Control method, system and device of cable extrusion equipment and extrusion equipment

Abstract

The invention relates to the technical field of cable production automation control, and discloses a control method, system and device of cable extrusion equipment and the extrusion equipment, the method comprises the steps that sensor parameters are acquired, and the sensor parameters comprise the cable extrusion temperature and the extrusion speed; performing time delay correction on the sensor parameter to obtain a corrected parameter value; inputting the correction parameter value into a pre-trained extrusion speed and extrusion temperature dynamic compensation model to generate an adjustment instruction; and sending the adjustment instruction to equipment to adjust the heating temperature and the extrusion speed of the cable. Compared with the prior art, the method has the following effects that the heating temperature and the extrusion speed of the cable can be accurately adjusted in real time through combined use of time delay correction and an extrusion speed and extrusion temperature dynamic compensation model, and the waiting time and the adjustment time in the production process are reduced, so that the control precision and the production efficiency of cable extrusion equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable production automation control, and in particular to a control method, system, device and extrusion equipment for cable extrusion equipment. Background Art

[0002] With the rapid development of the cable production industry and the continuous advancement of industrial automation technology, the control accuracy and efficiency of cable extrusion equipment have become key factors in determining cable product quality and production capacity. Scientific and rational control methods are crucial to ensuring cable uniformity, stability, and durability during the cable extrusion process. However, as market demands for cable quality continue to rise, traditional control methods are no longer able to meet current production needs.

[0003] One prior art method, such as that in Chinese patent application number CN105670506, proposes a method for matching setpoints and actual values ​​for a high-precision extrusion device. This method primarily controls the extrusion equipment by collecting extrusion temperature and thickness as control parameters and using their average value as the basis for matching control parameters. This method improves the control accuracy of the extrusion equipment to a certain extent, providing more stable process conditions for cable production.

[0004] However, some problems still exist in the existing technology. Specifically, although the method takes into account the extrusion temperature and thickness parameters, it does not conduct in-depth modeling and analysis of these parameters, resulting in a lack of real-time and dynamic control process. Summary of the Invention

[0005] The present invention provides a control method, device, electronic device and storage medium for cable extrusion equipment, so as to achieve real-time and accurate adjustment of cable heating temperature and extrusion speed, reduce waiting time and adjustment time in the production process, and improve the control accuracy and production efficiency of the cable extrusion equipment.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a control method for a cable extrusion device, comprising:

[0007] Acquiring sensor parameters, wherein the sensor parameters include cable extrusion temperature and extrusion speed;

[0008] Performing time delay correction on the sensor parameters to obtain corrected parameter values;

[0009] Inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction;

[0010] The adjustment instructions are sent to the device to adjust the cable heating temperature and the extrusion speed.

[0011] In an optional embodiment, the training process of the extrusion speed and extrusion temperature dynamic compensation model includes:

[0012] Building a single neuron PID controller model based on historical extrusion speed and extrusion temperature data, and training the single neuron PID controller model;

[0013] When the number of training times is greater than or equal to the preset maximum number of training times, or when the loss function value of the single neuron PID controller model is less than the preset loss threshold, the training is determined to be completed and a dynamic compensation model of extrusion speed and extrusion temperature is obtained.

[0014] In an optional implementation, performing delay correction on the sensor parameter to obtain a corrected parameter value includes:

[0015] The correction parameter value is obtained by the following formula:

[0016]

[0017] Among them, T(t), V(t), T(t-Δt) and V(t-Δt) are the actual extrusion temperature and extrusion speed, Δt is the time interval, τ represents the time correction coefficient, T s (t) and V s (t) is the extrusion temperature and extrusion speed measured by the sensor at time t, and these measured values ​​are the values ​​τ time ago, where T s (t) = T(t-τ) and V s (t) = V(t-τ), T′ s (t) and V′ s (t) is the extrusion temperature and extrusion speed after time delay correction.

[0018] In an optional embodiment, inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction includes:

[0019] The adjustment instruction includes a heating temperature adjustment value, which is obtained by the following formula:

[0020]

[0021] T final (k) = T meas (k)+Δu(T)(k)

[0022] Among them, K p is the proportional gain, K i is the integral gain, K d is the differential gain, T iis the integration time constant, T s is the sampling period, and the deviation value e(T)(k)=T is calculated at time step k. set -T meas (k), T set is the set extrusion temperature, T meas (k) is the extrusion temperature value measured by the sensor at time step k, the integral of the calculated deviation ∑e(T)(k)=∑e(T)(k-1)+e(T)(k), ∑e(T)(k-1) is the accumulation of all previous calculated deviation values, the differential of the calculated deviation Δe(T)(k)=e(T)(k)-e(T)(k-1), e(T)(k-1) is the calculated deviation at time step k-1, Δu(T)(k) is the control increment of the heating temperature at time step k, T final (k) is the final equipment heating temperature adjustment value.

[0023] In an optional embodiment, inputting the corrected parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to obtain a final parameter value includes:

[0024] The adjustment instruction includes an extrusion speed adjustment value, which is obtained by the following formula:

[0025]

[0026] V final (k)=V meas (k)+Δu(V)(k)

[0027] in, is the proportional gain, is the integral gain, is the differential gain, is the integration time constant, T s is the sampling period, and the deviation value e(V)(k)=V is calculated at time step k. set -V meas (k), V set is the set extrusion speed, V meas (k) is the extrusion speed value measured by the sensor at time step k, the integral of the calculated deviation ∑e(V)(k)=∑e(V)(k-1)+e(V)(k), ∑e(V)(k-1) is the accumulation of all previous calculated deviation values, the differential of the calculated deviation Δe(V)(k)=e(V)(k)-eV(k-1), eV(k-1) is the calculated deviation at time step k-1, Δu(V)(k) is the control increment of the extrusion speed at time step k, V final (k) is the final device extrusion speed adjustment value.

[0028] In an optional implementation, before performing delay correction on the sensor parameter to obtain the corrected parameter value, the method includes:

[0029] Performing numerical screening on the acquired sensor parameters to obtain sensor parameter values ​​that are within a normal range;

[0030] The sensor parameter values ​​that are within the normal range are filtered to obtain smoothed and noise-removed sensor parameters.

[0031] In an optional embodiment, sending the adjustment instruction to the device to adjust the cable heating temperature and the extrusion speed includes:

[0032] When the adjustment instruction exceeds the preset threshold range, an exception handling process is triggered.

[0033] In a second aspect, the present invention provides a control system for a cable extrusion device, comprising:

[0034] A data acquisition module, used to acquire sensor parameters, including cable extrusion temperature and extrusion speed;

[0035] A data correction module, configured to perform time delay correction on the sensor parameters to obtain corrected parameter values;

[0036] An instruction generation module is used to input the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction;

[0037] The device adjustment module is used to send the adjustment instruction to the device to adjust the cable heating temperature and the extrusion speed.

[0038] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the control method of the cable extrusion equipment described in any one of the above is implemented.

[0039] In a fourth aspect, the present invention further provides a cable extrusion device, characterized in that it includes the control system of the cable extrusion device described above.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The method of the present invention includes obtaining sensor parameters, which include cable extrusion temperature and extrusion speed; performing time delay correction on the sensor parameters to obtain corrected parameter values; inputting the corrected parameter values ​​into a pre-trained dynamic compensation model for extrusion speed and extrusion temperature to generate adjustment instructions; and sending the adjustment instructions to the device to adjust the cable heating temperature and extrusion speed. This method achieves precise control of the heating temperature and extrusion speed of the cable extrusion equipment by acquiring and correcting sensor parameters in real time and generating accurate adjustment instructions using a dynamic compensation model. This control method not only improves control accuracy and production efficiency, but also helps to reduce energy consumption and improve product quality. In addition, when the adjustment instruction exceeds the preset threshold range, the method triggers an exception handling process to ensure the stability and safety of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of a control method for a cable extrusion device provided by a first embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the control system structure of the cable extrusion equipment provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] Reference Figure 1 A first embodiment of the present invention provides a control method for a cable extrusion device, comprising the following steps:

[0046] S11, obtaining sensor parameters, wherein the sensor parameters include cable extrusion temperature and extrusion speed;

[0047] S12, performing time delay correction on the sensor parameters to obtain corrected parameter values;

[0048] S13, inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction;

[0049] S14, sending the adjustment instruction to the device to adjust the cable heating temperature and the squeezing speed.

[0050] In step S11, the purpose is to obtain real-time cable extrusion temperature and extrusion speed data from the sensors of the cable extrusion equipment. These data are the basis for subsequent time delay correction, dynamic compensation model calculation and adjustment instruction generation. The temperature during the cable extrusion process is one of the key factors affecting product quality and production efficiency. Too high or too low temperature may cause changes in the performance of the cable material, thereby affecting the uniformity, stability and durability of the cable. Therefore, accurately obtaining the cable extrusion temperature is a prerequisite for ensuring production quality. In addition, the extrusion speed determines the production efficiency of the cable. During the cable extrusion process, the extrusion speed needs to match factors such as the extrusion temperature and material properties to ensure the stability and production efficiency of the product. Therefore, obtaining real-time extrusion speed data is of great significance for adjusting production parameters and optimizing production processes. The sensors used to obtain the cable extrusion temperature and extrusion speed usually include temperature sensors and speed sensors. These sensors need to have high precision, high stability and good environmental adaptability to ensure that the acquired data is accurate and reliable. The data collected by the sensor is transmitted to the control system via a wired manner. In practical applications, the following factors also need to be considered when obtaining sensor parameters:

[0051] Sensor placement: The placement of the sensors is crucial for obtaining accurate data. It is necessary to ensure that the sensors can accurately reflect the actual temperature and speed conditions during the cable extrusion process.

[0052] Sensor calibration: Regularly calibrating the sensor can ensure the accuracy of its measurement data, which includes calibrating the sensor's zero point, range, sensitivity and other parameters.

[0053] Reliability of data transmission: During the data transmission process, it is necessary to ensure the integrity and accuracy of the data, which can be achieved by adopting redundant transmission and check code methods.

[0054] In summary, step S11 plays a crucial role in the control method of the cable extrusion equipment. By accurately acquiring the cable extrusion temperature and extrusion speed data, it provides a reliable basis for subsequent time delay correction and dynamic compensation model calculation, thereby achieving precise control of the cable extrusion equipment.

[0055] In step S12, the sensor parameters are subjected to time delay correction to obtain more accurate, real-time parameters, providing a reliable basis for subsequent dynamic compensation model calculations and adjustment command generation. Corrected parameter values ​​are more accurate parameter values ​​obtained by taking into account factors such as time delay, based on the original sensor parameters. These corrected parameter values ​​can more accurately reflect the actual operating status of the cable extrusion equipment, thereby improving the accuracy and effectiveness of control commands. During the cable extrusion process, sensor parameters often exhibit a certain time lag due to various factors such as sensor response delay and data transmission delay. The purpose of time delay correction is to eliminate this lag, so that the corrected parameter values ​​can reflect the current extrusion temperature and speed in real time. Time delay correction can be implemented using mathematical formulas or algorithms. For example, historical data can be used to train a prediction model that can predict the actual extrusion temperature and speed based on the current sensor parameters and known time delay. Alternatively, simple mathematical transformations such as weighted average or sliding average can be used to smooth the sensor parameters and reduce the impact of time delay.

[0056] In summary, by performing time delay correction on sensor parameters, more accurate and real-time production parameters can be obtained, providing a reliable basis for subsequent control instruction generation.

[0057] In a preferred implementation, before performing delay correction on the sensor parameter to obtain a corrected parameter value, the method further includes:

[0058] Performing numerical screening on the acquired sensor parameters to obtain sensor parameter values ​​that are within a normal range;

[0059] The sensor parameter values ​​that are within the normal range are filtered to obtain smoothed and noise-removed sensor parameters.

[0060] It's important to note that these processing steps are crucial to ensuring data quality and model accuracy. Numerical filtering is used to identify and remove anomalous data points, which may be caused by sensor failure, environmental interference, or operational errors. This prevents erroneous data from affecting subsequent correction calculations and model predictions. Filtering is used to reduce noise in the data and improve signal stability. During the cable extrusion process, sensor signals may contain noise due to factors such as mechanical vibration and electrical interference. Filtering typically uses mathematical methods, such as low-pass filters and Kalman filters, to effectively remove noise and extract smoother, more accurate data trends.

[0061] After numerical screening and filtering, combined with time delay correction, the accuracy and reliability of sensor parameters can be improved, so that the input data received by the dynamic compensation model is more precise.

[0062] In a preferred implementation, performing time delay correction on the sensor parameter to obtain a corrected parameter value includes:

[0063] The correction parameter value is obtained by the following formula:

[0064]

[0065] Among them, T(t), V(t), T(t-Δt) and V(t-Δt) are the actual extrusion temperature and extrusion speed, Δt is the time interval, τ represents the time correction coefficient, T s (t) and V s (t) is the extrusion temperature and extrusion speed measured by the sensor at time t, and these measured values ​​are the values ​​τ time ago, where T s (t) = T(t-τ) and V s (t) = V(t-τ), T′ s (t) and V s (t) is the extrusion temperature and extrusion speed after time delay correction.

[0066] It should be noted that the selection of the time correction coefficient τ and the time interval Δt in the above formula plays a decisive role in the accuracy of the correction parameter value. The time correction coefficient τ reflects the degree of delay of the sensor measurement value relative to the actual physical process, while the time interval Δt represents the time difference between two measurements. These two parameters need to be accurately calibrated according to the specific application scenario and sensor characteristics. In practical applications, the purpose of the correction formula is to predict the actual extrusion temperature T(t) and extrusion speed V(t) at time t based on the measurement value and change rate at the previous time point t-Δt. In this way, the sensor reading can be corrected to compensate for the delay between the sensor detecting the physical change and the sensor output reading. Corrected extrusion temperature T' s (t) and extrusion speed V′ s(t) can provide the control system with more accurate current state information. This information is crucial for the dynamic compensation model, as it directly impacts the model's predictive accuracy and control effectiveness. Furthermore, to ensure the effectiveness of the correction formula, sensors must be regularly calibrated to ensure the accuracy and reliability of the measured values. Furthermore, the impact of environmental factors and equipment aging on sensor performance should be considered, and the parameters in the correction formula should be adjusted as necessary. When applying correction parameter values ​​to the control system, it is also necessary to consider whether the actual actuator can respond to these correction instructions promptly and accurately. For example, if the response time of a heating element is long, it may require advance adjustment to ensure that the desired temperature is reached when needed. In summary, by applying time delay correction to sensor parameters, more accurate estimates of extrusion temperature and speed can be obtained, which are crucial for improving the precision and efficiency of cable extrusion equipment control. Continuous optimization of this process can improve product quality, reduce production costs, and enhance the level of automation in the production process.

[0067] In step S13, the delay-corrected sensor parameter values ​​(specifically, the cable extrusion temperature and speed) are input into the pre-trained "dynamic compensation model for extrusion speed and temperature" to generate adjustment instructions. These adjustment instructions are the output of step S13 and are generated based on the corrected parameter values ​​(extrusion temperature and speed) input into the model. These instructions are used to adjust the heating temperature and extrusion speed of the cable extrusion equipment to achieve more precise production control. Adjustment instructions typically include a heating temperature adjustment value and an extrusion speed adjustment value, which together determine the equipment's operating state for the next stage. Adjusting the heating temperature is typically done by varying the power output of the heating element to maintain or achieve the desired extrusion temperature. Adjusting the extrusion speed is done by varying the speed of the extrusion screw to control the material flow rate and output. The dynamic compensation model for extrusion speed and temperature is a machine learning-based prediction model that predicts and generates adjustment instructions based on the current extrusion temperature and speed. This model is able to capture dynamic changes during the extrusion process and make accurate adjustment predictions accordingly.

[0068] The acquisition of the dynamic compensation model of extrusion speed and extrusion temperature requires the following steps:

[0069] Data collection: Collect historical extrusion speed and extrusion temperature data, as well as corresponding equipment adjustment records.

[0070] Constructing a single neuron PID controller model: Based on the collected data, a single neuron PID controller model is constructed as an initial model.

[0071] Model training: Train the initial model and continuously adjust model parameters to reduce prediction errors. During training, the model's loss function value is monitored. Training is considered complete when the loss function value is less than the preset loss threshold or the number of training times reaches the preset maximum number of training times.

[0072] Model validation and optimization: Use the validation dataset to validate the trained model to ensure its accuracy and stability in real-world applications. If necessary, further optimize the model.

[0073] In a preferred implementation, the training process of the extrusion speed and extrusion temperature dynamic compensation model includes:

[0074] Building a single neuron PID controller model based on historical extrusion speed and extrusion temperature data, and training the single neuron PID controller model;

[0075] When the number of training times is greater than or equal to the preset maximum number of training times, or when the loss function value of the single neuron PID controller model is less than the preset loss threshold, the training is determined to be completed and a dynamic compensation model of extrusion speed and extrusion temperature is obtained.

[0076] It should be noted that, in the process of training the extrusion speed and extrusion temperature dynamic compensation model, several key factors need to be carefully considered.

[0077] First, the selection of historical extrusion speed and temperature data is crucial for model accuracy. This data should come from the actual production process of the cable extrusion equipment and cover a wide range of possible operating conditions. Before building the single-neuron PID controller model, this data needs to be preprocessed, including data cleaning, outlier processing, and data normalization, to ensure data accuracy and consistency.

[0078] Secondly, training the single-neuron PID controller model requires the use of an appropriate optimization algorithm and learning rate. The choice of optimization algorithm should be based on the characteristics of the data and the complexity of the model to ensure the effectiveness and efficiency of the training process. The setting of the learning rate also requires repeated trial and error to avoid overfitting or underfitting the model.

[0079] Furthermore, during training, the model's loss function must be monitored and evaluated. The loss function is an important metric for measuring the difference between the model's predicted values ​​and actual values. When the number of training cycles reaches the preset maximum, or when the loss function value drops below the preset loss threshold, the model is considered to have fully learned the data characteristics and has developed good predictive capabilities. At this point, training can be stopped, and the final dynamic compensation model for extrusion speed and temperature is obtained.

[0080] It's important to note that trained models still need to be validated and tested to ensure their stability and reliability in real-world applications. This can be achieved by applying the model to actual production data and observing the deviation between its predicted and actual results. If the deviation is large, further adjustments and optimizations to the model are necessary.

[0081] In a preferred implementation, inputting the modified parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to obtain the final parameter value includes:

[0082] The adjustment instruction includes an extrusion speed adjustment value, which is obtained by the following formula:

[0083]

[0084] V final (k)=V meas (k)+Δu(V)(k)

[0085] in, is the proportional gain, is the integral gain, is the differential gain, is the integration time constant, T s is the sampling period, and the deviation value e(V)(k)=V is calculated at time step k. set -V meas (k), V set is the set extrusion speed, V meas (k) is the extrusion speed value measured by the sensor at time step k, the integral of the calculated deviation ∑e(V)(k)=∑e(V)(k-1)+e(V)(k), ∑e(V)(k-1) is the accumulation of all previous calculated deviation values, the differential of the calculated deviation Δe(V)(k)=e(V)(k)-eV(k-1), eV(k-1) is the calculated deviation at time step k-1, Δu(V)(k) is the control increment of the extrusion speed at time step k, V final (k) is the final device extrusion speed adjustment value.

[0086] It should be noted that when calculating the extrusion speed adjustment value, each parameter and variable in the above formula plays an important role. First, the deviation value e(V)(k) reflects the set extrusion speed V set The extrusion speed V measured by the current sensor meas (k). This difference is the basis for adjusting the extrusion speed. It tells us whether the current extrusion speed deviates from the set value and how much it deviates. Secondly, the proportional gain Integral gain and differential gain Together they form the core of the PID controller. Proportional gain Determines the direct impact of the current deviation on the control quantity increment; integral gain The cumulative effect of the deviation is taken into account, which helps to eliminate the long-term deviation; the differential gain Paying attention to the trend of deviation changes will help predict future deviations and make adjustments in advance. and sampling period Ts are related to the integral gain Together, they determine the calculation method and accuracy of the deviation integral. It is usually used to adjust the scope and time scale of the integral gain, and the sampling period T s This determines the time interval between each calculation of the deviation and the control increment. When calculating the control increment Δu(V)(k), we comprehensively consider the deviation value e(V)(k), the integral of the deviation ∑e(V)(k) and the differential of the deviation Δe(V)(k). These three factors together determine how the extrusion speed should be adjusted at the current time step k. Finally, the control increment Δu(V)(k) is added to the currently measured extrusion speed V meas (k), we get the final equipment extrusion speed adjustment value V final (k), this adjustment value will be used as the new extrusion speed setting value to control the operation of the extrusion equipment. It should be noted that in actual applications, the parameters of the PID controller (i.e. and T s ) usually needs to be debugged and optimized according to the specific extrusion equipment and process conditions. Through reasonable parameter settings, it can be ensured that the dynamic compensation model of extrusion speed and extrusion temperature can accurately predict and adjust parameter changes during the extrusion process, thereby improving the quality of extruded products and production efficiency.

[0087] In a preferred implementation, inputting the correction parameter value into a pre-trained dynamic compensation model for extrusion speed and extrusion temperature to generate an adjustment instruction includes:

[0088] The adjustment instruction includes a heating temperature adjustment value, which is obtained by the following formula:

[0089]

[0090] T final (k) = T meas (k)+Δu(T)(k)

[0091] Among them, K p is the proportional gain, K i is the integral gain, K d is the differential gain, Ti is the integration time constant, T s is the sampling period, and the deviation value e(T)(k)=T is calculated at time step k. set -T meas (k), T set is the set extrusion temperature, T meas (k) is the extrusion temperature value measured by the sensor at time step k, the integral of the calculated deviation ∑e(T)(k)=∑e(T)(k-1)+e(T)(k), ∑e(T)(k-1) is the accumulation of all previous calculated deviation values, the differential of the calculated deviation Δe(T)(k)=e(T)(k)-e(T)(k-1), e(T)(k-1) is the calculated deviation at time step k-1, Δu(T)(k) is the control increment of the heating temperature at time step k, T final (k) is the final equipment heating temperature adjustment value.

[0092] It should be noted that, when calculating the heating temperature adjustment value, each parameter and variable in the above formula plays an important role, and the relationship between them is interdependent and works together.

[0093] First, the deviation value e(T)(k) is the set extrusion temperature T set The extrusion temperature T measured by the current sensor meas The difference between (k) reflects the degree of deviation of the current extrusion temperature from the set value. This deviation is the basis for adjusting the heating temperature, which determines how we need to adjust the heating temperature.

[0094] Secondly, the proportional gain K p This directly determines the degree of influence of the current deviation on the increment of the heating temperature control amount. When the deviation is large, the proportional gain will cause a large increment in the control amount to quickly reduce the deviation. This ensures that the extrusion temperature can quickly approach the set value.

[0095] Integral gain K i The cumulative effect of the deviation is taken into account, that is, the influence of the deviation values ​​of all past time steps on the current control value. Through the integral gain, we can gradually eliminate the long-term deviation and keep the extrusion temperature more stably near the set value. The integral time constant T i It is used to adjust the scope and time scale of the integral gain, which determines the growth rate of the integral term.

[0096] Differential gain K d The differential gain focuses on the deviation trend, that is, the difference between the current deviation and the deviation at the previous time step. The differential gain helps predict future deviation changes and make adjustments in advance to prevent further deviation expansion. This can improve the system's response speed and stability.

[0097] When calculating the control increment Δu(T)(k), we consider three factors: the current deviation e(T)(k), the integral of the deviation ∑e(T)(k), and the differential of the deviation Δe(T)(k). These three factors together determine the appropriate adjustment to the heating temperature at the current time step k. Through appropriate parameter settings and calculations, we can obtain an appropriate control increment, thereby achieving precise adjustment of the heating temperature.

[0098] Finally, the calculated control increment Δu(T)(k) is added to the currently measured extrusion temperature T meas (k), the final equipment heating temperature adjustment value T is obtained final (k). This adjustment value will be used as the new heating temperature setting value to control the heating system of the extrusion equipment.

[0099] It should be noted that in practical applications, the parameters of the PID controller (i.e. K p , K i , K d and T s ) usually needs to be debugged and optimized according to the specific extrusion equipment and process conditions. Through reasonable parameter setting and debugging process, it can be ensured that the dynamic compensation model of extrusion temperature and extrusion speed can accurately predict and adjust the parameter changes in the extrusion process, thereby improving the quality of extruded products and production efficiency.

[0100] In step S14, the adjustment instructions, as the core output of the cable extrusion equipment automation control system, are actually a set of specific adjustment values ​​for the cable heating temperature and extrusion speed. These adjustment values ​​are obtained based on the precise acquisition of sensor parameters in the early stage, time delay correction, and in-depth calculation of the extrusion speed and extrusion temperature dynamic compensation model completed through pre-training. The accuracy and timeliness of the adjustment instructions are directly related to the control accuracy and production efficiency of the cable extrusion equipment. The model will calculate the optimal adjustment instructions, including heating temperature adjustment values ​​and extrusion speed adjustment values, based on the input correction parameter values. These adjustment values ​​are obtained through a series of complex calculation processes, aiming to keep the actual operating state of the cable extrusion equipment consistent with the preset ideal state. The core task of step S14 is to send these adjustment instructions to the cable extrusion equipment to adjust its heating temperature and extrusion speed. The implementation of this process depends on the communication capabilities of the automation control system and the responsiveness of the equipment.

[0101] Specifically, the automated control system encodes and packages these adjustment instructions and then sends them to the cable extrusion equipment via a specific communication protocol. Upon receiving these instructions, the equipment immediately decodes and processes them, adjusting the heating temperature and extrusion speed based on the specific instructions.

[0102] In practical applications, the transmission and reception of adjustment instructions must be highly accurate and timely. Any communication delays or instruction errors can lead to unstable equipment operation, which in turn affects the quality and efficiency of cable production. Therefore, automated control systems must employ reliable communication technologies and protocols to ensure accurate transmission of adjustment instructions and timely equipment responses.

[0103] In a preferred implementation, when the adjustment instruction exceeds a preset threshold range, an exception handling process is triggered.

[0104] It should be noted that this process is designed to ensure that the extrusion equipment can operate safely and stably in the face of abnormal or extreme conditions, avoiding equipment damage or product quality degradation. Specifically, when the adjustment instruction (whether for extrusion speed or heating temperature) exceeds the preset threshold range, the system will immediately detect this abnormality and automatically enter the abnormality handling process. The process includes the following steps:

[0105] Alarm notification: The system will first send out an alarm signal to inform the operator of the current abnormal situation. This can be achieved through audio and video alarms, screen displays, etc., to ensure that the operator can quickly detect the problem.

[0106] Pause or slow down operation: To avoid further exacerbation of abnormal conditions, the system automatically pauses or slows down the operation of the extrusion equipment. This protects the equipment from damage and prevents abnormal conditions from having a serious impact on product quality.

[0107] Data recording and analysis: During the exception handling process, the system will record relevant data, including the value of the adjustment instruction, the time when the exception occurred, the operating status of the equipment, etc. This data can be used for subsequent analysis and troubleshooting.

[0108] Automatic adjustment or manual intervention: Based on the exception handling strategy, the system will attempt to automatically adjust relevant parameters to restore normal operation of the equipment. If automatic adjustment is ineffective or more specific action is required to resolve the problem, the system will prompt the operator to manually intervene.

[0109] Resuming Operation: After the abnormal situation has been properly handled and the equipment is able to operate safely and stably, the system will resume normal operation of the extruder. This requires the operator to confirm the relevant parameters and status to ensure that the equipment can work as expected.

[0110] To facilitate understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0111] In one implementation, we use a traditional time-delay correction model that determines the time correction factor τ based on an empirical formula and manual operator adjustment. The operator manually sets a fixed time-delay correction factor based on historical extrusion equipment performance data and known sensor delay characteristics. This method is simple to implement and easy to use, but may not be suitable for all operating conditions and relies on operator experience.

[0112] Another implementation approach involves introducing a machine learning algorithm to automatically adjust the delay correction factor τ. First, a large amount of raw sensor data and corrected data is collected, and then a machine learning model is trained using this data. This model learns the time-varying relationships between the data and automatically adjusts τ to suit current production conditions. For example, a neural network can be used to handle complex pattern recognition tasks to predict the optimal delay correction factor. This approach improves correction accuracy and adaptability, but requires significant computing resources and expertise to implement.

[0113] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0114] The operator of the cable extrusion equipment starts the machine and begins cable extrusion production. The equipment's built-in sensors acquire two key parameters, the cable's extrusion temperature and speed, in real time. The equipment then applies time delay correction to these sensor parameters. Using an algorithm, the error introduced by this delay is compensated, resulting in more accurate corrected parameter values. These corrected parameter values ​​are then fed into a pre-trained dynamic compensation model for extrusion speed and temperature.

[0115] After iterative calculations, the model outputs an adjustment command, which includes adjustment values ​​for the heating temperature and extrusion speed. This adjustment command is sent to the cable extrusion equipment, which automatically adjusts the heating temperature and extrusion speed accordingly, achieving precise control of the cable extrusion process.

[0116] In addition, when the adjustment instruction exceeds the preset threshold range, the control method will also trigger the exception handling process, issue an alarm in time and take corresponding countermeasures to ensure the safety and stability of the production process.

[0117] In summary, this control method can accurately adjust the cable heating temperature and extrusion speed in real time during actual production, significantly reducing waiting and adjustment time during production. Furthermore, the use of a dynamic compensation model significantly improves the control accuracy of the cable extrusion equipment, ensuring the quality and stability of the cable products.

[0118] Reference Figure 2 A second embodiment of the present invention provides a control system for a cable extrusion device, comprising:

[0119] A data acquisition module, used to acquire sensor parameters, including cable extrusion temperature and extrusion speed;

[0120] A data correction module, configured to perform time delay correction on the sensor parameters to obtain corrected parameter values;

[0121] An instruction generation module is used to input the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction;

[0122] The device adjustment module is used to send the adjustment instruction to the device to adjust the cable heating temperature and the extrusion speed.

[0123] It should be noted that the control system of a cable extrusion device provided in an embodiment of the present invention is used to execute all the process steps of the control method of a cable extrusion device in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated.

[0124] An embodiment of the present invention further provides a cable extrusion device, including the control system of the cable extrusion device as described above.

[0125] It should be noted that the cable extrusion device provided in the embodiment of the present invention is used to execute all the process steps of the control method of the cable extrusion device in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0126] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a control program for a cable extrusion device. When the processor executes the computer program, the steps in the above-mentioned control method embodiments of the cable extrusion device are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the instruction generation module.

[0127] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0128] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0129] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0130] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0131] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0132] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0133] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A control method for a cable extrusion device, characterized in that: include: Acquiring sensor parameters, wherein the sensor parameters include cable extrusion temperature and extrusion speed; Performing time delay correction on the sensor parameters to obtain corrected parameter values; Inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction; The adjustment instructions are sent to the device to adjust the cable heating temperature and the extrusion speed.

2. The control method of the cable extrusion equipment according to claim 1, characterized in that: The training process of the extrusion speed and extrusion temperature dynamic compensation model includes: Building a single neuron PID controller model based on historical extrusion speed and extrusion temperature data, and training the single neuron PID controller model; When the number of training times is greater than or equal to the preset maximum number of training times, or when the loss function value of the single neuron PID controller model is less than the preset loss threshold, the training is determined to be completed and a dynamic compensation model of extrusion speed and extrusion temperature is obtained.

3. The control method of the cable extrusion equipment according to claim 1, characterized in that: The performing delay correction on the sensor parameter to obtain a corrected parameter value includes: The correction parameter value is obtained by the following formula: Among them, t(t), V(t), t(t-Δt) and V(t-Δt) are the actual extrusion temperature and extrusion speed, Δt is the time interval, τ represents the time correction coefficient, T s (t) and V s (t) is the extrusion temperature and extrusion speed measured by the sensor at time t. These measured values ​​are the values ​​τ time ago, where t s (t) = t(t-τ) and V s (t) = V(t-τ), T′ s (t) and V′ s (t) is the extrusion temperature and extrusion speed after time delay correction.

4. The control method of the cable extrusion equipment according to claim 1, characterized in that: The step of inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction includes: The adjustment instruction includes a heating temperature adjustment value, which is obtained by the following formula: T final (k)=T mneas (k)+Δu(T)(k) Among them, K p is the proportional gain, K i is the integral gain, K d is the differential gain, T i is the integration time constant, T s is the sampling period, and the deviation value e(T)(k)=t is calculated at time step k. set -t meas (k), t set is the set extrusion temperature, T meas (k) is the extrusion temperature value measured by the sensor at time step k, the integral of the calculated deviation ∑e(T)(k)=∑e(T)(k-1)+e(t)(k), ∑e(T)(k-1) is the accumulation of all previous calculated deviation values, the differential of the calculated deviation Δe(t)(k)=e(T)(k)-e(T)(k-1), e(T)(k-1) is the calculated deviation at time step k-1, Δu(t)(k) is the control increment of the heating temperature at time step k, T final (k) is the final equipment heating temperature adjustment value.

5. The control method of the cable extrusion equipment according to claim 1, characterized in that: The step of inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to obtain a final parameter value includes: The adjustment instruction includes an extrusion speed adjustment value, which is obtained by the following formula: In final (k)=V meas (k)+Δu(V)(k) in, is the proportional gain, is the integral gain, is the differential gain, is the integration time constant, t s is the sampling period, and the deviation value e(V)(k)=V is calculated at time step k. set -V meas (k), V set is the set extrusion speed, V meas (k) is the extrusion speed value measured by the sensor at time step k. The integral of the calculated deviation is ∑e(V)(k)=∑e(V)(k-1)+e(V)(k). ∑e(V)(k-1) is the accumulation of all previously calculated deviation values. The differential of the calculated deviation is Δe(V)(k)=e(V)(k)-eV(k-1). eV(k-1) is the calculated deviation at time step k-1. Δu(V)(k) is the control increment of the extrusion speed at time step k. V final (k) is the final equipment extrusion speed adjustment value.

6. The control method of the cable extrusion equipment according to claim 1, characterized in that: Before performing delay correction on the sensor parameter to obtain the corrected parameter value, the method includes: Performing numerical screening on the acquired sensor parameters to obtain sensor parameter values ​​within a normal range; The sensor parameter values ​​that meet the normal range are filtered to obtain smoothed and noise-removed sensor parameters.

7. The control method of the cable extrusion equipment according to claim 1, characterized in that: The step of sending the adjustment instruction to the device to adjust the cable heating temperature and the squeezing speed includes: When the adjustment instruction exceeds the preset threshold range, an exception handling process is triggered.

8. A control system for a cable extrusion device, characterized in that: include: A data acquisition module, used to acquire sensor parameters, wherein the sensor parameters include cable extrusion temperature and extrusion speed; A data correction module, used for performing time delay correction on the sensor parameters to obtain corrected parameter values; An instruction generation module, used for inputting the correction parameter value into a pre-trained dynamic compensation model of extrusion speed and extrusion temperature to generate an adjustment instruction; The device adjustment module is used to send the adjustment instruction to the device to adjust the cable heating temperature and the squeezing speed.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the control method of the cable extrusion equipment according to any one of claims 1 to 7 when executing the computer program.

10. A cable extrusion device, characterized in that: A control system comprising the cable extrusion equipment as claimed in claim 8.

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