CN113297687A

CN113297687A - Clothing control and clothing method, equipment, system and storage medium

Info

Publication number: CN113297687A
Application number: CN202110062893.2A
Authority: CN
Inventors: 杨硕; 高翔
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2021-01-18
Filing date: 2021-01-18
Publication date: 2021-08-24

Abstract

The embodiment of the application provides a clothes making control and clothes making method, equipment, a system and a storage medium. In the embodiment of the application, parameter recommendation of the garment making equipment in a garment making scene is combined with artificial intelligence, an equipment parameter recommendation-determination model based on fabric attribute information and garment making process parameters is constructed, when the garment making equipment needs to execute a garment making task, the fabric attribute information and the garment making process parameters related to the current garment making task are combined, the required equipment parameters can be efficiently and reasonably recommended to the garment making equipment based on the equipment parameter recommendation-determination model, the parameter recommendation mode is not limited by the experience of machine repairment, and the equipment parameters recommended based on the model are more reasonable, the number of trial and error adjustment is not needed or can be reduced, so the trial and error cost of the equipment parameters can be saved.

Description

Clothing control and clothing method, equipment, system and storage medium

Technical Field

The application relates to the technical field of Internet of things, in particular to a clothes making control and clothes making method, equipment, system and storage medium.

Background

In the sewing production process of the clothes, due to the switching of the fabric and the sewing process, the equipment parameters of the sewing machine and other clothes making equipment also need to be correspondingly adjusted. In the prior art, the parameters of the equipment used in the historical production process are collected and stored in the sewing machine in advance; when the historical fabric and the corresponding sewing process occur again, the machine repair personnel select equipment parameters from the prestored equipment parameters through the electronic control panel or the mechanical piece by virtue of skill experience, and the sewing effect of the sewing workers in the production field is continuously debugged according to the equipment parameters. The method has higher requirements on the skill and experience of machine repair personnel, the whole trial and error process is time-consuming, and if the equipment parameters are not properly selected, sewing quality problems such as loose lines, wrinkling fabrics and the like can be caused.

Disclosure of Invention

Aspects of the present application provide a clothing control and clothing method, device, system and storage medium, so as to enable clothing equipment to rapidly and efficiently obtain appropriate equipment parameters, thereby improving clothing quality.

The embodiment of the application provides a clothing control system, includes: the system comprises a cloud server, a control terminal and at least one piece of clothes making equipment, wherein the control terminal and the at least one piece of clothes making equipment are deployed in a production environment; the control terminal is used for receiving a clothing order, issuing a current clothing task corresponding to the clothing order to a target clothing device in the at least one clothing device, and sending fabric attribute information and clothing process parameters related to the clothing order to the cloud server so that the cloud server can provide device parameters for the target clothing device; the cloud server is used for operating a target equipment parameter recommendation-determination model to obtain target equipment parameters according to the fabric attribute information and the clothing making process parameters, and issuing the target equipment parameters to the target clothing making equipment; and the target clothes making equipment is used for operating according to the target equipment parameters issued by the cloud server so as to complete the current clothes making task according to the clothes making technological parameters.

The embodiment of the application also provides a clothing making control method, which comprises the following steps: receiving a clothing order; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to target equipment parameters; and sending the fabric attribute information and the clothing process parameters related to the clothing order to a cloud server, so that the cloud server can acquire target equipment parameters required by the current clothing task for the target clothing equipment and send the target equipment parameters to the target clothing equipment.

The embodiment of the application also provides a clothing making control method, which comprises the following steps: receiving fabric attribute information and garment manufacturing process parameters related to a garment manufacturing order sent by a control terminal; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and issuing the target equipment parameters to target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters.

The embodiment of the application also provides a clothing making control method, which comprises the following steps: receiving a clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters related to the clothing order to obtain target equipment parameters; and issuing the current clothing task corresponding to the clothing order and the target equipment parameters to target clothing equipment so that the target clothing equipment can complete the current clothing task corresponding to the clothing order according to the target equipment parameters.

The embodiment of the application also provides a clothes making method, which is suitable for clothes making equipment and comprises the following steps: receiving a current clothing task corresponding to a clothing order; receiving target equipment parameters, wherein the target equipment parameters are obtained by operating a target equipment parameter recommendation-determination model according to fabric attribute information and clothing making process parameters related to the clothing making order; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

The embodiment of the application also provides a clothes making method, which comprises the following steps: receiving a current clothing task corresponding to a clothing order, fabric attribute information and clothing process parameters related to the clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

The embodiment of the present application further provides a management and control terminal, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving a clothing order; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to target equipment parameters; and sending the fabric attribute information and the clothing process parameters related to the clothing order to a cloud server, so that the cloud server can acquire target equipment parameters required by the current clothing task for the target clothing equipment and send the target equipment parameters to the target clothing equipment.

An embodiment of the present application further provides a cloud server, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving fabric attribute information and garment manufacturing process parameters related to a garment manufacturing order sent by a control terminal; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and issuing the target equipment parameters to target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters.

The embodiment of the present application further provides a management and control terminal, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving a clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters related to the clothing order to obtain target equipment parameters; and issuing the current clothing task corresponding to the clothing order and the target equipment parameters to target clothing equipment so that the target clothing equipment can complete the current clothing task corresponding to the clothing order according to the target equipment parameters.

The embodiment of this application still provides a clothing equipment, includes: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving a current clothing task corresponding to a clothing order; receiving target equipment parameters, wherein the target equipment parameters are obtained by operating a target equipment parameter recommendation-determination model according to fabric attribute information and clothing making process parameters related to the clothing making order; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

The embodiment of this application still provides a clothing equipment, includes: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving a current clothing task corresponding to a clothing order, fabric attribute information and clothing process parameters related to the clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

The embodiment of the present application further provides a clothing control system, including: the system comprises a cloud server, a control terminal and at least one piece of clothes making equipment, wherein the control terminal and the at least one piece of clothes making equipment are deployed in a production environment; the control terminal is used for receiving a clothing order, issuing a current clothing task corresponding to the clothing order to a target clothing device in the at least one clothing device, and providing fabric attribute information, clothing process parameters and other auxiliary information related to the clothing order to the cloud server so that the cloud server can provide device parameters for the target clothing device; the cloud server is used for operating an equipment parameter recommendation-determination model to obtain target equipment parameters according to the fabric attribute information, the clothing process parameters and other auxiliary information, and issuing the target equipment parameters to the target clothing equipment; the target clothes making equipment is used for operating according to target equipment parameters issued by the cloud server so as to complete the current clothes making task according to the clothes making technological parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

The embodiment of the application also provides a clothing making control method, which comprises the following steps: receiving a clothing order; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to target equipment parameters; fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment manufacturing order are sent to a cloud server, so that the cloud server can obtain target equipment parameters required by the current garment manufacturing task for the target garment manufacturing equipment based on an equipment parameter recommendation-determination model and send the target equipment parameters to the target garment manufacturing equipment; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

The embodiment of the application also provides a clothing making control method, which comprises the following steps: receiving fabric attribute information, garment manufacturing process parameters and other auxiliary information related to a garment manufacturing order sent by a control terminal; operating an equipment parameter recommendation-determination model according to the fabric attribute information, the garment manufacturing process parameters and other auxiliary information to obtain target equipment parameters; the target equipment parameters are issued to target clothes making equipment, so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

The embodiment of the present application further provides a management and control terminal, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving a clothing order; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to target equipment parameters; fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment manufacturing order are sent to a cloud server, so that the cloud server can obtain target equipment parameters required by the current garment manufacturing task for the target garment manufacturing equipment based on an equipment parameter recommendation-determination model and send the target equipment parameters to the target garment manufacturing equipment; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

An embodiment of the present application further provides a cloud server, including: a memory and a processor; the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for: receiving fabric attribute information, garment manufacturing process parameters and other auxiliary information related to a garment manufacturing order sent by a control terminal; operating an equipment parameter recommendation-determination model according to the fabric attribute information, the garment manufacturing process parameters and other auxiliary information to obtain target equipment parameters; the target equipment parameters are issued to target clothes making equipment, so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

Embodiments of the present application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps in any of the method embodiments provided by embodiments of the present application.

In the embodiment of the application, parameter recommendation of the garment making equipment in a garment making scene is combined with artificial intelligence, an equipment parameter recommendation-determination model based on fabric attribute information and garment making process parameters is constructed, when the garment making equipment needs to execute a garment making task, the fabric attribute information and the garment making process parameters related to the current garment making task are combined, the required equipment parameters can be efficiently and reasonably recommended to the garment making equipment based on the equipment parameter recommendation-determination model, the parameter recommendation mode is not limited by the experience of machine repairment, and the equipment parameters recommended based on the model are more reasonable, the number of trial and error adjustment is not needed or can be reduced, so the trial and error cost of the equipment parameters can be saved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1a is a schematic diagram of the structure and operation of a clothing control system according to an exemplary embodiment of the present disclosure;

FIG. 1b is a block diagram of a process for training a unified model according to an exemplary embodiment of the present application;

FIG. 1c is a block diagram of a process for classifying a training model according to an exemplary embodiment of the present application;

FIG. 2a is a schematic view of another operating principle of a garment control system provided in an exemplary embodiment of the present application;

FIG. 2b is a block diagram of another model training process and model use process provided in exemplary embodiments of the present application;

FIG. 3a is a schematic flow chart of a garment control method provided in an exemplary embodiment of the present application;

FIG. 3b is a schematic flow chart of another garment control method provided in an exemplary embodiment of the present application;

FIG. 4a is a schematic flow chart of yet another garment control method provided in an exemplary embodiment of the present application;

FIG. 4b is a schematic flow chart of yet another garment control method provided by an exemplary embodiment of the present application;

FIG. 5a is a schematic flow chart of a method of making a garment according to an exemplary embodiment of the present application;

FIG. 5b is a schematic flow chart illustrating another garment control method provided in an exemplary embodiment of the present application;

FIG. 5c is a schematic flow chart of another garment manufacturing method provided by an exemplary embodiment of the present application;

fig. 6 is a schematic structural diagram of a management terminal according to an exemplary embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of a cloud server according to an exemplary embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a garment manufacturing apparatus according to an exemplary embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Fig. 1a is a schematic diagram illustrating a structure of a clothing control system and an operation principle thereof according to an exemplary embodiment of the present application. As shown in fig. 1a, the clothing control system 100 includes: the system comprises a cloud server 101, a management and control terminal 102 and at least one piece of clothes making equipment 103, wherein the management and control terminal 102 and the at least one piece of clothes making equipment are deployed in a production environment. The control terminal 102 is in communication connection with at least one piece of clothes making equipment 103 and the cloud server 101, and the at least one piece of clothes making equipment 103 is also in communication connection with the cloud server 101.

Optionally, as shown in fig. 1a, at least one edge computing device 104 is also deployed in the production environment, where the edge computing device 104 refers to any device with communication function close to the clothing manufacturing device 103, and may be, for example, a mobile phone of a clothing worker, a camera in a workshop, a sensor installed on the clothing manufacturing device 103, or a gateway device in a factory. Wherein, the at least one edge computing device 104, the at least one clothing manufacturing device 103 and the control terminal 102 are interconnected with each other to form an internet of things (IoT) in a clothing scene; additionally, at least one edge computing device 104 may be communicatively coupled to the cloud server 101.

The at least one edge computing device 104 is connected between the at least one clothing manufacturing device 103 and the cloud server 101, and is capable of providing data forwarding service for the at least one clothing manufacturing device 103 and is responsible for data forwarding between the at least one clothing manufacturing device 103 and the cloud server 101. Each edge computing device 104 can provide data forwarding service for one or more clothing devices 103, and each clothing device 103 is communicatively connected to the cloud server 101 by means of at least one edge computing device 104.

Further optionally, the edge computing device 104 may be implemented as an independent communication module, such as a network card, and a communication module may be externally connected to each clothing manufacturing device 103, and is responsible for reporting data generated by the clothing manufacturing device 103 to the cloud server 101 and forwarding the data sent by the cloud server 101 to the clothing manufacturing device 103. It should be noted that, in addition to the communication with the cloud server 101 by the edge computing device 104, the clothing manufacturing device 103 may also have a communication module, and directly perform communication connection with the cloud server 101 based on the communication module.

In the present embodiment, the production environment mainly refers to a factory, an office, and the like of a clothing service provider. The clothing manufacturing equipment 103 is a mechanical equipment capable of producing or sewing clothing, and may be various types of sewing machines, cutting machines, printing machines, dyeing machines, ironing machines, and the like. The garment manufacturing facility 103 is typically deployed in a factory's garment manufacturing plant and is responsible for certain garment manufacturing operations during the garment manufacturing process. The administrative terminal 102 refers to a terminal device that is closer to the clothing-making device 103 in the deployment position, and may be, for example and without limitation: the management and control terminal 102 may be deployed in a clothing workshop of a factory, or may be deployed in a management room or an office of the factory. On one hand, the control terminal 102 interfaces with a clothing requiring party, such as a brand company, a service designer, or a small-scale clothing merchant, and receives a clothing order submitted by the clothing requiring party on line, and obtains clothing requirements of the clothing requiring party, such as clothing styles required to be sewn, fabrics required to be used, required clothing processes, and the like, as shown in (r) in fig. 1 a. On the other hand, the control terminal 102 is connected to at least one garment-making device 103 and is responsible for performing various control related to garment-making on the garment-making device 103, such as issuing a garment-making task to the garment-making device 103, collecting the execution progress and completion condition of the garment-making task, counting the production efficiency and quality inspection passing rate of the garment-making task, and maintaining the corresponding relationship between the garment-making device 103 and a garment-making worker (i.e., which garment-making worker uses which garment-making device 103 to perform work), skill level information of the garment-making worker, device model of the garment-making device 103, wear condition and other related information.

In this embodiment, after receiving the clothing order, the management and control terminal 102 may determine, by combining with the distribution application of each clothing apparatus 103 on the production line, the clothing apparatus 103 that may be responsible for the clothing order, and record the determined clothing apparatus as the target clothing apparatus 103 a. For example, the administrative terminal 102 may select an idle garment-making device, or a garment-making device that is about to complete a garment-making task currently being performed, as the target garment-making device 103 a. The target garment apparatus 103a may be one or a plurality of apparatuses, and may be all garment apparatuses in a production environment, for example. Then, the control terminal 102 generates a tailoring task according to the tailoring order and issues the tailoring task to the target tailoring device 103a, as shown in the second part of fig. 1 a. For ease of description and distinction, this tailoring task received by the target tailoring apparatus 103a will be referred to as the current tailoring task.

The control terminal 102 may analyze the clothing order, and on one hand, obtain, from the clothing order, identification information of the fabric required to be used by the clothing demander, for example, name or picture of the fabric. After acquiring the identification information of the fabric required to be used by the clothing manufacturing requirement, the control terminal 102 may acquire the attribute information of the fabric according to the identification information. Optionally, the management and control terminal 102 may query a database for maintaining the corresponding relationship between the fabric identifier and the fabric attribute according to the identifier information; and if the identification information is inquired in the database, acquiring corresponding fabric attribute information. Further, if the attribute information of the fabric is not inquired in the database, a notification message can be sent to the tester, and the tester detects the fabric attribute in real time by means of some testing equipment or tools to obtain the attribute information of the fabric.

In an optional embodiment, the attribute information of the fabric includes but is not limited to: at least one of the transverse elasticity and the longitudinal elasticity of the fabric, the thickness of the fabric, the front surface horizontal friction of the fabric, the back surface horizontal friction of the fabric and the weave structure of the fabric. If the attribute information cannot be inquired from the database, tests such as transverse and longitudinal elasticity test, thickness test, front and back surface horizontal friction force test, fabric weaving structure and the like can be carried out on the fabric, so that the transverse elasticity, the longitudinal elasticity, the thickness, the front surface horizontal friction force, the back surface horizontal friction force, the fabric weaving structure and the like of the fabric are obtained.

On the other hand, the control terminal 102 can also analyze the requirements of the clothing-making demander on the clothing-making process from the clothing-making order; and further carrying out digital processing on the requirements of the clothing making process by the clothing making demand party to obtain clothing making process parameters. Wherein, the clothing technological parameters include but are not limited to: at least one of a material laying mode, a sewing line mode and a sewing line position of the fabric. The material laying mode of the fabric refers to a mode of placing the fabric to be sewn in a sewing area of the garment making equipment according to the garment making sequence so as to be convenient for sewing, for example, the front surface of the fabric laying A is laid upwards in the sewing area, then the fabric B is laid on the fabric A, the front surface of the fabric B is opposite to the front surface of the fabric A, and then the fabric A and the fabric B are sewn together along the sewing line position according to the set sewing line mode. The sewing line position refers to a position where a sewing needle of the clothing manufacturing equipment sews on the fabric, and is usually a position of an edge of the fabric or a position close to the inner part of the edge (which can prevent the thread from being off). The sewing line mode refers to a stitch mode adopted by the garment manufacturing equipment when the fabric is sewn, and can be a flat stitch, a Z-shaped lock stitch, a single-needle chain stitch, a double-needle bottom covering stitch, a cross-stitch and the like.

After the fabric attribute information and the garment manufacturing process parameters are obtained, a current garment manufacturing task can be generated, the current garment manufacturing task carries the fabric attribute information and the garment manufacturing process parameters, and the current garment manufacturing task is issued to the target garment manufacturing equipment 103a so that the target garment manufacturing equipment 103a can execute the current garment manufacturing task accordingly.

It should be noted that the clothing-making equipment 103 of the present embodiment has some equipment parameters, and before the clothing-making task is performed, the equipment parameters can be set for the clothing-making equipment 103, and the equipment parameters can affect the operation mode of the clothing-making equipment 103 in the clothing-making task, so that the setting of the equipment parameters actually is a requirement for the operation mode of the clothing-making equipment in the clothing-making task. It should be noted that the device parameters set for the clothing-making device 103 are not equal to the device parameters of the clothing-making device 103 during actual operation, and the device parameters of the clothing-making device 103 during actual operation may differ from the set device parameters due to various factors such as the wear of the device of the clothing-making device 103. In addition, the garment manufacturing equipment 103 may vary in equipment parameters. Taking the garment manufacturing apparatus 103 as an example, then these apparatus parameters include, but are not limited to: at least one of start time, stop time, rotation speed, presser foot pressure and thread tension of the sewing machine.

In this embodiment, the equipment parameters of the clothing manufacturing equipment 103 are affected by the fabric and the clothing manufacturing process, that is, different fabric and different clothing manufacturing process require different equipment parameters to be set for the clothing manufacturing equipment 103, so as to ensure the clothing quality of the clothing manufacturing equipment 103.

Based on the above, it is necessary to set the desired equipment parameters for the target garment-making equipment 103a in advance before the target garment-making equipment 103a performs the current garment-making task. In the present embodiment, the cloud server 101 recommends, for the target garment device 103a, the device parameters it needs when performing the current garment task based on the device parameter recommendation-determination model. The cloud server 101 may train in advance a device parameter recommendation-determination model that may recommend device parameters for the target clothing-making device 103a, and for convenience of description and distinction, the device parameter recommendation-determination model that may recommend device parameters for the target clothing-making device 103a is referred to as a target device parameter recommendation-determination model. Since the fabric attribute information and the garment manufacturing process parameters have an influence on the equipment parameters of the garment manufacturing equipment 103, the equipment parameter recommendation-determination model of the embodiment takes the fabric attribute information and the garment manufacturing process parameters as input, so that the proper equipment parameters can be recommended for the garment manufacturing equipment 103 by combining the fabric attribute information required to be sewn by the garment manufacturing equipment 103 and the garment manufacturing process parameters required to be adopted.

In order to facilitate the cloud server 101 to accurately recommend the device parameters for the target clothing device 103a, as shown in fig. 1a, the management and control terminal 102 may send the fabric attribute information and the clothing making process parameters related to the clothing making order to the cloud server 101, so that the cloud server 101 provides the device parameters for the target clothing device 103 a. Further, as shown in the fourth step in fig. 1a, after receiving fabric attribute information and garment manufacturing process parameters related to a garment-making order, the cloud server 101 may operate a target device parameter recommendation-determination model according to the received fabric attribute information and garment manufacturing process parameters to obtain target device parameters; thereafter, as indicated by the fifth in fig. 1a, the cloud server 101 issues the target device parameters to the target garment device 103 a. Further, as shown in the sixth step in fig. 1a, the target clothing-making device 103a receives the target device parameter issued by the cloud server 101, and operates according to the target device parameter, so as to complete the current clothing-making task according to the clothing-making process parameter.

In some embodiments of the present application, the cloud server 101 may pre-train a unified device parameter recommendation-determination model, which takes fabric attribute information and garment manufacturing process parameters as inputs, and may recommend device parameters for each garment manufacturing device 103 under various conditions or conditions. Based on this, after receiving the fabric attribute information and the garment manufacturing process parameters provided by the control terminal 102, the cloud server 101 may directly run the maintained device parameter recommendation-determination model to obtain the target device parameters required by the target garment manufacturing device 103 a. The training process of the device parameter model can refer to the embodiment shown in fig. 1 b.

In other embodiments of the present application, the cloud server 101 may pre-train a plurality of device parameter recommendation-determination models, each device parameter recommendation-determination model taking fabric attribute information and garment manufacturing process parameters as inputs, but different device parameter recommendation-determination models are responsible for recommending device parameters for different garment manufacturing devices 103 under different situations or conditions. In this embodiment, the number of the device parameter recommendation-determination models pre-trained by the cloud server 101 is not limited, and the conditions or conditions to which the device parameter recommendation-determination models are applicable are not limited, so that the device parameter recommendation-determination models can be flexibly classified and trained according to application scenarios and requirements. The following examples illustrate:

in example A1Considering that the operation flows of different clothes making devices may be different, the operation flows are different, and the required device parameters are different, the cloud server 101 may train corresponding device parameter recommendation-determination models respectively for the clothes making devices of different operation flows in advance. For example, assuming that the garment machines in the garment environment are involved in a total of three types of operational flows, the machine parameter recommendation-determining model M1 may be pre-trained for garment machines of a first type of operational flow, the machine parameter recommendation-determining model M2 may be pre-trained for garment machines of a second type of operational flow, and the machine parameter recommendation-determining model M3 may be pre-trained for garment machines of a third type of operational flow. The operation process of the clothing manufacturing equipment is a physical attribute of the clothing manufacturing equipment, is determined when the clothing manufacturing equipment is factory set, and is related to the type, the function, the model, the hardware structure and the like of the clothing manufacturing equipment. In this embodiment, the operation flow of the clothing manufacturing equipment can be represented by building the object model of the clothing manufacturing equipment and the object model information, so that the operation flow of the clothing manufacturing equipment is parameterized. In this embodiment, the material model information is a data model defining the clothing apparatus, is a digital representation of the clothing apparatus, and can describe the operation flow (or function) of the clothing apparatus. Further, in this embodiment, the object model information describes what the clothing manufacturing equipment is, what can be done, and what information can be provided externally from three dimensions of attribute, service, and event, respectively, so as to define the operation flow (or function) of the clothing manufacturing equipment. Wherein the attributes in the object model information may describe the state of the clothing manufacturing equipment in operation to some extent, for example, including but not limited to: type, model, dimension and specification of clothing-making equipmentVoltage/current, supportable power, rotational speed, etc.; services in the object model information are used for describing the capability or method of the clothing making device which can be called externally, for example, the device parameters required for the clothing making task are allowed to be set externally for the clothing making device; events in the object model information are used to describe events of the garment manufacturing apparatus during operation, including but not limited to: the information of the clothes-making equipment is the notification information which needs to be sensed and processed by the outside, the notification information after the clothes-making task is completed, or the temperature when the clothes-making equipment breaks down or gives an alarm, etc. Based on this, the cloud server 101 may maintain the device parameter recommendation-determination model corresponding to different object model information in advance.

In order to facilitate the cloud server 101 to select a target device parameter recommendation-determination model capable of recommending device parameters for the target clothing device 103a from the maintained multiple device parameter recommendation-determination models, the management and control terminal 102 may obtain the object model information corresponding to the target clothing device 103a besides sending the fabric attribute information and the clothing process parameters to the cloud server 101, and send the object model information corresponding to the target clothing device 103a to the cloud server 101. Based on this, the cloud server 101 may select, from the multiple maintained device parameter recommendation-determination models, a device parameter recommendation-determination model corresponding to the object model information as the target device parameter recommendation-determination model according to the object model information corresponding to the target garment device 103 a.

Optionally, an object model may be constructed in advance for at least one piece of clothing-making equipment 103 controlled by the control system, so as to obtain a corresponding relationship between the object model information and the clothing-making equipment identifier. In the case that there are a plurality of clothes-making devices 103, it is possible to construct a plurality of object model information, and different object model information represents different operation flows. For example, when a plurality of clothes making devices 103 are all of the same model, the clothes making devices 103 correspond to the same object model information; the plurality of clothes making devices 103 are of different models, and the clothes making devices 103 of different models correspond to different object model information. It should be noted that the object model information corresponding to the clothing manufacturing apparatus 103 may be different according to the type of the clothing manufacturing apparatus, the type of the apparatus, the manufacturer of the apparatus, the service provided by the clothing manufacturing apparatus, and the event during operation. It should be noted that the object model building operation may be performed by the management and control terminal 102, or may be performed by other devices, which is not limited to this. Based on the correspondence between the pre-obtained object model information and the clothing-making device identifier, when the object model information corresponding to the target clothing-making device 103a needs to be acquired, the management and control terminal 102 may query the maintained correspondence according to the identifier of the target clothing-making device 103a to acquire the object model information corresponding to the target clothing-making device 103 a. The identification of the target garment device 103a may be its device ID, model information, or manufacturer information, etc.

In example A2Considering that the skill level of different clotheshorses may be different, the skill level of the clotheshorse may also affect the equipment parameters required by the clothing equipment. For example, for a clothing worker working for 10 years, the skill level is more skilled, and the sewing speed is faster, the rotating speed of the clothing-making equipment 103 can be set higher; for a clotheshorse working for 2 years, the skill level is relatively loose, and if the rotating speed of the clotheshorse 103 is increased, the sewing quality may not be ensured, or even the sewing may not be performed. In view of this, the cloud server 101 may respectively train corresponding device parameter recommendation-determination models in advance for different skill levels. That is, the cloud server 101 may maintain device parameter recommendation-determination models corresponding to different skill levels in advance. The grading of the skill level of the clotheshorse can be flexibly set according to application requirements, for example, the skill level of the clotheshorse can be divided into a plurality of grades according to the age of the clotheshorse, or the skill level of the clotheshorse can be divided into a plurality of grades according to the quality of the finished clotheshorse, or the skill level of the clotheshorse can be divided into a plurality of grades by simultaneously combining the age of the clotheshorse and the quality of the finished clotheshorse sewn by the age of the clotheshorse; and respectively training an equipment parameter recommendation-determination model for recommending equipment parameters for the clothes-making equipment used by the clothes-making workers of the grade for each grade.

In order to facilitate the cloud server 101 to select a target device parameter recommendation-determination model capable of recommending device parameters for the target clothing device 103a from the maintained multiple device parameter recommendation-determination models, the management and control terminal 102 may obtain skill level information of clothing workers corresponding to the target clothing device 103a, in addition to sending fabric attribute information and clothing process parameters to the cloud server 101, and send the skill level information of clothing workers corresponding to the target clothing device 103a to the cloud server 101. Based on this, the cloud server 101 may select, from the plurality of maintained device parameter recommendation-determination models, a device parameter recommendation-determination model adapted to the skill level of the clotheshorse as the target device parameter recommendation-determination model according to the skill level information of the clotheshorse corresponding to the target clotheshorse 103 a. The corresponding relationship between the clothes making equipment and the clothes making workers can be maintained in advance, and certainly, the clothes making workers can replace the clothes making equipment due to post adjustment and the like, so that the corresponding relationship between the clothes making equipment and the clothes making workers can be updated in real time. For example, the manager may update or maintain the correspondence between the clotheshorse and the clotheshorse in real time through a clotheshorse and clotheshorse management page provided by the management and control terminal 102. Based on this, the control terminal 102 may obtain, according to the corresponding relationship, an identifier of a clotheshorse corresponding to the target clotheshorse device 103 a; and then inquiring a pre-maintained information table of the clotheshorse according to the identification of the clotheshorse, and acquiring the skill level information of the clotheshorse from the information table. Besides the skill level of the clotheshorse, other basic information such as the name and the contact information of the clotheshorse is maintained in the information table.

In example A3The operation process of the clothing manufacturing equipment and the skill level of the clothing manufacturing worker can be considered at the same time, and the cloud server 101 can respectively train corresponding equipment parameter recommendation-determination models in advance according to different combinations between the operation process and the skill level. For example, assuming that three types of operation flows are involved in the clothes-making equipment in the clothes-making environment, and the skill level of the clothes-making worker is divided into three levels, namely, high, medium and low, the cloud server 101 may target the clothes-making equipment of the first type of operation flow to the high, medium and lowRespectively training three equipment parameter recommendation-determination models in the lower three skill level levels; respectively training three equipment parameter recommendation-determination models for the clothing-making equipment of the second type of operation process according to the high, medium and low skill level grades; respectively training three equipment parameter recommendation-determination models for the third type of clothing equipment in the operation process according to the high, medium and low skill level grades; a total of nine equipment parameter recommendation-determination models are maintained in advance.

In order to facilitate the cloud server 101 to select a target device parameter recommendation-determination model capable of recommending device parameters for the target clothing device 103a from the maintained multiple device parameter recommendation-determination models, the management and control terminal 102 may obtain the object model information and the skill level information of clothing workers corresponding to the target clothing device 103a, and send the object model information and the skill level information of clothing workers corresponding to the target clothing device 103a to the cloud server 101, in addition to sending the fabric attribute information and the clothing process parameters to the cloud server 101. Based on this, the cloud server 101 may select, from the maintained multiple device parameter recommendation-determination models, a device parameter recommendation-determination model adapted to both the object model information corresponding to the target clothing device 103a and the skill level information of the clothing worker as the target device parameter recommendation-determination model according to the object model information corresponding to the target clothing device 103a and the skill level information of the clothing worker.

Regardless of the classification method, the process of training a plurality of device parameter recommendation-determination models with respect to classification can be seen in the embodiment shown in fig. 1 c.

The following describes the process of pre-training a unified device parameter model and classifying and training a plurality of device parameter models with reference to fig. 1b and 1c, respectively.

As shown in fig. 1b, the process of training the unified device parameter model includes:

firstly, obtaining a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical clothing manufacturing process parameters and used historical equipment parameters related to a historical clothing manufacturing task. The historical fabric attribute information and the historical garment manufacturing process parameters related to the historical garment manufacturing tasks are obtained and stored by the management and control terminal 102 through analysis or actual measurement from the historical orders corresponding to the historical garment manufacturing tasks. In addition, the control terminal 102 records the equipment parameters used by the clothes making equipment in each clothes making task process and stores the equipment parameters into the database, so that the historical equipment parameters used by the clothes making equipment in the historical clothes making tasks can be obtained from the database. The multiple groups of sample data comprise historical data generated by various operating processes of clothes making equipment and various skill levels of clothes making workers.

And then, performing unified model training on the multiple groups of sample data to obtain a unified equipment parameter recommendation-determination model. During the model training process, an initial model can be selected, and the initial model can adopt a Recurrent Neural Network (RNN) model; alternatively, the initial model may employ a long short term memory network (LSTM) model; and then, taking the historical fabric attribute information and the historical clothing process parameters in each group of sample data as input samples, taking the historical equipment parameters as output samples, and continuously training the initial model by using multiple groups of sample data so as to enable the equipment parameters output by the initial model to be continuously close to the historical equipment parameters until the residual error of the model falls within a set residual error range, thereby obtaining a uniform equipment parameter recommendation-determination model. The RNN model or the LSTM model is a neural network model with a memory function, and the expression capacity of the model structure on time sequence information and semantic information can be fully mined, so that the RNN model or the LSTM model is more suitable for improving the parameter recommendation quality based on the model.

As shown in fig. 1c, the process of classifying and training a plurality of device parameter models includes:

firstly, obtaining a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical clothing manufacturing process parameters and historical equipment parameters related to a historical clothing manufacturing task. The historical fabric attribute information and the historical garment manufacturing process parameters related to the historical garment manufacturing tasks are obtained and stored by the management and control terminal 102 through analysis or actual measurement from the historical orders corresponding to the historical garment manufacturing tasks. In addition, the control terminal 102 records the equipment parameters used by the clothes making equipment in each clothes making task process and stores the equipment parameters into the database, so that the historical equipment parameters used by the clothes making equipment in the historical clothes making tasks can be obtained from the database. The multiple groups of sample data comprise historical data generated by various operating processes of clothes making equipment and various skill levels of clothes making workers.

And then, classifying a plurality of groups of sample data according to object model information corresponding to historical clothes making equipment used in the historical clothes making task and/or skill level information of a clothes making worker to obtain a plurality of sample sets.

Finally, model training is respectively carried out on the sample data in the plurality of sample sets, and a plurality of equipment parameter recommendation-determination models are obtained; wherein, different equipment parameter models correspond to different object model information and/or skill level information of the garment-making worker.

Further optionally, as shown in fig. 1c, before the model training, a feature engineering technique may be further adopted to process the sample data in each sample set, so as to convert the sample data in the sample set into training data suitable for the model training. In this embodiment, the manner of processing the sample data by using the feature engineering technology is not limited, and the following examples illustrate that:

processing mode 1 based on feature engineering:before model training, the production efficiency and/or quality inspection passing rate corresponding to the historical clothes-making task can be obtained. Based on this, as shown in fig. 1c, before the model training, the following processing may also be performed for each sample set: and determining the combination relation among the data dimensions contained in the sample data in the sample set and the weight in model training according to the production efficiency and/or quality inspection passing rate of the historical clothes-making tasks related to the sample set. The selection and dimension reduction of the data dimension combination can be realized through the processing, the weight of the important data dimension combination in the model training is larger, and the weight of the unimportant data dimension combination in the model training is smaller, so that the irrelevant data dimension combination can be removed on one handThe difficulty of learning tasks can be reduced, the model can be simplified, and the calculation complexity is reduced; on the other hand, different weights are configured for different data dimension combinations, so that the positive influence of high-quality data dimension combinations on model precision can be fully exerted, the negative influence of non-high-quality data dimension combinations on model precision is weakened, and the precision of the trained model is ensured.

For example, the transverse elasticity of the fabric is in the elasticity range (t1-t2), the horizontal friction force of the front side and the back side is in the friction force range (m1-m2), the fabric weaving method is an F-type weaving method, the shapes of the lines in the clothing manufacturing process parameters are curves or straight lines, the sewing line type is a decorative line, the data dimension combination is combined, the corresponding production efficiency and/or the quality inspection passing rate are high, the data dimension combination can be identified, and high weight is configured for the data dimension combination.

Processing mode 2 based on feature engineering:before model training, the actual operation data of the historical clothes making equipment in the historical clothes making tasks can be obtained, and the difference degree between the actual operation data of the historical clothes making equipment and the historical equipment parameters is analyzed. Based on this, as shown in fig. 1c, before the model training, the following processing may also be performed for each sample set: and adjusting the confidence level of the sample data in the sample set in the model training according to the difference degree between the actual operation data of the historical clothes making equipment related to the sample set and the historical equipment parameters. In the processing mode, the influence of the clothes making equipment is fully considered, namely the actual operation data of the clothes making equipment can not reach the set equipment parameters due to the factors of the wear degree of the clothes making equipment, the equipment model, the mechanical stability of the clothes making equipment and the like; furthermore, there are differences between the garment manufacturing devices, i.e. the same device parameters are set, but the actual operating data achieved by different garment manufacturing devices, such as wear level, model and mechanical stability, will also differ. Because the difference exists between the actual operation data and the set equipment parameters, the set historical equipment parameters are directly adopted for model training, the deviation is possible, and the difference between the actual operation data of the historical clothing manufacturing equipment and the historical equipment parameters set for the actual operation data can be considered to determine the historical equipmentThe confidence level of the parameters in the model training can be prepared, so that the deviation caused by the equipment can be reduced to a certain degree, and the precision of the model obtained by training is improved.

Optionally, for each group of sample data, if the difference between the historical equipment parameters in the group of sample data and the actual operating data of the historical clothes making equipment related to the group of sample data is large, it indicates that the influence of the historical clothes making equipment is large, the group of sample data cannot objectively express the relationship between the equipment parameters, the fabric attribute information and the clothes making process parameters, and the confidence level of the group of sample data can be reduced; if the difference between the historical equipment parameters in the set of sample data and the actual operating data of the historical clothes making equipment related to the set of sample data is small, the influence of the historical clothes making equipment is not large, the relation among the equipment parameters, the fabric attribute information and the clothes making process parameters can be objectively shown by the set of sample data, and the confidence level of the set of sample data can be increased. Therefore, the reasonability and the accuracy of the sample data can be improved, the deviation caused by the equipment can be reduced, and the precision of the model obtained by training can be improved.

The processing methods 1 and 2 based on the feature engineering may be used alternatively or in combination. Fig. 1c illustrates an example of the combined use of the processing methods 1 and 2 based on the feature engineering.

Further, as shown in fig. 1a, in the clothing-making environment of this embodiment, some sensors 105, such as a camera, a temperature sensor, a humidity sensor, and other sensors, may be further disposed, so as to collect various information in the clothing-making environment, and these sensors cooperate with the control terminal 102, the at least one clothing-making device 103, and the at least one edge computing device 104 to form an internet of things in a clothing-making scene. The sensors 105 include at least one sensor installed on at least one piece of clothing equipment 103, and are responsible for collecting actual operation data generated during operation of the clothing equipment 103 where the sensor is located, and sending the actual operation data to the cloud server 101 through the corresponding edge computing device 104. Actual operation data of the historical clothes making equipment in the model training process can be collected by a sensor 105 arranged on the historical clothes making equipment in the process of executing the historical clothes making tasks by the historical clothes making equipment and uploaded to the cloud server 101 through the corresponding edge computing equipment 104; the cloud server 101 receives and stores actual operating data generated by each historical clothes-making device in each historical clothes-making task for model training.

Of course, for the target clothes-making equipment 103a, at least one sensor mounted thereon will also collect actual operation data of the target clothes-making equipment 103a, such as actual start time, stop time, rotation speed, presser foot pressure, and line tension, during the course of the target clothes-making equipment 103a performing the current clothes-making task according to the target equipment parameters recommended by the cloud server 101, and send the actual operation data to the cloud server 101 through the corresponding edge computing equipment 104, as shown in fig. 1 a. Wherein, at least one sensor installed on each piece of clothing equipment includes but is not limited to: a camera mounted on the garment making device 103 at a thread pulling position, a speed sensor mounted on a motor of the garment making device 103, and a pressure sensor mounted on the garment making device 103 at a presser foot position. The camera can shoot the state information of the clothing making line of the clothing making equipment in the clothing making process, for example, the information includes but is not limited to: tension of the clothing making line, whether the clothing making line is broken, and the like; the speed sensor is used for acquiring information such as starting, stopping and rotating speed of the motor; the pressure sensor can acquire information such as the pressure of the presser foot at the position of the presser foot on the clothing manufacturing equipment.

For the cloud server 101, target device parameters, fabric attribute information and garment making process parameters used by the target garment device 103a in the current garment making task can be collected, and the parameters are added into the sample set as new sample data to update the target device parameter recommendation-determination model. In the process of updating the target equipment parameter recommendation-determination model, the cloud server 101 may receive actual operation data of the target clothing equipment 103a reported by a sensor installed on the target clothing equipment 103a through the edge computing device 104, and may analyze a difference between the actual operation data of the target clothing equipment 103a and a target equipment parameter recommended for the target clothing equipment 103 a; further, the difference can be combined to determine how to use the target equipment parameters and the group of parameters of the corresponding fabric attribute information and the clothing making process parameters as new sample data to update the target equipment parameter recommendation-determination model; thereafter, as indicated by the item ((r) in fig. 1 a), the target device parameter recommendation-determination model is updated in a certain manner using the target device parameters and the corresponding fabric attribute information and the garment manufacturing process parameters as new sample data.

Alternatively, in the case where the degree of difference between the actual operating data of the target garment apparatus 103a and the target apparatus parameters is within the set range, it is indicated that the difference between the two is not very large, which indicates that the target garment apparatus 103a is not much affected, and the set of parameters can be used as new sample data to update the target apparatus parameter model. Further, under the condition that the actual operation data of the target clothing device 103a has some difference from the target device parameters, the cloud server 101 may correct the target device parameters according to the actual operation data of the target clothing device 103a, and update the target device parameter recommendation-determination model according to the corrected target device parameters, and the corresponding fabric attribute information and clothing process parameters. In this case, the modified target device parameters, the corresponding fabric attribute information, and the garment manufacturing process parameters are used as normal sample data, and the confidence level of the modified target device parameters is relatively high, for example, higher than a set confidence level threshold, or has a hundred percent confidence level.

Alternatively, in the case where the degree of difference between the actual operation data of the target garment manufacturing device 103a and the target device parameter is outside the set range, it is indicated that the difference between the actual operation data and the target device parameter is large, which indicates that the influence of the target garment manufacturing device 103a is large, and although the target device parameter recommendation-determination model may be updated by using the set of parameters as new sample data, the degree of confidence of the set of parameters needs to be reduced, so that the degree of confidence of the target device parameter may be determined, which is relatively low, for example, lower than the set threshold value of the degree of confidence; and then, according to the reliability of the target equipment parameters, updating the recommendation-determination model of the target equipment parameters according to the target equipment parameters, the corresponding fabric attribute information and the clothing process parameters, so that the influence of the group of parameters on the model precision can be reduced.

It should be noted that, when the target device parameter recommendation-determination model is updated, in addition to considering the influence of the target clothing device 103a, the management and control terminal 102 may collect the production efficiency and/or quality inspection passing rate corresponding to the current clothing task, and send the production efficiency and/or historical quality inspection passing rate corresponding to the current clothing task to the cloud server 101. The cloud server 101 may also update the target device parameter recommendation-determination model by using the target device parameters and the corresponding fabric attribute information and the set of parameters of the garment manufacturing process parameters in combination with the production efficiency and/or the quality inspection passing rate corresponding to the current garment manufacturing task.

Optionally, the target equipment parameters, the corresponding fabric attribute information and the confidence level of the group of parameters of the clothing process parameters can be determined according to the production efficiency and/or the quality inspection passing rate corresponding to the current clothing task; and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and the clothing process parameters according to the confidence level. If the production efficiency and/or the quality inspection passing rate of the current clothing task are high, which indicates that the target equipment parameters are high in quality, high confidence level can be set for the target equipment parameters, the corresponding fabric attribute information and clothing process parameters, the advantages of the parameters are fully played, and the accuracy of the model is improved; if the production efficiency and/or the quality inspection passing rate of the current clothing task are low, which indicates that the target equipment parameters are not good, a low confidence level can be set for the target equipment parameters and the corresponding fabric attribute information and clothing process parameters, so as to reduce the negative influence of the parameters in the model updating process and ensure the accuracy of the model.

Further, the cloud server 101 may also determine how to use the target equipment parameters, the corresponding fabric attribute information, and the clothing process parameters in consideration of the difference between the actual operating data of the target clothing equipment and the target equipment parameters, and the production efficiency and/or the historical quality inspection passing rate corresponding to the current clothing task, and update the target equipment parameter recommendation-determination model by using the target equipment parameters, the corresponding fabric attribute information, and the clothing process parameters according to the determined use mode. The determined use mode can be that the target equipment parameters are corrected according to higher confidence level, and then the corrected equipment parameters, the corresponding fabric attribute information and the clothing making process parameters are used as new sample data to update the target equipment parameter recommendation-determination model; or setting a lower confidence level for the group of parameters, and directly using the target equipment parameters and the fabric attribute information and the clothing process parameters corresponding to the target equipment parameters as new sample data to update the target equipment parameter recommendation-determination model.

No matter which model updating mode is adopted, the target equipment parameter recommending-determining model is continuously updated, the quality of the target equipment parameter recommending-determining model is higher and higher, more appropriate equipment parameters can be recommended for the clothing making equipment, the parameter obtaining efficiency is improved, the clothing making quality produced based on the equipment parameters is improved, and the probability of sewing quality problems such as line looseness, fabric wrinkling and the like caused by inappropriate equipment parameters can be reduced.

In the above embodiment, the auxiliary information may be used to classify sample data, so as to train a plurality of device parameter recommendation-determination models adapted to different auxiliary information. Besides the above, the auxiliary information may be directly incorporated into the model training process as a model parameter. Based on this, the present exemplary embodiment also provides another clothing control system, as shown in fig. 2a, the clothing control system 200 includes: the cloud server 201, a management and control terminal 202 and at least one piece of clothing equipment 203 which are deployed in a production environment. The control terminal 202 is in communication connection with at least one piece of clothes making equipment 203 and the cloud server 201, and the at least one piece of clothes making equipment 203 is also in communication connection with the cloud server 201.

Optionally, as shown in fig. 2a, at least one edge computing device 204 is further deployed in the production environment, and the at least one edge computing device 204 is connected between the at least one clothing device 203 and the cloud server 201, and can provide a data forwarding service for the at least one clothing device 203, and is responsible for data forwarding between the at least one clothing device 203 and the cloud server 201.

Further, as shown in fig. 2a, some sensors 205 are also deployed in the production environment, and can collect various information in the clothing environment, and these sensors cooperate with the control terminal 202, the at least one clothing manufacturing device 203, and the at least one edge computing device 204 to form an internet of things in a clothing scene. The sensors 205 include at least one sensor installed on at least one clothing device 203, and are responsible for collecting actual operation data generated by the clothing device 203 in which the sensor is located during operation, and sending the actual operation data to the cloud server 201 through the corresponding edge computing device 204.

For some descriptions of the cloud server 201, the control terminal 202, the clothing manufacturing device 203, the edge computing device 204, and the sensor 205, reference may be made to the embodiment shown in fig. 1a, which is not described herein again. In this embodiment, the cloud server 201 is trained with a unified device parameter recommendation-determination model in advance, and can recommend required device parameters for various clothes-making devices under various conditions or situations. The equipment parameter recommendation-determination model in this embodiment is different from the equipment parameter recommendation-determination model in the foregoing embodiments, specifically, the input of the equipment parameter recommendation-determination model in this embodiment is fabric attribute information, garment manufacturing process parameters, and other auxiliary information. Based on this, the clothes-making control principle of the clothes-making control system of the present embodiment is different from that of the clothes-making control system shown in fig. 1a, and the clothes-making control principle of the clothes-making control system shown in fig. 2a is described as follows:

as shown in (r) of fig. 2a, the policing terminal 202 receives a garment order. Then, the control terminal 202 issues the current clothing-making task corresponding to the clothing-making order to the target clothing-making device 203a, as shown in fig. 2 a; in addition, the management and control terminal 202 further provides fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment order to the cloud server 201, as shown in fig. 2a, so that the cloud server 201 provides device parameters for the target garment manufacturing device 203a based on the device parameter recommendation-determination model.

As shown in the ((r) in fig. 2 a), the cloud server 201 runs the equipment parameter recommendation-determination model to obtain target equipment parameters according to the fabric attribute information, the clothing making process parameters and other auxiliary information sent by the control terminal 202; further, as indicated by the fifth in fig. 2a, the cloud server 201 issues the target device parameters to the target garment device 203 a.

As shown in fig. 2a, the target clothes-making device 203a receives the target device parameter issued by the cloud server 201, and operates according to the target device parameter, so as to complete the current clothes-making task according to the clothes-making process parameter. In the present embodiment, the other auxiliary information includes, but is not limited to, the object model information corresponding to the target garment apparatus 203a and/or the skill level information of the garment maker.

The process diagram related to model training is shown in fig. 2b, and includes:

firstly, obtaining a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical garment manufacturing process parameters, other auxiliary information (such as object model information corresponding to historical garment manufacturing equipment and/or skill level information of garment workers) and historical equipment parameters related to a historical garment manufacturing task. The historical fabric attribute information and the historical garment manufacturing process parameters related to the historical garment manufacturing tasks are obtained and stored by the management and control terminal 202 through analysis or actual measurement from the historical orders corresponding to the historical garment manufacturing tasks. In addition, the control terminal 202 records the equipment parameters used by the garment making equipment in each garment making task process and stores the equipment parameters into the database, so that the historical equipment parameters used by the garment making equipment in the historical garment making tasks can be obtained from the database. The multiple groups of sample data comprise historical data generated by various operating processes of clothes making equipment and various skill levels of clothes making workers.

And then, carrying out unified model training on the multiple groups of sample data to obtain an equipment parameter recommendation-determination model. During the model training process, an initial model can be selected, and the initial model can adopt a Recurrent Neural Network (RNN) model; alternatively, the initial model may employ a long short term memory network (LSTM) model; and then, taking the historical fabric attribute information, the historical clothing process parameters and other auxiliary information in each group of sample data as input samples, taking the historical equipment parameters as output samples, and continuously training the initial model by using multiple groups of sample data so as to enable the equipment parameters output by the initial model to be continuously close to the historical equipment parameters until the residual error of the model falls within a set residual error range, thereby obtaining a uniform equipment parameter recommendation-determination model.

In the system embodiment of the application, parameter recommendation of the garment manufacturing equipment in a garment manufacturing scene is combined with artificial intelligence, an equipment parameter recommendation-determination model based on fabric attribute information and garment manufacturing process parameters is constructed, when the garment manufacturing equipment needs to execute a garment manufacturing task, the fabric attribute information and the garment manufacturing process parameters related to the current garment manufacturing task are combined, the required equipment parameters can be efficiently and reasonably recommended to the garment manufacturing equipment based on the equipment parameter recommendation-determination model, the parameter recommendation mode is not limited by the experience of machine repair personnel, and the equipment parameters recommended based on the model are more reasonable, the number of trial and error adjustment is not needed or can be reduced, so the trial and error cost of the equipment parameters can be saved.

In the system embodiment or the method embodiment, the parameters of the clothes making equipment are automatically set, so that the clothes making equipment can quickly and efficiently execute clothes making tasks, the clothes making efficiency is improved, and more clothes making orders are accepted. Further, in the case of a large number of clothing orders, the clothing orders can be scheduled reasonably, for example, large orders are better than small orders, or orders with sufficient raw materials are better than orders with short raw materials, or orders are scheduled according to the submitting time of the orders. And then, automatically recommending parameters for the clothes making equipment used by each order according to the scheduling sequence among the orders so as to improve the clothes making efficiency.

Furthermore, a manager is usually arranged in the production environment, so that the manager and a garment maker can know the parameters used by the garment making equipment more clearly and conveniently, and the garment making equipment can output the parameters in a display screen, voice broadcast and other modes after receiving the parameters recommended by the model; further, if the manager or the clotheshorse finds that the parameters recommended by the model are not reasonable, the manager or the clotheshorse can manually fine-tune or correct the parameters so as to realize the interaction between the manager or the clotheshorse and ensure that the clotheshorse performs the clotheshorse task by using the most reasonable parameters.

Furthermore, each garment manufacturing device can report the completion progress or state of the current garment manufacturing task to the control terminal in real time, so that the control terminal can analyze the production progress of the whole order according to the completion progress or state of the garment manufacturing task reported by each garment manufacturing device and feed back the production progress to a manager or an ordering user. Furthermore, the management and control terminal can not only feed back the production progress of the order, but also estimate the delivery time of the order according to the production progress of the order, and feed back the delivery time to a manager or an order placing user, thereby being beneficial to improving the experience of the order placing user. For the manager to make a sensible arrangement or schedule of the persons or equipment based on the production schedule or delivery time of the entire order, for example if the production schedule of the order is slow, or the delivery time is later than the scheduled delivery time, more garment equipment and more garment workers may be scheduled to participate in the production of the order; or if the production progress of the order is faster or the interaction time is earlier than the preset delivery time, the order taking task at the later stage can be arranged, and the production efficiency of the whole production line is improved. For the ordering user, the order production progress or delivery time can be obtained in real time, the feeling is better, particularly for the merchants needing further clothing sales service for downstream users, the delivery time or order receiving time of the downstream users can be reasonably predicted according to the order production progress or interaction time, the service quality is guaranteed, and the viscosity of the downstream users is increased.

Fig. 3a is a schematic flow chart of a clothes making control method according to an exemplary embodiment of the present application. The method is applicable to a management and control terminal in the system shown in fig. 1a, and as shown in fig. 3a, the method includes:

31a, receiving a clothing order;

32a, issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to the target equipment parameters;

and 33a, sending the fabric attribute information and the clothing process parameters related to the clothing order to the cloud server, so that the cloud server can obtain target equipment parameters required by the current clothing task for the target clothing equipment and send the target equipment parameters to the target clothing equipment.

In an optional embodiment, the method further comprises: sending object model information corresponding to the target clothes making equipment and/or skill level information of clothes making workers to a cloud server, so that the cloud server can select a target equipment parameter recommendation-determination model from a plurality of maintained equipment parameter recommendation-determination models; the object model information corresponding to the target garment apparatus represents an operation flow of the target garment apparatus.

In an optional embodiment, the method further comprises: and obtaining the production efficiency and/or quality inspection passing rate corresponding to the current clothing task and uploading the production efficiency and/or quality inspection passing rate to the cloud server, so that the cloud server updates the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and clothing making process parameters in combination with the production efficiency and/or quality inspection passing rate.

Fig. 3b is a schematic flow chart of another clothing control method according to an exemplary embodiment of the present application. The method is applicable to the cloud server in the system shown in fig. 1a, and as shown in fig. 3b, the method includes:

31b, receiving fabric attribute information and garment manufacturing process parameters related to the garment manufacturing order sent by the control terminal;

32b, operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters;

and 33b, issuing the target equipment parameters to the target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters.

In an optional embodiment, the method further comprises: receiving object model information and/or skill level information of a garment maker corresponding to target garment making equipment sent by a control terminal; and selecting a target equipment parameter recommendation-determination model from the maintained equipment parameter recommendation-determination models according to object model information corresponding to the target clothes-making equipment and/or skill level information of a clothes-making worker.

In an optional embodiment, the method further comprises: acquiring a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical clothing manufacturing process parameters and historical equipment parameters related to a historical clothing manufacturing task;

classifying a plurality of groups of sample data according to object model information corresponding to historical clothes making equipment used in a historical clothes making task and/or skill level information of a clothes making worker to obtain a plurality of sample sets;

respectively carrying out model training on sample data in a plurality of sample sets to obtain a plurality of equipment parameter recommendation-determination models; wherein, different equipment parameter models correspond to different object model information and/or skill level information of the garment-making worker.

In an optional embodiment, the method further comprises: receiving production efficiency and/or quality inspection passing rate corresponding to the historical clothes making tasks provided by the control terminal; and

prior to model training, for each sample set, performing at least one of:

according to the production efficiency and/or quality inspection passing rate of the historical clothes-making tasks related to the sample set, determining the combination relation among data dimensions contained in sample data in the sample set and the weight in model training;

and adjusting the confidence level of the sample data in the sample set in the model training according to the difference degree between the actual operation data of the historical clothes making equipment related to the sample set and the historical equipment parameters.

In an optional embodiment, the method further comprises: and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and the clothing processing parameters according to the difference between the actual operation data of the target clothing equipment and the target equipment parameters and the production efficiency and/or the historical quality inspection passing rate corresponding to the current clothing task sent by the control terminal.

Further optionally, the updating the target equipment parameter recommendation-determination model by using the target equipment parameters, the corresponding fabric attribute information and the clothing process parameters in combination with the difference between the actual operation data of the target clothing equipment and the target equipment parameters includes:

under the condition that the difference degree between the actual operation data of the target clothes making equipment and the target equipment parameters is within a set range, correcting the target equipment parameters according to the actual operation data of the target clothes making equipment, and updating a recommendation-determination model of the target equipment parameters according to the corrected target equipment parameters, the corresponding fabric attribute information and the clothes making process parameters;

and under the condition that the difference between the actual operation data of the target clothes making equipment and the target equipment parameters is out of the set range, determining the confidence level of the target equipment parameters, and updating the recommendation-determination model of the target equipment parameters according to the corrected target equipment parameters, the corresponding fabric attribute information and the clothes making process parameters according to the confidence level of the target equipment parameters.

In the embodiment shown in fig. 3a and 3b, the management and control terminal and the cloud server are matched with each other, parameter recommendation and artificial intelligence of the garment making equipment in a garment making scene can be combined, an equipment parameter recommendation-determination model based on fabric attribute information and garment making process parameters is constructed, when the garment making equipment needs to execute a garment making task, the fabric attribute information and the garment making process parameters related to the current garment making task are combined, the required equipment parameters can be efficiently and reasonably recommended to the garment making equipment based on the equipment parameter recommendation-determination model, the parameter recommendation mode is not limited by experience of machine maintenance personnel, and the equipment parameters recommended based on the model are more reasonable, and the number of trial and error adjustment is not needed or reduced, so trial and error cost of the equipment parameters can be saved.

Fig. 4a is a schematic flow chart of another clothes-making control method according to an exemplary embodiment of the present application. The method is applicable to the management and control terminal in the system shown in fig. 2a, and as shown in fig. 4a, the method includes:

41a, receiving a clothing order;

42a, issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to the target equipment parameters;

43a, sending fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment order to a cloud server, so that the cloud server can obtain target equipment parameters required by the current garment manufacturing task for target garment manufacturing equipment based on an equipment parameter recommendation-determination model and send the target equipment parameters to the target garment manufacturing equipment; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

Fig. 4b is a schematic flow chart of another clothes-making control method according to an exemplary embodiment of the present application. The method is applicable to the cloud server in the system shown in fig. 2a, and as shown in fig. 4b, the method includes:

41b, receiving fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment order sent by the control terminal;

42b, operating an equipment parameter recommendation-determination model according to the fabric attribute information, the clothing process parameters and other auxiliary information to obtain target equipment parameters;

43b, issuing the target equipment parameters to the target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

In an optional embodiment, the method further comprises: acquiring multiple groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical garment manufacturing process parameters, other auxiliary information (such as object model information corresponding to historical garment manufacturing equipment and/or skill level information of garment workers) and historical equipment parameters related to a historical garment manufacturing task; and carrying out unified model training on the multiple groups of sample data to obtain an equipment parameter recommendation-determination model.

In an optional embodiment, the method further comprises: and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and corresponding fabric attribute information, the clothing making process parameters and other auxiliary information (such as object model information corresponding to the current clothing making equipment and/or skill level information of clothing makers).

In the embodiment shown in fig. 4a and 4b, the management and control terminal and the cloud server are matched with each other, parameter recommendation and artificial intelligence of the garment making equipment in a garment making scene can be combined, an equipment parameter recommendation-determination model based on fabric attribute information and garment making process parameters is constructed, when the garment making equipment needs to execute a garment making task, the required equipment parameters can be efficiently and reasonably recommended to the garment making equipment based on the equipment parameter recommendation-determination model by combining the fabric attribute information, the garment making process parameters and other auxiliary information related to the current garment making task, the parameter recommendation mode is not limited by experience of machine repair personnel, and the equipment parameters recommended based on the model are more reasonable, the number of trial and error adjustment is not needed or can be reduced, so trial and error cost of the equipment parameters can be saved.

Fig. 5a is a schematic flow chart of a garment manufacturing method according to an exemplary embodiment of the present application. The method is suitable for but not limited to a garment making terminal in the system shown in fig. 1a or fig. 2a, and as shown in fig. 5a, the method comprises the following steps:

51a, receiving a current clothes making task corresponding to a clothes making order;

52a, receiving target equipment parameters, wherein the target equipment parameters are obtained by operating a target equipment parameter recommending-determining model according to fabric attribute information and clothing process parameters related to a clothing order;

and 53a, operating according to the target equipment parameters to complete the current clothes making task according to the clothes making technological parameters.

In the embodiment, the target equipment parameter recommendation-determination model is operated according to the fabric attribute information and the clothing process parameters related to the clothing order, the equipment parameters required by the clothing task are recommended for the clothing equipment, the efficiency is higher, the reasonability is higher, the parameter recommendation mode is not limited by the experience of machine repair personnel, and the trial-and-error cost of the equipment parameters can be saved because the equipment parameters recommended based on the model are more reasonable and the trial-and-error times of adjustment are not needed or can be reduced.

In this embodiment, the device parameter recommendation-determination model executed by the cloud server is used to recommend the device parameters required for performing the clothes-making task for the clothes-making device, but the invention is not limited thereto. Optionally, the device parameter recommendation-determination model may also be run on the control terminal, and the control terminal recommends the device parameters required for performing the clothes-making task for the clothes-making device by running the device parameter recommendation-determination model, and the detailed process is shown in fig. 5 b. Alternatively, the device parameter recommendation-determination model may be run on the garment manufacturing device, and the garment manufacturing device runs the device parameter recommendation-determination model when the garment manufacturing task needs to be performed, and the detailed process is shown in fig. 5 c.

Fig. 5b is a schematic flow chart of another clothes-making control method according to an exemplary embodiment of the present application. The method can be used for a control terminal in a production environment, as shown in fig. 5b, and the method includes:

51b, receiving a clothing order;

52b, operating a target equipment parameter recommending-determining model according to the fabric attribute information and the clothing process parameters related to the clothing order to obtain target equipment parameters;

and 53b, issuing the current clothing task and the target equipment parameters corresponding to the clothing order to the target clothing equipment so that the target clothing equipment can complete the current clothing task corresponding to the clothing order according to the target equipment parameters.

In this embodiment, a target device parameter recommendation-determination model may be trained in advance, and the model is run on a control terminal, so that after receiving a tailoring order, the control terminal runs the model to provide target device parameters required for performing a tailoring task for target tailoring devices on one hand, and generates the tailoring task according to the tailoring order on the other hand; and issuing the current clothing-making task and the target equipment parameters to the target clothing-making equipment. The control terminal can respectively issue the current tailoring task and the target equipment parameters to the target tailoring equipment through different communication processes, and can also issue the current tailoring task and the target equipment parameters to the target tailoring equipment through the same communication process. The target clothes making equipment can receive the current clothes making task and the target equipment parameters and operate according to the target equipment parameters to complete the current clothes making task.

In this embodiment, the management and control terminal may further update the device parameter recommendation-determination model. With respect to the processes of training the device parameter recommendation-determination model, recommending the target device parameters for the target garment manufacturing device according to the device parameter recommendation-determination model, updating the device parameter recommendation-determination model, and the like, except that the execution main body is changed from the cloud server to the management and control terminal, other contents are the same as or similar to those of the above embodiment, so that the detailed description can be referred to the above embodiment, and details are not repeated here.

Fig. 5c is a schematic flow chart of another garment manufacturing method provided in an exemplary embodiment of the present application. The method is suitable for a garment manufacturing apparatus, as shown in fig. 5c, the method comprising:

51c, receiving a current clothing task corresponding to the clothing order, fabric attribute information related to the clothing order and clothing technological parameters;

52c, operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters;

and 53c, operating according to the target equipment parameters to complete the current clothes making task according to the clothes making technological parameters.

In this embodiment, the target device parameter recommendation-determination model may be trained in advance, and the model is run on the clothing making device, so that the clothing making device may run the target device parameter recommendation-determination model to determine the target device parameters required for executing the clothing making task after receiving the current clothing making task, the fabric attribute information and the clothing making process parameters related to the clothing making order; and then, operating according to the parameters of the target equipment to complete the current clothes making task.

In this embodiment, the garment manufacturing facility may also update the facility parameter recommendation-determination model. With respect to the processes of training the device parameter recommendation-determination model, determining the target device parameter according to the device parameter recommendation-determination model, updating the device parameter recommendation-determination model, and the like, except that the execution main body is changed from the cloud server to the clothing manufacturing device, other contents are the same as or similar to those of the above embodiment, so that the detailed description may refer to the above embodiment, and will not be repeated herein.

Further, in the above embodiments of the present application, the model-based parameter recommendation scheme is described in detail by taking the example of recommending the device parameters for the clothes making device in the clothes making scene, but the present application is not limited thereto. The model-based parameter recommendation scheme provided by the embodiment of the application can be applied to various product processing scenes. Based on this, this application embodiment still provides a product processing system, and this system includes: the cloud server, the management and control terminal and the at least one product processing device are deployed in the production environment. The product processing equipment is responsible for executing a product processing task so as to complete product processing operation.

The control terminal is used for receiving the product processing order, issuing the current processing task corresponding to the product processing order to target product processing equipment in at least one piece of product processing equipment, and sending the raw material attribute information and the process parameters related to the product processing order to the cloud server so that the cloud server can provide equipment parameters for the target product processing equipment. The raw material attribute information refers to attribute information of raw materials required by a product processing task; the process parameters refer to the process parameters required by the product processing task.

And the cloud server is used for operating the target equipment parameter recommendation-determination model to obtain target equipment parameters according to the raw material attribute information and the process parameters sent by the control terminal, and sending the target equipment parameters to the target product processing equipment.

And the target product processing equipment is used for operating according to the target equipment parameters issued by the cloud server so as to complete the current processing task according to the process parameters.

The present embodiment does not limit the product processing scenario, and may be various automatic production lines for processing food such as instant noodles and ham sausages, processing medicine, and automobile parts. Except for different product processing scenarios, the system architecture and the working principle of the system implemented by the mutual cooperation between the devices are the same as or similar to the embodiment of the system shown in fig. 1a, and are not repeated herein, and the detailed description may refer to the embodiment shown in fig. 1 a.

It should be noted that, in the product processing system, the device parameter recommendation-determination model runs on the cloud server, but is not limited thereto. For example, the device parameter recommendation-determination model may also be run at a management and control terminal or a product processing device terminal, and related implementation schemes are also similar to the foregoing embodiments in the clothing-making scenario, and are not described herein again.

It should be noted that in some of the flows described in the above embodiments and the drawings, a plurality of operations are included in a specific order, but it should be clearly understood that the operations may be executed out of the order presented herein or in parallel, and the sequence numbers of the operations, such as 51, 52, etc., are merely used for distinguishing different operations, and the sequence numbers do not represent any execution order per se. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.

Fig. 6 is a schematic structural diagram of a management terminal according to an exemplary embodiment of the present application. As shown in fig. 6, the administrative terminal includes: memory 61, processor 62 and communication component 63.

The memory 61 is used for storing computer programs and can be configured to store other various data to support operations on the administrative terminal. Examples of such data include instructions for any application or method operating on the governing terminal, contact data, phonebook data, messages, pictures, videos, and the like.

A processor 62, coupled to the memory 61, for executing computer programs in the memory 61 for: receiving a garment order through the communication component 63; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to the target equipment parameters; and fabric attribute information and the clothing process parameters related to the clothing order are sent to the cloud server, so that the cloud server can acquire target equipment parameters required by the current clothing task for target clothing equipment and send the target equipment parameters to the target clothing equipment.

In an alternative embodiment, processor 62 is further configured to: sending object model information corresponding to the target clothes making equipment and/or skill level information of clothes making workers to a cloud server, so that the cloud server can select a target equipment parameter recommendation-determination model from a plurality of maintained equipment parameter recommendation-determination models; the object model information corresponding to the target garment apparatus represents an operation flow of the target garment apparatus.

In an alternative embodiment, processor 62 is further configured to: and obtaining the production efficiency and/or quality inspection passing rate corresponding to the current clothing task and uploading the production efficiency and/or quality inspection passing rate to the cloud server, so that the cloud server updates the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and clothing making process parameters in combination with the production efficiency and/or quality inspection passing rate.

Further, as shown in fig. 6, the management and control terminal further includes: a display 64, a power supply component 65, an audio component 66, and the like. Only some of the components are schematically shown in fig. 6, and it is not meant that the regulation terminal includes only the components shown in fig. 6.

In addition to the above-mentioned management and control terminal shown in fig. 6, an embodiment of the present application further provides another management and control terminal, where the structure of the management and control terminal is the same as or similar to that shown in fig. 6, and refer to fig. 6. The main difference between the management and control terminal of this embodiment and the management and control terminal shown in fig. 6 is: the functions performed by the processor executing the computer programs stored in the memory are different, and the following description focuses on the functions. In the policing terminal of this embodiment, the processor executes the computer program stored in the memory to:

receiving a clothing order; issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to the target equipment parameters; fabric attribute information, garment manufacturing process parameters and other auxiliary information related to the garment manufacturing order are sent to a cloud server, so that the cloud server can obtain target equipment parameters required by the current garment manufacturing task for target garment manufacturing equipment based on an equipment parameter recommendation-determination model and send the target equipment parameters to the target garment manufacturing equipment; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker. For a detailed description of each operation, reference may be made to the foregoing embodiments, which are not repeated herein.

In addition to the management and control terminal shown in fig. 6, the embodiment of the present application further provides another management and control terminal, and the structure of the management and control terminal is the same as or similar to that shown in fig. 6, and can be referred to as that shown in fig. 6. The main difference between the management and control terminal of this embodiment and the management and control terminal shown in fig. 6 is: the functions performed by the processor executing the computer programs stored in the memory are different, and the following description focuses on the functions. In the policing terminal of this embodiment, the processor executes the computer program stored in the memory to:

receiving a clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters related to the clothing order to obtain target equipment parameters; and issuing the current clothing task corresponding to the clothing order and the target equipment parameters to target clothing equipment so that the target clothing equipment can complete the current clothing task corresponding to the clothing order according to the target equipment parameters.

It should be noted that the management and control terminal may be applied to various product processing systems besides a clothing scene, and is responsible for product processing management, and specifically, may receive a product processing order, issue a current processing task corresponding to the product processing order to target product processing equipment in the at least one product processing equipment, and send material attribute information and process parameters related to the product processing order to the cloud server, so that the cloud server provides equipment parameters for the target product processing equipment.

Accordingly, the present application further provides a computer-readable storage medium storing a computer program, where the computer program is capable of implementing the steps that can be executed by the control terminal in the foregoing method embodiments when executed.

Fig. 7 is a schematic structural diagram of a cloud server according to an exemplary embodiment of the present application. As shown in fig. 7, the cloud server includes: memory 71, processor 72, and communication component 73.

The memory 71 is used for storing computer programs, and may be configured to store other various data to support operations on the cloud server. Examples of such data include instructions, messages, pictures, videos, etc. for any application or method operating on the cloud server.

A processor 72, coupled to the memory 71, for executing computer programs in the memory 71 for: fabric attribute information and garment manufacturing process parameters related to a garment order sent by the control terminal are received through the communication component 73; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and issuing the target equipment parameters to the target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters.

In an alternative embodiment, processor 72 is further configured to: receiving object model information and/or skill level information of a garment maker corresponding to target garment making equipment sent by a control terminal; and selecting a target equipment parameter recommendation-determination model from the maintained equipment parameter recommendation-determination models according to object model information corresponding to the target clothes-making equipment and/or skill level information of a clothes-making worker.

In an alternative embodiment, processor 72 is further configured to: acquiring a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical clothing manufacturing process parameters and historical equipment parameters related to a historical clothing manufacturing task; classifying a plurality of groups of sample data according to object model information corresponding to historical clothes making equipment used in a historical clothes making task and/or skill level information of a clothes making worker to obtain a plurality of sample sets; respectively carrying out model training on sample data in a plurality of sample sets to obtain a plurality of equipment parameter recommendation-determination models; wherein, different equipment parameter models correspond to different object model information and/or skill level information of the garment-making worker.

In an alternative embodiment, processor 72 is further configured to: receiving production efficiency and/or quality inspection passing rate corresponding to the historical clothes making tasks provided by the control terminal; and prior to model training, for each sample set, performing at least one of:

In an alternative embodiment, processor 72 is further configured to: and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and the clothing processing parameters according to the difference between the actual operation data of the target clothing equipment and the target equipment parameters and the production efficiency and/or the historical quality inspection passing rate corresponding to the current clothing task sent by the control terminal.

Further optionally, when the recommendation-determination model of the target device parameter is updated, the processor 72 is specifically configured to:

Further, as shown in fig. 7, the cloud server further includes: power supply components 74, and the like. Only some of the components are schematically shown in fig. 7, and it is not meant that the cloud server includes only the components shown in fig. 7.

In addition to the cloud server shown in fig. 7, an embodiment of the present application provides another cloud server, where the structure of the cloud server is the same as or similar to that shown in fig. 7, and can be seen in fig. 7. The main differences between the cloud server of this embodiment and the cloud server shown in fig. 7 are: the functions performed by the processor executing the computer programs stored in the memory are different, and the following description focuses on the functions. In the cloud server of this embodiment, the processor executes the computer program stored in the memory to:

receiving fabric attribute information, garment manufacturing process parameters and other auxiliary information related to a garment manufacturing order sent by a control terminal; operating an equipment parameter recommendation-determination model according to the fabric attribute information, the clothing process parameters and other auxiliary information to obtain target equipment parameters; target equipment parameters are issued to target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker. For a detailed description of each operation, reference may be made to the foregoing embodiments, which are not repeated herein.

It should be noted that the cloud server can be applied to various product processing systems besides a clothing scene, and is responsible for recommending equipment parameters required for executing a product processing task for product processing equipment, and specifically can receive raw material attribute information and process parameters related to a product processing order sent by a control terminal; and operating a target equipment parameter recommendation-determination model to obtain target equipment parameters according to the raw material attribute information and the process parameters, and issuing the target equipment parameters to target product processing equipment so that the target product processing equipment operates according to the target equipment parameters to complete a product processing task corresponding to the product processing order.

Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, where the computer program can implement the steps that can be executed by the cloud server in the foregoing method embodiments when executed.

Fig. 8 is a schematic structural diagram of a garment manufacturing apparatus according to an exemplary embodiment of the present application. As shown in fig. 8, the clothing manufacturing apparatus includes: memory 81, processor 82, and communication component 83.

The memory 81 is used for storing a computer program and may be configured to store other various data to support operations on the garment apparatus. Examples of such data include instructions, messages, pictures, videos, etc. for any application or method operating on the garment device.

A processor 82 coupled to the memory 81 for executing the computer program in the memory 81 for: receiving a current garment task corresponding to a garment order via the communication component 83; receiving target equipment parameters, wherein the target equipment parameters are obtained by operating a target equipment parameter recommendation-determination model according to fabric attribute information and clothing process parameters related to a clothing order; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

Further, as shown in fig. 8, the clothing manufacturing apparatus further includes: a display 84, a power supply assembly 85, a sewing assembly 86, and the like. The sewing assembly 86 may vary depending on the garment manufacturing equipment, and in the case of a sewing machine, the sewing assembly 86 includes, but is not limited to: a head, a base, a transmission and some accessories; the machine head is provided with four mechanisms of material pricking, thread hooking, thread picking and feeding and auxiliary mechanisms of wire winding, material pressing, tooth falling and the like. The transmission part consists of a frame, a motor and the like; the motor may be mounted on the handpiece, but is not limited thereto. The accessories of the sewing machine comprise a needle, a bobbin, a knife, an oil can and the like. Only some of the components are shown schematically in fig. 8, and the fact that the garment apparatus includes only the components shown in fig. 8 is not meant.

In addition to the garment manufacturing apparatus shown in fig. 8, the present embodiment provides another garment manufacturing apparatus, which has the same or similar structure as that shown in fig. 8, and can be seen in fig. 8. The main differences between the garment making apparatus of the present embodiment and the garment making apparatus shown in fig. 8 are: the functions performed by the processor executing the computer programs stored in the memory are different, and the following description focuses on the functions. In the clothing manufacturing apparatus of the present embodiment, the processor executes the computer program stored in the memory to:

receiving a current clothing task corresponding to a clothing order, fabric attribute information and clothing process parameters related to the clothing order; operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters; and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

The memory in the above embodiments may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The communication component in the above embodiments is configured to facilitate communication between the device in which the communication component is located and other devices in a wired or wireless manner. The device where the communication component is located can access a wireless network based on a communication standard, such as a WiFi, a 2G, 3G, 4G/LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

The display in the above embodiments includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

The power supply components in the embodiments described above provide power to the various components of the device in which the power supply components are located. The power components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device in which the power component is located.

The audio component in the above embodiments may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive an external audio signal when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A garment control system, comprising: the system comprises a cloud server, a control terminal and at least one piece of clothes making equipment, wherein the control terminal and the at least one piece of clothes making equipment are deployed in a production environment;

the control terminal is used for receiving a clothing order, issuing a current clothing task corresponding to the clothing order to a target clothing device in the at least one clothing device, and sending fabric attribute information and clothing process parameters related to the clothing order to the cloud server so that the cloud server can provide device parameters for the target clothing device;

the cloud server is used for operating a target equipment parameter recommendation-determination model to obtain target equipment parameters according to the fabric attribute information and the clothing making process parameters, and issuing the target equipment parameters to the target clothing making equipment;

and the target clothes making equipment is used for operating according to the target equipment parameters issued by the cloud server so as to complete the current clothes making task according to the clothes making technological parameters.

2. The system of claim 1, wherein the governing terminal is further configured to: sending object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker to the cloud server, wherein the object model information represents an operation process of the target clothes making equipment;

the cloud server is further configured to: selecting a target device parameter recommendation-determination model from the maintained plurality of device parameter recommendation-determination models according to the object model information and/or the skill level information.

3. The system of claim 2, wherein the governing terminal is further configured to: and constructing an object model of the at least one piece of clothes making equipment in advance to obtain the corresponding relation between the object model information and the clothes making equipment identification.

4. The system of claim 2, wherein the cloud server is further configured to:

acquiring a plurality of groups of sample data, wherein each group of sample data comprises historical fabric attribute information, historical clothing manufacturing process parameters and historical equipment parameters related to a historical clothing manufacturing task;

classifying the multiple groups of sample data according to object model information corresponding to historical clothes making equipment used in the historical clothes making task and/or skill level information of a clothes making worker to obtain multiple sample sets;

respectively carrying out model training on sample data in the plurality of sample sets to obtain a plurality of equipment parameter recommendation-determination models; wherein, different equipment parameter models correspond to different object model information and/or skill level information of the garment-making worker.

5. The system of claim 4, wherein the cloud server is further configured to:

obtaining the production efficiency and/or quality inspection passing rate corresponding to the historical clothes-making task; and

prior to model training, for each sample set, performing at least one of:

according to the production efficiency and/or quality inspection passing rate of the historical clothes-making tasks related to the sample set, determining a combination relation among data dimensions contained in sample data in the sample set and a weight in model training;

and adjusting the confidence level of the sample data in the sample set in model training according to the difference between the actual operation data of the historical clothes making equipment related to the sample set and the historical equipment parameters.

6. The system of any one of claims 1-5, further comprising: the system comprises at least one piece of edge computing equipment deployed in a production environment, wherein the at least one piece of edge computing equipment is connected between the at least one piece of clothing equipment and the cloud server and is responsible for data forwarding between the at least one piece of clothing equipment and the cloud server.

7. The system of claim 6, wherein the target garment device is equipped with at least one sensor, and the sensor is responsible for collecting actual operation data generated by the target garment device when the target garment device operates according to the target device parameters, and sending the actual operation data to the cloud server through a corresponding edge computing device, so that the cloud server can update the target device parameter recommendation-determination model.

8. The system of claim 7, wherein the at least one sensor comprises: the device comprises a camera arranged at the upper thread pulling position of the target clothes making equipment, a speed sensor arranged on a motor of the target clothes making equipment and a pressure sensor arranged at the upper presser foot position of the target clothes making equipment.

9. The system of claim 7, wherein the cloud server is further configured to:

and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and the clothing processing parameters in combination with the difference between the actual operation data of the target clothing equipment and the target equipment parameters and the production efficiency and/or quality inspection passing rate corresponding to the current clothing task sent by the control terminal.

10. The system of claim 9, wherein the cloud server is specifically configured to:

under the condition that the difference degree is within a set range, correcting the target equipment parameters according to actual operation data of the target garment making equipment, and updating the target equipment parameter recommendation-determination model according to the corrected target equipment parameters, the corresponding fabric attribute information and garment making process parameters;

and under the condition that the difference degree is out of the set range, determining the confidence degree of the target equipment parameters, and updating the recommendation-determination model of the target equipment parameters according to the corrected target equipment parameters, the corresponding fabric attribute information and the clothing manufacturing process parameters according to the confidence degree of the target equipment parameters.

11. A garment control method, comprising:

receiving a clothing order;

issuing the current clothing task corresponding to the clothing order to target clothing equipment so that the target clothing equipment completes the current clothing task according to target equipment parameters;

and sending the fabric attribute information and the clothing process parameters related to the clothing order to a cloud server, so that the cloud server can acquire the target equipment parameters required by the current clothing task for the target clothing equipment and send the target equipment parameters to the target clothing equipment.

12. The method of claim 11, further comprising:

sending object model information corresponding to the target clothes making equipment and/or skill level information of clothes making workers to the cloud server, so that the cloud server can select a target equipment parameter recommendation-determination model from the maintained equipment parameter recommendation-determination models; the object model information represents an operation flow of the target garment facility.

13. The method of claim 11 or 12, further comprising:

and acquiring the production efficiency and/or quality inspection passing rate corresponding to the current clothing task and uploading the production efficiency and/or quality inspection passing rate to the cloud server, so that the cloud server updates the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and clothing making process parameters in combination with the production efficiency and/or quality inspection passing rate.

14. A garment control method, comprising:

receiving fabric attribute information and garment manufacturing process parameters related to a garment manufacturing order sent by a control terminal;

operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters to obtain target equipment parameters;

and issuing the target equipment parameters to target clothes making equipment so that the target clothes making equipment can complete the current clothes making task corresponding to the clothes making order according to the target equipment parameters.

15. The method of claim 14, further comprising:

receiving object model information and/or skill level information of a garment maker corresponding to the target garment making equipment, which is sent by a control terminal;

selecting a target device parameter recommendation-determination model from the maintained plurality of device parameter recommendation-determination models according to the object model information and/or the skill level information.

16. The method of claim 15, further comprising:

17. The method of claim 16, further comprising:

receiving the production efficiency and/or quality inspection passing rate corresponding to the historical clothes-making task provided by the control terminal; and

prior to model training, for each sample set, performing at least one of:

18. The method of claim 16, further comprising:

and updating the target equipment parameter recommendation-determination model by using the target equipment parameters and the corresponding fabric attribute information and the clothing processing parameters in combination with the difference between the actual operation data of the target clothing equipment and the target equipment parameters, and the production efficiency and/or the historical quality inspection passing rate corresponding to the current clothing task sent by the control terminal.

19. The method of claim 18, wherein updating the target equipment parameter recommendation-determination model with the target equipment parameters and corresponding fabric property information and garment process parameters in combination with the degree of difference between the target equipment actual operating data and the target equipment parameters comprises:

20. A garment control method, comprising:

receiving a clothing order;

operating a target equipment parameter recommendation-determination model according to the fabric attribute information and the clothing process parameters related to the clothing order to obtain target equipment parameters;

and issuing the current clothing task corresponding to the clothing order and the target equipment parameters to target clothing equipment so that the target clothing equipment can complete the current clothing task corresponding to the clothing order according to the target equipment parameters.

21. A method of making a garment, comprising:

receiving a current clothing task corresponding to a clothing order;

receiving target equipment parameters, wherein the target equipment parameters are obtained by operating a target equipment parameter recommendation-determination model according to fabric attribute information and clothing making process parameters related to the clothing making order;

and operating according to the target equipment parameters to complete the current clothing-making task according to the clothing-making technological parameters.

22. A method of making a garment, comprising:

receiving a current clothing task corresponding to a clothing order, fabric attribute information and clothing process parameters related to the clothing order;

23. A management terminal, comprising: a memory and a processor;

the memory for storing a computer program; the processor is coupled with the memory for executing the computer program for:

receiving a clothing order;

24. A cloud server, comprising: a memory and a processor;

25. A management terminal, comprising: a memory and a processor;

receiving a clothing order;

26. A garment manufacturing apparatus, comprising: a memory and a processor;

receiving a current clothing task corresponding to a clothing order;

27. A garment manufacturing apparatus, comprising: a memory and a processor;

28. A garment control system, comprising: the system comprises a cloud server, a control terminal and at least one piece of clothes making equipment, wherein the control terminal and the at least one piece of clothes making equipment are deployed in a production environment;

the control terminal is used for receiving a clothing order, issuing a current clothing task corresponding to the clothing order to a target clothing device in the at least one clothing device, and providing fabric attribute information, clothing process parameters and other auxiliary information related to the clothing order to the cloud server so that the cloud server can provide device parameters for the target clothing device;

the cloud server is used for operating an equipment parameter recommendation-determination model to obtain target equipment parameters according to the fabric attribute information, the clothing process parameters and other auxiliary information, and issuing the target equipment parameters to the target clothing equipment;

the target clothes making equipment is used for operating according to target equipment parameters issued by the cloud server so as to complete the current clothes making task according to the clothes making technological parameters; wherein the other auxiliary information comprises object model information corresponding to the target clothes making equipment and/or skill level information of a clothes making worker.

29. A product processing system, comprising: the system comprises a cloud server, a control terminal and at least one product processing device, wherein the control terminal and the at least one product processing device are deployed in a production environment;

the management and control terminal is used for receiving a product processing order, issuing a current processing task corresponding to the product processing order to target product processing equipment in the at least one piece of product processing equipment, and sending raw material attribute information and technological parameters related to the product processing order to the cloud server so that the cloud server can provide equipment parameters for the target product processing equipment;

the cloud server is used for operating a target equipment parameter recommendation-determination model to obtain target equipment parameters according to the raw material attribute information and the process parameters, and issuing the target equipment parameters to the target product processing equipment;

30. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 11-22.