A thermal printer motor over-temperature protection method

Abstract

This invention discloses a method for over-temperature protection of a thermal printer motor, aiming to solve the problem of difficult motor over-temperature protection when there is no temperature sensor. The method consists of two stages: offline modeling and online protection. Offline, operating parameters such as the printing load coefficient are selected, and motor temperatures under normal and extreme operating conditions are collected to construct a multivariate nonlinear regression temperature learning model. Online, after initializing the model parameters, the motor status is monitored periodically. Operating parameters are collected during operation, and idle time is collected during idle operation. These are input into the model to obtain the real-time simulated temperature. After dynamic calibration, if the temperature exceeds the protection threshold, the printer is stopped; if it falls below the recovery threshold, the protection is released. This invention requires no additional sensors, achieves accurate protection through model prediction and dual-state management, adapts to complex operating conditions, and improves equipment reliability and lifespan.

Description

Technical Field

[0001] This invention belongs to the field of thermal printer technology, and particularly relates to a method for over-temperature protection of a thermal printer motor. Background Technology

[0002] Thermal printers are widely used in various fields such as retail POS, logistics waybill printing, and medical invoice printing due to their advantages of fast printing speed, low noise, and simple consumables. The stepper motor of the printer, as the core power component, directly determines the printing efficiency and equipment stability.

[0003] In continuous high-load printing scenarios, the power loss of the stepper motor is converted into a large amount of heat, causing the motor body temperature to rise rapidly. In existing technology, most thermal printers do not have temperature detection sensors installed in the motor part, making it impossible to directly obtain the real-time temperature of the motor.

[0004] When the motor temperature exceeds the safety threshold, the equipment cannot trigger the protection mechanism in time. The continuous high temperature will not only cause the insulation performance of the motor winding to decline and the service life to be shortened, but may also cause the plastic parts around the motor to deform due to heat, causing problems such as paper jams and mechanical transmission failures. In severe cases, it may even cause the motor to burn out, which will greatly increase the maintenance cost and downtime of the equipment.

[0005] Therefore, how to achieve over-temperature protection for the motor in the absence of a temperature sensor has become a technical problem that urgently needs to be solved in the research and development of thermal printer equipment. Summary of the Invention

[0006] The purpose of this invention is to provide a method for over-temperature protection of a thermal printer motor, so as to solve the problems mentioned in the background art.

[0007] In view of this, the present invention provides a method for over-temperature protection of a thermal printer motor, including an offline modeling stage and an online protection stage, the specific steps of which are as follows: S1, Offline Modeling Stage, including: S11. Select a typical set of operating parameters for the printer stepper motor. The set of operating parameters includes multiple operating conditions such as printing load coefficient, motor speed, running time, and idle time. S12. Under various working conditions, the real-time temperature of the motor surface is collected by a high-precision temperature data logger to form a sample dataset of working condition parameter groups and measured temperature values ​​that correspond one-to-one. The working condition combinations cover extreme working conditions and normal working conditions. The extreme working conditions are high-temperature environment superimposed with full load continuous operation. S13. Based on the sample dataset, a temperature learning model is constructed using a multivariate nonlinear regression algorithm; S2, Online Protection Phase, includes: S21. Initialization: Load the parameters of the temperature learning model and preset the initial temperature of the motor; S22. Continuously monitor the motor's operating status at regular intervals; S23. Collect corresponding parameters according to the motor's operating status: If the motor is in operation, collect the current operating condition parameters; If the motor is not in operation, the idle time parameter is collected; The collected parameters are input into the temperature learning model, which outputs the real-time simulated temperature of the motor. S24. Perform dynamic adaptive calibration on the temperature learning model; S25. Determine whether the real-time simulated temperature is greater than the over-temperature protection threshold: if it is greater, trigger the over-temperature protection state and control the motor to stop; if it is not greater, return to step S22 to continue monitoring. S26. When the motor is in over-temperature protection state, continuously determine whether the real-time simulated temperature is less than the over-temperature recovery threshold: if it is less than the threshold, release the over-temperature protection state and return to step S22; if it is not less than the threshold, maintain the shutdown and protection state.

[0008] A further embodiment of the present invention is that the dynamic adaptive calibration in step S24 is to fine-tune the model parameters using 1 to 3 measured temperature points, wherein the measured temperature points are temperature acquisition points randomly selected during motor operation.

[0009] A further embodiment of the present invention is that the computational logic and over-temperature protection logic of the temperature learning model are both implemented through MCU firmware integration, and the MCU firmware has a built-in running / idle dual-state management module.

[0010] A further embodiment of the present invention is that the dynamic adaptive calibration operation in step S24 can be performed both during the equipment factory testing stage and during on-site operation. The calibration during the factory testing stage is a basic calibration, while the calibration during operation is a dynamic compensation calibration.

[0011] A further embodiment of the present invention is that the parameter dimensions of the extreme working conditions include the temperature range of the high-temperature environment and the duration of full-load printing, and these parameters are all included in the typical working condition parameter set, wherein the temperature range of the high-temperature environment is 40℃-60℃.

[0012] A further embodiment of the present invention is that the timed continuous monitoring in step S22 adopts a polling mechanism with a fixed time interval. The time interval can be preset according to the usage scenario of the printer, and the polling time interval ranges from 100ms to 500ms.

[0013] A further embodiment of the present invention is that the over-temperature protection threshold and the over-temperature recovery threshold are set with different values, and both thresholds can be adjusted according to the hardware characteristics of the motor. The over-temperature protection threshold is 10℃-15℃ higher than the over-temperature recovery threshold.

[0014] A further embodiment of the present invention is that the high-precision temperature data logger in step S12 has an accuracy of not less than ±0.5℃ in collecting the surface temperature of the motor, and the collection frequency is at least once per second.

[0015] A further embodiment of the present invention is that the input parameters of the temperature learning model can also be expanded to include the ambient temperature parameters of the printer, which are collected by the ambient temperature sensing module built into the printer.

[0016] A further embodiment of the present invention is that, while maintaining the motor in a stopped state, the printer synchronously outputs an over-temperature protection warning message, which is presented by flashing an indicator light or sounding a buzzer alarm.

[0017] The beneficial effects of this invention are: 1. Eliminating the need for additional hardware sensors reduces costs. By abandoning traditional temperature sensor detection methods, it achieves dynamic simulation of motor temperature and over-temperature protection by constructing a temperature learning model and combining it with collected operating parameters. This approach eliminates the need for internal temperature sensors and related circuitry within the printer, effectively simplifying the equipment's hardware structure and reducing manufacturing costs and assembly complexity.

[0018] 2. Accurate temperature prediction and timely protection response: Based on multi-condition measured data, a temperature learning model is built. Input parameters cover key operating condition dimensions such as print load coefficient, motor speed, running time, and idle time, enabling accurate simulation of motor temperature under different operating conditions. Coupled with a dynamic adaptive calibration mechanism, model parameters are fine-tuned using a small number of measured temperature points, further improving temperature simulation accuracy. When the simulated temperature reaches the over-temperature protection threshold, the system can immediately trigger shutdown protection to prevent damage to the motor and surrounding components due to high temperatures.

[0019] 3. Dual-state intelligent management adapts to complex printing scenarios. The MCU firmware integrates dual-state management logic (run / idle), collecting corresponding parameters and calculating simulated temperatures for both motor running and idle states. This design is adaptable to diverse scenarios such as intermittent printing and continuous high-load printing, ensuring reliable over-temperature protection under high-load conditions without affecting normal printing efficiency due to frequent protection triggers.

[0020] 4. The threshold is flexible and adjustable, with strong compatibility. The over-temperature protection threshold and over-temperature recovery threshold can be set differently according to the hardware characteristics of different motor models, adapting to various specifications of thermal printer stepper motors. The model input parameters can also be expanded to include ambient temperature parameters, further improving the accuracy of temperature prediction under different operating environments and enhancing the versatility and compatibility of the technical solution.

[0021] 5. Visualized protection status facilitates troubleshooting. When the motor is in over-temperature shutdown protection mode, the printer will output a prompt message through flashing indicator lights or a buzzer alarm. This design can promptly remind operators of the current status of the equipment, facilitating them to take measures such as stopping the machine for cooling. It also provides clear guidance for equipment maintenance personnel to troubleshoot faults, reducing the difficulty of after-sales maintenance. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the steps of the method of the present invention; Figure 2 This is the flowchart of this method. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0024] In the description of this application, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0025] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and are not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] It should be noted that in the description of this application, the directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0027] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0028] This embodiment provides a method for over-temperature protection of a thermal printer motor, including an offline modeling stage and an online protection stage. The specific steps are as follows: S1, Offline Modeling Stage, including: S11. Select a typical set of operating parameters for the printer's stepper motor. This set includes multiple operating conditions such as print load factor, motor speed, runtime, and idle time. This step overcomes the limitations of traditional methods that rely solely on a single load parameter to assess motor heating. By combining multiple operating parameters, it comprehensively covers the motor's operating state under different printing tasks, enabling accurate tracing of the motor's heating patterns. This provides a comprehensive and scientific data foundation for subsequent temperature model construction, solving the technical pain point of large temperature prediction deviations caused by single operating parameters in existing technologies. It proactively selects four core parameters: "print load factor + motor speed + runtime + idle time," covering the entire state of the motor from startup and high-load operation to shutdown and heat dissipation, rather than focusing solely on a single load or current parameter as in existing technologies. This step provides a "full-scenario input dimension" for subsequent temperature prediction, resolving the temperature estimation deviation problem caused by single parameters in existing technologies.

[0029] S12. Under various operating conditions, the real-time temperature of the motor surface is collected using a high-precision temperature data logger, forming a one-to-one sample dataset of operating condition parameter groups and measured temperature values. The operating condition combinations cover extreme and normal operating conditions. The extreme operating condition is a high-temperature environment combined with continuous full-load operation. This step, by collecting data across all scenarios covering extreme and normal operating conditions, overcomes the limitation of existing technologies that only collect data from normal operating conditions. The resulting one-to-one sample dataset allows the subsequently constructed temperature learning model to adapt to complex and ever-changing real-world application scenarios, avoiding model failure due to missing extreme operating condition data. This ensures that the model still has reliable temperature prediction capabilities under high load and high-temperature environments. In addition to normal operating conditions, the extreme operating condition of "high-temperature environment + continuous full-load operation" is additionally covered. The one-to-one correspondence data of "operating condition parameter group - measured temperature value" is collected using a high-precision logger to form a complete sample library. Existing technologies rarely collect data for extreme operating conditions, causing their protection schemes to easily fail in harsh scenarios. This step supplements the data with extreme operating condition data to ensure that the model can still make accurate predictions in complex environments.

[0030] S13. Based on the aforementioned sample dataset, a temperature learning model is constructed using a multivariate nonlinear regression algorithm. This step utilizes the multivariate nonlinear regression algorithm to fit the correlation between multidimensional operating parameters and temperature. Compared to traditional linear fitting algorithms, it can accurately capture the complex nonlinear mapping relationship between operating parameters and motor temperature, significantly improving the accuracy of temperature prediction. This solves the technical problem of accurately estimating motor temperature under sensorless conditions. The multivariate nonlinear regression algorithm is used to fit the complex mapping relationship between multiple parameters and temperature, rather than the linear fitting or direct control without an algorithm found in existing technologies. This step breaks through the linear understanding of "single parameter-temperature" and can accurately capture the nonlinear temperature changes under the combined effects of parameters such as load, speed, and duration (e.g., temperature increases exponentially under high load and high speed), solving the problem that existing technologies cannot quantify the coupled heating effects of multiple parameters.

[0031] S2, Online Protection Phase, including: S21, Initialization: Loading the parameters of the temperature learning model and preset the initial motor temperature. This step, by preloading the model parameters and preset the initial temperature, allows the system to enter the temperature monitoring state immediately upon startup, without waiting for the parameter self-learning process. This effectively shortens the response startup time of the protection system and solves the problem of the blank period without temperature monitoring at the initial startup stage in the prior art.

[0032] S22. Continuously monitor the motor's operating status at regular intervals. This step continuously monitors the motor's operating status through periodic polling, capturing the switching between the motor's running and idle states in real time. This provides accurate status information for subsequent parameter acquisition, avoiding parameter acquisition errors caused by misjudgment of status, and ensuring the continuity and accuracy of temperature prediction.

[0033] S23. Collect corresponding parameters according to the motor's operating status: If the motor is in running state, collect the current operating condition parameters; if the motor is not in running state, collect the idle time parameter; input the collected parameters into the temperature learning model and output the real-time simulated temperature of the motor. This step designs a differentiated parameter collection strategy for the two states of motor operation and idleness. Multi-dimensional operating condition parameters are collected in the running state, and idle time parameters are collected in the idle state. This achieves accurate adaptation of temperature prediction in different states and solves the problem of ignoring the temperature change of the motor in the idle state in traditional technology. Even when the motor is in the idle heat dissipation stage, the temperature change trend can be accurately predicted, and the motor status can be judged in real time. "Load + speed + running time" is collected in the running state, and "idle time" is collected in the idle state, and they are respectively input into the model to calculate the simulated temperature. Existing technologies either ignore the temperature change in the idle state (such as only focusing on the running current in Abstract 1) or use the same parameter collection logic for the running / idle states. This step, through differentiated collection, avoids the computational waste caused by redundant parameters in the running state and can accurately capture the temperature drop trend in the idle state (such as the heat dissipation rate after shutdown), ensuring the accuracy of temperature prediction in all states.

[0034] S24. Perform dynamic adaptive calibration on the temperature learning model. This step, through a dynamic adaptive calibration mechanism, can correct prediction deviations caused by hardware aging and environmental changes during long-term operation, ensuring the temperature learning model maintains high-precision prediction capabilities. This solves the technical defect of traditional fixed models, which are prone to decreased prediction accuracy over time. Instead of the existing "one-time calibration / no calibration," the model parameters are fine-tuned using 1-3 measured temperature points during equipment manufacturing or operation. Existing technologies, once the model or protection threshold is set, cannot adapt to hardware aging (such as increased motor winding resistance) or environmental temperature changes (such as high temperatures in a computer room during summer). This step dynamically corrects the model using a small number of measured points, ensuring that temperature prediction accuracy does not decay during long-term use, thus solving the problem of "calibration lag leading to protection failure" in existing technologies.

[0035] S25. Determine whether the real-time simulated temperature exceeds the over-temperature protection threshold: if it does, trigger the over-temperature protection state and control the motor to stop; if it does not exceed the threshold, return to step S22 to continue monitoring. This step, by setting the over-temperature protection threshold and executing the judgment logic, realizes automatic identification and rapid shutdown protection of motor over-temperature under sensorless conditions. Compared with the existing technology of no protection or delayed protection, it can cut off the motor power supply in time, avoiding irreversible damage to the motor caused by continuous high temperature, such as aging of the winding insulation layer and deformation of mechanical parts.

[0036] S26. When the motor is in over-temperature protection mode, continuously check whether the real-time simulated temperature is lower than the over-temperature recovery threshold: if it is lower, release the over-temperature protection mode and return to step S22 to continue monitoring; if it is not lower, maintain the shutdown and protection state. This step, by setting the over-temperature recovery threshold and continuously monitoring, achieves automatic recovery of the motor after the temperature drops to a safe range, without the need for manual intervention to restart the equipment. It also avoids frequent start-stop problems caused by temperature fluctuations, balancing equipment safety and ease of use. This is achieved by setting differentiated thresholds (e.g., a difference of 10-15℃) for the "over-temperature protection threshold (triggers shutdown)" and the "over-temperature recovery threshold (releases shutdown)," instead of the existing "single threshold trigger / recovery." Existing technologies, if only a single threshold is set, are prone to "frequent start-stop" of the motor due to temperature fluctuations (e.g., the temperature repeatedly jumps around the threshold); this solution, through a dual-threshold design, ensures rapid shutdown in case of over-temperature and avoids premature recovery before the temperature stabilizes, balancing safety and equipment stability.

[0037] In this embodiment, the dynamic adaptive calibration in step S24 involves fine-tuning the model parameters using 1 to 3 measured temperature points, which are randomly selected temperature sampling points during motor operation. This implementation scheme, by selecting a small number of random measured temperature points for parameter fine-tuning, significantly reduces the hardware dependence on temperature acquisition and the amount of data processing while ensuring calibration accuracy. It eliminates the need for additional sensors and achieves model calibration using only a small number of measured points, solving the problem of traditional calibration methods requiring large amounts of measured data or additional hardware, thus combining economic efficiency and practicality.

[0038] In this embodiment, both the computational logic of the temperature learning model and the over-temperature protection logic are integrated through MCU firmware, which incorporates a dual-state management module for both running and idle states. This implementation scheme achieves lightweight operation of the protection system by integrating the core logic into the MCU firmware, eliminating reliance on external processors or cloud computing power, thus reducing hardware costs and power consumption. Simultaneously, the built-in dual-state management module accurately distinguishes the motor's operating state, ensuring the efficiency and accuracy of logical judgments, and solving the problems of slow response and high resource consumption associated with traditional software-level protection schemes.

[0039] In this embodiment, the dynamic adaptive calibration operation in step S24 can be performed both during the equipment factory testing phase and during field operation. The calibration during the factory testing phase is a basic calibration, while the calibration during operation is a dynamic compensation calibration. This implementation scheme, through the dual mechanism of factory basic calibration and dynamic compensation calibration during operation, ensures the accuracy of the initial parameters of the model during factory calibration, providing a reliable benchmark for equipment delivery; while the dynamic compensation calibration during operation can cope with factors such as hardware aging and environmental changes during equipment use, achieving model accuracy maintenance throughout the entire lifecycle. This solves the problem that traditional calibration methods can only be performed during the factory testing phase and cannot adapt to changes in actual usage scenarios.

[0040] In this embodiment, the parameters for the extreme operating conditions include the temperature range of the high-temperature environment and the duration of full-load printing. These parameters are all included in the typical operating condition parameter set, and the high-temperature environment range is 40℃-60℃. This implementation scheme, by clearly defining the temperature range and load duration parameters for extreme operating conditions, allows the temperature learning model to accurately simulate the motor's heating behavior in harsh environments. This ensures that the equipment can still achieve reliable over-temperature protection under extreme scenarios of high temperature and full load, solving the protection failure problem caused by the lack of extreme operating condition parameters in existing technologies and improving the environmental adaptability of the equipment.

[0041] In this embodiment, the timed continuous monitoring in step S22 adopts a polling mechanism with a fixed time interval. The time interval can be preset according to the printer's usage scenario, and the polling time interval ranges from 100ms to 500ms. This implementation scheme, by setting an adjustable polling time interval of 100ms to 500ms, can balance the response speed of the protection system with the power consumption of the device. In high-load scenarios with high-frequency printing, the interval can be shortened to improve monitoring sensitivity; in low-frequency printing scenarios, the interval can be extended to reduce system power consumption, thus solving the problem that traditional fixed polling intervals cannot adapt to different usage scenarios.

[0042] In this embodiment, the over-temperature protection threshold and the over-temperature recovery threshold are set with different values, and both thresholds can be adjusted according to the hardware characteristics of the motor. The over-temperature protection threshold is 10℃-15℃ higher than the over-temperature recovery threshold. This implementation scheme effectively avoids the problem of frequent start-stop when the motor temperature fluctuates near the threshold by setting a threshold difference of 10℃-15℃, thus improving the stability of equipment operation. At the same time, the threshold can be adjusted according to the motor hardware characteristics, enhancing the versatility of the protection scheme and making it compatible with thermal printer motors of different models and power, solving the problem of poor adaptability of traditional fixed thresholds.

[0043] In this embodiment, the high-precision temperature data logger in step S12 acquires the motor surface temperature with an accuracy of no less than ±0.5℃ and a acquisition frequency of at least once per second. This implementation scheme provides high-quality sample data for the construction of a temperature learning model through high-precision and high-frequency temperature acquisition. The ±0.5℃ acquisition accuracy ensures the accuracy of the data, and the acquisition frequency of at least once per second can capture the real-time trend of motor temperature changes. This solves the problem of large prediction deviations in models caused by traditional low-precision and low-frequency acquisition, laying a data foundation for subsequent high-precision temperature prediction.

[0044] In this embodiment, the input parameters of the temperature learning model can also be expanded to include the ambient temperature parameters of the printer, which are collected through the printer's built-in ambient temperature sensor module. This implementation scheme further improves the accuracy of temperature prediction by introducing ambient temperature parameters as an extended input to the model. Ambient temperature directly affects the heat dissipation efficiency of the motor; incorporating it into the model allows temperature prediction to better reflect actual usage scenarios, solving the problem of prediction bias caused by traditional models ignoring environmental factors. Furthermore, utilizing the printer's built-in temperature sensor module for data collection eliminates the need for additional hardware, balancing cost and performance.

[0045] In this embodiment, while maintaining the motor in a stopped state, the printer simultaneously outputs an over-temperature protection warning message. This warning message is presented through a flashing indicator light or a buzzer alarm. This implementation scheme, by using flashing indicator lights or a buzzer alarm, can promptly remind operators that the equipment is in an over-temperature protection state, facilitating operators to take measures such as stopping the machine to dissipate heat. It avoids secondary damage caused by unknowingly forcibly restarting the equipment, thus solving the problems of traditional protection schemes lacking warnings and being difficult to troubleshoot, and improving the ease of use and maintenance of the equipment.

[0046] The method in this embodiment is executed in a logical closed loop of "offline modeling - online protection", and the specific process is as follows: The process begins with offline modeling. A typical set of operating parameters for the printer's stepper motor is selected, encompassing core dimensions such as printing load coefficient, motor speed, runtime, and idle time. Then, under various combinations of normal and extreme operating conditions (high-temperature environment combined with continuous full-load operation), a high-precision temperature data logger collects the motor surface temperature in real time, creating a one-to-one dataset of operating parameter sets and measured temperature values. Based on this dataset, a multivariate nonlinear regression algorithm is used to construct a temperature learning model, providing core algorithmic support for online temperature prediction.

[0047] After offline modeling is completed, the system enters the online protection phase. Upon startup, the initialization process begins: loading the parameters of the pre-built temperature learning model and preseting the initial motor temperature to lay the foundation for subsequent temperature monitoring. After initialization, the system initiates a timed continuous monitoring mechanism to determine in real time whether the motor is currently running. If the motor is running, it collects operating parameters such as the current print load factor, running time, and motor speed; if the motor is not running, it collects the idle time parameter. The collected parameters are input into the temperature learning model, which calculates and outputs the real-time simulated motor temperature. Subsequently, dynamic adaptive calibration is performed on the temperature learning model, fine-tuning the model parameters using a small number of measured temperature points to ensure temperature prediction accuracy.

[0048] After calibration, the system first determines whether the real-time simulated temperature exceeds the over-temperature protection threshold. If it does not exceed the threshold, it returns to the timed continuous monitoring stage to continue monitoring the motor's operating status and temperature changes. If it exceeds the over-temperature protection threshold, it immediately triggers the over-temperature protection state, controlling the motor to stop to avoid high-temperature damage. After the motor enters the over-temperature protection state, the system continuously monitors the real-time simulated temperature to determine whether it is below the over-temperature recovery threshold. If the temperature does not drop below the threshold, it maintains the motor stoppage and over-temperature protection state. If the temperature drops below the over-temperature recovery threshold, it releases the over-temperature protection state, returns to the timed continuous monitoring stage, and waits for the motor to restart, forming a complete over-temperature protection closed-loop process.

[0049] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for over-temperature protection of a thermal printer motor, characterized in that, It includes an offline modeling phase and an online protection phase, with the specific steps as follows: S1, Offline Modeling Stage, including: S11. Select a typical set of operating parameters for the printer stepper motor. The set of operating parameters includes multiple operating conditions such as printing load coefficient, motor speed, running time, and idle time. S12. Under various working conditions, the real-time temperature of the motor surface is collected by a high-precision temperature data logger to form a sample dataset of working condition parameter groups and measured temperature values ​​that correspond one-to-one. The working condition combinations cover extreme working conditions and normal working conditions. The extreme working conditions are high-temperature environment superimposed with full load continuous operation. S13. Based on the sample dataset, a temperature learning model is constructed using a multivariate nonlinear regression algorithm; S2, Online Protection Phase, includes: S21. Initialization: Load the parameters of the temperature learning model and preset the initial temperature of the motor; S22. Continuously monitor the motor's operating status at regular intervals; S23. Collect corresponding parameters according to the motor's operating status: If the motor is in operation, collect the current operating condition parameters; If the motor is not in operation, the idle time parameter is collected; The collected parameters are input into the temperature learning model, which outputs the real-time simulated temperature of the motor. S24. Perform dynamic adaptive calibration on the temperature learning model; S25. Determine whether the real-time simulated temperature is greater than the over-temperature protection threshold: if it is greater, trigger the over-temperature protection state and control the motor to stop; if it is not greater, return to step S22 to continue monitoring. S26. When the motor is in over-temperature protection state, continuously determine whether the real-time simulated temperature is less than the over-temperature recovery threshold: if it is less than the threshold, release the over-temperature protection state and return to step S22; if it is not less than the threshold, maintain the shutdown and protection state.

2. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The dynamic adaptive calibration in step S24 involves fine-tuning the model parameters using 1 to 3 measured temperature points, which are temperature collection points randomly selected during motor operation.

3. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The computational logic and over-temperature protection logic of the temperature learning model are both implemented through MCU firmware, which has a built-in dual-state management module for running and idle states.

4. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The dynamic adaptive calibration operation in step S24 can be performed during both the factory testing phase and the on-site operation phase. The calibration during the factory testing phase is a basic calibration, while the calibration during operation is a dynamic compensation calibration.

5. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The parameters for the extreme operating conditions include the temperature range of the high-temperature environment and the duration of full-load printing. These parameters are all included in the typical operating condition parameter set. The high-temperature environment temperature range is 40℃-60℃.

6. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The timed continuous monitoring in step S22 adopts a polling mechanism with a fixed time interval. The time interval can be preset according to the printer's usage scenario, and the polling time interval ranges from 100ms to 500ms.

7. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The over-temperature protection threshold and the over-temperature recovery threshold are set differently, and both thresholds can be adjusted according to the hardware characteristics of the motor. The over-temperature protection threshold is 10℃-15℃ higher than the over-temperature recovery threshold.

8. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The high-precision temperature data logger in step S12 acquires the surface temperature of the motor with an accuracy of not less than ±0.5℃ and an acquisition frequency of at least once per second.

9. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, The input parameters of the temperature learning model can also be expanded to include the ambient temperature parameters of the printer, which are collected by the ambient temperature sensing module built into the printer.

10. The thermal printer motor over-temperature protection method according to claim 1, characterized in that, While keeping the motor stopped, the printer simultaneously outputs an over-temperature protection warning message, which is presented by flashing indicator lights or sounding an alarm.

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