Barrel heating control method, system and controller

Abstract

The application provides a barrel heating control method, system and controller, the method comprising: periodically monitoring the state of the main communication module and / or the main heating output module; if a fault is detected in a plurality of consecutive monitoring periods, triggering a redundancy switching mechanism to switch to a backup module and synchronize the state; self-learning to establish a standard energy consumption reference for each heating section and monitoring the degree of deviation of the actual energy consumption based on the standard energy consumption reference, so as to automatically optimize the control parameters when the deviation degree is lower than a first preset threshold or diagnose the fault type based on the energy consumption characteristics when the deviation degree is higher than a second preset threshold. The application solves the problems of easy production downtime caused by single communication link or heating output point failure, lack of energy consumption refinement management and heating uniformity control.

Description

Technical Field

[0001] This invention relates to the field of barrel heating control technology, and in particular to a barrel heating control method, system and controller. Background Technology

[0002] In industrial production processes such as plastics processing and chemical reactions, barrel heating systems are core equipment. Their operational stability, heating uniformity, and energy efficiency directly impact product quality and production efficiency. Currently, existing barrel heating control systems generally use a single communication link (such as RS-485 communication) for data exchange, and the heating output section employs a single-point output structure, achieving basic closed-loop temperature control through temperature sensor feedback. Such systems can complete basic heating tasks under normal operating conditions and have a certain market application base.

[0003] However, the aforementioned control system suffers from the following technical shortcomings in practical applications: When the communication link is interrupted or the heating output point is damaged, the entire system will malfunction, leading to heating interruption and forcing production shutdown; simultaneously, the system cannot collect and analyze energy consumption data during the heating process, making it difficult for enterprises to grasp energy consumption distribution and optimization directions; furthermore, uneven temperatures are prone to occur in different sections during the heating process, with prominent issues of localized overheating or underheating, affecting the uniformity of material melting and the stability of product quality. Therefore, how to achieve controllable energy consumption and uniform heating while ensuring continuous and reliable system operation has become a pressing technical problem to be solved in this field.

[0004] Therefore, it is necessary to propose a barrel heating control scheme to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a barrel heating control method, system and controller to solve the problems that production stoppage is easily caused by a single communication link or heating output point failure and the lack of refined energy consumption management and heating uniformity control.

[0006] To achieve the above objectives, in a first aspect, a barrel heating control method is provided, the method comprising: Periodically monitor the status of the main communication module and / or the main heating output module; If a fault is detected in multiple consecutive monitoring cycles, the redundancy switching mechanism is triggered to switch to the backup module and synchronize the status. The system automatically learns to establish standard energy consumption benchmarks for each heating section and monitors the actual energy consumption deviation based on these benchmarks. When the deviation is below a first preset threshold, the system automatically optimizes control parameters or diagnoses fault types based on energy consumption characteristics when the deviation is above a second preset threshold.

[0007] Furthermore, when an aging fault of the heating element is diagnosed based on energy consumption characteristics and the degree of aging exceeds a preset safety threshold, the output control of the corresponding heating section is switched from the main heating output module to the backup heating output module in advance.

[0008] Secondly, a barrel heating control system is provided, comprising: The central control unit is configured to periodically monitor the status of the main communication module and / or the main heating output module, and trigger redundancy switching when a fault is detected in multiple consecutive monitoring cycles. The communication unit includes a main communication module and a backup communication module, both of which are bidirectionally connected to the central control unit and are used to realize data interaction between the central control unit and the host computer. The heating execution unit includes a main heating output module and a backup heating output module, both of which are electrically connected to the central control unit and are used to receive heating commands and drive the barrel heating element. The fault monitoring and redundancy switching unit is configured to disconnect the main module, activate the corresponding backup module, and synchronize the operating status after a permanent fault is confirmed. The energy consumption statistics and intelligent diagnosis unit is configured to establish a standard energy consumption benchmark for each heating section through self-learning and monitor the actual energy consumption deviation based on the standard energy consumption benchmark. When the deviation is lower than a first preset threshold, the control parameters are automatically optimized, and when the deviation is higher than a second preset threshold, the fault type is diagnosed based on the energy consumption characteristics.

[0009] Thirdly, a barrel heating controller is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0010] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0011] The beneficial effects of this invention are as follows: The barrel heating control method of this invention systematically solves the technical defects of existing barrel heating control systems, such as low reliability, uncontrollable energy consumption, and uneven heating, through a triple collaborative mechanism. First, by periodically monitoring the status of the main communication module and / or the main heating output module, redundancy switching is triggered only when a fault is detected in multiple consecutive monitoring cycles. This effectively filters out common electromagnetic interference and instantaneous communication jitter in industrial settings, avoiding erroneous switching due to single anomalies. Furthermore, it can immediately switch to the backup module and synchronize its operation after a permanent fault is confirmed, thereby ensuring uninterrupted heating and unbroken communication, and solving the reliability problem of production shutdown caused by a single communication link or output point failure. Secondly, by establishing standard energy consumption benchmarks for each heating section through self-learning, the system can dynamically adapt to normal energy consumption fluctuations under different operating conditions, avoiding false alarms or missed alarms caused by fixed thresholds. Furthermore, it implements a tiered response based on the degree to which actual energy consumption deviates from the standard benchmark: when the deviation is below a first preset threshold, it automatically optimizes heating control parameters (such as PID parameters or output duty cycle) to compensate for energy consumption deviations without changing the process temperature, achieving energy saving and consumption reduction; when the deviation is above a second preset threshold, it accurately diagnoses fault types (such as heating element aging or insulation layer damage) based on the combined characteristics of energy consumption changes and temperature response, thereby transforming energy consumption data into operable maintenance guidelines and solving the problem of lacking refined energy consumption management. Simultaneously, under the premise of ensuring continuous system operation through redundant switching, the energy consumption self-learning and parameter optimization process does not rely on manual intervention, ensuring that the heating system always maintains a near-optimal energy efficiency state, indirectly improving the stability and uniformity of temperature control and alleviating defects such as local overheating or insufficient heating. Therefore, this invention, with continuous confirmation of redundant switching and self-learning tiered energy consumption response as its core, achieves a unified high reliability, low energy consumption, and high quality in the barrel heating process.

[0012] Furthermore, by setting a fault counter and a "disconnect-then-start" switching logic, the reliability and smoothness of redundant switching are significantly improved. Specifically, the counter is incremented when a fault is detected in each monitoring cycle and immediately reset when no fault is detected. Switching is triggered only when the counter continuously accumulates to a preset threshold (e.g., 3 times). This mechanism effectively eliminates occasional false alarms caused by electromagnetic interference, instantaneous voltage fluctuations, or communication jitter in industrial settings, ensuring that switching only targets genuine permanent faults and avoiding system instability and unnecessary wear and tear on backup modules due to frequent false switching. Simultaneously, after confirming a fault, the main module's output enable signal is first cut off before activating the backup module and synchronizing current operating control parameters (such as temperature control setpoints and PID output values). This prevents electrical shocks caused by parallel conflicts between the two outputs and achieves seamless control parameter transitions. As a result, the heating process maintains stable temperature even when a fault occurs, and operators may not even perceive the switching process, significantly improving production continuity and system anti-interference capabilities, and reducing maintenance intervention frequency.

[0013] Furthermore, this invention relies on self-learning to establish standard energy consumption benchmarks for each heating section and a refined hierarchical response to the degree of deviation, achieving a leap from "passive statistics" to "active intelligent diagnosis." By automatically identifying stable production conditions and dynamically updating the energy consumption benchmark curves for each section, the system can adapt to normal energy consumption fluctuations under different materials, ambient temperatures, and equipment aging conditions. When the actual energy consumption deviates from the benchmark below the first preset threshold, it automatically optimizes heating control parameters (such as PID coefficients or output duty cycle) to compensate for efficiency decay and maintain equipment operation in a low-carbon consumption state. When the deviation continues to exceed the second preset threshold and meets the time condition, the system accurately distinguishes two types of hidden equipment faults based on the combination of energy consumption change characteristics and temperature response characteristics (e.g., "increased energy consumption + slower heating + increased duty cycle" indicates aging of the heating element, and "high energy consumption + large temperature fluctuations + increased heat dissipation" indicates damage to the insulation layer), and outputs specific maintenance prompts. This diagnostic capability enables maintenance personnel to quickly pinpoint the root cause of problems (whether to replace the heating coil or repair the insulation layer), avoiding blind replacement of parts or ineffective repairs, significantly reducing maintenance costs and energy waste, while isolating serious faults from minor adaptive optimizations to ensure that non-emergency anomalies do not affect normal production. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of a barrel heating control method according to an embodiment of the present invention; Figure 2 This is a schematic flowchart illustrating a barrel heating control method according to another embodiment of the present invention; Figure 3 This is a schematic flowchart of a barrel heating control method according to another embodiment of the present invention; Figure 4 This is a schematic flowchart of a barrel heating control method according to another embodiment of the present invention; Figure 5 This is a schematic flowchart of a barrel heating control method according to another embodiment of the present invention; Figure 6 This is a multi-channel heating parameter setting interface according to an embodiment of the present invention; Figure 7 This is an energy consumption page according to an embodiment of the present invention; Figure 8 This is a structural block diagram of a barrel heating control system according to an embodiment of the present invention; Figure 9 Electrical schematic diagram of the main circuit of the heating actuator; Figure 10 Diagram showing the power distribution and signal connection of the control system; Figure 11 This is a topological diagram of a computer-readable storage medium disclosed in this invention. Detailed Implementation

[0015] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present invention. All equivalent changes or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are within the scope of protection of the present invention.

[0016] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Example 1: As Figure 1 As shown, this embodiment provides a barrel heating control method (hereinafter referred to as the "control method" or "method") for use in a barrel heating control system or barrel heating controller. The method includes: Step 101. Periodically monitor the status of the main communication module and / or the main heating output module. Specifically, the central control unit sends status query commands to the main communication module and / or the main heating output module at a preset fixed period, and receives status response signals containing module operating status and verification information.

[0018] It should be understood that the central control unit sends status query commands to the main communication module and the main heating output module at preset fixed intervals (e.g., 200ms, configurable according to system response requirements). Upon receiving the query, the main communication module returns a response frame containing the module's operating status (normal / fault), communication link status (connected / disconnected), and verification information for data integrity (e.g., CRC checksum or frame checksum). The main heating output module provides real-time feedback on its power supply status (whether power is supplied normally), output level status (high / low), and load current status (current value or normal / abnormal indicator). The central control unit analyzes the received response signals. If the return value is non-zero or the verification fails, a fault is determined to exist in the current monitoring cycle; if the return value is normal and the verification passes, the status is determined to be normal. Simultaneously, the central control unit accurately identifies the fault type based on the abnormal characteristics of the response signals, including: communication timeout (no response received for more than one cycle), data verification error (CRC failure or frame loss), output open circuit (no current feedback after command), output short circuit (current exceeds rated threshold), output no response (no change in status after command), and module crash (no heartbeat for multiple cycles).

[0019] Thus, by using a pre-set, fixed-cycle active query and response mechanism, the status of the main communication module and the main heating output module is actively scanned at regular intervals, rather than passively waiting for fault reports. This keeps fault detection delays within a single cycle, ensuring real-time monitoring. Simultaneously, by utilizing response frames containing verification information, transmission errors at the data link layer (such as data corruption caused by electromagnetic interference) can be effectively identified, preventing misjudgments of module status due to data errors. Refined identification of different fault types allows the system to not only determine "whether there is a fault," but also "what kind of fault," providing accurate data for subsequent counter accumulation, fault log recording, and maintenance location. Furthermore, real-time monitoring of the power supply, output level, and load current of the main heating output module covers the entire output link from the control chip to the load coil, preventing hidden faults such as "instructions but no actual heating" from being overlooked.

[0020] Step 102. If a fault is detected in multiple consecutive monitoring cycles, the redundancy switching mechanism is triggered to switch to the backup module and synchronize the status.

[0021] like Figure 2 As shown, the specific operation of "if a fault is detected in multiple consecutive monitoring cycles, the redundancy switching mechanism is triggered" in step 102 includes: Step 201. If a fault is detected within each monitoring cycle, the counter is incremented; otherwise, the counter is reset to zero. Faults include communication faults and output faults; communication faults include at least communication timeout faults and data verification error faults, while output faults include at least output open circuit faults, output short circuit faults, output no-response faults, and module crash faults.

[0022] Step 202. When the counter continuously accumulates to the preset fault count threshold, the redundancy switching mechanism is triggered.

[0023] exist Figure 2In this embodiment, the central control unit maintains a fault anomaly counter. This counter retains its current value at the beginning of each monitoring cycle and updates it based on the detection results at the end of the cycle. When any communication fault (such as communication timeout, CRC check error) or output fault (such as output open circuit, short circuit, no response, module crash) is detected within the current monitoring cycle, the counter increments by 1, and the fault type, fault time, and corresponding channel information are recorded. If no fault is detected in the current cycle, the counter is immediately reset to zero. Only when the counter continuously accumulates to a preset fault count threshold (e.g., 3 times by default, configurable according to the level of interference on site) is it determined to be a permanent real fault, thereby triggering the redundancy switching mechanism. If the fault disappears in any cycle during the continuous accumulation process (the counter is reset to zero), all previously accumulated counts are invalidated, determined to be a transient disturbance, and no switching is performed. Thus, through the fault-tolerant logic of "continuous multiple confirmations + single recovery and zeroing," non-permanent anomalies commonly found in industrial environments, such as electromagnetic interference, power fluctuations, and instantaneous communication jitter, are effectively filtered out. This prevents redundant switching from being mistakenly triggered by a single, occasional fault, avoiding unnecessary start-up, shutdown, and output jumps of the backup module. Simultaneously, the configurable accumulation threshold is retained, enabling the system to adapt to field environments with varying interference intensities. Compared to traditional "fault-based switching" solutions, the method in this embodiment significantly improves the system's anti-interference capability and operational stability, ensuring that the backup module is only activated in the event of genuine, permanent hardware or link failure, extending the backup module's lifespan and reducing maintenance costs.

[0024] like Figure 3 As shown, the specific operations for "switching to the backup module and synchronizing the status" in step 102 include: Step 301. Disconnect the output enable signal of the malfunctioning main communication module or main heating output module.

[0025] Step 302. Activate the corresponding backup communication module or backup heating output module and synchronize the current operating control parameters to the backup module.

[0026] Figure 3In this embodiment, upon confirmation of a permanent fault, the central control unit first performs a safety isolation action: immediately disconnecting the output enable signal of the faulty main communication module or main heating output module (for the communication module, disconnecting data transmission enable; for the output module, disconnecting power supply and PWM output enable), preventing the faulty module from continuing to output potentially causing electrical conflicts or erroneous data. Subsequently, after a very short preset delay (e.g., 50~100ms, ensuring the main module completely exits), the central control unit activates the corresponding backup communication module or backup heating output module and synchronously loads all currently running control parameters (including the target temperature of each heating segment, the integral cumulative value of the PID controller, the current output duty cycle, and the queue of incomplete communication commands, etc.) to the backup module, enabling the backup module to continue operating in the same working state as the main module, achieving a seamless transition of output signals and communication data.

[0027] The sequential design of this embodiment, which prioritizes interruption before activation, avoids potential load short circuits or signal conflicts that might occur when both the main and backup outputs are active simultaneously, ensuring electrical safety. The extremely short delay guarantees smooth switching without affecting the temperature continuity of the heating process. State synchronization technology ensures that the backup module accurately continues the control output of the main module at the moment of takeover, without temperature fluctuations, power surges, or communication interruptions; the switching process is virtually imperceptible to operators. Therefore, the system can achieve truly seamless redundancy in the event of a permanent failure of the main module, solving the core defect of existing barrel heating control systems where production shutdowns are caused by the failure of a single communication link or output point, significantly improving production continuity and equipment reliability.

[0028] Step 103. Self-learn to establish standard energy consumption benchmarks for each heating section and monitor the actual energy consumption deviation based on the standard energy consumption benchmarks, so as to automatically optimize control parameters when the deviation is lower than the first preset threshold or diagnose fault types based on energy consumption characteristics when the deviation is higher than the second preset threshold.

[0029] like Figure 4 As shown, the specific operations of "self-learning to establish standard energy consumption benchmarks for each heating section" in step 103 include: Step 401. Automatically identify stable production conditions of the barrel. The central control unit continuously monitors parameters such as temperature fluctuations, equipment start-up and shutdown status, and production speed of each heating section of the barrel, and automatically filters out stable production conditions. The judgment conditions include: temperature fluctuations of each section ≤ ±0.5℃, no equipment alarms, no start-up and shutdown actions, and constant production speed.

[0030] Step 402. During the continuous operation under stable conditions, collect the energy consumption parameters and temperature parameters of each heating section to fit and generate the standard energy consumption reference curve for each heating section, and dynamically update the reference curve according to the preset update cycle.

[0031] During the stable operating period, real-time power, cumulative energy consumption, duty cycle, steady-state temperature, and ambient temperature data for each heating segment are continuously collected at a fixed sampling period (e.g., once per second) to form a time-series dataset. Weighted mean filtering or median filtering algorithms are used to remove abnormal jumps during the data collection process. Then, curve fitting is performed on each data segment to generate a standard time-series energy consumption baseline curve reflecting the normal heating efficiency of that segment. Simultaneously, this embodiment supports automatic re-execution of the above learning process at a preset update cycle (configurable to 7 days, 15 days, or 30 days) to dynamically adapt to normal energy consumption drift caused by seasonal temperature changes, material batch differences, or slight equipment aging, avoiding misjudgments caused by a fixed baseline curve. Thus, by automatically identifying stable operating conditions and excluding non-steady-state data (such as the heating phase or fault recovery phase), the representativeness and purity of the learning samples are ensured, avoiding interference from transient processes on the baseline curve. Filtering enhances data reliability, and the resulting customizable baseline curves accurately reflect the energy consumption characteristics of each heating section under normal production conditions, rather than using a fixed value. This enables differentiated and refined management between sections. A dynamic update mechanism allows the baseline to evolve slowly with equipment usage time and environmental changes, preventing false alarms caused by model aging and avoiding sudden changes in the baseline due to short-term anomalies. This self-learning process eliminates the need for manual calibration, significantly reducing on-site debugging and maintenance costs and providing a scientific and adaptive reference system for subsequent energy consumption deviation monitoring.

[0032] The operation of "automatically optimizing control parameters when the deviation is below the first preset threshold" in step 103 specifically includes: When the actual energy consumption deviates from the standard energy consumption benchmark by less than the first preset threshold, the heating control parameters are automatically adjusted to compensate for the energy consumption deviation without changing the target temperature of each heating section.

[0033] Specifically, when the actual energy consumption deviates from the standard energy consumption benchmark by less than the first preset threshold (the upper limit of normal fluctuations, such as a deviation rate ≤10%) and continues for more than several monitoring cycles (e.g., 3 cycles), the system determines it as a mild to moderate energy consumption anomaly and does not trigger an equipment alarm. At this time, the central control unit automatically starts the parameter optimization process while keeping the target process temperature of each heating section constant: first, it attempts to fine-tune the proportional band, integral time, or derivative time of the PID controller to improve the dynamic characteristics of the temperature response and reduce unnecessary overshoot and oscillation; if the effect is insufficient, it further slightly adjusts the upper or lower limit of the heating output duty cycle to reduce energy waste caused by ineffective heating or overcompensation. All optimization parameters are gradually increased within the safe range allowed by the system, and the changing trend of the energy consumption deviation rate is observed in real time. If the deviation rate falls back to the normal range, the current parameter is locked; if there is no improvement, fine-tuning continues until the preset adjustment limit is reached. This process runs entirely online without interrupting production.

[0034] It should be understood that this embodiment can automatically compensate for energy efficiency decline caused by slight aging of heating elements, minor damage to the insulation layer, or changes in ambient temperature without manual intervention, bringing the energy consumption level back to near the baseline. This avoids long-term high-energy-consumption operation due to fixed parameters. At the same time, since the target temperature is not changed, product quality is not affected in any way, achieving "energy saving without quality degradation". The adaptive optimization adopts a gradual step adjustment to prevent temperature fluctuations caused by sudden parameter changes and ensure the stability of the heating process. By monitoring the optimization effect in real time and locking effective parameters, it can continuously approach the optimal control parameters under the current equipment state, so that the equipment always works in a high-efficiency range, significantly reducing long-term electricity costs and delaying the hardware replacement cycle.

[0035] The specific operations in step 103, "diagnosing the fault type based on energy consumption characteristics when the deviation is higher than the second preset threshold," include: When the actual energy consumption deviates from the standard energy consumption benchmark by a greater degree than the second preset threshold and continues for more than a set time, the energy consumption change characteristics and temperature response characteristics are combined to distinguish and diagnose either the heating element aging fault or the insulation layer damage fault.

[0036] It should be understood that when the actual energy consumption deviates from the standard energy consumption benchmark by a degree higher than the second preset threshold (e.g., deviation rate > 20%) and continues for more than a set time (e.g., 3 consecutive monitoring cycles or 10 minutes), it is judged as a serious energy consumption anomaly, triggering a device-level alarm and initiating fault tracing and diagnosis. The diagnostic process is based on logical judgment using a combination of multi-dimensional features: if the actual energy consumption of a certain heating section is continuously increasing, and the heating rate is significantly slower (the time to reach the target temperature is extended by more than 30%), and the output duty cycle required for steady-state operation is significantly increased (e.g., from 40% to more than 70%), then it is determined to be an aging fault of the heating element (e.g., increased resistance of the resistance heating coil, or short circuit between turns of the electromagnetic heating coil leading to decreased heating efficiency); if the energy consumption is continuously high, and the temperature fluctuation range is significantly increased (e.g., from ±1℃ to ±5℃), and the calculated heat loss (estimated based on output power and temperature difference) continues to increase, but the possibility of heating element failure is ruled out, then it is determined to be a damage fault of the insulation layer (e.g., the insulation cotton of the barrel falls off, or the outer shell is damaged, resulting in a large amount of heat loss); the diagnostic results (including fault type, fault location, and confidence level) are uploaded to the host computer in real time, and specific maintenance guidance information is pushed.

[0037] Therefore, compared to traditional systems that only output general alarms for excessive energy consumption, this embodiment can accurately distinguish between two completely different types of latent equipment faults—heating element aging and insulation layer damage—by analyzing the correlation between energy consumption changes and temperature response. Maintenance personnel can identify the root cause of the fault without disassembling the machine, directly prepare corresponding spare parts (heating coils or insulation materials), and implement targeted repairs, avoiding blind replacement of parts or ineffective repairs, and significantly shortening the mean time to repair (MTTR). Simultaneously, this diagnostic logic utilizes existing collected data (power, temperature, duty cycle), requiring no additional hardware and achieving extremely low implementation costs. For severe faults that cannot be automatically repaired, the system proactively disables parameter adaptive correction to prevent further deterioration of energy consumption or product quality issues due to continued operation with the fault, and forcibly pushes maintenance reminders to ensure long-term reliability and economy of production.

[0038] Based on this embodiment, the degree of energy consumption deviation is further subdivided into three levels to achieve a more refined graded response. After the system learns and obtains the standard energy consumption baseline curve for each heating segment, it sets a normal deviation threshold (a first preset threshold, such as a deviation rate ≤ 5%) and a severe deviation threshold (a second preset threshold, such as a deviation rate > 15%). During real-time monitoring, when the actual energy consumption deviation rate does not exceed 5%, it is determined to be a normal deviation, and the system does not perform any intervention actions, but only continuously records data; when the deviation rate is greater than 5% but not more than 15% (i.e., higher than the first preset threshold and lower than the second preset threshold), it is determined to be a moderate deviation, and the system triggers an energy consumption warning to the host computer and automatically performs PID parameter fine-tuning or output duty cycle optimization to compensate for energy consumption deviation without changing the target temperature of each segment; when the deviation rate is greater than 15% and continues for more than 3 monitoring cycles (e.g., 3 200ms cycles), it is determined to be a severe deviation, and the system immediately triggers an equipment-level alarm, disables automatic parameter correction, and simultaneously initiates fault tracing and diagnosis based on a combination of energy consumption change characteristics and temperature response characteristics. Through the above three-level deviation classification processing, this embodiment can achieve complete closed-loop control from normal no action and moderate adaptive optimization to severe precise diagnosis and predictive switching, further improving the system's energy efficiency, reliability and intelligence level.

[0039] like Figure 5 As shown, the operations after switching to the backup module and synchronizing the status in this embodiment also include: Step 501. Continuously monitor the status of the disconnected original main module.

[0040] Step 502. When the original main module is detected to have returned to normal and the preset switchback conditions are met, the running status is switched back from the standby module to the original main module.

[0041] It should be understood that after the system successfully switches to the backup module and synchronizes its operation, the central control unit does not stop monitoring the original faulty main module. Specifically, the system continues to send status query commands to the disconnected original main communication module or the original main heating output module at preset fixed intervals, receives its response signals, and uses the same "fault detected in multiple consecutive monitoring cycles" logic as in steps 101-102 to determine whether it has returned to normal. When the heartbeat response of the original main module returns to the normal status value for multiple consecutive cycles (e.g., 3 consecutive cycles) and no fault type (e.g., communication timeout, data verification error, output open circuit / short circuit, etc.) is triggered, the central control unit determines that the original main module has been repaired and can be put back into use. On this basis, the system also sets preset switchback conditions, such as the backup module has been running stably for more than a certain period of time (e.g., 30 minutes), there are currently no unresolved system-level alarms, and the barrel heating is in a steady-state production condition (temperature fluctuation ≤ ±0.5℃). When all switchback conditions are met, the central control unit executes the switchback operation: First, it cuts off the output enable signal of the standby module, delays for a very short time (e.g., 50ms), then reactivates the output enable of the original main module, and synchronizes the currently running heating control parameters (target temperature, PID integral value, output duty cycle, etc.) from the standby module back to the original main module, so that the original main module continues to operate in a state completely consistent with the standby module. Then, the standby module is switched back to standby mode. The entire switchback process also follows the seamless principle of "disconnect before restart + state synchronization" to ensure that heating output and communication are not interrupted and parameters do not change during the switchback process.

[0042] Thus, the switchback mechanism in this embodiment enables the automatic recovery capability of the redundant system, avoiding resource waste caused by the standby module working for a long time while the main module is idle, and maintaining the original redundant architecture of the system (main module working, standby module on standby). By continuously monitoring the status of the original main module, the system can promptly detect and repair faults, automatically restoring the default working mode without manual intervention, reducing the workload of maintenance personnel. The preset switchback conditions prevent blind switchback when the original main module has just recovered but is still unstable, avoiding secondary faults or temperature fluctuations. The switchback process adopts the same seamless logic of "disconnect before restart + parameter synchronization" as the initial switch, ensuring that the heating output power and communication data do not change before and after the switchback, and the operator cannot perceive the switchback action, thereby ensuring the continuity of the production process and temperature stability. It can be seen that this switchback mechanism enables the system to have a complete fault switching → continuous monitoring → condition recovery → seamless switchback closed-loop capability, significantly improving the system's automation level and long-term operational reliability.

[0043] This embodiment provides a barrel heating control method, applied to the barrel heating of a plastic extruder. The specific implementation process is as follows: 1. System initialization and parameter configuration After the system is powered on, the central control unit (using a high-performance microprocessor, operating voltage 24V) completes initialization and loads preset control algorithms (including PID control algorithm, fault detection algorithm, and self-learning algorithm). The main communication module (485 communication, baud rate 9600bps, communication distance 100m) and the main heating output module (SSR solid-state relay, model SAP4860D+G60, rated voltage 220V, rated current 40A, connected to the electromagnetic induction heating coil) enter the working state, while the backup communication module (Modbus TCP, communication distance 15m) and the backup heating output module (same model as the main module) are in standby mode. The operator sets the heating parameters through the host computer touch screen: target temperature of 180℃ for the feeding section, 220℃ for the melting section, 200℃ for the homogenization section, and heating time of 60 minutes. The host computer sends the parameters to the central control unit through the main 485 communication.

[0044] The operator sets the heating parameters via the host computer touchscreen: target temperature of 180℃ for the feeding section, 220℃ for the melting section, and 200℃ for the homogenization section, with a heating time of 60 minutes. Figure 6 As shown, the interface displays the operating status, measured temperature, heating setpoint, PID parameters (P, I, D), bias, over-temperature threshold, feedforward time (FT), and differential delay time (DTR) of the hot runner, each barrel, standby, and oil temperature, arranged by channel. Operators can modify parameters in the corresponding channel input boxes and individually shut down the heating output of any channel using the "Stop" button. In this embodiment, the PID parameters for the feeding section, melting section, and homogenizing section can be set to P=26.0, I=174.0, D=43.0; P=19.0, I=591.0, D=147.0; and P=17.0, I=342.0, D=85.0, respectively. The bias value is set to 30.00 for all sections, the over-temperature protection threshold is set to 300℃, and the FT and DTR are set to 10.00 and 20.00, respectively, to achieve rapid response and over-temperature protection.

[0045] 2. Periodic condition monitoring and fault confirmation The central control unit sends heartbeat query commands to the main communication module and the main heating output module at a fixed cycle of 200ms. The main communication module returns a response frame containing the module's operating status, communication status, and CRC checksum; the main heating output module provides real-time feedback on power supply status, output level, and load current. The central control unit analyzes and verifies the response signals, identifying six types of faults: communication timeout (no response for more than one cycle), data verification error (CRC failure), output open circuit (no current feedback after command), output short circuit (current exceeds rated threshold), output no response (no change in status after command), and module crash (no heartbeat for multiple cycles). The system maintains a fault anomaly counter. If any fault is detected in each cycle, the counter increments by 1 and records the fault type and time; if no fault is detected, it is immediately reset to zero. Only when the counter increments three times consecutively and is never reset is it considered a permanent, real fault; otherwise, it is considered a transient disturbance and does not trigger a switchover.

[0046] 3. Redundancy switching and status synchronization Upon confirmation of a permanent fault, the central control unit executes a switchover: First, it cuts off the output enable signal of the faulty main module (disconnects power supply and PWM output), delays for 50ms to ensure the main module completely exits, and then activates the corresponding backup communication module or backup heating output module. Simultaneously, the current operating control parameters (target temperature for each segment, PID integral cumulative value, current output duty cycle, and queue of incomplete communication commands) are synchronously loaded into the backup module, allowing the backup module to seamlessly continue operation in the same state as the main module. The entire switchover process is seamless, without output jumps or communication interruptions, and is imperceptible to the operator. After the switchover is complete, the system continues to monitor the status of the original main module at the same interval, records the fault log, and uploads alarm information to the host computer.

[0047] 4. Switchback Mechanism During the stable operation of the standby module, the central control unit continuously monitors the disconnected original main module. When normal heartbeat responses are received for three consecutive monitoring cycles without any fault type being triggered, the original main module is determined to be repaired. Simultaneously, the system checks preset switchback conditions: the standby module has been running stably for more than 30 minutes, there are no other system-level alarms, and the barrel is in steady-state production (temperature fluctuations in each section ≤ ±0.5℃). When these conditions are met, a switchback is executed: first, the output enable of the standby module is disconnected; after a 50ms delay, the original main module is reactivated, and the current operating parameters are synchronized from the standby module back to the original main module. Then, the standby module is switched back to standby mode, restoring the default architecture of main operation and standby standby.

[0048] 5. Self-learning to establish energy consumption benchmarks The system automatically initiates self-learning after the barrel enters a stable production condition. The stable operating condition is determined by the following criteria: temperature fluctuations in each heating section ≤ ±0.5℃, no start / stop alarms, and constant production speed. During the stable operating period, the central control unit collects real-time power, cumulative energy consumption, duty cycle, steady-state temperature, and ambient temperature for each heating section (feeding section, melting section, homogenization section) at a sampling frequency of once per second. A weighted average filtering algorithm is used to eliminate abnormal fluctuations, and a standard time-series energy consumption baseline curve is generated by fitting data for each section. The system automatically relearns and dynamically updates the baseline curve every 7 days to adapt to seasonal changes and equipment aging.

[0049] 6. Energy consumption classification response and fault diagnosis The system compares the actual energy consumption curves of each segment with the baseline curve point by point in real time and calculates the deviation rate. A first preset threshold (deviation rate ≤ 5%) and a second preset threshold (deviation rate > 15%) are set. When the deviation rate is ≤ 5%, it is considered a normal deviation, and monitoring continues without intervention. When 5% < deviation rate ≤ 15%, it is considered a moderate deviation, and the host computer displays an energy consumption warning. Simultaneously, without changing the target temperature of each segment, the system automatically fine-tunes the PID parameters (proportional band, integral time, derivative time) or heating output duty cycle to compensate for the energy consumption deviation until the deviation rate returns to the normal range. When the deviation rate exceeds 15% and persists for more than 3 monitoring cycles, it is judged as a serious deviation, immediately triggering an equipment-level alarm, disabling automatic parameter correction, and initiating fault tracing and diagnosis: If a combination of increased energy consumption, slower heating, and increased steady-state duty cycle is detected, it is diagnosed as "heating element aging fault," and a "recommend replacing heating device" prompt is sent; if high energy consumption, increased temperature fluctuation, and increased heat dissipation loss are detected, and heating element aging is ruled out, it is diagnosed as "insulation layer damage fault," and a "recommend repairing insulation layer" prompt is sent. When the heating element is diagnosed as severely aged and the degree of aging exceeds the preset safety threshold, the system can trigger in advance to switch the corresponding segment output to the backup heating module, achieving predictive redundancy switching.

[0050] 7. Balanced heating and temperature control During the heating process, three K-type thermocouples (model MT-2052J-M12×1-φ5×300-2.5M, measuring range 0-500℃, accuracy ±0.1℃) collect real-time temperature data from the feeding section, melting section, and homogenization section, respectively, and feed the data back to the central control unit. The central control unit compares the actual temperature with the target temperature (180℃ / 220℃ / 200℃), calculates the required heating power for each section using a PID algorithm, and independently adjusts the power of the heating resistance coils in each section via a thyristor voltage regulation module (model SAP4860D+G60). Simultaneously, the system employs a soft-start strategy: the heating target value is set in segments, and the system automatically allocates the heating time and sequence for each zone, suppressing starting current surges and achieving rapid and stable temperature rise.

[0051] 8. Energy consumption statistics and data storage The energy consumption acquisition module calculates energy consumption for each segment and cycle by integrating real-time cumulative power and working time. The central control unit stores data such as real-time power, cumulative energy consumption, time-period energy consumption, deviation records, and diagnostic results in non-volatile memory (EEPROM) and uploads it to the host computer via a communication unit (primary RS485 or backup TCP). The host computer displays the energy consumption baseline, real-time energy consumption, deviation trend, fault log, and equipment health status for each segment in the form of curves and reports. It supports energy consumption threshold settings and over-limit alarms, and outputs hourly energy consumption reports.

[0052] like Figure 7 As shown, the energy consumption page is divided into two sections. The upper section displays the real-time voltage, current, and power of each equipment unit (hot runner, barrel 3, barrel 2, barrel 1, pressurization, injection, pre-plasticizing), with all values ​​initially displayed as 0.0. The middle section displays the total system power and total cumulative power consumption, both initially set to 0.0. The lower section lists historical energy consumption records in reverse chronological order in a table format. Each record includes a serial number, date / hour / minute, total power consumption (kWh), and total power consumption per hour (kWh / Hr), allowing users to easily track power consumption over different time periods. Through this page, operators can intuitively monitor the real-time electrical parameters of each heating unit and the entire machine, and use historical data tables to analyze energy consumption trends, providing data support for energy-saving optimization.

[0053] 9. Normal heating cycle and its end The system continues to run according to the above process until the heating time reaches 60 minutes. The central control unit receives the stop command from the host computer, shuts down the heating execution unit, completes one heating cycle, and stores the cumulative energy consumption, deviation records, fault logs, etc., to the data storage module for subsequent query, analysis, and process optimization.

[0054] In the above process, if the main communication module or the main heating output module experiences a permanent failure during operation, the redundancy switching mechanism will automatically execute (step 3) to ensure uninterrupted heating; if energy consumption is abnormal, the graded response mechanism will automatically intervene or issue an alarm (step 6); if repair or replacement of components is required, the back-off mechanism (step 4) and fault diagnosis prompts (step 6) provide complete operation and maintenance support. This implementation method covers the entire process of system initialization, monitoring, switching, back-off, self-learning, energy consumption grading, balanced heating, and data storage, achieving highly reliable, low-energy-consumption, and highly uniform barrel heating control.

[0055] Example 2: As Figure 8As shown, this embodiment also provides a barrel heating control system 800, including a central control unit 801 configured to periodically monitor the status of the main communication module and / or the main heating output module, and trigger redundancy switching when a fault is detected in multiple consecutive monitoring cycles; a communication unit 802, including a main communication module 802a and a backup communication module 802b, both bidirectionally connected to the central control unit 801, for realizing data interaction between the central control unit and the host computer; a heating execution unit 803, including a main heating output module 803a and a backup heating output module 803b, both electrically connected to the central control unit 801, for receiving heating commands and driving the barrel heating element; a fault monitoring and redundancy switching unit 804 configured to disconnect the main module, activate the corresponding backup module, and synchronize the operating status after confirming a permanent fault; and an energy consumption statistics and intelligent diagnosis unit 805 configured to self-learn to establish a standard energy consumption benchmark for each heating section and monitor the actual energy consumption deviation based on the standard energy consumption benchmark, so as to automatically optimize the control parameters when the deviation is lower than a first preset threshold, and diagnose the fault type based on energy consumption characteristics when the deviation is higher than a second preset threshold.

[0056] It should be understood that the barrel heating control system 800 achieves a highly reliable, low-energy-consumption, and uniformly heated control effect through the coordinated operation of the central control unit 801, communication unit 802, heating execution unit 803, fault monitoring and redundancy switching unit 804, and energy consumption statistics and intelligent diagnosis unit 805. Specifically, the fault monitoring and redundancy switching unit 804 can disconnect the main module, activate the backup module, and synchronize the operating status after confirming a permanent fault, ensuring continuous and uninterrupted system operation and improving the equipment's fault resistance. The energy consumption statistics and intelligent diagnosis unit 805, based on energy consumption benchmarks established through self-learning, adaptively optimizes control parameters or diagnoses fault types according to the degree of deviation, integrating energy saving and maintenance guidance. At the same time, the central control unit 801 also supports balanced heating adjustment to avoid local overheating or underheating. The complementary functions and data communication of each unit form a closed-loop control system from fault prevention, seamless switching to energy consumption optimization, and intelligent diagnosis, effectively solving the combined defects of low reliability, uncontrollable energy consumption, and uneven heating in existing technologies.

[0057] Figure 9The diagram illustrates the main circuit and control logic of the heating actuator 803 in the barrel heating control system. In the diagram, three-phase power is supplied via circuit breakers QF8 to QF12 (all C32-1P-20A) to the heating elements in the hot runner (manifold, 5kW), nozzle (Nozzle), barrel zone 1 (BARREL 1, 750×6W), barrel zone 2 (BARREL 2, 750×6W), and barrel zone 3 (BARREL 3, 750×6W+500W), respectively. The on / off state of each heating circuit is controlled by corresponding solid-state relays SSR1 to SSR5. The input terminals of the SSRs receive output signals OUT1 to OUT5 (typically PWM or switching signals) from the central control unit (PLC). Figure 9 The circuit also includes a manual switch for forcibly cutting off a specific heating element during commissioning or maintenance. When the central control unit 801 issues a heating command, the corresponding SSR (Self-Signaling Regulator) is activated, powering the heating element. When the temperature reaches the set value or heating needs to be stopped, the SSR is deactivated, cutting off power to the heating element. This circuit achieves independent driving and safety protection for multiple heating elements, while the contactless switching characteristics of the SSR ensure rapid response and long lifespan.

[0058] Figure 10 The system's power distribution and control core connection circuit are demonstrated. AC 220V is connected to the control transformer via circuit breaker QF15 (6A), outputting 220V / 220V isolation voltage, which then powers three switching power supplies PSU1 (24V / 150W), PSU2 (10V / 35W), and PSU3 (24V / 200W) via circuit breaker QF14 (10A). PSU1 provides 24V operating power to the PLC and HMI; PSU2 provides 10V excitation power to the electronic ruler (position sensor); and PSU3 provides 24V power to other field devices (such as intermediate relay coils and sensors). The PLC, as the central control unit, connects to the HMI (Human-Machine Interface) via a communication interface, receiving commands from the host computer and providing feedback on the system status. Pins 4 and 9 in the diagram are one of the communication ports of the main communication module 802a (such as a 485 communication module), pins 7 and 8 are another communication port of the 485 communication module, and a physical interface is reserved for easy connection to MES. This circuit provides a stable, multi-voltage power supply for the entire control system and enables the execution of control logic and human-machine interaction through the PLC and HMI.

[0059] Building upon the above embodiments, the system in this embodiment also supports predictive maintenance and adaptive scheduling based on historical trends in energy consumption deviation. Specifically, the energy consumption statistics and intelligent diagnostic unit 805 not only monitors the current degree of deviation in real time but also records the energy consumption deviation rate change curves of each heating section over a past period (e.g., 7 days, 30 days). It then uses linear regression or exponential smoothing algorithms to predict the energy consumption deviation trend for a future period (e.g., one week). When the prediction results indicate that the energy consumption deviation rate of a certain heating section will exceed a second preset threshold (severe deviation threshold) within a preset time (e.g., within 3 days), the system generates a predictive maintenance warning in advance and displays it on the host computer interface in the form of "Severe deviation is expected in XX days; it is recommended to schedule maintenance in advance." Simultaneously, the system automatically adjusts the adaptive optimization strategy for this segment based on the predicted trend: if the predicted deviation trend is a slow increase, the adjustment step size of the adaptive optimization is gradually widened to more actively compensate and delay deterioration; if the predicted trend is a sharp increase, the target temperature upper limit for this segment is proactively reduced (within the process allowable range) to reduce thermal stress, and the heating output priority of this segment is lowered, allowing adjacent heating segments to share some of the heat, thereby extending the effective service life of the heating elements in this segment and avoiding production interruptions. This embodiment upgrades energy consumption deviation from "real-time diagnosis" to "trend prediction + forward scheduling," achieving a leap from passive response to proactive prevention, thereby improving the system's intelligence level and equipment lifecycle management capabilities.

[0060] Furthermore, this system introduces a thermal coupling decoupling algorithm and a dynamic power redistribution mechanism between multiple heating sections. Specifically, the central control unit pre-stores or identifies the heat transfer coefficient matrix between adjacent heating sections (such as the thermal influence coefficient of zone 1 to zone 2 of the barrel, and the thermal influence coefficient of zone 2 to zone 3). When a heating section switches to the backup output module due to a fault or is determined by energy consumption diagnosis to be aging of the heating element and requiring power reduction, the system does not simply adjust independently by that section. Instead, it initiates dynamic power redistribution: based on the thermal coupling coefficient matrix, a portion of the heating power that should have been borne by that section is proportionally redistributed to adjacent healthy sections with strong thermal coupling. At the same time, the PID settings of adjacent sections are fine-tuned, so that adjacent sections, in addition to completing their own temperature control, additionally compensate for the heat loss of the faulty or aging section, thereby maintaining the uniformity of the overall temperature distribution of the barrel.

[0061] For example, when the output efficiency of the melting section decreases due to the aging of heating elements, the system automatically increases the power output of the feeding section and the homogenization section (within their respective safe limits), using the thermal conduction effect of the metal barrel to compensate for the heat deficit in the melting section, so that the actual temperature of the melting section can still be stabilized near the target value. In this way, redundancy switching, energy consumption diagnosis and multi-segment thermal coupling control are deeply integrated to achieve a coordinated heating mode of "inter-segment mutual assistance". Even if the performance of individual heating sections deteriorates, the overall heating uniformity can be guaranteed, thereby reducing the risk of overall machine performance degradation due to single-segment failure or aging.

[0062] It should be noted that for the same or similar technical solutions as in Embodiment 1, please refer to Embodiment 1, and will not be repeated here.

[0063] Example 3: This example provides a barrel heating controller, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as in Example 1.

[0064] Example 4: Combination Figure 11 For illustrative purposes, this embodiment also discloses a specific implementation of a computer-readable storage medium 1100. This computer-readable storage medium 1100 can be configured wholly or partially within a physical computer, server, cluster server, or data center.

[0065] In this embodiment, the computer-readable storage medium 1100 stores computer program instructions 1101. The computer program instructions 1101 are read and executed by a processor 1102 to perform the steps in the barrel heating control method disclosed in Embodiment 1.

[0066] Optionally, the computer-readable storage medium 1100 can be configured as a server, and the server runs on a physical device used to build a private cloud, hybrid cloud, or public cloud. Additionally, the computer-readable storage medium 1100 can also be configured as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), or erasable read-only memory (EROM). Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0067] The computer-readable storage medium 1100 is used to store a program, and the processor 1102, upon receiving an execution instruction, executes the barrel heating control method disclosed in Embodiment 1.

[0068] Meanwhile, the processor 1102 disclosed in this embodiment may be an integrated circuit chip with signal processing capabilities. The processor 1102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0069] The technical solution of the same part in the computer-readable storage medium 1100 disclosed in this embodiment as in Embodiment 1 and / or Embodiment 2 is described in Embodiment 1 and / or Embodiment 2, and will not be repeated here.

[0070] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0072] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for controlling barrel heating, characterized in that, The method includes: Periodically monitor the status of the main communication module and / or the main heating output module; If a fault is detected in multiple consecutive monitoring cycles, the redundancy switching mechanism is triggered to switch to the backup module and synchronize the status. The system establishes a standard energy consumption benchmark for each heating section through self-learning and monitors the actual energy consumption deviation based on the standard energy consumption benchmark. When the deviation is lower than the first preset threshold, the system automatically optimizes the control parameters or diagnoses the fault type based on the energy consumption characteristics when the deviation is higher than the second preset threshold. The method of diagnosing fault types based on energy consumption characteristics when the deviation exceeds a second preset threshold specifically includes: when the actual energy consumption deviates from the standard energy consumption benchmark by a degree exceeding the second preset threshold and continues for more than a set time, the fault is distinguished as either heating element aging fault or insulation layer damage fault based on the combination of energy consumption change characteristics and temperature response characteristics; wherein, when a combination of characteristics is detected, such as continuously increasing energy consumption, slowing heating rate, and significantly increased output duty cycle required for steady-state operation, the fault is diagnosed as heating element aging fault; when a combination of continuously high energy consumption, significantly increased temperature fluctuation amplitude, and continuously increased heat dissipation loss is detected, and the possibility of heating element fault is ruled out, the fault is diagnosed as insulation layer damage fault.

2. The method according to claim 1, characterized in that, The periodic monitoring of the status of the main communication module and / or the main heating output module specifically includes: The central control unit sends status query commands to the main communication module and / or the main heating output module at a preset fixed period, and receives status response signals containing the module's working status and verification information.

3. The method according to claim 1, characterized in that, If a fault is detected in multiple consecutive monitoring cycles, a redundancy switching mechanism is triggered, including: If a fault is detected during each monitoring cycle, the counter is incremented; if no fault is detected, the counter is reset to zero. The redundancy switching mechanism is triggered when the counter continuously accumulates to a preset fault count threshold.

4. The method according to claim 3, characterized in that, The faults include communication faults and output faults; wherein, the communication faults include at least communication timeout faults and data verification error faults, and the output faults include at least output open circuit faults, output short circuit faults, output no response faults, and module crash faults.

5. The method according to claim 1, characterized in that, The switching to the backup module and synchronizing the status includes: Disconnect the output enable signal of the faulty main communication module or main heating output module; Activate the corresponding backup communication module or backup heating output module and synchronize the current operating control parameters to the backup module.

6. The method according to claim 1, characterized in that, The self-learning process establishes standard energy consumption benchmarks for each heating section, including: Automatically identifies the stable production conditions of the barrel; During the continuous operation under stable conditions, energy consumption parameters and temperature parameters of each heating section are collected to fit and generate a standard energy consumption benchmark curve for each heating section, and the benchmark curve is dynamically updated according to a preset update cycle.

7. The method according to claim 1, characterized in that, The automatic optimization of control parameters when the deviation is below a first preset threshold includes: When the actual energy consumption deviates from the standard energy consumption benchmark by less than the first preset threshold, the heating control parameters are automatically adjusted to compensate for the energy consumption deviation without changing the target temperature of each heating section.

8. The method according to claim 1, characterized in that, After switching to the backup module and synchronizing the status, the process also includes: Continuously monitor the status of the disconnected original main module; When the original main module is detected to have recovered to normal and the preset switchback conditions are met, the running status will be switched back from the standby module to the original main module.

9. A barrel heating control system, characterized in that, include: The central control unit is configured to periodically monitor the status of the main communication module and / or the main heating output module, and trigger redundancy switching when a fault is detected in multiple consecutive monitoring cycles. The communication unit includes a main communication module and a backup communication module, both of which are bidirectionally connected to the central control unit and are used to realize data interaction between the central control unit and the host computer. The heating execution unit includes a main heating output module and a backup heating output module, both of which are electrically connected to the central control unit and are used to receive heating commands and drive the barrel heating element. The fault monitoring and redundancy switching unit is configured to disconnect the main module, activate the corresponding backup module, and synchronize the operating status after a permanent fault is confirmed. The energy consumption statistics and intelligent diagnosis unit is configured to establish a standard energy consumption benchmark for each heating section through self-learning and monitor the actual energy consumption deviation based on the standard energy consumption benchmark. When the deviation is lower than a first preset threshold, the control parameters are automatically optimized, and when the deviation is higher than a second preset threshold, the fault type is diagnosed based on the energy consumption characteristics. The method of diagnosing fault types based on energy consumption characteristics when the deviation exceeds a second preset threshold specifically includes: when the actual energy consumption deviates from the standard energy consumption benchmark by a degree exceeding the second preset threshold and continues for more than a set time, the fault is distinguished as either heating element aging fault or insulation layer damage fault based on the combination of energy consumption change characteristics and temperature response characteristics; wherein, when a combination of characteristics is detected, such as continuously increasing energy consumption, slowing heating rate, and significantly increased output duty cycle required for steady-state operation, the fault is diagnosed as heating element aging fault; when a combination of continuously high energy consumption, significantly increased temperature fluctuation amplitude, and continuously increased heat dissipation loss is detected, and the possibility of heating element fault is ruled out, the fault is diagnosed as insulation layer damage fault.

10. A barrel heating controller, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in claim 1.

Images