Artificial work rhythm analysis optimization method and device for intelligent manufacturing workshop

Abstract

The application provides a manual work rhythm analysis optimization method and device for a smart manufacturing workshop, and belongs to the field of intelligent production and manufacturing. The method comprises the following steps: acquiring real-time station conditions and historical data of a production line to determine a manual station planning rhythm range; judging whether a production line material shortage problem occurs in a current production workshop; acquiring a manual station rhythm under the condition that the production line material shortage problem occurs in the current production workshop; judging whether the manual station rhythm is within the manual station planning rhythm range; determining that the cause of the current production line material shortage problem is a production line material shortage caused by a machine rhythm under the condition that the manual station rhythm is within the manual station planning rhythm range; and adjusting the real-time working state of the production line according to the cause. The method can accurately locate a material shortage point under the production line material shortage problem in the production workshop, and improves the production efficiency of the production line in the workshop.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, and more specifically to a method and apparatus for analyzing and optimizing the work cycle of human workers in intelligent manufacturing workshops. Background Technology

[0002] Production takt time is an indicator of a production line's capacity, speed, and efficiency, and a key concept for achieving lean manufacturing. Takt time planning personnel, given a production cycle, utilize mathematical modeling and structural design to determine the complete production line takt time, including the takt time of each process in the body-in-white welding assembly and the takt time of manual work. Simultaneously, the takt time of each process needs to be as close as possible to the set takt time; otherwise, idle equipment will result in wasted resources. Therefore, takt time analysis and optimization are integral to the entire takt time planning process for the automated body-in-white welding line. Unlike automated workstations, the takt time of manual workstations is affected by factors such as the on-site environment, work difficulty, and worker emotions, making precise calculation and optimization difficult.

[0003] The manual work cycle is conducted under idealized conditions, ignoring the time from the workstation indicator light illuminating to the worker entering the grating, and only considering the manual work cycle in the manual loading area. This leads to a discrepancy between the manual work cycle analysis results and actual working conditions. Capacity analysis technology identifies blockages, missing parts, material shortages, and downtime in automated welding production lines based on real-time workstation status, but it lacks in-depth analysis of manual workstation capacity. Therefore, it cannot accurately pinpoint whether material shortages are caused by human error or machine malfunction. Furthermore, when planning the manual work cycle, those involved in cycle planning did not fully consider the impact of the actual on-site environment, work difficulty, and worker emotions. When on-site personnel cannot operate according to the planned cycle, it directly leads to the stagnation of subsequent processes on the production line, affecting the production line capacity analysis results. Summary of the Invention

[0004] The purpose of this invention is to provide a method and device for analyzing and optimizing the work cycle of manual labor in intelligent manufacturing workshops. By analyzing and determining the causes of material shortages on the production line, adjustments can be made based on the causes, thereby improving the efficiency of the production cycle.

[0005] To achieve the above objectives, the method includes:

[0006] Obtain real-time workstation status and historical data on the production line to determine the planned cycle time range for manual workstations;

[0007] Determine if there is a material shortage problem on the production line in the current production workshop;

[0008] In the event that the current production workshop is experiencing a material shortage problem, obtain the cycle time of manual workstations;

[0009] Determine whether the cycle time of the manual workstation is within the planned cycle time range of the manual workstation;

[0010] If it is determined that the cycle time of the manual workstation is within the planned cycle time range of the manual workstation, the cause of the current material shortage problem on the production line is determined to be the material shortage on the production line caused by the machine cycle time.

[0011] Adjust the real-time operating status of the production line based on the stated reasons.

[0012] Optionally, the method includes:

[0013] If it is determined that the cycle time of the manual workstation is not within the planned cycle time range of the manual workstation, the reason for the current material shortage problem on the production line is that the slow loading action of the manual workers leads to untimely material supply, resulting in a material shortage on the production line.

[0014] Determine whether the manual work cycle is within the planned cycle range of the manual workstation;

[0015] If it is determined that the manual work cycle is within the planned cycle range of the manual workstation, the cause of the current material shortage problem on the production line is determined to be the slow loading action of the manual workers, which leads to untimely material supply and thus material shortage on the production line.

[0016] Optionally, the method includes:

[0017] If it is determined that the manual work cycle is not within the planned cycle range of the manual workstation, the current material shortage problem on the production line is caused by excessive delay in the cycle.

[0018] Optionally, the manual workstation cycle time includes a delayed cycle time, a manual work cycle time, and a timeout cycle time, wherein the delayed cycle time, the manual work cycle time, the timeout cycle time, and the manual workstation cycle time satisfy formula (1).

[0019] Time dela +Time over +Time work =Time stat (1)

[0020] Among them, Time dela For delayed beats; Time work For manual work cycle time; Time over The portion of the manual work cycle that exceeds the planned cycle time at the manual workstation is considered an overtime cycle. stat For manual workstation rhythm.

[0021] Optionally, real-time workstation status and historical data of the production line can be obtained to determine the planned cycle time range for manual workstations, including:

[0022] Read the real-time working status and historical data, and construct a Gaussian distribution function;

[0023] The cycle time range of the manual workstation is determined based on the Gaussian distribution function.

[0024] Optionally, the density function of the Gaussian distribution function is Equation (2).

[0025]

[0026] Among them, Time stat For manual workstation cycle time, f(Time) stat Let σ be the Gaussian distribution function of the human workstation planning cycle time, σ be the standard deviation of the overall human work cycle time data, and ν be the mean of the overall human work cycle time data. stat The expected value of the density function.

[0027] On the other hand, the present invention also provides a device for analyzing and optimizing the human work cycle in a smart manufacturing workshop, the device including a processor configured to perform any of the methods described above.

[0028] Through the above technical solutions, the present invention provides a method and device for analyzing and optimizing the human work rhythm in intelligent manufacturing workshops. This method accurately locates the material shortage point in the production line under the problem of material shortage in the production workshop, establishes a more accurate rhythm analysis mode with human work modules for intelligent manufacturing workshops, and forms intelligent feedback to improve the production efficiency of the production workshop. The method includes: first, acquiring real-time workstation status and historical data of the production line to determine the planned cycle time range for manual workstations; then, determining whether there is a material shortage problem in the current production workshop; and if a material shortage problem is determined, acquiring the manual workstation cycle time; then, determining whether the manual workstation cycle time is within the planned cycle time range. If the manual workstation cycle time is within the planned cycle time range, the current material shortage problem is caused by the machine cycle time, and feedback can be provided based on this machine cycle time-related material shortage problem to improve the mechanical equipment and adjust the work efficiency of the manual workstations; if the manual workstation cycle time is not within the planned cycle time range, the current material shortage problem is determined to be caused by slow manual loading actions leading to untimely material supply, thus causing the material shortage problem. Based on this cause and an ideal value for a planned cycle time of a manual workstation, the work efficiency of the manual workstations can be monitored and adjusted. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for analyzing and optimizing the cycle time of manual work in a smart manufacturing workshop according to an embodiment of the present invention;

[0030] Figure 2 This is a Gaussian distribution function diagram of the cycle time range of manual workstation planning in a method for analyzing and optimizing the cycle time of manual work in a smart manufacturing workshop, according to one embodiment of the present invention. Detailed Implementation

[0031] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0032] In the embodiments of this application, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used to describe the relative positional relationships of components in relation to the directions shown in the accompanying drawings or in relation to the vertical, perpendicular, or gravitational directions.

[0033] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0034] like Figure 1 The diagram shows a flowchart of a method for analyzing and optimizing the cycle time of manual work in a smart manufacturing workshop, according to an embodiment of the present invention. Figure 1 In this method, the method includes:

[0035] In step S10, the real-time status of the workstations and historical data of the production line are obtained to determine the cycle time range of the manual workstations.

[0036] In step S11, it is determined whether there is a material shortage problem in the current production workshop.

[0037] In step S12, if it is determined that there is a material shortage problem in the current production workshop, the cycle time of the manual workstation is obtained.

[0038] In step S13, it is determined whether the manual workstation cycle time is within the planned cycle time range of the manual workstation. If the manual workstation cycle time is within the planned cycle time range of the manual workstation, then step S14 is performed; if the manual workstation cycle time is not within the planned cycle time range of the manual workstation, then step S15 is performed.

[0039] In step S14, if it is determined that the cycle time of the manual workstation is within the planned cycle time range of the manual workstation, it is determined that the current material shortage problem on the production line is caused by the machine cycle time.

[0040] In step S15, if it is determined that the cycle time of the manual workstation is not within the planned cycle time range of the manual workstation, it is determined that the current material shortage problem on the production line is caused by the slow loading action of the manual workers, which leads to untimely material supply and thus causes material shortage on the production line.

[0041] In step S16, it is determined whether the manual work rhythm is within the planned rhythm range of the manual work station; if the manual work rhythm is within the planned rhythm range of the manual work station, then proceed to step S17; if the manual work rhythm is not within the planned rhythm range of the manual work station, then proceed to step S18.

[0042] In step S17, if it is determined that the manual work cycle is within the planned cycle range of the manual workstation, the cause of the current material shortage problem on the production line is determined to be the slow loading action of the manual workers, which leads to untimely material supply and thus material shortage on the production line.

[0043] In step S18, if it is determined that the manual work cycle is not within the planned cycle range of the manual workstation, the cause of the current production line material shortage problem is determined to be the production line material shortage caused by excessive delay cycle.

[0044] In step S19, the real-time operating status of the production line is adjusted according to the cause.

[0045] In one embodiment of the present invention, to address the problem in the prior art where the cycle time of manual workstations, even under ideal conditions, easily overlooks the cycle time in other processes, the present invention, in step S10, acquires the real-time workstation status and historical data of the production line to determine the planned cycle time range for manual workstations, thus obtaining a production line database. This database includes the cycle time of all processes on the production line, process start time, process end time, process blockage time, process downtime, process material shortage time, process part shortage time, daily production line output, weekly production line output, monthly production line output, annual production line output, daily production line capacity, weekly production line capacity, monthly production line capacity, and annual production line capacity. In subsequent steps, the analysis process is refined, thereby making the results of the manual workstation cycle time analysis closer to actual working conditions.

[0046] In one embodiment of the present invention, considering that capacity analysis technology determines whether there are blockages, missing parts, material shortages, or downtime issues in automated welding production lines based on real-time workstation status, it does not conduct in-depth analysis of the capacity of manual workstations. Therefore, it is impossible to determine whether the material shortage problem in the production line is caused by human error or machine error. In one embodiment of the present invention, a planned cycle time range for a manual workstation is first determined based on the real-time workstation status and historical data of the production line. Then, it is determined whether there is a material shortage problem in the current production workshop. If a material shortage problem is determined to be present in the current workshop, the cycle time of the manual workstation is obtained through some production data. Finally, by comparing the planned cycle time of the manual workstation with the actual cycle time of the manual workstation, it is determined whether the material shortage problem in the current production workshop is caused by human error or machine error. If the cycle time of the manual workstation is within the planned cycle time range, the material shortage in the production line is caused by the machine cycle time. If the cycle time of the manual workstation is not within the planned cycle time range, the material shortage in the production line is caused by slow manual loading actions leading to untimely material supply. By conducting in-depth analysis of the production capacity of manual workstations, it is possible to determine whether the material shortage problem on the production line is caused by the machine cycle time or the manual workstation cycle time. Once the cause of the material shortage problem is identified, adjustments can be made to both the machines and the manual work to achieve the ideal manual workstation cycle time, thereby improving the production efficiency of the workshop production line.

[0047] In one embodiment of the present invention, the composition of the manual workstation cycle can be any of the various compositions known to those skilled in the art. In one embodiment of the present invention, the manual workstation cycle includes: a delayed cycle, a manual work cycle, and a timeout cycle. The delayed cycle, the manual work cycle, and the timeout cycle satisfy formula (1).

[0048] Time dela +Time over +Time work =Time stat (1)

[0049] Among them, Time dela For delayed beats; Time work For manual work cycle time; Time over The portion of the manual work cycle that exceeds the planned cycle time at the manual workstation is considered an overtime cycle. stat The time interval for manual workstation operation is defined as follows: In existing technologies, the time interval from when the workstation indicator light illuminates to when the worker enters the light grid is easily overlooked. Therefore, the time interval from when the worker sees the indicator light to when they enter the light grid is defined as Time. dela Therefore, under the most ideal circumstances, Time dela =0, meaning the delay time is 0.

[0050] In one embodiment of the present invention, considering that capacity analysis technology determines whether problems such as line blockage, missing parts, material shortages, and downtime in automated welding production lines are caused by human error, based on real-time workstation status, the present invention analyzes manual workstations to further determine whether the problem is a manual work cycle time. work Material shortages caused by substandard work procedures. In one embodiment of the present invention, the material shortage problem is addressed by determining the manual work cycle time. work Whether it falls within the planned cycle time range for manual workstations can be determined by analyzing the specific conditions of each workstation. If the manual work cycle time... work If the workstation's cycle time is within the planned range, then the current material shortage on the production line can be determined to be caused by slow manual loading, leading to untimely material supply. If the manual work cycle time is... work If the workstation is not within the planned cycle time range, then the current material shortage problem on the production line is determined to be caused by a delayed cycle time. dela The excessive size of the machine caused a material shortage on the production line. Through detailed analysis of the manual workstations, it was further determined whether the material shortage was due to slow manual loading actions leading to untimely material supply or a delay in cycle time. dela The excessive size caused a shortage of materials on the production line.

[0051] In one embodiment of the present invention, the time delay is... dela Scope, manual work cycle time work Range, Timeout over Scope and manual workstation cycle time stat The range can be obtained through various means known to those skilled in the art; in one embodiment of the invention, the delay time... dela Scope, manual work cycle time work Scope, manual work cycle time over Scope and manual workstation cycle time stat The range can be represented by a Gaussian distribution function, where the delay time is... dela and Time Overtime over It is generated by actual manual labor on site, so the most important thing to consider is the manual work cycle time. work Therefore, if the current production line material shortage is determined to be caused by slow manual loading, resulting in untimely material supply, then a specific analysis should be conducted to determine whether the manual work cycle is up to standard.

[0052] In one embodiment of the present invention, the method for obtaining the cycle time range of manual workstation planning can be one of various methods known to those skilled in the art. In one embodiment of the present invention, real-time workstation status and historical data of the production line are acquired to determine the cycle time range of manual workstation planning, resulting in a production line database. This database includes the cycle time of all processes on the production line, process start time, process end time, process blockage time, process downtime, process material shortage time, process part shortage time, daily production line output, weekly production line output, monthly production line output, annual production line output, daily production line capacity, weekly production line capacity, monthly production line capacity, and annual production line capacity. A Gaussian distribution function is constructed using these data, such as... Figure 2 As shown, Figure 2 This is a Gaussian distribution function diagram of the planned cycle time range of manual workstations in a method for analyzing and optimizing the cycle time of manual work in a smart manufacturing workshop, according to one embodiment of the present invention. The planned cycle time range of manual workstations can be determined by the Gaussian distribution function. The density function of the Gaussian distribution function is given by formula (2).

[0053]

[0054] Among them, Time stat For manual workstation cycle time, f(Time) stat Let σ be the Gaussian distribution function of the human workstation planning cycle time, σ be the standard deviation of the overall human work cycle time data, and ν be the mean of the overall human work cycle time data. stat The expected value of the density function.

[0055] In one embodiment of the present invention, when planning the manual work rhythm, the relevant personnel did not fully consider the influence of factors such as the actual on-site environment, work difficulty, and employee emotions. As a result, on-site personnel were unable to operate according to the planned rhythm at their workstations, directly leading to the stagnation of subsequent processes in the production line and affecting the production line capacity analysis results. To more accurately reflect a reasonable planned rhythm at workstations, it is necessary to read the daily manual work rhythm from the production line database, determine the range of planned rhythm at workstations using a Gaussian distribution function, and, while ensuring the required rhythm compliance rate on-site, assign a maximum value to the planned rhythm at each workstation, thereby ensuring that the planned rhythm at workstations meets the standards under the influence of various uncertain factors. The maximum value of the planned rhythm at each workstation can be any value known to those skilled in the art. In one embodiment of the present invention, the maximum value of the planned rhythm at each workstation can be the delayed rhythm Time. dela Manual work cycle time work Timeout overThe maximum values ​​obtained from the Gaussian distribution function are summed to obtain the final maximum value of the manual workstation cycle time. This maximum value is the optimal recommended value for the manual workstation cycle time, making the manual workstation cycle time closer to the ideal state.

[0056] In addition, the present invention also provides a device for analyzing and optimizing the human work cycle in a smart manufacturing workshop, the device including a processor configured to perform any of the methods described above.

[0057] Through the above technical solution, this invention provides a method and apparatus for analyzing and optimizing the work rhythm of manual workstations in intelligent manufacturing workshops. Considering that existing manual workstation rhythm processes operate under ideal conditions, neglecting situations in other processes, this invention refines the work rhythm of manual workstations, making the analysis results closer to actual working conditions. This invention refines the manual workstation rhythm into manual work rhythm, overtime rhythm, and delay rhythm. Data analysts can analyze each part of the rhythm to determine the cause of material shortages on the production line and provide solutions for material shortage problems to production workshop managers. Addressing the influence of various factors such as the on-site environment, work difficulty, and worker emotions in the manufacturing workshop, this invention utilizes manual workstation rhythm data from the production line database, combined with a Gaussian distribution function, to obtain the optimal recommended value for the manual workstation rhythm. This facilitates timely monitoring and understanding of the work status of manual workstations by workshop production managers and allows rhythm planners to make reasonable adjustments to the manual workstation rhythm.

[0058] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0059] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0060] Furthermore, various different embodiments of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for analyzing and optimizing the cycle time of manual work in intelligent manufacturing workshops, characterized in that, The method includes: Obtain real-time workstation status and historical data on the production line to determine the planned cycle time range for manual workstations; Determine if there is a material shortage problem on the production line in the current production workshop; In the event that the current production workshop is experiencing a material shortage problem, obtain the cycle time of manual workstations; Determine whether the cycle time of the manual workstation is within the planned cycle time range of the manual workstation; If it is determined that the cycle time of the manual workstation is within the planned cycle time range of the manual workstation, the cause of the current material shortage problem on the production line is determined to be the material shortage on the production line caused by the machine cycle time. Adjust the real-time operating status of the production line based on the stated reasons; If it is determined that the cycle time of the manual workstation is not within the planned cycle time range of the manual workstation, the reason for the current material shortage problem on the production line is that the slow loading action of the manual workers leads to untimely material supply, resulting in a material shortage on the production line. Determine whether the manual work cycle is within the planned cycle range of the manual workstation; If it is determined that the manual work cycle is within the planned cycle range of the manual workstation, the cause of the current material shortage problem on the production line is determined to be that the slow loading action of the manual workers leads to untimely material supply, resulting in a material shortage on the production line. If it is determined that the manual work cycle is not within the planned cycle range of the manual workstation, the cause of the current production line material shortage problem is determined to be the production line material shortage caused by excessive delay cycle. Obtaining real-time workstation status and historical data from the production line to determine the planned cycle time range for manual workstations includes: Read the real-time workstation status and historical data, and construct a Gaussian distribution function; The cycle time range of the manual workstation is determined based on the Gaussian distribution function.

2. The method according to claim 1, characterized in that, The manual workstation cycle includes a delayed cycle, a manual work cycle, and a timeout cycle, and the delayed cycle, the manual work cycle, the timeout cycle, and the manual workstation cycle satisfy formula (1). ，（1） in, To delay the beat; For manual work rhythm; The portion of the manual work cycle that exceeds the planned cycle time at the manual workstation is considered an overtime cycle time. For manual workstation rhythm.

3. The method according to claim 1, characterized in that, The density function of the Gaussian distribution function is given by formula (2). ，（2） in, For manual workstation cycle time, To plan the Gaussian distribution function of the cycle time for manual workstations, The standard deviation of the overall human work cycle data. The average value of the overall manual work cycle data The expected value of the density function.

4. A device for analyzing and optimizing the cycle time of manual work in a smart manufacturing workshop, characterized in that, The optimization device includes a processor configured to perform the method as described in any one of claims 1 to 3.

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