Sewing machine piece counting method and system based on lathe worker action analysis, medium and terminal

Abstract

The invention provides a sewing machine piece counting method and system based on lathe worker action analysis, a medium and a terminal. The method comprises the steps of obtaining a thread trimming time point of a sewing machine and a motor starting time point; obtaining the time difference between the adjacent motor trimming time point and the motor starting time point, and taking the adjacent motor trimming time point and the motor starting time point as a timestamp array; selecting the timestamp array of which the time difference is greater than a preset threshold as a target timestamp array; detecting whether cloth exists near the sewing machine; when it is detected that no cloth exists near a machine needle of the sewing machine, whether all sewing actions of a finished product are completed or not before the time period corresponding to the target timestamp array is judged; if yes, the number of sewn pieces is accumulated; otherwise, the number of the sewn pieces is kept unchanged. According to the sewing machine piece counting method and system based on lathe worker action analysis, the medium and the terminal, lathe worker action recognition is carried out based on the action data and the image data of the sewing machine, and then accurate sewing machine piece counting is achieved.

Description

Technical Field

[0001] This application belongs to the technical field of data processing, and relates to a sewing machine piece - counting method, system, medium and terminal based on lathe worker action analysis. Background Technique

[0002] In the prior art, Internet of Things (IoT) sewing equipment has basically replaced ordinary sewing machines, and factories have changed from manual piece - counting by workers to automatic piece - counting methods such as using work ticket software, hanging systems, and piece - counting algorithms. Existing sewing piece - counting algorithms only rely on the action data of sewing equipment. When there are errors in the acquisition of action data, it will lead to incorrect statistics of the piece - counting quantity.

[0003] With the development of the IoT system, IoT sewing equipment can provide real - time sewing data of employees. At the same time, with the continuous progress of artificial intelligence technology, especially the increasing maturity of object detection and depth detection technologies, it provides new possibilities for the update of piece - counting algorithms in the clothing production field. Summary of the Invention

[0004] The purpose of this application is to provide a sewing machine piece - counting method, system, medium and terminal based on lathe worker action analysis, which can identify lathe worker actions based on the action data and image data of the sewing machine, and then achieve accurate sewing machine piece - counting.

[0005] In a first aspect, this application provides a sewing machine piece - counting method based on lathe worker action analysis. The method includes the following steps: obtaining the thread - cutting time point and motor starting time point of the sewing machine; obtaining the time difference between adjacent motor thread - cutting time points and motor starting time points, and taking the adjacent motor thread - cutting time points and motor starting time points as a time - stamp array; selecting the time - stamp array with the time difference greater than a preset threshold as the target time - stamp array; detecting whether there is cloth near the sewing machine; when it is detected that there is no cloth near the sewing machine needle, determining whether all sewing actions of a finished product are completed before the period corresponding to the target time - stamp array; if so, accumulating the number of sewn pieces; otherwise, keeping the number of sewn pieces unchanged.

[0006] In one implementation manner of the first aspect, obtaining the thread - cutting time point and motor starting time point of the sewing machine includes the following steps: obtaining the event data of the sewing machine; the event data includes the event generation time and event type; taking the event generation times corresponding to the event types of thread - cutting and motor starting as the thread - cutting time point and motor starting time point.

[0007] In one implementation manner of the first aspect, detecting whether there is cloth near the sewing machine needle includes the following steps: obtaining multiple images of the needle area within the period corresponding to the target time - stamp array; Calculate the difference in pixel values between each sewing needle area image and the sewing needle reference image, where there is fabric near the sewing needle in the sewing needle reference image; When the proportion of pixel points with a difference greater than a preset difference in the sewing needle area image is greater than a first preset proportion, it is determined that there is fabric in the sewing needle area image; Among the multiple sewing needle area images, when the proportion of sewing needle area images determined to have fabric does not exceed a second preset proportion, it is determined that there is no fabric near the sewing needle of the sewing machine.

[0008] In one implementation of the first aspect, determining whether all sewing actions of a finished product are completed before the time period corresponding to the target timestamp array includes the following steps: Obtain the reference sewing action data required for a finished product: Determine whether there is previous actual sewing action data that is consistent with the reference sewing action data starting from the thread cutting time point corresponding to the target timestamp array; If so, it is determined that all sewing actions of a finished product are completed.

[0009] In one implementation of the first aspect, the actual sewing action data and the reference sewing action data adopt one or a combination of the number of sewing stitches, the number of times of lifting and lowering the presser foot, and the number of times of starting and stopping the motor between adjacent thread cuttings.

[0010] In one implementation of the first aspect, determining whether there is previous actual sewing action data that is consistent with the reference sewing action data starting from the thread cutting time point corresponding to the target timestamp array includes the following steps: Extract the segmented action features of the reference sewing action data; Extract the segmented action features of the actual sewing action data; Determine whether the segmented action features of the reference sewing action data and the segmented action features of the actual sewing action data match one by one; When they match one by one, it is determined that there is previous actual sewing action data that is consistent with the reference sewing action data.

[0011] In a second aspect, the present invention provides a sewing machine piece-counting system based on lathe worker action analysis, and the system includes a first acquisition module, a second acquisition module, a selection module, a detection module, and a piece-counting module; The first acquisition module is used to acquire the thread cutting time point and the motor start time point of the sewing machine; The second acquisition module is used to acquire the time difference between adjacent motor thread cutting time points and motor start time points, and use the adjacent motor thread cutting time points and motor start time points as a timestamp array; The selection module is configured to select the array of timestamps with the time difference greater than a preset threshold as the target array of timestamps; The detection module is configured to detect whether there is fabric near the sewing machine; The piece-counting module is configured to, when it is detected that there is no fabric near the sewing needle of the sewing machine, determine whether all sewing actions for one finished product are completed before the period corresponding to the target array of timestamps; if so, accumulate the number of sewn pieces; otherwise, keep the number of sewn pieces unchanged.

[0012] In a third aspect, the present application provides a terminal, which includes: a processor and a memory; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory, so that the terminal executes the above-mentioned sewing machine piece-counting method based on lathe worker motion analysis.

[0013] 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 terminal, the above-mentioned sewing machine piece-counting method based on lathe worker motion analysis is implemented.

[0014] In a fifth aspect, the present application provides a sewing machine piece-counting system based on lathe worker motion analysis, which includes the above-mentioned terminal and a sewing machine; The sewing machine includes an information collection module; The information collection module is configured to collect the thread-cutting time point and the motor start time point of the sewing machine and provide them to the terminal.

[0015] As described above, the sewing machine piece-counting method, system, medium, and terminal based on lathe worker motion analysis according to the present application have the following beneficial effects.

[0016] (1) Based on the motion data and image data of the sewing machine, lathe worker motion recognition is performed, and thus accurate sewing machine piece-counting is achieved.

[0017] (2) It avoids the mis-statistics caused by simply relying on the motion data of the sewing equipment for sewing piece-counting, and effectively improves the accuracy of sewing piece-counting. Description of the Drawings

[0018] Figure 1 It shows a flowchart of the sewing machine piece-counting method based on lathe worker motion analysis according to the present application in an embodiment.

[0019] Figure 2 It shows a schematic diagram of the event data according to the present application in an embodiment.

[0020] Figure 3 It shows a schematic diagram of the motor thread-cutting time point and the motor start time point according to the present application in an embodiment.

[0021] Figure 4 It shows a detection schematic diagram of the fabric near the sewing needle of the sewing machine in this application in one embodiment.

[0022] Figure 5 It shows a segmented schematic diagram of the reference sewing motion data in this application in one embodiment.

[0023] Figure 6 It shows a segmented schematic diagram of the number of sewing stitches between thread cuts in this application in one embodiment.

[0024] Figure 7 It shows a segmented feature schematic diagram of the reference sewing motion data in this application in one embodiment.

[0025] Figure 8 It shows a segmented matching schematic diagram of the reference sewing motion data and the actual sewing motion data in this application in one embodiment.

[0026] Figure 9 It shows a structural schematic diagram of the sewing machine piece-rate system based on the analysis of the lathe worker's actions in this application in one embodiment.

[0027] Figure 10 It shows a structural schematic diagram of the terminal in this application in one embodiment.

[0028] Figure 11 It shows a structural schematic diagram of the sewing machine piece-rate system based on the analysis of the lathe worker's actions in this application in another embodiment. Detailed implementation manners

[0029] The following uses specific specific examples to illustrate the implementation manners of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0030] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of this application in a schematic manner. Therefore, only the components related to this application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0031] In addition, in this application, descriptions such as "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may 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 ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0032] As Figure 1 shown, in one embodiment, the sewing machine piece-counting method based on lathe worker motion analysis of this application includes steps S1 to S5.

[0033] Step S1: Obtain the thread-cutting time point and the motor start time point of the sewing machine.

[0034] Specifically, an information collection module is provided in the sewing machine of this application. The information collection module is used to collect the event data of the sewing machine. As Figure 2 shown, the event data includes the event occurrence time, event type, and event parameters. In one embodiment, the event type can be represented by digital numbers such as 0, 1, 2, etc. For example, 2 represents motor start, 3 represents motor stop, and 6 and 7 respectively represent the thread-cutting and post-thread-cutting actions of the sewing machine. The event parameters represent the parameter information involved in the corresponding event type. For example, for the event with the event type of motor stop, its event parameter is the number of stitches sewn in this time. Therefore, obtain the event data of the sewing machine, and use the event generation times corresponding to the event types of thread-cutting and motor start as the thread-cutting time point and the motor start time point. As Figure 3 shown, according to event types 6 and 2, the corresponding thread-cutting time point and motor start time point can be obtained.

[0035] Step S2: Obtain the time difference between adjacent motor thread-cutting time points and motor start time points, and use the adjacent motor thread-cutting time points and motor start time points as a time stamp array.

[0036] Specifically, in this application, the sewing operations of the lathe worker are divided into four actions: picking up the fabric, sewing the fabric, straightening the fabric, and placing the fabric. Among them, the picking up the fabric is the starting process of the sewing operation, indicating going to pick up the fabric to be sewn. The sewing the fabric is the intermediate process of the sewing operation, indicating sewing the fabric. The straightening the fabric is the intermediate process of the sewing operation, indicating straightening the fabric. The placing the fabric is the ending process of the sewing operation, indicating placing the sewn fabric. Therefore, for one piece of fabric, its sewing operations are in sequence: picking up the fabric, sewing the fabric, straightening the fabric, sewing the fabric... (where the number of times of straightening the fabric and sewing the fabric depends on different sewing requirements), placing the fabric.

[0037] As can be seen from the above, during the sewing process of a sewing machine operator, the actions of picking up and placing the fabric are in one-to-one correspondence. Placing the fabric indicates that a finished product has been completed, and picking up the fabric indicates the start of sewing a new finished product. Therefore, it can be determined whether a finished product has been sewn by judging the actions of picking up and placing the fabric. Among them, the time points of the actions of picking up and placing the fabric occur between the thread cutting time point and the motor starting time point. Therefore, calculate the time difference between adjacent thread cutting time points and motor starting time points. During this time difference, the sewing machine operator may pick up and place the fabric. At the same time, use the adjacent thread cutting time point and motor starting time point as a time stamp array. For example, the time stamp array is expressed as [thread cutting time point, next motor starting time point].

[0038] Step S3: Select the time stamp array with the time difference greater than the preset threshold as the target time stamp array.

[0039] Specifically, it is possible for the sewing machine operator to pick up and place the fabric between the thread cutting time point and the motor starting time point, but the time length requirements of the above actions also need to be considered at the same time. If the time length is too short, it may only be a pause in the fabric sewing action, and the actions of picking up and placing the fabric are not carried out. Therefore, based on the above special situation, it is necessary to set the action time threshold for picking up and placing the fabric. Only when the time difference is greater than the preset threshold, such as 3s, can the actions of picking up and placing the fabric be further analyzed.

[0040] It should be noted that the time threshold can be obtained and updated through multiple learning.

[0041] Step S4: Detect whether there is fabric near the sewing machine.

[0042] Specifically, when analyzing the picking and placing actions of the sewing machine operator, it is first necessary to detect whether there is fabric near the sewing machine needle. Only when there is no fabric near the sewing machine needle, can the picking and placing actions of the sewing machine operator be identified and analyzed. If there is fabric near the sewing machine, it indicates that the operator may be sewing or straightening the fabric.

[0043] In one embodiment, detecting whether there is fabric near the sewing machine needle includes the following steps.

[0044] 41) Obtain multiple images of the needle area during the period corresponding to the target time stamp array.

[0045] Among them, an image acquisition module, such as a camera, is provided on the sewing machine. Based on the camera, multiple images of the needle area during the period corresponding to the target time stamp array are acquired.

[0046] 42) Calculate the difference in pixel values between each needle area image and the needle reference image, where there is fabric near the needle in the needle reference image.

[0047] Among them, the needle reference image is pre-acquired, that is, an image with fabric near the needle. The needle area image and the needle reference image are acquired by the camera at the same position. Calculate the difference in pixel values of the corresponding pixel points of the needle area image and the needle reference image.

[0048] 43) When the proportion of pixel points in the needle area image whose difference is greater than the preset difference does not exceed the first preset proportion, it is determined that there is no fabric in the needle area image; otherwise, it is determined that there is fabric in the needle area image.

[0049] Among them, count the proportion of pixel points in the needle area image whose difference is greater than the preset difference. When the proportion is greater than the first preset proportion, it is judged that there is no fabric in the needle area image.

[0050] 44) Among the multiple needle area images, when the proportion of the needle area images determined to have fabric does not exceed the second preset proportion, it is determined that there is no fabric near the needle of the sewing machine.

[0051] Among them, for multiple needle area images, count the proportion of the needle area images with fabric again. Only when the proportion exceeds the second preset proportion, it is determined that there is fabric near the needle of the sewing machine. When the proportion of the needle area images determined to have fabric does not exceed the second preset proportion, it is determined that there is no fabric near the needle of the sewing machine. As Figure 4 shown, the left side is the needle reference image, and the right side has the needle area image. Through the above analysis, it can be determined that there is fabric near the needle in the right image.

[0052] Step S5: When it is detected that there is no fabric near the needle of the sewing machine, determine whether all the sewing actions of a finished product have been completed before the time period corresponding to the target timestamp array; if so, accumulate the number of sewn pieces; otherwise, keep the number of sewn pieces unchanged.

[0053] Specifically, when it is detected that there is no fabric near the needle of the sewing machine, it can be determined whether the operator is performing pick-and-place actions. Before performing pick-and-place actions, it is necessary to ensure that the sewing machine has completed all the sewing actions of a finished product.

[0054] In one embodiment, determining whether all the sewing actions of a finished product have been completed before the time period corresponding to the target timestamp array includes the following steps.

[0055] 51) Obtain the reference sewing action data required for a finished product.

[0056] Among them, the reference sewing action data may be one or a combination of multiple of the number of sewing stitches between adjacent thread trimmings, the number of times the presser foot is lifted and lowered, and the number of times the motor starts and stops.

[0057] Taking the number of sewing stitches between adjacent thread trimmings as an example to obtain the reference sewing action data. The reference sewing action data can be obtained by manual input. In this application, the reference sewing action data is constructed according to the number of sewing times required between each thread trimming during the sewing process of a finished product.

[0058] 52) Determine whether there is previous actual sewing action data that is consistent with the reference sewing action data starting from the thread trimming time point corresponding to the target time stamp array.

[0059] Among them, the actual sewing action data may be one or a combination of multiple of the number of sewing stitches between adjacent thread trimmings, the number of times the presser foot is lifted and lowered, and the number of times the motor starts and stops. The data structures of the reference sewing action data and the actual sewing action data are consistent.

[0060] For example, starting from the thread trimming time point corresponding to the target time stamp array, obtain actual sewing action data that is greater than or equal to the length of the reference sewing action data forward. Compare the obtained actual sewing action data with the reference sewing action data to determine whether the actual sewing action data from a certain time point to the thread trimming time point corresponding to the target time stamp array is consistent with the reference sewing action data. If so, it is determined that there is previous actual sewing action data that is consistent with the reference sewing action data.

[0061] In one embodiment, the reference sewing action data is segmented according to different sewing states. For example, the sewing action data with cloth near the sewing needle is used as the first segment of data, the sewing action data with no cloth near the sewing needle and the time difference between the thread trimming time point and the motor start time point being less than or equal to a preset threshold is used as the second segment of data, and the sewing action data with no cloth near the sewing needle and the time difference between the thread trimming time point and the motor start time point being greater than the preset threshold is used as the third segment of data. For each segment of sewing action data, extract the corresponding segmented action features of the reference sewing action data and the actual sewing action data. Determine whether the segmented action features of the reference sewing action data and the segmented action features of the actual sewing action data match one by one; when they match one by one, it is determined that there is previous actual sewing action data that is consistent with the reference sewing action data.

[0062] Such as Figure 5As shown, the sewing data is divided into two segments based on the time difference between the motor stop time point and the motor start time point, the time difference between the thread cutting time point and the motor start time point, and whether there is fabric near the sewing needle. In the first segment of data, if the time difference is small and there is fabric near the sewing needle, it is an action of sewing the fabric; if the time difference is large and there is fabric near the sewing needle, it may be an action of straightening the fabric or an abnormal action. In the second segment of data, if the time difference is small and there is no fabric near the sewing needle, it is determined as an action of sewing the fabric. When the time difference is large and there is no fabric near the sewing needle, it is determined as an action of placing the fabric. Therefore, it is necessary to segment the sewing action data of a finished product for different sewing actions.

[0063] As Figure 6 shown, the number of thread cutting times and the number of sewing stitches between thread cuttings during the sewing process are collected. As Figure 7 shown, the number of sewing times between thread cuttings is classified, and category characters are used to replace the original number of stitches. Preferably, different stitch number intervals correspond to different categories. For example, 21 stitches and 16 stitches are close to category 0, 91 stitches and 83 stitches are close to category 1, and the remaining two segments of data are classified in the same way. Figure 5 The sewing data in Figure 8 can be divided into three segments. The first segment has 2 thread cuttings with categories 0 and 1, the second segment has 1 thread cutting with category 0, and the third segment has 1 thread cutting with category 1. It should be noted that only the number of sewing stitches is used for classification here, so only the numerical size needs to be judged for classification. For classification by selecting more features, classification methods such as clustering or SVM and other machine learning algorithms can be used. Therefore, the classification of the number of sewing times between thread cuttings corresponding to each segment of data is used as the segment action feature of the reference sewing action data and the segment action feature of the actual sewing action data. The respective segment action features of the reference sewing action data and the actual sewing action data are compared one by one. When both are the same, it is determined that the actual sewing action data and the reference sewing action data are consistent. As Figure 8 shown, each time a segment of actual sewing action data is taken to match the reference sewing action data to check whether there are features in the same order as the reference sewing action data. For Figure 6 the embodiment of

[0064] 53) If so, it is determined that all sewing actions of a finished product are completed.

[0065] Among them, when there is actual sewing action data that is consistent with the reference sewing action data before, it is determined that the sewing machine has completed all sewing actions for a finished product. At the same time, the operator will perform pick-and-place actions. Therefore, the sewing quantity can be accumulated, thus achieving accurate sewing piece counting.

[0066] The protection scope of the sewing machine piece counting method based on operator action analysis described in the embodiments of this application is not limited to the execution order of the steps listed in this embodiment. Any solutions achieved by adding or subtracting steps of the prior art and replacing steps according to the principles of this application are included in the protection scope of this application.

[0067] The embodiments of this application also provide a sewing machine piece counting system based on operator action analysis. The sewing machine piece counting system based on operator action analysis can implement the sewing machine piece counting method described in this application. However, the implementation devices of the sewing machine piece counting system based on operator action analysis described in this application include but are not limited to the structure of the sewing machine piece counting system listed in this embodiment. Any structural deformations and replacements of the prior art made according to the principles of this application are included in the protection scope of this application.

[0068] As Figure 9 shown, in one embodiment, the sewing machine piece counting system based on operator action analysis of this application includes a first acquisition module 91, a second acquisition module 92, a selection module 93, a detection module 94, and a piece counting module 95.

[0069] The first acquisition module 91 is used to acquire the thread cutting time point and the motor start time point of the sewing machine.

[0070] The second acquisition module 92 is connected to the first acquisition module 91 and is used to acquire the time difference between adjacent motor thread cutting time points and motor start time points, and use the adjacent motor thread cutting time points and motor start time points as a time stamp array.

[0071] The selection module 93 is connected to the second acquisition module 92 and is used to select the time stamp arrays with time differences greater than a preset threshold as target time stamp arrays.

[0072] The detection module 94 is connected to the selection module 93 and is used to detect whether there is cloth near the sewing machine.

[0073] The piece counting module 95 is connected to the detection module 94 and is used to determine whether all sewing actions for a finished product have been completed before the period corresponding to the target time stamp array when it is detected that there is no cloth near the sewing needle of the sewing machine; if so, accumulate the number of sewn pieces; otherwise, keep the number of sewn pieces unchanged.

[0074] Among them, the structures and principles of the first acquisition module 91, the second acquisition module 92, the selection module 93, the detection module 94, and the piece-counting module 95 correspond one by one to the steps in the above sewing machine piece-counting method based on lathe worker motion analysis, so they will not be elaborated here.

[0075] In several embodiments provided in 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 is that the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces, and the indirect coupling or communication connection of devices, modules, or units can be in electrical, mechanical, or other forms.

[0076] 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 distributed to multiple network units. One can select some or all of the modules / units 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.

[0077] Those of ordinary skill in the art should also 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 the present application.

[0078] The embodiments of the present application also provide a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiments can be completed by instructing a processor through a program. The 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 can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid-state drive (SSD)), etc.

[0079] The embodiments of the present application also provide a terminal. The terminal includes a processor and a memory.

[0080] The memory is used to store a computer program.

[0081] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disc.

[0082] The processor is connected to the memory and is used to execute the computer program stored in the memory, so that the terminal executes the above-mentioned sewing machine piece counting method based on lathe worker action analysis.

[0083] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0084] Such as Figure 10As shown, the terminal of the present application is presented in the form of a general-purpose computing device. The components of the terminal may include, but are not limited to: one or more processors or processing units 101, a memory 102, and a bus 103 that connects different system components (including the memory 102 and the processing unit 101).

[0085] The bus 103 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0086] The terminal typically includes a variety of computer system-readable media. These media can be any available media that can be accessed by the terminal, including volatile and non-volatile media, removable and non-removable media.

[0087] The memory 102 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 1021 and / or cache memory 1022. The terminal may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 1023 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 10 not shown, commonly referred to as a "hard disk drive"). Although Figure 10 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 103 through one or more data media interfaces. The memory 102 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present application.

[0088] A program / utility 1024 having a set (at least one) of program modules 10241 may be stored in, for example, the memory 102. Such program modules 10241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 10241 generally perform the functions and / or methods in the embodiments described in the present application.

[0089] The terminal can also communicate with one or more external devices (such as keyboards, pointing devices, displays, etc.), and can also communicate with one or more devices that enable users to interact with the terminal, and / or communicate with any device that enables the terminal to communicate with one or more other computing devices (such as network cards, modems, etc.). Such communication can be carried out through the input / output (I / O) interface 104. Moreover, the terminal can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through the network adapter 105. As Figure 10 shown, the network adapter 105 communicates with other modules of the terminal through the bus 103. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the terminal, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0090] As Figure 11 shown, in one embodiment, the sewing machine piece-rate system based on lathe worker motion analysis of the present application includes the above-mentioned terminal 111 and sewing machine 112.

[0091] The sewing machine 112 includes an information acquisition module.

[0092] The information acquisition module is used to acquire the thread cutting time point and the motor start time point of the sewing machine 112 and provide them to the terminal 111.

[0093] It should be noted that the terminal 111 can be set on the sewing machine 112 for local processing; or it can be set in the cloud for cloud processing. When the terminal 111 is set in the cloud, one terminal can perform sewing piece-rate calculation for multiple sewing machines at the same time.

[0094] The above embodiments only illustrate the principles and effects of the present application by way of example, rather than limiting the present application. Any person familiar with this technology can modify or change the above 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 idea disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A sewing machine piece-rate method based on lathe worker motion analysis, characterized in that, The method includes the following steps: Obtain the thread cutting time point and the motor start time point of the sewing machine; Obtain the time difference between adjacent motor thread cutting time points and motor start time points, and use the adjacent motor thread cutting time points and motor start time points as a time stamp array; Select the time stamp array with the time difference greater than the preset threshold as the target time stamp array; Detect whether there is fabric near the sewing machine; When it is detected that there is no fabric near the sewing needle of the sewing machine, determine whether all sewing actions of a finished product are completed before the period corresponding to the target time stamp array; if so, accumulate the number of sewn pieces; otherwise, keep the number of sewn pieces unchanged.

2. The sewing machine piece-rate method based on lathe worker motion analysis according to claim 1, wherein Obtaining the thread cutting time point and the motor start time point of the sewing machine includes the following steps: Obtain the event data of the sewing machine; the event data includes the event generation time and the event type; Use the event generation times corresponding to the thread cutting and motor start as the thread cutting time point and the motor start time point.

3. The sewing machine piece-rate method based on lathe worker motion analysis according to claim 1, characterized in that, Detecting whether there is fabric near the sewing needle of the sewing machine includes the following steps: Obtain multiple sewing needle area images within the period corresponding to the target time stamp array; Calculate the difference in pixel values between each sewing needle area image and the sewing needle reference image, where there is fabric near the sewing needle in the sewing needle reference image; When the proportion of pixel points with the difference greater than the preset difference in the sewing needle area image is greater than the first preset proportion, determine that there is fabric in the sewing needle area image; Among the multiple sewing needle area images, when the proportion of the sewing needle area images determined to have fabric does not exceed the second preset proportion, determine that there is no fabric near the sewing needle of the sewing machine.

4. The sewing machine piecework counting method based on lathe worker motion analysis according to claim 1, characterized in that, Judging whether all sewing actions of a finished product are completed before the period corresponding to the target time stamp array includes the following steps: Obtain the reference sewing action data required for a finished product: Judge whether there is previous actual sewing action data consistent with the reference sewing action data starting from the thread cutting time point corresponding to the target time stamp array; If so, determine that all sewing actions of a finished product are completed.

5. The sewing machine piece-rate method based on lathe worker motion analysis according to claim 4, characterized in that The actual sewing action data and the reference sewing action data adopt one or a combination of the number of sewing stitches, the number of times of lifting and pressing the presser foot, and the number of times of motor start and stop between adjacent thread cuttings.

6. The sewing machine piece-rate method based on lathe worker motion analysis according to claim 4, wherein, Judging whether there is previous actual sewing action data consistent with the reference sewing action data starting from the thread cutting time point corresponding to the target time stamp array includes the following steps: Extract the segmented action features of the reference sewing action data; Extract the segmented action features of the actual sewing action data; Judge whether the segmented action features of the reference sewing action data and the segmented action features of the actual sewing action data match one by one; When they match one by one, determine that there is previous actual sewing action data consistent with the reference sewing action data.

7. A sewing machine piece-rate system based on lathe worker motion analysis, characterized in that, The system includes a first acquisition module, a second acquisition module, a selection module, a detection module, and a piece counting module; The first acquisition module is used to obtain the thread cutting time point and the motor start time point of the sewing machine; The second acquisition module is configured to acquire the time difference between adjacent motor thread cutting time points and motor starting time points, and use the adjacent motor thread cutting time points and motor starting time points as a time stamp array; The selection module is configured to select the time stamp array with the time difference greater than a preset threshold as the target time stamp array; The detection module is configured to detect whether there is cloth near the sewing machine; The piece counting module is configured to, when it is detected that there is no cloth near the sewing needle of the sewing machine, determine whether all sewing actions of a finished product are completed before the time period corresponding to the target time stamp array; if so, accumulate the number of sewn pieces; otherwise, keep the number of sewn pieces unchanged.

8. A terminal, characterized in that, The terminal includes: a processor and a memory; The memory is used to store a computer program; The processor is configured to execute the computer program stored in the memory, so that the terminal executes the sewing machine piece counting method based on lathe worker action analysis according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the terminal, it implements the sewing machine piece counting method based on lathe worker action analysis according to any one of claims 1 to 6.

10. A sewing machine piece-rate system based on lathe worker motion analysis, characterized in that, It includes the terminal and the sewing machine according to claim 8; The sewing machine includes an information acquisition module; The information acquisition module is configured to acquire the thread cutting time point and motor starting time point of the sewing machine, and provide them to the terminal.

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