Intelligent manufacturing systems, methods, media, and computer equipment based on sewing piecework.

Abstract

The application provides an intelligent production system, method, medium and computer device based on sewing piece, the application can automatically recommend qualified employees according to uploaded process information; the coincidence degree of the recommendation and the idea of the manager is continuously improved through automatic learning; the time required for an employee to sew a piece can be generated, which can help the manager to perform order, production management and yield prediction; a process library and a worker information library can be established; the production does not excessively rely on personal skills, and the employee has strong universality; the manager does not need to know the operator, and can judge which process to match, allocate tasks and the like only through work ability evaluation. The management level can be reduced, and the production response speed is improved; the required data of the application is simple, and the required data can be generated in the sewing process of most types of sewing machines, and the coverage is wide; compared with the traditional manual piecework, the piecework accuracy is higher, and the wage settlement basis is more stable.

Description

Technical Field

[0001] This application relates to the field of sewing technology, and in particular to intelligent production systems, methods, media, and computer equipment based on sewing piecework. Background Technology

[0002] Currently, the management of the garment production process is generally severely lagging behind. Due to the industry's characteristics, it is accustomed to using orders as the unit of measurement, piecework, and quality inspection. To obtain daily production data such as output and yield rate, it often takes at least two or even three days, which is extremely detrimental to timely production management. The industry currently lacks a convincing personnel evaluation and rating system, thus requiring a large number of lower-level managers for task allocation. In actual production, it is often carried out in small groups, with tasks assigned by group leaders. Furthermore, the number of experienced group leaders is relatively large, requiring significant manpower costs.

[0003] Existing technologies typically address this issue in several ways, but each has its own drawbacks:

[0004] 1) Administrators use the web client in the visual terminal to generate one-to-one or one-to-many task files by matching operators and processes according to the matching method provided by the system, and then upload them to the server.

[0005] 2) The operator obtains the corresponding task file from the server, confirms the task and starts working. The operator uses the sewing equipment's working data obtained by the collector connected to the user terminal to perform machine learning on the first product. At the same time, the user terminal binds the machine learning results with the operator and the corresponding task and uploads them to the server.

[0006] 3) During the operation of the sewing equipment, if the data collected by the data collector meets the results of machine learning, the piece count will automatically increment by 1, and the user terminal will upload the piece count to the server in real time. Piece counting is then performed (CN202011605979.7).

[0007] The aforementioned prior art has the following disadvantages:

[0008] 1) The system matches operators and processes by administrators. While it offers recommended matching methods, it lacks specific reasons for these recommendations, leading to low reliability. Administrators lack information on worker skills when matching processes with workers; they can only match them to the appropriate process if they know the worker's capabilities. However, administrators have limited knowledge of workers, limiting the system's usability to small groups. Terminal communication is less efficient than verbal communication within small teams. Furthermore, the system requires manual import of process and personnel data, resulting in low efficiency.

[0009] 2) Existing technology only has the sewing process that is currently needed, but it does not have information on the relevant sewing processes that workers have done in the past, or information on the process library that the factory has done before. This information can help match workers' abilities with the process.

[0010] 3) Administrators can only obtain the number of pieces sewn by each employee, but cannot further adjust production scheduling or employee skills training based on the number of pieces sewn by each employee. This has no positive impact on the production process and does not improve factory operating efficiency.

[0011] 4) Using the operator's first sewing data as the machine learning object will not yield accurate results, as workers are often inexperienced and prone to errors on their first attempt. Using incorrect data as the machine learning object will lead to misjudgments of piecework results. Furthermore, piecework errors cannot be corrected, meaning employees still need to constantly check the piecework results and manually calculate piecework after errors occur. This has poor practicality and does not improve employee work efficiency. Summary of the Invention

[0012] In view of the shortcomings of the prior art described above, the purpose of this application is to provide an intelligent production system, method, medium and computer equipment based on sewing piecework, so as to solve the technical problems of low efficiency and low accuracy of manual operation in the prior art.

[0013] To achieve the above and other related objectives, the first aspect of this application provides an intelligent production system based on sewing piecework, comprising: a digital pattern room module, sewing equipment, a cloud service module, a managed terminal, and a manager terminal; the cloud service module establishes communication connections with the digital pattern room module, sewing equipment, managed terminal, and manager terminal respectively; the digital pattern room module is used to perform sample garment production and decompose the processes used after receiving an order to generate information on each process and its corresponding sewing requirements; the sewing equipment performs the corresponding sewing task according to each decomposed process, and sends the sewing operation parameters of each process to the cloud service module and saves them in the process library. The cloud service module is used to search for processes that match the sewing operation parameters from the process library, and to match the corresponding operators based on the process requirement characteristics corresponding to the matched processes. After the matching is completed, the corresponding process information and task information are sent to the backup manager terminal. After receiving the process information and task information, the backup manager terminal sends the sewing equipment number it uses to the cloud service module. The cloud service module also queries the equipment library based on the sewing equipment number to obtain equipment information, so as to bind the operator information, equipment information and process information. The sewing equipment information used by the operator at the workstation and the sewing process information are sent to the manager terminal.

[0014] In some embodiments of the first aspect of this application, the cloud service module is further configured to extract motion feature parameters of the sewing equipment during the sewing process, and compare them with the corresponding process template through a similarity comparison algorithm to obtain the number of sewn pieces by the sewing equipment and send them to the administrator terminal.

[0015] In some embodiments of the first aspect of this application, the calculation process of the similarity comparison algorithm includes: using the Euclidean distance algorithm to compare the similarity of two sequences; for two sequences of the same length, calculating the distance between every two points and then summing them, the smaller the distance, the higher the similarity; for two sequences of different lengths, using a sliding window, copying the shorter sequence until it is the same length as the longer sequence, then calculating the distance between every two points and summing them, the smaller the distance, the higher the similarity.

[0016] In some embodiments of the first aspect of this application, the cloud service module is further configured to use a skill analysis algorithm to analyze the sewing data of employees, obtain skill analysis results such as the employee's skill matrix and work efficiency, and save them to the personnel information database.

[0017] In some embodiments of the first aspect of this application, the cloud service module is further configured to use an anomaly analysis algorithm to obtain the difference between each sewn part and the process template, including: identifying the rework status of the sewn part by the difference in the number of stitches; or, identifying the location of the anomaly in the sewn part by the time point when the anomaly occurs during the sewing process.

[0018] In some embodiments of the first aspect of this application, the cloud service module is further configured to calculate the utilization rate and sewing speed information of the sewing equipment based on the sewing time required for each sewn piece and the motor running time, and send and display the information on the administrator terminal.

[0019] To achieve the above and other related objectives, a second aspect of this application provides an intelligent production method based on sewing piecework, comprising: acquiring information on each process and corresponding sewing requirements generated based on sample garment production and process breakdown, and sending the information on each process and corresponding sewing requirements to a sewing device for the sewing device to perform sewing tasks; receiving sewing operation parameters from the sewing device for each process and saving them to a process library, searching the process library for processes that match the sewing operation parameters, and matching the corresponding operator based on the process requirement characteristics corresponding to the matched process; after matching, sending the corresponding process information and task information to a management terminal, and obtaining the corresponding sewing device number from the management terminal; querying the equipment library based on the sewing device number to obtain equipment information, so as to bind the operator information, equipment information, and process information; and sending the sewing device information used at the operator's workstation and the sewing process information to the management terminal.

[0020] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent production method based on sewing piecework.

[0021] To achieve the above and other related objectives, a fourth aspect of this application provides a computer device, comprising: a processor and a memory; the memory for storing a computer program, and the processor for executing the computer program stored in the memory to cause the device to perform the intelligent production method based on sewing piecework.

[0022] As described above, the intelligent production system, method, medium, and computer equipment based on sewing piecework of this application have the following beneficial effects:

[0023] 1) This invention can automatically recommend qualified employees and provide reasons for the recommendation based on the uploaded process information; at the same time, it can automatically learn and continuously improve the alignment between the recommendations and the ideas of management personnel.

[0024] 2) This invention can generate the time required for an employee to sew one item, thus helping managers with order and production management and output forecasting. This greatly reduces the job threshold and workload for managers, allowing them to predict order completion time and optimize personnel allocation without requiring management experience.

[0025] 3) This invention can establish a process library and a worker information database, enabling conscious efforts to address workers' skill weaknesses and improve their overall skills. This prevents production from becoming overly reliant on individual skills and enhances the versatility of the workforce.

[0026] 4) Managers do not need to know the machinists; they can determine which process to match and assign tasks based solely on work performance evaluations. This reduces management layers and improves production response speed.

[0027] 5) The data required by this invention is simple, and most types of sewing machines generate the required data during the sewing process, covering a wide range.

[0028] 6) Compared with traditional manual piecework, the present invention has higher piecework accuracy and more stable wage settlement basis. Attached Figure Description

[0029] Figure 1 The diagram shown is a structural schematic of an intelligent production system based on sewing piecework in one embodiment of this application.

[0030] Figure 2 The diagram shown is a flowchart of an intelligent production method based on sewing piecework in one embodiment of this application.

[0031] Figure 3The diagram shown is a structural schematic of a computer device according to an embodiment of this application. Detailed Implementation

[0032] The following specific examples illustrate the implementation 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 embodiments, and 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, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0033] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of this application. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of this application. The following detailed description should not be considered limiting, and the scope of the embodiments of this application is defined only by the claims of the published patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. Spatially related terms, such as “upper,” “lower,” “left,” “right,” “below,” “below,” “lower part,” “above,” “upper part,” etc., may be used herein to illustrate the relationship between one element or feature shown in the figures and another element or feature.

[0034] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," and "holding" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0035] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some manner.

[0036] To address the problems in the background technology, this invention provides an intelligent production solution based on sewing piecework. The solution aims to establish a database on a cloud platform, including a database of all processes performed in the factory, categorized by process type, difficulty, and sewing time. A personnel information database includes personnel's years of experience, past work experience, and skill sets. Therefore, orders can be categorized and workers recommended based on their abilities during the sample garment production stage. Even if the administrator doesn't know the recommended workers, they can assess their suitability based on their past work experience and skill set. Piecework is calculated using sample garments sewn in a digital pattern room as templates, with piecework data generated during the sewing process recorded on the cloud platform. Workers can view their piecework in real time and request corrections. The cloud platform generates information such as the time required for each worker to sew and the estimated production capacity, which is displayed on the terminal for administrators to control production.

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the invention.

[0038] like Figure 1 The diagram illustrates the structure of an intelligent production system based on sewing piecework according to an embodiment of the present invention. The intelligent production system in this embodiment includes: a digital prefabricated housing module 11, a sewing machine 12, a cloud service module 13, a managed terminal 14, and a manager terminal 15. The digital prefabricated housing module 11 is communicatively connected to the cloud service module 13, the sewing machine 12 is also communicatively connected to the cloud service module 13, and the cloud service module 13 is further communicatively connected to the managed terminal 14 and the manager terminal 15.

[0039] It is worth noting that the cloud service module 13 can be a server, which can be deployed on one or more physical servers based on factors such as function and load, or it can be composed of distributed or centralized server clusters; it can also be computer devices such as desktop computers, laptops, tablets, smartphones, smart bracelets, smartwatches, smart helmets, and smart TVs. The managed terminal 14 is an electronic terminal (such as mobile phones, tablets, smart bracelets, smartwatches, smart helmets, etc.) suitable for those being managed. Those being managed typically refer to staff under the supervision of an administrator, especially sewing operators. The manager terminal 15 is an electronic terminal (such as mobile phones, tablets, smart bracelets, smartwatches, smart helmets, etc.) suitable for administrators.

[0040] In this embodiment, the intelligent production process of the intelligent production system is as follows:

[0041] Step a. After receiving the order, the digital pattern room module 11 performs sample garment production and breaks down the processes used to generate information on each process and its corresponding sewing requirements. The sewing requirements include, but are not limited to, the sewing methods for the broken-down processes and the parameters required for the sewing process.

[0042] Step b. The sewing equipment 12 performs the corresponding sewing task according to each of the disassembled processes and sends the sewing operation parameters of the sewing equipment 12 in each process to the cloud service module 13 and saves them to the process library. Specifically, each process can be sewn by a sample garment worker, and the sewing machine's operation data during the sewing process can be saved to the cloud platform process library through a gateway device, which is a database used to store process-related data. It should be noted that the aforementioned sample garment worker refers to a skilled person with strong work skills. The sewing data and sewing time of the garments sewn by them can serve as the optimal template.

[0043] Step c. The cloud service module 13 finds the matching process from the process library and matches the operator according to the process requirement characteristics corresponding to the matched process. The process requirement characteristics include, but are not limited to, the difficulty level of the process, the skill proficiency required for the process, and whether the operator has done the relevant process before. Based on the above process requirements, the most suitable operator is found, and the matched operator and the matching degree are displayed on the administrator terminal 15 so that the administrator can judge whether the operator recommended by the result should be used to sew the process.

[0044] Furthermore, the manager's selection results for each operation are saved. This saved information is used as a labeled training dataset and input into a deep learning AI model for supervised training. This results in a predictive model that can output results consistent with the manager's style, improving the accuracy of the system's recommendations. The input parameters of the deep learning AI model include the difficulty level of the process, the required skill level, and whether the operator needs relevant experience in the process. The output parameters include information about the selected operator, including but not limited to operator ID and basic information (such as name, gender, and length of service). Additionally, the deep learning AI model includes, but is not limited to, convolutional neural network models, feedforward neural network models, and radial basis function neural network models; this embodiment does not impose any limitations.

[0045] Step d. After the matching is completed, the cloud service module 13 sends the process information and task information to the managed terminal 14; after receiving the process information and task information, the managed terminal 14 sends the sewing equipment number it uses to the cloud service module 13.

[0046] Step e. Cloud service module 13 queries the equipment database based on the sewing equipment number to obtain equipment information, so as to bind the operator information, equipment information and process information; and sends the sewing equipment information used by the operator at the workstation and the sewing process information to the manager terminal 15 for the manager to view.

[0047] In some examples, the cloud service module 13 also extracts motion characteristic parameters of the sewing equipment during the sewing process, including but not limited to start-stop actions such as lifting the presser foot and the motor, their corresponding timestamps, and the number of stitches generated by the motor movement. Then, it compares these parameters with the corresponding process template using a similarity comparison algorithm to obtain the number of pieces sewn by the sewing machine. The number of pieces sewn is sent to the managed terminal 14 in real time for employees to verify the results. If employees disagree with the piece count, they can request a modification.

[0048] It is worth noting that the similarity calculation in this embodiment belongs to the field of time series similarity. For calculating curve similarity or curve matching, the Euclidean distance algorithm is preferably used. Its principle is as follows: For sequences of the same length, the distance between every two points is calculated and then summed; the smaller the distance, the higher the similarity. For sequences of different lengths, there are generally two methods: one is subsequence matching (finding the part of the long sequence that is most similar to the short sequence), and the other is a sliding window, which involves copying the short sequence until it is the same length as the long sequence.

[0049] In some examples, cloud service module 13 uses a skills analysis algorithm to analyze employees' sewing data, obtaining skills analysis results such as employee skills matrices and work efficiency, which are then saved to the personnel information database on the cloud platform. The skills analysis algorithm is a commonly used method for analyzing non-management work; it is applicable to both simple and complex tasks. The key to this method is its systematic nature, providing ample data for training program design. Currently, skills analysis algorithms typically involve the following: 1) Whether the work facilities are suitable for the employee's physical condition; 2) Whether the work environment affects the employee's physical and mental health; 3) Whether the employee's work attitude is positive and their enthusiasm is high; 4) A detailed analysis of the employee's work process. Skills analysis is achieved through these analyses. Furthermore, the aforementioned skills matrix refers to displaying the distribution of employees' various skills in the form of a matrix diagram.

[0050] In some examples, the cloud service module 13 predicts daily production capacity using piecework results and a capacity prediction algorithm and displays it on the administrator terminal 15.

[0051] In some examples, the cloud service module 13 also uses anomaly analysis algorithms to identify garment anomalies based on differences between the sewing process and the template for each garment. Specifically, this includes: identifying rework needs based on differences in stitch count; and identifying the location of anomalies on the garment based on the time point of occurrence during the sewing process. The cloud service module 13 sends this anomaly information to the managed terminal 14, allowing anomalies to be detected and repaired during the employee's sewing stage. This reduces the workload of quality inspectors and prevents defective products from being missed during quality control.

[0052] Furthermore, by using information such as the sewing time required for each sewn item and the motor running time, machine utilization rate and sewing speed can be calculated. This information is displayed on the management terminal 15, enabling managers to understand the factory's production status in real time for precise management. Utilization rate refers to the proportion of time that equipment occupies to create value within its available time.

[0053] It should be understood that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software through processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the cloud service module can be a separate processing element, or it can be integrated into a chip within the above system. Alternatively, it can be stored as program code in the system's memory, and its functions can be called and executed by a processing element within the system. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, the steps of the above method or the various modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0054] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0055] like Figure 2 The diagram illustrates a flowchart of an intelligent production method based on sewing piecework, as described in this embodiment of the invention. The intelligent production method in this embodiment is applied to the cloud service module described in the previous embodiment. As mentioned above, the cloud service module can be a server, or a computer device such as a desktop computer, laptop computer, tablet computer, smartphone, smart bracelet, smartwatch, smart helmet, or smart TV.

[0056] In this embodiment, the intelligent production method based on sewing piecework mainly includes the following steps:

[0057] Step S21: Obtain the information on each process and corresponding sewing requirements generated based on the sample garment production and process breakdown, and send the information on each process and corresponding sewing requirements to the sewing equipment for the sewing equipment to perform the sewing task.

[0058] Step S22: After receiving the sewing operation parameters of each process from the sewing equipment, save them to the process library, search for the process that matches the sewing operation parameters from the process library, and match the corresponding operator according to the process requirement characteristics of the matched process.

[0059] Step S23: After the matching is completed, send the corresponding process information and task information to the manager terminal, and obtain the corresponding sewing equipment number from the manager terminal.

[0060] Step S24: Query the equipment database according to the sewing equipment number to obtain equipment information, so as to bind the operator information, equipment information and process information; send the sewing equipment information used by the operator at the workstation and the sewing process information to the administrator terminal.

[0061] It should be noted that the intelligent production method based on sewing piecework in this embodiment is similar in implementation to the intelligent production system based on sewing piecework described above, so it will not be repeated here.

[0062] like Figure 3 The diagram illustrates the structure of a computer device according to an embodiment of the present invention. The computer device provided in this example includes: a processor 31, a memory 32, and a communicator 33; the memory 32 is connected to the processor 31 and the communicator 33 via a system bus and communicates with them; the memory 32 stores computer programs; the communicator 33 communicates with other devices; and the processor 31 runs the computer program, enabling the electronic terminal to execute the various steps of the intelligent production method based on sewing piecework as described above.

[0063] The system bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). Memory may include Random Access Memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0064] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0065] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent production method based on sewing piecework.

[0066] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0067] In the embodiments provided in this application, the computer-readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of the medium. However, it should be understood that computer-readable and writable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0068] In summary, this application provides an intelligent production system, method, medium, and computer equipment based on sewing piecework. This invention can automatically recommend qualified employees and provide reasons for the recommendation based on uploaded process information. Simultaneously, it performs automatic learning to continuously improve the alignment between recommendations and management's ideas. It can generate the time required for an employee to sew one piece, thus assisting managers in order and production management and output forecasting. This significantly reduces the work threshold and workload for managers, allowing them to predict order completion times and optimize personnel allocation without management experience. It enables the establishment of a process library and worker information database, allowing for conscious cultivation of workers' skill weaknesses and improvement of their skills. Production does not overly rely on individual skills, and employees are highly versatile. Managers do not need to know the sewing machine operators; they can determine which process to match and assign tasks based solely on work ability evaluation. It reduces management layers and improves production response speed. The data required by this invention is simple, as most types of sewing machines generate the necessary data during the sewing process, providing broad coverage. Compared to traditional manual piecework, this invention offers higher piecework accuracy and more stable wage settlement. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0069] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A smart production system based on sewing gauge parts, characterized by, The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process.

2. The smart production system based on sewing machine elements according to claim 1, characterized in that, The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database.

3. The smart production system based on sewing machine components of claim 1, wherein, The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process.

4. The smart production system based on sewing machine components of claim 1, wherein, The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database. The cloud service module is further configured to use an abnormality analysis algorithm to obtain differences between each sewing piece and a process template, which includes: identifying rework of the sewing piece through differences in the number of stitches; or identifying a position of an abnormality in the sewing piece through a time point of an abnormality in the sewing process. The cloud service module is further configured to use a skill analysis algorithm to analyze sewing data of the employee, obtain a skill matrix and a work efficiency of the employee, and save the skill analysis result to a personnel information database.

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