Process working hour splitting method and system, electronic equipment and storage medium

Abstract

The invention provides a working procedure and working hour splitting method and system, electronic equipment and a storage medium. The working procedure and working hour splitting method comprises the steps of obtaining event data of Internet of Things sewing equipment; based on the event data, acquiring a plurality of relatively long downtime intervals in a specific time period; for each long downtime interval, event data of finishing sewing of the last garment by the employee is screened out, and an event data set is formed; the event data set comprises event data of motor starting, motor stopping and preset thread trimming times; based on all the event data sets, obtaining data of working hours of the splitting process; and based on the data of the working hours of the splitting process, splitting event data of preset thread trimming times to obtain the working hours of the employees for completing a single process. According to the method, refined splitting of single-process working hours can be realized, and the problem of out-of-order process data caused by rework or low proficiency of staff is solved. In addition, only the number of times of thread trimming needed to be split needs to be provided, and the difficulty of scheme application is reduced.

Description

Technical Field

[0001] This application belongs to the technical field of sewing, and specifically relates to a method, system, electronic device and storage medium for splitting process working hours. Background Art

[0002] With the application of Internet of Things sewing equipment, working hour statistics technology has developed towards a more intelligent and refined direction. Traditional stopwatch timing has gradually been replaced by hanging timing methods. However, the current hanging timing methods still have some defects, such as being unable to obtain the specific working hours of employees for completing a single process.

[0003] In actual garment production management, the working hours of combined processes can help determine the bottleneck points and balance of the production line. While the working hours of individual processes can provide guidance for the reasonable allocation of processes, thereby improving the overall production efficiency.

[0004] Therefore, it is necessary to provide a method for splitting process working hours based on Internet of Things sewing equipment to achieve refined splitting of process working hours and bring innovation to garment management. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, electronic device and storage medium for splitting process working hours, which is used to solve the problem that the prior art cannot perform refined splitting of process working hours.

[0006] In a first aspect, this application provides a method for splitting process working hours, including:

[0007] Obtain event data of Internet of Things sewing equipment; the event data includes a timestamp, an event type and event parameters;

[0008] Based on the event data, obtain multiple long downtime intervals within a specific time period; for each of the long downtime intervals, filter out the event data of the last piece of garment sewn by the employee before the long downtime interval to form an event data group; the event data group includes event data with the event type of motor start, event data with the event type of motor stop, and n pieces of event data with the event type of thread cutting, where n is the total number of thread cutting times required by the employee to complete a preset combined process pre-input by the administrator;

[0009] Based on all the event data groups, obtain data for splitting process working hours; the data for splitting process working hours is related to a specific event data group;

[0010] Based on the data for splitting process working hours, split the n pieces of event data with the event type of thread cutting in the specific event data group to obtain the working hours of the employee for completing a single process.

[0011] In one implementation of the first aspect, obtaining multiple long downtime intervals of an employee based on the event data includes:

[0012] Obtaining the timestamps t2 of all event data with the event type of motor start and the timestamps within the specific time period;

[0013] For each of the timestamps t2, obtaining the timestamp t6 of the event data that is closest in time to the timestamp t2 before the timestamp t2 and has the event type of thread cutting;

[0014] Calculating the difference between the timestamp t2 and the timestamp t6;

[0015] Taking the difference as a downtime interval of the employee within the specific time period;

[0016] Sorting all the downtime intervals;

[0017] Determining a preset number of long downtime intervals according to the sorting result.

[0018] In one implementation of the first aspect, obtaining the data of the splitting process working hours based on all the event data groups includes:

[0019] Based on each event data group, calculating the actual working hours and the sum of the actual number of stitches required for the employee to complete the sewing of the last piece of clothing, and forming an [working hours, sum of stitches] array;

[0020] Using the standard working hours and the standard sum of stitches to filter out the [working hours, sum of stitches] arrays that meet specific conditions as the data of the splitting process working hours, and the event data group corresponding to the [working hours, sum of stitches] array that meets the specific conditions is the specific event data group.

[0021] In one implementation of the first aspect, calculating the actual working hours and the sum of the actual number of stitches required for the employee to complete the sewing of the last piece of clothing based on each event data group, and forming an [working hours, sum of stitches] array includes:

[0022] For each event data group, obtaining the timestamps of n event data with the event type of thread cutting in the event data group;

[0023] Calculating the difference between the nth timestamp and the first timestamp to obtain the actual working hours required for the employee to complete the sewing of the last piece of clothing;

[0024] Obtaining the event parameters of all event data with the event type of motor stop;

[0025] Sum the event parameters of all event data with the event type of motor stop to obtain the actual total number of stitches required for the employee to complete the last piece of clothing sewing.

[0026] In an implementation manner of the first aspect, it further includes obtaining the standard working hours and the standard total number of stitches required for the employee to complete the sewing of one piece of clothing:

[0027] Based on the total number of thread trimming times n, divide the historical event data to obtain multiple historical event data groups; each of the historical event data groups is a set of historical event data for the employee to complete the sewing of one piece of clothing;

[0028] Based on the historical event data groups, calculate the historical working hours and the historical total number of stitches for the employee to complete the sewing of one piece of clothing;

[0029] Based on the normal distribution, screen out the stable historical working hours and the stable historical total number of stitches;

[0030] Based on the stable historical working hours, calculate the root mean square of working hours and the standard deviation of working hours;

[0031] Based on the stable historical total number of stitches, calculate the root mean square of the total number of stitches and the standard deviation of the total number of stitches.

[0032] In an implementation manner of the first aspect, the [working hours, total number of stitches] arrays that meet specific conditions screened out at least satisfy:

[0033] (Root mean square of working hours - Standard deviation of working hours) < Working hours < (Root mean square of working hours + Standard deviation of working hours) and

[0034] (Root mean square of the total number of stitches - Standard deviation of the total number of stitches) < Total number of stitches < (Root mean square of the total number of stitches + Standard deviation of the total number of stitches).

[0035] In an implementation manner of the first aspect, based on the data of the split working hours of each process, split the n event data with the event type of thread trimming in the specific event data group to obtain the working hours for the employee to complete a single process, including:

[0036] Obtain the number of thread trimming times for a single process pre-entered by the administrator, and the sum of the number of thread trimming times for a single process is the total number of thread trimming times;

[0037] Based on the number of thread trimming times for a single process, divide the n event data with the event type of thread trimming in the specific event data group to obtain single-process event groups corresponding to the number of single processes;

[0038] Calculate the difference between the last timestamp and the first timestamp in each single-process event group to obtain the working hours for the employee to complete a single process.

[0039] In an implementation of the first aspect, it further includes: if the number of the split process working hours data is greater than 1, then screen the split process working hours, and screen out the item with the smallest sum of the split process working hours.

[0040] In a second aspect, the present application provides a system for splitting process working hours, including:

[0041] An event data acquisition module, configured to acquire event data of an Internet of Things sewing device; the event data includes a timestamp, an event type, and event parameters;

[0042] An event data screening module, configured to, based on the event data, acquire a plurality of long downtime intervals within a specific time period; for each of the long downtime intervals, screen out the event data of the last piece of clothing sewn by an employee before the long downtime interval to form an event data group; the event data group includes event data with an event type of motor start, event data with an event type of motor stop, and n pieces of event data with the event type of thread cutting, where n is the total number of thread cutting times required for an employee to complete a preset combined process pre-input by an administrator;

[0043] A working hour allocation data acquisition module, configured to, based on all the event data groups, acquire data of split process working hours; the data of the split process working hours is related to a specific event data group;

[0044] A single process working hour splitting module, configured to, based on the data of the split process working hours, split the n pieces of event data with the event type of thread cutting in the specific event data group to obtain the working hours for an employee to complete a single process.

[0045] In a third aspect, the present application provides an electronic device, including: a processor and a memory;

[0046] The memory is used to store a computer program;

[0047] The processor is configured to execute the computer program stored in the memory so that the electronic device executes the method described in any one of the above.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method described in any one of the above.

[0049] As described above, the method, system, electronic device, and storage medium for splitting process working hours according to the present application have the following beneficial effects:

[0050] (1) It can achieve refined splitting of single process working hours;

[0051] (2) It solves the problem of disordered process data caused by employee rework or low proficiency;

[0052] (3) Only the number of thread cutting times that need to be split needs to be provided, without additional data input, thus reducing the difficulty of applying the solution. Description of the Drawings

[0053] Figure 1 It shows a flowchart of the process working hour splitting method described in the present application in an embodiment.

[0054] Figure 2 It shows a schematic diagram of event data of the process working hour splitting method described in the present application in an embodiment.

[0055] Figure 3 It shows a schematic diagram of actual sewing data of the process working hour splitting method described in the present application in an embodiment.

[0056] Figure 4 It shows a schematic diagram of time stamps t2 corresponding to 10 relatively long downtime intervals respectively in an embodiment of the process working hour splitting method described in the present application.

[0057] Figure 5 It shows a schematic diagram of the split process working hours in an embodiment of the process working hour splitting method described in the present application.

[0058] Figure 6 It shows a schematic diagram of the split process working hours in another embodiment of the process working hour splitting method described in the present application.

[0059] Figure 7 It shows a schematic structural diagram of the process working hour splitting system described in the present application in an embodiment.

[0060] Figure 8 It shows a schematic structural diagram of the electronic device described in the present application in an embodiment.

[0061] Description of Component Numbers

[0062] 71 Event data acquisition module

[0063] 72 Event data screening module

[0064] 73 Man-hour allocation data acquisition module

[0065] 74 Single-process working hour splitting module

[0066] 81 Processor

[0067] 82 Memory Detailed Description of the Invention

[0068] The following describes the implementation modes of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation modes. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0069] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0070] In addition, in the present application, descriptions such as "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those skilled in the art can implement them. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present application.

[0071] In an actual sewing scenario, multiple employees are involved in sewing a piece of clothing. To achieve production line balance, multiple processes may be assigned to each employee. For example, sewing a piece of clothing includes N processes. Suppose the administrator assigns the combined process formed by process 1 and process 2 to employee 1, where process 1 requires 2 thread cutting tasks to be completed, and process 2 requires 2 thread cutting tasks to be completed. Therefore, employee 1 needs to perform 2 / 2 thread cuttings to sew a piece of clothing, that is, a total of 4 thread cuttings, where 2 / 2 is the number of thread cuttings for a single process, and 4 is the total number of thread cuttings. Usually, the completion time of the employee is recorded by pressing the timing beat device after the employee completes 4 thread cutting tasks. However, this recorded time can only reflect the combined time for the employee to complete process 1 and process 2, and it is impossible to obtain the specific time for the employee to complete one of the processes (process 1 or process 2).

[0072] If the total time for Employee 1 to complete Process 1 and Process 2 is 120 seconds, which is much higher than that of other workers, this may indicate a bottleneck in the production line. To maintain the balance of the production line, the administrator may need to reallocate some processes to other workers. However, to do this, it is first necessary to understand the specific working hours of these two processes respectively in order to decide which process is most suitable to be assigned to which worker.

[0073] The following embodiments of this application provide a method, system, electronic device, and storage medium for splitting process working hours, which are applied to the clothing production scenario and can solve the problem that the prior art cannot perform refined splitting of process working hours. The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings in the embodiments of this application.

[0074] Please refer to Figure 1 , which shows a flowchart of the method for splitting process working hours according to this application in an embodiment.

[0075] As Figure 1 shown, this embodiment provides a method for splitting process working hours, including the following steps S100 to S400.

[0076] Step S100: Obtain the event data of the Internet of Things sewing equipment.

[0077] In an embodiment of this application, the event data is provided by the acquisition module of the Internet of Things sewing equipment.

[0078] Please refer to Figure 2 , which shows a schematic diagram of the event data in an embodiment of the method for splitting process working hours according to this application.

[0079] As Figure 2 shown, the event data includes a timestamp, an event type, and event parameters, separated by commas.

[0080] Specifically, the recording format of the timestamp is "yyyy-MM-dd HH:mm:ss.SSS", where "yyyy-MM-dd" represents the year, month, and day, "HH:mm:ss" represents the hour, minute, and second, and ".SSS" represents milliseconds.

[0081] The numerical range of the event type is 0 to 10, respectively representing various actions or states of the Internet of Things sewing equipment. For example, 0 represents the equipment shutdown, 1 represents the equipment startup, 2 represents the motor startup, 3 represents the motor stop, 4 represents the presser foot lift, 5 represents the presser foot lower, 6 represents the thread cutting (front cutting for overlock sewing), 7 represents the rear thread cutting (applicable to overlock sewing), 8 represents the electronic control counts one piece, 9 represents the reinforcement stitch, and 10 represents the stitch density adjustment.

[0082] Event parameters correspond one-to-one with event types. For example, when the value of the event type is 0, the event parameter represents the working time of this operation (unit: second); when the value of the event type is 1, the event parameter represents the stitch density value at startup; when the value of the event type is 3, the event parameter represents the number of stitches generated from motor startup to motor shutdown; when the value of the event type is 9, the event parameter being 101 represents 1 time of front tacking, the equipment parameter being 102 represents 2 times of front tacking, the event parameter being 201 represents 1 time of back tacking, the event parameter being 202 represents 2 times of back tacking, the event parameter being 301 represents 1 time of manual reverse stitching, the event parameter being 42 represents W-stitching; when the value of the event type is 10, the event parameter represents the adjusted stitch density value.

[0083] Please refer to Figure 3 , which shows a schematic diagram of the actual sewing data in an embodiment of the process working hour splitting method described in this application.

[0084] As Figure 3 The figure shows that during the actual sewing process, employees will perform a series of operations using the Internet of Things sewing equipment, such as lifting the presser foot, lowering the presser foot, and tacking.

[0085] It should be noted that when an employee sews clothes for a period of time, there may be multiple motor startups and shutdowns in the middle. Therefore, the number of stitches generated by several motor startups and shutdowns needs to be added together to obtain the total number of stitches for that period.

[0086]

[0087] Step S200: Based on the event data, obtain multiple long downtime intervals within a specific time period. For each of the long downtime intervals, filter out the event data of the last piece of clothing sewn by the employee before the long downtime interval to form an event data group.

[0088] Specifically, the event data group includes event data of the event type of motor startup, event data of the event type of motor shutdown, and n pieces of event data of the event type of thread cutting, where n is the total number of thread cutting times required by the administrator for the employee to complete the preset combined process.

[0088] When splitting the working hours of a single process, problems may occur where the process data is out of order due to employee rework or low proficiency. For example, assume that employee A needs 2 / 2 times (4 times) of thread cutting to complete the sewing of a piece of clothing, and the characteristics of each thread cutting are A, B, C, and D respectively. Then the ideal sewing data of employee A is ABCD / ABCD / ABCD. When splitting according to 2 / 2 times of thread cutting, the working hours obtained are AB / CD. However, if employee A has rework or other situations, and the characteristics of rework are recorded as -1, then the actual sewing data of employee A may be ABCD / AB-1C / DABC / DABC. When splitting and counting the data of employee A for a whole day or a period of time, the combination with the highest frequency after splitting may be DA / BC, while it should actually be AB / CD, which does not match the actual situation.

[0089] It should be noted that the above example uses the rework of a single thread cutting for illustration. The actual sewing will be more complex, but the problems that occur are the same as the above problems.

[0090] Combined with the actual sewing scenario, employees generally complete the sewing of the current piece of clothing before a long downtime. Therefore, this application selects the event data of the employee completing the sewing of the last piece of clothing before a long downtime as the research object, which can effectively avoid the situation where the combination split after disorder does not match the actual combination.

[0091] In an embodiment of the present application, based on the event data, obtaining multiple long downtime intervals of an employee within a specific time period includes steps S201 to S206.

[0092] Step S201: Obtain the time stamps t2 of all event data with the event type of motor start and the time stamps within the specific time period.

[0093] Step S202: For each of the time stamps t2, obtain the time stamp t6 of the event data with the event type of thread cutting that is closest in time to the time stamp t2 and before the time stamp t2.

[0094] Step S203: Calculate the difference between the time stamp t2 and the time stamp t6.

[0095] Step S204: Use the difference as a downtime interval of the employee within the specific time period.

[0096] Step S205: Sort all the downtime intervals.

[0097] Step S206: Determine a preset number of long downtime intervals according to the sorting result.

[0098] It should be noted that the specific time period in this embodiment can be set according to actual needs. For example, it can be any whole day or any time period within a day. When sorting all the downtime intervals, descending or ascending order can be adopted. The number of the selected longer downtime intervals in this application should not be too large or too small. Too large will increase the calculation amount, and too small will affect the accuracy of the result.

[0099] Please refer to Figure 4 , which shows a schematic diagram of the time stamps t2 corresponding to 10 longer downtime intervals in an embodiment of the process working hour splitting method described in this application.

[0100] Step S300: Based on all the event data groups, obtain the data for splitting the process working hours; the data for splitting the process working hours is related to a specific event data group.

[0101] In an embodiment of this application, obtaining the data for splitting the process working hours based on all the event data groups includes:

[0102] Step S301: Based on each event data group, calculate the actual working hours and the sum of the actual number of stitches required for an employee to complete the sewing of the last piece of clothing, and form an [actual working hours, sum of number of stitches] array.

[0103] Specifically, calculating the actual working hours and the sum of the actual number of stitches required for an employee to complete the sewing of the last piece of clothing based on each event data group and forming an [actual working hours, sum of number of stitches] array includes: for each event data group, obtain the time stamps of n event data with the event type of thread cutting in the event data group; calculate the difference between the nth time stamp and the first time stamp to obtain the actual working hours required for an employee to complete the sewing of the last piece of clothing; obtain the event parameters of all the event data with the event type of motor stop; sum up the event parameters of all the event data with the event type of motor stop to obtain the sum of the actual number of stitches required for an employee to complete the sewing of the last piece of clothing.

[0104] Specifically, Table 1 shows the corresponding table of the event data group and the [actual working hours, sum of number of stitches] array in an embodiment of the process working hour splitting method of this application. As shown in Table 1, each event data group corresponds to an [actual working hours, sum of number of stitches] array.

[0105] Any event data group includes event data with the event type of motor start, event data with the event type of motor stop, and 4 event data with the event type of thread cutting (not specifically shown in Table 1), and at this time n = 4.

[0106] Table 1. Corresponding table of event data group and [actual working hours, sum of number of stitches] array

[0107] Event data group [Man-hours, number of stitches and] array Event data group 1 [57.69700002670288,236.0] Event data group 2 [107.59899997711182,266.0] Event data group 3 [137.10000014305115,268.0] Event data group 4 [57.99900007247925,275.0] Event data group 5 [128.69700002670288,273.0]

[0108] Further, this embodiment provides a method for splitting the working hours of a process, which further includes obtaining the standard working hours and the standard number of stitches required for an employee to complete the sewing of a piece of clothing.

[0109] In a specific time period, assume that the normal working hours for employee A to complete the sewing of a piece of clothing is 40 seconds. However, sometimes it may be sewn faster, such as 30 seconds before meals; and sometimes it may be slower, such as affected by breaks, and the working hours become 60 seconds. It should be noted that the situations of 30 seconds and 60 seconds are not common, and most of the working hours are distributed near the median value of 40 seconds. Therefore, the working hours for employee A to complete the sewing of a piece of clothing in this specific time period follow a normal distribution. By the same principle, the number of stitches and for employee A to complete the sewing of a piece of clothing in a specific time period also follows a normal distribution.

[0110] In an embodiment of the present application, obtaining the standard working hours and the standard number of stitches required for an employee to complete the sewing of a piece of clothing includes: dividing the historical event data based on the total number of thread trimming times n to obtain a plurality of historical event data groups; each of the historical event data groups is a set of historical event data for an employee to complete the sewing of a piece of clothing; based on the historical event data groups, calculating the historical working hours and the historical number of stitches and for an employee to complete the sewing of a piece of clothing; screening out the stable historical working hours and the stable historical number of stitches and based on the normal distribution; calculating the root mean square of working hours and the standard deviation of working hours based on the stable historical working hours; calculating the root mean square of the number of stitches and and the standard deviation of the number of stitches and based on the stable historical number of stitches and.

[0111] Specifically, Table 2 shows an example of the standard working hours and the standard number of stitches and in an embodiment of the method for splitting the working hours of a process of the present application.

[0112] Table 2. Example of standard working hours and standard number of stitches and

[0113] Root mean square of man-hours Standard deviation of man-hours Root mean square of total number of stitches Standard deviation of total number of stitches 69.53948714133055 15.540762897502733 274.0142766977606 28.485258431725608

[0114] In this implementation manner, screening out the stable historical working hours and the stable historical number of stitches based on the normal distribution helps to exclude the influence of outliers on the experimental results and ensure the accuracy and reliability of the data. After obtaining the stable historical working hours and the historical number of stitches, further calculating the root mean square of working hours, the standard deviation of working hours, the root mean square of the number of stitches and, and the standard deviation of the number of stitches and can help the enterprise better understand the distribution and fluctuation of the working hours and the number of stitches required for an employee to complete the sewing of a piece of clothing, and provide an important reference basis for formulating production plans and evaluating employee performance.

[0115] Step S302: Using the standard working hours and the standard number of stitches and, screening out the [working hours, number of stitches and] arrays that meet specific conditions as the data for splitting the working hours of the process, and the event data group corresponding to the [working hours, number of stitches and] array that meets specific conditions is the specific event data group.

[0116] In one embodiment of the present application, the [man-hour, number of stitches] arrays that meet the specific conditions satisfy at least (1) and (2):

[0117] (1) (Root mean square of man-hours - Standard deviation of man-hours) < Man-hours < (Root mean square of man-hours + Standard deviation of man-hours);

[0118] (2) (Root mean square of total number of stitches - Standard deviation of total number of stitches) < Total number of stitches < (Root mean square of total number of stitches + Standard deviation of total number of stitches).

[0119] Specifically, based on the standard man-hours and the example of the standard total number of stitches shown in Table 2, it can be calculated that:

[0120] Root mean square of man-hours - Standard deviation of man-hours = 69.53948714133055 - 15.540762897502733 = 53.9987242438;

[0121] Root mean square of man-hours + Standard deviation of man-hours = 69.53948714133055 + 15.540762897502733 = 85.0802500388;

[0122] Root mean square of total number of stitches - Standard deviation of total number of stitches = 274.0142766977606 - 28.485258431725608 = 245.529018266;

[0123] Root mean square of total number of stitches + Standard deviation of total number of stitches = 274.0142766977606 + 28.485258431725608 = 302.499535129.

[0124] Among the 5 [man-hour, total number of stitches] array examples shown in Table 1, only [57.99900007247925, 275.0] meets the above specific conditions (1) and (2). Therefore, [57.99900007247925, 275.0] is determined as the data for splitting the man-hours of the process. At the same time, the event data group 4 corresponding to [57.99900007247925, 275.0] is defined as the specific event data group.

[0125] Step S400: Based on the data of the split process man-hours, split the event data of the n events of the event type of thread cutting in the specific event data group to obtain the man-hours for an employee to complete a single process.

[0126] Specifically, based on the data of the split process working hours, the n event data with the event type of thread cutting in the specific event data group are split. The working hours for an employee to complete a single process include: obtaining the number of thread cuts for a single process pre-input by the administrator, and the sum of the number of thread cuts for a single process is the total number of thread cuts; based on the number of thread cuts for a single process, the n event data with the event type of thread cutting in the specific event data group are segmented to obtain a single-process event group corresponding to the number of single processes; calculate the difference between the last timestamp and the first timestamp in each single-process event group to obtain the working hours for an employee to complete a single process.

[0127] In an embodiment of the present application, there is only one piece of data for splitting process working hours, and at this time there is also only one specific event data group. For example, the specific event data group includes 4 event data with the event type of thread cutting, such as A, B, C, and D. The number of thread cuts for a single process input by the administrator is 2 / 2, and the total number of thread cuts is 4. In this case, for an employee to complete the sewing of a piece of clothing, a combined process composed of two single processes needs to be completed. The first process needs to complete the thread cutting tasks of A and B, and the second process needs to complete the thread cutting tasks of C and D.

[0128] Using the process working hour splitting method provided by the present application, ABCD can be split into AB / CD, where " / " is the delimiter, A and B form a single-process event group, and C and D form another single-process event group. Subsequently, calculate the timestamp difference between B and A = (B timestamp - A timestamp); calculate the timestamp difference between D and C = (D timestamp - C timestamp). Therefore, the split process working hours can be expressed as: [(B timestamp - A timestamp), (D timestamp - C timestamp)].

[0129] In another embodiment of the present application, there are multiple pieces of data for splitting process working hours. At this time, there are also multiple specific event data groups, and the number of specific event data groups is equal to the number of data for splitting process working hours. If the number of data for splitting process working hours is greater than 1, then screen the split process working hours and select the item with the smallest sum of the split process working hours.

[0130] For example, the specific event data group 1 includes 4 event data with the event type of thread cutting, such as A1, B1, C1, and D1; the specific event data group 2 includes 4 event data with the event type of thread cutting, such as A2, B2, C2, and D2; the specific event data group 3 includes 4 event data with the event type of thread cutting, such as A3, B3, C3, and D3. The number of thread cuts for a single process input by the administrator is 2 / 2, and the total number of thread cuts is 4.

[0131] For a specific event data group 1, using the process working hour splitting method provided by this application, A1B1C1D1 can be split into A1B1 / C1D1, where " / " is the delimiter, A1 and B1 form a single process event group, and C1 and D1 form another single process event group. Subsequently, calculate the time stamp difference between B1 and A1 = (B1 time stamp - A1 time stamp); calculate the time stamp difference between D1 and C1 = (D1 time stamp - C1 time stamp).

[0132] Similarly, for a specific event data group 2, using the process working hour splitting method provided by this application, A2B2C2D2 can be split into A2B2 / C2D2, the time stamp difference between B2 and A2 = (B2 time stamp - A2 time stamp); calculate the time stamp difference between D2 and C2 = (D2 time stamp - C2 time stamp).

[0133] For a specific event data group 3, using the process working hour splitting method provided by this application, A3B3C3D3 can be split into A3B3 / C3D3, the time stamp difference between B3 and A3 = (B3 time stamp - A3 time stamp); calculate the time stamp difference between D3 and C3 = (D3 time stamp - C3 time stamp).

[0134] Therefore, the split process working hours can be expressed as:

[0135] [(B1 time stamp - A1 time stamp), (D1 time stamp - C1 time stamp)]

[0136] [(B2 time stamp - A2 time stamp), (D2 time stamp - C2 time stamp)]

[0137] [(B3 time stamp - A3 time stamp), (D3 time stamp - C3 time stamp)]]。

[0138] Please refer to Figure 5 , which shows a schematic diagram of the split process working hours in an embodiment of the process working hour splitting method described in this application.

[0139] Please refer to Figure 6 , which shows a schematic diagram of the split process working hours in another embodiment of the process working hour splitting method described in this application.

[0140] Furthermore, it is necessary to screen the split process working hours according to the operations that employees may perform before shutdown (such as rework to find the team leader for guidance, etc.), including: calculating the sum of single process working hours; screening the single process working hour with the smallest sum value as the split process working hours. The split process working hours are the working hours for employees to complete a single process.

[0141] For example, based on Figure 5For the split process working hours shown, since the sum of the two single process working hours 31.799 and 44.605 in [31.799, 44.605] is the smallest, [31.799, 44.605] is selected as the split process working hours. That is, the working hour for the employee to complete Process 1 is 31.799, and the working hour for the employee to complete Process 2 is 44.605. Based on Figure 6 For the split process working hours shown, since the sum of 47.756 and 71.944 in [47.756, 71.944] is the smallest, [47.756, 71.944] is selected as the split process working hours. That is, the working hour for the employee to complete Process 1 is 47.756, and the working hour for the employee to complete Process 2 is 71.944.

[0142] The protection scope of the process working hour splitting method described in the embodiments of the present application is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or subtracting steps of the prior art and replacing steps according to the principle of the present application is included in the protection scope of the present application.

[0143] Please refer to Figure 7 , which shows the structural schematic diagram of the process working hour splitting system described in the present application in one embodiment.

[0144] As Figure 7 shown, this embodiment provides a process working hour splitting system, including:

[0145] An event data acquisition module 71, configured to acquire event data of the Internet of Things sewing equipment; the event data includes a timestamp, an event type, and event parameters.

[0146] An event data screening module 72, configured to obtain multiple long downtime intervals within a specific time period based on the event data; for each of the long downtime intervals, screen out the event data of the last piece of clothing sewn by the employee before the long downtime interval to form an event data group; the event data group includes event data with an event type of motor start, event data with an event type of motor stop, and n pieces of event data with the event type of thread cutting, where n is the total number of thread cutting times required for the employee to complete a preset combined process pre-input by the administrator.

[0147] A working hour allocation data acquisition module 73, configured to obtain data of split process working hours based on all the event data groups; the data of split process working hours is related to a specific event data group.

[0148] A single process working hour splitting module 74, configured to split the n pieces of event data with the event type of thread cutting in the specific event data group based on the data of split process working hours to obtain the working hours for the employee to complete a single process.

[0149] It should be noted that the structures and principles of the event data acquisition module 71, event data screening module 72, man-hour allocation data acquisition module 73, and single-process man-hour splitting module 74 in the embodiments of the present application correspond one by one to the steps in the above-mentioned process man-hour splitting method, so they will not be elaborated here.

[0150] The process man-hour splitting system provided by the embodiments of the present application can implement the process man-hour splitting method described in the present application. However, the implementation devices of the process man-hour splitting method described in the present application include but are not limited to the structures of the process man-hour splitting systems listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0151] Please refer to Figure 8 , which shows the structural schematic diagram of the electronic device of the present application in an embodiment.

[0152] As shown in Figure 8 , this embodiment provides an electronic device, including: a processor 81 and a memory 82.

[0153] The memory 82 is used to store computer programs.

[0154] The processor 81 is used to execute the computer program stored in the memory 82, so that the electronic device executes the process man-hour splitting method described in any one of the above.

[0155] In several embodiments provided by the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules or units can be in electrical, mechanical or other forms.

[0156] The modules / units described as separate components may or may not be physically separated. The components displayed as modules / units may or may not be physical modules, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, in each embodiment of the present application, the various functional modules / units can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0157] Those of ordinary skill in the art should further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0158] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the process time splitting method described in any one of the above. Those of ordinary skill in the art can understand that all or part of the steps in the method of implementing the above embodiments can be completed by instructing the processor through a program. The described program can be stored in a computer-readable storage medium. The storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)).

[0159] The embodiments of the present application can also provide a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, they generate all or part of the processes or functions described in the embodiments of the present application. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, or data center to another website, computer, or data center in a wired manner (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or a wireless manner (e.g., infrared, wireless, microwave, etc.).

[0160] When the computer program product is executed by a computer, the computer executes the method described in the foregoing method embodiments. The computer program product may be a software installation package. In the case where the foregoing method needs to be used, the computer program product can be downloaded and executed on the computer.

[0161] The descriptions of the processes or structures corresponding to the foregoing various drawings each have their own focuses. For parts not detailed in a certain process or structure, reference may be made to the relevant descriptions of other processes or structures.

[0162] The foregoing embodiments merely illustrate the principles and effects of the present application, rather than limiting the present application. Any person familiar with this technology can modify or change the foregoing embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A method for splitting process working hours, characterized in that, Including: Obtain event data of the Internet of Things sewing equipment; the event data includes a timestamp, an event type, and event parameters; Based on the event data, obtain multiple long downtime intervals within a specific time period; for each of the long downtime intervals, filter out the event data of the last piece of clothing sewn by the employee before the long downtime interval to form an event data group; the event data group includes event data with an event type of motor start, event data with an event type of motor stop, and n pieces of event data with an event type of thread cutting, where n is the total number of thread cuts required for the employee to complete a preset combined process pre-entered by the administrator; Based on all the event data groups, obtain data on the working hours of the split process; the data on the working hours of the split process is related to a specific event data group; Based on the data on the working hours of the split process, split the n pieces of event data with an event type of thread cutting in the specific event data group to obtain the working hours for the employee to complete a single process.

2. The method according to claim 1, wherein Based on the event data, obtaining multiple long downtime intervals of the employee within a specific time period includes: Obtain the timestamps t2 of all event data with a timestamp within the specific time period and an event type of motor start; For each of the timestamps t2, obtain the timestamp t6 of the event data with an event type of thread cutting that is closest in time to the timestamp t2 and before the timestamp t2; Calculate the difference between the timestamp t2 and the timestamp t6; Take the difference as a downtime interval of the employee within a specific time period; Sort all the downtime intervals; According to the sorting result, determine a preset number of long downtime intervals.

3. The method according to claim 1, characterized in that, Based on all the event data groups, obtaining data on the working hours of the split process includes: Based on each event data group, calculate the actual working hours and the sum of the actual number of stitches required for the employee to complete the last piece of clothing sewn to form a [working hours, sum of stitches] array; Using the standard working hours and the standard sum of stitches, filter out the [working hours, sum of stitches] arrays that meet specific conditions as the data on the working hours of the split process, and the event data group corresponding to the [working hours, sum of stitches] array that meets the specific conditions is the specific event data group.

4. The method according to claim 3, wherein Based on each event data group, calculating the actual working hours and the sum of the actual number of stitches required for the employee to complete the last piece of clothing sewn to form a [working hours, sum of stitches] array includes: For each event data group, obtain the timestamps of the n pieces of event data with an event type of thread cutting in the event data group; Calculate the difference between the nth timestamp and the first timestamp to obtain the actual working hours required for the employee to complete the last piece of clothing sewn; Obtain the event parameters of all event data with an event type of motor stop; Sum up the event parameters of all event data with an event type of motor stop to obtain the sum of the actual number of stitches required for the employee to complete the last piece of clothing sewn.

5. The method according to claim 3, wherein It also includes obtaining the standard working hours and the standard sum of stitches required for the employee to complete one piece of clothing sewn: Segment the historical event data based on the total number of thread cutting times \(n\) to obtain multiple groups of historical event data; each group of historical event data is a set of historical event data for an employee to complete the sewing of one piece of clothing; Based on the group of historical event data, calculate the historical working hours and the sum of historical needle counts for an employee to complete the sewing of one piece of clothing; Select stable historical working hours and stable sum of historical needle counts based on the normal distribution; Based on the stable historical working hours, calculate the root mean square of working hours and the standard deviation of working hours; Based on the stable sum of historical needle counts, calculate the root mean square of the sum of needle counts and the standard deviation of the sum of needle counts.

6. The method according to claim 5, wherein The selected \([working\ hours, sum\ of\ needle\ counts]\) array that meets specific conditions should at least satisfy: (Root mean square of working hours - Standard deviation of working hours) < working hours < (Root mean square of working hours + Standard deviation of working hours) and (Root mean square of the sum of needle counts - Standard deviation of the sum of needle counts) < sum of needle counts < (Root mean square of the sum of needle counts + Standard deviation of the sum of needle counts).

7. The method according to claim 1, characterized in that Based on the data of the split working hours of each process, split the \(n\) event data of the event type of thread cutting in the specific event data group to obtain the working hours for an employee to complete a single process, including: Obtain the number of thread cutting times for a single process pre - input by the administrator, and the sum of the number of thread cutting times for a single process is the total number of thread cutting times; Based on the number of thread cutting times for a single process, split the \(n\) event data of the event type of thread cutting in the specific event data group to obtain a single - process event group corresponding to the number of single processes; Calculate the difference between the last timestamp and the first timestamp in each single - process event group to obtain the working hours for an employee to complete a single process.

8. The method according to claim 1, wherein It also includes: If the data of the split working hours of each process is greater than 1 in quantity, screen the split working hours of each process and select the one with the smallest sum of the split working hours of each process.

9. A process working hour splitting system, characterized in that, It includes: An event data acquisition module, used to acquire the event data of the IoT sewing device; the event data includes a timestamp, an event type, and event parameters; An event data screening module, used to obtain multiple long downtime intervals within a specific time period based on the event data; for each long downtime interval, screen the event data of the last piece of clothing sewn by the employee before the long downtime interval to form an event data group; the event data group includes event data of the event type of motor start, event data of the event type of motor stop, and \(n\) event data of the event type of thread cutting, where \(n\) is the total number of thread cutting times required for an employee to complete a preset combined process pre - input by the administrator; A working - hour allocation data acquisition module, used to obtain the data of the split working hours of each process based on all the event data groups; The data of the split working hours of each process is related to a specific event data group; A single - process working - hour splitting module, used to split the \(n\) event data of the event type of thread cutting in the specific event data group based on the data of the split working hours of each process to obtain the working hours for an employee to complete a single process.

10. An electronic device, characterized in that, It includes: A processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer programs stored in the memory so that the electronic device executes the method described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method according to any one of claims 1 to 8.

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