A weft insertion control method, device and electronic equipment for an air jet loom

Abstract

The application provides a weft insertion control method, device and electronic equipment for an air-jet loom, the weft insertion control method comprising the following steps: selecting an acquisition point in a profiled reed channel and obtaining a transient airflow velocity of the acquisition point; performing variational mode decomposition on the transient airflow velocity to obtain a final intrinsic mode component of the transient airflow velocity; obtaining time-domain characteristics, frequency-domain characteristics and / or entropy theory characteristics of the final intrinsic mode component; predicting a movement displacement of a weft yarn head end through the time-domain characteristics, frequency-domain characteristics and / or entropy theory characteristics, predicting a distance between the weft yarn head end and a profiled reed channel wall surface according to the movement displacement, and adjusting a control parameter of the air-jet loom if the predicted distance between the weft yarn head end and the profiled reed channel wall surface is less than a preset value. The method can control the transient airflow velocity in the profiled reed channel online and timely adjust the movement displacement of the weft yarn head end, thereby reducing the weft breakage rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of weft yarn leading, in particular to a weft leading control method, device and electronic equipment for an air-jet loom. BACKGROUND

[0002] An air-jet loom uses the frictional drag force of high-speed airflow emitted by a main nozzle and an auxiliary nozzle to guide the weft yarn to fly through a shed. When the weft yarn fluctuates and flies forward in the profiled reed channel, it will produce a movement displacement under the action of the airflow frictional force. When the displacement of the weft yarn head end is too large, it will hit the profiled reed wall and cause weft breakage, resulting in horizontal stoppage and affecting the efficiency and weft leading quality of the whole loom.

[0003] The existing air-jet loom is provided with two photoelectric sensors at the end of the weft yarn movement track. When the weft yarn has been broken, the photoelectric sensor sends an on-off signal, and the air-jet loom stops horizontally. The air-jet loom cannot make a prediction before the weft yarn is about to break, and cannot timely adjust the movement state of the weft yarn, so as to reduce the weft breakage rate. SUMMARY

[0004] In view of the problem that the existing air-jet loom cannot make a prediction before the weft yarn is about to break, and cannot timely adjust the movement state of the weft yarn to reduce the weft breakage rate, the present application provides a weft leading control method, device and electronic equipment for an air-jet loom.

[0005] In a first aspect, a weft leading control method for an air-jet loom is provided, comprising the following steps:

[0006] selecting a collection point in the profiled reed channel, and obtaining a transient airflow velocity of the collection point;

[0007] performing variational mode decomposition on the transient airflow velocity to obtain a final intrinsic modal component of the transient airflow velocity;

[0008] obtaining a time-domain feature, a frequency-domain feature and / or an entropy theory feature of the final intrinsic modal component;

[0009] predicting a movement displacement of the weft yarn head end through the time-domain feature, the frequency-domain feature and / or the entropy theory feature, predicting a distance between the weft yarn head end and the profiled reed channel wall surface according to the movement displacement, and adjusting a control parameter of the air-jet loom if the predicted distance between the weft yarn head end and the profiled reed channel wall surface is less than a preset value.

[0010] Optionally, the variational mode decomposition on the transient airflow velocity to obtain the final intrinsic modal component of the transient airflow velocity comprises:

[0011] performing variational mode decomposition on the transient airflow velocity to obtain a plurality of initial intrinsic modal components;

[0012] respectively calculate envelope entropy corresponding to a plurality of initial intrinsic modal components;

[0013] Optimize the variational modal decomposition algorithm with the minimum envelope entropy as the fitness function to obtain the optimal decomposition number and the quadratic penalty factor; and perform variational modal decomposition on the transient airflow velocity again based on the optimal decomposition number and the quadratic penalty factor to obtain the final intrinsic modal component.

[0014] Optionally, the obtaining of the time domain feature and the entropy theory feature of the final intrinsic modal component includes:

[0015] Obtaining the time domain feature and / or the entropy theory feature of the final intrinsic modal component signal with the maximum kurtosis value.

[0016] Optionally, the prediction of the movement displacement of the weft yarn head end through the time domain feature, the frequency domain feature and / or the entropy theory feature includes:

[0017] Using the time domain feature, the frequency domain feature and / or the entropy theory feature as the input feature of machine learning, establishing a regression relationship between the input feature and the movement displacement of the weft yarn head end, and predicting the movement displacement of the weft yarn head end based on the regression relationship between the input feature and the movement displacement of the weft yarn head end.

[0018] Optionally, the time domain feature includes: mean absolute error, root mean square error, kurtosis value; and the entropy theory feature includes: approximate entropy, sample entropy, fuzzy entropy, information entropy, envelope entropy.

[0019] Optionally, the final intrinsic modal component includes a first component for predicting the average weft insertion speed, a second component for representing the weft yarn fluctuation amplitude, and a third component for representing noise.

[0020] Optionally, the control parameters include the air supply pressure at the auxiliary nozzle, the energization timing and the opening and closing time of the electromagnetic valve.

[0021] If the distance between the predicted weft yarn head end and the profiled reed channel wall surface is less than a preset value, the air supply pressure of the servo pressure valve at the auxiliary nozzle and the energization timing and the opening and closing time of the electromagnetic valve are adjusted.

[0022] In a second aspect, a weft insertion control device is provided, including:

[0023] A data acquisition module is configured to acquire the transient airflow velocity of the acquisition point in the profiled reed channel.

[0024] an intrinsic modal component optimization module, configured to perform variational modal decomposition on the transient airflow velocity to obtain a plurality of initial intrinsic modal components, calculate envelope entropy corresponding to each of the plurality of initial intrinsic modal components, and perform optimization on the variational modal decomposition algorithm by taking the minimum envelope entropy as a fitness function to obtain an optimal decomposition number and a quadratic penalty factor, and perform variational modal decomposition based on the optimal decomposition number and the quadratic penalty factor to obtain a final intrinsic modal component;

[0025] a transient velocity signal reconstruction module, configured to obtain the final intrinsic modal component with the maximum kurtosis value and an index value of the intrinsic modal component with the maximum kurtosis value;

[0026] a feature acquisition module, configured to obtain time-domain features, frequency-domain features and / or entropy theory features of the final intrinsic modal component;

[0027] a control module, configured to use the time-domain features and the entropy theory features as input features of machine learning, establish a regression relationship between the input features and the weft displacement, predict the motion displacement of the weft head end based on the regression relationship between the input features and the weft displacement, predict the distance between the weft head end and the profile reed channel wall surface based on the motion displacement, and adjust the control parameters of the air-jet loom if the predicted distance between the weft head end and the profile reed channel wall surface is less than a preset value.

[0028] In a third aspect, an electronic device is provided, and the electronic device includes:

[0029] a processor;

[0030] a memory for storing executable instructions of the processor;

[0031] the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of the first aspect.

[0032] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program for executing the method of the first aspect.

[0033] Beneficial effects: The weft insertion control method for the air-jet loom provided in the present application can obtain the final intrinsic modal component of the transient airflow velocity by performing variational modal decomposition on the transient airflow velocity, predict the motion displacement of the weft head end based on the time-domain features, frequency-domain features and / or entropy theory features of the final intrinsic modal component, and adjust the control parameters of the air-jet loom if the predicted distance between the weft head end and the profile reed channel wall surface is less than a preset value, thereby reducing the weft breakage rate. BRIEF DESCRIPTION OF DRAWINGS

[0034] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Figure 1 A flowchart of a weft insertion control method according to the embodiment is shown.

[0036] Figure 2 A flowchart of another weft insertion control method for a jet loom according to the embodiment is shown.

[0037] Figure 3 A flowchart of a weft insertion control method for a jet loom according to the embodiment is shown.

[0038] Figure 4 A structural diagram of a jet loom is shown.

[0039] Figure 5 A graph of the amplitude versus time of a final eigenmode function component according to the embodiment is shown.

[0040] Figure 6 A spectral graph of a final eigenmode function component according to the embodiment is shown.

[0041] Figure 7 A structural diagram of an apparatus according to the embodiment is shown.

[0042] Figure 8 A schematic diagram of an electronic device according to the embodiment is shown.

[0043] Figure 9 A schematic diagram of a computer readable medium according to the embodiment is shown. DETAILED DESCRIPTION

[0044] The technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0046] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "linking" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances. In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0047] As Figure 1 shown, the first aspect of the embodiment provides a weft insertion control method for an air jet loom, comprising the following steps:

[0048] S11, selecting a collection point in the profiled reed channel, and obtaining the transient airflow velocity of the collection point. Specifically, in this embodiment, a plurality of collection points are selected, and the transient airflow velocity of each collection point is obtained respectively;

[0049] S12, variational mode decomposition is performed on the transient airflow velocity to obtain the final intrinsic mode component.

[0050] Specifically, it includes:

[0051] The variational mode decomposition is performed on the transient airflow velocity to obtain a plurality of initial intrinsic mode components;

[0052] The envelope entropy corresponding to each of the plurality of initial intrinsic mode components is calculated respectively;

[0053] The variational mode decomposition algorithm is optimized with the minimum envelope entropy as the fitness function to obtain the optimal decomposition number and the quadratic penalty factor; the transient airflow velocity is again subjected to variational mode decomposition based on the optimal decomposition number and the quadratic penalty factor to obtain the final intrinsic mode component. S13, the time domain feature, the frequency domain feature and / or the entropy theory feature of the final intrinsic mode component are obtained. Specifically, it includes:

[0054] The time domain feature, the frequency domain feature and / or the entropy theory feature of the final intrinsic mode component signal with the maximum kurtosis value are obtained.

[0055] The entropy theory feature includes: approximate entropy, sample entropy, fuzzy entropy, information entropy, envelope entropy.

[0056] The time domain feature includes: mean absolute error, root mean square error, kurtosis value.

[0057] S14, predicting the motion displacement of the weft head end through the time domain feature, the frequency domain feature and / or the entropy theory feature, predicting the distance between the weft head end and the profile reed channel wall surface according to the motion displacement, adjusting the control parameter of the air jet loom if the predicted distance between the weft head end and the profile reed channel wall surface is less than the preset value, adjusting the control parameter of the air jet loom if the predicted distance between the weft head end and the profile reed channel wall surface is less than the preset value.

[0058] Predicting the motion displacement of the weft head end through the time domain feature, the frequency domain feature and / or the entropy theory feature includes:

[0059] Using the time domain feature, the frequency domain feature and / or the entropy theory feature as the input feature of machine learning, establishing the regression relationship between the input feature and the motion displacement of the weft head end, and predicting the motion displacement of the weft head end based on the regression relationship between the input feature and the motion displacement of the weft head end.

[0060] The control parameter includes the gas supply pressure at the auxiliary nozzle, the energization timing and the opening and closing time of the electromagnetic valve.

[0061] If the predicted distance between the weft head end and the profile reed channel wall surface is less than the preset value, the gas supply pressure at the auxiliary nozzle and the energization timing and the opening and closing time of the electromagnetic valve are adjusted.

[0062] The second aspect of the embodiment provides a weft insertion control method for an air jet loom.

[0063] As shown in Figure 3 , the air jet loom includes a main nozzle, a plurality of auxiliary nozzles and a profile reed. The airflow direction of the main nozzle is parallel to the axial direction of the profile reed. Each group of auxiliary nozzles includes a plurality of auxiliary nozzles, and the axial direction of the auxiliary nozzles is perpendicular to the axial direction of the profile reed. The profile reed includes a profile reed channel, and the weft yarn shuttles in the profile reed channel. The airflow direction of the main nozzle is parallel to the axial direction of the profile reed channel, and the axial direction of the auxiliary nozzles is perpendicular to the axial direction of the profile reed. In this embodiment, the transient airflow velocity of the auxiliary nozzle is collected. The auxiliary nozzle is controlled by an electromagnetic valve and a pressure valve (a servo pressure valve is used in this embodiment).

[0064] In this embodiment, the Pitot tube is used to measure the pressure in the profile reed channel, and the NI acquisition card is used to send the pressure to the computer, and then the pressure is converted into a velocity signal. Alternatively, a hot-wire anemometer or a PIV particle velocity laser (laser Doppler velocimeter) can be used to directly measure the instantaneous transient airflow velocity of the collection point in the profile reed channel and send it to the computer. Due to the complex working condition of the air jet loom, the transient airflow velocity is accompanied by noise, and the noise contains information about the fluctuation degree of the weft head end.

[0065] As shown in Figure 2 , the weft insertion control method includes the following steps:

[0066] S21. Select multiple collection points within the irregularly shaped reed channel and acquire the transient airflow velocity at each collection point multiple times.

[0067] like Figure 4 The diagram shows the transient airflow velocity at a certain detection point. The high-pressure airflow exits from the auxiliary nozzle and enters the irregular reed channel. The velocity still fluctuates significantly within a considerable time and space scale. The transient velocity of the auxiliary jet is a non-stationary signal, accompanied by noise signals.

[0068] S22. Perform variational mode decomposition on the transient airflow velocity to obtain the final intrinsic mode components of the transient airflow velocity. Specifically, this includes:

[0069] Variational mode decomposition is performed on transient airflow velocity to obtain multiple initial intrinsic mode components;

[0070] Calculate the envelope entropy corresponding to each of the multiple initial intrinsic mode components;

[0071] The variational mode decomposition algorithm is optimized by using the minimum envelope entropy as the fitness function to obtain the optimal number of decompositions and the quadratic penalty factor;

[0072] Based on the optimal number of decompositions and the secondary penalty factor, the transient airflow velocity is subjected to variational mode decomposition again to obtain the final intrinsic mode components.

[0073] The final intrinsic mode components include a first component representing the predicted velocity, a second component representing the fluctuation amplitude, and a third component representing noise. The first component, as the average velocity of the auxiliary air jet, determines the weft insertion speed; the larger the first component, the higher the weft insertion efficiency. The third component, as noise interference, is removed in this embodiment. The second component is consistent with the trend of the weft yarn end motion displacement, and the weft yarn end motion displacement can be predicted using the second component.

[0074] In this implementation, an optimization algorithm is used to optimize the variational mode decomposition algorithm. The optimization algorithm can be the Northern Eagle algorithm, the Energy Valley optimization algorithm, the Gold Mining optimization algorithm, etc. In this embodiment, the Northern Eagle algorithm is used for optimization, yielding an optimal number of decompositions of 3 and a secondary penalty factor of 500. Obtaining the optimal number of decompositions and the secondary penalty factor through optimization effectively avoids under-decomposition or mode aliasing phenomena caused by human selection of specific values ​​in the variational mode decomposition algorithm, achieving a better decomposition effect.

[0075] S23. Obtain the time-domain characteristics, frequency-domain characteristics, and / or entropy theory characteristics of the final intrinsic mode components; specifically including:

[0076] The kurtosis value of the final intrinsic modal component is solved first, the final intrinsic modal component with the maximum kurtosis value is obtained, and the index value of the maximum kurtosis value is obtained, and then the signal is reconstructed to obtain the time domain characteristics, frequency domain characteristics and / or entropy theory characteristics of the final intrinsic modal component with the maximum kurtosis value. Because the kurtosis value is relatively sensitive, selecting the final intrinsic modal component with the maximum kurtosis value can improve the accuracy of the weft yarn displacement prediction.

[0077] The time domain characteristics, frequency domain characteristics and / or entropy theory characteristics of the transient velocity reconstruction signal are obtained. In this embodiment, the final intrinsic modal component with the maximum kurtosis value is the second component.

[0078] The amplitude-time relationship of the final intrinsic modal function component is shown in Figure 5 In the formula, IMF1 is the first component, IMF2 is the second component, and IMF3 is the third component. Figure 5 In the formula, IMF1 is the first component, IMF2 is the second component, and IMF3 is the third component. As can be seen from the figure, there is no frequency aliasing, and it is more optimal to perform variational modal decomposition on the transient airflow velocity again based on the optimal number of decompositions and the quadratic penalty factor to obtain the final intrinsic modal component.

[0079] The frequency spectrum of the final intrinsic modal function component is shown in Figure 6 In the formula, IMF1 is the first component, IMF2 is the second component, and IMF3 is the third component. Figure 6 In the formula, IMF1 is the first component, IMF2 is the second component, and IMF3 is the third component. As can be seen from the figure, there is no frequency aliasing, and it is more optimal to perform variational modal decomposition on the transient airflow velocity again based on the optimal number of decompositions and the quadratic penalty factor to obtain the final intrinsic modal component.

[0080] S24, the motion displacement of the weft yarn head end is predicted by using the time domain characteristics, frequency domain characteristics and / or entropy theory characteristics. One of the time domain characteristics, frequency domain characteristics and entropy theory characteristics can be used to obtain the motion displacement. If all of the time domain characteristics, frequency domain characteristics and entropy theory characteristics are used, the prediction accuracy is the highest. In this embodiment, the prediction accuracy and the prediction speed are considered, and the time domain characteristics and the entropy theory characteristics are used to predict the motion displacement.

[0081] Specifically, the time domain characteristics and the entropy theory characteristics are used as input features of machine learning to establish a regression relationship between the input features and the motion displacement of the weft yarn head end, and the motion displacement of the weft yarn head end is predicted based on the regression relationship between the input features and the motion displacement of the weft yarn head end.

[0082] The distance between the weft yarn head end and the channel wall surface of the profiled reed is predicted according to the motion displacement. If the predicted distance between the weft yarn head end and the channel wall surface of the profiled reed is less than a preset value, the control parameters of the air jet loom are adjusted.

[0083] The control parameters include the gas supply pressure at the auxiliary nozzle, the energization timing and the opening and closing time of the electromagnetic valve.

[0084] If the predicted distance between the weft yarn head end and the channel wall surface of the profiled reed is less than a preset value, the gas supply pressure of the servo pressure valve at the auxiliary nozzle and the energization timing and the opening and closing time of the electromagnetic valve are adjusted.

[0085] The maximum kurtosis value of the final eigenvector component is used as an airflow fluctuation amplitude evaluation index, and the accuracy of the predicted motion displacement of the weft end is evaluated.

[0086] The average absolute error is calculated according to the following formula:

[0087]

[0088] The root mean square error is calculated according to the following formula:

[0089]

[0090] The kurtosis value is calculated according to the following formula:

[0091]

[0092] In the formula, E MAE is the average absolute error, E RMSE is the root mean square error, E QD is the kurtosis value, n is the number of transient airflow velocities obtained at a sampling point; x i (t) is the amplitude of the transient airflow velocity at the sampling point, is the average value of the amplitude of the transient airflow velocity signal at the sampling point;

[0093] The final eigenvector component of a sampling point is used to calculate the average absolute error, root mean square error, kurtosis value and envelope entropy, and the results are shown in Table 1.

[0094] Table 1: Evaluation table of final eigenvector component

[0095]

[0096]

[0097] As can be seen from the table, the kurtosis value of the second component is the largest, and the envelope entropy is the smallest.

[0098] The control parameters include the gas supply pressure at the auxiliary nozzle, the energization time of the electromagnetic valve, etc. By reducing the gas supply pressure, reducing the energization time of the electromagnetic valve, etc., the degree of the second component is reduced, thereby reducing the motion deformation caused by the weft advancing in the auxiliary jet flow, and reducing the weft breakage rate.

[0099] Variational mode decomposition (VMD) is a time-frequency processing method, which can decompose a signal into multiple intrinsic mode components (IMFs), and is commonly used for detection of complex input signals. Compared with traditional Fourier transform, wavelet transform method, and empirical mode decomposition, VMD is widely used in non-destructive fault detection and structure health state prediction. VMD has good anti-noise performance and non-stationary signal processing effect.

[0100] The VMD method is used to decompose the jet transient velocity signal, and the parameters include the number of decompositions, the quadratic penalty factor, the noise tolerance, and the discrimination accuracy. The number of decompositions and the quadratic penalty factor have a greater impact on the decomposition result. At present, the intelligent algorithm is rarely used to determine the optimal parameters of VMD. The best number of decompositions is determined by trying the change of the center frequency under different intrinsic mode components, and the impact of the quadratic penalty factor on the decomposition result is ignored. In this embodiment, the best number of decompositions and the quadratic penalty factor are obtained by optimization, so as to avoid the under-decomposition or mode aliasing phenomenon caused by the selection of specific values by human factors, and a good decomposition effect is achieved.

[0101] There is a strong coupling effect between the high-speed airflow and the weft yarn. The more impact signal components contained in the transient airflow velocity, the greater the displacement of the weft yarn in the airflow. Once the limit position of the profile reed wall is exceeded, the weft yarn will be broken. Therefore, the final intrinsic mode component (second component in this embodiment) corresponding to the maximum kurtosis value and the index value are selected, the fluctuation degree of the final intrinsic mode component is evaluated by using the average absolute error, the root mean square error, and the kurtosis value, and the fluctuation size of the weft yarn head end is predicted by using the fluctuation index of the second component. If the distance between the predicted weft yarn head end and the profile reed wall surface is less than the preset value, the control parameters of the air-jet loom are adjusted, so as to reduce the weft yarn breakage rate.

[0102] The third aspect of the embodiment of the present application provides a device, an electronic device, and a computer readable medium, which are described below with reference to the accompanying drawings.

[0103] The present application also provides a device, and the device provided in the embodiment of the present application can implement the method described above, and the device can be implemented by software, hardware, or a combination of software and hardware. For example, the device can include integrated or separate functional modules or units to perform the corresponding steps in the above methods.

[0104] Please refer to Figure 7 which shows a schematic diagram of a device provided by some embodiments of the present application. Since the device embodiment is basically similar to the method embodiment, it is described more simply, and the related parts refer to the part of the method embodiment. The device embodiment described below is only illustrative.

[0105] As Figure 7As shown, the device 600 can include:

[0106] a data acquisition module 601 configured to acquire the transient airflow velocity of the collection point in the special reed channel;

[0107] an intrinsic modal component optimization module 602 configured to perform variational modal decomposition on the transient airflow velocity to obtain a plurality of initial intrinsic modal components, calculate the envelope entropy corresponding to each of the plurality of initial intrinsic modal components, and perform optimization on the variational modal decomposition algorithm by taking the minimum envelope entropy as the fitness function to obtain the optimal decomposition number and the quadratic penalty factor, and perform variational modal decomposition based on the optimal decomposition number and the quadratic penalty factor to obtain the final intrinsic modal component;

[0108] a transient velocity signal reconstruction module 603 configured to obtain the final intrinsic modal component with the maximum kurtosis value and the index value of the intrinsic modal component with the maximum kurtosis value;

[0109] a feature acquisition module 604 configured to obtain the time domain feature, the frequency domain feature, and / or the entropy theory feature of the final intrinsic modal component;

[0110] a control module 605 configured to use the time domain feature, the frequency domain feature, and the entropy theory feature as the input feature of machine learning, establish a regression relationship between the input feature and the weft displacement, predict the movement displacement of the weft head end according to the regression relationship between the input feature and the weft displacement, predict the distance between the weft head end and the wall surface of the special reed channel according to the movement displacement, and adjust the control parameter of the air jet loom if the predicted distance between the weft head end and the wall surface of the special reed channel is less than a preset value.

[0111] In some embodiments of the present application, the device 900 provided by the present application has the same beneficial effects as the method provided by the aforementioned embodiments of the present application.

[0112] The present application also provides an electronic device corresponding to the method provided by the aforementioned embodiments. The electronic device can be an electronic device for a server, such as a server, including a standalone server and a distributed server cluster, etc., to execute the above method. The electronic device can also be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to execute the above method.

[0113] Please refer to Figure 8 which shows a schematic diagram of an electronic device provided by some embodiments of the present application. As shown in Figure 8As shown, the electronic device 40 comprises a processor 400, a memory 401, a bus 402 and a communication interface 403, the processor 400, the communication interface 403 and the memory 401 are connected through the bus 402; the memory 401 stores a computer program which can run on the processor 400, and the processor 400 executes the computer program to perform the foregoing method of the present application.

[0114] The memory 401 can include a high-speed random access memory (RAM) and can also include a non-volatile memory such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which can be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. can be used.

[0115] The bus 402 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 401 is used to store programs, and the processor 400 executes the programs after receiving execution instructions. The method disclosed in any of the foregoing embodiments of the present application can be applied to the processor 400 or implemented by the processor 400.

[0116] The processor 400 can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor 400 or the instruction in the form of software. The processor 400 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-program gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware coding processor for execution, or a combination of hardware and software modules in the coding processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is mature in the art. The storage medium is located in the memory 401, and the processor 400 reads the information in the memory 401 and combines the hardware to complete the steps of the above method.

[0117] The electronic device provided by the embodiments of the present application and the method provided by the embodiments of the present application have the same inventive concept and the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0118] The embodiments of the present application also provide a computer readable medium corresponding to the method provided by the preceding embodiments. Please refer to Figure 9 The computer readable storage medium shown in the figure is an optical disc 50, and a computer program (i.e. program product) is stored on the optical disc 50. When the computer program is run by a processor, the preceding method is executed.

[0119] It should be noted that examples of the computer readable storage medium can also include, but are not limited to, a phase change memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), other types of random access memory (RAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a flash memory or other optical or magnetic storage medium, which will not be described one by one here.

[0120] The computer readable storage medium provided by the embodiments of the present application and the method provided by the embodiments of the present application have the same inventive concept and the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0121] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order from that shown in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the preceding method embodiments, which will not be described here.

[0123] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. The apparatus embodiments described above are merely illustrative, for example, the division of the units is merely a logical function division, and actual implementation can have another division manner, and for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some communication interfaces, apparatuses or units, which can be electrical, mechanical or other forms.

[0124] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0125] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0126] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various storage program codes.

[0127] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A weft insertion control method for an air-jet loom, characterized in that, The method comprises the following steps: selecting a collection point in the special reed channel and obtaining the transient airflow velocity of the collection point; performing variational mode decomposition on the transient airflow velocity to obtain final intrinsic mode components of the transient airflow velocity, including: performing variational mode decomposition on the transient airflow velocity to obtain a plurality of initial intrinsic mode components; calculating the envelope entropy corresponding to each of the plurality of initial intrinsic mode components; optimizing the variational mode decomposition algorithm with the minimum envelope entropy as the fitness function to obtain the optimal number of decompositions and a quadratic penalty factor; and performing variational mode decomposition again on the transient airflow velocity based on the optimal number of decompositions and the quadratic penalty factor to obtain the final intrinsic mode components; obtaining the time domain feature, the frequency domain feature and / or the entropy theory feature of the final intrinsic mode components; obtaining the time domain feature and the entropy theory feature of the final intrinsic mode components includes: obtaining the time domain feature and / or the entropy theory feature of the final intrinsic mode component signal with the maximum kurtosis value; predicting the motion displacement of the weft head end through the time domain feature, the frequency domain feature and / or the entropy theory feature, including: using the time domain feature, the frequency domain feature and / or the entropy theory feature as the input feature of machine learning, establishing a regression relationship between the input feature and the motion displacement of the weft head end, and predicting the motion displacement of the weft head end based on the regression relationship between the input feature and the motion displacement of the weft head end; predicting the distance between the weft head end and the wall surface of the special reed channel according to the motion displacement, and adjusting the control parameter of the air jet loom if the predicted distance between the weft head end and the wall surface of the special reed channel is less than a preset value.

2. The weft insertion control method for an air jet loom according to claim 1, characterized in that: the time domain feature includes: mean absolute error, root mean square error, kurtosis value; the entropy theory feature includes: approximate entropy, sample entropy, fuzzy entropy, information entropy, envelope entropy.

3. A weft insertion control method for an air jet loom according to claim 1, characterized in that, The final intrinsic mode components include a first component for predicting the average weft insertion speed, a second component for representing the weft fluctuation amplitude, and a third component for representing noise.

4. A weft insertion control method for an air jet loom according to claim 1, wherein The control parameter includes the air supply pressure at the auxiliary nozzle, the energization timing of the electromagnetic valve, and the opening and closing time of the electromagnetic valve. If the predicted distance between the weft head end and the wall surface of the special reed channel is less than a preset value, adjust the servo pressure valve air supply pressure at the auxiliary nozzle and the energization timing and opening and closing time of the electromagnetic valve.

5. An insertion control device for carrying out the insertion control method according to claim 1, characterized by It includes: a data acquisition module for obtaining the transient airflow velocity of the collection point in the special reed channel; an intrinsic mode component optimization module for performing variational mode decomposition on the transient airflow velocity to obtain a plurality of initial intrinsic mode components; calculating the envelope entropy corresponding to each of the plurality of initial intrinsic mode components; optimizing the variational mode decomposition algorithm with the minimum envelope entropy as the fitness function to obtain the optimal number of decompositions and a quadratic penalty factor; and performing variational mode decomposition based on the optimal number of decompositions and the quadratic penalty factor to obtain the final intrinsic mode components; a transient velocity signal reconstruction module for obtaining the final intrinsic mode component with the maximum kurtosis value and the index value of the intrinsic mode component with the maximum kurtosis value; a feature acquisition module for obtaining the time domain feature, the frequency domain feature and / or the entropy theory feature of the final intrinsic mode components; A control module is configured to use the time domain features and the entropy theory features as input features of machine learning to establish a regression relationship between the input features and the weft displacement; predict the motion displacement of the weft head end according to the regression relationship between the input features and the weft displacement, predict the distance between the weft head end and the profile reed wall surface according to the motion displacement, and adjust the control parameters of the air jet loom if the predicted distance between the weft head end and the profile reed wall surface is less than a preset value.

6. An electronic device, comprising: The electronic device comprises: a processor: a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 4. The computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.

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