Weaving machine control method, weaving machine control cabinet, and weaving machine

Abstract

This application discloses a loom control method, a loom control cabinet, and a loom, belonging to the field of loom control technology. The method includes: acquiring user-defined information; obtaining process control information corresponding to the user-defined information based on a process control library; driving the movement of each heald frame according to the shedding control information; detecting the motion state information of the shedding mechanism in real time; using an adaptive learning model to obtain process adjustment information based on the motion state information; and adjusting the motion state of each heald frame according to the shedding adjustment information. This application solves the problems of inconvenience and reduced production efficiency when changing fabric patterns or types in a loom, achieving the effect of process control of the shedding mechanism and real-time adjustment of the motion state of each heald frame for different fabric patterns or types.

Description

Technical Field

[0001] This application relates to the field of loom control technology, and in particular to a loom control method, a loom control cabinet, and a loom. Background Technology

[0002] Currently, some looms use electronic shedding mechanisms. By adding an electromagnetic actuator to the cam box of the traditional mechanical shedding mechanism, the movement and swing range of each cam can be independently controlled. When it is necessary to change the fabric pattern or fabric type, it can be adjusted simply by using the electromagnetic actuator.

[0003] However, these electronic opening mechanisms still require various mechanical components such as rocker arms and connecting rods found in traditional mechanical opening mechanisms. When changing fabric patterns or types, adjustments must be made after stopping the machine to avoid damaging the fabric. This not only causes many inconveniences to production but also easily affects production efficiency. Summary of the Invention

[0004] The main purpose of this application is to provide a loom control method, a loom control cabinet, and a loom, in order to solve the technical problems in the related art where looms face many inconveniences and affect production efficiency when changing fabric patterns or types.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application proposes a loom control method, applied to a loom, the loom including a shedding mechanism, the shedding mechanism including a multi-healing frame, the method including:

[0007] Obtain user settings information;

[0008] Based on the process control library, obtain the process control information corresponding to the user-defined information. The process control information includes opening control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and the process control information.

[0009] Drive each heald frame to move according to the opening control information, and detect the motion status information of the opening mechanism in real time;

[0010] Using an adaptive learning model, process adjustment information is obtained based on motion state information, including opening adjustment information.

[0011] Adjust the movement state of each heald frame according to the opening adjustment information.

[0012] Optionally, in the above loom control method, the step of obtaining user setting information includes:

[0013] User-defined information can be obtained through manual settings, recognition of fabric images, or scanning of finished fabrics.

[0014] Optionally, in the above loom control method, the adaptive learning model includes an optimization algorithm model with target constraints or a machine learning model trained based on preset convergence conditions, wherein the target constraints include the performance requirements of the loom, and the preset convergence conditions are determined based on the performance requirements of the loom.

[0015] The steps for obtaining process adjustment information based on motion state information using an adaptive learning model include:

[0016] The motion state information is input into the optimization algorithm model, and iterative optimization is performed based on the target constraints to obtain the target motion information of each crochet frame; or, the motion state information is input into the machine learning model to output the target motion information of each crochet frame.

[0017] The opening adjustment information is determined based on the motion status information and the target motion information of each heald frame.

[0018] Optionally, in the above loom control method, the step of obtaining process adjustment information based on motion state information using an adaptive learning model includes:

[0019] Real-time acquisition of the overall status information of the loom;

[0020] By using an adaptive learning model, process adjustment information is obtained based on the overall machine status information and motion status information.

[0021] Optionally, in the above loom control method, the step of acquiring the overall status information of the loom in real time includes:

[0022] Real-time acquisition of sensor data from the loom, and use of this data as overall machine status information; or,

[0023] During the synchronous simulation of the digital prototype loom operation, the simulation data of the digital prototype is acquired in real time and used as the overall machine status information.

[0024] Optionally, in the above loom control method, after the step of adjusting the motion state of each heald frame according to the sheath adjustment information, the method further includes:

[0025] The quality of the target fabric is evaluated to obtain the quality evaluation result; or, the quality of the target fabric is evaluated to obtain the quality evaluation result, and the fabric quality is predicted to obtain the quality prediction result.

[0026] The process control library is optimized based on fabric evaluation results or quality prediction results to obtain an optimized process control library; or...

[0027] The adaptive learning model is optimized based on the fabric evaluation results or quality prediction results to obtain the optimized adaptive learning model.

[0028] Optionally, in the above-described loom control method, before the steps of evaluating the quality of the obtained target fabric and obtaining a quality evaluation result; or, evaluating the quality of the obtained target fabric, obtaining a quality evaluation result, and then performing fabric quality prediction to obtain a quality prediction result, the method further includes:

[0029] The fabric obtained in real time from the loom is used as the target fabric; or, the fabric simulated by the digital prototype is used as the target fabric when the digital prototype is running synchronously to simulate the loom.

[0030] Optionally, in the above loom control method, before the step of obtaining the process control information corresponding to the user-defined information based on the process control library, the method further includes:

[0031] Establish the correspondence between different fabric patterns and / or different fabric types and the process control information of the loom, and obtain the process control library.

[0032] Optionally, in the above loom control method, the loom further includes multiple actuators, and the process control information includes multiple execution control information.

[0033] After obtaining the process control information corresponding to the user-defined information from the process control library, the method further includes:

[0034] The corresponding execution mechanism is controlled to work according to each execution control information.

[0035] Secondly, this application also proposes a loom control cabinet for use in a loom, the loom including a shearing mechanism, the shearing mechanism including a multi-heal frame, the loom control cabinet including:

[0036] The human-machine interaction system, the loom main control system, and the shedding drive system are connected in sequence.

[0037] The shedding drive system includes a control unit and multiple drive units connected to the control unit. The control unit is connected to the loom main control system, and the multiple drive units are connected to multiple heald frames one by one.

[0038] Human-computer interaction systems are used to obtain user-defined information;

[0039] The loom main control system is used to obtain process control instructions corresponding to user-set information based on the process control library. The process control instructions include shedding control instructions. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control instructions.

[0040] The control unit is used to generate drive control signals according to the opening control command;

[0041] Each drive unit is used to drive the corresponding heald frame to move according to the drive control signal;

[0042] The loom main control system is also used to generate process adjustment instructions based on the motion state information of the shedding mechanism using an adaptive learning model. These process adjustment instructions include shedding adjustment instructions.

[0043] The control unit is also used to generate drive control signals according to the opening adjustment command in order to adjust the motion state of each heald frame.

[0044] Thirdly, this application also proposes a loom comprising:

[0045] An opening mechanism, which includes a multi-piece heald frame;

[0046] The loom control cabinet is used to implement the loom control method described above.

[0047] The above-mentioned one or more technical solutions provided in this application may have the following advantages or at least achieve the following technical effects:

[0048] This application proposes a loom control method, a loom control cabinet, and a loom. By acquiring user-defined information and obtaining corresponding process control information, including shedding control information, from a process control library, the method drives the movement of each heald frame according to the shedding control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control information, enabling process control of the shedding mechanism for different fabric patterns or types, achieving online adjustment of the shedding mechanism. Furthermore, by real-time detection of the motion state information of the shedding mechanism and using an adaptive learning model, process adjustment information, including shedding adjustment information, is generated based on the motion state information. The motion state of each heald frame is then adjusted based on the shedding adjustment information, achieving real-time optimization and adjustment of the motion state of each heald frame in the shedding mechanism. This application allows for various process controls on the electronic shedding mechanism. During normal loom operation, when it is necessary to change the fabric pattern or type, there is no need to stop the machine, greatly facilitating production and improving production efficiency. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the first embodiment of the loom control method of this application;

[0051] Figure 2 These are schematic diagrams of the looms involved in the various embodiments of this application;

[0052] Figure 3 This is a schematic diagram of the shedding control process in the second embodiment of the loom control method of this application;

[0053] Figure 4 This is a schematic diagram of the optimized control flow in the second embodiment of the loom control method of this application;

[0054] Figure 5 This is a system connection diagram of the first embodiment of the loom control cabinet of this application;

[0055] Figure 6 This is a schematic diagram of the system connection of the second embodiment of the loom control cabinet of this application.

[0056] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] It should be noted that in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an apparatus or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an apparatus or system. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the apparatus or system including that element. The meaning of "and / or" throughout the text includes three parallel options; for example, "A and / or B" includes option A, option B, or options where both A and B are satisfied. In this application, unless otherwise expressly specified and limited, the terms "connected," "fixed," etc., should be interpreted broadly. For example, "connected" can be a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication of two elements or the interaction between two elements. In this application, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0059] For those skilled in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only on the basis that those skilled in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0060] Analysis of the relevant technologies revealed that the mechanical shedding mechanism used in traditional looms drives the heald frame to move up and down by mechanical linkages. Specifically, the main shaft of the loom drives the cam box of the shedding mechanism. The rotational motion of the main shaft is converted into the reciprocating oscillation of the rocker arm through the action of the gear combination and cam combination in the cam box, and then into the up and down motion of the linkage, thereby driving the heald frame to move up and down.

[0061] For this type of mechanical opening mechanism, when it is necessary to change the fabric pattern or fabric type, the only way to adjust the opening stroke is to change the reciprocating swing angle of the rocker arm by changing the cam plate combination in the cam box. Different fabric patterns or different fabric types require the design of specific cam plate combinations. Changing the fabric pattern or fabric type requires cumbersome mechanical adjustments, which brings many inconveniences to the production work.

[0062] Currently, some looms use electronic shedding mechanisms. By adding electromagnetic actuators to the cam box of the traditional mechanical shedding mechanism, the movement and swing range of each cam can be independently controlled. Compared with the traditional mechanical shedding mechanism, when it is necessary to change the fabric pattern or fabric type, there is no need to change the cam combination through mechanical adjustment. It can be adjusted only by electromagnetic actuators.

[0063] However, these electronic opening mechanisms still require various mechanical components such as rocker arms and connecting rods found in traditional mechanical opening mechanisms. When changing fabric patterns or types, adjustments must be made after stopping the machine to avoid damaging the fabric. This not only causes many inconveniences to production but also easily affects production efficiency.

[0064] In view of the numerous inconveniences and production efficiency problems that exist in related technologies when changing fabric patterns or types on looms, this application provides a loom control method, a loom control cabinet, and a loom. Specific embodiments and implementation methods are as follows:

[0065] Example 1

[0066] Reference Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the loom control method of this application. This embodiment proposes a loom control method that can be applied to a loom, specifically to a loom control cabinet, to control the loom.

[0067] like Figure 2 The diagram shows the structure of a loom; the loom includes a shedding mechanism, which includes multiple heald frames.

[0068] In this embodiment, the loom may also include a main shaft, various actuators such as a winding mechanism, a beating mechanism, a weft insertion mechanism, a warp feeding mechanism, etc., as well as components connecting the various actuators such as a breast beam and a back beam. The specific configuration can be set according to actual needs, and is not limited here.

[0069] Specifically, the shedding mechanism can be a direct-drive electronic shedding mechanism, which is different from the traditional mechanical shedding mechanism or the electronic shedding mechanism that requires various mechanical components such as rocker arms and connecting rods in the traditional mechanical shedding mechanism. The direct-drive electronic shedding mechanism has no mechanical cams or connecting rods to convert motion. It can directly drive the heald frames to reciprocate or move up and down by the linear drive unit. Therefore, the loom control cabinet can control the motion of each heald frame by controlling the linear drive unit, that is, realize the electronic shedding process control.

[0070] like Figure 1 As shown, the loom control method may include:

[0071] S100: Obtain user settings information.

[0072] Specifically, users can operate the loom control cabinet, such as inputting various parameters or data information, and the loom control cabinet will generate user-defined information accordingly. User-defined information can be stored and transmitted in the form of instructions.

[0073] In practical implementation, the loom control cabinet may include an HMI (Human Machine Interface), through which users can perform user operations. After the user inputs preset parameters or information, the loom control cabinet obtains the user-defined information accordingly. User-defined information may include loom information, fabric information, and operational information, as well as other necessary system control information. Loom information includes, for example, loom type, loom width, system configuration, and mechanical structure configuration; fabric information includes fabric pattern information and / or fabric type information, such as weft tension, weft density, and weave structure type, and yarn type, yarn hardness, and fabric variety; operational information includes loom performance requirements, loom running time, and target loom speed; and system control information includes, for example, the target operating speed of the loom's main shaft, the acceleration and deceleration time of the main shaft, the control method of the main shaft, the start-up enable signal, system protection limits, etc. Specific settings can be configured according to actual needs and are not limited here.

[0074] Optionally, the HMI on the loom control cabinet can also be used for visual display, showing users the user settings, detected loom status, and operating status of each mechanism, so that users can know the real-time status of the loom. The information displayed includes, but is not limited to, the real-time operating status of each mechanism in the loom, such as motion information, electrical information, process information, and other comprehensive analysis information such as the overall energy consumption and efficiency of the loom.

[0075] S200: Based on the process control library, obtain the process control information corresponding to the user-defined information. The process control information includes opening control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and the process control information.

[0076] Specifically, after the loom control cabinet obtains the user-defined information, it can search the process configuration parameters that match the user-defined information in the process control library, which serve as the process control information corresponding to the user-defined information. For example, for a certain yarn type set by the user, the process configuration parameters of the loom corresponding to that yarn type are searched as the process control information. The process control information can include the control parameters required for each component or actuator of the loom to perform one cycle of operation. Here, the control parameters of the shedding mechanism are included to obtain the shedding control information. The shedding control information can include the control information of each heald frame in the shedding mechanism, as well as the up-and-down operation enable information and operation protection limit information of the shedding mechanism. The control information of the heald frame can include the grouping information, action sequence information, motion trajectory information, motion displacement, motion speed, motion acceleration, etc. of the heald frame movement. The specific settings can be configured according to actual needs, which are not limited here.

[0077] In practice, after receiving the process control information, the loom control cabinet can generate process control instructions and send them to the loom to perform specific control on the actuators, including the sheathing mechanism, such as timing scheduling, operation control, speed control, etc.

[0078] S300: Drives each heald frame to move according to the opening control information, and detects the motion status information of the opening mechanism in real time.

[0079] Specifically, after receiving the shedding control information, the loom control cabinet can issue commands to the shedding mechanism of the loom to control the multiple heald frames individually, or control the multiple heald frames in the shedding mechanism by controlling the linear drive units connected to each heald frame, causing the heald frames to move up and down or reciprocate. During the movement of the heald frames, the loom control cabinet can also monitor the status of each heald frame in real time to obtain the motion status information of the shedding mechanism. The detected motion status information may include the speed, acceleration, etc. of the shedding mechanism, which are not limited here.

[0080] In practice, the motion status information detected in real time by the loom control cabinet may include real-time operating current, voltage, output force and power of the sheathing mechanism, and real-time displacement, speed and acceleration of the heald frame movement, etc., which are not limited here.

[0081] Optionally, after obtaining the motion status information of the shedding mechanism, the loom control cabinet can extract or analyze the motion status information to obtain status feedback information, which is then displayed to the user. Alternatively, it can utilize an adaptive learning model to generate process adjustment information based on the status feedback information to adjust the motion status of the shedding mechanism and / or other mechanisms on the loom. The status feedback information obtained after information extraction or analysis may include electronic shedding output, power, efficiency, vibration and energy consumption information during the movement of each heald frame, as well as the overall energy consumption, efficiency, vibration, and temperature information of the loom, etc. The information displayed to the user can be the real-time detected motion status information, or it can include the processed status feedback information, displayed in real-time or updated.

[0082] S400: Utilizes an adaptive learning model to obtain process adjustment information based on motion state information, including opening adjustment information.

[0083] Specifically, after the loom control cabinet obtains the real-time motion status information of the shedding mechanism on the loom, it can input it into the adaptive learning model to obtain the optimized process configuration parameters of the shedding mechanism at this time. Then, based on the real-time motion status information, it can calculate the process configuration parameters that the shedding mechanism needs to be adjusted, i.e., the process adjustment information. Here, the adjustment parameters are for the shedding mechanism, so the process adjustment information includes the shedding adjustment information. In practical applications, the adjustment parameters of other actuators can also be obtained, i.e., the process adjustment parameters can also include the adjustment parameters corresponding to other actuators.

[0084] In practice, after the loom control cabinet receives the process adjustment information, it can generate process adjustment instructions and send them to the loom to make specific adjustments to the actuators, including the shedding mechanism, such as operating status control and speed control.

[0085] S500: Adjusts the movement state of each heald frame based on the opening adjustment information.

[0086] Specifically, after receiving the shedding adjustment information, the loom control cabinet can issue instructions to the shedding mechanism of the loom to control the multiple heald frames or the heald frames that need to be adjusted in the shedding mechanism, so as to adjust the movement state of each heald frame.

[0087] In practice, adjustments are made to the shedding mechanism based on the normal operation of each heald frame, thereby optimizing the movement of the shedding mechanism. The movement state of the shedding mechanism is adjusted online to adapt to changes in fabric patterns or types, ensuring optimized operation and maintaining production efficiency.

[0088] The loom control method of this embodiment obtains user-defined information and corresponding process control information, including shedding control information, from a process control library. Then, it drives the movement of each heald frame according to the shedding control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control information, enabling process control of the shedding mechanism for different fabric patterns or types, thus achieving online adjustment of the shedding mechanism. Furthermore, by real-time detection of the motion state information of the shedding mechanism and using an adaptive learning model, it generates process adjustment information, including shedding adjustment information, based on the motion state information. Then, it adjusts the motion state of each heald frame according to the shedding adjustment information, achieving real-time optimization and adjustment of the motion state of each heald frame in the shedding mechanism. This application can perform various process controls on the electronic shedding mechanism. During normal loom operation, when it is necessary to change the fabric pattern or type, there is no need to stop the machine, greatly facilitating production and improving production efficiency.

[0089] Example 2

[0090] Building upon Example 1, this example further proposes a loom control method. This method may include:

[0091] S100: Obtain user settings information.

[0092] Specifically, S100 may include:

[0093] S110: Obtain user-defined information through manual settings, recognition of fabric images, or scanning of finished fabrics.

[0094] like Figure 3 The diagram illustrating the opening control process first involves "user setting." During this process, the user can choose one of three methods to obtain user-defined information: manual setting, recognition of a fabric image, or scanning of a finished fabric. User-defined information may include loom information, fabric information, operational information, etc., as detailed in the aforementioned embodiment, and will not be repeated here.

[0095] Manual setup by the user involves the user entering the required information on the HMI panel as prompted. Image recognition of the fabric involves importing an image of the finished fabric into the loom control cabinet for automatic recognition to obtain the necessary information. Scanning the finished fabric involves scanning the actual finished fabric to identify the required information. When scanning the finished fabric, specific scanning equipment, such as handheld or wireless scanners, can be used to upload the scanned information to the loom control cabinet, obtaining the user-defined settings. Optionally, the loom control cabinet can also extract information such as fabric pattern and type from the scanned information to serve as the target fabric style desired by the user. This allows the loom to obtain the user-defined information based on this style and perform subsequent operations.

[0096] In this embodiment, the pattern information and / or variety information of the target fabric can be obtained in a variety of ways to meet the needs of different usage environments. This also facilitates the process control cabinet to query the process control library and obtain the process control information that matches the pattern information and / or variety information of the target fabric, thereby obtaining the fabric pattern and / or variety that the user wants.

[0097] S200: Based on the process control library, obtain the process control information corresponding to the user-defined information. The process control information includes opening control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and the process control information.

[0098] Specifically, such as Figure 3 As shown, the process control library includes the correspondence between different fabric patterns and / or different fabric types and process control information. The process control information includes, but is not limited to, weft insertion control information, weft beating control information, warp feed control information, take-up control information, and shedding control information.

[0099] S300: Drives each heald frame to move according to the opening control information, and detects the motion status information of the opening mechanism in real time.

[0100] like Figure 3 As shown, the loom control cabinet obtains the shedding control information corresponding to the user-defined information from the process control library, i.e., "obtain shedding configuration information." This shedding configuration information includes the grouping motion information, motion trajectory information, and standard operating information of each heald frame on the shedding mechanism. Grouping motion information includes, for example, the running sequence; motion trajectory information includes, for example, displacement, velocity, and acceleration; and standard operating information includes, for example, vibration and noise levels. Then, as... Figure 3 As shown, the loom control cabinet can "generate shedding drive instructions" to drive each heald frame to move according to the running sequence, displacement, speed, acceleration, vibration index, noise index, etc. in the shedding control information.

[0101] S400: Utilizes an adaptive learning model to obtain process adjustment information based on motion state information, including opening adjustment information.

[0102] Specifically, the adaptive learning model includes an optimization algorithm model with set objective constraints or a machine learning model trained based on preset convergence conditions. The objective constraints include the performance requirements of the loom, and the preset convergence conditions are determined based on the performance requirements of the loom. Correspondingly, S400 may include:

[0103] S410: Input the motion state information into the optimization algorithm model, perform iterative optimization calculations based on the target constraints, and obtain the target motion information of each frame; or, input the motion state information into the machine learning model and output the target motion information of each frame.

[0104] S420: Determine the opening adjustment information based on the motion status information and the target motion information of each heald frame.

[0105] Specifically, the optimization algorithm model refers to directly using an algorithm to calculate the input data, continuously iterating and optimizing until the target constraints are met, and then stopping to obtain the final calculation result. This final result, as the output of the algorithm model, is the target motion information of each heald frame. The target motion information of each heald frame is the optimized motion information of the heald frame, ensuring that the shedding mechanism of the loom operates according to this motion information, thus meeting the performance requirements of the loom. Specifically, this can include motion information such as displacement, velocity, and acceleration of each heald frame. The optimization algorithm model can use the motion information of each heald frame, such as displacement, velocity, and acceleration, as optimization variables to perform iterative optimization calculations, achieving optimal control of the electronic shedding mechanism. The target constraints during the iterative optimization calculations include the performance requirements of the loom, which may include optimal vibration, optimal energy consumption, optimal noise, optimal machine life, etc., etc., without limitation here.

[0106] A machine learning model refers to a model that has been trained using training data. In practical applications, data is directly input, and the output, namely the target motion information of each heald frame, can be obtained immediately. Machine learning models can include AI (Artificial Intelligence) self-learning models, neural network models, or other optimization models with similar functions that are trained by self-learning using training data. In specific applications, real-time operating information of the loom, real-time operating status information of the sheathing mechanism, and historical operating status information can all be used as training data for the machine learning model. At the same time, based on the performance requirements of the loom, the preset convergence conditions of the model are determined to train the machine learning model. The trained model is then put into use to achieve optimal control of the electronic sheathing mechanism.

[0107] In this embodiment, two adaptive learning models are provided to meet the needs of more different practical applications, improve the adaptability of the method, and expand the application range of the loom control cabinet.

[0108] S500: Adjusts the movement state of each heald frame based on the opening adjustment information.

[0109] For more details on the implementation of the above specific embodiments, please refer to the description of Embodiment 1, which will not be repeated here.

[0110] In an optional embodiment of this example, after S500, the method may further include:

[0111] S700: Evaluate the quality of the target fabric obtained and obtain the quality evaluation result; or, evaluate the quality of the target fabric obtained and obtain the quality evaluation result, perform fabric quality prediction and obtain the quality prediction result.

[0112] S800a: Optimize the process control library based on fabric evaluation results or quality prediction results to obtain an optimized process control library; or...

[0113] S800b: The adaptive learning model is optimized based on the fabric evaluation results or quality prediction results to obtain the optimized adaptive learning model.

[0114] In practical implementation, when weaving production on a loom, obtaining one target fabric constitutes one loom control cycle. During the loom's cycle operation, multiple target fabrics can be generated. Based on these target fabrics, the quality of the target fabrics from previous cycles can be evaluated, yielding corresponding quality evaluation results. These results include, but are not limited to, evaluations of pattern structure, fabric defect information, etc., and can be numerical or graded results. Alternatively, based on these target fabrics, after evaluating the quality of the target fabrics from previous cycles and obtaining multiple quality evaluation results for multiple target fabrics, fabric quality prediction can be performed. This prediction simulation of fabric quality for future cycles yields quality prediction results, which can also be numerical or graded results.

[0115] Regardless of whether the obtained result is a quality evaluation or a quality prediction, the process control library can be optimized based on this result. Appropriate process control information can be matched to specific process control schemes or different process control schemes can be updated to ensure that the process control information corresponding to different fabric patterns and / or different fabric types in the process control library can optimize the loom performance and fabric quality. Subsequently, the optimized process control library can be used to obtain process control information to adjust the loom's motion state, such as adjusting the motion state of each heald frame, adjusting the weft insertion process state, adjusting the warp feeding process state, etc.

[0116] The adaptive learning model can also be optimized based on the fabric evaluation results or quality prediction results to obtain an optimized adaptive learning model. Subsequently, the optimized adaptive learning model can be used to obtain process adjustment information and adjust the motion state of the loom.

[0117] In this embodiment, in addition to optimizing the process control to ensure the performance of the loom, further optimization of the process control to ensure the quality of the fabric can also be achieved. This is because, under the same loom performance, different operating states of the shedding mechanism may result in different fabric qualities. Therefore, the operating state of the shedding mechanism can be further optimized to ensure both the performance of the loom and the quality of the fabric.

[0118] Furthermore, before S700 "evaluating the quality of the obtained target fabric and obtaining a quality evaluation result; or, evaluating the quality of the obtained target fabric and obtaining a quality evaluation result, performing fabric quality prediction and obtaining a quality prediction result", the method may further include:

[0119] S600: Use the fabric obtained in real time from the loom as the target fabric; or, when the digital prototype is running synchronously to simulate the loom, use the fabric simulated by the digital prototype as the target fabric.

[0120] Specifically, the loom control cabinet can use the fabric obtained by the loom in real time as the target fabric for quality evaluation or prediction; alternatively, it can use the fabric obtained after the digital prototype virtually observes and simulates the loom's operating status and then use that fabric as the target fabric for quality evaluation or prediction. Using a digital prototype to virtually observe the weaving process allows for the simulation of fabric quality characteristics, more closely resembling actual physical weaving. Furthermore, the simulation process using a digital prototype does not require the use of actual fabric, thus avoiding resource waste.

[0121] like Figure 4 The diagram illustrates the optimized control flow. The process control library can also include preset target quality information, such as the performance of different quality levels for each fabric type. Then, after fabric quality evaluation is performed in the fabric control cabinet, or fabric quality prediction is made based on the evaluation results, the process control library or the adaptive learning model is optimized. Figure 4The diagram illustrates further optimization of the process control library. After updating the process control library using an adaptive learning model, further optimization can be performed based on quality evaluation or prediction results to obtain an even better optimized process control library. This allows for the optimization of various process control information within the library, including optimizing weft insertion and beat-up control information, warp feed and crimping control information, and shedding control information. Subsequently, during future control cycles of the loom, when optimizing and adjusting the shedding mechanism, the optimal shedding adjustment information can be obtained based on the optimized shedding control information.

[0122] In an optional embodiment of this example, S400 "using an adaptive learning model to obtain process adjustment information based on motion state information" may include:

[0123] S401: Real-time acquisition of the overall status information of the loom;

[0124] S402: Using an adaptive learning model, process adjustment information is obtained based on the overall machine status information and motion status information.

[0125] Specifically, while optimizing the control of the loom's shedding mechanism, the loom control cabinet can also monitor the overall status information of the loom in real time, including the position, speed, acceleration, torque, yarn tension, back beam undulation angle, warp beam and fabric roll radius, and winding and unwinding speed of various actuators or transmission mechanisms of the loom, such as the main shaft, weft insertion mechanism, back beam, warp feed mechanism and take-up mechanism. These are not limited here.

[0126] The loom control cabinet can obtain process control information corresponding to the user-set information based on the process control library. It can also use an adaptive learning model to obtain process adjustment information based on the overall machine status information and motion status information. The process control information or process adjustment information may include standard target information such as loom vibration index and heald frame vibration index.

[0127] In this embodiment, the shedding mechanism is adjusted based on the operating status information of the shedding mechanism and the overall status information of the loom. The loom control cabinet can perform comprehensive analysis and use an adaptive learning model to adjust the operating status of each heald frame, thereby achieving optimal performance of the loom, such as machine life, noise, vibration, and energy consumption. This optimizes process control while ensuring the performance of the loom.

[0128] Furthermore, S401 "real-time acquisition of the overall status information of the loom" may include:

[0129] S401a: Collect sensor data of the loom in real time and use the sensor data as the overall machine status information; or, S401b: When the digital prototype is synchronously simulating the loom, acquire the simulation data of the digital prototype in real time and use the simulation data as the overall machine status information.

[0130] Specifically, the loom control cabinet can collect real-time sensor data from various sensors installed on the loom, such as those for voltage, current, tension, speed, angle, and acceleration. It can also use a digital prototype to virtually observe and simulate the loom's operating status. This digital prototype can be a 1:1 mapping to the actual loom, ensuring a consistent mapping between the virtual observation data (simulated data) and the actual physical state data of the loom. This guarantees that the simulated data accurately reflects the overall state of the loom. In practical applications, one or both monitoring methods can be used, with the user choosing or selecting via the HMI (Hybrid Management System).

[0131] In an optional embodiment of this example, before step S200 "obtaining process control information corresponding to user-defined information according to the process control library", the method may further include:

[0132] S900: Establish the correspondence between different fabric patterns and / or different fabric types and the process control information of the loom, and obtain the process control library.

[0133] Establish a correspondence between different fabric patterns and / or different fabric types and the process control information of the loom, i.e. Figure 3 The diagram illustrates a process control library obtained through a predefined method. In practical applications, the process control library can be established by predefining process configuration information corresponding to different fabric patterns and / or different fabric types based on expert experience, on-site user experience, etc.

[0134] Optionally, after S400 "obtains process adjustment information based on motion state information using an adaptive learning model", the method may further include:

[0135] S910: Update the process control information corresponding to different fabric patterns and / or different fabric types in the process control library according to the process adjustment information to obtain the updated process control library.

[0136] The process control database is updated using process adjustment information obtained from an adaptive learning model. Figure 3 The process control library is updated by an adaptive learning model to obtain the updated process control library. In subsequent loom control cycles, step S200 can match the updated process control library with the process control information corresponding to the user-defined information.

[0137] This embodiment proposes a method for acquiring and updating the process control library. Optimized data from each loom control cycle is promptly stored in the process control library, leading to more optimized subsequent loom operations, improved method reliability, and guaranteed loom stability. The online self-learning function of an adaptive learning model is used to update the process control library, ensuring that the process configuration information remains optimal.

[0138] In an optional embodiment of this example, the loom further includes multiple actuators, and the process control information includes multiple execution control information; after S200 "obtaining the process control information corresponding to the user-defined information according to the process control library", the method may further include:

[0139] S1000: Controls the operation of the corresponding actuators according to each execution control information.

[0140] Specifically, the actuators may include any one or more of the following: the loom's main shaft, beating-up mechanism, weft insertion mechanism, warp feed mechanism, and crimping mechanism. Multiple execution control information refers to control information corresponding to multiple actuators, and may include any one or more of the following: main shaft control information, beating-up control information, weft insertion control information, warp feed control information, and crimping control information.

[0141] In this embodiment, after the loom control cabinet obtains various execution control information corresponding to the user-set information according to the process control library, it can control each heald frame in the opening structure, and can also control other execution mechanisms of the loom respectively, so as to ensure the normal operation of the entire loom and the coordinated actions between various execution mechanisms.

[0142] In practice, when the loom control cabinet controls the main shaft, it mainly controls the target speed of the main shaft in its acceleration / deceleration or stable operation state. When controlling the operation of the weft insertion mechanism, warp feeding mechanism, and winding mechanism, the process timing can be scheduled and controlled based on the angle timing of the main shaft.

[0143] Optionally, S401 "real-time acquisition of the overall status information of the loom" may include: real-time detection of the status of each actuator to obtain the status information of each actuator;

[0144] S402 "Using an adaptive learning model to obtain process adjustment information based on overall machine status information and motion status information" may include: using an adaptive learning model to obtain process adjustment information based on overall machine status information and the status information and / or motion status information of each actuator, and the process adjustment information may also include multiple execution adjustment information.

[0145] Correspondingly, S500 "adjusting the motion state of each heald frame according to the shedding adjustment information" may include: adjusting the motion state of each heald frame according to the shedding adjustment information, and adjusting the working state of each corresponding actuator on the loom according to multiple execution adjustment information.

[0146] In this embodiment, the overall control and scheduling of various actuators in the loom are realized, which meets the process timing in actual applications, ensures the accuracy of the timing of the shedding process control, and ensures the normal operation of other actuators.

[0147] The loom control method proposed in this embodiment improves the loom's adaptability to different fabric patterns and types through an adaptive learning model. It can adaptively match various process control schemes, enhancing the ease of use and flexibility of process adjustments for different fabric patterns or varieties. It proposes multiple intelligent control methods, optimizing and adjusting process control by acquiring real-time loom status information and combining this information with motion status information. Real-time adjustment of the shedding mechanism's motion status ensures the overall performance of the loom. Furthermore, it optimizes the process control library or adaptive learning model through fabric evaluation or quality prediction, thereby optimizing the motion status of the shedding mechanism and improving fabric surface quality while ensuring loom performance. Finally, optimization of the process control library and adaptive learning model ensures both loom performance and fabric quality in subsequent process control.

[0148] Example 3

[0149] Reference Figure 5 , Figure 5 This is a schematic diagram of the system connection of the first embodiment of the loom control cabinet of this application; this embodiment proposes a loom control cabinet. The loom control cabinet can be applied to a loom, which includes a sheathing mechanism, and the sheathing mechanism includes multiple heald frames.

[0150] like Figure 5 As shown, the loom control cabinet may include:

[0151] The human-machine interaction system, the loom main control system, and the shedding drive system are connected in sequence.

[0152] The shedding drive system includes a control unit and multiple drive units connected to the control unit. The control unit is connected to the loom main control system, and the multiple drive units are connected to multiple heald frames one by one.

[0153] Human-computer interaction systems are used to obtain user-defined information;

[0154] The loom main control system is used to obtain process control instructions corresponding to user-set information based on the process control library. The process control instructions include shedding control instructions. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control instructions.

[0155] The control unit is used to generate drive control signals according to the opening control command;

[0156] Each drive unit is used to drive the corresponding heald frame to move according to the drive control signal;

[0157] The loom main control system is also used to generate process adjustment instructions based on the motion state information of the shedding mechanism using an adaptive learning model. These process adjustment instructions include shedding adjustment instructions.

[0158] The control unit is also used to generate drive control signals according to the opening adjustment command in order to adjust the motion state of each heald frame.

[0159] Specifically, after receiving the user's operation information, the human-machine interaction system generates user setting information, which can be sent to the loom main control system in the form of instructions. The loom main control system receives the user setting information, obtains the corresponding process configuration information from the process control library, and can generate instructions containing the process configuration information, i.e., process control instructions. The process control instructions can include control instructions for multiple actuators, specifically including control instructions for the shedding mechanism, i.e., shedding control instructions, and sends the shedding control instructions to the control unit of the shedding drive system. The control unit can further generate control signals, i.e., drive control signals, in response to the shedding control instructions, and then send them to each drive unit, so that the drive unit drives the corresponding heald frame to move up and down or reciprocate according to the received drive control signals. The specific selection can be made according to actual needs and is not limited here.

[0160] Optionally, the motion status information of the shedding mechanism can be directly obtained by the drive unit, that is, the drive unit is also used to detect the motion status information of the shedding mechanism in real time and forward it to the loom main control system through the control unit; or, as... Figure 5 As shown, the information can also be obtained by a separate shedding monitoring unit connected to the control unit in the shedding drive system. This shedding monitoring unit is connected to multiple heald frames and is used to detect the motion status information of the shedding mechanism in real time and forward it to the loom main control system through the control unit.

[0161] During the movement of the heald frames, the shedding monitoring unit of the shedding drive system can detect the status of each heald frame in real time, obtain the motion status information of the shedding mechanism, and forward it to the loom main control system through the control unit. The loom main control system can use an adaptive learning model to adjust the process configuration information, specifically generating instructions containing the adjusted process configuration information, i.e., process adjustment instructions, which may specifically include the adjustment instructions for the shedding mechanism, i.e., shedding adjustment instructions, and send the shedding adjustment instructions to the control unit of the shedding drive system. The control unit can further generate control signals, i.e., drive control signals, in response to the shedding adjustment instructions to adjust the motion status of each heald frame.

[0162] In the opening drive system, the control unit and multiple drive units work together to realize the timing scheduling, motion control, energy efficiency control, vibration control and so on of the opening mechanism; the drive control signal can include information such as displacement, velocity and acceleration related to the movement of the heald frame, so that the drive unit can directly drive the operation of each heald frame, and the number of drive units is the same as the number of heald frames.

[0163] The loom control cabinet of this embodiment controls the shedding mechanism of the loom. Within the control cabinet, a human-machine interface system acquires user-defined information, and the loom main control system obtains corresponding process control instructions, including shedding control instructions, from a process control library. The control unit then generates drive control signals based on these shedding control instructions to drive the corresponding heald frames through various drive units. The process control library includes the correspondence between different fabric patterns and / or different fabric types and the process control instructions, enabling the loom control cabinet to perform process control of the shedding mechanism for different fabric patterns or types, achieving online adjustment of the shedding mechanism. Furthermore, the shedding monitoring unit detects the motion status information of the shedding mechanism in real time. The loom main control system uses an adaptive learning model to generate process adjustment instructions, including shedding adjustment instructions, based on the motion status information. The control unit then generates drive control signals based on these shedding adjustment instructions to adjust the motion status of each heald frame, achieving real-time adjustment of the motion status of each heald frame in the shedding mechanism. This application allows for various process controls on the electronic shedding mechanism. During normal loom operation, when it is necessary to change the fabric pattern or type, there is no need to stop the machine, greatly facilitating production and improving efficiency.

[0164] Example 4

[0165] Reference Figure 6 , Figure 6 This is a system connection diagram of the second embodiment of the loom control cabinet of this application; based on the third embodiment, this embodiment further proposes a loom control cabinet.

[0166] Furthermore, the human-machine interaction system may include a manual setting module and / or an import recognition module and / or a scan recognition module, which are respectively connected to the loom main control system;

[0167] The manual setting module is used to obtain user-defined information through manual settings.

[0168] The import recognition module is used to obtain user-defined information by recognizing fabric images;

[0169] The scanning and recognition module is used to obtain user-defined information by scanning finished fabrics.

[0170] Specifically, the user-manual setup method refers to the user entering the corresponding fabric information on the HMI panel as prompted; the fabric image recognition method involves importing an image of the finished fabric into the import recognition module of the human-computer interaction system for automatic recognition to obtain the fabric information; and the finished fabric scanning method involves scanning the actual finished fabric to identify the fabric information. When scanning finished fabric, specific scanning devices, such as handheld or wireless scanners, can be used to upload the scanned fabric information to the scanning recognition module to obtain the user-set information. Optionally, the scanning recognition module can also extract information such as pattern structure and fabric type from the scanned fabric information.

[0171] Furthermore, the adaptive learning model includes an optimization algorithm model with set target constraints or a machine learning model trained based on preset convergence conditions, wherein the target constraints include the performance requirements of the loom, and the preset convergence conditions are determined based on the performance requirements of the loom.

[0172] The loom main control system is also used to input motion state information into the optimization algorithm model, perform iterative optimization calculations based on target constraints to obtain the target motion information of each heald frame, or input motion state information into the machine learning model to output the target motion information of each heald frame; and generate opening adjustment instructions based on the motion state information and the target motion information of each heald frame.

[0173] In optional implementations of this embodiment, such as Figure 6 As shown, the loom main control system includes a main control module and a loom monitoring module connected to the main control module. The main control module is connected to the human-machine interaction system and the control unit, respectively, and the loom monitoring module is connected to the loom.

[0174] The loom monitoring module is used to acquire the overall status information of the loom in real time and send it to the main control module;

[0175] The main control module is used to obtain process control commands and generate process adjustment commands based on the overall machine status information and motion status information using an adaptive learning model.

[0176] In this embodiment, based on the operating status information of the shedding mechanism, the overall machine status information of the loom is also combined to adjust the shedding mechanism. The main control module can perform comprehensive analysis and use an adaptive learning model to adjust the operating status of each heald frame. Specifically, the control unit generates drive control signals for each heald frame, and drive control is achieved through the drive unit corresponding to the heald frame that needs to be adjusted. This achieves optimal performance of the loom, such as machine life, noise, vibration, and energy consumption, and optimizes process control while ensuring the performance of the loom.

[0177] Furthermore, the loom monitoring module includes a real-time acquisition submodule and / or an analog acquisition submodule connected to the main control module;

[0178] The real-time acquisition submodule is used to acquire sensor data of the loom in real time and send the sensor data as the overall machine status information to the main control module;

[0179] The simulation acquisition submodule is used to acquire the simulation data of the digital prototype in real time when the digital prototype is synchronously simulating the loom, and send the simulation data as the overall machine status information to the main control module.

[0180] In optional implementations of this embodiment, such as Figure 6 As shown, the loom main control system also includes a quality observation module connected to the main control module;

[0181] The quality observation module is used to evaluate the quality of the target fabric and obtain the quality evaluation result; or, to evaluate the quality of the target fabric, obtain the quality evaluation result, perform fabric quality prediction, and obtain the quality prediction result.

[0182] The main control module is also used to optimize the process control library based on the fabric evaluation results or quality prediction results to obtain an optimized process control library; or, to optimize the adaptive learning model based on the fabric evaluation results or quality prediction results to obtain an optimized adaptive learning model.

[0183] Furthermore, the quality observation module includes a real-time observation sub-module and / or a simulation observation sub-module connected to the main control module;

[0184] The real-time observation submodule is used to take the fabric obtained by the loom in real time as the target fabric, so as to obtain quality evaluation results or quality prediction results based on the target fabric.

[0185] The simulation observation submodule is used to take the fabric simulated by the digital prototype as the target fabric when the digital prototype is running synchronously to obtain quality evaluation results or quality prediction results based on the target fabric.

[0186] In an optional embodiment of this example, the loom main control system is also used to obtain the process control library through a predefined method or to update the process control library through an adaptive learning model.

[0187] The process control library can predefine the matching process configuration information and the corresponding process control instructions for different fabric patterns and / or different fabric types based on expert experience and on-site user experience. After the process control library is established, the online self-learning function of the adaptive learning model is used to update the process control library so that the process configuration information and process control instructions are kept optimal, so that the loom main control system can obtain better process control instructions based on the updated process control library.

[0188] In an optional embodiment of this invention, the loom may further include multiple actuators, and the process control instructions may further include multiple execution control instructions.

[0189] The loom main control system also includes multiple electrical execution modules that are connected to the main control module, and each of the multiple electrical execution modules is connected to a corresponding multiple actuator.

[0190] Each electrical actuator module is used to control the corresponding actuator to work according to the execution control command.

[0191] Specifically, the actuators can include any one or more of the following: the loom's main shaft, winding mechanism, beating-up mechanism, weft insertion mechanism, and warp feed mechanism. Multiple execution control commands are control commands corresponding to the control of multiple actuators, and can include any one or more of the following: main shaft control commands, winding control commands, beating-up control commands, weft insertion control commands, and warp feed control commands. The main control module can obtain the execution control commands corresponding to the user-defined information from the process control library. Multiple electrical execution modules can receive the multiple execution control commands output by the main control module to control the corresponding actuators.

[0192] In the specific implementation process, when the electrical execution module controls the spindle to work according to the spindle control command, it mainly controls the target speed of the spindle in the acceleration / deceleration state or the stable operation state. When the electrical execution module controls the corresponding winding mechanism, weft insertion mechanism, weft insertion mechanism, warp feeding mechanism and other mechanisms to work according to the winding control command, weft insertion control command, weft feeding control command and warp feeding control command, it can perform process timing scheduling control based on the spindle angle timing.

[0193] Optionally, the loom monitoring module can detect the status of each actuator in real time and obtain the status information of each actuator; the main control module can use an adaptive learning model to generate process adjustment instructions based on the status information and / or motion status information of each actuator, and the process adjustment instructions can also include multiple execution adjustment instructions; each electrical actuator module can also be used to adjust the working status of the corresponding actuator according to the execution adjustment instructions.

[0194] The loom control cabinet proposed in this embodiment improves the loom's adaptability to different fabric patterns and types through an adaptive learning model. It can adaptively match various process control schemes, enhancing the ease of use and flexibility of process adjustments for different fabric patterns or varieties. It proposes multiple intelligent control methods, optimizing and adjusting process control through the loom monitoring module and main control module, and adjusting the motion state of the shedding mechanism in real time to ensure the overall performance of the loom. Furthermore, it optimizes the process control library or adaptive learning model through the quality observation module and main control module, thereby optimizing the motion state of the shedding mechanism and improving fabric surface quality while ensuring loom performance. Finally, by optimizing the process control library and adaptive learning model, it ensures both loom performance and fabric quality in subsequent process control.

[0195] Example 5

[0196] This embodiment proposes a loom, which may include:

[0197] An opening mechanism, which includes a multi-piece heald frame;

[0198] loom control cabinet;

[0199] The loom control cabinet is used to control the movement of multiple heald frames and adjust the movement status of each heald frame in real time.

[0200] The loom control cabinet can be a device that implements the loom control method of Embodiment 1 or 2 above, or it can be the loom control cabinet of Embodiment 3 or 4 above.

[0201] Alternatively, the loom may also include:

[0202] spindle;

[0203] Multiple actuators, which may include one or more of the following: a warp winding mechanism, a weft insertion mechanism, a weft propagation mechanism, and a warp delivery mechanism;

[0204] Components that connect various actuators, such as the front beam and rear beam.

[0205] The specific structure and functions of the loom control cabinet can be referred to in the above embodiments. Since this embodiment adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, and will not be described in detail here.

[0206] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above embodiments are only optional embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the concept of this application and using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A loom control method characterized by, Applied to a loom, the loom including a shedding mechanism, the shedding mechanism being a direct-drive electronic shedding mechanism comprising multiple heald frames, the method comprising: Obtain user settings information; According to the process control library, process control information corresponding to the user setting information is obtained. The process control information includes opening control information. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control information. The opening control information is used to drive the movement of each of the heald frames, and the motion status information of the opening mechanism is detected in real time. Using an adaptive learning model, process adjustment information is obtained based on the motion state information, and the process adjustment information includes opening adjustment information. During normal operation of the loom, when it is necessary to change the fabric pattern or type, the machine is not stopped. Instead, the movement state of each heald frame is adjusted according to the opening adjustment information. The step of obtaining process adjustment information based on the motion state information using an adaptive learning model includes: Real-time acquisition of the overall status information of the loom; Using an adaptive learning model, process adjustment information is obtained based on the overall machine status information and the motion status information. The adaptive learning model includes an optimization algorithm model with target constraints or a machine learning model trained based on preset convergence conditions. The target constraints include the performance requirements of the loom, and the preset convergence conditions are determined based on the performance requirements of the loom. The step of acquiring the overall status information of the loom in real time includes: During the synchronous simulation of the loom's operation using a digital prototype, the simulation data of the digital prototype is acquired in real time and used as the overall machine status information; there is a mapping relationship between the simulation data and the physical status data of the loom.

2. The loom control method according to claim 1, characterized by, The step of obtaining user-defined information includes: The user-defined information can be obtained through manual settings, identification of fabric images, or scanning of finished fabrics.

3. The loom control method according to claim 1, characterized by, The step of obtaining process adjustment information based on the motion state information using an adaptive learning model includes: The motion state information is input into the optimization algorithm model, and iterative optimization is performed based on the target constraints to obtain the target motion information of each of the bounding boxes; or, the motion state information is input into the machine learning model to output the target motion information of each of the bounding boxes. The opening adjustment information is determined based on the motion state information and the target motion information of each of the heald frames.

4. The loom control method according to any one of claims 1 to 3, characterized in that, After the step of adjusting the motion state of each of the heald frames according to the opening adjustment information, the method further includes: The quality of the target fabric is evaluated to obtain the quality evaluation result; or, the quality of the target fabric is evaluated to obtain the quality evaluation result, and the fabric quality is predicted to obtain the quality prediction result. The process control library is optimized based on the quality evaluation results or the quality prediction results to obtain an optimized process control library; or, the adaptive learning model is optimized based on the quality evaluation results or the quality prediction results to obtain an optimized adaptive learning model.

5. The loom control method as described in claim 4, characterized in that, Before the step of evaluating the quality of the obtained target fabric and obtaining a quality evaluation result; or, evaluating the quality of the obtained target fabric and obtaining a quality evaluation result, and then performing fabric quality prediction and obtaining a quality prediction result, the method further includes: The fabric obtained in real time by the loom is used as the target fabric; or, the fabric simulated by the digital prototype is used as the target fabric when the digital prototype is running synchronously to simulate the loom.

6. The loom control method according to any one of claims 1 to 3, characterized in that, Before the step of obtaining the process control information corresponding to the user-defined information based on the process control library, the method further includes: Establish a correspondence between different fabric patterns and / or different fabric types and the process control information of the loom, and obtain the process control library.

7. The loom control method according to any one of claims 1 to 3, characterized in that, The loom also includes multiple actuators, and the process control information includes multiple execution control information. After the step of obtaining the process control information corresponding to the user-defined information based on the process control library, the method further includes: The corresponding actuators are controlled to operate according to the execution control information provided.

8. A loom control cabinet characterized by Applied to a loom, the loom includes a shedding mechanism, the shedding mechanism being a direct-drive electronic shedding mechanism comprising multiple heald frames, and the loom control cabinet including: The human-machine interaction system, the loom main control system, and the shedding drive system are connected in sequence. The shedding drive system includes a control unit and multiple drive units respectively connected to the control unit. The control unit is connected to the loom main control system, and the multiple drive units are connected to multiple heald frames one by one. The human-computer interaction system is used to obtain user-defined information; The loom main control system is used to obtain process control instructions corresponding to the user-set information according to the process control library. The process control instructions include shedding control instructions. The process control library includes the correspondence between different fabric patterns and / or different fabric types and process control instructions. The control unit is used to generate a drive control signal according to the opening control command; Each of the driving units is used to drive the corresponding heald frame to move according to the driving control signal; The loom main control system is also used to generate process adjustment instructions based on the motion state information of the shedding mechanism using an adaptive learning model, wherein the process adjustment instructions include shedding adjustment instructions; The control unit is also used to generate the drive control signal according to the opening adjustment command during the normal operation of the loom, so as to adjust the movement state of each heald frame without stopping the machine when it is necessary to change the fabric pattern or type. The loom main control system includes a main control module and a loom monitoring module connected to the main control module. The main control module is connected to the human-machine interaction system and the control unit, respectively, and the loom monitoring module is connected to the loom. The loom monitoring module is used to acquire the overall status information of the loom in real time and send it to the main control module; The main control module is used to obtain process control instructions and generate process adjustment instructions based on the overall machine status information and motion status information using an adaptive learning model. The adaptive learning model includes an optimization algorithm model with target constraints or a machine learning model trained based on preset convergence conditions. The target constraints include the performance requirements of the loom, and the preset convergence conditions are determined based on the performance requirements of the loom. The loom monitoring module includes an analog acquisition submodule connected to the main control module. The analog acquisition submodule is used to acquire the analog data of the digital prototype in real time when the digital prototype is synchronously simulating the loom operation, and send the analog data as the overall machine status information to the main control module.

9. A loom characterized in that, The loom includes: An opening mechanism, the opening mechanism comprising multiple heald frames; A loom control cabinet for implementing the loom control method as described in any one of claims 1 to 7.

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