A method and system for accounting effective labor hours of clothing production based on the Internet of Things

Abstract

The application discloses a kind of based on clothing production effective labor hour accounting method and system of Internet of Things, its method includes: obtaining the production assignment task data of current sewing machine station, whether current sewing machine station is effective labor station according to production assignment task data determination;If, through Internet of Things sensing equipment, the bottom operation parameter sequence of current sewing machine station is continuously monitored and collected, effective labor state determination is carried out to bottom operation parameter sequence based on predefined effective working hour determination logic rule;With high-precision timer, the duration of each time entering effective labor state is recorded and accumulated to generate effective working hour structured data;Based on effective working hour structured data, the visual report and analysis data for performance management, production efficiency analysis and process optimization are generated.It can realize automatic accounting based on equipment real running data completely, eliminate artificial error and fraud, ensure that data is fair, credible and objective and accurate.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method and system for calculating effective labor hours in garment production based on IoT. Background Technology

[0002] At present, as a typical labor-intensive industry, the garment manufacturing industry relies heavily on the accurate management of the working hours of employees on the production line for its production efficiency and cost control. Effective working hours, which are the pure working time spent by employees to directly add value to the product (such as sewing and ironing), are the core basis for measuring the production efficiency of individuals and teams, conducting fair pay, optimizing production processes, and cost accounting. At present, the industry mainly relies on the following two traditional methods for calculating effective working hours: quality inspectors manually record the start-up, stop-up, and output of employees through paper forms or simple electronic forms, and then the clerk summarizes and calculates them. This method has inherent defects: (1) the subjective judgment and negligence of the recorder can easily lead to errors, and it is not accurate to the second level. It is also difficult to accurately distinguish between effective work and non-effective time such as picking up materials, changing spools, and debugging equipment. (2) the recorded data usually needs to be sorted out after the shift, and real-time feedback cannot be achieved. It is difficult for managers to grasp the real efficiency of the production site in a timely manner. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides an Internet of Things-based method and system for calculating effective labor hours in garment production. This system solves the problems mentioned in the background art, where quality inspectors manually record employees' start-up, stop-work, and output completion information using paper forms or simple spreadsheets, followed by data aggregation and calculation by clerks. This results in subjective data recording, low accuracy, data lag, lack of transparency, and high management costs.

[0004] A method for calculating effective labor hours in garment production based on the Internet of Things includes the following steps:

[0005] Obtain the production assignment task data for the current sewing machine station, and determine whether the current sewing machine station is a valid work station based on the production assignment task data;

[0006] If so, the underlying operating parameter sequence of the current sewing machine station is continuously monitored and collected through IoT sensing devices, and the effective working status is determined based on the predefined effective working time determination logic rules.

[0007] A high-precision timer records the duration of each entry into an effective working state and accumulates the data to generate structured data of effective working hours;

[0008] Based on structured data of effective working hours, visualized reports and analytical data are generated for performance management, production efficiency analysis, and process optimization.

[0009] Preferably, the step of obtaining the production assignment task data of the current sewing machine station and determining whether the current sewing machine station is a valid work station based on the production assignment task data includes:

[0010] Detect the current working status of the sewing machine station, and determine whether the current sewing machine station is active based on the working status;

[0011] If so, the production assignment task data for the current sewing machine station is determined through the manufacturing execution system on the online server based on the current sewing machine's equipment number;

[0012] The operator's identity, task order number, product model, and process code for the current sewing machine station are determined based on the production assignment data.

[0013] The labor status of the current sewing machine station is determined by the operator's identity, task order number, product model, and process code. Based on the determination result, it is determined whether the current sewing machine station is a valid labor station.

[0014] Among them, labor attributes are divided into effective labor and ineffective labor.

[0015] Preferably, if so, the underlying operating parameter sequence of the current sewing machine station is continuously monitored and collected through IoT sensing devices, and the effective working state is determined based on predefined effective working time determination logic rules, including:

[0016] The system continuously monitors and collects the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal, and needle action counter signal of the current sewing machine station through IoT sensing devices, and performs analog-to-digital conversion to generate the underlying operating parameter sequence.

[0017] The multi-dimensional synchronous judgment conditions for effective working state are determined according to the predefined effective working time judgment logic rules. The multi-dimensional synchronous judgment conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle puncture action counter detects the number of effective needles per unit time.

[0018] The synchronization parameters of the underlying operating parameter sequence are screened and evaluated through multi-dimensional synchronization judgment conditions;

[0019] Based on the evaluation results, determine the start and end clock parameters for assessing the effective working status of the current sewing machine station.

[0020] Preferably, the step of recording the duration of each entry into an effective working state using a high-precision timer and accumulating the data to generate structured effective working time data includes:

[0021] The start and end clock points of each entry into an effective working state are recorded by a high-precision timer, and the first working duration is generated based on the start and end clock points.

[0022] Collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters;

[0023] The time-varying line chart of the running trend is split to obtain multiple linear and non-linear line segments. The comprehensive time period parameters of the non-linear line are statistically analyzed. The duration of the second labor is obtained by subtracting the duration corresponding to the comprehensive time period parameters from the duration of the first labor.

[0024] All durations of secondary labor are summed up and linked to production task information to generate structured data of effective working hours.

[0025] Preferably, the generation of visual reports and analytical data based on structured data of effective working hours for performance management, production efficiency analysis, and process optimization includes:

[0026] The efficiency achievement rate of the current sewing machine station is calculated based on the ratio of effective working hours structured data to preset standard working hours, and performance-based wages and bonuses are calculated based on the efficiency achievement rate.

[0027] By comparing the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, production efficiency loss and production bottlenecks can be determined.

[0028] Perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results;

[0029] The production process of the current sewing machine station is optimized based on the motion unit to be optimized and its optimization parameters.

[0030] An Internet of Things (IoT)-based system for calculating effective labor hours in garment production, comprising:

[0031] The first determination module is used to obtain the production assignment task data of the current sewing machine station and determine whether the current sewing machine station is a valid work station based on the production assignment task data.

[0032] The second determination module is used to continuously monitor and collect the underlying operating parameter sequence of the current sewing machine station through IoT sensing devices, and determine the effective working status of the underlying operating parameter sequence based on the predefined effective working time determination logic rules.

[0033] The first generation module is used to record the duration of each entry into the effective working state with a high-precision timer and accumulate it to generate structured data of effective working hours;

[0034] The second generation module is used to generate visual reports and analytical data for performance management, production efficiency analysis, and process optimization based on structured data of effective working hours.

[0035] Preferably, the first determination module includes:

[0036] The first determination submodule is used to detect the working status of the current sewing machine station and determine whether the current sewing machine station is active based on the working status.

[0037] The second determination submodule is used to determine the production assignment task data for the current sewing machine station based on the device number of the current sewing machine through the manufacturing execution system on the online server;

[0038] The third determination submodule is used to determine the operator's identity, task order number, product model, and process code of the current sewing machine station based on the production assignment task data;

[0039] The judgment submodule is used to judge the labor status of the current sewing machine station by operator identity, task order number, processing product model and process code, and determine whether the current sewing machine station is a valid labor station based on the judgment result;

[0040] Among them, labor attributes are divided into effective labor and ineffective labor.

[0041] Preferably, the second determination module includes:

[0042] The first generation submodule is used to continuously monitor and collect the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal and needle action counter signal of the current sewing machine station through IoT sensing devices, and perform analog-to-digital conversion to generate the underlying operating parameter sequence.

[0043] The fourth determination submodule is used to determine the multi-dimensional synchronous determination conditions of the effective working state according to the predefined effective working time determination logic rules. The multi-dimensional synchronous determination conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle action counter detects the number of effective needles per unit time.

[0044] The screening and evaluation submodule is used to screen and evaluate the synchronization parameters of the underlying operating parameter sequence through multi-dimensional synchronization judgment conditions;

[0045] The fifth determination submodule is used to determine the start clock parameters and end clock parameters for determining the effective working status of the current sewing machine station based on the evaluation results.

[0046] Preferably, the first generation module includes:

[0047] The second generation submodule is used to record the start and end clock points of each entry into the effective working state using a high-precision timer, and to generate the first working duration based on the start and end clock points.

[0048] A submodule is constructed to collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and to construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters;

[0049] The acquisition submodule is used to split the time-varying line chart of the running trend to obtain multiple linear and non-linear line segments, calculate the comprehensive time period parameters of the non-linear line, and subtract the duration corresponding to the comprehensive time period parameters from the first labor duration to obtain the second labor duration.

[0050] The third generation submodule is used to accumulate the duration of all second labor and bind it with production task information to generate effective working hour structured data.

[0051] Preferably, the second generation module includes:

[0052] The calculation submodule is used to calculate the efficiency achievement rate of the current sewing machine station based on the ratio of effective working hours structured data to preset standard working hours, and to calculate performance wages and bonuses based on the efficiency achievement rate.

[0053] The comparison submodule is used to compare the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, in order to determine the production efficiency loss and production bottlenecks.

[0054] The analysis submodule is used to perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results.

[0055] The optimization submodule is used to optimize the production process of the current sewing machine station based on the action unit to be optimized and its optimization parameters.

[0056] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0057] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0059] Figure 1 A flowchart illustrating the effective labor hours calculation method for garment production based on the Internet of Things provided by this invention;

[0060] Figure 2 A flowchart illustrating the effective labor hours calculation method for garment production based on the Internet of Things provided by this invention;

[0061] Figure 3 A schematic diagram of the structure of an Internet of Things-based effective labor hour accounting system for garment production provided by the present invention;

[0062] Figure 4 This is a schematic diagram of the structure of the first determination module in an Internet of Things-based effective labor hour calculation system for garment production provided by the present invention. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0064] At present, as a typical labor-intensive industry, the garment manufacturing industry relies heavily on the accurate management of the working hours of employees on the production line for its production efficiency and cost control. Effective working hours, which are the pure working time spent by employees to directly add value to the product (such as sewing and ironing), are the core basis for measuring the production efficiency of individuals and teams, conducting fair pay, optimizing production processes, and cost accounting. At present, the industry mainly relies on the following two traditional methods for calculating effective working hours: quality inspectors manually record the start-up, stop-up, and output of employees through paper forms or simple electronic forms, and then the clerk summarizes and calculates the data. This method has inherent defects: (1) the subjective judgment and negligence of the recorder can easily lead to errors, and it is impossible to be accurate to the second level. It is also difficult to accurately distinguish between effective work and ineffective time such as picking up materials, changing spools, and debugging equipment. (2) the recorded data usually needs to be sorted out after the shift, and real-time feedback cannot be achieved. It is difficult for managers to grasp the real efficiency of the production site in a timely manner. In order to solve the above problems, this embodiment discloses a method for calculating effective working hours of garment production based on the Internet of Things.

[0065] A method for calculating effective labor hours in garment production based on the Internet of Things, such as Figure 1 As shown, it includes the following steps:

[0066] Step S101: Obtain the production assignment task data of the current sewing machine station, and determine whether the current sewing machine station is a valid work station based on the production assignment task data;

[0067] Step S102: If so, continuously monitor and collect the underlying operating parameter sequence of the current sewing machine station through IoT sensing devices, and determine the effective working status of the underlying operating parameter sequence based on the predefined effective working time determination logic rules.

[0068] Step S103: Record the duration of each entry into effective working state using a high-precision timer and accumulate the data to generate structured data of effective working hours;

[0069] Step S104: Generate visualized reports and analytical data for performance management, production efficiency analysis and process optimization based on the structured data of effective working hours.

[0070] The working principle of the above technical solution is as follows: acquire the production assignment task data of the current sewing machine station, determine whether the current sewing machine station is an effective work station based on the production assignment task data; if so, continuously monitor and collect the underlying operating parameter sequence of the current sewing machine station through IoT sensing devices, and determine the effective work status of the underlying operating parameter sequence based on predefined effective work time determination logic rules; record the duration of each entry into the effective work state with a high-precision timer and accumulate it to generate structured effective work time data; generate visualized reports and analysis data for performance management, production efficiency analysis and process optimization based on the structured effective work time data.

[0071] The beneficial effects of the above technical solution are as follows: By collecting a series of parameters of the current sewing machine station based on IoT sensing devices and accurately determining the effective working hours through preset effective working hour determination logic, it is possible to achieve automatic calculation based entirely on the actual operation data of the equipment, eliminating human error and falsification, ensuring the fairness, credibility, objectivity and accuracy of the data. At the same time, managers can view the effective working hour progress of all employees in real time, making the production status clear at a glance, supporting rapid decision-making. Automated calculation saves a lot of time for manual recording, statistics and verification, reducing management costs. It solves the problems mentioned in the background technology, such as the high subjectivity, low accuracy, data lag and lack of transparency, and high management costs caused by quality inspectors manually recording employees' start-up, stop-up and output through paper forms or simple electronic spreadsheets, followed by clerks summarizing and calculating.

[0072] In one embodiment, such as Figure 2 As shown, the step of obtaining the production assignment task data of the current sewing machine station and determining whether the current sewing machine station is a valid work station based on the production assignment task data includes:

[0073] Step S201: Detect the working status of the current sewing machine station, and determine whether the current sewing machine station is active based on the working status;

[0074] Step S202: If so, determine the production assignment task data for the current sewing machine station based on the device number of the current sewing machine through the manufacturing execution system on the online server;

[0075] Step S203: Determine the operator's identity, task order number, product model, and process code for the current sewing machine station based on the production assignment task data;

[0076] Step S204: Determine the labor status of the current sewing machine station by using the operator's identity, task order number, product model, and process code; and determine whether the current sewing machine station is a valid labor station based on the determination result.

[0077] Among them, labor attributes are divided into effective labor and ineffective labor.

[0078] The beneficial effects of the above technical solution are as follows: by determining the activation status of the current sewing machine station, it is possible to intuitively determine whether the current sewing machine station is in a working state, thereby quickly determining the task assignment status of the current sewing machine station. Furthermore, by determining the multi-dimensional data of the processing task, it is possible to evaluate whether the current sewing machine station is an effective working station. Based on the operator and processing object of the current sewing machine station, it is possible to accurately determine whether it is an effective working station, avoiding misidentification caused by sewing machine equipment debugging, and improving accuracy and reliability.

[0079] In one embodiment, if so, the process involves continuously monitoring and collecting the underlying operating parameter sequence of the current sewing machine station through an IoT sensing device, and determining the effective working state of the underlying operating parameter sequence based on predefined effective working time determination logic rules, including:

[0080] The system continuously monitors and collects the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal, and needle action counter signal of the current sewing machine station through IoT sensing devices, and performs analog-to-digital conversion to generate the underlying operating parameter sequence.

[0081] The multi-dimensional synchronous judgment conditions for effective working state are determined according to the predefined effective working time judgment logic rules. The multi-dimensional synchronous judgment conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle puncture action counter detects the number of effective needles per unit time.

[0082] The synchronization parameters of the underlying operating parameter sequence are screened and evaluated through multi-dimensional synchronization judgment conditions;

[0083] Based on the evaluation results, determine the start and end clock parameters for assessing the effective working status of the current sewing machine station.

[0084] In this embodiment, the preset idling threshold is set differently according to the sewing machine model and process type, such as 500–800 rpm;

[0085] In this embodiment, the criterion for determining the number of valid needles is: a needle count of ≥3 per unit time (e.g., 1 second) is considered valid;

[0086] The beneficial effects of the above technical solution are as follows: by defining and detecting effective working hours through multi-dimensional synchronous judgment, accurate assessment of working hours can be carried out based on the precise definition of working hours, ensuring the accuracy, objectivity and completeness of the assessment results, laying the foundation for subsequent pay settlement, and further improving practicality.

[0087] In one embodiment, the step of recording the duration of each entry into an effective working state with a high-precision timer and accumulating the data to generate structured effective working hours includes:

[0088] The start and end clock points of each entry into an effective working state are recorded by a high-precision timer, and the first working duration is generated based on the start and end clock points.

[0089] Collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters;

[0090] The time-varying line chart of the running trend is split to obtain multiple linear and non-linear line segments. The comprehensive time period parameters of the non-linear line are statistically analyzed. The duration of the second labor is obtained by subtracting the duration corresponding to the comprehensive time period parameters from the duration of the first labor.

[0091] All durations of secondary labor are summed up and linked to production task information to generate structured data of effective working hours.

[0092] In this embodiment, the time-varying line graph of the running trend is represented as a time-series variation graph drawn based on the target running parameters;

[0093] In this embodiment, the nonlinear broken line represents the curve segment of the sewing machine station in an unstable operating state, and its specific identification method is the slope change detection method.

[0094] The beneficial effects of the above technical solution are as follows: by constructing a time-varying line graph of the current sewing machine station's operating trend, and by checking and eliminating non-linear non-production stage durations based on the linear attributes of parameter synchronization judgment conditions, the high accuracy of effective working hours can be further guaranteed, data quality and reference value can be improved, and the overall working hour calculation efficiency can be improved.

[0095] In one embodiment, the generation of visual reports and analytical data for performance management, production efficiency analysis, and process optimization based on structured data of effective working hours includes:

[0096] The efficiency achievement rate of the current sewing machine station is calculated based on the ratio of effective working hours structured data to preset standard working hours, and performance-based wages and bonuses are calculated based on the efficiency achievement rate.

[0097] By comparing the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, production efficiency loss and production bottlenecks can be determined.

[0098] Perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results;

[0099] The production process of the current sewing machine station is optimized based on the motion unit to be optimized and its optimization parameters.

[0100] In this embodiment, the operation heatmap is generated based on the pedal action frequency and hand infrared sensor data.

[0101] The beneficial effects of the above technical solution are as follows: it can intuitively evaluate the production efficiency and performance information of each operator, thereby optimizing and adjusting to a certain extent to ensure production efficiency, improving overall work efficiency and stability. Furthermore, by conducting operation heatmap analysis to determine the optimized actions, the initial actions can be optimized based on the habitual actions of high-efficiency operators to ensure production efficiency. It can significantly optimize the process from the details of production operation, further improving work efficiency and practicality.

[0102] In this embodiment, an operation heatmap analysis is performed on the target operation data of a high-efficiency operator. Based on the analysis results, the action units to be optimized and their optimization parameters are determined, including:

[0103] Simultaneously collect multimodal data of the target operator while performing standard operating procedures and perform data fusion to generate high-precision free-degree motion trajectories;

[0104] Multidimensional operational heatmaps are generated based on degrees of freedom motion trajectories, including time density, action frequency, path overlap, and physiological load.

[0105] The motion unit segmentation algorithm is used to decompose the free-degree motion trajectory into multiple basic motion units. The feature regions on the multidimensional operation heatmap are associated and mapped with the basic motion units to identify the target motion unit to be optimized.

[0106] The specific optimization parameters of the target action unit are determined based on the quantitative characteristics of the multidimensional operation heatmap, and the optimization scheme is output according to the specific optimization parameters.

[0107] The optimization schemes include, but are not limited to: material layout adjustment suggestions, standardized motion trajectories, ergonomic improvement parameters, and tool / fixture design parameters.

[0108] In this embodiment, the multimodal data of the target operator during the execution of standard operating procedures is collected synchronously and fused to generate a high-precision motion trajectory with degrees of freedom, including:

[0109] Visual data streams are obtained by capturing two-dimensional image sequences of the global position, posture, and object movement of highly efficient operators through multi-angle high-definition cameras deployed in the operating area.

[0110] IMU data streams are acquired by collecting three-dimensional spatial coordinates, acceleration, angular velocity, and orientation of the operator's hand / limbs using miniature sensors on the operator's wristband, finger sleeve, or tooling.

[0111] Track the precise position of workpieces and tools on the workbench using RFID or UWB positioning systems, and use pressure sensors to sense the pressure distribution during operation to obtain environmental data streams;

[0112] Software timestamp technology is used to synchronize visual data streams, IMU data streams, and environmental data streams at the millisecond level;

[0113] By employing Kalman filtering or factor graph optimization algorithms, and based on the fusion of visual absolute position information and IMU high-frequency relative motion information from synchronous data streams, high-precision, high-refresh-rate operator hand and key joint degree-of-freedom motion trajectory data are generated.

[0114] In this embodiment, a multidimensional operational heatmap is generated based on the degree-of-freedom motion trajectory, including time density, action frequency, path overlap, and physiological load, comprising:

[0115] The dwell time of the operation trajectory at each position is determined based on the motion trajectory of the degrees of freedom, and Gaussian kernel density is estimated and visualized to generate a time density heatmap;

[0116] Based on the motion trajectory of the degrees of freedom, the density of the start / end points of the actions occurring in the operating space area is determined and a heat map of the action frequency is generated.

[0117] Based on the motion trajectory of the degree of freedom, the repetitive operation and its motion trajectory are determined and the standard deviation is calculated by superimposing them to generate a path overlap heatmap.

[0118] Based on the free-degree motion trajectory, joint torque and muscle load are estimated using a biomechanical model and visualized on the operator's human body model to generate a physiological load heatmap.

[0119] The beneficial effects of the above technical solution are as follows: it transforms subjective motion analysis into objective measurement of heat map data, eliminating human bias. Furthermore, it conducts comprehensive analysis from four dimensions—time, space, frequency, and physiological load—revealing problems that cannot be discovered by a single dimension. Through data fusion, motion segmentation, and correlation analysis algorithms, it realizes the entire process from raw data to optimized motion suggestions, improving overall optimization efficiency, ensuring the working stability of the sewing machine station, and improving work efficiency.

[0120] In one embodiment, this embodiment also discloses an Internet of Things-based system for calculating effective labor hours in garment production, such as... Figure 3 As shown, the system includes:

[0121] The first determination module 301 is used to obtain the production assignment task data of the current sewing machine station and determine whether the current sewing machine station is a valid work station based on the production assignment task data.

[0122] The second determination module 302 is used to continuously monitor and collect the underlying operating parameter sequence of the current sewing machine station through IoT sensing devices, and determine the effective working status of the underlying operating parameter sequence based on the predefined effective working time determination logic rules.

[0123] The first generation module 303 is used to record the duration of each entry into the effective working state with a high-precision timer and accumulate it to generate structured data of effective working hours;

[0124] The second generation module 304 is used to generate visual reports and analytical data for performance management, production efficiency analysis and process optimization based on structured data of effective working hours.

[0125] The working principle and beneficial effects of the above technical solution have been explained in the method embodiments, and will not be repeated here.

[0126] In one embodiment, such as Figure 4 As shown, the first determination module 301 includes:

[0127] The first determining submodule 3011 is used to detect the working status of the current sewing machine station and determine whether the current sewing machine station is active based on the working status.

[0128] The second determining submodule 3012 is used to determine the production assignment task data of the current sewing machine station based on the device number of the current sewing machine through the manufacturing execution system of the online server;

[0129] The third determination submodule 3013 is used to determine the operator's identity, task order number, processed product model, and process code of the current sewing machine station based on the production assignment task data;

[0130] The judgment submodule 3014 is used to judge the labor status of the current sewing machine station by operator identity, task order number, processing product model and process code, and determine whether the current sewing machine station is a valid labor station based on the judgment result;

[0131] Among them, labor attributes are divided into effective labor and ineffective labor.

[0132] In one embodiment, the second determining module includes:

[0133] The first generation submodule is used to continuously monitor and collect the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal and needle action counter signal of the current sewing machine station through IoT sensing devices, and perform analog-to-digital conversion to generate the underlying operating parameter sequence.

[0134] The fourth determination submodule is used to determine the multi-dimensional synchronous determination conditions of the effective working state according to the predefined effective working time determination logic rules. The multi-dimensional synchronous determination conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle action counter detects the number of effective needles per unit time.

[0135] The screening and evaluation submodule is used to screen and evaluate the synchronization parameters of the underlying operating parameter sequence through multi-dimensional synchronization judgment conditions;

[0136] The fifth determination submodule is used to determine the start clock parameters and end clock parameters for determining the effective working status of the current sewing machine station based on the evaluation results.

[0137] In one embodiment, the first generation module includes:

[0138] The second generation submodule is used to record the start and end clock points of each entry into the effective working state using a high-precision timer, and to generate the first working duration based on the start and end clock points.

[0139] A submodule is constructed to collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and to construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters;

[0140] The acquisition submodule is used to split the time-varying line chart of the running trend to obtain multiple linear and non-linear line segments, calculate the comprehensive time period parameters of the non-linear line, and subtract the duration corresponding to the comprehensive time period parameters from the first labor duration to obtain the second labor duration.

[0141] The third generation submodule is used to accumulate the duration of all second labor and bind it with production task information to generate effective working hour structured data.

[0142] In one embodiment, the second generation module includes:

[0143] The calculation submodule is used to calculate the efficiency achievement rate of the current sewing machine station based on the ratio of effective working hours structured data to preset standard working hours, and to calculate performance wages and bonuses based on the efficiency achievement rate.

[0144] The comparison submodule is used to compare the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, in order to determine the production efficiency loss and production bottlenecks.

[0145] The analysis submodule is used to perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results.

[0146] The optimization submodule is used to optimize the production process of the current sewing machine station based on the action unit to be optimized and its optimization parameters.

[0147] Those skilled in the art should understand that the "first" and "second" in this invention simply refer to different application stages.

[0148] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0149] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for calculating effective labor hours in garment production based on the Internet of Things, characterized in that, Includes the following steps: Obtain the production assignment task data for the current sewing machine station, and determine whether the current sewing machine station is a valid work station based on the production assignment task data; If so, the underlying operating parameter sequence of the current sewing machine station is continuously monitored and collected through IoT sensing devices, and the effective working status is determined based on the predefined effective working time determination logic rules. A high-precision timer records the duration of each entry into an effective working state and accumulates the data to generate structured data of effective working hours; Based on structured data of effective working hours, visualized reports and analytical data are generated for performance management, production efficiency analysis, and process optimization. The visualization reports and analytical data generated based on structured data of effective working hours for performance management, production efficiency analysis, and process optimization include: The efficiency achievement rate of the current sewing machine station is calculated based on the ratio of effective working hours structured data to preset standard working hours, and performance-based wages and bonuses are calculated based on the efficiency achievement rate. By comparing the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, production efficiency loss and production bottlenecks can be determined. Perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results; The production process of the current sewing machine station is optimized based on the motion unit to be optimized and its optimization parameters; Perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results, including: Simultaneously collect multimodal data of the target operator while performing standard operating procedures and perform data fusion to generate high-precision free-degree motion trajectories; Multidimensional operational heatmaps based on degrees of freedom motion trajectories are generated, including time density, action frequency, path overlap, and physiological load. The motion trajectory with degrees of freedom is decomposed into multiple basic motion units using an action unit segmentation algorithm. Feature regions on the multidimensional operation heatmap are associated and mapped with basic action units to identify the target action unit to be optimized. The specific optimization parameters of the target action unit are determined based on the quantitative characteristics of the multidimensional operation heatmap, and the optimization scheme is output according to the specific optimization parameters. The optimization scheme includes: material layout adjustment suggestions, standardized motion trajectories, ergonomic improvement parameters, and tool / fixture design parameters; Synchronously collect multimodal data of the target operator while performing standard operating procedures and perform data fusion to generate high-precision free-degree motion trajectories, including: Visual data streams are obtained by capturing two-dimensional image sequences of the global position, posture, and object movement of highly efficient operators through multi-angle high-definition cameras deployed in the operating area. IMU data streams are acquired by collecting three-dimensional spatial coordinates, acceleration, angular velocity, and orientation of the operator's hand / limbs using miniature sensors on the operator's wristband, finger sleeve, or tooling. Track the precise position of workpieces and tools on the workbench using RFID or UWB positioning systems, and use pressure sensors to sense the pressure distribution during operation to obtain environmental data streams; Software timestamp technology is used to synchronize visual data streams, IMU data streams, and environmental data streams at the millisecond level; Using Kalman filtering or factor graph optimization algorithms, based on the fusion of visual absolute position information and IMU high-frequency relative motion information from synchronous data stream, high-precision, high-refresh-rate operator hand and key joint degree-of-freedom motion trajectory data are generated. Based on the generation of motion trajectories with degrees of freedom, a multidimensional operational heatmap is generated, including time density, action frequency, path overlap, and physiological load. The dwell time of the operation trajectory at each position is determined based on the motion trajectory of the degrees of freedom, and Gaussian kernel density is estimated and visualized to generate a time density heatmap; Based on the motion trajectory of the degrees of freedom, the density of the start / end points of the actions occurring in the operating space area is determined and a heat map of the action frequency is generated. Based on the motion trajectory of the degree of freedom, the repetitive operation and its motion trajectory are determined and the standard deviation is calculated by superimposing them to generate a path overlap heatmap. Based on the free-degree motion trajectory, joint torque and muscle load are estimated using a biomechanical model and visualized on the operator's human body model to generate a physiological load heatmap.

2. The method for calculating effective labor hours in garment production based on the Internet of Things according to claim 1, characterized in that, The step of obtaining the production assignment task data for the current sewing machine station and determining whether the current sewing machine station is a valid work station based on the production assignment task data includes: Detect the current working status of the sewing machine station, and determine whether the current sewing machine station is active based on the working status; If so, the production assignment task data for the current sewing machine station is determined through the manufacturing execution system on the online server based on the current sewing machine's equipment number; The operator's identity, task order number, product model, and process code for the current sewing machine station are determined based on the production assignment data. The labor status of the current sewing machine station is determined by the operator's identity, task order number, product model, and process code. Based on the determination result, it is determined whether the current sewing machine station is a valid labor station. Among them, labor attributes are divided into effective labor and ineffective labor.

3. The method for calculating effective labor hours in garment production based on the Internet of Things as described in claim 1, characterized in that, If so, the underlying operating parameter sequence of the current sewing machine station is continuously monitored and collected through IoT sensing devices, and the effective working state is determined based on predefined effective working time determination logic rules, including: The system continuously monitors and collects the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal, and needle action counter signal of the current sewing machine station through IoT sensing devices, and performs analog-to-digital conversion to generate the underlying operating parameter sequence. The multi-dimensional synchronous judgment conditions for effective working state are determined according to the predefined effective working time judgment logic rules. The multi-dimensional synchronous judgment conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle puncture action counter detects the number of effective needles per unit time. The synchronization parameters of the underlying operating parameter sequence are screened and evaluated through multi-dimensional synchronization judgment conditions; Based on the evaluation results, determine the start and end clock parameters for assessing the effective working status of the current sewing machine station.

4. The method for calculating effective labor hours in garment production based on the Internet of Things according to claim 1, characterized in that, The process of recording the duration of each entry into an effective working state using a high-precision timer and accumulating this data to generate structured effective working hours includes: The start and end clock points of each entry into an effective working state are recorded by a high-precision timer, and the first working duration is generated based on the start and end clock points. Collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters; The time-varying line chart of the running trend is split to obtain multiple linear and non-linear line segments. The comprehensive time period parameters of the non-linear line are statistically analyzed. The duration of the second labor is obtained by subtracting the duration corresponding to the comprehensive time period parameters from the duration of the first labor duration. All durations of secondary labor are summed up and linked to production task information to generate structured data of effective working hours.

5. A system for calculating effective labor hours in garment production based on the Internet of Things, characterized in that, The system includes: The first determination module is used to obtain the production assignment task data of the current sewing machine station and determine whether the current sewing machine station is a valid work station based on the production assignment task data. The second determination module is used to continuously monitor and collect the underlying operating parameter sequence of the current sewing machine station through IoT sensing devices, and determine the effective working status of the underlying operating parameter sequence based on the predefined effective working time determination logic rules. The first generation module is used to record the duration of each entry into the effective working state with a high-precision timer and accumulate it to generate structured data of effective working hours; The second generation module is used to generate visual reports and analytical data for performance management, production efficiency analysis and process optimization based on structured data of effective working hours; The second generation module includes: The calculation submodule is used to calculate the efficiency achievement rate of the current sewing machine station based on the ratio of effective working hours structured data to preset standard working hours, and to calculate performance wages and bonuses based on the efficiency achievement rate. The comparison submodule is used to compare the effective working hours of different operators in the same process and the effective working hours of the same operator in different time periods based on the structured data of effective working hours, in order to determine the production efficiency loss and production bottlenecks. The analysis submodule is used to perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results. The optimization submodule is used to optimize the production process of the current sewing machine station based on the action unit to be optimized and its optimization parameters; Perform operation heatmap analysis on the target operation data of high-efficiency operators, and determine the action units to be optimized and their optimization parameters based on the analysis results, including: Simultaneously collect multimodal data of the target operator while performing standard operating procedures and perform data fusion to generate high-precision free-degree motion trajectories; Multidimensional operational heatmaps based on degrees of freedom motion trajectories are generated, including time density, action frequency, path overlap, and physiological load. The motion trajectory with degrees of freedom is decomposed into multiple basic motion units using an action unit segmentation algorithm. Feature regions on the multidimensional operation heatmap are associated and mapped with basic action units to identify the target action unit to be optimized. The specific optimization parameters of the target action unit are determined based on the quantitative characteristics of the multidimensional operation heatmap, and the optimization scheme is output according to the specific optimization parameters. The optimization scheme includes: material layout adjustment suggestions, standardized motion trajectories, ergonomic improvement parameters, and tool / fixture design parameters; Synchronously collect multimodal data of the target operator while performing standard operating procedures and perform data fusion to generate high-precision free-degree motion trajectories, including: Visual data streams are obtained by capturing two-dimensional image sequences of the global position, posture, and object movement of highly efficient operators through multi-angle high-definition cameras deployed in the operating area. IMU data streams are acquired by collecting three-dimensional spatial coordinates, acceleration, angular velocity, and orientation of the operator's hand / limbs using miniature sensors on the operator's wristband, finger sleeve, or tooling. Track the precise position of workpieces and tools on the workbench using RFID or UWB positioning systems, and use pressure sensors to sense the pressure distribution during operation to obtain environmental data streams; Software timestamp technology is used to synchronize visual data streams, IMU data streams, and environmental data streams at the millisecond level; Using Kalman filtering or factor graph optimization algorithms, based on the fusion of visual absolute position information and IMU high-frequency relative motion information from synchronous data stream, high-precision, high-refresh-rate operator hand and key joint degree-of-freedom motion trajectory data are generated. Based on the generation of motion trajectories with degrees of freedom, a multidimensional operational heatmap is generated, including time density, action frequency, path overlap, and physiological load. The dwell time of the operation trajectory at each position is determined based on the motion trajectory of the degrees of freedom, and Gaussian kernel density is estimated and visualized to generate a time density heatmap; Based on the motion trajectory of the degrees of freedom, the density of the start / end points of the actions occurring in the operating space area is determined and a heat map of the action frequency is generated. Based on the motion trajectory of the degree of freedom, the repetitive operation and its motion trajectory are determined and the standard deviation is calculated by superimposing them to generate a path overlap heatmap. Based on the free-degree motion trajectory, joint torque and muscle load are estimated using a biomechanical model and visualized on the operator's human body model to generate a physiological load heatmap.

6. The IoT-based effective labor hour calculation system for garment production according to claim 5, characterized in that, The first determination module includes: The first determination submodule is used to detect the working status of the current sewing machine station and determine whether the current sewing machine station is active based on the working status. The second determination submodule is used to determine the production assignment task data for the current sewing machine station based on the device number of the current sewing machine through the manufacturing execution system on the online server; The third determination submodule is used to determine the operator's identity, task order number, product model, and process code of the current sewing machine station based on the production assignment task data; The judgment submodule is used to judge the labor status of the current sewing machine station by operator identity, task order number, processing product model and process code, and determine whether the current sewing machine station is a valid labor station based on the judgment result; Among them, labor attributes are divided into effective labor and ineffective labor.

7. The IoT-based effective labor hour accounting system for garment production according to claim 5, characterized in that, The second determination module includes: The first generation submodule is used to continuously monitor and collect the motor current / voltage signal, spindle rotary encoder signal, pedal position sensor signal and needle action counter signal of the current sewing machine station through IoT sensing devices, and perform analog-to-digital conversion to generate the underlying operating parameter sequence. The fourth determination submodule is used to determine the multi-dimensional synchronous determination conditions of the effective working state according to the predefined effective working time determination logic rules. The multi-dimensional synchronous determination conditions are: the motor is in driving state, the spindle speed continuously exceeds the preset idling threshold, and the needle action counter detects the number of effective needles per unit time. The screening and evaluation submodule is used to screen and evaluate the synchronization parameters of the underlying operating parameter sequence through multi-dimensional synchronization judgment conditions; The fifth determination submodule is used to determine the start clock parameters and end clock parameters for determining the effective working status of the current sewing machine station based on the evaluation results.

8. The IoT-based effective labor hour accounting system for garment production according to claim 5, characterized in that, The first generation module includes: The second generation submodule is used to record the start and end clock points of each entry into the effective working state using a high-precision timer, and to generate the first working duration based on the start and end clock points. A submodule is constructed to collect the target operating parameters of the current sewing machine station during the time period between the start clock point and the end clock point, and to construct a time-varying line graph of the operating trend of the current sewing machine station based on the target operating parameters; The acquisition submodule is used to split the time-varying line chart of the running trend to obtain multiple linear and non-linear line segments, calculate the comprehensive time period parameters of the non-linear line, and subtract the duration corresponding to the comprehensive time period parameters from the first labor duration to obtain the second labor duration. The third generation submodule is used to accumulate the duration of all second labor and bind it with production task information to generate effective working hour structured data.

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