Automatic control method and device of textile manufacturing equipment and electronic equipment

Abstract

The invention relates to an equipment automatic control technology, and discloses an automatic control method and device for textile manufacturing equipment and electronic equipment, and the method comprises the steps: obtaining historical parameter data of a textile loom, and building a textile tension mechanical model according to the historical parameter data; generating a tension compensation curve according to the spinning tension mechanical model and the historical parameter data; generating a fuzzy neural network controller by using the historical parameter data and the tension compensation curve; and performing dynamic parameter adjustment on the pre-acquired real-time parameters of the textile loom by using a fuzzy neural network controller and the tension compensation curve so as to realize automatic control of the textile manufacturing equipment. The tension control capability of the textile manufacturing equipment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment automatic control, and in particular to an automatic control method, device and electronic equipment for textile manufacturing equipment. Background Art

[0002] Textile manufacturing equipment refers to mechanical equipment used in the textile industry, which transforms yarn into grey or finished fabric through processes such as weaving preparation, weaving, and finishing. With the rapid development of industrial automation, automated control of textile manufacturing equipment has become a key way to improve production efficiency, ensure product quality, and reduce labor costs.

[0003] Traditional automatic control methods usually use pre-set control parameters to adjust the tension of textile looms in the textile manufacturing process to maintain the uniformity of fabric texture and the high quality of fabric products. However, such fixed control parameters cannot flexibly adapt to sudden changes and abnormal situations in the production process, which can easily lead to slow response of textile manufacturing equipment during the weaving process and inability to accurately achieve tension control, ultimately affecting the stability and reliability of textile manufacturing equipment product quality. Summary of the Invention

[0004] The present invention provides an automatic control method, device and electronic equipment for textile manufacturing equipment, the main purpose of which is to solve the problem of unstable fabric quality caused by the inability to accurately control tension.

[0005] To achieve the above-mentioned object, the present invention provides an automatic control method for textile manufacturing equipment, comprising: Acquiring historical parameter data of a textile loom, and establishing a textile tension mechanics model based on the historical parameter data; generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data; generating a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The fuzzy neural network controller and the tension compensation curve are used to dynamically adjust the pre-acquired real-time parameters of the textile loom to achieve automatic control of the textile manufacturing equipment.

[0006] Optionally, establishing a textile tension mechanics model according to the historical parameter data includes: Filtering key features from the historical parameter data; calculating derivative parameters of the key features; Constructing a tension correlation model based on the key features and the derived parameters; Parameters of the tension correlation model are optimized to obtain a tension mechanics model.

[0007] Optionally, generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data includes: Calculating tension transformation data based on historical parameter data and the textile tension mechanics model; Calculating a tension adjustment coefficient based on the tension conversion data, the historical parameter data, and a preset reference tension; A tension compensation curve is constructed according to the reference tension, the tension adjustment coefficient, and the tension transformation data.

[0008] Optionally, the calculating of the tension adjustment coefficient according to the tension conversion data, the historical parameter data and a preset reference tension includes: Constructing a tension distribution function according to the tension transformation data and the historical parameter data; The tension distribution function is parameter optimized using the tension conversion data and the reference tension to obtain a tension adjustment coefficient.

[0009] Optionally, the generating of a fuzzy neural network controller using the historical parameter data and the tension compensation curve includes: Calculating the tension deviation based on the historical parameter data and the tension compensation curve; Filtering adjustment parameters from the historical parameter data according to the tension deviation; A preset neural network structure is fitted according to the tension deviation and the adjustment parameters to obtain a fuzzy neural network controller.

[0010] Optionally, fitting a preset neural network structure according to the tension deviation and the adjustment parameter to obtain a fuzzy neural network controller includes: defining a fuzzy set according to the tension deviation and the adjustment parameter; Constructing a membership function according to the fuzzy set; generating a fuzzy rule according to the fuzzy combination and the membership function; Constructing a neural network layer according to the fuzzy rules and the membership function; Data optimization is performed on the neural network layer to obtain a fuzzy neural network controller.

[0011] Optionally, screening out key features from the historical parameter data includes: Calculating the correlation coefficients between all parameters in the historical parameter data and textile tension; Key features are screened out from the historical parameter data based on the correlation coefficient results.

[0012] Optionally, constructing a tension correlation model based on the key features and the derived parameters includes: Establishing a basic equation based on the key features and the derived parameters; The basic equations are integrated to obtain a tension correlation model.

[0013] In order to solve the above problems, the present invention also provides an automatic control device for textile manufacturing equipment, the device comprising: A model building module is used to obtain historical parameter data of textile looms and establish a textile tension mechanics model based on the historical parameter data; A curve generating module, configured to generate a tension compensation curve based on the textile tension mechanics model and the historical parameter data; A controller construction module, configured to generate a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The automatic adjustment module is used to dynamically adjust the pre-acquired real-time parameters of the textile loom using the fuzzy neural network controller and the tension compensation curve to achieve automatic control of the textile manufacturing equipment.

[0014] In order to solve the above problem, the present invention further provides an electronic device, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the automatic control method of the textile manufacturing equipment described above.

[0015] By establishing a textile tension mechanics model based on historical parameter data, the embodiments of the present invention can timely capture the dynamic behavior of yarn tension changes during the weaving process, and then adjust the parameters of the textile loom based on the obtained tension data to improve the uniformity of the resulting fabric. By predicting the tension at various parts of the loom based on the tension mechanics model, and then using the tension and historical parameter data, a tension compensation curve is constructed to guide the tension changes of the textile loom during operation, resulting in a more uniform fabric structure. Therefore, the automatic control method, device, and electronic device proposed by the present invention for textile manufacturing equipment can solve the problem of unstable fabric quality caused by the inability to accurately control tension. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of an automatic control method for textile manufacturing equipment provided by one embodiment of the present invention; Figure 2A schematic diagram of a process for generating a tension compensation curve according to an embodiment of the present invention; Figure 3 A schematic diagram of a process for generating a fuzzy neural network controller according to an embodiment of the present invention; Figure 4 A functional module diagram of an automatic control device for textile manufacturing equipment provided by one embodiment of the present invention; Figure 5 A schematic structural diagram of an electronic device for implementing the automatic control method for textile manufacturing equipment provided by one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] The embodiment of the present application provides an automatic control method for textile manufacturing equipment. The execution subject of the automatic control method of the textile manufacturing equipment includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the automatic control method of the textile manufacturing equipment can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 FIG. 1 is a flow chart of an automatic control method for textile manufacturing equipment according to an embodiment of the present invention. In this embodiment, the automatic control method for textile manufacturing equipment includes: S1. Obtain historical parameter data of a textile loom, and establish a textile tension mechanics model based on the historical parameter data.

[0021] In the embodiment of the present invention, the historical parameter data refers to the operating data in the production record of the textile loom, including: yarn type, yarn diameter, yarn tension, warp density, weft density, weaving speed parameters, etc.

[0022] In an embodiment of the present invention, by establishing a relationship model between textile tension and key parameters for the historical parameter data, tension data can be predicted under given parameters, and then the parameter data can be adjusted in time according to the predicted tension data and the preset tension data to improve the operating uniformity of the textile loom.

[0023] In an embodiment of the present invention, establishing a textile tension mechanics model based on the historical parameter data includes: Filtering key features from the historical parameter data; calculating derivative parameters of the key features; Constructing a tension correlation model based on the key features and the derived parameters; Parameters of the tension correlation model are optimized to obtain a tension mechanics model.

[0024] In the embodiment of the present invention, the step of screening out key features from the historical parameter data includes: Calculating the correlation coefficients between all parameters in the historical parameter data and textile tension; Key features are screened out from the historical parameter data based on the correlation coefficient results.

[0025] In detail, the covariance between each parameter in the historical parameter data and the textile tension and the standard deviation of each parameter and the textile tension are calculated, and then the ratio of the covariance to the product of the standard deviation is calculated to obtain the correlation coefficient, wherein a correlation coefficient close to 1 indicates a positive correlation, close to -1 indicates a negative correlation, and close to 0 indicates no correlation.

[0026] Specifically, the calculated correlation coefficient is compared with a preset correlation threshold, and only when the absolute value of the correlation coefficient is greater than the preset threshold, the parameter corresponding to the correlation coefficient is used as the key feature.

[0027] In the embodiment of the present invention, the derived parameters refer to parameters that can reflect the trend of tension changes, such as the friction coefficient of the yarn, the contact pressure between the yarn and the loom components, the deformation of the yarn during the weaving process, etc.

[0028] In an embodiment of the present invention, constructing a tension correlation model based on the key features and the derived parameters includes: Establishing a basic equation based on the key features and the derived parameters; The basic equations are integrated to obtain a tension correlation model.

[0029] In detail, the basic equation refers to establishing corresponding basic equations for each key feature based on physical principles such as Newton's second law (F=ma) and the law of conservation of energy, combined with the mechanical properties of the yarn (such as elasticity, friction, etc.), for example, the relationship between the tensile force of the yarn and the yarn diameter and elastic modulus, or the relationship between the friction force and friction coefficient of the yarn during the weaving process.

[0030] Specifically, equation integration involves integrating all of the aforementioned basic equations to form a correlation model. This correlation model incorporates an analysis of the forces acting on the yarn during the weaving process, including but not limited to tensile forces, bending forces, and frictional forces, while also accounting for yarn deformation and recovery characteristics. Loom parameters, such as loom speed and weft yarn feed rate, are selected from the historical parameter data. These loom parameters are converted into mathematical expressions, which are then incorporated into the correlation model to produce a tension correlation model.

[0031] In an embodiment of the present invention, the parameters of the association relationship model are initialized, and then the parameters of the tension association relationship model are adjusted using the gradient descent method and the historical parameter data, so that the tension value predicted by the final model is consistent with the tension value in the historical parameter data.

[0032] In an embodiment of the present invention, by constructing a textile tension mechanics model, it is possible to simulate the weaving process in a virtual environment, thereby discovering possible problems in the textile manufacturing process in advance, reducing the number of trial and error times in actual textile manufacturing, and thus improving weaving efficiency and weaving quality.

[0033] S2. Generate a tension compensation curve according to the textile tension mechanics model and the historical parameter data.

[0034] In the embodiments of the present invention, the tension compensation curve refers to a constant tension curve set during the textile manufacturing process to ensure more uniform yarn tension. During the textile manufacturing process, yarns with different characteristics will elongate and deform to varying degrees when subjected to the same tension. Therefore, using a constant tension can easily lead to varying tightness and structure across the fabric, ultimately impacting fabric quality.

[0035] In the embodiment of the present invention, the tension compensation curve can provide a clear target curve for the automatic control of textile manufacturing, effectively reduce weaving defects caused by uneven tension, and improve the quality of the final textile product.

[0036] Ginseng Figure 2 As shown, generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data includes: S21, calculating tension transformation data based on historical parameter data and the textile tension mechanics model; S22, calculating a tension adjustment coefficient based on the tension conversion data, the historical parameter data, and a preset reference tension; S23: Constructing a tension compensation curve according to the reference tension, the tension adjustment coefficient, and the tension transformation data.

[0037] In an embodiment of the present invention, the calculation of the tension adjustment coefficient based on the tension conversion data, the historical parameter data, and the preset reference tension includes: Constructing a tension distribution function according to the tension transformation data and the historical parameter data; The tension distribution function is parameter optimized using the tension conversion data and the reference tension to obtain a tension adjustment coefficient.

[0038] In detail, the tension transformation data refers to the tension measurement values ​​at different time points or yarn points on the textile loom, and the tension distribution function is used to describe the relationship between tension and yarn position, time and yarn properties.

[0039] Specifically, the difference between the tension distribution function and the corresponding tension transformation data is calculated, and the parameters of the tension distribution function are adjusted according to the difference using the mean square error method. The parameters of the tension distribution function finally obtained are the tension adjustment coefficients.

[0040] Furthermore, the tension adjustment coefficient indicates how to adjust the tension to achieve a preset reference tension level under different conditions (such as yarn position, yarn type, environmental conditions, etc.).

[0041] In the embodiment of the present invention, by establishing a tension compensation curve, the uniformity of the fabric structure can be ensured, and loose or tight areas can be avoided, thereby improving the quality and appearance of the final product.

[0042] S3. Generate a fuzzy neural network controller using the historical parameter data and the tension compensation curve.

[0043] In an embodiment of the present invention, the fuzzy neural network controller is used for tension control. According to the real-time tension deviation and the rate of change of the deviation, the speed and other parameters of the textile loom are adjusted to maintain the set tension level, thereby achieving the expected effect corresponding to the tension compensation curve.

[0044] In an embodiment of the present invention, the tension compensation curve provides tension set values ​​under different yarns, and the fuzzy neural network controller is responsible for adjusting various parameters of the textile loom in real time to follow the set values ​​and dynamically adjust the control output to ensure that the tension meets the requirements of the compensation curve.

[0045] In an embodiment of the present invention, the generated fuzzy neural network controller can not only handle complex nonlinear relationships, but also quickly and accurately generate and adjust tension control strategies in the presence of system parameter changes or external interference, such as yarn quality fluctuations, loom wear, etc., thereby ensuring the stability of tension control and the optimal final fabric weaving effect.

[0046] Ginseng Figure 3 As shown, the method of generating a fuzzy neural network controller using the historical parameter data and the tension compensation curve includes: S31, calculating the tension deviation according to the historical parameter data and the tension compensation curve; S32, filtering out adjustment parameters from the historical parameter data according to the tension deviation; S33 , fitting a preset neural network structure according to the tension deviation and the adjustment parameters to obtain a fuzzy neural network controller.

[0047] In an embodiment of the present invention, the neural network structure includes a fuzzy layer, a neural network layer and an output layer; wherein the fuzzy layer is used to fuzzify input variables, the neural network layer is used to learn and adjust weights, and the output layer is used to defuzzify and generate control outputs.

[0048] In an embodiment of the present invention, fitting a preset neural network structure according to the tension deviation and the adjustment parameter to obtain a fuzzy neural network controller includes: defining a fuzzy set according to the tension deviation and the adjustment parameter; Constructing a membership function according to the fuzzy set; generating a fuzzy rule according to the fuzzy combination and the membership function; Constructing a neural network layer according to the fuzzy rules and the membership function; Data optimization is performed on the neural network layer to obtain a fuzzy neural network controller.

[0049] In detail, the fuzzy set refers to the set division of the tension deviation and adjustment parameters, such as "small deviation", "medium deviation", "large deviation", etc., and the corresponding "slight adjustment", "moderate adjustment", and "substantial adjustment".

[0050] Specifically, a membership function is a function used to quantify the degree to which an element belongs to a fuzzy set. For tension deviation, various membership functions are possible, such as triangular, trapezoidal, or Gaussian. For example, a membership function for "small deviation" might reach a maximum value of 1 when the deviation is close to zero, and then rapidly decrease to 0 after deviating from zero within a certain range.

[0051] Specifically, fuzzy rules are decision criteria developed based on domain expert knowledge and experience to guide the fuzzy inference process. In tension control, a fuzzy rule might be expressed as follows: "If the tension deviation is 'large,' then the adjustment parameter should be 'largely adjusted.'" These fuzzy rules form the basis for controller decision-making, helping determine which control action to take given input conditions.

[0052] Specifically, the neural network layers in a fuzzy neural network are responsible for implementing the mathematical operations of fuzzy logic, including the fuzzification, fuzzy inference, and defuzzification processes. Each layer can contain multiple nodes, each of which performs a specific operation, such as calculating membership functions, applying fuzzy rules, or performing defuzzification. Through this multi-layered structure, the neural network is able to learn and adjust the parameters of membership functions and fuzzy rules to optimize control performance.

[0053] Specifically, data optimization refers to the process of adjusting neural network parameters to minimize output error. In fuzzy neural networks, the goal of data optimization is to enable the network to accurately predict adjustment parameters and make reasonable control decisions in the face of various tension deviations. This optimization utilizes a backpropagation algorithm to update network weights by calculating error gradients until a predetermined convergence criterion is reached or performance indicators are met.

[0054] In an embodiment of the present invention, by generating a fuzzy neural network controller, it is possible to make optimal control decisions when faced with various tension deviations, and quickly adjust the parameters of the textile loom so that the tension is maintained within the range required by the tension compensation curve, thereby achieving effective tension control.

[0055] S4. Using the fuzzy neural network controller and the tension compensation curve, dynamically adjust the pre-acquired real-time parameters of the textile loom to achieve automatic control of the textile manufacturing equipment.

[0056] In the embodiment of the present invention, by using the fuzzy neural network controller to dynamically adjust the parameters of the tension curve, the tension can be maintained within a preset range, thereby avoiding quality problems caused by tension fluctuations.

[0057] In an embodiment of the present invention, by obtaining the implementation parameters of the textile loom, the fuzzy neural network controller calculates the motor speed and tension settings that need to be adjusted based on the real-time parameters of the textile loom and the tension compensation curve, and produces control instructions based on the calculation results. The textile manufacturing equipment adjusts the textile loom in real time according to the production control instructions.

[0058] In an embodiment of the present invention, instructions are output to the drive system to automatically adjust the various components of the loom, such as changing the motor speed, to respond to tension fluctuations, maintain tension stability, and achieve closed-loop dynamic adjustment of tension control, thereby improving weaving efficiency and product quality.

[0059] like Figure 4 FIG. 1 is a functional module diagram of an automatic control device for textile manufacturing equipment provided by one embodiment of the present invention.

[0060] The automatic control device 100 for textile manufacturing equipment described in the present invention can be installed in an electronic device. Depending on the functionality to be implemented, the automatic control device 100 for textile manufacturing equipment may include a model building module 101, a curve generation module 102, a controller building module 103, and an automatic adjustment module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by a processor in an electronic device and perform a fixed function. These modules are stored in the memory of the electronic device.

[0061] In this embodiment, the functions of each module / unit are as follows: The model building module 101 is used to obtain historical parameter data of the textile loom and establish a textile tension mechanics model based on the historical parameter data; The curve generating module 102 is configured to generate a tension compensation curve based on the textile tension mechanics model and the historical parameter data; The controller construction module 103 is used to generate a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The automatic adjustment module 104 is used to dynamically adjust the pre-acquired real-time parameters of the textile loom using the fuzzy neural network controller and the tension compensation curve to achieve automatic control of the textile manufacturing equipment.

[0062] In detail, each module in the automatic control device 100 of the textile manufacturing equipment in the embodiment of the present invention adopts the same Figures 1 to 3 The automatic control method for textile manufacturing equipment described in the foregoing is the same technical means and can produce the same technical effects, so I will not go into details here.

[0063] like Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an automatic control method for textile manufacturing equipment provided by an embodiment of the present invention.

[0064] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as an automatic control program for textile manufacturing equipment.

[0065] In some embodiments, the processor 10 may be composed of an integrated circuit. The processor 10 is the control core (Control Unit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory 11 (for example, executing automatic control programs for textile manufacturing equipment, etc.) and calling data stored in the memory 11.

[0066] The memory 11 includes at least one type of readable storage medium, including a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 can be used not only to store application software installed in the electronic device and various data, such as the code of the automatic control program of the textile manufacturing equipment, but also to temporarily store data that has been output or is about to be output.

[0067] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0068] The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface.

[0069] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0070] For example, although not shown, the electronic device may further include a power source (e.g., a battery) to power various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power source may further include any of one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, and the like, which are not further detailed here.

[0071] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0072] The automatic control program for the textile manufacturing equipment stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following: Acquiring historical parameter data of a textile loom, and establishing a textile tension mechanics model based on the historical parameter data; generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data; generating a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The fuzzy neural network controller and the tension compensation curve are used to dynamically adjust the pre-acquired real-time parameters of the textile loom to achieve automatic control of the textile manufacturing equipment.

[0073] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0074] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0075] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0076] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a system may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automatic control method for textile manufacturing equipment, characterized in that: The method comprises: Acquiring historical parameter data of a textile loom, and establishing a textile tension mechanics model according to the historical parameter data; Generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data; Generate a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The fuzzy neural network controller and the tension compensation curve are used to dynamically adjust the pre-acquired real-time parameters of the textile loom to achieve automatic control of the textile manufacturing equipment.

2. The automatic control method of textile manufacturing equipment according to claim 1, characterized in that: The step of establishing a textile tension mechanics model according to the historical parameter data comprises: Filtering key features from the historical parameter data; calculating a derivative parameter of the key feature; Constructing a tension correlation model according to the key features and the derived parameters; The tension correlation model is optimized in parameters to obtain a tension mechanics model.

3. The automatic control method of textile manufacturing equipment according to claim 2, characterized in that: The generating of the tension compensation curve according to the textile tension mechanics model and the historical parameter data comprises: Calculating tension transformation data based on historical parameter data and the textile tension mechanics model; Calculating a tension adjustment coefficient according to the tension conversion data, the historical parameter data and a preset reference tension; A tension compensation curve is constructed according to the reference tension, the tension adjustment coefficient, and the tension transformation data.

4. The automatic control method of textile manufacturing equipment according to claim 1, characterized in that: The calculating of the tension adjustment coefficient according to the tension conversion data, the historical parameter data and the preset reference tension includes: Constructing a tension distribution function according to the tension transformation data and the historical parameter data; The tension distribution function is parameter optimized using the tension transformation data and the reference tension to obtain a tension adjustment coefficient.

5. The automatic control method of textile manufacturing equipment according to claim 1, characterized in that: The method of generating a fuzzy neural network controller by using the historical parameter data and the tension compensation curve includes: Calculating the tension deviation according to the historical parameter data and the tension compensation curve; Filtering out adjustment parameters from the historical parameter data according to the tension deviation; A preset neural network structure is fitted according to the tension deviation and the adjustment parameters to obtain a fuzzy neural network controller.

6. The automatic control method of textile manufacturing equipment as claimed in claim 5, characterized in that: The method of fitting a preset neural network structure according to the tension deviation and the adjustment parameter to obtain a fuzzy neural network controller includes: defining a fuzzy set according to the tension deviation and the adjustment parameter; Constructing a membership function according to the fuzzy set; Generate a fuzzy rule according to the fuzzy combination and the membership function; Constructing a neural network layer according to the fuzzy rules and the membership function; The neural network layer is subjected to data optimization to obtain a fuzzy neural network controller.

7. The automatic control method of textile manufacturing equipment according to claim 2, characterized in that: The step of selecting key features from the historical parameter data includes: Calculate the correlation coefficient between all parameters in the historical parameter data and textile tension; Key features are screened out from the historical parameter data based on the correlation coefficient results.

8. The automatic control method of textile manufacturing equipment according to claim 2, characterized in that: The constructing of a tension correlation model according to the key features and the derived parameters includes: Establishing a basic equation according to the key features and the derived parameters; The basic equations are integrated to obtain a tension correlation model.

9. An automatic control device for textile manufacturing equipment, characterized in that: The device comprises: A model building module, used to obtain historical parameter data of a textile loom and establish a textile tension mechanics model according to the historical parameter data; A curve generating module, used for generating a tension compensation curve according to the textile tension mechanics model and the historical parameter data; A controller construction module, used to generate a fuzzy neural network controller using the historical parameter data and the tension compensation curve; The automatic adjustment module is used to dynamically adjust the pre-acquired real-time parameters of the textile loom by using the fuzzy neural network controller and the tension compensation curve, so as to realize automatic control of the textile manufacturing equipment.

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