WO2024027224A1 - 基于缝纫计件的智能生产系统、方法、介质及计算机设备 - Google Patents

基于缝纫计件的智能生产系统、方法、介质及计算机设备 Download PDF

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Publication number
WO2024027224A1
WO2024027224A1 PCT/CN2023/090523 CN2023090523W WO2024027224A1 WO 2024027224 A1 WO2024027224 A1 WO 2024027224A1 CN 2023090523 W CN2023090523 W CN 2023090523W WO 2024027224 A1 WO2024027224 A1 WO 2024027224A1
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WIPO (PCT)
Prior art keywords
sewing
information
equipment
cloud service
service module
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PCT/CN2023/090523
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English (en)
French (fr)
Inventor
韩安太
曾树杰
栗硕
Original Assignee
杰克科技股份有限公司
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Application filed by 杰克科技股份有限公司 filed Critical 杰克科技股份有限公司
Publication of WO2024027224A1 publication Critical patent/WO2024027224A1/zh

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Classifications

    • D—TEXTILES; PAPER
    • D05—SEWING; EMBROIDERING; TUFTING
    • D05B—SEWING
    • D05B19/00—Programme-controlled sewing machines
    • D—TEXTILES; PAPER
    • D05—SEWING; EMBROIDERING; TUFTING
    • D05B—SEWING
    • D05B19/00—Programme-controlled sewing machines
    • D05B19/02—Sewing machines having electronic memory or microprocessor control unit
    • D—TEXTILES; PAPER
    • D05—SEWING; EMBROIDERING; TUFTING
    • D05B—SEWING
    • D05B69/00—Driving-gear; Control devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]

Definitions

  • This application relates to the field of sewing technology, and in particular to intelligent production systems, methods, media and computer equipment based on sewing piece counting.
  • the administrator uses the Web client in the visual terminal to form a one-to-one or one-to-many task file for operators and processes according to the matching method provided by the system, and uploads it to the server.
  • the operator obtains the task file corresponding to the operator from the server, confirms the task and starts working.
  • the operator completes machine learning on the first product through the working data of the sewing equipment obtained by the collector connected to the user terminal.
  • the user terminal will The machine learning results are bound to operators and corresponding tasks and then uploaded to the server.
  • the administrator matches operators and processes. Although the system provides recommended matching methods, there is no specific reason for the recommendation. This results in a low credibility of the recommended matching method.
  • the administrator does not have manual skill information when matching processes and labor. Only when the administrator understands the employee's work ability can the corresponding process be matched. However, managers know only a limited number of workers, which means that the existing system can only be used within a small group. The communication efficiency of terminals in a small area is lower than that of verbal communication. This system requires manual import of process and personnel data, which is inefficient.
  • the administrator can only get the number of pieces worked by employees, and cannot further regulate production, employee skill training, etc. through the number of pieces sewn by each employee. It has no positive impact on the production process and does not improve the efficiency of factory operations.
  • the purpose of this application is to provide an intelligent production system, method, medium and computer equipment based on sewing piece counting to solve the technical problems in the prior art such as low manual operation efficiency and low accuracy.
  • the first aspect of this application provides an intelligent production system based on sewing piecework, including: a digital board room module, sewing equipment, cloud service module, managed terminal, and manager terminal;
  • the cloud service module establishes communication connections with the digital board room module, sewing equipment, managed terminals and manager terminals respectively; the digital board room module is used to execute sample production after obtaining the order and review the used processes. Disassemble to generate each process and corresponding sewing requirement information; the sewing equipment performs corresponding sewing tasks according to each disassembled process and sends its sewing operation parameters in each process.
  • the cloud service module is used to search for the process that matches the sewing operation parameters from the process library, and based on the process requirement characteristics corresponding to the matched process. Match the corresponding operators; after the matching is completed, send the corresponding process information and task information to the backup manager terminal; after receiving the process information and task information, the backup manager terminal sends the process information and task information to the cloud service module.
  • the number of the sewing equipment used; the cloud service module also queries the equipment library according to the sewing equipment number to obtain the equipment information to bind the operator information, equipment information and process information; The sewing equipment information and sewing process information are sent to the manager terminal.
  • the cloud service module is also used to extract motion characteristic parameters of the sewing equipment during the sewing process, and compare them with the corresponding process templates through a similarity comparison algorithm. Yes, the number of pieces sewn by the sewing equipment is obtained and sent to the administrator terminal.
  • the calculation process of the similarity comparison algorithm includes: using the Euclidean distance algorithm to calculate the similarity between two sequences; for two sequences with the same length, calculating the similarity between each two sequences. The distances between points are then summed. The smaller the distance, the higher the similarity. For two sequences with different lengths, use a sliding window to copy the short sequence until it is the same length as the long sequence, and then calculate the distance between each two points. Then sum up. The smaller the distance, the higher the similarity.
  • the cloud service module is also used to analyze the sewing data of employees using a skill analysis algorithm to obtain skill analysis results such as the employee's skill matrix and work efficiency, and save them to in the personnel information database.
  • the cloud service module is also configured to use anomaly analysis algorithms to obtain each The difference between a sewn part and the process template includes: identifying the rework situation of the sewn part through the difference in stitch number; or identifying the abnormal position in the sewn part through the abnormal time point during the sewing process.
  • the cloud service module is also used to calculate the utilization rate and sewing time of the sewing equipment based on the time required for sewing each sewing piece and the motor running time. Speed information is sent and displayed on the administrator terminal.
  • the second aspect of the present application provides an intelligent production method based on sewing piece counting, including: obtaining each process and corresponding sewing requirement information generated based on sample garment production and process disassembly, And send each process and the corresponding sewing requirement information to the sewing equipment for the sewing equipment to perform sewing tasks; receive the sewing operation parameters in each process from the sewing equipment and save them in the process library.
  • a third aspect of the present application provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the intelligent production method based on sewing piecework is implemented. .
  • a fourth aspect of the present application provides a computer device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory. , so that the device executes the intelligent production method based on sewing piece counting.
  • This invention can automatically recommend qualified employees based on uploaded process information and give reasons for recommendation; at the same time, it performs automatic learning to continuously improve the fit between recommendations and managers' ideas.
  • the present invention can generate the time required for employees to sew a piece, so it can help managers carry out orders, production management and output forecasting. This greatly reduces the work threshold and work intensity for managers. No management experience is required to predict order completion time and optimally allocate personnel.
  • the present invention can establish a process database and a worker information database, and can consciously cultivate the weak points of workers' skills and improve workers' skills and abilities. This prevents production from overly relying on personal skills and making employees highly versatile.
  • Managers do not need to know lathes. They can judge which process is suitable and assign tasks only through work ability evaluation. It can reduce management levels and improve production response speed.
  • the data required by the present invention is simple, most types of sewing machines will generate the required data during the sewing process, and the coverage is wide.
  • the present invention has higher piece counting accuracy and a more stable basis for wage settlement.
  • Figure 1 shows a schematic structural diagram of an intelligent production system based on sewing piece counting in an embodiment of the present application.
  • Figure 2 shows a schematic flow chart of an intelligent production method based on sewing piece counting in an embodiment of the present application.
  • Figure 3 shows a schematic structural diagram of a computer device in an embodiment of the present application.
  • connection can be a fixed connection or a fixed connection. It is a detachable connection or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be an internal connection between two components.
  • connection can be a fixed connection or a fixed connection. It is a detachable connection or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be an internal connection between two components.
  • A, B or C or "A, B and/or C” means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C” . Exceptions to this definition occur only when a combination of elements, functions, or operations is inherently mutually exclusive in some manner.
  • the present invention provides an intelligent production solution based on sewing piecework, aiming to establish a database on the cloud platform, including a process library performed by the factory that is classified according to information such as process category, process difficulty, sewing time, and personnel.
  • the information database includes the personnel’s years of employment, past processes, capability matrix, etc.
  • the piece counting method uses the sample garments sewn in the digital board room as a template, and uses the data generated during the sewing process to calculate the pieces on the cloud platform. Workers can view the piece counting in real time and apply for corrections to the results.
  • the cloud platform generates information such as the time required for workers to sew a piece and estimated production capacity, which is displayed on the terminal for administrators to control production.
  • FIG. 1 a schematic structural diagram of an intelligent production system based on sewing piece counting is shown in an embodiment of the present invention.
  • the intelligent production system in this embodiment includes: digital board room module 11, sewing equipment 12, cloud service module 13, managed terminal 14, manager terminal 15.
  • the digital board room module 11 is communicatively connected to the cloud service module 13
  • the sewing equipment 12 is also communicatively connected to the cloud service module 13
  • the cloud service module 13 is also communicatively connected to the managed terminal 14 and the manager terminal 15.
  • the cloud service module 13 can be a server, which can be arranged on one or more physical servers according to functions, loads and other factors, or can be composed of distributed or centralized server clusters; or It can be a computer device such as a desktop computer, laptop computer, tablet computer, smartphone, smart bracelet, smart watch, smart helmet, smart TV, etc.
  • the managed terminal 14 is an electronic terminal suitable for being managed (such as mobile phones, pad computers, smart bracelets, smart watches, smart helmets and other devices).
  • the managed terminal usually refers to work under the jurisdiction of the administrator. Personnel, especially sewing operators.
  • the administrator terminal 15 is an electronic terminal suitable for administrators (such as mobile phones, pad computers, smart bracelets, smart watches, smart helmets and other devices).
  • the digital board room module is a functional module for generating a digital board room; the digital board room is aimed at the factory's research and development links such as style design, sample production, and process disassembly to realize styles and processes.
  • a solution for full-process management and control of information such as labor prices, design materials, etc.
  • the principle of the digital board room module to realize process disassembly is: in the sample sewing process, the sample worker uses A sewing machine with human-computer interaction function operates at the beginning and end of each process.
  • the machine data collected during this process is the digital template of the process, which can be used for automatic piece counting.
  • the intelligent production process of the intelligent production system is as follows:
  • Step a After obtaining the order, the digital board room module 11 executes sample production and disassembles the processes used to generate each process and corresponding sewing requirement information.
  • the sewing requirements include but are not limited to the sewing method of the disassembled process, parameters required for the sewing process, etc.
  • Step b The sewing equipment 12 performs corresponding sewing tasks according to each disassembled process and sends the sewing operation parameters of the sewing equipment 12 in each process to the cloud service module 13 and then saves them to the process library. middle. Specifically, each process can be sewn by the sample worker and the operating data of the sewing machine during the sewing process can be saved to the cloud platform process library through the gateway device, which is a database used to store process-related data. It should be noted that the aforementioned sample workers refer to skilled personnel with strong work skills. The sewing data and sewing time of the clothes sewn by them can be used as optimal templates.
  • 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 corresponding to the process. Level, skill proficiency required for the process, whether the operator has been required to do the relevant process, etc., find the most matching operator based on the above process requirements, and display the matched operator and matching degree on the manager terminal 15 for The manager determines whether the operator recommended by the result will sew the process through the matched operator and matching process.
  • the administrator saves the operator selection result information each time, uses the saved information as a labeled training data set, and inputs it into the deep learning artificial intelligence model for supervised training, so as to obtain an output that conforms to the administrator's style. prediction model to improve the accuracy of system recommendations.
  • the input parameters of the deep learning artificial intelligence model include the difficulty level of the process, the skill proficiency required for the process, whether the operator is required to have relevant process operation experience, and other parameters; the output parameters include the information of the selected operator, including but not limited to such as Operator number, basic information (such as name, gender, length of service, etc.), etc.
  • the deep learning artificial intelligence model includes but is not limited to a convolutional neural network model, a feedforward neural network model, a radial basis neural network model, etc., which are not limited in this embodiment.
  • Step d After completing the matching, 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 process information and task information to the cloud service module 13.
  • Step e The cloud service module 13 queries the equipment library according to the sewing equipment number to obtain equipment information to bind operator information, equipment information and process information; The processed process information is sent to the manager terminal 15 for the manager to view.
  • the cloud service module 13 also extracts motion characteristic parameters of the sewing equipment during the sewing process, including but Not limited to start and stop actions such as lifting the presser foot and motor and corresponding time stamps, data such as the number of stitches generated by motor movement are processed to extract features; then the similarity comparison algorithm is compared with the corresponding process template to obtain the sewing machine's sewing Number of pieces. The number of pieces sewn will be sent to the managed terminal 14 in real time for employees to verify the results. If employees have objections to the piece rate results, they can apply for modification.
  • start and stop actions such as lifting the presser foot and motor and corresponding time stamps
  • data such as the number of stitches generated by motor movement are processed to extract features
  • the similarity comparison algorithm is compared with the corresponding process template to obtain the sewing machine's sewing Number of pieces.
  • the number of pieces sewn will be sent to the managed terminal 14 in real time for employees to verify the results. If employees have objections to the piece rate results, they can apply for modification.
  • the similarity calculation in this embodiment belongs to the field of time series similarity.
  • the Euclidean distance algorithm is preferably used for similarity calculation. The principle is as follows: for sequences of the same length , calculate the distance between each two points and then sum. The smaller the distance, the higher the similarity. For sequences of different lengths, there are generally two methods to deal with them. One is subsequence matching (finding the part of the long sequence that is most similar to the short sequence), and the other is sliding window, which refers to copying the short sequence until it matches the long sequence. long.
  • the cloud service module 13 uses a skill analysis algorithm to analyze the employee's sewing data, obtains the employee's skill matrix and work efficiency and other skill analysis results, and saves them to the personnel information database of the cloud platform.
  • the skill analysis algorithm is the most commonly used method for analyzing non-managerial work. It is suitable for the analysis of simple jobs as well as complex jobs.
  • the cloud service module 13 predicts the daily production capacity through the piece counting results and the production capacity prediction algorithm and displays it on the manager terminal 15 .
  • the cloud service module 13 also uses an anomaly analysis algorithm to obtain abnormal conditions of the clothing based on the differences between the sewing process of each piece and the template. Specifically, it includes: identifying the rework status of clothing through the difference in stitch number; identifying abnormal locations on clothing through the time points when abnormalities occur during the sewing process. The cloud service module 13 sends these abnormal point information to the managed terminal 14, so that the abnormal points can be discovered and inspected and repaired during the sewing stage of the employees, which can reduce the workload of the quality inspector during the quality inspection process. Prevent defective products from being missed.
  • Utilization rate refers to the proportion of time that equipment takes to create value within the time it can provide.
  • each module of the above system is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or they can also be physically separated. And these modules can all be processed in software through processing components It can be implemented in the form of calling; it can also be all implemented in the form of hardware; it can also be implemented in the form of some modules calling software through processing elements, and some modules can be implemented in the form of hardware.
  • the cloud service module can be a separate processing element, or it can be integrated into a chip of the above system.
  • it can also be stored in the memory of the above system in the form of program code, and processed by one of the above systems. The component calls and executes the functions of the above cloud service module.
  • each step of the above method or each of the above modules can be completed by instructions in the form of hardware integrated logic circuits or software in the processor element.
  • the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs for short), or one or more microprocessors ( digital signal processor (DSP for short), or one or more field programmable gate arrays (Field Programmable Gate Array (FPGA for short)), etc.
  • ASICs Application Specific Integrated Circuits
  • DSP digital signal processor
  • FPGA Field Programmable Gate Array
  • the processing element can be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU for short) or other processors that can call program code.
  • these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).
  • SOC system-on-a-chip
  • the cloud service module can be a server, or a desktop computer, notebook computer, tablet computer, smart phone, Smart bracelets, smart watches, smart helmets, smart TVs and other computer equipment.
  • the intelligent production method based on sewing piece counting mainly includes the following steps:
  • Step S21 Obtain each process and corresponding sewing requirement information generated based on sample garment production and process disassembly, and send each process and corresponding sewing requirement information to the sewing equipment for the sewing equipment to perform sewing. Make tasks.
  • Step S22 Receive the sewing operation parameters in each process from the sewing equipment and save them in the process library, and search for the process matching the sewing operation parameters from the process library, and use the matched process according to the The corresponding process requires characteristics to match the corresponding operator.
  • 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.
  • Step S24 Query the equipment library according to the sewing equipment number to obtain equipment information to bind operator information, equipment information and process information; combine the sewing equipment information used at the operator's station and the sewing items The process information is sent to the manager terminal.
  • FIG. 3 a schematic structural diagram of a computer device in an embodiment of the present invention is shown.
  • the computer equipment 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 through a system bus and completes mutual communication.
  • the memory 32 is used to store computer programs and communicate with each other.
  • the processor 33 is used to communicate with other devices, and the processor 31 is used to run a computer program so that the electronic terminal executes each step of the intelligent production method based on sewing piecework.
  • the system bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the system bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
  • the communication interface is used to implement communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries).
  • the memory may include random access memory (RAM), or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, referred to as CPU), a network processor (Network Processor, referred to as NP), etc.; it can also be a digital signal processor (Digital Signal Processing, referred to as DSP) , Application Specific Integrated Circuit (ASIC for short), Field-Programmable Gate Array (FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.
  • CPU Central Processing Unit
  • NP Network Processor
  • DSP Digital Signal Processing
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • the present invention also provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the intelligent production method based on sewing piecework is implemented.
  • the aforementioned computer program can be stored in a computer-readable storage medium.
  • the steps including the above-mentioned method embodiments are executed; and the aforementioned storage media include: ROM, RAM, magnetic disks, optical disks and other media that can store program codes.
  • the computer readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, A USB flash drive, a mobile hard disk, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by the computer. Also, any connection is properly termed a computer-readable medium.
  • 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 medium.
  • coaxial cable, fiber optic Cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of medium.
  • computer readable and writable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, and are instead intended for non-transitory, tangible storage media.
  • Disks and optical disks include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs. Disks typically copy data magnetically, while discs use lasers to optically copy data. Copy the data locally.
  • this application provides an intelligent production system, method, medium and computer equipment based on sewing piecework.
  • the present invention can automatically recommend qualified employees based on uploaded process information and give reasons for the recommendation; at the same time, it can automatically learn , continuously improve the fit between recommendations and managers’ ideas; it can generate the time required for employees to sew a piece, so it can help managers conduct orders, production management and output forecasting. This greatly reduces the work threshold and work intensity for managers. It is possible to predict order completion time and optimally allocate personnel without management experience; it is possible to establish a process library and worker information library, and to consciously cultivate the weak points of workers' skills and improve their skills. Worker skills and abilities.

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  • Engineering & Computer Science (AREA)
  • Textile Engineering (AREA)
  • Computer Hardware Design (AREA)
  • Mechanical Engineering (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Quality & Reliability (AREA)
  • Manufacturing & Machinery (AREA)
  • General Factory Administration (AREA)
  • Sewing Machines And Sewing (AREA)

Abstract

一种基于缝纫计件的智能生产系统、方法、介质及计算机设备，包括：数字板房模块（11）、缝制设备（12）、云服务模块（13）、被管理者终端（14）、管理者终端（15）；云服务模块（13）分别与数字板房模块（11）、缝制设备（12）、被管理者终端（14）及管理者终端（15）建立通信连接；数字板房模块（11）用于在获取订单后执行样衣制作并对所用工序进行拆解，以生成各工序及对应的缝制要求信息；缝制设备（12）按照被拆解的每个工序执行相应的缝制任务，并将其在每个工序中的缝制运行参数发送至云服务模块（13）后保存至工序库中；云服务模块（13）用于从工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员；在匹配完成后将对应的工艺信息和任务信息发送至被管理者终端（14）；被管理者终端（14）在接收到工艺信息和任务信息后向云服务模块（13）发送其所使用的缝制设备编号；云服务模块（13）还根据缝制设备（12）编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备（12）信息及所缝制的工艺信息发送至管理者终端（15）。该系统能够根据上传的工艺信息自动推荐符合条件的员工，帮助管理人员进行订单、生产管理与产量预测；能够建立工艺库与工人信息库，管理人员不需要认识车工仅仅通过工作能力评价即可判断匹配哪种工序，分配任务等，减少管理的层级提高生产响应速度；相比于传统的人工计件，计件准确性更高，工资结算依据更稳定。

Description

基于缝纫计件的智能生产系统、方法、介质及计算机设备 技术领域
本申请涉及缝制技术领域，特别是涉及基于缝纫计件的智能生产系统、方法、介质及计算机设备。
背景技术
目前服装生产过程的管理普遍严重滞后，由于产业特性习惯以订单为总结单位、人工计件、质检等因素。如果要得知当天的产能、生产良品率等生产数据往往至少需要二天甚至三天，这对于生产的及时管理非常不利。行业目前没有令人信服的人员评价、评级方式，因此需要人数众多的底层管理者进行任务分配。在实际生产中往往以小组的形式生产，由组长分配任务。且组长人数较多且经验丰富，需要大量的人力成本。
现有技术通常通过如下几种方式解决，但各有各的弊端：
1)管理员通过可视化终端中的Web客户端将操作工和工艺按照系统提供的匹配方式，形成一对一或者一对多的任务文件，上传至服务器。
2)操作工从服务器获取对应操作工的任务文件，确认任务并开始工作，操作工通过与用户终端连接的采集器获取的缝纫设备的工作数据对第一件产品完成机器学习，同时用户终端将机器学习结果与操作工和对应任务绑定后上传至服务器。
3)操作工使用缝纫设备过程中，采集器获取的缝纫设备的工作数据满足机器学习的结果，计件数自动加1，同时用户终端将计件数实时上传至服务器。由此进行计件(CN202011605979.7)。
上述现有技术有如下缺点：
1)由管理员进行操作工和工艺进行匹配，虽然系统有提供推荐匹配方式，但是没有推荐的具体原因。这会导致推荐的匹配方式可信度低。管理员在进行工艺与人工匹配时无人工的技能信息，只有管理员了解该员工工作能力时才能匹配对应的工序。但管理人员了解的工人有限，也就导致现有系统只能小组内使用，小范围使用终端交流效率要比口头联系的效率低。此系统需要人工导入工艺与人员数据，效率低下。
2)现有技术只有目前需要缝制的工艺，但没有工人过往缝制的相关工艺的信息，以及工厂曾经做过的工序库等信息，这些信息对工人能力及工序的匹配有促进效果。
3)管理员只能得到员工工作件数，无法通过每位员工的缝制件数进行进一步的排产，员工技能培养等进一步调控。没有对生产过程产生正面影响，也没有提高工厂运转效率。
4)由操作工的第一个缝制数据作为机器学习对象无法获得正确的数据，工人在第一次缝制时往往不熟练、误操作比较多。以错误的数据作为机器学习对象会对计件结果产生误判。且计件错误后无法修正这就意味着员工依然要不停查看计件结果并且产生错误后依然要手动计件。实用性较差，没有提高员工工作效率。
发明内容
鉴于以上所述现有技术的缺点，本申请的目的在于提供基于缝纫计件的智能生产系统、方法、介质及计算机设备，用于解决现有技术中人工操作效率低准确率低等技术问题。
为实现上述目的及其他相关目的，本申请的第一方面提供一种基于缝纫计件的智能生产系统，包括：数字板房模块、缝制设备、云服务模块、被管理者终端、管理者终端；所述云服务模块分别与所述数字板房模块、缝制设备、被管理者终端及管理者终端建立通信连接；所述数字板房模块用于在获取订单后执行样衣制作并对所用工序进行拆解，以生成各工序及对应的缝制要求信息；所述缝制设备按照被拆解的每个工序执行相应的缝制任务，并将其在每个工序中的缝制运行参数发送至所述云服务模块后保存至工序库中；所述云服务模块用于从所述工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员；在匹配完成后将对应的工艺信息和任务信息发送至所述备管理者终端；所述备管理者终端在接收到所述工艺信息和任务信息后向云服务模块发送其所使用的缝制设备编号；所述云服务模块还根据缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至所述管理者终端。
于本申请的第一方面的一些实施例中，所述云服务模块还用于提取缝制过程中缝制设备的运动特征参数，并通过相似度比对算法将之与对应的工序模板进行比对，得到缝制设备的缝制件数并发送至所述管理者终端。
于本申请的第一方面的一些实施例中，所述相似度比对算法的计算过程包括：利用欧式距离算法对两个序列的相似度进行不对；对于长度相同的两个序列，计算每两点之间的距离然后求和，距离越小表示相似度越高；对于长度不同的两个序列，利用滑动窗口，复制短序列直至与长序列等长后，再计算每两点之间的距离然后求和，距离越小表示相似度越高。
于本申请的第一方面的一些实施例中，所述云服务模块还用于使用技能分析算法对员工的缝制数据进行分析，得到员工的技能矩阵和工作效率等技能分析结果，并保存至人员信息库中。
于本申请的第一方面的一些实施例中，所述云服务模块还用于使用异常分析算法得到每 个缝制件与工序模板之间的差异，其包括：通过针数的差异识别缝制件的返工情况；或者，通过缝制过程中出现异常的时间点识别缝制件中出现异常的位置。
于本申请的第一方面的一些实施例中，所述云服务模块还用于根据每个缝制件的缝制所需时间、电机运行时间，计算得到缝制设备的稼动率和缝制速度信息，发送并显示于所述管理者终端。
为实现上述目的及其他相关目的，本申请的第二方面提供一种基于缝纫计件的智能生产方法，包括：获取根据样衣制作及工序拆解所生成的各工序及对应的缝制要求信息，并将所述各工序及对应的缝制要求信息发送至缝制设备，供缝制设备执行缝制任务；从所述缝制设备接收每个工序中的缝制运行参数后保存至工序库中，并从所述工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员；在匹配完成后将对应的工艺信息和任务信息发送至管理者终端，并从所述管理者终端获取对应的缝制设备编号；根据所述缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至管理者终端。
为实现上述目的及其他相关目的，本申请的第三方面提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现所述基于缝纫计件的智能生产方法。
为实现上述目的及其他相关目的，本申请的第四方面提供一种计算机设备，包括：处理器及存储器；所述存储器用于存储计算机程序，所述处理器用于执行所述存储器存储的计算机程序，以使所述设备执行所述基于缝纫计件的智能生产方法。
如上所述，本申请的基于缝纫计件的智能生产系统、方法、介质及计算机设备，具有以下有益效果：
1)本发明能够根据上传的工艺信息自动推荐符合条件的员工并给出推荐理由；与此同时，进行自动学习，不断提高推荐与管理人员想法的契合度。
2)本发明能够生成员工缝制一件所需时间，因此能够帮助管理人员进行订单、生产管理与产量预测。这大大降低了对管理人员的工作门槛和工作强度，无需管理经验也能够预测订单完成时间，并进行人员最优分配。
3)本发明能够建立工艺库与工人信息库，能够有意识进行培养工人技能薄弱点，提高工人技能能力。使生产不会过度依赖个人技能，员工通用性强。
4)管理人员不需要认识车工仅仅通过工作能力评价即可判断匹配哪种工序，分配任务等。 能够减少管理的层级提高生产响应速度。
5)本发明所需数据简单，大多数种类的缝纫机缝制过程中都会产生所需数据，覆盖面广。
6)本发明相比于传统的人工计件，计件准确性更高，工资结算依据更稳定。
附图说明
图1显示为本申请一实施例中基于缝纫计件的智能生产系统的结构示意图。
图2显示为本申请一实施例中基于缝纫计件的智能生产方法的流程示意图。
图3显示为本申请一实施例中计算机设备的结构示意图。
具体实施方式
以下通过特定的具体实例说明本申请的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本申请的其他优点与功效。本申请还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本申请的精神下进行各种修饰或改变。需说明的是，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
需要说明的是，在下述描述中，参考附图，附图描述了本申请的若干实施例。应当理解，还可使用其他实施例，并且可以在不背离本申请的精神和范围的情况下进行机械组成、结构、电气以及操作上的改变。下面的详细描述不应该被认为是限制性的，并且本申请的实施例的范围仅由公布的专利的权利要求书所限定。这里使用的术语仅是为了描述特定实施例，而并非旨在限制本申请。空间相关的术语，例如“上”、“下”、“左”、“右”、“下面”、“下方”、“下部”、“上方”、“上部”等，可在文中使用以便于说明图中所示的一个元件或特征与另一元件或特征的关系。
在本申请中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”、“固持”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
再者，如同在本文中所使用的，单数形式“一”、“一个”和“该”旨在也包括复数形式，除非上下文中有相反的指示。应当进一步理解，术语“包含”、“包括”表明存在所述的特征、操作、元件、组件、项目、种类、和/或组，但不排除一个或多个其他特征、操作、元件、组件、项目、种类、和/或组的存在、出现或添加。此处使用的术语“或”和“和/或” 被解释为包括性的，或意味着任一个或任何组合。因此，“A、B或C”或者“A、B和/或C”意味着“以下任一个：A；B；C；A和B；A和C；B和C；A、B和C”。仅当元件、功能或操作的组合在某些方式下内在地互相排斥时，才会出现该定义的例外。
为解决背景技术中的问题，本发明提供基于缝纫计件的智能生产方案，旨在云平台建立数据库，包括工厂所做过的工序库根据工序类别、工序难度、缝制时长等信息进行分类，人员信息库包括人员的从业年限、过往做过的工艺、能力矩阵等。由此，订单在制作样衣阶段就能进行工序的分类判断、根据工人能力进行推荐。管理员即便完全不认识所推荐人员也能够通过工人所做过的工艺、能力矩阵等信息判断此人能否胜任。计件方式则是以数字板房缝制的样衣为模板、通过缝制过程中产生的数据在云平台进行计件且工人能够实时查看计件并可申请对结果进行修正。云平台生成工人缝制一件所需时间、预计产能等信息展示在终端，用于管理员进行生产调控。
为了使本发明的目的、技术方案及优点更加清楚明白，通过下述实施例并结合附图，对本发明实施例中的技术方案的进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定发明。
如图1所示，展示了本发明实施例中的一种基于缝纫计件的智能生产系统的结构示意图。本实施例中的智能生产系统包括：数字板房模块11、缝制设备12、云服务模块13、被管理者终端14、管理者终端15，数字板房模块11通信连接所述云服务模块13，缝制设备12也通信连接所述云服务模块13，所述云服务模块13还通信连接所述被管理者终端14和管理者终端15。
值得说明的是，所述云服务模块13可以是服务器，所述服务器可以根据功能、负载等多种因素布置在一个或多个实体服务器上，也可以由分布的或集中的服务器集群构成；也可以是台式电脑、笔记本电脑、平板电脑、智能手机、智能手环、智能手表、智能头盔、智能电视等计算机设备。所述被管理者终端14是适用于被管理者的一种电子终端(例如手机、pad电脑、智能手环、智能手表、智能头盔等设备)，被管理者通常是指受管理员管辖的工作人员，尤指缝纫操作工。所述管理者终端15是适用于管理员的一种电子终端(例如手机、pad电脑、智能手环、智能手表、智能头盔等设备)。
另需说明的是，所述数字板房模块是一种生成数字板房的功能模块；所述数字板房是针对工厂的款式设计、样衣制作、工序拆解等研发环节，实现款式、工序、工价、设计资料等信息全流程管控的一个解决方案。
于本实施例中，数字板房模块实现工序拆解的原理在于：在样衣缝制环节，样衣工使用 具有人机交互功能的缝纫机，在每个工序开始、结束时进行操作，这中间采集到的机器数据即为工序的数字模板，可用于自动计件。
在本实施例中，智能生产系统的智能生产流程如下：
步骤a.在获取到订单后由数字板房模块11执行样衣制作并对所用工序进行拆解，以生成各工序及对应的缝制要求信息。所述缝制要求包括但不限于被拆解的工序缝制方法、缝制工工艺所需参数等。
步骤b.缝制设备12按照被拆解的每个工序执行相应的缝制任务并将所述缝制设备12在每个工序中的缝制运行参数发送至云服务模块13后保存至工序库中。具体而言，可由样衣工缝制每个工序并将缝纫过程中缝纫机的运行数据通过网关设备保存至云平台工序库，即用于存储工序相关数据的数据库。需说明的是，前述样衣工是指具有较强工作技能的娴熟人员，经由他们缝制的衣物，其缝制数据和缝制时间可以作为最优模板。
步骤c.云服务模块13从所述工序库中找出与之匹配的工序，并根据匹配出的工序所对应的工序要求特征匹配操作人员；所述工序要求特征包括但不限于工序对应的难度等级、工序所需技能熟练度、要求操作人员是否做过相关工序等，根据上述这些工序要求去寻找最为匹配的操作人员，并将匹配到的操作人员及匹配程度展示于管理者终端15，供管理者通过所述匹配到的操作人员及匹配程判断是否由该结果推荐的操作人员来缝制该工艺。
进一步地，将管理者每次的操作人员选择结果信息进行保存，将保存的这些信息作为标签后的训练数据集，输入到深度学习人工智能模型中进行监督训练，从而得到能够输出符合管理员风格的预测模型，提高系统推荐的准确性。所述深度学习人工智能模型的输入参数包括工序的难度等级、工序所需技能熟练度、是否需要操作人员有相关工序操作经验等参数；输出参数包括被选中操作人员的信息，包括但不限于如操作人员编号、基本信息(如姓名、性别、工龄等)等。另外，所述深度学习人工智能模型包括但不限于卷积神经网络模型、前馈神经网络模型、径向基神经网络模型等，本实施例不做限定。
步骤d.在完成匹配后，云服务模块13将工艺信息和任务信息发送至被管理者终端14；被管理者终端14在接收到所述工艺信息和任务信息后，向云服务模块13发送其所使用的缝制设备编号。
步骤e.云服务模块13根据缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至管理者终端15供管理者查看。
在一些示例中，云服务模块13还通过提取缝制过程中缝制设备的运动特征参数，包括但 不限于如抬压脚和电机等启停动作及对应时间戳，电机运动产生的针数等数据进行处理提取特征；然后通过相似度对比算法与对应的工序模板进行比对，得到缝纫机的缝制件数。缝制的件数将实时发送至被管理者终端14，供员工对结果进行验证。若员工对计件结果产生异议则可申请修改。
值得说明的是，本实施例中的相似度计算隶属于时间序列相似性领域，为计算曲线相似性或曲线匹配问题，优选采用欧式距离算法进行相似度计算，其原理如下：对于长度相同的序列，计算每两点之间的距离然后求和，距离越小表示相似度越高。对于不同长度的序列，一般有两种方法来处理，一个是子序列匹配(找出长序列中与短序列最相似的部分)，另一个是滑动窗口，是指复制短序列直至与长序列等长。
在一些示例中，云服务模块13使用技能分析算法对员工的缝制数据进行分析，得到员工的技能矩阵和工作效率等技能分析结果，并保存至云平台的人员信息库中。所述技能分析算法是用以分析非管理性工作最常使用的一种方法，它既适用于对简单工作的分析，也适用于对复杂性工作的分析，这种方法的关键之处在于其系统性，从而为培训项目的设计提供充分的资料依据；目前，技能分析算法通常应涉及到如下内容：1)工作的设施与员工身体条件是否相适应；2)工作环境条件对职工生理和心理是否有影响；3)职工的工作态度是否端正，积极性是否高涨；4)对职工工作过程进行详细分析。通过以上分析来实现技能分析。另外，前述的技能矩阵是指以矩阵图的形式来展示员工的各项技能分布情况。
在一些示例中，云服务模块13通过计件结果及产能预测算法对每日产能进行预测并显示于管理者终端15。
在一些示例中，云服务模块13还对每一件的缝制过程与模板的差异通过异常分析算法得到衣物的异常情况。具体包括：通过针数的差异识别衣物的返工情况；通过缝制过程中异常出现的时间点识别衣物上出现异常的位置。云服务模块13将这些异常点信息发送至被管理者终端14，由此可在员工缝制阶段就能发现异常点并检查修复，这在质检过程中就能减少质检员的工作量，防止瑕疵品被遗漏。
进一步地，通过每一件缝制品的缝制所需时间、电机运动时间等信息，能够计算得到机器稼动率、缝制速度等信息；将这些信息显示于管理者终端15，从而能使管理者实时了解工厂生产情况便于精细管理。稼动率是指设备在所能提供的时间内为了创造价值而占用的时间所占的比重。
应理解以上系统的各个模块的划分仅仅是一种逻辑功能的划分，实际实现时可以全部或部分集成到一个物理实体上，也可以物理上分开。且这些模块可以全部以软件通过处理元件 调用的形式实现；也可以全部以硬件的形式实现；还可以部分模块通过处理元件调用软件的形式实现，部分模块通过硬件的形式实现。例如，云服务模块可以为单独设立的处理元件，也可以集成在上述系统的某一个芯片中实现，此外，也可以以程序代码的形式存储于上述系统的存储器中，由上述系统的某一个处理元件调用并执行以上云服务模块的功能。其它模块的实现与之类似。此外这些模块全部或部分可以集成在一起，也可以独立实现。这里所述的处理元件可以是一种集成电路，具有信号的处理能力。在实现过程中，上述方法的各步骤或以上各个模块可以通过处理器元件中的硬件的集成逻辑电路或者软件形式的指令完成。
例如，以上这些模块可以是被配置成实施以上方法的一个或多个集成电路，例如：一个或多个特定集成电路(Application Specific Integrated Circuit，简称ASIC)，或，一个或多个微处理器(digital signal processor，简称DSP)，或，一个或者多个现场可编程门阵列(Field Programmable Gate Array，简称FPGA)等。再如，当以上某个模块通过处理元件调度程序代码的形式实现时，该处理元件可以是通用处理器，例如中央处理器(Central Processing Unit，简称CPU)或其它可以调用程序代码的处理器。再如，这些模块可以集成在一起，以片上系统(system-on-a-chip，简称SOC)的形式实现。
如图2所示，展示了本发明实施例中的一种基于缝纫计件的智能生产方法的流程示意图。本实施例中的智能生产方法应用于上文实施例中的云服务模块，如上文所述的，所述云服务模块可以是服务器，也可以是台式电脑、笔记本电脑、平板电脑、智能手机、智能手环、智能手表、智能头盔、智能电视等计算机设备。
在本实施例中，所述基于缝纫计件的智能生产方法主要包括如下各步骤：
步骤S21：获取根据样衣制作及工序拆解所生成的各工序及对应的缝制要求信息，并将所述各工序及对应的缝制要求信息发送至缝制设备，供缝制设备执行缝制任务。
步骤S22：从所述缝制设备接收每个工序中的缝制运行参数后保存至工序库中，并从所述工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员。
步骤S23：在匹配完成后将对应的工艺信息和任务信息发送至管理者终端，并从所述管理者终端获取对应的缝制设备编号。
步骤S24：根据所述缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至管理者终端。
需说明的是，本实施例中基于缝纫计件的智能生产方法，其实施方式与上文中的基于缝 纫计件的智能生产系统类似，故不再赘述。
如图3所示，展示了本发明实施例中的一种计算机设备的结构示意图。本实例提供的计算机设备，包括：处理器31、存储器32、通信器33；存储器32通过系统总线与处理器31和通信器33连接并完成相互间的通信，存储器32用于存储计算机程序，通信器33用于和其他设备进行通信，处理器31用于运行计算机程序，使电子终端执行如上基于缝纫计件的智能生产方法的各个步骤。
上述提到的系统总线可以是外设部件互连标准(Peripheral Component Interconnect，简称PCI)总线或扩展工业标准结构(Extended Industry Standard Architecture，简称EISA)总线等。该系统总线可以分为地址总线、数据总线、控制总线等。为便于表示，图中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。通信接口用于实现数据库访问装置与其他设备(例如客户端、读写库和只读库)之间的通信。存储器可能包含随机存取存储器(Random Access Memory，简称RAM)，也可能还包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。
上述的处理器可以是通用处理器，包括中央处理器(Central Processing Unit，简称CPU)、网络处理器(Network Processor，简称NP)等；还可以是数字信号处理器(Digital Signal Processing，简称DSP)、专用集成电路(Application Specific Integrated Circuit，简称ASIC)、现场可编程门阵列(Field－Programmable Gate Array，简称FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。
本发明还提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现所述基于缝纫计件的智能生产方法。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过计算机程序相关的硬件来完成。前述的计算机程序可以存储于一计算机可读存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
于本申请提供的实施例中，所述计算机可读写存储介质可以包括只读存储器、随机存取存储器、EEPROM、CD-ROM或其它光盘存储装置、磁盘存储装置或其它磁存储设备、闪存、U盘、移动硬盘、或者能够用于存储具有指令或数据结构形式的期望的程序代码并能够由计算机进行存取的任何其它介质。另外，任何连接都可以适当地称为计算机可读介质。例如，如果指令是使用同轴电缆、光纤光缆、双绞线、数字订户线(DSL)或者诸如红外线、无线电和微波之类的无线技术，从网站、服务器或其它远程源发送的，则所述同轴电缆、光纤光 缆、双绞线、DSL或者诸如红外线、无线电和微波之类的无线技术包括在所述介质的定义中。然而，应当理解的是，计算机可读写存储介质和数据存储介质不包括连接、载波、信号或者其它暂时性介质，而是旨在针对于非暂时性、有形的存储介质。如申请中所使用的磁盘和光盘包括压缩光盘(CD)、激光光盘、光盘、数字多功能光盘(DVD)、软盘和蓝光光盘，其中，磁盘通常磁性地复制数据，而光盘则用激光来光学地复制数据。
综上所述，本申请提供基于缝纫计件的智能生产系统、方法、介质及计算机设备，本发明能够根据上传的工艺信息自动推荐符合条件的员工并给出推荐理由；与此同时，进行自动学习，不断提高推荐与管理人员想法的契合度；能够生成员工缝制一件所需时间，因此能够帮助管理人员进行订单、生产管理与产量预测。这大大降低了对管理人员的工作门槛和工作强度，无需管理经验也能够预测订单完成时间，并进行人员最优分配；能够建立工艺库与工人信息库，能够有意识进行培养工人技能薄弱点，提高工人技能能力。使生产不会过度依赖个人技能，员工通用性强；管理人员不需要认识车工仅仅通过工作能力评价即可判断匹配哪种工序，分配任务等。能够减少管理的层级提高生产响应速度；本发明所需数据简单，大多数种类的缝纫机缝制过程中都会产生所需数据，覆盖面广；本发明相比于传统的人工计件，计件准确性更高，工资结算依据更稳定。所以，本申请有效克服了现有技术中的种种缺点而具高度产业利用价值。
上述实施例仅例示性说明本申请的原理及其功效，而非用于限制本申请。任何熟悉此技术的人士皆可在不违背本申请的精神及范畴下，对上述实施例进行修饰或改变。因此，举凡所属技术领域中具有通常知识者在未脱离本申请所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本申请的权利要求所涵盖。

Claims (9)

  1. 一种基于缝纫计件的智能生产系统，其特征在于，包括：
    数字板房模块、缝制设备、云服务模块、被管理者终端、管理者终端；所述云服务模块分别与所述数字板房模块、缝制设备、被管理者终端及管理者终端建立通信连接；
    所述数字板房模块用于在获取订单后执行样衣制作并对所用工序进行拆解，以生成各工序及对应的缝制要求信息；所述缝制设备按照被拆解的每个工序执行相应的缝制任务，并将其在每个工序中的缝制运行参数发送至所述云服务模块后保存至工序库中；所述云服务模块用于从所述工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员；在匹配完成后将对应的工艺信息和任务信息发送至所述备管理者终端；所述备管理者终端在接收到所述工艺信息和任务信息后向云服务模块发送其所使用的缝制设备编号；所述云服务模块还根据缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至所述管理者终端。
  2. 根据权利要求1所述的基于缝纫计件的智能生产系统，其特征在于，所述云服务模块还用于提取缝制过程中缝制设备的运动特征参数，并通过相似度比对算法将之与对应的工序模板进行比对，得到缝制设备的缝制件数并发送至所述管理者终端。
  3. 根据权利要求1所述的基于缝纫计件的智能生产系统，其特征在于，所述相似度比对算法的计算过程包括：利用欧式距离算法对两个序列的相似度进行不对；对于长度相同的两个序列，计算每两点之间的距离然后求和，距离越小表示相似度越高；对于长度不同的两个序列，利用滑动窗口，复制短序列直至与长序列等长后，再计算每两点之间的距离然后求和，距离越小表示相似度越高。
  4. 根据权利要求1所述的基于缝纫计件的智能生产系统，其特征在于，所述云服务模块还用于使用技能分析算法对员工的缝制数据进行分析，得到员工的技能矩阵和工作效率等技能分析结果，并保存至人员信息库中。
  5. 根据权利要求1所述的基于缝纫计件的智能生产系统，其特征在于，所述云服务模块还用于使用异常分析算法得到每个缝制件与工序模板之间的差异，其包括：通过针数的差异识别缝制件的返工情况；或者，通过缝制过程中出现异常的时间点识别缝制件中出现异常的位置。
  6. 根据权利要求1所述的基于缝纫计件的智能生产系统，其特征在于，所述云服务模块还用于根据每个缝制件的缝制所需时间、电机运行时间，计算得到缝制设备的稼动率和缝制速度信息，发送并显示于所述管理者终端。
  7. 一种基于缝纫计件的智能生产方法，其特征在于，包括：
    获取根据样衣制作及工序拆解所生成的各工序及对应的缝制要求信息，并将所述各工序及对应的缝制要求信息发送至缝制设备，供缝制设备执行缝制任务；
    从所述缝制设备接收每个工序中的缝制运行参数后保存至工序库中，并从所述工序库中查找与缝制运行参数相匹配的工序，并根据匹配出的工序所对应的工序要求特征来匹配对应的操作人员；
    在匹配完成后将对应的工艺信息和任务信息发送至管理者终端，并从所述管理者终端获取对应的缝制设备编号；
    根据所述缝制设备编号查询设备库以获取设备信息，以将操作人员信息、设备信息和工艺信息进行绑定；将操作人员所在工位使用的缝制设备信息及所缝制的工艺信息发送至管理者终端。
  8. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求7所述基于缝纫计件的智能生产方法。
  9. 一种计算机设备，其特征在于，包括：处理器及存储器；
    所述存储器用于存储计算机程序；
    所述处理器用于执行所述存储器存储的计算机程序，以使所述终端执行如权利要求7所述基于缝纫计件的智能生产方法。
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