A method and system for intelligent optimization of low temperature resistant cable production process

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for intelligent optimization of the production process of low-temperature resistant cables. The method includes: real-time acquisition of the actual speed of a wrapping machine; determining the reliability of the actual speed at each historical moment; determining the degree of change of the actual speed at each moment; obtaining the number of moments of the final backtracking window required at the current moment in the WMA algorithm; obtaining the variance increment of the degree of change of the actual speed at the current moment; using the average speed of each moment in the final backtracking window of the current moment obtained by the WMA algorithm as the predicted speed at the next moment, so as to realize the intelligent optimization of the production process of low-temperature resistant cables. The present invention significantly improves the prediction accuracy of the actual speed of the wrapping machine by the traditional weighted moving average algorithm through comprehensive evaluation of the reliability of the actual speed at each moment and the degree of change of the actual speed at each moment, combined with the variance adaptive backtracking window of the degree of change, thereby improving the production quality of low-temperature resistant cables.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for intelligently optimizing a low-temperature resistant cable production process. Background Art

[0002] With the growing global demand for renewable energy, the wind power industry has experienced unprecedented rapid development. Wind farms are often deployed in extremely harsh environments, such as high altitude or cold regions. The climatic conditions in these regions pose extremely severe challenges to the low temperature resistance of cables. Once the low temperature resistance is reduced, the cable cannot maintain stable electrical and signal transmission under low temperature conditions, thus affecting the reliable operation of the wind power generation system. In the manufacturing process of low temperature resistant cables, one of the key processes is to use a wrapping machine to evenly wrap a layer of polyurethane insulation material around the outside of each core insulation layer. In this process, the speed stability of the wrapping machine is crucial and directly affects the quality and low temperature resistance of the product. If the speed is unstable, the polyurethane insulation material will be unevenly wrapped, which will affect the low temperature resistance of the cable. In the production of low-temperature resistant cables, the stability of the actual speed of wrapping is the key to ensuring the uniform winding of polyurethane insulation materials. In order to ensure the uniformity of wrapping, it is necessary to monitor the actual speed of the wrapping machine in real time and predict the predicted speed at the next moment. Once the predicted speed deviates from the preset speed, it needs to be corrected in real time; when making predictions, the traditional weighted moving average (WMA) algorithm can be used to obtain the predicted speed at the next moment.

[0003] The patent document with the announcement number CN104318053B discloses a traffic volume prediction method based on single variable regression calculation of weighted moving average weight, and calculates the weights of the call center traffic from Monday to Sunday based on objective historical traffic volume data, and establishes a traffic volume prediction model. The present invention uses the traffic volume data of the last seven days in the historical traffic volume data to directly obtain the traffic volume prediction value of the first day after the historical traffic volume data, and can continue to obtain the traffic volume prediction value of the second day after the historical traffic volume data, and so on, in a progressive manner by date. The traffic volume prediction method of this patent document can calculate the traffic volume prediction value after the historical traffic volume data day by day through the above-mentioned traffic volume prediction model.

[0004] However, the above patent document is not aimed at the prediction of rotational speed, and does not solve the problem that the traditional weighted moving average algorithm sets a fixed window, assigns weights to the data in each window, and obtains a weighted average value as the predicted value for the next moment. However, during the production process, the performance of the rotating shaft of the wrapping machine is reduced due to interference such as mechanical vibration and long-term load, and the actual rotational speed gradually decreases. The fixed window cannot adapt to this trend change in time, thereby reducing the accuracy of the prediction, causing the preset value adjusted based on the predicted value to deviate from the actual production demand, which in turn causes the polyurethane insulation layer to be wrapped unevenly, weakening the thermal insulation performance of the insulation material in a low temperature environment. Summary of the invention

[0005] In order to solve the problem that the traditional weighted moving average algorithm sets a fixed window, but the fixed window cannot adapt to the actual production needs, resulting in reduced accuracy of predicted rotation speed, further causing uneven wrapping of the polyurethane insulation layer, and weakening the thermal insulation performance of the insulation material in a low-temperature environment, the present invention provides a method and system for intelligent optimization of the production process of low-temperature resistant cables.

[0006] In the first aspect, the present invention provides an intelligent optimization method for the production process of low-temperature resistant cables, which adopts the following technical solutions:

[0007] A method for intelligent optimization of low-temperature resistant cable production process, comprising: real-time acquisition of the actual speed of a wrapping machine; determining the reliability of the actual speed at each historical moment according to the difference between the actual speed at each historical moment and the preset speed and the standard deviation of the actual speed at each historical moment, wherein the reliability is negatively correlated with the difference and the standard deviation; determining the degree of change of the actual speed at each moment according to the absolute value of the difference between the actual speed at each moment and the mean of the actual speed at each historical moment and the reliability, wherein the degree of change is positively correlated with the absolute value of the difference between the mean and the reliability; iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment, and taking the number of moments of the backtracking window when the iteration is terminated as the number of moments of the backtracking window at the current moment in the WMA algorithm; The number of moments of the final backtracking window required in the MA algorithm, in the process of iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment: according to the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment and the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment, the variance increment of the degree of change of the actual speed at the current moment is obtained; according to the size of the variance increment, the number of moments of the backtracking window required in the WMA algorithm at the current moment is obtained; the average speed of each moment in the final backtracking window at the current moment obtained by the WMA algorithm is used as the predicted speed at the next moment, so as to realize the intelligent optimization of the production process of low-temperature resistant cables.

[0008] The beneficial effects are as follows: by collecting the actual speed of the wrapping machine in real time and determining the reliability and degree of change based on the historical data of the speed, abnormal fluctuations in the speed can be discovered in time, which helps to warn potential problems in advance, thereby reducing production interruptions and failures and improving the stability of the production process; the size of the lookback window is dynamically adjusted according to the variance increment, so that the algorithm can adaptively adjust the degree of dependence on historical data. When the variance increment is large, it means that the reliability of the current data point is low, and the size of the lookback window is kept unchanged; conversely, when the variance increment is small, it means that the current data point is consistent with the historical data. At this time, expanding the lookback window can better cope with the uncertainty in the production process and improve the robustness of the model; based on the adaptive final lookback window, the weighted moving average algorithm is used to obtain the mean speed, and it is used as the predicted speed at the next moment, which improves the accuracy of the prediction and can better capture the trend changes of the speed, thereby providing more reliable data support for production planning.

[0009] Furthermore, the reliability satisfies the following relationship:

[0010] ; In the formula, For the The reliability of the actual speed at the historical moment, For the The standard deviation of the actual speed at the historical moment, For the The first moment in history The actual speed at that moment, is the preset speed of the wrapping machine. For the The number of historical moments.

[0011] The beneficial effects are: by monitoring and evaluating the relationship between the actual speed and the preset speed, deviations can be discovered and corrected in a timely manner, thereby improving the operational stability of the machine; real-time monitoring of reliability can help to detect potential faults in a timely manner, thereby performing preventive maintenance and reducing downtime.

[0012] Furthermore, the degree of change satisfies the following relationship:

[0013] ; In the formula, For the The degree of change of the actual speed at a moment, For the The reliability of the actual speed at the historical moment, For the The actual speed at that moment, For the The average of the actual speed at each historical moment, is a hyperparameter.

[0014] The beneficial effects are: by calculating the degree of change, the fluctuation of the rotation speed can be monitored in real time and abnormal conditions can be discovered in time; the specific degree of change can be used for subsequent data analysis to help enterprises carry out more effective production planning and resource allocation.

[0015] Furthermore, the variance increment satisfies the following relationship:

[0016] ; In the formula, is the variance increment of the actual speed change at the current moment, is the variance of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment, is the variance of the actual speed change at each moment within the initial lookback window at the current moment.

[0017] The beneficial effects are: by calculating the variance increment, the trend of speed fluctuation can be identified, and abnormal conditions in the production process can be discovered in time, which helps to take appropriate measures to prevent production problems caused by serious fluctuations; by analyzing the variance increment, the rationality of the current lookback window can be evaluated. If it is found that the window selection affects the variance calculation results, the length of the lookback window can be optimized to better reflect the actual situation of speed changes.

[0018] Further, the step of obtaining the number of time points of the backtracking window required in the WMA algorithm at the current moment according to the size of the variance increment includes: in response to the variance increment being greater than , then the number of moments in the initial lookback window remains unchanged, and the number of moments in the lookback window is obtained; in response to the variance increment being no greater than , then add the moment before the first moment in the initial lookback window to the initial lookback window to obtain the number of moments in the lookback window.

[0019] The beneficial effects are: the dynamic update mechanism can avoid unnecessary window expansion when the variance increment is greater than 0, reduce the consumption of computing resources, thereby improving the overall efficiency of the system, and appropriately expand the window when the variance increment is not greater than 0, thereby improving accuracy.

[0020] Furthermore, the process of iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment also includes: in response to the number of moments of the backtracking window and the initial backtracking window being unequal, using the backtracking window at the current moment obtained in this round of iteration as the initial backtracking window at the current moment in the next round of iteration; in response to the number of moments of the backtracking window and the initial backtracking window being equal, terminating the iteration.

[0021] Furthermore, the intelligent optimization of the low-temperature resistant cable production process includes: taking the difference between the predicted rotational speed at the next moment and the preset rotational speed as the adjustment amplitude of the wrapping machine at the next moment, adjusting the preset rotational speed of the wrapping machine according to the adjustment amplitude, and completing the intelligent optimization of the low-temperature resistant cable production process.

[0022] In a second aspect, the present invention provides an intelligent optimization system for low-temperature resistant cable production process, which adopts the following technical solution:

[0023] A low-temperature resistant cable production process intelligent optimization system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned low-temperature resistant cable production process intelligent optimization method is implemented.

[0024] By adopting the above technical solution, the above-mentioned intelligent optimization method for the production process of low-temperature resistant cables is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is manufactured based on the memory and the processor for easy use.

[0025] The present invention has the following technical effects:

[0026] By comprehensively evaluating the reliability of the actual speed at each moment and the degree of change of the actual speed at each moment, and adaptively adjusting the duration of the window in combination with the variance of the degree of change, it is ensured that the data in the backtracking window at each moment presents a change trend that is as consistent as possible, thereby accurately distinguishing the normal stable state from the performance degradation state caused by interference, and significantly improving the prediction accuracy of the actual speed of the wrapping machine by the traditional weighted moving average algorithm. According to the more accurate predicted speed, the preset speed is adjusted in time, avoiding the problem of uneven wrapping of the polyurethane insulation layer due to unstable actual speed, and improving the production quality of low-temperature resistant cables. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers are the same or corresponding parts.

[0028] Figure 1 The present invention is a flowchart of a method for intelligently optimizing a low-temperature resistant cable production process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0031] The embodiment of the present invention discloses a method for intelligently optimizing the production process of a low-temperature resistant cable. Figure 1 , comprising steps S1 to S6:

[0032] S1: Collect the actual speed of the wrapping machine in real time.

[0033] By fixing the magnetoresistive speed sensor on a fixed structure near the rotating shaft, ensuring that the sensor is positioned to identify the magnetic mark on the rotating shaft, the actual speed of the wrapping machine for low-temperature resistant cable production is obtained using the output signal of the sensor.

[0034] Implementers can set the collection frequency according to specific implementation conditions, for example, 1 minute / time.

[0035] S2: Determine the reliability of the actual speed at each historical moment.

[0036] It should be noted that in the production process, the wrapping machine starts and performs wrapping operations through the preset speed parameter value. However, during long-term use, due to the limitations of the equipment's own physical characteristics and the influence of external environmental factors, there will always be a certain deviation in the actual speed, but this deviation always exists in the entire process. In other words, if the actual speed value at a historical moment is closer to the preset speed, and this degree of proximity is stable, it means that the equipment is in normal operation during the historical period. Therefore, it is necessary to analyze the reliability of the actual speed at each moment, and evaluate the reliability of the speed at each moment through the deviation value between the actual speed and the preset speed at each moment and the change of the actual speed at each moment.

[0037] The reliability of the actual speed at each historical moment is determined based on the difference between the actual speed at each historical moment and the preset speed and the standard deviation of the actual speed at each historical moment.

[0038] Specifically, the reliability satisfies the following relationship:

[0039] ;

[0040] In the formula, For the The reliability of the actual speed at the historical moment, For the The standard deviation of the actual speed at the historical moment, For the The first moment in history The actual speed at that moment, is the preset speed of the wrapping machine. For the The number of historical moments.

[0041] Implementers can set the speed according to specific implementation conditions, for example, 20r / min.

[0042] in, Indicates The standard deviation of the actual speed at each historical moment. The smaller the value, the more stable the actual speed change at each historical moment. Indicates the difference between the actual speed at each moment and the preset speed. The smaller the value, the closer the actual speed at the historical moment is to the preset speed. Indicates The stability of the actual speed at the historical moment is a comprehensive measure of the difference between the actual speed and the preset speed. The smaller the value, the more stable the actual speed at the historical moment and the closer the actual speed at the historical moment is to the preset speed, indicating that the The more reliable the actual speed is during the historical period.

[0043] S3: Determine the degree of change of the actual rotation speed at each moment.

[0044] It should be noted that in the actual wrapping process, the stability of the rotation speed is crucial to ensure the uniformity of wrapping and the distribution of materials. A sudden change in the rotation speed will directly lead to uneven wrapping, which will in turn affect the thermal insulation performance of the product. Therefore, it is particularly important to accurately monitor and analyze the degree of change in the actual rotation speed at each moment. If the reliability of the actual rotation speed at a certain moment in history is higher, it means that the historical change trend is more similar, which means that the average value of the actual rotation speed at the historical moment can reflect the actual average level within the historical moment. This average level is used as a reference to compare the actual rotation speed at that moment. If the difference between the actual rotation speed at that moment and the long-term average level is greater, it can be explained that the degree of change in the actual rotation speed at that moment is greater.

[0045] The degree of change of the actual speed at each moment is determined according to the absolute value of the difference between the actual speed at each moment and the average of the actual speed at historical moments and the reliability.

[0046] Specifically, the degree of change satisfies the following relationship:

[0047] ;

[0048] In the formula, For the The actual speed change at a certain moment is For the The reliability of the actual speed at the historical moment, For the The actual speed at that moment, For the The average of the actual speed at each historical moment, is a hyperparameter.

[0049] Implementers can set hyperparameters according to specific implementation conditions. For example, 0.01 is set to prevent situation occurs.

[0050] in, Indicates The difference between the actual speed at the moment and the average of the actual speed at the historical moments. The larger the value, the The greater the difference between the actual speed at a certain moment and the average speed at a certain historical moment, the greater the difference between the actual speed at a certain moment and the average speed at a certain historical moment. The more significant the change in actual speed is at the moment; The more reliable the actual speed at a historical moment is, The larger the value, the The greater the difference between the actual speed at the moment and the average speed at the historical moment, the The greater the change in the actual speed at a given moment.

[0051] S4: Obtain the number of moments of the final backtracking window required in the WMA algorithm at the current moment.

[0052] Iterate to obtain the number of moments of the backtracking window required in the WMA algorithm at the current moment, and use the number of moments of the backtracking window when the iteration is terminated as the number of moments of the final backtracking window required in the WMA algorithm at the current moment.

[0053] S5: Obtain the variance increment of the degree of change of the actual speed at the current moment.

[0054] It should be noted that in normal operation, the actual speed of the wrapping process based on the preset speed is relatively stable, and there are many factors that affect the production process. For example, when the production volume is significant in a certain period of time, long-term high-load operation not only aggravates mechanical wear, but may also cause problems such as shaft overheating and insufficient lubrication, resulting in a significant decrease in the actual speed over time; and the traditional fixed window cannot distinguish between different changing trends of the actual speed, which may result in data with two different changing trends in the fixed window. Subsequent smooth prediction will be affected by these different trends and there will be deviations. Therefore, the degree of change of the actual speed at any time can be used to retrospectively divide the data with the same trend, and a corresponding window can be divided for each moment to ensure that the speed change trend is consistent.

[0055] In the process of iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment: according to the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment and the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment, the variance increment of the degree of change of the actual speed at the current moment is obtained; wherein, in the first round of iteration at the current moment, the initial backtracking window only includes the current moment.

[0056] Specifically, the variance increment satisfies the following relationship:

[0057] ;

[0058] In the formula, is the variance increment of the actual speed change at the current moment, is the variance of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment, is the variance of the actual speed change at each moment within the initial lookback window at the current moment.

[0059] According to the size of the variance increment, the number of time points of the backtracking window required in the WMA algorithm at the current moment is obtained.

[0060] It should be noted that the variance increment is greater than , indicating that the newly added time increases the overall volatility, which means that the newly added time has a different trend from the window. At this time, the window size remains unchanged; on the contrary, if the variance increment is less than or equal to , indicating that the newly added moment does not significantly change the overall volatility or make the overall more stable, which means that the newly added moment is consistent with the overall trend, so the previous moment is added to the window.

[0061] Specifically, the step of obtaining the number of time points of the backtracking window required in the WMA algorithm at the current moment according to the size of the variance increment includes:

[0062] In response to a variance increase greater than , then the number of moments in the initial lookback window remains unchanged, and the number of moments in the lookback window is obtained;

[0063] In response to the variance increment being no greater than , then add the moment before the first moment in the initial lookback window to the initial lookback window to obtain the number of moments in the lookback window.

[0064] Specifically, the process of iteratively obtaining the number of time points of the backtracking window required in the WMA algorithm at the current time also includes:

[0065] In response to the time numbers of the backtracking window and the initial backtracking window being unequal, the backtracking window at the current time obtained in the current iteration is used as the initial backtracking window at the current time in the next iteration;

[0066] In response to the number of time points of the lookback window and the initial lookback window being equal, the iteration is terminated.

[0067] S6: The average speed of each moment in the final backtracking window of the current moment obtained by the WMA algorithm is used as the predicted speed of the next moment to achieve intelligent optimization of the low-temperature resistant cable production process.

[0068] It should be noted that the traditional weighted moving average algorithm calculates the average value as the predicted value of the actual speed at the next moment by giving a larger weight to the recent data in the window. However, if the size of the window setting includes two trend changes, the calculated predicted value will be too biased. Therefore, by dynamically adjusting the size of the lookback window, it can be ensured that the data trend contained in the window corresponding to each moment is consistent. Dividing each moment into a window that adapts to itself and then performing smooth prediction can improve the accuracy of the prediction, thereby optimizing the preset speed.

[0069] Specifically, the intelligent optimization of the low-temperature resistant cable production process includes:

[0070] The difference between the predicted speed at the next moment and the preset speed is used as the adjustment amplitude of the wrapping machine at the next moment, and the preset speed of the wrapping machine is adjusted according to the adjustment amplitude. Specifically, the adjustment amplitude is added to the preset speed to obtain the adjusted preset speed at the next moment to ensure that the actual speed of the equipment is stable at the expected optimal speed level, thereby completing the intelligent optimization of the low-temperature resistant cable production process.

[0071] An embodiment of the present invention also discloses a low-temperature resistant cable production process intelligent optimization system, including a processor and a memory, the memory storing computer program instructions, and when the computer program instructions are executed by the processor, a low-temperature resistant cable production process intelligent optimization method according to the present invention is implemented.

[0072] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0073] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0074] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0075] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent optimization of low temperature resistant cable production process, characterized in that: include: Collect the actual speed of the wrapping machine in real time; The reliability of the actual speed at each moment in history is determined based on the difference between the actual speed at each moment in history and the preset speed and the standard deviation of the actual speed at each moment in history. ; In the formula, For the The reliability of the actual speed at the historical moment, For the The standard deviation of the actual speed at the historical moment, For the The first moment in history The actual speed at that moment, is the preset speed of the wrapping machine. For the The reliability is negatively correlated with the difference and standard deviation of the historical moments. The degree of change of the actual speed at each moment is determined according to the absolute value and reliability of the difference between the actual speed at each moment and the mean of the actual speed at the historical moments. ; In the formula, For the The degree of change of the actual speed at a moment, For the The actual speed at that moment, For the The average of the actual speed at each historical moment, is a hyperparameter, and the degree of change is positively correlated with the absolute value of the mean difference and the reliability; the number of moments of the backtracking window required in the WMA algorithm at the current moment is iteratively obtained, and the number of moments of the backtracking window when the iteration is terminated is used as the number of moments of the final backtracking window required in the WMA algorithm at the current moment. In the process of iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment: according to the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment and the variance of the degree of change of the actual speed at each moment in the initial backtracking window at the current moment, the variance increment of the degree of change of the actual speed at the current moment is obtained. ; In the formula, is the variance increment of the actual speed change at the current moment, is the variance of the actual speed at each moment in the initial backtracking window at the current moment and the actual speed at the previous moment, is the variance of the degree of change of the actual speed at each moment in the initial lookback window at the current moment; according to the size of the variance increment, the number of moments of the lookback window required in the WMA algorithm at the current moment is obtained; The average speed of each moment in the final backtracking window of the current moment obtained by the WMA algorithm is used as the predicted speed at the next moment to achieve intelligent optimization of the low-temperature resistant cable production process.

2. The intelligent optimization method for low-temperature resistant cable production process according to claim 1 is characterized in that: The step of obtaining the number of time points of the backtracking window required in the WMA algorithm at the current moment according to the size of the variance increment includes: In response to a variance increase greater than , then the number of moments in the initial lookback window remains unchanged, and the number of moments in the lookback window is obtained; In response to the variance increment being no greater than , then add the moment before the first moment in the initial lookback window to the initial lookback window to obtain the number of moments in the lookback window.

3. The intelligent optimization method for low-temperature resistant cable production process according to claim 2 is characterized in that: The process of iteratively obtaining the number of moments of the backtracking window required in the WMA algorithm at the current moment also includes: In response to the time numbers of the backtracking window and the initial backtracking window being unequal, the backtracking window at the current time obtained in the current iteration is used as the initial backtracking window at the current time in the next iteration; In response to the number of time points of the lookback window and the initial lookback window being equal, the iteration is terminated.

4. The intelligent optimization method for low-temperature resistant cable production process according to claim 1 is characterized in that: The intelligent optimization of the production process of low temperature resistant cables includes: The difference between the predicted speed at the next moment and the preset speed is used as the adjustment amplitude of the wrapping machine at the next moment. The preset speed of the wrapping machine is adjusted according to the adjustment amplitude to complete the intelligent optimization of the low-temperature resistant cable production process.

5. An intelligent optimization system for low-temperature resistant cable production process, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for intelligent optimization of a low-temperature resistant cable production process according to any one of claims 1 to 4 is implemented.

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