Control system and method of intelligent screw grouting extruder

Abstract

The invention discloses a control system and method for an intelligent screw grouting extruder, belonging to the technical field of wire and cable production. The control system comprises: a flow control module that monitors material flow in real time and adjusts the feed amount through fluctuation detection and frequency analysis; a temperature control module that monitors and predicts temperature deviation through a temperature sensor and adjusts heating power; a speed control module that initializes the screw speed according to material characteristics and monitors the screw load through a torque sensor to dynamically adjust the screw speed; a pressure regulation module that monitors and adjusts the pressure in the extruder cavity in real time and adjusts the distance between the die and the screw by calculating the pressure deviation; a quality detection module that detects the physical and visual quality of the product online through a laser rangefinder and image recognition technology and performs comprehensive quality evaluation. The invention solves the problem of how to dynamically adjust the operating state of the extruder according to parameters such as real-time temperature, flow, pressure and speed, thereby ensuring the consistency between the thickness of the cable sheath layer and the product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of wire and cable production, and more particularly to a control system and method for an intelligent screw grouting extruder. Background Art

[0002] In the wire and cable manufacturing industry, single-screw extruders are key pieces of equipment used to process and shape the insulation and sheathing of cables. Traditional single-screw extruder control systems rely primarily on manual experience and simple mechanical control to achieve process stability, but this approach presents numerous challenges. For example, manual experience is difficult to standardize, leading to inconsistent product quality. Simple mechanical control also prevents precise temperature, pressure, and speed regulation, impacting production efficiency and quality. Furthermore, traditional systems are complex to operate, costly to maintain, and consume a lot of energy, hindering the long-term development of the enterprise. To overcome these shortcomings, advanced control technologies and algorithms, such as fuzzy logic control, adaptive control, and predictive control, are being adopted to precisely control key parameters in the extrusion process. The introduction of intelligent technologies not only improves production efficiency and product quality, but also reduces energy consumption and waste, thereby enhancing the overall competitiveness of the enterprise.

[0003] The Chinese patent application with publication number CN118404791A discloses an extruder weight-per-meter control system for cable production, including an extrusion part, a feeding part and a control part. The extrusion part includes at least one extrusion screw and a driving device arranged at one end of the extrusion screw. The other end of the extrusion screw is provided with an extrusion head. A feed port is provided between the extrusion screw and the driving device. The feeding part includes a feeder and at least two storage hoppers arranged at the output end of the feeder. The output end of each storage hopper is respectively connected to the feed port. A weighing sensor device is provided between the feeder and the storage hopper. The input end of each storage hopper is respectively provided with a feed solenoid valve. The control part includes a main control machine and a double air filter arranged at the output end of the main control machine. The double air filters are respectively connected to the feed solenoid valves. It belongs to the technical field of wire and cable production.

[0004] Although existing technologies accurately control the thickness of cable sheaths by adjusting the extrusion screw speed and the feed rate of the hopper through a control unit, effectively improving resource utilization, they still fail to address the problem of how to precisely adjust multiple key parameters during the extrusion process in a dynamic environment to further improve production efficiency and product quality consistency. Therefore, to overcome these limitations, the present invention proposes a control system and method for an intelligent screw grouting extruder. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a control system and method for an intelligent screw grouting extruder, which solves the problem of how to dynamically adjust the operating state of the extruder according to real-time parameters such as temperature, flow, pressure and speed, thereby ensuring the precise thickness of the cable sheath layer and the consistency of product quality.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A control system for an intelligent screw grouting extruder, comprising a flow control module, a temperature control module, a speed control module, a pressure regulation module, a quality detection module and a safety warning module;

[0008] The flow control module is used to monitor and control the extruder feed rate. The flow meter monitors the extruder material flow in real time and adjusts the feed rate through fluctuation detection and frequency analysis.

[0009] The temperature control module is used to monitor and adjust the temperature of the extruder in real time. It collects the temperature data of the extruder in real time through the temperature sensor, predicts the temperature deviation, and adjusts the heating power of the key area according to the predicted temperature deviation.

[0010] The speed control module is used to initialize the extruder screw speed according to the key characteristics of the material, monitor the screw load through the torque sensor, and dynamically adjust the screw speed according to the screw load;

[0011] The pressure regulating module is used to monitor the pressure in the extruder cavity in real time. It collects the pressure data of the extruder cavity in real time through the pressure sensor and calculates the pressure deviation. It adjusts the distance between the extruder die and the screw according to the pressure deviation.

[0012] The quality inspection module is used to detect the quality of production products online. It uses a laser rangefinder to monitor the physical properties of the production products in real time to obtain a physical quality score, and uses image recognition to detect defects on the cable surface to obtain a visual quality score. A comprehensive quality assessment of the product is performed based on the physical quality score and the visual quality score.

[0013] Specifically, the steps of monitoring and regulating the extruder feed rate include:

[0014] Use a flow meter to collect material flow data in the extruder in real time to obtain real-time flow data of the extruder, including instantaneous flow data and average flow data;

[0015] Fluctuation detection is performed on real-time traffic data, and the fluctuation level of traffic data at each moment is quantified by standard deviation, namely:

[0016]

[0017] in, It's time The fluctuation level of traffic data, It's time The weighting factor of It's time The average flow value, It's time The instantaneous flow value, The value range is [ , ], is the size of the time window;

[0018] Configure a fluctuation threshold. If the fluctuation level of traffic data exceeds the fluctuation threshold, the traffic data exceeding the fluctuation threshold will be marked as an abnormal fluctuation point. The abnormal fluctuation point will be continuously tested and the abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold will be calculated. The calculation formula for the abnormal fluctuation frequency is as follows:

[0019]

[0020] in, It is at the moment The abnormal fluctuation point in the time window The abnormal frequency of fluctuations, is the time window size used to detect abnormal frequency fluctuations, It is the time when the traffic data marked as abnormal fluctuation point is located. It's time The fluctuation level of traffic data, is the fluctuation threshold, Is an indicator function, which takes 1 when the condition is met, otherwise it takes 0. is a nonlinear correction function.

[0021] Specifically, the steps of monitoring and regulating the extruder feed rate also include:

[0022] Configure a frequency threshold. If the abnormal fluctuation frequency exceeds the threshold, the continuity check fails. Otherwise, the continuity check passes.

[0023] If the continuity test fails, the feed rate is adjusted based on the fluctuation level of the flow data. The feed rate adjustment formula is as follows:

[0024]

[0025] in, It's the time after adjustment Feed amount, It is the time before adjustment Feed amount, is the predetermined target material flow rate, It's time The average flow value, It's time The fluctuation level of traffic data, is the proportionality coefficient, is the influence coefficient of flow fluctuation on regulation amount;

[0026] Configure the flow qualification threshold. Based on the adjusted feed volume, collect the adjusted real-time flow data through the flow meter, calculate the adjusted average flow value and the fluctuation level of the flow data. If the absolute value of the difference between the real-time flow data and the target material flow is greater than the flow qualification threshold, continue to adjust the feed volume based on the flow deviation and fluctuation level. Otherwise, stop adjusting the feed volume.

[0027] Specifically, the steps of monitoring and regulating the temperature of the extruder include:

[0028] Arrange temperature sensors in key areas of the extruder to collect temperature data of each key area in real time;

[0029] Set the target temperature for the key area and predict the deviation trend from the target temperature based on the historical temperature data of the key area. The formula for predicting the deviation trend is as follows:

[0030]

[0031] in, It is Key areas at the moment The predicted value of temperature deviation, It is Key areas at the moment temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the moment The predicted value of temperature deviation, is the time window size used for temperature deviation trend prediction, is the time length control coefficient, is the prediction coefficient, is the weighting coefficient.

[0032] Specifically, the step of monitoring and regulating the temperature of the extruder also includes:

[0033] Configure a temperature qualification threshold. If the predicted temperature deviation value of the key area is less than the temperature qualification threshold, the temperature data will be continuously monitored. If the predicted temperature deviation value is greater than the temperature qualification threshold, the heating power will be adjusted.

[0034] According to the deviation prediction results, the heating power of the key area is adjusted to correct the temperature deviation so that the temperature data gradually approaches the target temperature. The adjustment formula of the heating power is as follows:

[0035]

[0036] in, It is Key areas at the moment The heating power, It is Key areas at the moment The heating power, is a non-negative heating power adjustment coefficient, is a nonlinear index.

[0037] Specifically, the steps of dynamically adjusting the speed of the extruder screw include:

[0038] Identify the type of material being processed by the extruder, obtain the key characteristics of the material, and set the initial screw speed and speed adjustment threshold based on the key characteristics of the material and the screw speed standard table;

[0039] Use a torque sensor to monitor the screw load in real time, and dynamically adjust the screw speed based on the screw load and the screw speed range. The formula for dynamic adjustment of the screw speed is:

[0040]

[0041] in, It's time The screw speed, is the initial screw speed, is the screw speed adjustment threshold, is the load regulation factor, It's time The screw load, is the ideal screw load to be set, is the maximum screw load, is the minimum screw load, is the maximum function, is the minimum function.

[0042] Specifically, the steps of monitoring and regulating the pressure in the extruder cavity include:

[0043] The pressure data is collected in real time by the pressure sensors installed at the key parts of the extruder, and the pressure deviation between the pressure data and the target pressure is calculated in real time;

[0044] Configure the pressure fluctuation threshold. When the pressure deviation is greater than the pressure fluctuation threshold, the time window is calculated. The frequency at which the pressure deviation within the pressure range is greater than the pressure fluctuation threshold ,Right now:

[0045]

[0046] in, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is [ p * , p * + T p ] , is the pressure fluctuation threshold, is an indicator function, which takes 1 when the condition is met and 0 otherwise;

[0047] Configure the fluctuation frequency threshold. When the pressure deviation inside is greater than the pressure fluctuation threshold and the frequency is greater than the fluctuation frequency threshold, the die opening is automatically adjusted, otherwise the pressure data continues to be monitored.

[0048] Specifically, the steps of automatically adjusting the die opening include:

[0049] According to the size of the pressure deviation and the changing trend of the pressure deviation, the distance between the die and the screw is automatically adjusted, that is:

[0050]

[0051] in, is the distance between the adjusted die and the screw, It is the distance between the die and the screw before adjustment. is the adjustment control parameter, is the size of the time window, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is [ p * , p * + T p ] , is the adjustment control parameter, Is the time window The frequency at which the pressure deviation within is greater than the pressure fluctuation threshold, is the adjustment control parameter.

[0052] Specifically, the steps of online testing of production products include:

[0053] The products produced by the extruder are transported to the monitoring area, where the outer diameter and wall thickness of the products are measured using a laser rangefinder and physical quality scores are performed, namely:

[0054]

[0055] in, is the physical quality rating of the product, is the number of laser ranging detection points, It is The outer diameter of the product is measured at each inspection point. is the target product outer diameter, The value range is [ , ], It is The wall thickness of the product is measured at each inspection point. is the target product wall thickness, and is a non-negative weight parameter;

[0056] The camera captures the product surface image, uses image recognition to check whether there are defects on the product surface, and performs visual quality scoring, namely:

[0057]

[0058] in, is the product visual quality score, is the number of images detected by image recognition, is the number of product defect types, It is In the detection image The number of defect types that occur, The value range is [1, ], The value range is [1, ];

[0059] Combine the physical quality score and visual quality score to comprehensively evaluate the overall quality of the product, namely:

[0060]

[0061] in, It is a comprehensive rating of product quality. is the physical quality rating of the product, is the product visual quality score, and is a non-negative weight coefficient;

[0062] Configure the scoring threshold. If the comprehensive product quality score is greater than the scoring threshold, the product is considered unqualified. Otherwise, the product is considered qualified. For products that are judged as unqualified, sorting and rejection operations are automatically performed.

[0063] The control method of the intelligent screw grouting extruder comprises the following steps:

[0064] Step S1: The flow meter monitors the material flow of the extruder in real time and adjusts the feed amount through fluctuation detection and frequency analysis;

[0065] Step S2: collecting temperature data of the extruder in real time through a temperature sensor, predicting temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation;

[0066] Step S3: Initialize the extruder screw speed according to the key characteristics of the material, monitor the screw load through the torque sensor, and dynamically adjust the screw speed according to the screw load;

[0067] Step S4: collecting pressure data of the extruder cavity in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the extruder die and the screw according to the pressure deviation;

[0068] Step S5: The physical properties of the production product are monitored in real time by a laser rangefinder to obtain a physical quality score, and defects on the cable surface are detected by image recognition to obtain a visual quality score. A comprehensive quality assessment of the product is performed based on the physical quality score and the visual quality score;

[0069] Step S6: Monitor the production status of the extruder in real time, perform regression detection, and issue abnormal warnings.

[0070] Beneficial effects of the present invention:

[0071] 1. Flow meters monitor material flow in real time, detecting fluctuations and analyzing frequency, allowing timely adjustments to feed rates to avoid quality issues or excessive equipment loads caused by flow fluctuations during production. Simultaneously, temperature sensors collect real-time temperature data from key areas and adjust heating power based on predicted temperature deviations. This allows precise control of the temperature in each area, preventing overheating or overcooling, thereby improving material flow and product quality.

[0072] 2. Dynamically adjusts screw speed based on the material's key characteristics and screw load, eliminating the need for manual intervention and enhancing the automation and intelligence of the production process. A torque sensor monitors the load and adjusts the speed based on the actual load, ensuring the equipment is always in optimal working condition and reducing equipment wear and energy consumption. Furthermore, by real-time monitoring of the internal cavity pressure and adjusting for pressure deviation, the distance between the die and the screw is automatically adjusted to ensure that the pressure remains within the target range, avoiding pressure instability or uneven material distribution caused by improper die adjustment, further improving the stability of the production process.

[0073] 3. Using laser rangefinders and image recognition technology, we conduct real-time physical and visual quality assessments of manufactured products, promptly identifying defects or substandard parts and determining their quality through comprehensive quality scoring. This automated quality inspection and sorting system effectively improves product consistency and yield rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of the control system of the intelligent screw grouting extruder;

[0075] Figure 2 A flowchart of the specific steps for monitoring and adjusting the feed rate of the extruder;

[0076] Figure 3 A flowchart of the specific steps for monitoring and adjusting the temperature of the extruder;

[0077] Figure 4 A flowchart of the specific steps for monitoring and regulating the pressure in the extruder cavity;

[0078] Figure 5 A flowchart of the specific steps for online testing of production products;

[0079] Figure 6 The figure is a flow chart of the control method of the intelligent screw grouting extruder. DETAILED DESCRIPTION

[0080] Example 1

[0081] See also Figure 1 ,This embodiment introduces the control system of the intelligent screw grouting extruder, including a flow control module, a temperature control module, a speed control module, a pressure regulation module, a quality detection module and a safety warning module;

[0082] The flow control module is used to monitor and control the extruder feed rate. The flow meter monitors the extruder material flow in real time and adjusts the feed rate through fluctuation detection and frequency analysis to ensure the stable flow of material in the screw.

[0083] In this embodiment, the flow control module uses a flow meter to monitor the flow rate of the material in the intelligent screw grouting extruder (hereinafter referred to as the "intelligent screw grouting extruder" is referred to as the "extruder") in real time. The flow meter includes a turbine flowmeter or an electromagnetic flowmeter. The flow data is analyzed in real time, and the drive motor of the feeder is automatically adjusted according to the required feeding rate, and the motor speed is adjusted to accurately control the feeding amount, ensuring that the material is evenly and stably fed into the screw cavity, avoiding uneven feeding or material jamming caused by flow fluctuations. Precise control of the flow rate can effectively reduce the retention or uneven flow of the material, improve production efficiency, and avoid fluctuations in processing quality caused by unstable flow. At the same time, by optimizing flow control, it is also helpful to improve product consistency and reduce scrap rate.

[0084] See also Figure 2 Preferably, the specific steps of monitoring and adjusting the extruder feed rate include:

[0085] Use a flow meter to collect material flow data in the extruder in real time to obtain real-time flow data of the extruder, including instantaneous flow data and average flow data. The instantaneous flow data is the flow value collected every second, reflecting the instantaneous change of the current flow data. The average flow data is the average of the instantaneous flow values ​​within the configured time window, providing smooth information on the change trend of the flow data. The calculation formula for the average flow data is as follows:

[0086]

[0087] in, It's time The average flow value, It's time The average flow value, is the size of the time window, The value range is [ , ].

[0088] Fluctuation detection is performed on real-time traffic data, and the fluctuation level of traffic data at each moment is quantified by standard deviation, namely:

[0089]

[0090] in, It's time The fluctuation level of traffic data quantifies the magnitude of traffic fluctuations. It's time The weighting factor is used to control the impact of historical data on volatility calculations. It's time The average flow value, It's time The average flow value, The value range is [ , ], The standard deviation is the size of the time window, which indicates how many historical data points are considered. The standard deviation quantifies the level of flow fluctuation and accurately identifies the magnitude of flow fluctuation, reflecting the stability of the extruder feed.

[0091] Configure a fluctuation threshold. If the fluctuation level of traffic data exceeds the fluctuation threshold, the traffic data exceeding the fluctuation threshold will be marked as an abnormal fluctuation point. The abnormal fluctuation point will be continuously tested. The abnormal fluctuation frequency when the fluctuation level exceeds the fluctuation threshold is calculated to monitor the stability of the traffic data fluctuation level over a period of time. The calculation formula for the abnormal fluctuation frequency is as follows:

[0092]

[0093] in, It is at the moment The abnormal fluctuation point in the time window The abnormal frequency of fluctuations, is the time window size used to detect abnormal frequency fluctuations, It is the time when the traffic data marked as abnormal fluctuation point is located. It's time The fluctuation level of traffic data, is the fluctuation threshold, Is an indicator function, which takes 1 when the condition is met, otherwise it takes 0. is a nonlinear correction function used to increase frequency sensitivity. For example, If the frequency of abnormal fluctuations is higher than a predetermined threshold, it indicates frequent flow fluctuations, which may lead to instability in the production process. The frequency of abnormal fluctuations plays a key role in analyzing process stability and can help determine whether adjustments are needed.

[0094] Configure the frequency threshold. If the abnormal fluctuation frequency is greater than the frequency threshold, the continuity test fails. Otherwise, the continuity test passes. If the continuity test fails, the feed rate is adjusted. The feed rate is adjusted based on the fluctuation level of the flow data. First, the feed rate is adjusted based on the difference between the current flow data and the target material flow rate. Then, the feed rate is further adjusted based on the size of the flow fluctuation level. In the case of large fluctuations, the amplitude of the feed rate adjustment needs to be reduced to avoid instability caused by over-adjustment. The feed rate adjustment formula is as follows:

[0095]

[0096] in, It's the time after adjustment Feed amount, It is the time before adjustment Feed amount, is the predetermined target material flow rate, It's time The average flow value, It's time The fluctuation level of traffic data, Is the proportional coefficient, which is used to control the adjustment range of flow deviation, and its value range is [0.01, 0.1]. This is the coefficient of influence of flow fluctuation on the adjustment amount, with a value range of [0.001, 0.05]. Adjust the feed amount based on the deviation between the detected flow fluctuation level and the target material flow rate. Especially in cases of large fluctuations, reduce the adjustment range to avoid over-adjustment. This effectively prevents system instability caused by over-adjustment and ensures that the flow rate remains as stable as possible during the production process.

[0097] Configure a flow rate qualification threshold. Based on the adjusted feed rate, collect the adjusted real-time flow data through the flow meter, calculate the adjusted average flow rate and the fluctuation level of the flow data, and if the absolute difference between the real-time flow data and the target material flow rate exceeds the flow qualification threshold, continue to adjust the feed rate based on the flow deviation and fluctuation level. Otherwise, stop adjusting the feed rate. The dynamic feedback mechanism verifies the effectiveness of the adjustment after adjustment, ensuring that the final flow rate reaches the target value and that the flow fluctuation is within an acceptable range. If the flow rate is stable, stop further adjustment to avoid unnecessary adjustments.

[0098] The temperature control module is used to monitor and adjust the temperature of the extruder in real time. It collects the temperature data of the extruder in real time through the temperature sensor, predicts the temperature deviation, and adjusts the heating power of the key areas according to the predicted temperature deviation to ensure that the temperature of the extruder is within the set range.

[0099] In this embodiment, the temperature control module employs multiple temperature sensors, including thermocouples or RTDs, located in each heating zone, screw, and die area of ​​the extruder. These sensors monitor the temperature of each critical area in real time. Based on the preset target temperature, the module automatically adjusts the heater power output and the operating state of the thermostat to ensure stable material processing conditions, preventing viscosity changes or thermal stability issues caused by temperature fluctuations. Precise temperature control minimizes the impact of temperature fluctuations on material processing performance, thereby ensuring stable product quality and avoiding production failures or material loss caused by temperature anomalies.

[0100] See also Figure 3 Preferably, the specific steps of monitoring and adjusting the temperature of the extruder include:

[0101] Temperature sensors are arranged in key areas of the extruder to collect temperature data of each key area in real time. Key areas include heating section, screw, and die. Temperature sensors include thermocouples or RTD temperature sensors to ensure comprehensive monitoring of the extruder temperature.

[0102] According to the process requirements of the material, the target temperature of the key areas is set, and the temperature data of each key area is received and processed in real time. Based on the historical temperature data of the key areas, the deviation trend from the target temperature is predicted. The deviation trend prediction formula is as follows:

[0103]

[0104] in, It is Key areas at the moment The predicted value of temperature deviation, It is Key areas at the moment temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the moment The predicted value of temperature deviation, is the time window size used for temperature deviation trend prediction, is the time length control coefficient, which is a positive integer greater than 2. is the prediction coefficient, which depends on the influence of historical data. is a weighting coefficient with a value range of [0, 1]. The temperature deviation prediction formula can be used to predict the temperature deviation trend of each key area based on historical temperature data. This can identify the trend of temperature deviation from the target, provide early warning, and adjust the temperature more effectively to avoid instability in the processing process caused by excessive temperature deviation.

[0105] Configure a temperature qualification threshold. If the predicted temperature deviation value of the key area is less than the temperature qualification threshold, the temperature data will be continuously monitored. If the predicted temperature deviation value is greater than the temperature qualification threshold, the heating power will be adjusted.

[0106] According to the deviation prediction results, the heating power of the key area is adjusted to correct the temperature deviation so that the temperature data gradually approaches the target temperature. The adjustment formula of the heating power is as follows:

[0107]

[0108] in, It is Key areas at the moment The heating power, It is Key areas at the moment The heating power, Is a non-negative heating power adjustment coefficient, used to adjust the power response sensitivity, is a nonlinear index used to adjust the nonlinear relationship between heating power and temperature deviation, with a value range of [1,10];

[0109] After the heating power of the key area is adjusted, the temperature data of the adjusted key area is continuously monitored. If the temperature deviation prediction value is less than the temperature qualification threshold, the heating power adjustment is stopped. Otherwise, the heating power is continued to be adjusted to make the temperature data approach the target temperature.

[0110] The speed control module is used to initialize the extruder screw speed based on the key characteristics of the material, and monitor the screw load through the torque sensor. The screw speed is dynamically adjusted according to the screw load to ensure that the speed is within the effective range to optimize the production process. Key material characteristics include melt index, viscosity and thermal stability.

[0111] In this embodiment, the speed control module monitors the screw speed in real time via an encoder mounted on the motor. The screw speed is dynamically adjusted based on the characteristics of different materials. For example, for materials with a higher melt index, the speed is automatically reduced to prevent overheating; for materials with higher viscosities, the speed is increased to ensure that the materials are fully mixed and flow smoothly. The output of the motor driver is precisely adjusted to achieve precise control of the screw speed, ensuring that each material is processed under the most suitable conditions. This avoids problems such as uneven heating, insufficient mixing, or poor fluidity caused by improper speed, thereby ensuring the stability of the extrusion process.

[0112] Preferably, the specific steps of dynamically adjusting the speed of the extruder screw include:

[0113] Identify the type of material being processed by the extruder and obtain the key characteristics of the material, including melt index, viscosity, and thermal stability; based on the key characteristics of the material and in combination with the screw speed standard table, set the initial screw speed and speed adjustment threshold;

[0114] Use a torque sensor to monitor the screw load in real time, and dynamically adjust the screw speed based on the screw load and the screw speed range. The formula for dynamic adjustment of the screw speed is:

[0115]

[0116] in, It's time The screw speed, is the initial screw speed, is the screw speed adjustment threshold, is the load adjustment coefficient, which is used to control the sensitivity of load changes to speed adjustment. Its value range is [0,1]. It's time The screw load, is the ideal screw load to be set, is the maximum screw load, is the minimum screw load, is the maximum function, It is a minimum function; by limiting the maximum and minimum values, it ensures that the adjusted speed is always within the effective range, avoiding adverse effects on the equipment or production process. According to the material characteristics and real-time load data, the screw speed is accurately controlled to improve production efficiency and ensure the quality and stability of material processing. By dynamically adjusting the speed, equipment overload or unstable operation caused by load fluctuations is avoided, ensuring that the production process is carried out under optimal conditions.

[0117] The pressure regulating module is used to monitor the pressure in the extruder cavity in real time. It collects the pressure data of the extruder cavity in real time through the pressure sensor and calculates the pressure deviation. It adjusts the distance between the extruder die and the screw according to the pressure deviation to keep the pressure in the extruder cavity stable.

[0118] In this embodiment, the pressure regulating module monitors the pressure conditions in the extruder cavity in real time by installing multiple pressure sensors in the inner cavity of the extruder. According to the set target pressure value, the internal pressure is adjusted by adjusting the distance between the die and the screw. When the pressure fluctuates abnormally, it is automatically adjusted to ensure that the pressure is always maintained within a stable working range, thereby avoiding material jamming or uneven extrusion due to unstable pressure. Accurate pressure regulation can effectively prevent the extruder from jamming or equipment failure, ensuring the smooth progress of the production process. Through stable pressure control, product quality problems caused by pressure fluctuations are reduced, and the operating stability and product consistency of the extruder are improved.

[0119] See also Figure 4 Preferably, the specific steps of monitoring and regulating the pressure in the extruder cavity include:

[0120] Pressure data is collected in real time by pressure sensors installed at key locations of the extruder, and the pressure deviation between the pressure data and the target pressure is calculated in real time. Key locations include the front end of the screw and the die area. By providing instant feedback on the pressure deviation, a rapid response is achieved, reducing the time lag of pressure fluctuations.

[0121] Configure the pressure fluctuation threshold. When the pressure deviation is greater than the pressure fluctuation threshold, the time window is calculated. The frequency at which the pressure deviation within the pressure range is greater than the pressure fluctuation threshold ,Right now:

[0122]

[0123] in, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is [ p * , p * + T p ] , is the pressure fluctuation threshold, It is an indicator function, which takes 1 when the condition is met and 0 otherwise. By setting the pressure fluctuation threshold, it avoids erroneous actions caused by small fluctuations or normal fluctuations. By calculating the frequency of the pressure deviation being greater than the pressure fluctuation threshold within the time window, it can quantitatively evaluate the severity of the pressure fluctuation and determine from a quantitative perspective whether the pressure is in an abnormal fluctuation state.

[0124] Configure the fluctuation frequency threshold. When the pressure deviation inside the die is greater than the pressure fluctuation threshold and the frequency is greater than the fluctuation frequency threshold, the die is automatically adjusted, otherwise the pressure data is continued to be monitored; unnecessary adjustments are avoided and only serious pressure problems are responded to, thereby improving the stability of the extrusion process.

[0125] Specifically, the steps of automatic die adjustment include:

[0126] According to the size of the pressure deviation and the changing trend of the pressure deviation, the distance between the die and the screw is automatically adjusted to optimize the flow channel cross-sectional area to restore a stable pressure distribution, namely:

[0127]

[0128] in, is the distance between the adjusted die and the screw, It is the distance between the die and the screw before adjustment. is the adjustment control parameter, and its value range is [0, 1]. is the size of the time window, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is [ p * , p * + T p ] , is the adjustment control parameter, and its value range is [0, 1]. Is the time window The frequency at which the pressure deviation within is greater than the pressure fluctuation threshold, is the adjustment control parameter, and its value range is [0, 1];

[0129] The quality inspection module is used to detect the quality of production products online. It uses a laser rangefinder to monitor the physical properties of the production products in real time to obtain a physical quality score, and uses image recognition to detect defects on the cable surface to obtain a visual quality score. A comprehensive quality assessment of the product is performed based on the physical quality score and the visual quality score.

[0130] In this embodiment, the quality inspection module integrates a laser rangefinder and a high-definition camera, which measures the product's outer diameter and wall thickness in real time and performs image recognition inspections on the product surface. The laser rangefinder accurately monitors the outer diameter of each cable or product to ensure compliance with design standards. The high-definition camera uses image recognition technology to automatically detect defects such as cracks, bubbles, and wrinkles on the cable surface, and compares the test data with a standard database to promptly identify and flag anomalies. This allows real-time monitoring of product quality during the production process, ensuring that the appearance and dimensions of each product meet standards, and promptly identifying and eliminating substandard products. This quality control approach significantly improves product qualification rates, reduces the flow of substandard products into the market, and ensures high-quality output throughout the production process.

[0131] See also Figure 5 Preferably, the specific steps of online testing of production products include:

[0132] The products produced by the extruder are transported to the monitoring area, where the outer diameter and wall thickness of the products are measured using a laser rangefinder and physical quality scores are performed, namely:

[0133]

[0134] in, is the physical quality rating of the product, is the number of laser ranging detection points, It is The outer diameter of the product is measured at each inspection point. is the target product outer diameter, The value range is [ , ], It is The wall thickness of the product is measured at each inspection point. is the target product wall thickness, and It is a non-negative weight parameter. The physical quality score reflects the physical quality of the product by weighted calculation of the relative deviation of the outer diameter and wall thickness. The lower the score, the closer the product is to the target size and the better the quality.

[0135] The camera captures the product surface image and uses image recognition to check whether there are defects on the product surface. Defects include cracks, bubbles, wrinkles, and scratches. Visual quality scoring is performed, namely:

[0136]

[0137] in, is the product visual quality score, is the number of images detected by image recognition, is the number of product defect types, It is In the detection image The number of defect types that occur, The value range is [1, ], The value range is [1, The visual quality score reflects the completeness and refinement of a product's surface quality by counting the frequency of each defect type. A higher score indicates more surface defects and lower quality.

[0138] Combined with the physical quality score and visual quality score, the overall quality of the product is comprehensively evaluated to determine whether the product is qualified, namely:

[0139]

[0140] in, It is a comprehensive rating of product quality. is the physical quality rating of the product, is the product visual quality score, and It is a non-negative weight coefficient; the physical quality score and the visual quality score are weighted and combined to obtain the comprehensive quality score of the product, which reflects the overall quality performance of the product in both physical and visual aspects.

[0141] Configure the scoring threshold. If the comprehensive product quality score is greater than the scoring threshold, the product is considered unqualified. Otherwise, the product is considered qualified. For products that are judged as unqualified, sorting and rejection operations are automatically performed.

[0142] The safety warning module is used to monitor the production status of the extruder in real time, automatically detect abnormal conditions and generate warning information. Abnormal conditions include abnormal feed volume, abnormal temperature, abnormal speed, abnormal pressure, and abnormal product quality.

[0143] In this embodiment, the safety warning module is based on the safety thresholds of various key parameters, including flow, temperature, pressure, etc., and monitors the changes in various data in real time. When one or more parameters exceed the set safety thresholds, the warning mechanism is automatically triggered, and an audible alarm is issued to display abnormal information. In addition, the warning information is sent to relevant operators via text message or email to remind them to deal with abnormal situations in a timely manner. The safety warning module can detect potential faults or abnormalities in real time during the production process, and issue warnings to operators through a rapid response mechanism, effectively reducing the occurrence of equipment failures. Through timely warnings, the risk of production accidents can be greatly reduced, ensuring production safety and the long-term operation of equipment.

[0144] Preferably, the specific steps of monitoring and early warning of the extruder production status include:

[0145] Configure the observation threshold. When the flow control module adjusts the feed rate, perform flow data regression detection to obtain the time length of the feed rate adjustment. If the time length of the feed rate adjustment is greater than the observation threshold, a flow abnormality warning will be issued.

[0146] When the temperature control module adjusts the heating power, it performs temperature regression detection to obtain the length of time the heating power is adjusted. If the length of time the heating power is adjusted is greater than the observation threshold, a temperature anomaly warning is issued.

[0147] When the speed control module dynamically adjusts the screw speed, it performs speed regression detection to obtain the length of time the screw speed continuously changes. When the length of time the screw speed continuously changes is greater than the observation threshold, an abnormal speed warning is issued;

[0148] When the pressure regulating module is automatically adjusting the die mouth, a pressure regression test is performed to obtain the length of time the module automatically adjusts. If the length of time the module automatically adjusts is greater than the observation threshold, a pressure abnormality warning is issued;

[0149] Configure the unqualified threshold and count the number of unqualified products within the observation threshold. If the number is greater than the unqualified threshold, a product quality warning will be issued.

[0150] Example 2

[0151] See also Figure 6 , a control method for an intelligent screw grouting extruder comprises the following steps:

[0152] Step S1: The flow meter monitors the material flow of the extruder in real time and adjusts the feed amount through fluctuation detection and frequency analysis;

[0153] Step S2: collecting temperature data of the extruder in real time through a temperature sensor, predicting temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation;

[0154] Step S3: Initialize the extruder screw speed according to the key characteristics of the material, monitor the screw load through the torque sensor, and dynamically adjust the screw speed according to the screw load;

[0155] Step S4: collecting pressure data of the extruder cavity in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the extruder die and the screw according to the pressure deviation;

[0156] Step S5: The physical properties of the production product are monitored in real time by a laser rangefinder to obtain a physical quality score, and defects on the cable surface are detected by image recognition to obtain a visual quality score. A comprehensive quality assessment of the product is performed based on the physical quality score and the visual quality score;

[0157] Step S6: Monitor the production status of the extruder in real time, perform regression detection, and issue abnormal warnings.

[0158] Preferably, the specific steps of adjusting the feed amount include:

[0159] Use a flow meter to collect material flow data in the extruder in real time to obtain real-time flow data of the extruder, including instantaneous flow data and average flow data;

[0160] Perform fluctuation detection on real-time traffic data and quantify the fluctuation level of traffic data at each moment through standard deviation;

[0161] Configure a fluctuation threshold. If the fluctuation level of traffic data exceeds the fluctuation threshold, the traffic data exceeding the fluctuation threshold will be marked as an abnormal fluctuation point. The abnormal fluctuation point will be continuously tested and the abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold will be calculated. The calculation formula for the abnormal fluctuation frequency is as follows:

[0162]

[0163] in, It is at the moment The abnormal fluctuation point in the time window The abnormal frequency of fluctuations, is the time window size used to detect abnormal frequency fluctuations, It is the time when the traffic data marked as abnormal fluctuation point is located. It's time The fluctuation level of traffic data, is the fluctuation threshold, Is an indicator function, which takes 1 when the condition is met, otherwise it takes 0. is a nonlinear correction function;

[0164] Configure a frequency threshold. If the abnormal fluctuation frequency exceeds the threshold, the continuity check fails. Otherwise, the continuity check passes.

[0165] If the continuity test fails, the feed rate is adjusted based on the fluctuation level of the flow data. The feed rate adjustment formula is as follows:

[0166]

[0167] in, It's the time after adjustment Feed amount, It is the time before adjustment Feed amount, is the predetermined target material flow rate, It's time The average flow value, It's time The fluctuation level of traffic data, is the proportionality coefficient, is the influence coefficient of flow fluctuation on regulation amount;

[0168] Configure the flow qualification threshold. Based on the adjusted feed volume, collect the adjusted real-time flow data through the flow meter, calculate the adjusted average flow value and the fluctuation level of the flow data. If the absolute value of the difference between the real-time flow data and the target material flow is greater than the flow qualification threshold, continue to adjust the feed volume based on the flow deviation and fluctuation level. Otherwise, stop adjusting the feed volume.

[0169] Preferably, the specific steps of dynamically adjusting the screw speed include:

[0170] Identify the type of material being processed by the extruder, obtain the key characteristics of the material, and set the initial screw speed and speed adjustment threshold based on the key characteristics of the material and the screw speed standard table;

[0171] Use a torque sensor to monitor the screw load in real time, and dynamically adjust the screw speed based on the screw load and the screw speed range. The formula for dynamic adjustment of the screw speed is:

[0172]

[0173] in, It's time The screw speed, is the initial screw speed, is the screw speed adjustment threshold, is the load regulation factor, It's time The screw load, is the ideal screw load to be set, is the maximum screw load, is the minimum screw load, is the maximum function, is the minimum function.

[0174] Preferably, the specific steps of adjusting the distance between the extruder die and the screw include:

[0175] The pressure data is collected in real time by the pressure sensors installed at the key parts of the extruder, and the pressure deviation between the pressure data and the target pressure is calculated in real time;

[0176] Configure the pressure fluctuation threshold. When the pressure deviation is greater than the pressure fluctuation threshold, the time window is calculated. The frequency at which the pressure deviation within the pressure range is greater than the pressure fluctuation threshold ,Right now:

[0177]

[0178] in, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is [ p * , p * + T p ] , is the pressure fluctuation threshold, is an indicator function, which takes 1 when the condition is met and 0 otherwise;

[0179] Configure the fluctuation frequency threshold. When the pressure deviation inside is greater than the pressure fluctuation threshold and the frequency is greater than the fluctuation frequency threshold, the die opening is automatically adjusted, otherwise the pressure data continues to be monitored.

[0180] Working principle and its effect:

[0181] The control system and method of the intelligent screw grouting extruder significantly improves the stability, efficiency and product quality of the production process through a series of advanced monitoring and adjustment technologies.

[0182] By real-time monitoring of material flow and performing fluctuation detection and frequency analysis, the feed rate can be adjusted promptly to avoid fluctuations in production process that could lead to quality fluctuations or excessive equipment load. This ensures stable material flow during extrusion and reduces equipment failures and material waste caused by unstable flow. Real-time temperature monitoring of key areas and adjustment of heating power based on predicted temperature deviations enable precise temperature control in each zone to avoid overheating or undercooling, thereby improving material flow and product quality during extrusion. Dynamic adjustment of screw speed based on key material characteristics and screw load eliminates the need for manual intervention, enhancing automation and intelligent production processes. A torque sensor monitors load and adjusts speed based on actual load conditions, ensuring optimal equipment operation and reducing wear and energy consumption. Real-time monitoring of internal pressure and pressure deviation adjustment automatically adjusts the distance between the die and screw to ensure pressure remains within the target range. This avoids unstable pressure and uneven material distribution caused by improper die adjustment, further improving production stability. Laser rangefinders and image recognition technology enable real-time physical and visual quality assessment of manufactured products, promptly identifying defects or substandard parts and determining their quality through comprehensive quality scoring. Unqualified products can be automatically rejected, preventing them from entering the market, reducing manual intervention, and improving production efficiency and product reliability.

[0183] Through comprehensive monitoring and automated regulation, the extruder's production efficiency and product quality have been improved. The system's various modules work together to not only optimize various parameters in the production process, but also significantly reduce manual intervention through intelligent feedback mechanisms, simplifying operations and improving the stability and safety of the production line.

[0184] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. The control system of the intelligent screw grouting extruder is characterized by: Including flow control module, temperature control module, speed control module, pressure regulation module, quality detection module and safety warning module; The flow control module is used to monitor and control the feed rate of the extruder. The flow meter monitors the material flow of the extruder in real time and adjusts the feed rate through fluctuation detection and frequency analysis. The temperature control module is used to monitor and adjust the temperature of the extruder in real time, collect the temperature data of the extruder in real time through the temperature sensor, predict the temperature deviation, and adjust the heating power of the key area according to the predicted temperature deviation; The speed control module is used to initialize the speed of the extruder screw according to the key characteristics of the material, monitor the screw load through the torque sensor, and dynamically adjust the screw speed according to the screw load; The pressure regulating module is used to monitor the pressure of the extruder cavity in real time, collect the pressure data of the extruder cavity in real time through the pressure sensor and calculate the pressure deviation, and adjust the distance between the extruder die and the screw according to the pressure deviation; The quality inspection module is used to detect the quality of the produced products online, and the physical properties of the produced products are monitored in real time by a laser rangefinder to obtain a physical quality score, and defects on the cable surface are detected by image recognition to obtain a visual quality score. The product is comprehensively evaluated based on the physical quality score and the visual quality score; The steps of monitoring and regulating the extruder feed rate include: Use a flow meter to collect material flow data in the extruder in real time to obtain real-time flow data of the extruder, including instantaneous flow data and average flow data; Fluctuation detection is performed on real-time traffic data, and the fluctuation level of traffic data at each moment is quantified by standard deviation, namely: ； in, It's time The fluctuation level of traffic data, It's time The weighting factor of It's time The average flow rate value, It's time The instantaneous flow value, The value range is [ , ], is the size of the time window; Configure a fluctuation threshold. If the fluctuation level of traffic data exceeds the fluctuation threshold, the traffic data exceeding the fluctuation threshold will be marked as an abnormal fluctuation point. The abnormal fluctuation point will be continuously tested and the abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold will be calculated. The calculation formula for the abnormal fluctuation frequency is as follows: ； in, It is at the moment The abnormal fluctuation point in the time window The abnormal frequency of fluctuations, is the time window size used to detect abnormal frequency fluctuations, It is the time when the traffic data marked as abnormal fluctuation point is located. It's time The fluctuation level of traffic data, is the fluctuation threshold, Is an indicator function, which takes 1 when the condition is met, otherwise it takes 0. is a nonlinear correction function.

2. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that, The step of monitoring and regulating the extruder feed rate also includes: Configure a frequency threshold. If the abnormal fluctuation frequency exceeds the threshold, the continuity check fails. Otherwise, the continuity check passes. If the continuity test fails, the feed rate is adjusted based on the fluctuation level of the flow data. The feed rate adjustment formula is as follows: ； in, It's the time after adjustment Feed amount, It is the time before adjustment Feed amount, is the predetermined target material flow rate, It's time The average flow value, It's time The fluctuation level of traffic data, is the proportionality coefficient, is the influence coefficient of flow fluctuation on regulation amount; Configure the flow qualification threshold. Based on the adjusted feed volume, collect the adjusted real-time flow data through the flow meter, calculate the adjusted average flow value and the fluctuation level of the flow data. If the absolute value of the difference between the real-time flow data and the target material flow is greater than the flow qualification threshold, continue to adjust the feed volume based on the flow deviation and fluctuation level. Otherwise, stop adjusting the feed volume.

3. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that, The steps of temperature monitoring and regulating of the extruder include: Arrange temperature sensors in key areas of the extruder to collect temperature data of each key area in real time; Set the target temperature for the key area and predict the deviation trend from the target temperature based on the historical temperature data of the key area. The formula for predicting the deviation trend is as follows: ； in, It is Key areas at the moment The predicted value of temperature deviation, It is Key areas at the moment temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the moment The predicted value of temperature deviation, is the time window size used for temperature deviation trend prediction, is the time length control coefficient, is the prediction coefficient, is the weighting coefficient.

4. The control system of the intelligent screw grouting extruder according to claim 3, characterized in that: The step of temperature monitoring and regulating the extruder also includes: Configure a temperature qualification threshold. If the predicted temperature deviation value of the key area is less than the temperature qualification threshold, the temperature data will be continuously monitored. If the predicted temperature deviation value is greater than the temperature qualification threshold, the heating power will be adjusted. According to the deviation prediction results, the heating power of the key area is adjusted to correct the temperature deviation so that the temperature data gradually approaches the target temperature. The adjustment formula of the heating power is as follows: ； in, It is Key areas at the moment The heating power, It is Key areas at the moment The heating power, is a non-negative heating power adjustment coefficient, is a nonlinear index.

5. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that: The step of dynamically adjusting the speed of the extruder screw comprises: Identify the type of material being processed by the extruder, obtain the key characteristics of the material, and set the initial screw speed and speed adjustment threshold based on the key characteristics of the material and the screw speed standard table; Use a torque sensor to monitor the screw load in real time, and dynamically adjust the screw speed based on the screw load and the screw speed range. The formula for dynamic adjustment of the screw speed is: ； in, It's time The screw speed, is the initial screw speed, is the screw speed adjustment threshold, is the load regulation factor, It's time The screw load, is the ideal screw load to be set, is the maximum screw load, is the minimum screw load, is the maximum function, is the minimum function.

6. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that: The steps of monitoring and regulating the pressure in the extruder cavity include: The pressure data is collected in real time by the pressure sensors installed at the key parts of the extruder, and the pressure deviation between the pressure data and the target pressure is calculated in real time; Configure the pressure fluctuation threshold. When the pressure deviation is greater than the pressure fluctuation threshold, the time window is calculated. The frequency at which the pressure deviation within the pressure range is greater than the pressure fluctuation threshold ,Right now: ； in, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is , is the pressure fluctuation threshold, is an indicator function, which takes 1 when the condition is met and 0 otherwise; Configure the fluctuation frequency threshold. When the pressure deviation inside is greater than the pressure fluctuation threshold and the frequency is greater than the fluctuation frequency threshold, the die opening is automatically adjusted, otherwise the pressure data continues to be monitored.

7. The control system of the intelligent screw grouting extruder according to claim 6, characterized in that: The step of automatically adjusting the die opening comprises: According to the size of the pressure deviation and the changing trend of the pressure deviation, the distance between the die and the screw is automatically adjusted, that is: ； in, is the distance between the adjusted die and the screw, It is the distance between the die and the screw before adjustment. is the adjustment control parameter, is the size of the time window, is the moment when the pressure deviation is greater than the pressure fluctuation threshold, It's time The pressure deviation, The value range is , is the adjustment control parameter, Is the time window The frequency at which the pressure deviation within is greater than the pressure fluctuation threshold, is the adjustment control parameter.

8. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that: The steps of online detection of the production product include: The products produced by the extruder are transported to the monitoring area, where the outer diameter and wall thickness of the products are measured using a laser rangefinder and physical quality scores are performed, namely: ； in, is the physical quality rating of the product, is the number of laser ranging detection points, It is The outer diameter of the product is measured at each inspection point. is the target product outer diameter, The value range is [ , ], It is The wall thickness of the product is measured at each inspection point. is the target product wall thickness, and is a non-negative weight parameter; The camera captures the product surface image, uses image recognition to check whether there are defects on the product surface, and performs visual quality scoring, namely: ； in, is the product visual quality score, is the number of images detected by image recognition, is the number of product defect types, It is In the detection image The number of defect types that occur, The value range is [1, ], The value range is [1, ]; Combine the physical quality score and visual quality score to comprehensively evaluate the overall quality of the product, namely: ； in, It is a comprehensive rating of product quality. is the physical quality rating of the product, is the product visual quality score, and is a non-negative weight coefficient; Configure the scoring threshold. If the comprehensive product quality score is greater than the scoring threshold, the product is considered unqualified. Otherwise, the product is considered qualified. For products that are judged as unqualified, sorting and rejection operations are automatically performed.

9. A control method for an intelligent screw grouting extruder, which is implemented based on the control system of the intelligent screw grouting extruder according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step S1: The flow meter monitors the material flow of the extruder in real time and adjusts the feed amount through fluctuation detection and frequency analysis; Step S2: collecting temperature data of the extruder in real time through a temperature sensor, predicting temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation; Step S3: Initialize the extruder screw speed according to the key characteristics of the material, monitor the screw load through the torque sensor, and dynamically adjust the screw speed according to the screw load; Step S4: collecting pressure data of the extruder cavity in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the extruder die and the screw according to the pressure deviation; Step S5: The physical properties of the production product are monitored in real time by a laser rangefinder to obtain a physical quality score, and defects on the cable surface are detected by image recognition to obtain a visual quality score. A comprehensive quality assessment of the product is performed based on the physical quality score and the visual quality score; Step S6: Monitor the production status of the extruder in real time, perform regression detection, and issue abnormal warnings.

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