Control system and method of intelligent screw grouting extruder

Abstract

The invention discloses a control system and method of an intelligent screw grouting extruder, and belongs to the technical field of wire and cable production, and the control system comprises the following steps: a flow control module monitors the material flow in real time, and adjusts the feeding amount through fluctuation detection and frequency analysis; the temperature control module monitors and predicts temperature deviation through a temperature sensor and adjusts heating power; the rotating speed control module initializes the rotating speed of the screw according to material characteristics and monitors the load of the screw through the torque sensor so as to dynamically adjust the rotating speed of the screw; the pressure adjusting module monitors and adjusts the pressure of the inner cavity of the extruder in real time, and adjusts the distance between the die orifice and the screw rod by calculating the pressure deviation; the quality detection module detects the physical and visual quality of the product on line through a laser range finder and an image recognition technology, and carries out comprehensive quality evaluation; according to the invention, the problem of how to dynamically adjust the running state of the extruder according to real-time parameters such as temperature, flow, pressure and rotating speed is solved, so that the consistency of the thickness of a cable sheath layer and the product quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric 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 extruder is one of the key equipment used to process and shape the insulation layer and sheath of cables. The traditional single-screw extruder control system mainly relies on manual experience and simple mechanical control to achieve the stability of the production process, but this approach has many problems. For example, manual experience is difficult to standardize, resulting in unstable product quality; simple mechanical control cannot achieve precise temperature, pressure and speed regulation, thus affecting production efficiency and product quality. In addition, the traditional system is complex to operate, has high maintenance costs, and consumes a lot of energy, which is not conducive to the long-term development of the enterprise. In order to overcome the shortcomings of the traditional system, advanced control technologies and algorithms such as fuzzy logic control, adaptive control and predictive control are adopted to achieve precise control of key parameters in the extrusion process. By introducing intelligent technology, not only can production efficiency and product quality be improved, but also energy consumption and waste can be reduced, thereby enhancing the overall competitiveness of the enterprise.

[0003] A Chinese patent application with publication number CN118404791A discloses an extruder weight-per-meter control system for cable production, comprising an extrusion unit, a feeding unit and a control unit, wherein the extrusion unit comprises at least one extrusion screw and a driving device arranged at one end of the extrusion screw, an extrusion head is arranged at the other end of the extrusion screw, a feed port is arranged between the extrusion screw and the driving device, the feeding unit comprises 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 arranged between the feeder and the storage hopper, a feed solenoid valve is respectively arranged at the input end of each storage hopper, the control unit comprises 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, and belongs to the technical field of wire and cable production.

[0004] Although the prior art adjusts the extrusion screw speed and the feeding amount of the storage hopper through the control unit to accurately control the thickness of the cable sheath layer and effectively improve the efficiency of resource utilization, it still fails to solve the problem of how to accurately adjust multiple key parameters in the extrusion process in a dynamic environment to further improve production efficiency and product quality consistency. Therefore, in order 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 prior art, 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 regulating module, a quality detection module and a safety warning module;

[0008] 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.

[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 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;

[0011] The pressure regulating module is used to monitor the pressure in the extruder cavity in real time, collect the pressure data in 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;

[0012] The quality inspection module is used to detect the quality of production products online. It monitors the physical properties of production products in real time through laser rangefinder to obtain physical quality scores, and detects defects on the cable surface through image recognition to obtain visual quality scores. Comprehensive quality assessment of products is performed based on physical quality scores and visual quality scores.

[0013] Specifically, the steps of monitoring and adjusting 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 the fluctuation threshold. If the fluctuation level of the traffic data is greater than the fluctuation threshold, the traffic data greater than the fluctuation threshold will be marked as an abnormal fluctuation point, and the abnormal fluctuation point will be continuously tested. The abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold is calculated. The calculation formula of 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 and 0 otherwise. is a nonlinear correction function.

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

[0022] Configure the frequency threshold. If the abnormal fluctuation frequency is greater than the frequency threshold, the continuity test fails. Otherwise, the continuity test 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 is 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 qualified flow 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 qualified flow threshold, continue to adjust the feed volume according to the flow deviation and fluctuation level, otherwise stop the feed volume adjustment.

[0027] Specifically, the steps of monitoring and adjusting 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 through the temperature sensors;

[0029] Set the target temperature of 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 time The predicted value of temperature deviation, It is Key areas at the time The temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the time 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 adjusting the temperature of the extruder also includes:

[0033] Configure the temperature qualified threshold. If the predicted temperature deviation value of the key area is less than the qualified temperature threshold, the temperature data is continuously monitored. If the predicted temperature deviation value is greater than the qualified temperature threshold, the heating power is 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 time The heating power, It is Key areas at the time The heating power, is a non-negative heating power adjustment factor, 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 speed and speed adjustment threshold of the screw based on the key characteristics of the material and the screw speed standard table;

[0039] The torque sensor is used to monitor the screw load in real time, and the screw speed is dynamically adjusted 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 value 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 sensor installed at the key position 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, calculate the pressure fluctuation threshold in the time window. The frequency at which the pressure deviation 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. 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, It 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, and the outer diameter and wall thickness of the products are measured using a laser rangefinder to perform physical quality scoring, 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, and uses image recognition to check whether there are defects on the product surface, and then performs visual quality scoring, namely:

[0057]

[0058] in, is the product visual quality rating, 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 the 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 rating, 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 to be 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 the temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation;

[0066] Step S3: 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;

[0067] Step S4: collecting pressure data of the inner cavity of the extruder in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the die and the screw of the extruder 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, and 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. Use flowmeters to monitor material flow in real time, perform fluctuation detection and frequency analysis, and adjust feed volume in a timely manner to avoid quality problems or excessive equipment load caused by flow fluctuations during production. At the same time, temperature sensors collect temperature data in key areas in real time, and adjust heating power based on temperature deviation predictions to accurately control the temperature of each area to avoid overheating or overcooling, thereby improving material fluidity and product quality.

[0072] 2. The screw speed is adjusted dynamically according to the key characteristics of the material and the screw load, avoiding the need for manual intervention and improving the automation and intelligence of the production process. The torque sensor monitors the load and adjusts the speed according to the actual load conditions to ensure that the equipment is always in the best working state and reduce equipment wear and energy consumption. In addition, by real-time monitoring of the inner cavity pressure and adjusting the pressure deviation, the distance between the die and the screw is automatically adjusted to ensure that the pressure is maintained within the target range, avoiding unstable pressure or uneven materials caused by improper die adjustment, and further improving the stability of the production process.

[0073] 3. Through laser rangefinder and image recognition technology, the physical and visual quality of the products produced are evaluated in real time, defects or unqualified parts of the products are discovered in time, and judged through comprehensive quality scores. The automated quality inspection and sorting system effectively improves the consistency and qualified rate of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a structural 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 temperature monitoring and regulation 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 a control system of an 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 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 to ensure the stable flow of the material in the screw.

[0083] In this embodiment, the flow control module uses a flow meter to monitor the flow rate of materials in the intelligent screw grouting extruder (hereinafter referred to as the intelligent screw grouting extruder: 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 to ensure that the material is evenly and stably fed into the screw cavity to avoid uneven feeding or jamming caused by flow fluctuations. Accurate control of flow can effectively reduce material retention or uneven flow, improve production efficiency, and avoid processing quality fluctuations 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 the flow meter to collect the material flow data in the extruder in real time to obtain the 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 value of the instantaneous flow value within the configured time window, providing smooth information on the change trend of the flow data. The calculation formula of 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 [ , ], It is the size of the time window, which is used to indicate how many historical data points are considered. The standard deviation is used to quantify the fluctuation level of the flow rate, which can accurately identify the fluctuation amplitude of the flow rate and reflect the stability of the extruder feed.

[0091] Configure the fluctuation threshold. If the fluctuation level of the traffic data is greater than the fluctuation threshold, the traffic data greater than the fluctuation threshold will be marked as an abnormal fluctuation point, and the abnormal fluctuation point will be continuously tested. By calculating the abnormal fluctuation frequency when the fluctuation level exceeds the fluctuation threshold, the stability of the traffic data fluctuation level over a period of time can be monitored. 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 and 0 otherwise. is a nonlinear correction function used to increase frequency sensitivity. For example, If the frequency of abnormal fluctuations is higher than the predetermined threshold, it indicates that there are frequent flow fluctuations, which may lead to instability in the production process. The frequency of abnormal fluctuations plays a key role in the stability analysis of the process 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 amount is adjusted. The feed amount is adjusted in combination with the fluctuation level of the flow data. First, the feed amount is adjusted according to the difference between the current flow data and the target material flow, and then the feed amount is further adjusted according to the size of the flow fluctuation level. In the case of large fluctuations, the amplitude of the feed amount adjustment needs to be reduced to avoid instability caused by over-adjustment. The formula for feed amount adjustment is as follows:

[0095]

[0096] in, It is 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. Its value range is [0.01, 0.1]. It is the coefficient of influence of flow fluctuation on the adjustment amount, and its value range is [0.001, 0.05]. The feed amount is adjusted according to the deviation between the detected flow data fluctuation level and the target material flow. Especially in the case of large fluctuations, the adjustment range is reduced to avoid over-adjustment, effectively avoiding system instability caused by over-adjustment, and ensuring that the flow in the production process can be kept as stable as possible.

[0097] Configure the qualified flow 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 qualified flow threshold, then continue to adjust the feed volume according to the flow deviation and fluctuation level, otherwise stop the feed volume adjustment. Through the dynamic feedback mechanism, the effectiveness of the adjustment can be verified after the adjustment to ensure that the final flow reaches the target value and the flow fluctuation is within an acceptable range. If the flow 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 area 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 arranges multiple temperature sensors in each heating section, screw and die area of ​​the extruder, and the temperature sensors include thermocouples or RTD temperature sensors to monitor the temperature of each key area in real time, and automatically adjusts the power output of the heater according to the preset target temperature, adjusts the working state of the temperature controller, ensures that the material is processed under stable temperature conditions, and prevents material viscosity changes or thermal stability problems caused by temperature fluctuations. Accurate temperature control avoids the impact of temperature fluctuations on material processing performance, thereby ensuring the stability of product quality and avoiding production failures or material losses caused by abnormal temperature.

[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. The temperature data of each key area is collected in real time through the temperature sensors. The key areas include the heating section, screw, and die. The 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 area is set, the temperature data of each key area is received and processed in real time, and the deviation trend from the target temperature is predicted based on the historical temperature data of the key area. The formula for deviation trend prediction is as follows:

[0103]

[0104] in, It is Key areas at the time The predicted value of temperature deviation, It is Key areas at the time The temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the time 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, give early warning, and adjust the temperature more effectively to avoid instability in the processing process caused by excessive temperature deviation.

[0105] Configure the temperature qualified threshold. If the predicted temperature deviation value of the key area is less than the qualified temperature threshold, the temperature data is continuously monitored. If the predicted temperature deviation value is greater than the qualified temperature threshold, the heating power is 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 time The heating power, It is Key areas at the time 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, and its value range is [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 predicted temperature deviation value is less than the qualified temperature 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 speed of the extruder screw according to 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. The key characteristics of the material include: melt index, viscosity and thermal stability;

[0111] In this embodiment, the speed control module monitors the speed of the screw in real time through an encoder installed on the motor. The speed of the screw is dynamically adjusted according to the characteristics of different materials. For example, for materials with a higher melt index, the speed is automatically reduced to avoid overheating of the material; for materials with higher viscosity, the speed is increased to ensure that the material can be 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 can be processed under the most suitable conditions, thereby avoiding problems such as uneven heating, insufficient mixing or poor fluidity caused by improper speed, and 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; set the initial speed and speed adjustment threshold of the screw based on the key characteristics of the material and the screw speed standard table;

[0114] The torque sensor is used to monitor the screw load in real time, and the screw speed is dynamically adjusted 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 value function, It is a minimum function; through the maximum and minimum value limits, it ensures that the adjusted speed is always within the effective range to avoid 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. The pressure sensor collects the pressure data in the extruder cavity in real time and calculates the pressure deviation. The distance between the extruder die and the screw is adjusted 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 condition in the machine 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 jamming or uneven extrusion due to unstable pressure. Accurate pressure regulation can effectively prevent the extruder from jamming or equipment failure, and ensure 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] The pressure data is collected in real time by installing pressure sensors at key locations of the extruder, and the pressure deviation between the pressure data and the target pressure is calculated in real time. The 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, calculate the pressure fluctuation threshold in the time window. The frequency at which the pressure deviation 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 due to small fluctuations or normal fluctuations, and 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 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; unnecessary adjustments are avoided and only serious pressure problems are responded to, thereby improving the stability of the extrusion process.

[0125] Specifically, the specific 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. 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]. It 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 monitors the physical properties of production products in real time through laser rangefinder to obtain physical quality scores, and detects defects on the cable surface through image recognition to obtain visual quality scores. Comprehensive quality assessment of products is performed based on physical quality scores and visual quality scores.

[0130] In this embodiment, the quality inspection module integrates a laser rangefinder and a high-definition camera, which measures the outer diameter and wall thickness of the product in real time and performs image recognition inspection on the product surface. The laser rangefinder can accurately monitor the outer diameter of each cable or product to ensure that it meets the design standards; the high-definition camera uses image recognition technology to automatically detect whether there are cracks, bubbles, wrinkles and other defects on the cable surface, and compares the detection data with the standard database to promptly detect and mark abnormalities. The quality of the products in the production process can be detected in real time to ensure that the appearance and size of each product meet the standards, and unqualified products can be detected and eliminated in a timely manner. Through such quality control, the qualified rate of products can be significantly improved, the flow of unqualified products into the market can be reduced, and high-quality output in the production process can be ensured.

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

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

[0136]

[0137] in, is the product visual quality rating, 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 integrity and refinement of the product surface quality by counting the frequency of each defect type. The higher the score, the more surface defects the product has and the lower the quality.

[0138] Combine the physical quality score and the visual quality score to comprehensively evaluate the overall quality of the product 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 rating, and It is a non-negative weight coefficient; the physical quality score and the visual quality score are weighted together 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 to be 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 each key parameter, including flow, temperature, pressure, etc., and monitors the changes of various data in real time. When one or more parameters exceed the set safety threshold, the early warning mechanism is automatically triggered, and a sound alarm is issued to display abnormal information. In addition, the early warning information is sent to relevant operators via text messages or emails to remind them to deal with abnormal situations in a timely manner. The safety early 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 early warnings, the risk of production accidents can be greatly reduced, ensuring production safety and 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 volume, perform flow data regression detection to obtain the time length of the feed volume adjustment. If the time length of the feed volume adjustment is greater than the observation threshold, an abnormal flow warning is issued.

[0146] When the temperature control module adjusts the heating power, a temperature regression test is performed to obtain the time length of the heating power adjustment. If the time length of the heating power adjustment is greater than the observation threshold, a temperature abnormality 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 performs automatic die adjustment, a pressure regression test is performed to obtain the length of time for the module to automatically adjust. If the length of time for the module to automatically adjust is greater than the observation threshold, an abnormal pressure warning is issued.

[0149] Configure the unqualified threshold and count the number of unqualified products within the observation threshold. If it 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 the temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation;

[0154] Step S3: 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;

[0155] Step S4: collecting pressure data of the inner cavity of the extruder in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the die and the screw of the extruder 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, and 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 the fluctuation threshold. If the fluctuation level of the traffic data is greater than the fluctuation threshold, the traffic data greater than the fluctuation threshold will be marked as an abnormal fluctuation point, and the abnormal fluctuation point will be continuously tested. The abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold is calculated. The calculation formula of 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 and 0 otherwise. is a nonlinear correction function;

[0164] Configure the frequency threshold. If the abnormal fluctuation frequency is greater than the frequency threshold, the continuity test fails. Otherwise, the continuity test 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 is 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 qualified flow 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 qualified flow threshold, continue to adjust the feed volume according to the flow deviation and fluctuation level, otherwise stop the feed volume adjustment.

[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 speed and speed adjustment threshold of the screw based on the key characteristics of the material and the screw speed standard table;

[0171] The torque sensor is used to monitor the screw load in real time, and the screw speed is dynamically adjusted 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 value 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 sensor installed at the key position 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, calculate the pressure fluctuation threshold in the time window. The frequency at which the pressure deviation 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 amount can be adjusted in time to avoid quality fluctuations or excessive equipment load caused by fluctuations in the production process, thereby ensuring stable material flow during the extrusion process and reducing equipment failures and raw material waste caused by unstable flow during production. By real-time monitoring of temperature data in key areas and adjusting the heating power according to temperature deviation prediction, the temperature of each area can be accurately controlled to avoid overheating or overcooling, thereby improving material fluidity and product quality during the extrusion process. According to the key characteristics of the material and the screw load, the screw speed can be dynamically adjusted to avoid the need for manual intervention and improve the automation and intelligence of the production process. The load is monitored by a torque sensor and the speed is adjusted according to the actual load to ensure that the equipment is always in the best working state, reducing equipment wear and energy consumption. By real-time monitoring of the inner cavity pressure and adjusting the pressure deviation, the distance between the die and the screw is automatically adjusted to ensure that the pressure is maintained within the target range, avoiding pressure instability or uneven materials caused by improper die adjustment, and further improving the stability of the production process. Through laser rangefinder and image recognition technology, it is possible to conduct real-time physical and visual quality assessment of the products produced, timely discover defects or unqualified parts of the products, and make judgments through comprehensive quality scores. Unqualified products can be automatically eliminated, avoiding unqualified products from entering the market, reducing manual intervention, and improving production efficiency and product reliability.

[0183] Through comprehensive monitoring and automatic adjustment, the production efficiency and product quality of the extruder are improved. The various modules of the system work together, not only optimizing various parameters in the production process, but also greatly reducing manual intervention through intelligent feedback mechanisms, reducing the difficulty of operation, and improving the stability and safety of the production line.

[0184] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. The control system of the intelligent screw grouting extruder is characterized by: It includes 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 inner cavity of the extruder in real time, collect the pressure data of the inner cavity of the extruder 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 production products online, monitor the physical properties of production products in real time through a laser rangefinder to obtain a physical quality score, and detect defects on the cable surface through image recognition to obtain a visual quality score, and conduct a comprehensive quality assessment of the product based on the physical quality score and the visual quality score.

2. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that: 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 value, It's time The instantaneous flow value, The value range is [ , ], is the size of the time window; Configure the fluctuation threshold. If the fluctuation level of the traffic data is greater than the fluctuation threshold, the traffic data greater than the fluctuation threshold will be marked as an abnormal fluctuation point, and the abnormal fluctuation point will be continuously tested. The abnormal fluctuation frequency of the fluctuation level exceeding the fluctuation threshold is calculated. The calculation formula of 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 and 0 otherwise. is a nonlinear correction function.

3. The control system of the intelligent screw grouting extruder according to claim 2, characterized in that: The step of monitoring and adjusting the extruder feed rate also includes: 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 based on the fluctuation level of the flow data. The feed rate adjustment formula is as follows: ； in, It is 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 qualified flow 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 qualified flow threshold, continue to adjust the feed volume according to the flow deviation and fluctuation level, otherwise stop the feed volume adjustment.

4. The control system of the intelligent screw grouting extruder according to claim 1, characterized in that: The steps of temperature monitoring and regulating the extruder include: Arrange temperature sensors in key areas of the extruder to collect temperature data of each key area in real time through the temperature sensors; Set the target temperature of 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 time The predicted value of temperature deviation, It is Key areas at the time The temperature, The value range is [ , ], It is The target temperature of each key area, It is Key areas at the time 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.

5. The control system of the intelligent screw grouting extruder according to claim 4, characterized in that: The step of temperature monitoring and regulating the extruder also includes: Configure the temperature qualified threshold. If the predicted temperature deviation value of the key area is less than the qualified temperature threshold, the temperature data is continuously monitored. If the predicted temperature deviation value is greater than the qualified temperature threshold, the heating power is 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 time The heating power, It is Key areas at the time The heating power, is a non-negative heating power adjustment factor, is a nonlinear index.

6. 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 speed and speed adjustment threshold of the screw based on the key characteristics of the material and the screw speed standard table; The torque sensor is used to monitor the screw load in real time, and the screw speed is dynamically adjusted 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 value function, is the minimum function.

7. 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 inner cavity of the extruder include: The pressure data is collected in real time by the pressure sensor installed at the key position 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, calculate the pressure fluctuation threshold in the time window. The frequency at which the pressure deviation 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.

8. The control system of the intelligent screw grouting extruder according to claim 7, 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. 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, It is the time window The frequency at which the pressure deviation within is greater than the pressure fluctuation threshold, is the adjustment control parameter.

9. 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, and the outer diameter and wall thickness of the products are measured using a laser rangefinder to perform physical quality scoring, 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, and uses image recognition to check whether there are defects on the product surface, and then performs visual quality scoring, namely: ； in, is the visual quality rating of the product, 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 the 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 visual quality rating of the product, 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 to be unqualified, sorting and rejection operations are automatically performed.

10. 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 9, 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 the temperature deviation, and adjusting the heating power of the key area according to the predicted temperature deviation; Step S3: 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; Step S4: collecting pressure data of the inner cavity of the extruder in real time through a pressure sensor and calculating the pressure deviation, and adjusting the distance between the die and the screw of the extruder 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, and 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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