Multipath length monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of spinning, in particular to a multi-path length monitoring method and device, computer equipment and a storage medium, and the multi-path length monitoring method comprises the steps: obtaining a spinning start message, and obtaining a monitoring basic parameter from the spinning start message; after the monitoring basic parameters are obtained, a wire length standard value is obtained, and a wire length control range is obtained according to the wire length standard value; triggering a wire length monitoring instruction according to the wire length control range, acquiring a current value of the wire length in real time, and comparing the current value of the wire length with the wire length control range to obtain a comparison result; and when the comparison result exceeds the thread length control range, a spinning shutdown instruction is triggered. The method and the device have the effect of improving the accuracy of the monitoring result in the wire length monitoring process.

Description

Technical Field

[0001] The present application relates to the technical field of textiles, and in particular to a multi-line length monitoring method, device, computer equipment and storage medium. Background Art

[0002] At present, the textile industry is pursuing the dual challenges of high precision and high speed, and line length monitoring has become an indispensable part of ensuring product quality. However, for a long time, this link has mainly relied on human visual inspection with line measuring instruments. Although it is intuitive and easy to use, its limitations are increasingly prominent in the fast-paced modern factory environment. For example, the monitoring efficiency is limited by manpower and it is difficult to avoid error accumulation.

[0003] In order to improve the accuracy and timeliness of monitoring, the industry generally adopts the method of adding multiple sets of independent line measuring instruments to cover all key lines. The data collected by each line measuring instrument is then regularly checked by staff or manually imported into the central control system to formulate an early warning mechanism.

[0004] The above-mentioned prior art solutions have the following defects: Although the increase in hardware investment seems to have solved the need for comprehensive monitoring, the redundant manual data transmission and processing steps in this model have greatly weakened the response time, especially in emergency situations. It is difficult to make quick adjustments. In addition, each new monitoring point means more complex early warning settings that need to be verified one by one, which invisibly increases the workload of management and maintenance and hinders the pace of intelligent transformation. Summary of the invention

[0005] In order to improve the accuracy of monitoring results during line length monitoring, the present application provides a multi-line length monitoring method, apparatus, computer equipment and storage medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A multi-route length monitoring method, the multi-route length monitoring method comprising: Obtaining a spinning start message, and obtaining basic monitoring parameters from the spinning start message; After obtaining the basic monitoring parameters, obtaining a line length standard value, and obtaining a line length control range according to the line length standard value; Triggering a line length monitoring instruction according to the line length control range, acquiring a current line length value in real time, and comparing the current line length value with the line length control range to obtain a comparison result; When the comparison result exceeds the line length control range, a textile shutdown instruction is triggered.

[0007] By adopting the above technical solution, the monitoring basic parameters are obtained from the textile start message, and the standard value of the wire length and its control range are calculated based on these parameters. Subsequently, the system triggers the wire length monitoring instruction according to these standard values and control range to achieve real-time monitoring. When it is detected that the current value of the wire length exceeds the preset control range, the system immediately triggers the textile shutdown instruction. This linkage strategy of real-time monitoring and intelligent shutdown completely solves the problem of delayed feedback caused by traditional manual monitoring. The response time of the production line to abnormal wire length is greatly shortened, and problems can be discovered and measures can be taken in the first time, effectively curbing the accumulation of defective products caused by the lag of wire length monitoring, and greatly improving the product quality stability and the controllability of the production process. Before textile starts, the system can automatically obtain various basic parameters required for monitoring from the start message, such as textile demand data, number of textile needles, etc. This step eliminates the errors that may be brought by manual input and ensures the accuracy of subsequent monitoring. Through the automated parameter acquisition process, not only the work efficiency is improved, but also the work burden of the operator is reduced, making the whole monitoring process more efficient and reliable. After obtaining the monitoring basic parameters, the system calculates the perimeter parameters of each line to be detected according to the specific number of textile needles and the number of lines to be monitored, and then determines the corresponding standard value of the wire length and the error range. This method ensures that each monitoring point has personalized and accurate control standards, avoiding misjudgment that may be caused by fixed thresholds. At the same time, the control range is dynamically generated based on the specific situation of actual production, making the monitoring more in line with the actual production needs and improving the flexibility and adaptability of the monitoring. During the wire length monitoring process, the system will collect the current textile data in real time and input it into the pre-trained wire length prediction model for analysis to obtain the current wire length value. This real-time data analysis ability, combined with the support of advanced algorithms, enables the system to quickly and accurately identify abnormal wire length situations and make timely responses. Compared with the traditional static threshold judgment method, this method has higher sensitivity and accuracy and can better cope with the complex and changeable production environment. Before formally entering the monitoring stage, the system will also use a large amount of data in the historical wire length statistical records to train and optimize the initial model to obtain a more accurate wire length prediction model. The advantage of doing this is that the system can not only make real-time judgments based on the current data, but also refer to past experience and rules to make more scientific and reasonable decisions. This is of great significance for preventing potential risks and improving production efficiency. By integrating a series of automated and intelligent functions, this technical solution greatly simplifies the daily work process of the operator. For example, there is no need to manually set the specific parameters of each monitoring point, nor to frequently check and adjust the warning logic. All configuration and management work can be completed on a unified platform, which is convenient and fast. In addition, the visual interface and friendly interaction design provided by the system enable the operator to easily master the overall operation status of the production line and discover and solve problems in time.

[0008] In a preferred example, this application can be further configured as follows: obtaining the textile start message and acquiring the monitoring basic parameters from the textile start message, specifically including: acquiring the textile demand data from the textile start message and obtaining the textile needle count data from the textile demand data; calculating the number of textile lines to be monitored based on the textile needle count data and generating the monitoring basic data according to the number of textile lines to be monitored.

[0009] By adopting the above technical solution, it can effectively solve the problem of lagging reaction of abnormal line length caused by manual monitoring in traditional textile production lines. Specifically: by analyzing the textile demand data in the textile start message, the system can quickly obtain the textile needle count data, which ensures the timely acquisition of monitoring basic parameters. The traditional manual input method is prone to omissions or errors, while this technical solution reduces the influence of human factors greatly through automatic analysis, improving the accuracy and reliability of the monitoring starting point. Based on the obtained textile needle count data, the system can accurately calculate the specific number of lines to be monitored. This is particularly important for large textile workshops because different types of textile equipment may have different combinations of needle counts, so personalized settings are required for each case. Through automatic calculation, not only can a large amount of labor costs be saved, but also losses caused by manual calculation errors can be avoided. On the basis of clearly knowing the number of textile lines to be monitored, the system further generates the corresponding monitoring basic data. These basic data include but are not limited to key parameters such as the standard length and maximum allowable deviation of each line, providing solid data support for subsequent real-time monitoring. This pre-prepared basic data structure makes the entire monitoring process smoother and reduces various uncertainties that may occur during online monitoring. This technical solution is not only applicable to single types or scales of textile equipment, but also has strong universality and scalability. Whether it is a small family workshop or large-scale industrial production, as long as the same communication protocol (such as the textile start message format) is followed, it can easily access this multi-line length monitoring system. In the future, even if new production lines are added or more advanced equipment is replaced, there is no need to redesign the entire system, and only some parameters need to be simply updated to meet the monitoring requirements in the new scenario. By introducing the process of extracting monitoring basic parameters from the textile start message, not only the speed and accuracy of line length monitoring are significantly improved, but also the operation process is greatly simplified, the operation and maintenance difficulty is reduced, thus comprehensively ensuring the smooth progress of textile production and the stable output of its product quality.

[0010] In a preferred example, this application can be further configured as follows: after obtaining the monitoring basic parameters, obtaining the line length standard value and acquiring the line length control range according to the line length standard value, specifically including: Calculate according to the textile stitch number data and the number of textile lines to be monitored, and obtain the perimeter parameter data of each textile line to be detected; Obtain the line length standard value and the corresponding error range data according to the perimeter parameter data, and calculate the line length control range from the line length standard value and the error range data.

[0011] By adopting the above technical solution, first, after obtaining the monitoring basic parameters, it is possible to perform detailed calculations based on the textile needle count data and the number of textile lines to be monitored, and obtain the perimeter parameter data of each textile line to be detected. This process not only ensures that the specific measurement starting and ending points of each line are clear, but also provides a scientific basis for setting the standard line length value in the subsequent process through accurate calculation of the lengths of different lines. Since the perimeter parameter data covers the actual physical dimensions of each line, it can more accurately reflect the specific situation on the production line, avoiding the accumulation of errors caused by the inapplicability of the general standard value to specific lines. Secondly, according to the obtained perimeter parameter data, the standard line length value and its corresponding error range data are further obtained. Here, the standard line length value is formulated based on statistical principles. Considering various factors that may occur in actual production (such as equipment wear, material differences, etc.), a reasonable control range is introduced. The setting of this range not only reflects the fluctuation range of the line length under normal working conditions, but also can effectively distinguish abnormal conditions, improving the sensitivity and reliability of monitoring. Specifically, the combination of the standard line length value and the error range data forms a dynamic threshold system, which can maintain consistency between different production batches and at the same time can flexibly handle the occurrence of individual special situations. Thirdly, this calculation method of the standard line length value and the error range based on the perimeter parameter data fundamentally solves the problem that the traditional fixed standard value is difficult to adapt to the diverse production requirements. Although the traditional multi-line measuring instrument configuration attempts to cover all key lines, due to the lack of a unified standardization process, the data between different measuring instruments cannot be effectively integrated, the warning logic is scattered and needs to be verified one by one, increasing the workload of management and maintenance. And this technical solution realizes the effective management and adjustment of the synchronous operation of multiple production lines through centralized and systematic parameter settings, greatly improving the overall production efficiency. Especially when multiple production lines are running simultaneously, the administrator can view the status of each line in real time through the central control system, discover and handle potential problems in a timely manner, ensuring the continuity and stability of production. In addition, by combining high-precision sensors and deep learning algorithms, this technical solution can further improve the accuracy and reliability of line length monitoring. In practical applications, high-precision sensors can capture subtle line length changes, and deep learning algorithms continuously optimize the prediction model through learning a large amount of historical data, and can identify possible abnormal situations in advance. This not only reduces the number of misjudgments of non-genuine abnormalities, but also significantly improves the response speed of the system, ensuring that the production line can still maintain a high degree of stability and efficiency in the face of complex and changeable environments.

[0012] In a preferred example of the present application, it can be further configured as follows: triggering a line length monitoring instruction according to the line length control range, obtaining the current line length value in real time, and comparing the current line length value with the line length control range to obtain a comparison result, specifically including: Obtain the current textile data according to the line length monitoring instruction, and extract the current textile features from the current textile data; Input the current textile features into a preset line length prediction model for prediction to obtain the current value of the line length.

[0013] By adopting the above technical solution, the process of obtaining the current value of the line length in real time is more accurate and efficient. First, obtain the current textile data according to the line length monitoring instruction and extract the current textile features from it. This step ensures the accuracy and representativeness of the data. Subsequently, input the current textile features into a preset line length prediction model for prediction to obtain the current value of the line length. This method not only improves the accuracy of line length measurement but also reduces the influence of human factors, ensuring the reliability and consistency of the monitoring results. In addition, training the initial model in combination with historical line length statistical records further improves the robustness and adaptability of the prediction model, enabling the system to better cope with line length changes in different production environments, thereby improving the stability of the overall production process and product quality.

[0014] In a preferred example of the present application, it can be further configured as follows: Before triggering the line length monitoring instruction according to the line length control range, obtaining the current value of the line length in real time, and comparing the current value of the line length with the line length control range to obtain a comparison result, the multi-line length monitoring method further includes: Obtain historical line length statistical records, and obtain historical line length data and textile feature data from the historical line length statistical records, where the textile feature data includes textile line type information and textile equipment operating status; Train the initial model according to the textile feature data to obtain the line length prediction model.

[0015] By adopting the above technical solution, first obtain historical line length statistical records, and extract historical line length data and textile feature data from them. These data cover different types of textile line type information and the equipment status when operating in various states. Through the accumulation and analysis of a large amount of historical data, the change law of the line length under specific conditions can be understood and predicted more accurately. Subsequently, use the extracted historical line length data and textile feature data as the training set and input them into the initial model for deep learning training. This process not only improves the sensitivity of the model to line length changes but also enhances its ability to identify abnormal situations. After sufficient training, the line length prediction model can quickly adapt and make accurate predictions in a new production environment.

[0016] The above second invention object of the present application is achieved through the following technical solutions: A multi-line length monitoring device, the multi-line length monitoring device includes: A parameter acquisition module, configured to acquire a textile start message and obtain monitoring basic parameters from the textile start message; A control range acquisition module, configured to acquire a standard line length value after obtaining the monitoring basic parameters, and obtain a line length control range according to the standard line length value; A line length monitoring module, configured to trigger a line length monitoring instruction according to the line length control range, acquire a current line length value in real time, and compare the current line length value with the line length control range to obtain a comparison result; An emergency handling module, configured to trigger a textile shutdown instruction when the comparison result exceeds the line length control range.

[0017] By adopting the above technical solution, the monitoring basic parameters are obtained from the textile start message, and the standard value of the wire length and its control range are calculated based on these parameters. Subsequently, the system triggers the wire length monitoring instruction according to these standard values and control ranges to achieve real-time monitoring. When it is detected that the current value of the wire length exceeds the preset control range, the system immediately triggers the textile shutdown instruction. This linkage strategy of real-time monitoring and intelligent shutdown completely solves the problem of delayed problem feedback caused by traditional manual monitoring. The reaction time of the production line to abnormal wire length is greatly shortened, and problems can be discovered and measures can be taken in the first time, effectively curbing the accumulation of defective products caused by the lag of wire length monitoring, and greatly improving the product quality stability and the controllability of the production process. Before textile starts, the system can automatically obtain various basic parameters required for monitoring from the start message, such as textile demand data, the number of textile needles, etc. This step eliminates the errors that may be brought by manual input and ensures the accuracy of subsequent monitoring. Through the automated parameter acquisition process, not only the work efficiency is improved, but also the work burden of the operator is reduced, making the whole monitoring process more efficient and reliable. After obtaining the monitoring basic parameters, the system calculates the perimeter parameters of each line to be detected according to the specific number of textile needles and the number of lines to be monitored, and then determines the corresponding standard value of the wire length and the error range. This method ensures that each monitoring point has personalized and accurate control standards, avoiding misjudgment that may be caused by fixed thresholds. At the same time, the control range is dynamically generated based on the specific situation of actual production, making the monitoring more in line with the actual production needs and improving the flexibility and adaptability of the monitoring. During the wire length monitoring process, the system will collect the current textile data in real time and input it into the pre-trained wire length prediction model for analysis to obtain the current wire length value. This real-time data analysis ability, combined with the support of advanced algorithms, enables the system to quickly and accurately identify abnormal wire length situations and make timely responses. Compared with the traditional static threshold judgment method, this method has higher sensitivity and accuracy and can better cope with the complex and changeable production environment. Before officially entering the monitoring stage, the system will also use a large amount of data in the historical wire length statistical records to train and optimize the initial model to obtain a more accurate wire length prediction model. The advantage of doing this is that the system can not only make real-time judgments based on the current data, but also refer to past experience and rules to make more scientific and reasonable decisions. This is of great significance for preventing potential risks and improving production efficiency. By integrating a series of automated and intelligent functions, this technical solution greatly simplifies the daily work process of the operator. For example, there is no need to manually set the specific parameters of each monitoring point, nor to frequently check and adjust the warning logic. All configuration and management work can be completed on a unified platform, which is convenient and fast. In addition, the visual interface and friendly interaction design provided by the system enable the operator to easily master the overall operation status of the production line and discover and solve problems in time.

[0018] The above-mentioned third object of the present application is achieved by the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above multi-line length monitoring method are implemented.

[0019] The above-mentioned fourth object of the present application is achieved by the following technical solutions: A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above multi-line length monitoring method are implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. The application of the real-time monitoring and intelligent shutdown linkage strategy significantly shortens the reaction time of the production line to abnormal line lengths, effectively curbs the accumulation of defective products caused by lagging line length monitoring, and greatly improves the product quality stability and the controllability of the production process; 2. The introduction of the dynamic threshold adjustment mechanism overcomes the problem of decentralized setting of warning logics in the configuration of multiple measuring line instruments, realizes the function of adaptively adjusting the control range based on the actual production situation, ensures the flexibility and accuracy of the monitoring strategy, and thus greatly improves the production efficiency and resource utilization rate; 3. The integration of data-driven advanced sensing technology and intelligent analysis architecture significantly enhances the accuracy and reliability of line length monitoring, reduces the number of misjudgments of non-genuine anomalies, and ensures that the production line can still maintain a high degree of stability and efficiency in the face of complex and changing environments; 4. The use of high-precision line length sensors improves the signal capture ability and anti-interference characteristics, makes the monitoring results more accurate and reliable, and further improves the overall performance of the system; 5. The design of an interactive and efficient management software greatly facilitates the operator's management and adjustment of line length monitoring parameters, eliminates the need for cumbersome manual setting steps, effectively alleviates the workload of technical personnel, and at the same time promotes the transformation process of the textile manufacturing industry towards a higher level of intelligentization. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flowchart of a multi-line length monitoring method in an embodiment of the present application; Figure 2 is a flowchart for implementing step S10 in the multi-line length monitoring method in an embodiment of the present application; Figure 3 is a flowchart for implementing step S20 in the multi-line length monitoring method in an embodiment of the present application; Figure 4It is the implementation flowchart of step S30 in the multi-route length monitoring method in an embodiment of the present application; Figure 5 It is another implementation flowchart of the multi-route length monitoring method in an embodiment of the present application; Figure 6 It is a principle block diagram of a multi-route length monitoring system in an embodiment of the present application; Figure 7 It is a schematic diagram of the device in an embodiment of the present application. Detailed implementation manner

[0022] The present application will be further described in detail below with reference to the accompanying drawings.

[0023] In one embodiment, as Figure 1 shown, the present application discloses a multi-route length monitoring method, which specifically includes the following steps: S10: Obtain the textile start message, and obtain the monitoring basic parameters from the textile start message.

[0024] In this embodiment, the monitoring basic parameters refer to the basic data of the textile thread used in this textile task.

[0025] Specifically, after the textile start message is triggered, the editing box for the monitoring basic parameters is triggered, and relevant personnel are notified to input data such as the perimeter parameter of the textile coil and the total number of needles in the editing box as the monitoring basic parameters.

[0026] S20: After obtaining the monitoring basic parameters, obtain the line length standard value, and obtain the line length control range according to the line length standard value.

[0027] In this embodiment, the line length standard value refers to the maximum length allowed for this textile thread.

[0028] Specifically, after obtaining the monitoring basic parameters, the input box for the line length standard value changes from non-editable to editable, that is, if the monitoring basic parameters cannot be obtained, the input of the line length standard value is restricted. Further, after obtaining the line length standard value, the line length control range is generated according to the preset standard.

[0029] S30: Trigger the line length monitoring instruction according to the line length control range, obtain the current line length value in real time, and compare the current line length value with the line length control range to obtain a comparison result.

[0030] Specifically, after obtaining the line length control range, trigger the line length monitoring instruction, calculate the current line length value according to the rotation speed of the coil during operation, and compare the current line length value with the line length control range to obtain the comparison result.

[0031] S40: When the comparison result exceeds the line length control range, trigger the textile shutdown instruction.

[0032] Specifically, when the current value of the thread length is within the thread length control range or lower than the minimum value of the thread length control range, the textile work continues. If the current value of the thread length exceeds the maximum value of the thread length control range, a textile stop instruction is triggered.

[0033] In this embodiment, by obtaining the monitoring basic parameters from the textile start message and calculating the standard value of the wire length and its control range based on these parameters. Subsequently, the system triggers the wire length monitoring instruction according to these standard values and control ranges to achieve real-time monitoring. When it is detected that the current value of the wire length exceeds the preset control range, the system immediately triggers the textile shutdown instruction. This linkage strategy of real-time monitoring and intelligent shutdown completely solves the problem of delayed feedback caused by traditional manual monitoring. The response time of the production line to abnormal wire length is greatly shortened, and problems can be discovered and measures can be taken in the first time, effectively curbing the accumulation of defective products caused by the lag of wire length monitoring, and greatly improving the product quality stability and the controllability of the production process. Before textile starts, the system can automatically obtain various basic parameters required for monitoring from the start message, such as textile demand data, the number of textile needles, etc. This step eliminates the errors that may be brought by manual input and ensures the accuracy of subsequent monitoring. Through the automated parameter acquisition process, not only the work efficiency is improved, but also the work burden of the operator is reduced, making the entire monitoring process more efficient and reliable. After obtaining the monitoring basic parameters, the system calculates the perimeter parameters of each line to be detected according to the specific number of textile needles and the number of lines to be monitored, and then determines the corresponding standard value of the wire length and the error range. This method ensures that each monitoring point has a personalized and accurate control standard, avoiding misjudgment that may be caused by fixed thresholds. At the same time, the control range is dynamically generated based on the specific situation of actual production, making the monitoring more in line with the actual production needs and improving the flexibility and adaptability of the monitoring. During the wire length monitoring process, the system will collect the current textile data in real time and input it into the pre-trained wire length prediction model for analysis to obtain the current wire length value. This real-time data analysis ability, combined with the support of advanced algorithms, enables the system to quickly and accurately identify abnormal wire length situations and make timely responses. Compared with the traditional static threshold judgment method, this method has higher sensitivity and accuracy and can better cope with the complex and changeable production environment. Before officially entering the monitoring stage, the system will also use a large amount of data in the historical wire length statistical records to train and optimize the initial model to obtain a more accurate wire length prediction model. The advantage of doing this is that the system can not only make real-time judgments based on the current data, but also refer to past experience and rules to make more scientific and reasonable decisions. This is of great significance for preventing potential risks and improving production efficiency. By integrating a series of automated and intelligent functions, this technical solution greatly simplifies the daily work process of the operator. For example, there is no need to manually set the specific parameters of each monitoring point, nor to frequently check and adjust the warning logic. All configuration and management work can be completed on a unified platform, which is convenient and fast. In addition, the visual interface and friendly interaction design provided by the system enable the operator to easily master the overall operation status of the production line and discover and solve problems in a timely manner.

[0034] In one embodiment, as Figure 2 shown, in step S10, that is, obtaining a textile start message and obtaining monitoring basic parameters from the textile start message, specifically including: S11: Obtain textile demand data from the textile start message, and obtain textile needle count data from the textile demand data.

[0035] Specifically, when triggering the textile start message, obtain the textile demand data input by relevant personnel, such as the type of textile product, type number, size, and quantity, etc., as the textile demand data.

[0036] Further, obtain the data of the production output of the product from the textile product information in the textile demand data, and calculate the textile needle count data.

[0037] S12: Calculate the number of textile lines to be monitored according to the textile needle count data, and generate monitoring basic data according to the number of textile lines to be monitored.

[0038] Specifically, calculate the total number of textile lines required according to the product type and textile needle count data in the textile demand data, that is, the number of textile lines to be monitored, and generate the monitoring basic data according to the number of textile lines to be monitored.

[0039] In one embodiment, as Figure 3 shown, in step S20, that is, after obtaining the monitoring basic parameters, obtain the line length standard value, and obtain the line length control range according to the line length standard value, specifically including: S21: Calculate according to the textile needle count data and the number of textile lines to be monitored to obtain the perimeter parameter data of each textile line to be detected.

[0040] Specifically, obtain the total required line length according to the required textile needle count data, and calculate the theoretical line length required for each coil according to the total number of textile lines, so as to calculate the perimeter parameter data corresponding to each coil in the wound state according to the theoretical line length.

[0041] S22: Obtain the line length standard value and the corresponding error range data according to the perimeter parameter data, and calculate the line length control range according to the line length standard value and the error range data.

[0042] Specifically, obtain the error range data corresponding to the preset or custom line length standard, so as to calculate the line length control range corresponding to each line length standard value.

[0043] In one embodiment, as Figure 4As shown, in step S30, that is, according to the wire length control range, a wire length monitoring instruction is triggered, the current value of the wire length is obtained in real time, and the current value of the wire length is compared with the wire length control range to obtain a comparison result, which specifically includes: S31: Obtain the current textile data according to the wire length monitoring instruction, and extract the current textile features from the current textile data.

[0044] Specifically, after the wire length monitoring instruction is triggered, according to the textile equipment started by the textile line to be monitored, the operating state during equipment operation is obtained, and the corresponding features are extracted from the data of the operating state as the current textile features.

[0045] S32: Input the current textile features into a preset wire length prediction model for prediction to obtain the current value of the wire length.

[0046] Specifically, input the current textile features into a pre-trained wire length prediction model, so that the model predicts the current wire length according to the operating state of the equipment, thereby obtaining the current value of the wire length.

[0047] In one embodiment, as Figure 5 shown, before step S30, the multi-wire length monitoring method further includes: S301: Obtain the historical wire length statistical record, and obtain the historical wire length data and textile feature data from the historical wire length statistical record, where the textile feature data includes the textile line type information and the operating state of the textile equipment.

[0048] Specifically, when monitoring the wire length during past textile production, the operating state of the equipment and the corresponding wire length data, that is, the historical wire length data, are obtained at the same time, and the features of the equipment operating state and the textile line are used as the textile feature data.

[0049] S302: Train the initial model according to the textile feature data to obtain a wire length prediction model.

[0050] Specifically, train the initial model according to the textile feature data, so as to obtain a wire length prediction model according to the correlation between the placement feature data and the wire length.

[0051] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0052] In one embodiment, a multi-wire length monitoring device is provided, and the multi-wire length monitoring device corresponds one-to-one to the multi-wire length monitoring method in the above embodiment. As Figure 6As shown in the figure, the multi-route length monitoring device includes a parameter acquisition module, a control range acquisition module, a line length monitoring module, and an emergency handling module. The detailed descriptions of each functional module are as follows: The parameter acquisition module is used to obtain the textile start message and acquire the monitoring basic parameters from the textile start message; The control range acquisition module is used to obtain the line length standard value after obtaining the monitoring basic parameters, and acquire the line length control range according to the line length standard value; The line length monitoring module is used to trigger the line length monitoring instruction according to the line length control range, obtain the current line length value in real time, and compare the current line length value with the line length control range to obtain a comparison result; The emergency handling module is used to trigger the textile shutdown instruction when the comparison result exceeds the line length control range.

[0053] Optionally, the parameter acquisition module includes: The stitch number acquisition sub-module is used to obtain the textile demand data from the textile start message and acquire the textile stitch number data from the textile demand data; The monitoring data acquisition sub-module is used to calculate the number of textile lines to be monitored according to the textile stitch number data, and generate the monitoring basic data according to the number of textile lines to be monitored.

[0054] Optionally, the control range acquisition module includes: The perimeter calculation sub-module is used to calculate according to the textile stitch number data and the number of textile lines to be monitored to obtain the perimeter parameter data of each textile line to be detected; The control range acquisition sub-module is used to obtain the line length standard value and the corresponding error range data according to the perimeter parameter data, and calculate the line length control range from the line length standard value and the error range data.

[0055] Optionally, the line length monitoring module includes: The feature extraction sub-module is used to obtain the current textile data according to the line length monitoring instruction and extract the current textile feature from the current textile data; The line length prediction sub-module is used to input the current textile feature into a preset line length prediction model for prediction to obtain the current line length value.

[0056] Optionally, the multi-route length monitoring device further includes: The historical feature acquisition module is used to obtain the historical line length statistical record, and acquire the historical line length data and the textile feature data from the historical line length statistical record, where the textile feature data includes the textile line type information and the running state of the textile equipment; The model training module is used to train the initial model according to the textile feature data to obtain the line length prediction model.

[0057] For the specific limitations of the multi-route length monitoring device, reference can be made to the limitations of the multi-route length monitoring method in the above text, which will not be elaborated here. Each module in the above multi-route length monitoring device can be implemented in whole or in part by software, hardware, and their combinations. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0058] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 7 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a multi-route length monitoring method.

[0059] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain a textile start message, and obtain monitoring basic parameters from the textile start message; After obtaining the monitoring basic parameters, obtain a line length standard value, and obtain a line length control range according to the line length standard value; Trigger a line length monitoring instruction according to the line length control range, obtain the current line length value in real time, and compare the current line length value with the line length control range to obtain a comparison result; When the comparison result exceeds the line length control range, trigger a textile stop instruction.

[0060] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are implemented: Obtain a textile start message, and obtain monitoring basic parameters from the textile start message; After obtaining the monitoring basic parameters, obtain a line length standard value, and obtain a line length control range according to the line length standard value; Trigger a line length monitoring instruction according to the line length control range, obtain the current line length value in real time, and compare the current line length value with the line length control range to obtain a comparison result; When the comparison result exceeds the line length control range, trigger a textile stop instruction.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0062] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0063] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-route length monitoring method, characterized in that: The multi-line length monitoring method comprises: Obtaining a spinning start message, and obtaining basic monitoring parameters from the spinning start message; After obtaining the basic monitoring parameters, obtaining a line length standard value, and obtaining a line length control range according to the line length standard value; Triggering a line length monitoring instruction according to the line length control range, acquiring a current line length value in real time, and comparing the current line length value with the line length control range to obtain a comparison result; When the comparison result exceeds the line length control range, a textile shutdown instruction is triggered.

2. The multi-line length monitoring method according to claim 1, characterized in that: The step of obtaining a spinning start message and obtaining basic monitoring parameters from the spinning start message specifically includes: Acquire textile demand data from the textile start message, and acquire textile needle count data from the textile demand data; The number of textile lines to be monitored is calculated based on the textile needle count data, and the basic monitoring data is generated based on the number of textile lines to be monitored.

3. The multi-line length monitoring method according to claim 2, characterized in that: After the basic monitoring parameters are obtained, a line length standard value is obtained, and a line length control range is obtained according to the line length standard value, specifically including: Calculating according to the textile needle count data and the number of textile lines to be monitored, obtaining perimeter parameter data of each textile line to be detected; The standard value of the line length and the corresponding error range data are obtained according to the perimeter parameter data, and the line length control range is obtained by calculating the standard value of the line length and the error range data.

4. The multi-line length monitoring method according to claim 1, characterized in that: The triggering of the line length monitoring instruction according to the line length control range, obtaining the current value of the line length in real time, and comparing the current value of the line length with the line length control range to obtain a comparison result specifically includes: Acquire current textile data according to the line length monitoring instruction, and extract current textile features from the current textile data; The current textile feature is input into a preset line length prediction model for prediction to obtain the current value of the line length.

5. The multi-line length monitoring method according to claim 4, characterized in that: Before triggering the line length monitoring instruction according to the line length control range, acquiring the current value of the line length in real time, and comparing the current value of the line length with the line length control range to obtain the comparison result, the multi-line length monitoring method further includes: Obtaining historical line length statistical records, and obtaining historical line length data and textile feature data from the historical line length statistical records, wherein the textile feature data includes textile line type information and textile equipment operation status; The initial model is trained according to the textile feature data to obtain the line length prediction model.

6. A multi-route length monitoring device, characterized in that: The multi-line length monitoring device comprises: A parameter acquisition module, used to acquire a spinning start message, and acquire basic monitoring parameters from the spinning start message; A control range acquisition module, used to acquire a line length standard value after acquiring the basic monitoring parameters, and acquire a line length control range according to the line length standard value; A line length monitoring module, used to trigger a line length monitoring instruction according to the line length control range, obtain a current value of the line length in real time, and compare the current value of the line length with the line length control range to obtain a comparison result; The emergency processing module is used to trigger a textile shutdown instruction when the comparison result exceeds the line length control range.

7. The multi-route length monitoring device according to claim 6, characterized in that: The parameter acquisition module includes: A needle number acquisition submodule, used for acquiring textile demand data from the textile start message, and acquiring textile needle number data from the textile demand data; The monitoring data acquisition submodule is used to calculate the number of textile circuits to be monitored based on the textile needle count data, and generate the monitoring basic data based on the number of textile circuits to be monitored.

8. The multi-route length monitoring device according to claim 7, characterized in that: The control range acquisition module includes: A perimeter calculation submodule, used for calculating according to the textile needle number data and the number of textile lines to be monitored, to obtain perimeter parameter data of each textile line to be detected; The control range acquisition submodule is used to acquire the line length standard value and the corresponding error range data according to the perimeter parameter data, and the line length control range is calculated by the line length standard value and the error range data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the multi-path length monitoring method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-path length monitoring method according to any one of claims 1 to 5 are implemented.

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