Method and system for calculating operation time

Abstract

The present invention relates to the technical field of data statistics, and in particular to a method and system for calculating operation hours, the method comprising: collecting a number of historical parameter groups of historical sewing objects and their corresponding historical operation hours, calculating a historical sewing evaluation value according to the historical parameter groups, and constructing an operation hour model according to the number of historical sewing evaluation values ​​and the corresponding historical operation hours; collecting the actual size, actual weaving density and actual pattern complexity of the target sewing object, calculating the target sewing evaluation value according to the collected data, and obtaining the target operation hours through the operation hour model; collecting the sewing parameters of the target sewing object, and obtaining the actual operation hours according to the sewing parameters; updating the operation hour model according to the actual operation hours and the target sewing evaluation value to obtain an operation hour optimization model; and using the operation hour optimization model to judge the evaluation operation hours of any sewing object to be evaluated. The present invention improves the accuracy of the operation hour calculation result.

Description

Technical Field

[0001] The present invention relates to the technical field of data statistics, and in particular to a method and system for calculating operation hours. Background Art

[0002] Working hours statistics is one of the important links in enterprise management and is crucial to achieving effective production planning and reasonable personnel deployment.

[0003] The patent document with Chinese patent publication number CN116912026A discloses a statistical system for employee operation data, including: a plurality of lower computers and a host computer; wherein each lower computer corresponds to a workstation; each of the lower computers is used to measure the operation data of the corresponding workstation, and after each operation is completed, it generates corresponding operation measurement data, and transmits the operation measurement data to the host computer; the host computer is used to calculate the real-time plan completion status of all operators corresponding to each lower computer, the real-time headquarters group operation time of each operator corresponding to each lower computer, the real-time comprehensive energy efficiency of each operator corresponding to each lower computer, each operation working hour of the workstation corresponding to each lower computer, and the real-time comprehensive energy efficiency of the workstation corresponding to each lower computer.

[0004] In the prior art, the collection of operation hours is performed by calculating the number of operations or the start time and end time of the operations, but the time of problems occurring during the operation is not calculated, resulting in inaccurate calculation results of the operation hours. Summary of the invention

[0005] To this end, the present invention provides a method for calculating operation hours, which establishes an operation hour model through historical data and collects target data to continuously update the operation model, thereby solving the problem of inaccurate operation hour calculation results.

[0006] To achieve the above object, the present invention provides a method for calculating operation hours, the method comprising:

[0007] Collecting a number of historical parameter groups of historical sewn objects and the historical operation hours corresponding to each historical sewn object, calculating the historical parameter evaluation value of each parameter in any historical parameter group, calculating the historical sewing evaluation value according to the historical parameter evaluation values, and constructing the operation hour model according to the historical sewing evaluation values ​​and the corresponding historical operation hours;

[0008] Collecting the actual size, actual weaving density and actual pattern complexity of the target sewing object, respectively calculating corresponding evaluation values ​​according to the actual size, the actual weaving density and the pattern complexity, obtaining a target sewing evaluation value according to the calculation results, and inputting the target sewing evaluation value into the operation time model to obtain the target operation time, wherein the actual weaving density is obtained by the initial weaving density and the humidity calibration coefficient, and the actual pattern complexity is obtained by the actual pattern area ratio and the actual number of colors;

[0009] Collecting sewing parameters of the target sewing object during the sewing process, calculating a correction coefficient according to the sewing parameters, and correcting the target operation time by the correction coefficient to obtain actual operation time;

[0010] The operation time model is updated according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, wherein the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range;

[0011] The operation time optimization model is used to determine the estimated operation time of any sewn product to be evaluated, so as to adjust the production plan according to the estimated operation time.

[0012] Furthermore, the step of collecting a plurality of historical parameter groups of historical sewn objects and their corresponding historical operation hours comprises:

[0013] Measuring the length and width of the historical sewn object to obtain the historical size of the historical sewn object;

[0014] Acquiring a historical weight value of the historical sewn object according to a weight sensor, and dividing the historical weight value by the area value of the historical sewn object to acquire a historical weaving density;

[0015] By analyzing the historical area and the number of historical colors of the pattern on the historical sewn object, the historical pattern complexity is obtained according to the analysis result;

[0016] The historical size, the historical weave density and the historical pattern complexity are used as the historical parameter group;

[0017] The historical start time and the historical end time of the historical sewing object are obtained, and the historical operation hours are obtained according to the historical start time and the historical end time.

[0018] Furthermore, obtaining the historical pattern complexity according to the analysis results includes:

[0019] Acquiring an initial image of the historical sewn object by an image acquisition device;

[0020] Processing the initial image by an edge detection algorithm to obtain pattern edges;

[0021] Calculating the ratio of the area included in the pattern edge to the total area of ​​the initial image, and recording the obtained ratio as the historical pattern area ratio;

[0022] Identify the historical sewing objects by using a color sensor to obtain the number of historical colors;

[0023] The product of the historical pattern area ratio and the number of historical colors is recorded as the historical pattern complexity.

[0024] Further, the step of calculating the historical sewing evaluation value according to any of the historical parameter groups comprises:

[0025] Comparing the historical dimensions with the standard dimensions, and obtaining a historical dimension evaluation value according to the comparison result;

[0026] Comparing the historical weaving density with the standard weaving density, and obtaining a historical weaving density evaluation value according to the comparison result;

[0027] Comparing the historical pattern complexity with the standard complexity, and obtaining a historical complexity evaluation value according to the comparison result;

[0028] The historical sewing evaluation value is obtained by performing calculation according to the historical size evaluation value, the historical weaving density evaluation value, the historical complexity evaluation value and a preset weight.

[0029] Furthermore, the step of constructing a work time model according to the historical sewing evaluation values ​​and the corresponding historical work time comprises:

[0030] Draw a trend change graph based on the historical sewing evaluation values ​​and the historical operation hours;

[0031] Analyze the trend of graphic changes in the trend change graph, and determine a regression model according to the trend analysis result;

[0032] The regression model is trained by using a plurality of the historical sewing evaluation values ​​and a plurality of the historical operation hours to obtain the operation hour model.

[0033] Furthermore, the step of obtaining the actual weave density through the initial weave density and the humidity calibration coefficient includes:

[0034] placing the target sewn object on the weight sensor to obtain a plurality of measured weight values;

[0035] Taking the average of the several measured weight values ​​obtained as the actual weight value;

[0036] Recording the ratio of the actual weight value to the area of ​​the target sewn object as the initial weaving density;

[0037] The actual humidity value of the environment is detected and measured in real time through the humidity sensor;

[0038] Comparing the actual humidity value with the standard humidity value, and when the actual humidity value is greater than the standard humidity value, calculating a humidity calibration coefficient;

[0039] The initial weave density is calibrated according to the humidity calibration coefficient to obtain an actual weave density.

[0040] Furthermore, the step of collecting sewing parameters during the sewing process of the target sewn object includes:

[0041] The moving value of the mark on the target sewing object is collected in real time by a photoelectric sensor, and the real-time moving speed of the target sewing object at adjacent moments is calculated by a plurality of the moving values;

[0042] The pressure sensor is used to detect the real-time skipped stitch signal during sewing;

[0043] The distance sensor detects the distance between the sewing needle and the target sewing object in real time to obtain the real-time distance value;

[0044] The real-time moving speed, the real-time skipped stitch signal and the real-time distance value are used as the sewing parameters.

[0045] Furthermore, the step of correcting the target operation time according to the sewing parameters includes:

[0046] Acquire the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values;

[0047] Obtaining the skipping needle duration according to the real-time skipping needle signals;

[0048] Obtaining the stopping time according to the real-time moving speeds;

[0049] Calculating a correction coefficient according to the skipped stitch duration, the stop duration and the sewing duration;

[0050] The target operation time is corrected according to the correction coefficient to obtain the actual operation time.

[0051] Furthermore, the step of obtaining the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values ​​comprises:

[0052] Draw a distance variation diagram of the sewing process of the target sewing object according to the plurality of real-time distance values;

[0053] Analyze the distance change graph and calculate the actual time difference corresponding to the points on adjacent horizontal axes;

[0054] The actual time difference is compared with the preset time difference, and a number of actual time differences whose actual time difference is less than or equal to the preset time difference are summed to obtain the sewing time length.

[0055] Furthermore, the present invention also provides a system for calculating working hours of an operation, the system comprising:

[0056] A model building module, for collecting a number of historical parameter groups of historical sewing objects and their corresponding historical operation hours, calculating a historical parameter evaluation value of each parameter in any of the historical parameter groups, calculating a historical sewing evaluation value based on the historical parameter evaluation values, and building an operation hour model based on the historical sewing evaluation values ​​and the corresponding historical operation hours;

[0057] a data acquisition module connected to the model building module, for collecting the actual size, actual weaving density and actual pattern complexity of the target sewing object, wherein the actual weaving density is obtained by the initial weaving density and the humidity calibration coefficient; the data acquisition module is also used to obtain a target sewing evaluation value according to a calculation result obtained based on the actual size, the actual weaving density and the pattern complexity, and to obtain a target working time by inputting the target sewing evaluation value into the working time model;

[0058] a correction module connected to the data acquisition module, for acquiring sewing parameters during the sewing process of the target sewing object, and for determining actual operation hours according to a correction coefficient obtained based on the sewing parameters;

[0059] An updating module, connected to the correction module, for updating the operation time model according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, wherein the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range;

[0060] An evaluation module is connected to the updating module and is used to use the operation time optimization model to determine the evaluation operation time of any sewn object to be evaluated, so as to adjust the production plan according to the evaluation operation time.

[0061] Compared with the prior art, the beneficial effect of the present invention is that by collecting the parameter groups and corresponding operation working hours of historical sewing objects, it is helpful to analyze the influence of different parameter combinations on working hours, calculate the historical sewing evaluation value according to the historical parameter group, and build the operation working hour model based on the historical sewing evaluation value and the corresponding historical operation working hours, so that the model construction conforms to the historical sewing operation, so that the model construction result is accurate, and it is convenient to predict the target working hours of the target sewing object, and calculate the target sewing evaluation value by collecting the parameter group of the target sewing object, and input it into the operation working hour model, so that the prediction of the target operation working hours can be obtained quickly and accurately, which is helpful to improve the production efficiency and Production planning, by collecting the sewing parameters of the target sewing objects, correcting the target operation hours, taking into account various factors in the actual production process, obtaining a time estimate that is closer to the actual situation, making the time calculation result more accurate, improving production efficiency and the rationality of resource allocation, and updating the operation time model according to the actual operation time and the target sewing evaluation value, so that the model can more accurately predict the actual operation time, thereby improving the accuracy and efficiency of the production plan, and continuously updating the operation time model to achieve continuous improvement of the operation time model, so that the operation time calculation results can adapt to changes in the production environment, thereby effectively improving the accuracy of the operation time calculation.

[0062] In particular, accurately capturing the pattern area and number of colors through image acquisition equipment and color sensors helps to provide an accurate data basis for the subsequent quantitative calculation of pattern complexity. Processing the initial image through a precise edge detection algorithm can clearly distinguish between the pattern and the background, which helps to accurately calculate the area occupied by the pattern, thereby improving the accuracy of the pattern complexity assessment. Combining the pattern area ratio and the number of colors can more comprehensively describe the complexity of the pattern, making the pattern complexity calculation results accurate, which helps to predict the working hours required to produce specific sewn products.

[0063] In particular, through the trend change chart, the relationship and trend between the historical sewing evaluation values ​​and the historical operation hours can be intuitively observed, which is helpful to understand the data pattern and provide clues for selecting a suitable regression model. The regression model is determined based on the trend analysis results, ensuring that the model can accurately reflect the dependency between the evaluation value and the working hours, avoiding the mismatch problem caused by using a single or general model. The regression model is trained using actual historical data, so that the model can learn the specific relationship between parameters specific to sewing operations and working hours, thereby improving the prediction accuracy. As more data is accumulated, the model is constantly retrained and optimized to adapt to changes in production processes and the introduction of new products, ensuring that the model is always kept up to date, thereby effectively improving the accuracy of operation hour calculations.

[0064] In particular, by measuring multiple times and calculating the average value, the influence of single measurement errors is reduced, and a more accurate actual weight value is obtained. The actual area of ​​the target sewn object is used to calculate the initial weaving density, ensuring that the calculation of the weaving density is based on the actual physical size, thereby improving the accuracy of the overall evaluation. Humidity is an important environmental factor affecting the weight of sewn products. By real-time detection and consideration of the influence of humidity, the deviation in weaving density calculation caused by environmental changes is calibrated, the actual humidity is compared with the standard humidity, and the humidity calibration coefficient is calculated when necessary, ensuring that the weaving density values ​​obtained under different environmental conditions are effectively compared with the values ​​under the standard state, so that the weaving density calculation results are accurate. Through precise weaving density measurement, an accurate data basis is provided for the subsequent updating of the operation time model, thereby effectively improving the accuracy of the operation time calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a process flow of a method for calculating operation hours according to an embodiment of the present invention;

[0066] Figure 2 It is a schematic diagram of the process of collecting historical parameter groups and historical operation hours in the operation hour calculation method according to an embodiment of the present invention;

[0067] Figure 3 It is a schematic diagram of a flow chart of correcting the target operation time in the operation time calculation method according to an embodiment of the present invention;

[0068] Figure 4 This is a structural block diagram of a job working hours calculation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0071] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0072] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0073] See also Figure 1 As shown, an embodiment of the present invention provides a method for calculating operation hours, the method comprising:

[0074] Step S100, collecting a plurality of historical parameter groups of historical sewing objects and the historical operation hours corresponding to each historical sewing object, calculating the historical parameter evaluation value of each parameter in any historical parameter group, calculating the historical sewing evaluation value according to the plurality of historical parameter evaluation values, and constructing the operation hour model according to the plurality of historical sewing evaluation values ​​and the corresponding historical operation hours;

[0075] Step S200, collecting the actual size, actual weaving density and actual pattern complexity of the target sewing object, calculating corresponding evaluation values ​​according to the actual size, the actual weaving density and the pattern complexity, obtaining a target sewing evaluation value according to the calculation results, and inputting the target sewing evaluation value into the operation time model to obtain the target operation time, wherein the actual weaving density is obtained by the initial weaving density and the humidity calibration coefficient, and the actual pattern complexity is obtained by the actual pattern area ratio and the actual number of colors;

[0076] Step S300, collecting sewing parameters of the target sewing object during the sewing process, calculating a correction coefficient according to the sewing parameters, and correcting the target operation time by the correction coefficient to obtain actual operation time;

[0077] Step S400, updating the operation time model according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, wherein the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range;

[0078] Step S500: using the operation time optimization model to determine the estimated operation time of any sewn object to be evaluated, so as to adjust the production plan according to the estimated operation time.

[0079] Specifically, the plurality of historical sewn objects are a collection of all sewn objects produced in one day;

[0080] The target range is 0-1.

[0081] It is understandable that a possible example of adjusting the production plan according to the evaluated working hours in the embodiment of the present invention includes: if there are multiple sewn objects to be produced at the same time, adjusting the production sequence according to their evaluated working hours to ensure that those sewn objects with shorter evaluated working hours are produced first.

[0082] Specifically, an embodiment of updating the operation time model according to the actual operation time and the target sewing evaluation value to obtain the operation time optimization model may be: collecting five groups of historical sewing objects, whose historical operation time is 60min, 30min, 50min, 80min and 100min respectively, and their corresponding historical sewing evaluation values ​​are 0.6, 0.3, 0.5, 0.8 and 1 respectively, then the operation time model is y=100×Pi, wherein y is the historical operation time, and Pi is the historical sewing evaluation value. If the actual operation time is 70min and the target sewing evaluation value is 0.6, a supplementary value is calculated according to the actual operation time and the target sewing evaluation value, and the supplementary value=actual operation time-(100×target sewing evaluation value), then the operation time optimization model is y=100×Pi+10.

[0083] Specifically, the embodiment of the present invention collects parameter groups of historical sewing objects and corresponding operation working hours, which is helpful to analyze the influence of different parameter combinations on working hours, calculates historical sewing evaluation values ​​according to historical parameter groups, and constructs an operation working hour model based on historical sewing evaluation values ​​and corresponding historical operation working hours, so that the model construction conforms to the historical sewing operation, and the model construction result is accurate, which is convenient for predicting the target working hours of the target sewing object. By collecting the parameter group of the target sewing object and calculating the target sewing evaluation value, it is input into the operation working hour model, so that the prediction of the target operation working hours can be obtained quickly and accurately, which is helpful to improve the production efficiency and production plan of the production process. By collecting the sewing parameters of the target sewing object, the target operation time is corrected. Taking into account various factors in the actual production process, a time estimate that is closer to the actual situation is obtained, making the time calculation result more accurate, improving production efficiency and the rationality of resource allocation, and updating the operation time model according to the actual operation time and the target sewing evaluation value, so that the model can more accurately predict the actual operation time, thereby improving the accuracy and efficiency of the production plan. By continuously updating the operation time model, the continuous improvement of the operation time model is achieved, so that the operation time calculation results can adapt to changes in the production environment, thereby effectively improving the accuracy of the operation time calculation.

[0084] See also Figure 2 As shown, the step of collecting a plurality of historical parameter groups of historical sewn objects and their corresponding historical operation hours includes:

[0085] Step S110, measuring the length and width of the historical sewn object to obtain the historical size of the historical sewn object;

[0086] Step S120, obtaining a historical weight value of the historical sewn object according to a weight sensor, and dividing the historical weight value by the area value of the historical sewn object to obtain a historical weaving density;

[0087] Step S130, analyzing the historical area and the number of historical colors of the pattern on the historical sewn object, and obtaining the complexity of the historical pattern according to the analysis result;

[0088] Step S140, taking the historical size, the historical weaving density and the historical pattern complexity as the historical parameter group;

[0089] Step S150, obtaining the historical start time and the historical end time of the historical sewing object, and obtaining the historical operation hours according to the historical start time and the historical end time.

[0090] Specifically, in the embodiment of the present invention, the size of the sewn product is an important factor affecting the production time. By accurately measuring the length and width of the historical product, the time required to manufacture the historical sewn product can be better estimated, and basic data can be provided for the modeling of the subsequent operation time model. The weaving density is directly related to the production difficulty. The weaving density index provided by the combination of weight and area is helpful to evaluate the production requirements and corresponding time consumption of different sewn products, and provide basic data for the modeling of the subsequent operation time model. The complexity of the pattern will affect the production speed and difficulty, and therefore has a great impact on the time. By quantifying the area and number of colors of the historical pattern, basic data is provided for the modeling of the subsequent operation time model, and the accuracy of the prediction is improved. Integrating the historical size, weaving density and pattern complexity into a set of parameters helps to more accurately capture the various factors affecting the time, so as to reflect the comprehensive effects of these factors in the model. By recording the actual start and end time of each sewn product, accurate time data is obtained, and accurate basic data is provided for the modeling of the subsequent operation time model, thereby improving the accuracy of the operation time model construction result.

[0091] Specifically, the historical pattern complexity is obtained based on the analysis results including:

[0092] Acquiring an initial image of the historical sewn object by an image acquisition device;

[0093] Processing the initial image by an edge detection algorithm to obtain pattern edges;

[0094] Calculating the ratio of the area included in the pattern edge to the total area of ​​the initial image, and recording the obtained ratio as the historical pattern area ratio;

[0095] Identify the historical sewing objects by using a color sensor to obtain the number of historical colors;

[0096] The product of the historical pattern area ratio and the number of historical colors is recorded as the historical pattern complexity.

[0097] Specifically, directly multiplying the area ratio and the number of colors makes the calculation complexity simple and direct, thereby improving the calculation efficiency. Area and color are both important factors in pattern complexity. Combining them for evaluation can obtain more accurate complexity values, which helps to more accurately understand and analyze the characteristics of the pattern.

[0098] Specifically, the embodiments of the present invention accurately collect pattern area and color quantity through image acquisition equipment and color sensor, which helps to provide an accurate data basis for the subsequent quantitative calculation of pattern complexity. The initial image is processed by an accurate edge detection algorithm to clearly distinguish the pattern and the background, which helps to accurately calculate the area occupied by the pattern, thereby improving the accuracy of pattern complexity evaluation. The pattern area ratio and the number of colors are combined to more comprehensively describe the complexity of the pattern, so that the pattern complexity calculation result is accurate, which helps to predict the working hours required to produce specific sewn products.

[0099] Specifically, the step of calculating the historical sewing evaluation value according to any of the historical parameter groups includes:

[0100] Comparing the historical dimensions with the standard dimensions, and obtaining a historical dimension evaluation value according to the comparison result;

[0101] Comparing the historical weaving density with the standard weaving density, and obtaining a historical weaving density evaluation value according to the comparison result;

[0102] Comparing the historical pattern complexity with the standard complexity, and obtaining a historical complexity evaluation value according to the comparison result;

[0103] The historical sewing evaluation value is obtained by performing calculation according to the historical size evaluation value, the historical weaving density evaluation value, the historical complexity evaluation value and preset weights.

[0104] During the specific implementation process, the embodiments of the present invention set the preset weights to 0.2, 0.3 and 0.5 respectively, wherein 0.2 is the weight corresponding to the historical size evaluation value, 0.3 is the weight corresponding to the historical weave density evaluation value, and 0.5 is the weight corresponding to the historical complexity evaluation value. The arbitrary historical sewing evaluation value is obtained by summing up the product of each evaluation value and its corresponding weight, wherein a possible example of any historical sewing evaluation value is Pi=0.2×1.6+0.3×1.3+0.5×1=1.21, i=1, 2…n, n is an integer. In this embodiment, the preset weights are set to 0.2, 0.3 and 0.5 respectively. The actual weights and calculation methods may vary according to specific requirements. For example, invite field experts or conduct user surveys to understand their emphasis on size, weave density and complexity, and set weights based on feedback.

[0105] Specifically, the standard size is the average of several historical sizes of several historical sewn objects;

[0106] The standard weaving density is the average of several historical weaving densities of several historical sewn articles;

[0107] The standard complexity is the average of several pattern complexities of several historical sewn objects;

[0108] The historical dimension evaluation value Ci is 1 when the historical dimension is less than or equal to the standard dimension, and is Ci=e^(historical dimension / standard dimension-1) when the historical dimension is greater than the standard dimension. For example, when the standard dimension is 100cm×100cm and the historical dimension is 120cm×120cm, the historical dimension evaluation value Ci is Ci=e^((120×120) / (100×100)-1)≈1.6. When the historical dimension is greater than the standard dimension, the historical dimension evaluation value is positively correlated with the ratio of the historical dimension to the standard dimension, which means that the larger the historical dimension is than the standard dimension, the greater the manufacturing difficulty and the greater the historical dimension evaluation value. When the historical dimension is less than or equal to the standard dimension, the historical dimension evaluation value is 1, so as to subtract 1 from the formula for determining the historical dimension evaluation value Ci when the historical dimension is greater than the standard dimension.

[0109] The historical weaving density evaluation value Zi is 1 when the historical weaving density is less than or equal to the standard weaving density. When the historical weaving density is greater than the standard weaving density, the historical weaving density evaluation value Zi is Zi=e^(historical weaving density / standard weaving density-1). For example, when the standard weaving density is 200 needles / inch and the historical weaving density is 250 needles / inch, then Zi=e^(250 / 200-1)≈1.3. When the historical weaving density is greater than the standard weaving density, the historical weaving density evaluation value is positively correlated with the ratio of the historical weaving density to the standard weaving density, which means that the greater the historical weaving density is than the standard weaving density, the greater the manufacturing difficulty is, and the greater the historical weaving density evaluation value is.

[0110] The historical complexity evaluation value Fi is 1 when the historical pattern complexity is less than or equal to the standard complexity, and is the historical pattern complexity divided by the standard complexity when the historical pattern complexity is greater than the standard complexity.

[0111] Specifically, the embodiment of the present invention provides a multi-angle evaluation method by combining the evaluation values ​​of size, weaving density and pattern complexity, which helps to comprehensively consider all key factors affecting working hours and costs, so as to obtain more accurate production efficiency and quality indicators. By presetting weights, the importance of each factor can be flexibly adjusted according to actual conditions such as the specific needs of the enterprise or industry standards, so that the evaluation value calculation results are comprehensive and accurate.

[0112] Specifically, the step of constructing a work time model according to the historical sewing evaluation values ​​and the corresponding historical work time includes:

[0113] Draw a trend change graph based on the historical sewing evaluation values ​​and the historical operation hours;

[0114] Analyze the trend of graphic changes in the trend change graph, and determine a regression model according to the trend analysis result;

[0115] The regression model is trained by using a plurality of the historical sewing evaluation values ​​and a plurality of the historical operation hours to obtain the operation hour model.

[0116] Specifically, the regression models determined based on the trend analysis results include:

[0117] If the trend analysis result is a linear relationship, the regression model selects a linear regression model;

[0118] If the trend analysis result is a nonlinear relationship, the regression model selects a polynomial regression model.

[0119] Specifically, if the graph change trend is a straight line or can be fitted by a straight line, the trend analysis result is a linear relationship. If the graph change trend is a curve shape (such as a parabola, exponential curve, logarithmic curve, etc.) or the data cannot be fitted by a straight line, the trend analysis result is nonlinear.

[0120] Specifically, the embodiment of the present invention uses a trend change graph to intuitively observe the relationship and trend between historical sewing evaluation values ​​and historical operation hours, which helps to understand the data pattern and provide clues for selecting a suitable regression model. The regression model is determined based on the trend analysis results, ensuring that the model can accurately reflect the dependency between the evaluation value and the working hours, avoiding the mismatch problem caused by using a single or general model, and using actual historical data to train the regression model so that the model can learn the specific relationship between parameters specific to sewing operations and working hours, thereby improving prediction accuracy. As more data is accumulated, the model is continuously retrained and optimized to adapt to changes in production processes and the introduction of new products, ensuring that the model is always kept up to date, thereby effectively improving the accuracy of operation hour calculations.

[0121] Specifically, the step of obtaining the actual weave density through the initial weave density and the humidity calibration coefficient includes:

[0122] placing the target sewn object on the weight sensor to obtain a plurality of measured weight values;

[0123] Taking the average of the several measured weight values ​​obtained as the actual weight value;

[0124] Recording the ratio of the actual weight value to the area of ​​the target sewn object as the initial weaving density;

[0125] The actual humidity value of the environment is detected and measured in real time through the humidity sensor;

[0126] Comparing the actual humidity value with the standard humidity value, and when the actual humidity value is greater than the standard humidity value, calculating a humidity calibration coefficient;

[0127] The initial weave density is calibrated according to the humidity calibration coefficient to obtain an actual weave density.

[0128] Specifically, placing the target sewn object on the weight sensor and obtaining a number of measured weight values ​​includes:

[0129] Placing the target sewn object on the weight sensor so that the target sewn object completely covers the sensing area of ​​the weight sensor;

[0130] The weight sensor is started to measure, and the measured weight value is read from the weight sensor and recorded;

[0131] The target sewn object is removed from the weight sensor, and the measurement and recording are performed again according to the above steps. The operation is repeated ten times to obtain a number of measured weight values.

[0132] The standard humidity value is set at 65%. When the humidity in the air is higher than 65%, the sewn fabric may absorb more moisture and increase its weight.

[0133] The humidity calibration coefficient is the absolute value of the difference between the actual humidity value and the standard humidity value divided by the standard humidity value;

[0134] The actual weave density is the humidity calibration coefficient multiplied by the initial weave density.

[0135] Specifically, the embodiment of the present invention reduces the influence of single measurement error by measuring multiple times and calculating the average value, obtains a more accurate actual weight value, uses the actual area of ​​the target sewn product to calculate the initial weaving density, ensures that the calculation of the weaving density is based on the actual physical size, and improves the accuracy of the overall evaluation. Humidity is an important environmental factor affecting the weight of sewn products. By real-time detection and considering the influence of humidity, the deviation of weaving density calculation caused by environmental changes is calibrated, the actual humidity is compared with the standard humidity, and the humidity calibration coefficient is calculated when necessary, ensuring that the weaving density values ​​obtained under different environmental conditions are effectively compared with the values ​​under the standard state, so that the weaving density calculation result is accurate. Through precise weaving density measurement, an accurate data basis is provided for the subsequent updating of the operation time model, thereby effectively improving the accuracy of the operation time calculation.

[0136] Specifically, the target sewing evaluation value Q of the target sewn object in the embodiment of the present invention is Q=0.2×R+0.3×S+0.5×T, where R is the actual size evaluation value, S is the actual weaving density evaluation value, T is the actual complexity evaluation value, and the preset weight is the same as the historical sewing evaluation value weight.

[0137] Specifically, the actual size is compared with the standard size, and an actual size evaluation value is obtained according to the comparison result;

[0138] Comparing the actual weaving density with the standard weaving density, and obtaining an actual weaving density evaluation value according to the comparison result;

[0139] Comparing the actual pattern complexity with the standard complexity, and obtaining an actual complexity evaluation value according to the comparison result;

[0140] Calculate the actual sewing evaluation value according to the actual size evaluation value, the actual weaving density evaluation value, the actual complexity evaluation value and the preset weight to obtain the actual sewing evaluation value;

[0141] The actual size evaluation value, the actual weave density evaluation value and the actual complexity evaluation value correspond one-to-one with the calculation methods of the historical size evaluation value, the historical weave density evaluation value and the historical complexity evaluation value mentioned above, and will not be elaborated here.

[0142] Specifically, the step of collecting sewing parameters during the sewing process of the target sewn object includes:

[0143] The moving value of the mark on the target sewing object is collected in real time by a photoelectric sensor, and the real-time moving speed of the target sewing object at adjacent moments is calculated by a plurality of the moving values;

[0144] The pressure sensor is used to detect the real-time skipped stitch signal during sewing;

[0145] The distance sensor detects the distance between the sewing needle and the target sewing object in real time to obtain the real-time distance value;

[0146] The real-time moving speed, the real-time skipped stitch signal and the real-time distance value are used as the sewing parameters.

[0147] Specifically, the real-time detection of the skipped stitch signal during sewing by the pressure sensor includes:

[0148] Acquire a plurality of pressure values ​​through the pressure sensor;

[0149] Draw a pressure variation graph based on several pressure values;

[0150] Analyze the pressure change graph, if the difference between the pressure values ​​at adjacent moments is greater than the preset difference, a needle skipping situation occurs;

[0151] The preset difference is 1.

[0152] See also Figure 3 As shown, the step of correcting the target operation time according to the sewing parameters includes:

[0153] Step S210, obtaining the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values;

[0154] Step S220, obtaining the skipping needle duration according to the plurality of real-time skipping needle signals;

[0155] Step S230, obtaining a stop duration according to the real-time moving speeds;

[0156] Step S240, calculating a correction coefficient according to the skipped stitch duration, the stop duration and the sewing duration;

[0157] Step S250, correcting the target operation time according to the correction coefficient to obtain the actual operation time.

[0158] Specifically, obtaining the needle skipping duration according to the real-time needle skipping signals includes: obtaining the time when the needle skipping signal appears as the initial time of needle skipping;

[0159] The next sewing moment adjacent to the initial moment of the skipped stitch is obtained as the end moment of the skipped stitch;

[0160] The skipping time length is obtained according to the skipping initial time and the skipping end time.

[0161] Specifically, obtaining the stop duration according to the real-time moving speeds includes:

[0162] The moment when the real-time moving speed is 0 for the first time is taken as the initial stop moment;

[0163] The time adjacent to the initial stop time and the time when the real-time moving speed is greater than 0 and the moving speed trend increases is obtained as the stop end time;

[0164] The stop duration is acquired according to the stop initial time and the stop end time.

[0165] Specifically, the correction coefficient is the sum of the skipped stitch duration and the stop duration divided by the sum of the skipped stitch duration, the stop duration and the sewing duration;

[0166] The actual operation time is the correction coefficient multiplied by the target operation time.

[0167] Specifically, the embodiments of the present invention accurately measure and correct working hours to identify efficiency loss points in production and then take measures to improve them. Skipped stitches are one of the important factors affecting the quality of sewn products. Quickly responding to and recording the time when skipped stitches occur helps to adjust machine settings in a timely manner or take other necessary measures to ensure the quality of the final product. The calculation of the correction coefficient provides the ability to make decisions based on actual production data, which helps to accurately plan production and allocate resources.

[0168] Specifically, the step of obtaining the sewing time between the sewing needle and the target sewing object according to the real-time distance values ​​includes:

[0169] Draw a distance variation diagram of the sewing process of the target sewing object according to the plurality of real-time distance values;

[0170] Analyze the distance change graph and calculate the actual time difference corresponding to the points on adjacent horizontal axes;

[0171] The actual time difference is compared with the preset time difference, and a number of actual time differences whose actual time difference is less than or equal to the preset time difference are summed to obtain the sewing time length.

[0172] See also Figure 4 As shown, an embodiment of the present invention further provides a system for calculating working hours of an operation, the system comprising:

[0173] A model building module 10 is used to collect a number of historical parameter groups of historical sewing objects and their corresponding historical operation hours, calculate the historical parameter evaluation value of each parameter in any of the historical parameter groups, calculate the historical sewing evaluation value according to the historical parameter evaluation values, and build an operation hour model according to the historical sewing evaluation values ​​and the corresponding historical operation hours;

[0174] A data acquisition module 20, which is connected to the model building module 10, and is used to collect the actual size, actual weaving density and actual pattern complexity of the target sewing object, wherein the actual weaving density is obtained by the initial weaving density and the humidity calibration coefficient; the data acquisition module is also used to obtain a target sewing evaluation value according to a calculation result obtained based on the actual size, the actual weaving density and the pattern complexity, and to obtain a target working time by inputting the target sewing evaluation value into the working time model;

[0175] A correction module 30, connected to the data acquisition module 20, for collecting sewing parameters during the sewing process of the target sewing object, and for determining actual operation hours according to a correction coefficient obtained based on the sewing parameters;

[0176] An updating module 40, which is connected to the correction module 30, is used to update the operation time model according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, and the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range;

[0177] The evaluation module 50 is connected to the updating module 40 and is used to use the operation time optimization model to determine the evaluation operation time of any sewn object to be evaluated, so as to adjust the production plan according to the evaluation operation time.

[0178] Specifically, the system of the operation working hours calculation method provided in the embodiment of the present invention can execute the above-mentioned operation working hours calculation method and achieve the same technical effect, which will not be repeated here.

[0179] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0180] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating working hours, characterized in that: include: Collecting a number of historical parameter groups of historical sewn objects and the historical operation hours corresponding to each historical sewn object, calculating the historical parameter evaluation value of each parameter in any historical parameter group, calculating the historical sewing evaluation value according to the historical parameter evaluation values, and constructing the operation hour model according to the historical sewing evaluation values ​​and the corresponding historical operation hours; The step of constructing a work time model according to the historical sewing evaluation values ​​and the corresponding historical work time comprises: Draw a trend change graph based on the historical sewing evaluation values ​​and the historical operation hours; Analyze the trend of graphic changes in the trend change graph, and determine a regression model according to the trend analysis result; The regression model is trained by using a plurality of the historical sewing evaluation values ​​and a plurality of the historical operation hours to obtain the operation hour model; Collecting the actual size, actual weaving density and actual pattern complexity of the target sewing object, respectively calculating corresponding evaluation values ​​according to the actual size, the actual weaving density and the pattern complexity, obtaining a target sewing evaluation value according to the calculation results, and inputting the target sewing evaluation value into the operation time model to obtain the target operation time, wherein the actual weaving density is obtained by the initial weaving density and the humidity calibration coefficient, and the actual pattern complexity is obtained by the actual pattern area ratio and the actual number of colors; Collecting sewing parameters of the target sewing object during the sewing process, calculating a correction coefficient according to the sewing parameters, and correcting the target operation time by the correction coefficient to obtain actual operation time; The step of correcting the target operation time according to the sewing parameters comprises: Acquire the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values; Obtaining the skipping needle duration according to the real-time skipping needle signals; Obtaining the stopping time according to the real-time moving speeds; Calculating a correction coefficient according to the skipped stitch duration, the stop duration and the sewing duration; Correct the target operation time according to the correction coefficient to obtain the actual operation time; The operation time model is updated according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, wherein the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range; The operation time optimization model is used to determine the estimated operation time of any sewn product to be evaluated, so as to adjust the production plan according to the estimated operation time.

2. The method for calculating operation hours according to claim 1, characterized in that: The step of collecting a plurality of historical parameter groups of historical sewn objects and their corresponding historical operation hours comprises: Measuring the length and width of the historical sewn object to obtain the historical size of the historical sewn object; Acquiring a historical weight value of the historical sewn object according to a weight sensor, and dividing the historical weight value by the area value of the historical sewn object to acquire a historical weaving density; By analyzing the historical area and the number of historical colors of the pattern on the historical sewn object, the historical pattern complexity is obtained according to the analysis result; The historical size, the historical weave density and the historical pattern complexity are used as the historical parameter group; The historical start time and the historical end time of the historical sewing object are obtained, and the historical operation hours are obtained according to the historical start time and the historical end time.

3. The method for calculating operation hours according to claim 2, characterized in that: The historical pattern complexity obtained based on the analysis results includes: Acquiring an initial image of the historical sewn object by an image acquisition device; Processing the initial image by an edge detection algorithm to obtain pattern edges; Calculating the ratio of the area included in the pattern edge to the total area of ​​the initial image, and recording the obtained ratio as the historical pattern area ratio; Identify the historical sewing objects by using a color sensor to obtain the number of historical colors; The product of the historical pattern area ratio and the number of historical colors is recorded as the historical pattern complexity.

4. The method for calculating operation hours according to claim 3, characterized in that: The step of calculating the historical sewing evaluation value according to any of the historical parameter groups comprises: Comparing the historical dimensions with the standard dimensions, and obtaining a historical dimension evaluation value according to the comparison result; Comparing the historical weaving density with the standard weaving density, and obtaining a historical weaving density evaluation value according to the comparison result; Comparing the historical pattern complexity with the standard complexity, and obtaining a historical complexity evaluation value according to the comparison result; The historical sewing evaluation value is obtained by performing calculation according to the historical size evaluation value, the historical weaving density evaluation value, the historical complexity evaluation value and preset weights.

5. The method for calculating operation hours according to claim 4, characterized in that: The step of obtaining the actual weave density through the initial weave density and the humidity calibration coefficient comprises: placing the target sewn object on the weight sensor to obtain a plurality of measured weight values; Taking the average of the several measured weight values ​​obtained as the actual weight value; Recording the ratio of the actual weight value to the area of ​​the target sewn object as the initial weaving density; The actual humidity value of the environment is detected and measured in real time through the humidity sensor; Comparing the actual humidity value with the standard humidity value, and when the actual humidity value is greater than the standard humidity value, calculating a humidity calibration coefficient; The initial weave density is calibrated according to the humidity calibration coefficient to obtain an actual weave density.

6. The method for calculating operation hours according to claim 5, characterized in that: The step of collecting sewing parameters during the sewing process of the target sewn object comprises: The moving value of the mark on the target sewing object is collected in real time by a photoelectric sensor, and the real-time moving speed of the target sewing object at adjacent moments is calculated by a plurality of the moving values; The pressure sensor is used to detect the real-time skipped stitch signal during sewing; The distance sensor detects the distance between the sewing needle and the target sewing object in real time to obtain the real-time distance value; The real-time moving speed, the real-time skipped stitch signal and the real-time distance value are used as the sewing parameters.

7. The method for calculating operation hours according to claim 6, characterized in that: The step of obtaining the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values ​​comprises: Draw a distance variation diagram of the sewing process of the target sewing object according to the plurality of real-time distance values; Analyze the distance change graph and calculate the actual time difference corresponding to the points on adjacent horizontal axes; The actual time difference is compared with the preset time difference, and a number of actual time differences whose actual time difference is less than or equal to the preset time difference are summed to obtain the sewing time length.

8. An application as claimed in claim 1 7. The system of the method for calculating working hours according to any one of claims 7, characterized in that: include: A model building module, for collecting a number of historical parameter groups of historical sewing objects and their corresponding historical operation hours, calculating a historical parameter evaluation value of each parameter in any of the historical parameter groups, calculating a historical sewing evaluation value based on the historical parameter evaluation values, and building an operation hour model based on the historical sewing evaluation values ​​and the corresponding historical operation hours; The model building module is used to draw a trend change graph according to the historical sewing evaluation values ​​and the historical operation hours, analyze the trend of the graph change in the trend change graph, determine the regression model according to the trend analysis result, and train the regression model through the historical sewing evaluation values ​​and the historical operation hours to obtain the operation hour model; A data acquisition module, connected to the model building module, for collecting the actual size, actual weave density and actual pattern complexity of the target sewn object, wherein the actual weave density is obtained through the initial weave density and the humidity calibration coefficient; The data acquisition module is also used to obtain a target sewing evaluation value according to a calculation result obtained based on the actual size, the actual weaving density and the pattern complexity, and to obtain a target operation time by inputting the target sewing evaluation value into the operation time model; a correction module connected to the data acquisition module, for acquiring sewing parameters during the sewing process of the target sewing object, and for determining actual operation hours according to a correction coefficient obtained based on the sewing parameters; The correction module is used to obtain the sewing time between the sewing needle and the target sewing object according to the plurality of real-time distance values, obtain the skipping time according to the plurality of real-time skipping signals, obtain the stop time according to the plurality of real-time moving speeds, calculate the correction coefficient according to the skipping time, the stop time and the sewing time, and correct the target operation time according to the correction coefficient to obtain the actual operation time; An updating module, connected to the correction module, for updating the operation time model according to the actual operation time and the target sewing evaluation value to obtain an operation time optimization model, wherein the operation time optimization model can make the error range between the target operation time and the actual operation time within a target range; An evaluation module is connected to the updating module and is used to use the operation time optimization model to determine the evaluation operation time of any sewn object to be evaluated, so as to adjust the production plan according to the evaluation operation time.

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