 1 This is a flowchart illustrating a job task allocation method provided in an embodiment of this application.
[0020] Specific steps may include: S101: Use IoT devices to obtain the operation logs of employees performing historical tasks; This embodiment can be applied to electronic devices with processors and memory, which analyze employees' skill levels to assign appropriate tasks to the corresponding employees.
[0021] During the execution of historical tasks, employees need to control equipment to operate the corresponding procedures. This step can utilize IoT devices to obtain the employee's operation logs for executing historical tasks. These operation logs are data records of the employee's operational behaviors and results during the execution of historical tasks. The operation logs include work data, quality inspection data, and capability data.
[0022] The above operational data reflects the specific performance of employees when performing various procedures. The data includes the time spent by the employee performing each procedure and the number of times the employee performs each procedure. Specifically, the time spent refers to the time required for the employee to complete each procedure; this can be the time for a single execution of the procedure or the average time for multiple executions. The number of times refers to the number of times the employee performs each procedure. This operational data can be collected by sensors installed in the equipment or obtained by analyzing user operation videos.
[0023] The aforementioned quality inspection data refers to the quality inspection results that products undergo after processing by employees. Specifically, the quality inspection data includes the number of products requiring rework at each stage of the process. The number of products requiring rework refers to the number of products that need to be reprocessed due to quality issues. This quality inspection data can be data entered by quality inspectors or data obtained by using artificial intelligence models to analyze user operation videos.
[0024] The aforementioned capability data describes an employee's ability to operate equipment. Specifically, the capability data includes the types of equipment an employee can operate and the operational levels an employee can perform. Each type of equipment can perform multiple operations, and in this embodiment, operational levels can be pre-defined based on the difficulty of the operations. For example, each type of equipment can have four operational levels: A, B, C, and D. The aforementioned capability data can be determined based on the equipment and operations performed by the employee during historical tasks. For example, if an employee performed an A-level operation on the first type of equipment, and A-level and D-level operations on the second type of equipment during a historical task, then the capability data in the operation log includes: the types of equipment an employee can operate are the first type of equipment and the second type of equipment; and the operational levels an employee can perform are A-level operations on the first type of equipment, A-level operations on the second type of equipment, and D-level operations on the second type of equipment.
[0025] S102: Determine the values corresponding to the evaluation indicators based on the operation log; This embodiment can pre-set multiple evaluation indicators for evaluating employee skill levels. These evaluation indicators include work efficiency indicators, work experience indicators, work quality indicators, equipment operation capability indicators, and process capability indicators.
[0026] Specifically, this embodiment can calculate the employee's values for work efficiency, work experience, and work quality indicators based on work data and quality inspection data, and can calculate the employee's values for equipment operation capability and process capability indicators based on capability data. These values are the scores for the process capability indicators.
[0027] S103: Determine the employee's skill level based on the values corresponding to all the evaluation indicators, and assign new work tasks to the employee based on the skill level; In this embodiment, minimum numerical requirements for each skill level can be predetermined. These requirements include minimum values for work efficiency, work experience, work quality, equipment operation capability, and process capability. The employee's skill level can be determined by comparing their performance on each evaluation indicator with the minimum required values.
[0028] This embodiment can identify multiple new job tasks to be assigned, and can also determine the minimum skill level required by employees for each new job task. This embodiment can compare the employee's skill level with the minimum skill level required for each new job task, and assign the employee a new job task with a minimum skill level lower than the employee's skill level based on the comparison result. As a possible implementation method, the task details of the new job tasks can be sent to the user's terminal device.
[0029] This embodiment utilizes IoT devices to acquire operation logs of employees performing historical tasks. These logs record operational details of employees at different stages, including work data, quality inspection data, and capability data. Based on these logs, this embodiment can determine the employee's values for work efficiency, work experience, work quality, equipment operation capability, and process capability indicators. Then, based on these evaluation indicators, the employee's skill level is comprehensively assessed. This embodiment matches the task content of pending assignments with the employee's skill level, and then assigns new tasks based on the degree of matching. Therefore, this embodiment can accurately evaluate employee skill levels and improve the rationality of task allocation.
[0030] As a feasible implementation method, Figure 1 In the corresponding embodiment, the value corresponding to the work efficiency index can be calculated in the following way: Step A1: Calculate the average time spent by the employee on each process based on the work data in the operation log; Step A2: Calculate the process efficiency of each process based on the ratio of standard working hours to the average time consumed by the process; In this embodiment, GSD data can be pre-acquired, which includes the standard working time and standard number of operations for each process. This embodiment can use the ratio of the standard working time to the average time of the corresponding process as the process efficiency. This embodiment can also divide the total time by the number of effective operations to obtain the average time of the process.
[0031] Step A3: Calculate the efficiency assessment value corresponding to the operation efficiency of each process; Among them, the efficiency of the process operation is positively correlated with the efficiency assessment value.
[0032] Step A4: Perform a weighted calculation on the efficiency assessment values of all processes to obtain the numerical value corresponding to the work efficiency index.
[0033] In this embodiment, a corresponding weight can be set for each process, and then the efficiency assessment value of all processes can be weighted and calculated based on the above weights to obtain the value corresponding to the work efficiency index.
[0034] As a feasible implementation method, Figure 1 In the corresponding embodiment, the numerical value of the work experience index can be calculated in the following way: Step B1: Determine the effective number of times the employee performs each process based on the work data and quality inspection data in the operation log; The effective number of operations refers to the number of times a process can be executed to obtain a product that passes quality inspection. This step subtracts the number of reworked products for a specific process from the total number of operations in the work data to obtain the effective number of operations for that process.
[0035] Step B2: The ratio of the effective number of operations to the corresponding standard number of operations for each process is taken as the proficiency level of each process; Step B3: Calculate the proficiency assessment value corresponding to the proficiency level for each process. Among them, proficiency is positively correlated with proficiency assessment value.
[0036] Step B4: Perform a weighted calculation on the proficiency assessment values for all processes to obtain the numerical value corresponding to the work experience index.
[0037] In this embodiment, a corresponding weight can be set for each process, and then the proficiency assessment value of all processes can be weighted and calculated based on the above weights to obtain the value corresponding to the work experience index.
[0038] As a feasible implementation method, Figure 1 In the corresponding embodiment, the values corresponding to the work quality indicators can be calculated in the following way: Step C1: Calculate the rework rate for each process based on the work data and quality inspection data in the operation log; The rework rate for each process is equal to the ratio of the number of products reworked in each process to the total number of operations.
[0039] Step C2: Calculate the rework rate assessment value corresponding to the rework rate of each process; Among them, the rework rate is negatively correlated with the rework rate assessment value.
[0040] Step C3: Perform a weighted calculation on the rework rate assessment values of all processes to obtain the numerical values corresponding to the work quality indicators.
[0041] In this embodiment, a corresponding weight can be set for each process, and then the rework rate assessment value of all processes can be weighted and calculated based on the above weights to obtain the value corresponding to the work quality index.
[0042] As a feasible implementation method, Figure 1 In the corresponding embodiment, the value corresponding to the equipment operation capability index can be calculated in the following way: Step D1: Determine the highest process level for each type of equipment that the employee can operate based on the capability data in the operation log; Step D2: Calculate the equipment assessment value for each type of equipment operated by the employee based on the highest process level; Step D3: Add up the equipment assessment values of all equipment to obtain the numerical value corresponding to the equipment operation capability index.
[0043] An example illustrating the numerical calculation method for the above equipment operational capability indicators: If there are three types of equipment, the process levels of the three types of equipment are A, B, C and D. The assessment value of equipment at level A is 3 points, that at level B is 2 points, that at level C is 1 point and that at level D is 0.5 points.
[0044] If the capability data includes: Employee A can operate the A-level process of the first type of equipment, the B-level process of the second type of equipment, and the D-level process of the second type of equipment, then the value corresponding to the equipment operation capability index is 3+2=5 points.
[0045] If the capability data includes: Employee B can operate the A-level process of the first type of equipment, the B-level process of the second type of equipment, and the D-level process of the third type of equipment, then the value corresponding to the equipment operation capability index is 3+2+0.5=5.5 points.
[0046] As a feasible implementation method, Figure 1 In the corresponding embodiment, the process capability index can be calculated in the following way: Step E1: Determine the types of work processes that the employee can operate based on the capability data in the operation log; The process type is a combination of equipment type and process level.
[0047] Step E2: Query the process difficulty assessment value corresponding to each of the aforementioned process types; Step E3: Add up the process difficulty assessment values corresponding to all the process types to obtain the numerical value of the process capability index.
[0048] An example illustrating the numerical calculation method for the above process capability indicators: Each type of equipment has a process level of A, B, C and D. The process difficulty assessment value corresponding to A is 1.8 points, that of B is 0.6 points, that of C is 0.2 points and that of D is 0.1 points.
[0049] If the capability data includes: Employee A can operate the A-level process of the first type of equipment, the B-level process of the second type of equipment, and the D-level process of the second type of equipment, then the corresponding value of the process capability index is 1.8 + 0.6 + 0.1 = 2.5 points.
[0050] If the capability data includes: Employee B can operate the A-level process of the first type of equipment, the B-level process of the second type of equipment, and the D-level process of the third type of equipment, then the value corresponding to the process capability index is 1.8 + 0.6 + 0.1 = 2.5 points.
[0051] If the capability data includes: Employee C can operate the A-level process of the first type of equipment, the B-level process of the second type of equipment, the B-level process of the third type of equipment, and the C-level process of the third type of equipment, then the corresponding value of the process capability index is 1.8 + 0.6 + 0.6 + 0.2 = 3.2 points.
[0052] As a feasible implementation method, the operation log can be constructed in the following way: Step F1: Use the IoT device to capture video data of employees performing historical tasks; In this embodiment, a camera or other IoT device with video recording capabilities can be used to capture the operation process of employees when performing various procedures of historical work tasks in order to obtain video data.
[0053] Step F2: Obtain the job data by identifying the target operation action in the video data; The target operation actions include the actions performed by the employee during each process step. This embodiment can use computer vision technology (such as pose estimation and action recognition) to identify the target operation actions in the video. Specifically, this step can obtain the time and number of steps for each process step by identifying the target operation actions in the video data, and then summarize the time and number of steps to obtain the work data.
[0054] Step F3: Compare the target operation action in the video data with the corresponding standard action, and determine the quality inspection data based on the comparison result; This step involves comparing the target operation actions identified in the video data with preset standard actions to assess whether the operation is standardized. Specifically, this step compares the target operation actions in the video data with the corresponding standard actions, and the comparison results include the number of product reworks for each process. The quality inspection data is then determined based on the comparison results.
[0055] Step F4: Determine the capability data based on the equipment type and process level corresponding to the target operation action in the video data; This step involves analyzing the actions of employees in the video and assessing their abilities by combining the type of equipment and the level of the work process.
[0056] Step F5: Construct the operation log, which includes the job data, the quality inspection data, and the capability data.
[0057] The process described in the above embodiments is illustrated below through examples in practical applications.
[0058] The apparel industry has entered a phase of "many styles, small quantities," with shorter delivery cycles, higher quality requirements, and frequent style changes placing high demands on management. Effective production organization and optimal human-machine matching to maximize production efficiency are goals pursued by enterprises. In actual production, issues such as semi-finished product accumulation, production line blockages, and uneven workloads among employees often arise. The key lies in achieving the best match between employees, equipment, and processes. To achieve this optimal match, a fundamental task is to conduct thorough employee skill level assessments.
[0059] The skills assessment methods in related technologies are mainly based on the subjective judgment of managers. The skill status of employees often exists in the memory of managers, which is subject to a certain degree of subjectivity. Since there is no quantitative analysis of employees' skills, it is impossible to accurately reflect the true skill level of employees, and it is also impossible to provide effective data support for the human resource management of enterprises.
[0060] With the development of technology, more and more new technologies are being applied to production management. For example, 5G, the Internet of Things, and sensors are used to collect, transmit, and store data from the production site. This data includes information such as employee processing actions, work time, work efficiency, and workmanship quality, which can serve as an important basis for evaluating employee skill levels. However, how to use this production site data to objectively and accurately evaluate employee skills remains a challenge.
[0061] To address the above issues, this embodiment provides a method for evaluating garment sewing machine operators' skills based on production site data. This method analyzes and processes data from five aspects: production site time efficiency, quality, experience (number of process cycles), sewing machine skills (proficiency in operating the machine, i.e., equipment operation ability), and process skills (process difficulty). This establishes a scientific and reasonable skills evaluation system, thereby achieving an objective and accurate evaluation of employee skills.
[0062] Please see Figure 2 , Figure 2 This is a schematic diagram of a sewing employee skill evaluation system based on Internet of Things (IoT) technology, provided in an embodiment of this application. The solution involves inputting production processing data recorded by IoT sewing machines into the employee skill evaluation system via a cloud platform, along with quality inspection data recorded by a quality inspection tablet and standard working hour data from the GSD (Garment Sewing Data) system. After the system cleans, stores, and processes the data, it automatically determines the employee's skill level.
[0063] The garment sewing worker skills evaluation method based on production site data provided in this embodiment includes the following steps: Step G1: Data acquisition and data processing.
[0064] This embodiment adds sensors to the sewing machine's electronic control system to monitor actions such as raising and lowering the presser foot, needle movement, backstitching, and thread cutting, capturing the employee's work time and obtaining work data. This embodiment can clean, transform, and process the collected data on a cloud platform to ensure accuracy and consistency. This embodiment can also install quality control application software on a tablet computer, allowing quality control personnel to input inspection results to determine workmanship quality, with the data transmitted via Wi-Fi to the employee skills evaluation system.
[0065] Step G2: Construct metrics and perform data calculations.
[0066] This embodiment can construct an employee skill evaluation index system, including indicators such as work efficiency, work experience, work quality, sewing machine ability (i.e., equipment operation ability) and process ability; calculate the processed data, set the index value standards, and obtain the employee's actual value on each index.
[0067] Step G3: Output and display of results.
[0068] This step outputs and visualizes each employee's comprehensive skill score. This embodiment uses a skill radar chart to objectively demonstrate employee skills based on five aspects: efficiency, quality, experience, sewing ability, and process capability.
[0069] Specifically, each processing step can be divided into: pre-sewing (taking the cut piece + adjusting + aligning) + sewing (lifting the presser foot + placing the presser foot + starting the sewing + sewing thread (needle movement) + backstitching + lifting the presser foot, etc.) + post-sewing (removing the cut piece). Sensors record the processing time for each of these time periods by monitoring the electronic control actions. Each complete record represents the actual processing time for the process. This data is packaged and transmitted to the cloud platform, which processes the data and transmits the results to the employee skills assessment system.
[0070] Quality inspectors check the workmanship and record quality issues, such as the number of qualified parts, the number of rework parts, the reasons for rework, and the rework process. This data is transmitted to the skills assessment system in real time.
[0071] Work efficiency = Actual time / Standard time × 100%. The actual time is derived from the preprocessed time above, and then the average of the actual time is taken; the standard time is derived from the GSD standard time system.
[0072] Rework rate = number of reworked parts / number of inspected parts × 100%, where the number of reworked parts is derived from the quality inspection data mentioned above.
[0073] Experience points are derived from the number of qualified tablets mentioned above and can be used to determine whether a skill level needs to be upgraded.
[0074] The numerical rules for the above indicators are as follows: Efficiency score (i.e., the numerical value corresponding to the work efficiency index): Efficiency 0-100%, corresponding to a score of 0-10, the higher the efficiency, the higher the score; Quality score (i.e., the numerical value corresponding to the work quality indicator): calculated based on the number of rework and inspections in the lathe production process. If the rework rate is ≥10%, the score is 0; the smaller the rework rate, the higher the score. Experience score (i.e., the numerical value corresponding to the work experience index): Set the standard number of cycles for a process to N pieces, and the number of cycles for a process to 0-N pieces, with a corresponding score of 0-10 points. The more you do, the higher the score.
[0075] In actual production, some employees can operate two or more machines. For example, an employee can operate a level A sewing machine, a level B overlock sewing machine, and a level A coverstitch sewing machine. In actual operation, they are doing the A, B, C, and D level processes of each machine type. Therefore, it is necessary to add the equipment dimension, namely the sewing machine's capability, to make a comprehensive judgment.
[0076] This embodiment allows setting different equipment assessment values based on usage frequency. For example, sewing machines are scored out of 10 points, flatbed wrapping machines are scored as follows: Grade A 3 points, Grade B 2 points, Grade C 1 point, and Grade D 0.5 points, with a maximum score of 9 points. Other types of equipment are scored out of 1 point, with each type receiving 0.1 points. Regarding process capability, this embodiment allows assigning different process difficulty assessment values according to process levels A, B, C, and D, with the total score being a weighted average.
[0077] Based on the data collection and calculations above, employee skill levels are comprehensively evaluated across the dimensions of sewing ability, process capability, efficiency, quality, and experience. A radar chart clearly displays employee skill status; when all five dimensions reach certain values, the employee's level rises to a higher level. Please see [link / reference]. Figure 3 , Figure 3 This is a radar chart illustrating employee skills as provided in an embodiment of this application. The example in the chart shows employee number 123456 with skill level B, quality 100%, efficiency 29%, sewing type 1, and experience of 8490 pieces. In the skill analysis interface, sewing capability is 0.1, process capability is 2.6, quality is 10, efficiency is 2.9, and experience is 8.5.
[0078] The above process can automatically acquire data and calculate employee skills, thus digitizing employee skill evaluation. This process uses a large data sample, ensuring high data reliability and allowing managers to monitor employee skills in real time. This provides a basis for rationally planning production, personnel scheduling, and production ranking, and also serves as a reference for employee promotion.
[0079] This embodiment acquires data through on-site filming. Specifically, cameras replace IoT sewing machines on-site to film employees' sewing machine operations. The actual processing actions are then compared to a standard action model to determine output and working hours, and efficiency is calculated. Similarly, quality data can be automatically obtained using intelligent inspection equipment to automatically inspect and calculate rework rates, pass rates, and other data. This data is then used as the basis for determining employee skill levels. This embodiment applies IoT technology to acquire on-site production data, performs data processing and calculations, and provides a digital solution for evaluating employee skill levels. It enables automatic evaluation, updating, and real-time viewing of employee skills, solving problems such as errors from periodic employee skill assessments and subjective human judgment.
[0080] The following practical example illustrates how the skill levels mentioned above are calculated: The production line includes various types of equipment such as flatbed sewing machines, overlock sewing machines, coverstitch sewing machines, and others. The process levels for flatbed sewing machines, overlock sewing machines, and coverstitch sewing machines are categorized as A, B, C, and D. The equipment assessment score for a level A flatbed sewing machine process is 3 points, for a level B process it's 2 points, for a level C process it's 1 point, and for a level D process it's 0.5 points. The equipment assessment value for a Level A process on an overlock sewing machine is 3 points, for a Level B process it is 2 points, for a Level C process it is 1 point, and for a Level D process it is 0.5 points. The equipment assessment value for a grade A process of a cover-sewing machine is 3 points, the equipment assessment value for a grade B process of a cover-sewing machine is 2 points, the equipment assessment value for a grade C process of a cover-sewing machine is 1 point, and the equipment assessment value for a grade D process of a cover-sewing machine is 0.5 points. The equipment assessment score for other equipment processes is 1 point.
[0081] For any type of equipment, the difficulty assessment value for a Class A process is 1.8 points, for a Class B process it is 0.6 points, for a Class C process it is 0.2 points, and for a Class D process it is 0.1 points.
[0082] The standard working time for a Class B process on a flatbed sewing machine is 100 seconds, and the standard number of cycles (also known as the standard number of pieces) is 10,000.
[0083] In actual operation, employees use equipment to perform corresponding procedures to complete tasks. While employees are working, the system automatically collects data and generates operation logs. For example, if an employee performs the zipper installation procedure, with a standard working time of 100 seconds, using a flat sewing machine, at procedure level B, and performs the task between 8:00 and 10:00, the resulting operation log would include: (1) Quality inspection data: 62 finished products, of which 60 were qualified and 2 were reworked due to skipped needles.
[0084] (2) Operation data: The total time was 2 hours, and the total number of times the B-level process of the flat sewing machine was performed was 62.
[0085] (3) Ability data: The employee was assessed as a flatbed sewing machine B-level employee, and the employee is good at B-level processes.
[0086] Based on the above data, the following calculations are performed: The rework rate is 2 / 62 × 100% = 3.23%; The average time for the process is 2 × 3600 / (62 - 2) = 120 seconds; The process efficiency is: 100 / 120 × 100% = 83.3% The proficiency level of the process is (62-2) / 10000×100%=0.6%; The value corresponding to the equipment's operational capability indicator is 3; The value of the process capability index is 0.6.
[0087] The equipment operation capability index is determined by the highest level of the process that can be performed. For example, if the highest level of process is A, then it is 3 points, not 3+2=5 points.
[0088] If an employee can perform one A process in addition to the B-level process, then the process capability index value is: 0.6 + 1.8 = 2.4 points.
[0089] Furthermore, if other employees can operate the flatbed sewing machine (Level A), the overlock sewing machine (Level B), and the coverstitch sewing machine (Level D), then the equipment operation capability index value is 3 + 2 + 0.5 = 5.5; if other employees can operate the flatbed sewing machine (Level A), the overlock sewing machine (Level A), and the coverstitch sewing machine (Level A), then the equipment operation capability index value is 3 + 3 + 3 = 9; if other employees can only operate all other equipment, then the equipment operation capability index value is 1.
[0090] Furthermore, if other employees can operate 3 Level A processes and 3 Level B processes, then the process capability index score is 1.8 * 3 + 0.6 * 3 = 7.2 points. In addition, this embodiment can set a corresponding upper limit for each evaluation index. For example, if other employees can operate 6 Level A processes, then the process capability index score is 10 points.
[0091] Please see Figure 4 , Figure 4 This is a schematic diagram of a job assignment system provided in an embodiment of this application. The system may include: The data acquisition module 401 is used to acquire operation logs of employees performing historical tasks using IoT devices; wherein, the operation logs include operation data, quality inspection data and capability data; the operation data includes the time and number of times the employee operates each process, the quality inspection data includes the number of products reworked for each process, and the capability data includes the types of equipment and process levels that the employee can operate; The indicator value calculation module 402 is used to determine the value corresponding to the evaluation indicator based on the operation log; wherein, the evaluation indicator includes operation efficiency indicator, operation experience indicator, operation quality indicator, equipment operation capability indicator and process capability indicator. The task allocation module 403 is used to determine the employee's skill level based on the values corresponding to all the evaluation indicators, and to allocate new work tasks to the employee based on the skill level.
[0092] This embodiment utilizes IoT devices to acquire operation logs of employees performing historical tasks. These logs record operational details of employees at different stages, including work data, quality inspection data, and capability data. Based on these logs, this embodiment can determine the employee's values for work efficiency, work experience, work quality, equipment operation capability, and process capability indicators. Then, based on these evaluation indicators, the employee's skill level is comprehensively assessed. This embodiment matches the task content of pending assignments with the employee's skill level, and then assigns new tasks based on the degree of matching. Therefore, this embodiment can accurately evaluate employee skill levels and improve the rationality of task allocation.
[0093] Furthermore, the indicator value calculation module 402 includes: The first calculation unit is used to calculate the average time spent by the employee on each process based on the work data in the operation log; it is also used to calculate the process efficiency of each process based on the ratio of standard working hours to the average time spent on the process; it is also used to calculate the efficiency assessment value corresponding to the process efficiency of each process; and it is also used to perform a weighted calculation on the efficiency assessment values of all processes to obtain the value corresponding to the work efficiency index. The second calculation unit is used to determine the effective number of times the employee operates each process based on the work data and quality inspection data in the operation log; it is also used to take the ratio of the effective number of times each process is operated to the corresponding standard number of times as the proficiency of each process; it is also used to calculate the proficiency assessment value corresponding to the proficiency of each process; and it is also used to perform a weighted calculation on the proficiency assessment values of all processes to obtain the value corresponding to the work experience index. The third calculation unit is used to calculate the rework rate of each process based on the operation data and quality inspection data in the operation log; it is also used to calculate the rework rate assessment value corresponding to the rework rate of each process; and it is also used to perform weighted calculation on the rework rate assessment values of all processes to obtain the value corresponding to the operation quality index.
[0094] The fourth calculation unit is used to determine the highest process level of each type of equipment that the employee can operate based on the capability data in the operation log; it is also used to calculate the equipment assessment value of the employee operating each type of equipment based on the highest process level; and it is also used to add the equipment assessment values of all equipment to obtain the value corresponding to the equipment operation capability index.
[0095] The fifth calculation unit is used to determine the types of work processes that the employee can operate based on the capability data in the operation log; wherein the types of work processes are a combination of equipment type and work process level; it is also used to query the work process difficulty assessment value corresponding to each type of work process; and it is also used to add up the work process difficulty assessment values corresponding to all types of work processes to obtain the value of the work process capability index.
[0096] Furthermore, the process by which the data acquisition module 401 acquires the operation logs of employees performing historical tasks using IoT devices includes: using the IoT devices to capture video data of employees performing historical tasks; obtaining the task data by identifying target operation actions in the video data; wherein, the target operation actions include the actions of the employee when performing each process; comparing the target operation actions in the video data with the corresponding standard actions, and determining the quality inspection data based on the comparison results; and determining the capability data based on the equipment type and process level corresponding to the target operation actions in the video data.
[0097] Since the embodiments of the system part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the system part, and they will not be repeated here.
[0098] This application also provides a storage medium on which a computer program is stored, which, when executed, can perform the steps provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0099] This application also provides an electronic device that may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the electronic device may also include various network interfaces, power supplies, and other components.
[0100] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.
[0101] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
Claims
1. A method of assigning work tasks, characterized by, The method comprises the following steps: obtaining operation logs of employees performing historical work tasks by using Internet of Things devices; wherein the operation logs comprise work data, quality inspection data and ability data; the work data comprises time consumption and frequency of the employees operating each process, the quality inspection data comprises product rework quantity of each process, and the ability data comprises device types and process levels that can be operated by the employees; determining values corresponding to evaluation indexes according to the operation logs; wherein the evaluation indexes comprise work efficiency index, work experience index, work quality index, device operation ability index and process ability index; determining skill levels of the employees according to the values corresponding to all the evaluation indexes, and assigning new work tasks to the employees according to the skill levels.
2. The work task allocation method according to claim 1, wherein, The step of determining values corresponding to evaluation indexes according to the operation logs comprises the following steps: calculating average time consumption of the employees operating each process according to the work data in the operation logs; calculating process work efficiency of each process according to the ratio of standard working hours to average time consumption of the process; calculating efficiency evaluation values corresponding to the process work efficiency of each process; performing weighted calculation on the efficiency evaluation values of all processes to obtain the value corresponding to the work efficiency index.
3. The method of claim 1, wherein, The step of determining values corresponding to evaluation indexes according to the operation logs comprises the following steps: determining effective operation frequency of the employees operating each process according to the work data and the quality inspection data in the operation logs; taking the ratio of effective operation frequency of each process to corresponding standard frequency as proficiency of each process; calculating proficiency evaluation values corresponding to the proficiency of each process; performing weighted calculation on the proficiency evaluation values of all processes to obtain the value corresponding to the work experience index.
4. The method of claim 1, wherein, The step of determining values corresponding to evaluation indexes according to the operation logs comprises the following steps: calculating rework rate of each process according to the work data and the quality inspection data in the operation logs; calculating rework rate evaluation values corresponding to the rework rate of each process; performing weighted calculation on the rework rate evaluation values of all processes to obtain the value corresponding to the work quality index.
5. The method of claim 1, wherein, The step of determining values corresponding to evaluation indexes according to the operation logs comprises the following steps: determining the highest process level of each device that can be operated by the employees according to the ability data in the operation logs; calculating device evaluation values of each device that can be operated by the employees according to the highest process level; adding the device evaluation values of all devices to obtain the value corresponding to the device operation ability index.
6. The method of claim 1, wherein, The step of determining values corresponding to evaluation indexes according to the operation logs comprises the following steps: determining process types that can be operated by the employees according to the ability data in the operation logs; wherein the process types are combinations of device types and process levels; inquiring process difficulty evaluation values corresponding to each process type; adding the process difficulty evaluation values corresponding to all the process types to obtain the value of the process ability index.
7. The work task allocation method according to any one of claims 1 to 6, characterized by, The step of obtaining operation logs of employees performing historical work tasks by using Internet of Things devices comprises the following steps: capturing video data of employees performing historical work tasks by using the Internet of Things devices; obtaining the work data by recognizing a target operation action in the video data; wherein the target operation action includes an action of the employee operating each kind of process; comparing the target operation action in the video data with a corresponding standard action, and determining the quality inspection data according to a comparison result; determining the ability data according to a device type and a process level corresponding to the target operation action in the video data.
8. A work task assignment system characterized by, comprise: a data acquisition module configured to acquire an operation log of an employee performing a historical work task by using an Internet of Things device; wherein the operation log comprises work data, quality inspection data, and ability data; the work data comprises time consumption and a number of times of the employee operating each kind of process, the quality inspection data comprises a product rework quantity of each kind of process, and the ability data comprises a device type and a process level that the employee can operate; an index value calculation module configured to determine a value corresponding to an evaluation index according to the operation log; wherein the evaluation index comprises a work efficiency index, a work experience index, a work quality index, a device operation ability index, and a process ability index; a task allocation module configured to determine a skill level of the employee according to the values corresponding to all the evaluation indexes, and allocate a new work task to the employee according to the skill level.
9. An electronic device, comprising: comprise a memory and a processor, the memory has a computer program stored therein, and the processor realizes steps of the work task allocation method according to any one of claims 1 to 7 when calling the computer program in the memory.
10. A storage medium, characterized by the storage medium has computer executable instructions stored therein, and the computer executable instructions are loaded and executed by a processor to realize steps of the work task allocation method according to any one of claims 1 to 7.