An air-jet loom weft insertion control method, product, device and medium

Abstract

The application relates to the technical field of air-jet loom control, in particular to an air-jet loom weft insertion control method, a product, equipment and a medium. The method comprises the following steps: obtaining historical data corresponding to an air-jet loom, wherein the historical data comprises environment data, fabric data and air-jet device data of the air-jet loom; creating a prediction model according to the environment data, the fabric data and the air-jet device data; obtaining current environment data, current fabric data and current air-jet device data, inputting the current environment data and the current fabric data into the prediction model to obtain predicted air-jet device data; and performing parameter adjustment on the air-jet device of the air-jet loom according to the predicted air-jet device data and the current air-jet device data, so that the air-jet device performs weft insertion according to the adjusted parameters. The application can improve the accuracy and real-time performance of weft insertion control.

Description

Technical Field

[0001] This application relates to the field of air-jet loom control technology, and in particular to a method, product, equipment and medium for controlling the weft insertion of an air-jet loom. Background Technology

[0002] As a crucial piece of equipment in the modern textile industry, the precise control of the weft insertion process in air-jet looms is essential for improving product quality and production efficiency. The weft insertion process involves multiple factors, including environmental conditions, fabric type, and the operating status of the air-jet system.

[0003] While existing technologies can monitor the quality of fabrics produced by air-jet looms in real time and adjust the parameters of the air-jet system promptly when quality problems are detected, thus precisely controlling weft insertion, this reactive approach, which involves addressing issues after they have already occurred, inevitably introduces a certain degree of lag. This lag can not only increase production costs but also potentially cause irreversible damage to the overall quality of the fabric, thereby impacting the company's economic benefits and market competitiveness. Summary of the Invention

[0004] To address the lag issue in existing weft insertion control technology, this application provides a weft insertion control method, product, equipment, and medium for air-jet looms.

[0005] Firstly, this application provides a weft insertion control method for an air-jet loom, employing the following technical solution:

[0006] A method for controlling weft insertion on an air-jet loom, comprising:

[0007] Obtain historical data corresponding to the air-jet loom, including environmental data, fabric data, and air jet device data of the air-jet loom;

[0008] A predictive model is created based on the environmental data, the fabric data, and the jet device data.

[0009] Acquire current environmental data, current fabric data, and current jet device data; input the current environmental data and the current fabric data into the prediction model to obtain predicted jet device data.

[0010] Based on the predicted jet device data and the current jet device data, the parameters of the jet device of the jet loom are adjusted so that the jet device inserts weft according to the adjusted parameters.

[0011] By adopting the above technical solution, historical data corresponding to the air-jet loom can be obtained, providing a data foundation for the creation of the prediction model. The prediction model created based on historical data can learn the correlation and pattern between the environment, fabric and air-jet device, and can predict the ideal parameters that the air-jet device should have. Real-time acquisition of current environmental data and fabric data, and inputting them into the prediction model, can quickly obtain the ideal parameters of the air-jet device under the current conditions. This allows the air-jet loom to dynamically adjust according to different environments and fabrics, improving the loom's adaptability and operating efficiency. By comparing the differences between the predicted air-jet device data and the current air-jet device data, precise parameter adjustment of the air-jet device can be performed, improving the accuracy and real-time performance of weft insertion control, thereby improving the production quality and efficiency of the air-jet loom.

[0012] In a preferred embodiment, this application can be further configured to: create a predictive model based on the environmental data, the fabric data, and the jetting device data, including:

[0013] Independent matrices are created for the environmental data, fabric data, and jet device data respectively to obtain an environmental data matrix, a fabric data matrix, and a jet device data matrix. An association matrix is ​​then created based on the environmental data, fabric data, and jet device data.

[0014] Statistical analysis was performed on the environmental data matrix, the fabric data matrix, the jet device data matrix, and the correlation matrix respectively to obtain the statistical analysis results.

[0015] A predictive model is created based on the statistical analysis results.

[0016] By adopting the above technical solutions, environmental data matrices, fabric data matrices, and air jet device data matrices are created, which helps in the organization and management of data. Independent matrices are used for analysis and extraction of key features, while correlation matrices can reveal the relationships between different data types. Predictive models based on statistical analysis results can more accurately capture the complex relationships between the environment, fabric, and air jet device, which helps improve the operating efficiency and production quality of air jet looms.

[0017] In a preferred embodiment, this application can be further configured to: create a predictive model based on the statistical analysis results, including:

[0018] Based on the statistical analysis results, key features are identified from the environmental data, fabric data, and jet device data, and the prediction model type is determined.

[0019] Create a prediction model based on the key features and the prediction model type.

[0020] By adopting the above technical solutions and identifying key features, the prediction model can focus on the main factors, improve prediction accuracy, and select the appropriate prediction model type to ensure the effectiveness and reliability of the model.

[0021] In a preferred embodiment, this application can be further configured to: acquire current environmental data, current fabric data, and current jet device data, including:

[0022] Acquire current data collected by multiple acquisition devices, including acquisition devices corresponding to environmental data, fabric data, and air jet device data;

[0023] The current initial target data is adjusted according to the calibration value corresponding to the target acquisition device to obtain the current target data. The target acquisition device is any one of the plurality of acquisition devices, and the current initial target data is the data corresponding to the target acquisition device in the current data.

[0024] Each current data point is adjusted to obtain the current environmental data, current fabric data, and current jet device data.

[0025] By adopting the above technical solution and using multiple acquisition devices to collect current data, the diversity and comprehensiveness of the data are ensured. The calibration value helps to eliminate the errors or deviations that exist in the acquisition devices themselves, ensuring the accuracy and reliability of the data. The obtained current environmental data, current fabric data, and current jet device data can truly reflect the current actual situation, providing a reliable data basis for subsequent parameter adjustment and prediction, and improving the accuracy of prediction.

[0026] In a preferred embodiment, this application can be further configured such that the method also includes:

[0027] According to the preset acquisition frequency, target data is acquired to obtain the first target data acquired at the previous acquisition time adjacent to the current time and the second target data acquired at the current time. The target data is data of the same type as the data acquired by the target acquisition device.

[0028] Acquire the first initial target data corresponding to the previous acquisition time and the second initial target data corresponding to the current time, wherein the first initial target data and the second initial target data are real-time data acquired by the target acquisition device;

[0029] The first target data and the first initial target data are compared to obtain a first difference, and the second target data and the second initial target data are compared to obtain a second difference;

[0030] Based on the first difference and the second difference, the calibration value of the acquisition device corresponding to the target data is determined.

[0031] By adopting the above technical solution, target data is acquired according to a preset acquisition frequency, enabling effective comparison and analysis of data from adjacent acquisition times. The difference between the target data and the initial target data reflects the error or deviation of the acquisition equipment. By comparing the data differences at different acquisition times, the performance of the acquisition equipment can be understood more comprehensively. Based on the first and second differences obtained from the comparison, the calibration value of the acquisition equipment can be determined. The calibration value is a quantitative representation of the equipment error, used to eliminate equipment errors and improve data accuracy.

[0032] In a preferred embodiment, this application can be further configured to: determine the calibration value of the acquisition device corresponding to the target data based on the first difference and the second difference, including:

[0033] Determine whether the first difference and the second difference have the same sign, and determine whether the absolute values ​​of the first difference and the second difference exceed a preset difference;

[0034] If the first difference and the second difference have the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, then the mean of the first difference and the second difference is determined, and the mean is used as the calibration value of the target acquisition device.

[0035] By adopting the above technical solution, analyzing whether the signs of the first difference and the second difference are the same, it is possible to understand whether the data change trend of the acquisition device is consistent in two consecutive acquisition times. If the difference has the same sign, it indicates that there is a stable error in the acquisition device between two adjacent acquisition times. Setting a preset difference as an error threshold can filter out data with errors exceeding the threshold, eliminate small fluctuations, and avoid over-calibration. By calculating the average of the first difference and the second difference as the calibration value, the average error of the device can be reflected, thus improving the accuracy of the calibration value.

[0036] In a preferred embodiment, this application can be further configured such that the method also includes:

[0037] If the first difference and the second difference are not of the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, an alarm signal is issued to indicate that the target acquisition device is faulty.

[0038] By adopting the above technical solution, when the first difference and the second difference are not of the same sign and both of their absolute values ​​exceed the preset difference, it indicates that the data acquisition device has experienced unstable data fluctuations in a short period of time, and an alarm signal is issued. This can promptly warn of possible equipment failures, help reduce production losses, and ensure the normal operation of the air-jet loom.

[0039] Secondly, this application provides a computer program product, which adopts the following technical solution:

[0040] A computer program product includes a computer program that, when executed by a processor, implements the weft insertion control method for an air-jet loom as described in any of the first aspects.

[0041] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0042] One or more processors;

[0043] Memory;

[0044] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the jet loom weft insertion control method as described in any of the first aspects.

[0045] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0046] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the air-jet loom weft insertion control method as described in any of the first aspects.

[0047] In summary, this application includes the following beneficial technical effects:

[0048] This application provides a data foundation for creating a predictive model by acquiring historical data corresponding to the air-jet loom. The predictive model created based on historical data can learn the correlation and patterns between the environment, fabric, and air-jet device, and can predict the ideal parameters that the air-jet device should have. By acquiring current environmental and fabric data in real time and inputting them into the predictive model, the ideal parameters of the air-jet device under the current conditions can be quickly obtained. This allows the air-jet loom to dynamically adjust according to different environments and fabrics, improving the loom's adaptability and operating efficiency. By comparing the differences between the predicted air-jet device data and the current air-jet device data, the parameters of the air-jet device can be precisely adjusted, improving the accuracy and real-time performance of weft insertion control, thereby improving the production quality and efficiency of the air-jet loom. Attached Figure Description

[0049] Figure 1 This is a schematic flowchart of a weft insertion control method for an air-jet loom provided in an embodiment of this application;

[0050] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] The following is in conjunction with the appendix Figure 1 -Appendix Figure 2 This application will be described in further detail.

[0052] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0055] This application provides a weft insertion control method for an air-jet loom, such as... Figure 1 As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this connection. The method includes steps S101-S104, wherein:

[0056] S101. Obtain historical data corresponding to the air-jet loom. The historical data includes environmental data, fabric data, and air jet device data of the air-jet loom.

[0057] In this embodiment, the electronic device can be equipped with a database to store historical data related to the air-jet loom. The data types contained in the historical data can be set according to actual experience. Optionally, environmental data can include the temperature, humidity and air pressure of the manufacturing workshop where the air-jet loom is located, fabric data can include fabric type, weft tension, weft introduction time, etc., and air jet device data can include parameters such as air jet pressure, air jet time and air jet frequency.

[0058] S102. Create a predictive model based on environmental data, fabric data, and jet device data.

[0059] In this embodiment, statistical and correlation analyses can be performed on environmental data, fabric data, and jet device data to obtain statistical and correlation analysis results. The statistical analysis results can be used to identify key features in historical data, which can then be applied to train the prediction model. The correlation analysis results can be used to select the model type most suitable for the training dataset. Using environmental data, fabric data, and jet device data as a training set, a prediction model can be created. This model can predict the most suitable jet device data based on the current environmental data and current fabric data.

[0060] S103. Obtain current environmental data, current fabric data, and current jet device data. Input the current environmental data and current fabric data into the prediction model to obtain the predicted jet device data.

[0061] In this embodiment, multiple data acquisition devices can be installed on the air-jet loom to collect environmental data, fabric data, and air-jet device data in real time and store them in a database.

[0062] S104. Based on the predicted jet device data and the current jet device data, adjust the parameters of the jet device of the jet loom so that the jet device inserts weft according to the adjusted parameters.

[0063] In this embodiment, the predicted jet device data and the current jet device data can be compared to determine the difference between each type of data in the current jet device data and the corresponding type of data in the predicted jet device data, and the jet device parameters can be adjusted based on the difference.

[0064] This application embodiment obtains historical data corresponding to the air-jet loom, which provides a data foundation for the creation of a prediction model. The prediction model created based on historical data can learn the correlation and pattern between the environment, fabric, and air-jet device, and can predict the ideal parameters that the air-jet device should have. By acquiring current environmental data and fabric data in real time and inputting them into the prediction model, the ideal parameters of the air-jet device under the current conditions can be obtained quickly. This allows the air-jet loom to dynamically adjust according to different environments and fabrics, improving the loom's adaptability and operating efficiency. By comparing the difference between the predicted air-jet device data and the current air-jet device data, the parameters of the air-jet device can be precisely adjusted, which can improve the accuracy and real-time performance of weft insertion control, thereby improving the production quality and efficiency of the air-jet loom.

[0065] One possible implementation of this application embodiment involves creating a predictive model based on environmental data, fabric data, and jet device data, including:

[0066] Independent matrices are created for environmental data, fabric data, and jet device data respectively, resulting in environmental data matrix, fabric data matrix, and jet device data matrix. An association matrix is ​​then created based on the environmental data, fabric data, and jet device data.

[0067] Statistical analysis was performed on the environmental data matrix, fabric data matrix, and jet device data matrix respectively to obtain the statistical analysis results. Correlation analysis was performed on the correlation matrix to obtain the correlation analysis results.

[0068] A predictive model is created based on the results of statistical analysis and correlation analysis.

[0069] In this embodiment, the environmental data matrix contains all recorded environmental data, such as temperature, humidity, and air pressure. Each row in the environmental data matrix represents a record, and each column represents an environmental parameter. The fabric data matrix contains all recorded fabric data, such as fabric type, weft tension, and weft introduction time. Each row in the fabric data matrix represents a record, and each column represents a fabric parameter. The jet device data matrix contains all recorded jet device data, such as jet pressure, jet time, and jet frequency. Each row in the jet device data matrix represents a record, and each column represents a jet device parameter. The process of establishing the correlation matrix is ​​as follows: First, analyze the number of features corresponding to the environmental data, fabric data, and jet device data, that is, determine the dimension of each dataset. Assign corresponding rows or columns to each dataset according to the number of features. Based on the dimensions of the environmental data, fabric data, and jet device data, create a blank correlation matrix. The number of rows and columns of the correlation matrix depends on the number of features in the dataset. Each cell in the correlation matrix stores the correlation value between different features. For each pair of features in the environmental data, fabric data, and jet device data, calculate the correlation value between them. The correlation value can be calculated using various methods, such as Pearson correlation coefficient, Spearman rank correlation coefficient, mutual information, etc. Fill the calculated correlation value into the corresponding position in the correlation matrix.

[0070] Furthermore, statistical analysis can be performed on each independent matrix to obtain the statistics corresponding to each feature in that independent matrix, including mean, standard deviation, skewness, kurtosis, etc. This allows us to obtain the statistics for each feature corresponding to the environmental data matrix, fabric data matrix, and jet device data matrix. The statistical analysis results include the statistics corresponding to the independent matrices and the correlation values ​​corresponding to the correlation matrices. These results can be used to identify key features, which can then be used to train the predictive model. The statistical analysis results can also be used to determine the type of predictive model, such as linear regression, decision trees, random forests, or neural networks. Finally, based on the key features and the predictive model type, the predictive model can be trained.

[0071] This application embodiment facilitates data organization and management by creating an environmental data matrix, a fabric data matrix, and an air jet device data matrix. Independent matrices are used for analysis and extraction of key features, while correlation matrices can reveal the relationships between different data types. Predictive models based on statistical analysis results can more accurately capture the complex relationships between the environment, fabric, and air jet device, which helps improve the operating efficiency and production quality of air jet looms.

[0072] One possible implementation of this application embodiment involves creating a predictive model based on statistical analysis results, including:

[0073] Based on the statistical analysis results, key features were identified from environmental data, fabric data, and jet device data, and the type of prediction model was determined.

[0074] Create a prediction model based on key features and prediction model type.

[0075] In this embodiment, feature selection algorithms, such as tree-based feature importance scoring and recursive feature elimination, can be used to automatically evaluate the importance of each feature based on statistical analysis results. An importance threshold can also be set based on practical experience to select key features.

[0076] Furthermore, the trained prediction model is used to predict jet device data, which is a continuous variable. The expectation is that the model can predict specific numerical values; therefore, the problem type can be considered a regression problem. Consequently, an appropriate model type can be selected based on the problem type. Commonly used model types in regression problems include linear regression, decision tree regression, random forest regression, and gradient boosting regression. These models each have their own characteristics and application scenarios, and the model type can be determined based on actual needs. Specifically, linear regression models assume a linear relationship between the dependent and independent variables, offering high computational efficiency but failing to capture complex nonlinear relationships. Decision tree regression models can automatically perform feature selection and interaction effect detection, exhibiting good handling capabilities for nonlinear relationships, but may be prone to overfitting. Random forest regression models improve prediction accuracy and stability by integrating multiple decision trees, offering good performance but with high computational complexity. Gradient boosting regression models improve prediction results by iteratively fitting residuals, handling nonlinear relationships and outliers, but requiring significant computational resources. Furthermore, by combining statistical analysis results and actual needs, the type of prediction model can be determined. Optionally, when there is a linear relationship between key features, a linear regression model can be selected as the prediction model type; when there is no obvious linear relationship between key features, and automatic feature selection and interaction effect detection are required, a decision tree regression model can be selected as the prediction model type; when there may be complex interaction relationships between key features, a random forest regression model can be selected as the prediction model type; when there is a large amount of noise or outliers among key features, and nonlinear relationships and interaction effects exist among key features, a gradient boosting regression model can be selected as the prediction model type.

[0077] By identifying key features, the embodiments of this application enable the prediction model to focus on the main factors, thereby improving prediction accuracy. Selecting an appropriate prediction model type can ensure the effectiveness and reliability of the model.

[0078] One possible implementation of this application embodiment involves acquiring current environmental data, current fabric data, and current jet device data, including:

[0079] Acquire current data collected by multiple acquisition devices, including environmental data, fabric data, and jet device data, each corresponding to its own acquisition device;

[0080] The current initial target data is adjusted based on the calibration value corresponding to the target acquisition device to obtain the current target data. The target acquisition device can be any one of multiple acquisition devices, and the current initial target data can be any one of the current data.

[0081] Each current data point is adjusted to obtain the current environmental data, current fabric data, and current jet device data.

[0082] In this embodiment, multiple data acquisition devices can be set up to collect various types of data corresponding to the air-jet loom in real time, including environmental data, fabric data, and air-jet device data, and store the collected data in a database. During prolonged use, the monitoring accuracy of the acquisition devices may decrease. Therefore, a calibration value can be determined for each acquisition device, and the corresponding data can be adjusted based on the calibration value to obtain calibrated and accurate data.

[0083] This application embodiment utilizes multiple acquisition devices to collect current data, ensuring data diversity and comprehensiveness. Calibration values ​​help eliminate errors or deviations inherent in the acquisition devices themselves, ensuring data accuracy and reliability. The obtained current environmental data, current fabric data, and current jet device data can truly reflect the current actual situation, providing reliable data basis for subsequent parameter adjustment and prediction, and improving prediction accuracy.

[0084] One possible implementation of this application embodiment includes:

[0085] The target data is acquired according to the preset acquisition frequency, and the first target data acquired at the previous acquisition time adjacent to the current time and the second target data acquired at the current time are obtained. The target data is any type of data among environmental data, fabric data and jet device data.

[0086] Acquire the first initial target data corresponding to the previous acquisition time and the second initial target data corresponding to the current time. The first initial target data and the second initial target data are real-time data collected by the target acquisition device, which is set on the jet loom.

[0087] The first target data is compared with the first initial target data to obtain the first difference, and the second target data is compared with the second initial target data to obtain the second difference;

[0088] Based on the first and second differences, determine the calibration value of the acquisition device corresponding to the target data.

[0089] In this embodiment, the preset acquisition frequency can be set according to actual needs. Multiple standard acquisition devices can be controlled to acquire environmental data, fabric data, and air jet device data according to the preset acquisition frequency. The obtained data is accurate data. Then, by comparing the accurate data of each type with the real-time data, the calibration value of the corresponding acquisition device can be determined. Any standard acquisition device is called the target standard acquisition device, and the data acquired by the target standard acquisition device is called the target data. When the target standard acquisition device acquires the target data according to the preset acquisition frequency, a series of target data containing timestamps will be obtained. The target data acquired at the current moment is used as the second target data, and the target data acquired at the previous acquisition moment before the current moment is used as the first target data. The current moment is the moment corresponding to the current data acquired by multiple acquisition devices.

[0090] Furthermore, data of the same data type as the target data in the current data is used as the second initial target data, and data of the same data type as the target data in the historical data corresponding to the previous acquisition time on the target standard acquisition device is used as the first initial target data. Both the first and second initial target data are real-time data, and both are non-real-time data acquired at a preset acquisition frequency. The timestamps of the first and second initial target data correspond, as do those of the first target data and the second target data. Even further, the difference between the first target data and the first initial target data can be used as the first difference, and the difference between the second target data and the second initial target data can be used as the second difference. Based on the first and second differences, the calibration value of the acquisition device corresponding to the target data can be determined.

[0091] This application embodiment acquires target data according to a preset acquisition frequency, enabling effective comparison and analysis of data from adjacent acquisition times. By comparing the target data with the initial target data, the difference reflects the error or deviation of the acquisition device. By comparing the data differences at different acquisition times, the performance of the acquisition device can be understood more comprehensively. Based on the first and second differences obtained from the comparison, the calibration value of the acquisition device can be determined. The calibration value is a quantitative representation of the device error, used to eliminate the device error and improve data accuracy.

[0092] One possible implementation of this application embodiment involves determining the calibration value of the acquisition device corresponding to the target data based on a first difference and a second difference, including:

[0093] Determine whether the first difference and the second difference have the same sign, and determine whether the absolute values ​​of the first difference and the second difference exceed a preset difference;

[0094] If the first difference and the second difference have the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, then the mean of the first difference and the second difference is determined, and the mean is used as the calibration value of the target acquisition device.

[0095] In this embodiment, it is determined whether the first difference and the second difference have the same sign, that is, whether the first difference and the second difference are both positive or both negative. The preset difference can be set according to actual needs. If the first difference and the second difference have the same sign, and the absolute value of the first difference and the second difference exceeds the preset difference, then the average of the first difference and the second difference is calculated, and the average is used as the calibration value of the target acquisition device. The difference between the actual data and the calibration value can be used as the adjusted data.

[0096] This application's embodiments analyze whether the signs of the first difference and the second difference are the same, which can help understand whether the data change trends of the acquisition device are consistent between two consecutive acquisition times. If the difference values ​​have the same sign, it indicates that the acquisition device has a stable error between two adjacent acquisition times. By setting a preset difference as an error threshold, data with errors exceeding the threshold can be filtered out, small fluctuations can be eliminated, and over-calibration can be avoided. By calculating the average of the first difference and the second difference as the calibration value, the average error of the device can be reflected, thus improving the accuracy of the calibration value.

[0097] One possible implementation of this application embodiment includes:

[0098] If the first difference and the second difference are not of the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, an alarm signal will be issued to indicate that the target acquisition device is faulty.

[0099] In one possible scenario, if the first and second differences do not have the same sign, and both their absolute values ​​exceed a preset difference, it indicates that the target acquisition device is experiencing significant data fluctuations within a short period. In this case, the target acquisition device may be malfunctioning, and an alarm signal can be issued to alert relevant personnel to investigate. In another possible scenario, if the first and second differences have the same sign, and neither their absolute values ​​exceed the preset difference, it indicates that the data fluctuations of the target device are within an acceptable range, and no data adjustment is required. (This is repeated three times in the original text.)

[0100] In this embodiment of the application, when the first difference and the second difference are not of the same sign and their absolute values ​​both exceed the preset difference, it indicates that the data acquisition device has experienced unstable data fluctuations in a short period of time, and an alarm signal is issued. This can promptly warn of possible equipment failures, help reduce production losses, and ensure the normal operation of the air-jet loom.

[0101] This application provides a computer program product, including a computer program that, when executed by a processor, implements the content shown in the aforementioned embodiment of the air-jet loom weft insertion control method.

[0102] This application provides an electronic device, such as... Figure 2 As shown, Figure 2 The illustrated electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one type, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of this application.

[0103] Processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 201 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0104] Bus 202 may include a pathway for transmitting information between the aforementioned components. Bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 202 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0105] The memory 203 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0106] The memory 203 stores the application code that executes the solution of this application, and its execution is controlled by the processor 201. The processor 201 executes the application code stored in the memory 203 to implement the content shown in the aforementioned embodiment of the air-jet loom weft insertion control method.

[0107] Figure 2 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0108] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the contents shown in the aforementioned embodiment of the jet loom weft insertion control method.

[0109] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0110] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling weft insertion on an air-jet loom, characterized in that, include: Obtain historical data corresponding to the air-jet loom, including environmental data, fabric data, and air jet device data of the air-jet loom; A predictive model is created based on the environmental data, the fabric data, and the jet device data. Acquire current environmental data, current fabric data, and current jet device data; input the current environmental data and the current fabric data into the prediction model to obtain predicted jet device data. Based on the predicted jet device data and the current jet device data, the parameters of the jet device of the jet loom are adjusted so that the jet device inserts weft according to the adjusted parameters; Based on the environmental data, the fabric data, and the jetting device data, a predictive model is created, including: Independent matrices are created for the environmental data, fabric data, and jet device data respectively to obtain an environmental data matrix, a fabric data matrix, and a jet device data matrix. An association matrix is ​​then created based on the environmental data, fabric data, and jet device data. Statistical analysis was performed on the environmental data matrix, the fabric data matrix, the jet device data matrix, and the correlation matrix respectively to obtain the statistical analysis results. A predictive model is created based on the statistical analysis results.

2. The weft insertion control method for an air-jet loom according to claim 1, characterized in that, Based on the statistical analysis results, a predictive model is created, including: Based on the statistical analysis results, key features are identified from the environmental data, fabric data, and jet device data, and the prediction model type is determined. Create a prediction model based on the key features and the prediction model type.

3. The weft insertion control method for an air-jet loom according to claim 1, characterized in that, Acquire current environmental data, current fabric data, and current jet device data, including: Acquire current data collected by multiple acquisition devices, including acquisition devices corresponding to environmental data, fabric data, and air jet device data; The current initial target data is adjusted according to the calibration value corresponding to the target acquisition device to obtain the current target data. The target acquisition device is any one of the plurality of acquisition devices, and the current initial target data is the data corresponding to the target acquisition device in the current data. Each current data point is adjusted to obtain the current environmental data, current fabric data, and current jet device data.

4. The weft insertion control method for an air-jet loom according to claim 3, characterized in that, The method further includes: According to the preset acquisition frequency, target data is acquired to obtain the first target data acquired at the previous acquisition time adjacent to the current time and the second target data acquired at the current time. The target data is data of the same type as the data acquired by the target acquisition device. Acquire the first initial target data corresponding to the previous acquisition time and the second initial target data corresponding to the current time, wherein the first initial target data and the second initial target data are real-time data acquired by the target acquisition device; The first target data and the first initial target data are compared to obtain a first difference, and the second target data and the second initial target data are compared to obtain a second difference; Based on the first difference and the second difference, the calibration value of the acquisition device corresponding to the target data is determined.

5. The weft insertion control method for an air-jet loom according to claim 4, characterized in that, Based on the first difference and the second difference, the calibration value of the acquisition device corresponding to the target data is determined, including: Determine whether the first difference and the second difference have the same sign, and determine whether the absolute values ​​of the first difference and the second difference exceed a preset difference; If the first difference and the second difference have the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, then the mean of the first difference and the second difference is determined, and the mean is used as the calibration value of the target acquisition device.

6. The weft insertion control method for an air-jet loom according to claim 5, characterized in that, The method further includes: If the first difference and the second difference are not of the same sign, and the absolute values ​​of both the first difference and the second difference exceed the preset difference, an alarm signal is issued to indicate that the target acquisition device is faulty.

7. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of the air-jet loom weft insertion control method according to any one of claims 1 to 6.

8. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the air-jet loom weft insertion control method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, it causes the computer to perform the weft insertion control method for an air-jet loom as described in any one of claims 1-6.

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