Textile detection equipment control system

Abstract

The invention provides a textile detection equipment control system, and belongs to the technical field of control. According to the textile detection equipment control system provided by the invention, multiple pieces of detection data are input into the neural network model, and the target fault distance is obtained according to the output result of the model, so that the control terminal can accurately predict and position the fault. According to the textile detection equipment control system provided by the invention, the accuracy of fault prediction is improved, so that the efficient operation of a textile equipment assembly line is ensured.

Description

Technical Field

[0001] The present application relates to the field of control technology, and in particular to a textile testing equipment control system. Background Art

[0002] In the textile production process, efficient operation and fault prevention of assembly line equipment are the key to ensuring production quality and efficiency. Traditional textile testing equipment control systems usually rely on manual monitoring and simple sensor feedback, making it difficult to achieve real-time monitoring and predictive maintenance of equipment status. As the processing precision requirements for textiles continue to increase, equipment failure or abnormality may lead to production interruptions, material waste, and reduced product quality. Therefore, there is an urgent need for an intelligent control system that can monitor equipment status in real time, predict potential failures, and automatically adjust. Summary of the invention

[0003] The embodiment of the present application provides a textile testing equipment control system to improve the above-mentioned problem.

[0004] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, an embodiment of the present application provides a textile testing equipment control system, which is applicable to a textile equipment assembly line, the assembly line includes a plurality of textile equipment, and is used to continuously process textiles, the textile equipment includes a feed end and a discharge end, the plurality of textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment, the system includes a control terminal and a plurality of testing terminals, the control terminal is communicatively connected to the testing terminal, the plurality of testing terminals are respectively arranged at the discharge end of the textile equipment, and the control system is configured as follows: The control terminal is based on the detection data obtained by multiple detection terminals, the detection data including light transmittance, light reflectance and void ratio, where the void ratio is the ratio of the actual area of ​​the textile to the gap area; The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio; The control terminal inputs a plurality of detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model; The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device; The control terminal determines the target device and all textile devices before the target device as a target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device.

[0005] In conjunction with the first aspect, in some embodiments, the control system is configured as follows: The control terminal obtains a training set, which includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set. The neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio, including: The control terminal obtains a characteristic vector of a preset fault distance, wherein the characteristic vector of the preset fault distance has at least three dimensions, and each dimension corresponds to a preset transmittance, a preset reflectance, and a preset space ratio; The control terminal constructs a neural network training model based on a feature vector of a preset fault distance, wherein the neural network training model is used to iteratively calculate a preset transmittance, a preset reflectivity, a preset air ratio, and a preset fault distance; When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model.

[0006] In conjunction with the first aspect, in some embodiments, the control system is configured as follows: When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model, including: The control terminal feeds the data in the training set back to the neural network training model for iterative calculation, and evaluates the calculation result of each iteration according to the preset loss function; When the value of the loss function is less than a preset threshold or the number of iterations reaches a preset upper limit, the control terminal stops the iterative calculation and outputs the trained neural network training model.

[0007] In combination with the first aspect, in some implementations, the control terminal regulates all textile devices in the target device group according to the detection data acquired by the target device, including: The control terminal determines whether the target device has a fault or anomaly based on the detection data of the target device; If there is a fault or anomaly, the control terminal generates a control instruction, which includes adjusting the operating parameters of all textile equipment in the target equipment group; The control terminal sends the control instruction to each textile equipment in the target equipment group to control the textile equipment.

[0008] In conjunction with the first aspect, in some embodiments, the control system is configured as follows: The control instructions include adjusting at least one parameter of the operating speed, temperature, pressure or tension of the textile equipment.

[0009] In conjunction with the first aspect, in some embodiments, the control system is configured as follows: The detection terminal includes an optical sensor, which is used to detect the light transmittance, light reflectivity and air ratio of the textile.

[0010] In conjunction with the first aspect, in some embodiments, the control system is configured as follows: The control terminal is also used to record the detection data and the output results of the neural network model, and generate historical data reports, which are used to analyze the operating status of the textile equipment assembly line.

[0011] In a second aspect, an embodiment of the present application proposes a textile detection equipment control method, which is applicable to a textile equipment assembly line, the assembly line includes multiple textile equipment, and is used to continuously process textiles, the textile equipment includes a feed end and a discharge end, the multiple textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment, the system includes a control terminal and multiple detection terminals, the control terminal is communicatively connected to the detection terminal, and the multiple detection terminals are respectively arranged at the discharge end of the textile equipment, and the method includes: The control terminal is based on the detection data obtained by multiple detection terminals, the detection data including light transmittance, light reflectance and void ratio, where the void ratio is the ratio of the actual area of ​​the textile to the gap area; The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio; The control terminal inputs a plurality of detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model; The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device; The control terminal determines the target device and all textile devices before the target device as a target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device.

[0012] In conjunction with the second aspect, in some embodiments, the method includes: The control terminal obtains a training set, which includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set. The neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio, including: The control terminal obtains a characteristic vector of a preset fault distance, wherein the characteristic vector of the preset fault distance has at least three dimensions, and each dimension corresponds to a preset transmittance, a preset reflectance, and a preset space ratio; The control terminal constructs a neural network training model based on a feature vector of a preset fault distance, wherein the neural network training model is used to iteratively calculate a preset transmittance, a preset reflectivity, a preset air ratio, and a preset fault distance; When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model.

[0013] In conjunction with the second aspect, in some implementations, when the iterative calculation process meets a preset condition, the control terminal stops the calculation and outputs the trained neural network training model, including: The control terminal feeds the data in the training set back to the neural network training model for iterative calculation, and evaluates the calculation result of each iteration according to the preset loss function; When the value of the loss function is less than a preset threshold or the number of iterations reaches a preset upper limit, the control terminal stops the iterative calculation and outputs the trained neural network training model.

[0014] In conjunction with the second aspect, in some implementations, the control terminal regulates all textile devices in the target device group according to the detection data acquired by the target device, including: The control terminal determines whether the target device has a fault or anomaly based on the detection data of the target device; If there is a fault or anomaly, the control terminal generates a control instruction, which includes adjusting the operating parameters of all textile equipment in the target equipment group; The control terminal sends the control instruction to each textile equipment in the target equipment group to control the textile equipment.

[0015] In conjunction with the second aspect, in some embodiments, the control instruction includes adjusting at least one parameter of the operating speed, temperature, pressure or tension of the textile equipment.

[0016] In combination with the second aspect, in some embodiments, the detection terminal includes an optical sensor, and the optical sensor is used to detect the transmittance, reflectivity and air ratio of the textile.

[0017] In combination with the second aspect, in some embodiments, the control terminal is also used to record the detection data and the output results of the neural network model, and generate a historical data report, which is used to analyze the operating status of the textile equipment assembly line.

[0018] A third aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method proposed in the first aspect of the embodiment of the present invention.

[0019] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0020] In summary, the above method and device have the following technical effects: A textile detection equipment control system and control method proposed in the embodiment of the present application, firstly, the control terminal is based on the detection data obtained by multiple detection terminals, the detection data includes transmittance, reflectance and empty ratio, and the empty ratio is the ratio of the actual area of ​​the textile to the gap area, then, the control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset empty ratio and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output the predicted fault distance according to the input transmittance, reflectance and empty ratio, then, the control terminal inputs multiple detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model, then, the control terminal determines the target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device, finally, the control terminal determines the target device and all textile devices before the target device as the target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device. A textile detection equipment control system proposed in the embodiment of the present application, by inputting multiple detection data into the neural network model, and obtaining the target fault distance according to the output result of the model, the control terminal can realize accurate prediction and positioning of the fault. This approach improves the accuracy of fault prediction, thereby ensuring the efficient operation of textile equipment lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a textile testing equipment control method proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The present application proposes a control method for textile detection equipment, which is applicable to a textile equipment assembly line, which includes multiple textile equipment and is used to continuously process textiles. The textile equipment includes a feed end and a discharge end, and the multiple textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment. The system includes a control terminal and multiple detection terminals, and the control terminal is communicatively connected to the detection terminal. The multiple detection terminals are respectively arranged at the discharge end of the textile equipment. Exemplarily, the detection terminal includes an optical sensor, which is used to detect the light transmittance, reflectivity and air ratio of the textile. Please refer to Figure 1 , the method comprises steps S101-S105: S101: The control terminal is based on the detection data obtained by multiple detection terminals, the detection data including transmittance, reflectivity and void ratio, and the void ratio is the ratio of the actual area of ​​the textile to the gap area.

[0024] Specifically, light transmittance refers to the ability of a textile to transmit light, and is usually used to evaluate the density, thickness or material uniformity of a textile. Changes in light transmittance may reflect fluctuations in the quality of the textile or abnormal operation of the equipment. Reflectivity refers to the ability of the textile surface to reflect light, and is usually used to evaluate the smoothness of the textile surface, coating uniformity or whether there are defects. Abnormalities in reflectivity may indicate problems with surface treatment equipment (such as coating machines). The void ratio refers to the ratio of the actual area of ​​a textile to the gap area, and is used to evaluate the weaving density or processing accuracy of a textile. Changes in the void ratio may reflect the operating status of the weaving equipment or processing equipment.

[0025] S102: The control terminal obtains a training set, which includes a preset transmittance, a preset reflectance, a preset space ratio and a preset fault distance, and trains a neural network model based on the training set. The neural network model is used to output a predicted fault distance based on the input transmittance, reflectance and space ratio.

[0026] For example, assume that there are the following samples in the training set:

[0027] The control terminal uses these samples to train the neural network model.

[0028] After training, the model can predict the fault distance (e.g., 15 meters) based on real-time detection data (e.g., transmittance = 75%, reflectivity = 35%, and air ratio = 85%). By obtaining the training set and training the neural network model, the control terminal can accurately predict the fault distance and provide reliable data support for the intelligent control of the textile equipment assembly line. This method not only improves the accuracy of fault prediction, but also enhances the stability and efficiency of the production line.

[0029] Specifically, as an implementation method, the control terminal obtains a feature vector of a preset fault distance, wherein the feature vector of the preset fault distance has at least three dimensions, and each dimension corresponds to a preset transmittance, a preset reflectance, and a preset space ratio. Then, the control terminal constructs a neural network training model based on the feature vector of the preset fault distance, wherein the neural network training model is used to iteratively calculate the preset transmittance, the preset reflectance, the preset space ratio, and the preset fault distance. Finally, when the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model.

[0030] It can be understood that the control terminal feeds the data in the training set into the neural network training model for iterative calculation, and evaluates the calculation result of each iteration according to the preset loss function. When the value of the loss function is less than the preset threshold or the number of iterations reaches the preset upper limit, the control terminal stops the iterative calculation and outputs the trained neural network training model.

[0031] Specifically, the control terminal inputs the data in the training set (including transmittance, reflectivity, void ratio and preset fault distance) into the neural network training model. Each sample data passes through the input layer, hidden layer and output layer of the model in turn for forward propagation calculation.

[0032] In this embodiment, forward propagation is taken as an example:

[0033] in: , , They represent transmittance, reflectivity and space ratio respectively. is the model weight, b is the bias term, and ∫ is the activation function. A preset loss function (such as mean square error, MSE) is used to evaluate the error between the predicted result and the actual fault distance. For example, the loss function is as follows:

[0034] Where N is the number of samples; is the predicted value of the i-th sample; is the true value of the i-th sample.

[0035] It can be understood that according to the value of the loss function, the gradient of the model parameters is calculated by the back propagation algorithm. The gradient represents the partial derivative of the loss function with respect to the model parameters (weights and biases), which is used to guide the direction of parameter adjustment. The control terminal continuously monitors the value of the loss function and the number of iterations during the iterative calculation process. When any of the following conditions is met, the iterative calculation is stopped and the trained model is output: The loss function value is less than the preset threshold: it indicates that the prediction accuracy of the model has reached the requirement. The number of iterations reaches the preset upper limit: it indicates that the model training has reached the maximum number of calculations to avoid overfitting or waste of computing resources.

[0036] By feeding the data in the training set back to the neural network model for iterative calculation and evaluating the results based on the loss function, the control terminal can efficiently train a high-precision neural network model. This method provides reliable technical support for fault prediction and intelligent control of textile equipment production lines.

[0037] S103: The control terminal inputs multiple detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model.

[0038] S104: The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device.

[0039] S105: The control terminal determines the target device and all textile devices before the target device as a target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device.

[0040] Specifically, as an implementation method, the control terminal can determine whether the target device has a fault or anomaly based on the detection data of the target device; if a fault or anomaly exists, the control terminal generates a control instruction, and the control instruction includes adjusting the operating parameters of all textile devices in the target device group. Specifically, the control instruction includes adjusting at least one parameter of the operating speed, temperature, pressure or tension of the textile device.

[0041] The control terminal sends the control instruction to each textile equipment in the target equipment group to control the textile equipment.

[0042] Optionally, in some other embodiments, the control terminal is also used to record the detection data and the output results of the neural network model, and generate a historical data report, which is used to analyze the operating status of the textile equipment assembly line.

[0043] A textile detection device control method proposed in an embodiment of the present application, firstly, a control terminal acquires detection data based on multiple detection terminals, the detection data includes transmittance, reflectance and empty ratio, and the empty ratio is the ratio of the actual area of ​​the textile to the gap area, then, the control terminal acquires a training set, the training set includes a preset transmittance, a preset reflectance, a preset empty ratio and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance and empty ratio, then, the control terminal inputs multiple detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model, then, the control terminal determines the target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device, finally, the control terminal determines the target device and all textile devices before the target device as the target device group, and regulates all textile devices in the target device group according to the detection data acquired by the target device. A textile detection device control system proposed in an embodiment of the present application, by inputting multiple detection data into the neural network model, and obtaining the target fault distance according to the output result of the model, the control terminal can realize accurate prediction and positioning of the fault. This approach improves the accuracy of fault prediction, thereby ensuring the efficient operation of textile equipment lines.

[0044] Based on the same inventive concept, the embodiment of the present application further proposes that the embodiment of the present application provides a textile detection equipment control system, which is applicable to a textile equipment assembly line, the assembly line includes multiple textile equipment, and is used to continuously process textiles, the textile equipment includes a feed end and a discharge end, the multiple textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment, the system includes a control terminal and multiple detection terminals, the control terminal is communicatively connected to the detection terminal, the multiple detection terminals are respectively arranged at the discharge end of the textile equipment, and the control system is configured as follows: The control terminal is based on the detection data obtained by multiple detection terminals, the detection data including light transmittance, light reflectance and void ratio, where the void ratio is the ratio of the actual area of ​​the textile to the gap area; The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio; The control terminal inputs a plurality of detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model; The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device; The control terminal determines the target device and all textile devices before the target device as a target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device.

[0045] In some embodiments, the control system is configured to: The control terminal obtains a training set, which includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set. The neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio, including: The control terminal obtains a characteristic vector of a preset fault distance, wherein the characteristic vector of the preset fault distance has at least three dimensions, and each dimension corresponds to a preset transmittance, a preset reflectance, and a preset space ratio; The control terminal constructs a neural network training model based on a feature vector of a preset fault distance, wherein the neural network training model is used to iteratively calculate a preset transmittance, a preset reflectivity, a preset air ratio, and a preset fault distance; When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model.

[0046] In some implementations, when the iterative calculation process meets a preset condition, the control terminal stops the calculation and outputs the trained neural network training model, including: The control terminal feeds the data in the training set back to the neural network training model for iterative calculation, and evaluates the calculation result of each iteration according to the preset loss function; When the value of the loss function is less than a preset threshold or the number of iterations reaches a preset upper limit, the control terminal stops the iterative calculation and outputs the trained neural network training model.

[0047] In some implementations, the control terminal regulates all textile devices in the target device group according to the detection data acquired by the target device, including: The control terminal determines whether the target device has a fault or anomaly based on the detection data of the target device; If there is a fault or anomaly, the control terminal generates a control instruction, which includes adjusting the operating parameters of all textile equipment in the target equipment group; The control terminal sends the control instruction to each textile equipment in the target equipment group to control the textile equipment.

[0048] In some embodiments, the control instruction includes adjusting at least one parameter of the operating speed, temperature, pressure or tension of the textile equipment.

[0049] In some embodiments, the detection terminal includes an optical sensor, and the optical sensor is used to detect the light transmittance, light reflectivity and air ratio of the textile.

[0050] In combination with the first aspect, in some embodiments, the control terminal is also used to record the detection data and the output results of the neural network model, and generate a historical data report, which is used to analyze the operating status of the textile equipment assembly line.

[0051] A textile detection equipment control system proposed in an embodiment of the present application, firstly, a control terminal is based on the detection data obtained by multiple detection terminals, the detection data includes transmittance, reflectance and empty ratio, and the empty ratio is the ratio of the actual area of ​​the textile to the gap area, then, the control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset empty ratio and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance and empty ratio, then, the control terminal inputs multiple detection data into the neural network model, and obtains the target fault distance according to the output result of the neural network model, then, the control terminal determines the target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as the target device, finally, the control terminal determines the target device and all textile devices before the target device as the target device group, and regulates all textile devices in the target device group according to the detection data obtained by the target device. A textile detection equipment control system proposed in an embodiment of the present application, by inputting multiple detection data into the neural network model, and obtaining the target fault distance according to the output result of the model, the control terminal can realize accurate prediction and positioning of the fault. This approach improves the accuracy of fault prediction, thereby ensuring the efficient operation of textile equipment lines.

[0052] Based on the same inventive concept, an embodiment of the present application further proposes an electronic device, the electronic device comprising: At least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the textile detection device control method of the embodiment of the present application.

[0053] In addition, to achieve the above-mentioned purpose, an embodiment of the present application further proposes a computer-readable storage medium storing a computer program, which implements the textile detection device control method of the embodiment of the present application when the computer program is executed by a processor.

[0054] The following is a detailed introduction to the various components of electronic equipment: The processor is the control center of the electronic device, which can be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0055] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0056] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0057] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor, or may exist independently and be coupled to the processor through an interface circuit of the electronic device, which is not specifically limited in the embodiments of the present invention.

[0058] A transceiver is used to communicate with a network device or a terminal device.

[0059] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0060] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0061] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, which will not be repeated here.

[0062] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0063] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0064] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0065] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0066] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0067] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0068] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A textile testing equipment control system, characterized in that: Applicable to a textile equipment assembly line, the assembly line includes a plurality of textile equipment and is used for continuously processing textiles, the textile equipment includes a feed end and a discharge end, the plurality of textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment, the system includes a control terminal and a plurality of detection terminals, the control terminal is communicatively connected to the detection terminal, the plurality of detection terminals are respectively arranged at the discharge end of the textile equipment, and the control system is configured as follows: The control terminal is based on the detection data obtained by the plurality of detection terminals, the detection data including light transmittance, light reflectance and space ratio, the space ratio being the ratio of the actual area of ​​the textile to the gap area; The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio; The control terminal inputs the plurality of detection data into the neural network model, and acquires a target fault distance according to an output result of the neural network model; The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as a target device; The control terminal determines the target device and all the textile devices before the target device as a target device group, and regulates all the textile devices in the target device group according to the detection data acquired by the target device.

2. A textile testing equipment control system according to claim 1, characterized in that: The control system is configured to: The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio, including: The control terminal acquires a characteristic vector of the preset fault distance, wherein the characteristic vector of the preset fault distance has at least three dimensions, and each dimension corresponds to the preset transmittance, the preset reflectance, and the preset space ratio; The control terminal constructs a neural network training model based on the feature vector of the preset fault distance, wherein the neural network training model is used to iteratively calculate the preset transmittance, the preset reflectivity, the preset space ratio and the preset fault distance; When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model.

3. A textile testing equipment control system according to claim 2, characterized in that: The control system is configured to: When the iterative calculation process meets the preset conditions, the control terminal stops the calculation and outputs the trained neural network training model, including: The control terminal feeds back the data in the training set to the neural network training model for iterative calculation, and evaluates the calculation result of each iteration according to a preset loss function; When the value of the loss function is less than a preset threshold or the number of iterations reaches a preset upper limit, the control terminal stops the iterative calculation and outputs the trained neural network training model.

4. A textile testing equipment control system according to claim 1, characterized in that: The control system is configured to: The control terminal regulates all the textile devices in the target device group according to the detection data acquired by the target device, including: The control terminal determines whether the target device has a fault or an abnormality based on the detection data of the target device; If there is a fault or anomaly, the control terminal generates a control instruction, and the control instruction includes adjusting the operating parameters of all the textile equipment in the target equipment group; The control terminal sends the control instruction to each of the textile equipment in the target equipment group to control the textile equipment.

5. A textile testing equipment control system according to claim 4, characterized in that: The control system is configured to: The control instructions include adjusting at least one parameter of the operating speed, temperature, pressure or tension of the textile equipment.

6. A textile testing equipment control system according to claim 1, characterized in that: The control system is configured to: The detection terminal includes an optical sensor, and the optical sensor is used to detect the light transmittance, light reflectivity and air ratio of the textile.

7. A textile testing equipment control system according to claim 1, characterized in that: The control system is configured to: The control terminal is also used to record the detection data and the output results of the neural network model, and generate a historical data report, which is used to analyze the operating status of the textile equipment assembly line.

8. A textile testing equipment control method, characterized in that: Applicable to a textile equipment assembly line, the assembly line includes a plurality of textile equipment and is used for continuously processing textiles, the textile equipment includes a feed end and a discharge end, the plurality of textile equipment are linearly connected, and the discharge end of one textile equipment is connected to the feed end of the next textile equipment, the system includes a control terminal and a plurality of detection terminals, the control terminal is communicatively connected to the detection terminal, and the plurality of detection terminals are respectively arranged at the discharge end of the textile equipment, the method includes: The control terminal is based on the detection data obtained by the plurality of detection terminals, the detection data including light transmittance, light reflectance and space ratio, the space ratio being the ratio of the actual area of ​​the textile to the gap area; The control terminal obtains a training set, the training set includes a preset transmittance, a preset reflectance, a preset space ratio, and a preset fault distance, and trains a neural network model based on the training set, the neural network model is used to output a predicted fault distance according to the input transmittance, reflectance, and space ratio; The control terminal inputs the plurality of detection data into the neural network model, and acquires a target fault distance according to an output result of the neural network model; The control terminal determines a target detection terminal based on the target fault distance, and determines the textile device corresponding to the target detection terminal as a target device; The control terminal determines the target device and all the textile devices before the target device as a target device group, and regulates all the textile devices in the target device group according to the detection data acquired by the target device.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to at least one of the processors; The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute a textile testing equipment control method as proposed in claim 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method as set forth in claim 8 is implemented.

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