Efficient temperature control electric soldering iron and working method thereof

Abstract

The invention relates to the technical field of electric soldering iron temperature control, and discloses an efficient temperature control electric soldering iron and a working method thereof.The method comprises the steps that a PTC ceramic heating body in a soldering bit is rapidly heated, and when the real-time temperature of the soldering bit reaches a preset switching temperature threshold value, the soldering bit is switched to an alloy resistance heating body for constant-temperature heating; acquiring a sampling temperature of the solder tip, and calculating an instant error, an error change rate and an accumulative error based on a target temperature and the sampling temperature; and inputting the instant error, the error change rate and the accumulative error into a BP neural network for compensation calculation to obtain a compensation factor, and adjusting a pulse width modulation duty ratio instruction of the alloy resistance heating body according to the compensation factor through a fuzzy PID controller to obtain a pulse width modulation duty ratio of the alloy resistance heating body. The PTC ceramic heating body provides high-power sprint in a low-temperature area by utilizing material characteristics, and the alloy resistance heating body realizes linear adjustable precision control in a working temperature area.

Description

Technical Field

[0001] This invention relates to the field of soldering iron temperature control technology, and in particular to a high-efficiency temperature-controlled soldering iron and its working method. Background Technology

[0002] Traditional soldering irons use a single nickel-chromium alloy resistance wire as the heating element. During the start-up phase, the large thermal inertia results in a slow heating response, requiring a long time to heat from room temperature to the working temperature. This seriously affects the continuity and efficiency of soldering operations, especially in scenarios that require frequent start-ups.

[0003] Most existing soldering irons use a bimetallic mechanical temperature control scheme, which regulates the temperature by triggering a temperature threshold to achieve on / off control. This control method has the inherent defect of large temperature fluctuation range. After the soldering iron tip contacts the solder joint, the temperature drops sharply and the recovery time is long, making it impossible to maintain a stable soldering temperature. This can easily lead to cold solder joints or poor solder joints when soldering precision electronic components, making it difficult to guarantee the soldering quality. Summary of the Invention

[0004] This invention provides an efficient temperature-controlled soldering iron and its working method. It achieves a combination of rapid start-up and precise temperature control through a dual-mode heating element dynamic switching mechanism. The PTC ceramic heating element provides high power surge in the low-temperature range by utilizing the material properties, while the alloy resistance heating element achieves linearly adjustable precision control in the working temperature range. This solves the technical problems of slow heating and difficulty in precise temperature control of traditional soldering irons.

[0005] The first aspect of the present invention provides a working method, the working method comprising: The PTC ceramic heating element in the soldering iron tip is rapidly heated. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating. The sampling temperature of the soldering iron tip is obtained, and the instantaneous error, error change rate, and cumulative error are calculated based on the target temperature and the sampling temperature. The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. The pulse width modulation duty cycle command of the alloy resistance heater is then adjusted by a fuzzy PID controller according to the compensation factor.

[0006] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the PTC ceramic heating element in the soldering iron tip is rapidly heated, and when the real-time temperature of the soldering iron tip reaches a preset switching temperature threshold, the process switches to an alloy resistance heating element for constant-temperature heating, including: The main control chip receives the power-on command and applies a pulse width modulation signal to the PTC ceramic heating element, and monitors the real-time temperature of the soldering iron tip through the NTC temperature sensor; Compare the real-time temperature with a preset switching temperature threshold; When the real-time temperature reaches the preset switching temperature threshold, the power supply current of the PTC ceramic heating element is adjusted by the solid-state relay and the alloy resistance heating element is activated, and the pulse width modulation duty cycle of the alloy resistance heating element is adjusted to the preset constant temperature power duty cycle.

[0007] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, obtaining the sampling temperature of the soldering iron tip, and calculating the instantaneous error, error change rate, and cumulative error based on the target temperature and the sampling temperature, includes: The voltage signal output by the NTC temperature sensor is acquired, and the voltage signal is differentially amplified by an operational amplifier and then converted into a high-frequency analog-to-digital converter to obtain the sampling temperature of the soldering iron tip. The difference between the target temperature and the sampled temperature is calculated to obtain the instantaneous error; The error rate of change is calculated based on the instantaneous error of the current sampling period and the instantaneous error of the previous sampling period, and the cumulative error is obtained by summing the instantaneous errors of multiple consecutive sampling periods.

[0008] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. Then, a fuzzy PID controller adjusts the pulse width modulation duty cycle command of the alloy resistance heater according to the compensation factor, including: The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. A first adjustment value for the proportional gain, a second adjustment value for the integral gain, and a third adjustment value for the derivative gain are generated by pre-setting fuzzy rules, and the control output parameters of the fuzzy PID controller are calculated based on the first adjustment value, the second adjustment value, and the third adjustment value. The compensation factor and the control output parameter are used to perform correction calculations to obtain the correction result. The correction result is then added to the pulse width modulation duty cycle of the previous control cycle to obtain the pulse width modulation duty cycle command of the alloy resistance heater.

[0009] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present invention, a first adjustment amount for the proportional gain, a second adjustment amount for the integral gain, and a third adjustment amount for the derivative gain are generated by pre-setting fuzzy rules, and the control output parameters of the fuzzy PID controller are calculated based on the first adjustment amount, the second adjustment amount, and the third adjustment amount, including: The instantaneous error and the rate of change of error are divided into multiple preset fuzzy sets, and the membership values ​​of the instantaneous error and the rate of change of error in each fuzzy set are calculated by the triangular membership function. Calculate the first adjustment amount of the proportional gain, the second adjustment amount of the integral gain, and the third adjustment amount of the derivative gain based on the membership values. The proportional gain reference value is superimposed with the first adjustment amount to obtain the corrected proportional gain; the integral gain reference value is superimposed with the second adjustment amount to obtain the corrected integral gain; and the differential gain reference value is superimposed with the third adjustment amount to obtain the corrected differential gain. The control output parameters of the fuzzy PID controller are obtained by weighted summing of the instantaneous error, the cumulative error, and the error change rate based on the corrected proportional gain, the corrected integral gain, and the corrected derivative gain.

[0010] In conjunction with the first aspect, the fifth implementation of the first aspect of the present invention further includes: The real-time temperature of a continuous sampling period is differentially calculated to obtain the temperature drop rate, and the temperature drop rate is compared with a preset load threshold. When the temperature drop rate exceeds the preset load threshold, the zero-crossing moment of the AC voltage waveform is detected by the optocoupler circuit. At the zero-crossing moment, the power supply circuit of the alloy resistance heating element is connected through the bidirectional thyristor, and the pulse width modulation duty cycle command is increased to the preset boost value. The pulse width modulation duty cycle command is dynamically adjusted according to the temperature recovery rate of the soldering iron tip until the temperature recovers to the target temperature range.

[0011] In conjunction with the first aspect, the sixth implementation of the first aspect of the present invention further includes: Start an idle timer to accumulate standby time. When the standby time reaches a preset time and the temperature drop rate of the soldering iron tip does not exceed a preset drop threshold, reduce the pulse width modulation duty cycle of the alloy resistance heating element to zero and adjust the pulse width modulation duty cycle of the PTC ceramic heating element to the first sleep duty cycle. When the temperature of the soldering iron tip drops to the first sleep temperature threshold, the pulse width modulation duty cycle of the PTC ceramic heating element is adjusted to the second sleep duty cycle; When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain the low-power heat preservation state at the preset sleep temperature.

[0012] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, when the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain a low-power heat preservation state at the preset sleep temperature, including: When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is locked to the third sleep duty cycle; The temperature of the soldering iron tip is continuously monitored by an NTC temperature sensor. When the temperature of the soldering iron tip deviates from the preset sleep temperature, the third sleep duty cycle is finely adjusted to compensate, so that the output power of the PTC ceramic heating element and the heat dissipation power of the environment are dynamically balanced, so as to maintain the constant temperature and heat preservation state of the preset sleep temperature.

[0013] In conjunction with the first aspect, the eighth implementation of the first aspect of the present invention further includes: Calculate the temperature fluctuation variance of the soldering iron tip, and construct a comprehensive feature vector based on the instantaneous error, the error change rate, the cumulative error, and the temperature fluctuation variance; The loss function value is calculated based on the comprehensive feature vector, and the connection weights of the BP neural network are incrementally updated according to the loss function value to obtain the updated BP neural network.

[0014] A second aspect of the present invention provides a high-efficiency temperature-controlled soldering iron, the high-efficiency temperature-controlled soldering iron comprising: The heating module is used to rapidly heat the PTC ceramic heating element in the soldering iron tip. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating. The calculation module is used to obtain the sampling temperature of the soldering iron tip, and calculate the instantaneous error, error change rate and cumulative error based on the target temperature and the sampling temperature; The compensation module is used to input the instantaneous error, the error change rate, and the cumulative error into a BP neural network for compensation calculation to obtain a compensation factor, and then use a fuzzy PID controller to adjust the pulse width modulation duty cycle command of the alloy resistance heating element according to the compensation factor.

[0015] Compared with existing technologies, this invention has the following advantages: It combines rapid start-up and precise temperature control through a dual-mode heating element dynamic switching mechanism. The PTC ceramic heating element utilizes material properties to provide high-power surges in the low-temperature range, while the alloy resistance heating element achieves linearly adjustable precision control within the operating temperature range, solving the technical problems of slow heating and inaccurate temperature control in traditional soldering irons. Combining BP neural network dynamic compensation with fuzzy PID fusion control, the neural network compensation factor can correct the fuzzy PID output in real time to offset interference from non-modeling factors, effectively suppressing temperature fluctuations and improving temperature control accuracy. Zero-crossing triggering technology, combined with bidirectional thyristors, achieves instantaneous power boost during load impact, significantly shortening the recovery time while suppressing electromagnetic interference, ensuring rapid temperature recovery during soldering. Three-level power reduction adjustment allows the soldering tip to smoothly drop to the sleep temperature and maintain low-power heat preservation, both suppressing high-temperature oxidation and extending the soldering tip's lifespan, and significantly reducing standby power consumption for energy-saving operation. By continuously optimizing the neural network weight parameters through error backpropagation, the temperature control system can adapt to changes in thermal dynamic characteristics caused by the aging of the soldering iron tip. The grid voltage feedforward compensation further enhances the system's ability to suppress external disturbances, achieving the overall goal of efficient temperature control with rapid response, precise constant temperature, energy saving and environmental protection. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0018] Figure 1 This is a flowchart illustrating the working method provided in an embodiment of the present invention; Figure 2 This is a schematic block diagram of the structure of the high-efficiency temperature-controlled soldering iron in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items, and all possible combinations, and includes such combinations. See also Figure 1 One embodiment of the working method in this invention includes: Step 100: Rapidly heat the PTC ceramic heating element in the soldering iron tip. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, switch to the alloy resistance heating element for constant temperature heating. Step 200: Obtain the sampling temperature of the soldering iron tip, and calculate the instantaneous error, error change rate, and cumulative error based on the target temperature and the sampling temperature; Step 300: Input the instantaneous error, the error change rate, and the cumulative error into a BP neural network for compensation calculation to obtain a compensation factor, and then adjust the pulse width modulation duty cycle command of the alloy resistance heating element according to the compensation factor through a fuzzy PID controller.

[0023] In one specific embodiment, the process of performing step 100 may specifically include the following steps: The main control chip receives the power-on command and applies a pulse width modulation signal to the PTC ceramic heating element, and monitors the real-time temperature of the soldering iron tip through the NTC temperature sensor; Compare the real-time temperature with a preset switching temperature threshold; When the real-time temperature reaches the preset switching temperature threshold, the power supply current of the PTC ceramic heating element is adjusted by the solid-state relay and the alloy resistance heating element is activated, and the pulse width modulation duty cycle of the alloy resistance heating element is adjusted to the preset constant temperature power duty cycle.

[0024] Specifically, upon power-on, the main control chip starts its internal timer and outputs an initial pulse width modulation signal to the drive channel of the PTC ceramic heating element. Due to its low impedance at low temperatures, the PTC heating element can rapidly release a large amount of heat power, quickly heating the hot end of the soldering iron tip. Simultaneously, the main control chip continuously collects real-time temperature signals via an NTC temperature sensor thermally coupled to the hot end of the soldering iron tip. The output voltage signal of the temperature sensor is amplified by an operational amplifier circuit and then input to an analog-to-digital converter for high-frequency sampling, resulting in a continuous digital temperature data stream. The main control chip compares the real-time temperature acquired in each sampling cycle with a preset switching temperature threshold. This threshold is set close to the boundary temperature of the optimal temperature control range for the alloy resistance heating element, such as 350°C, to ensure maximum thermal efficiency of the PTC sprint heating without overshoot. When the main controller determines that the current real-time temperature is greater than or equal to the set threshold, it determines that the rapid heating stage is complete. At this time, the main control chip immediately sends a control command to drive the solid-state relay to work. By changing the on / off state of the relay output terminal, the main power supply path of the PTC ceramic heating element is cut off and it is put into standby mode. Then, the PWM drive module of the alloy resistance heating element is started through another control signal, and its initial duty cycle is set to the reference duty cycle value corresponding to the preset constant temperature power range. The reference duty cycle value is preset through a large number of thermal response experiments, such as a duty cycle of 50%-70% to provide a smooth transition power output.

[0025] In one specific embodiment, the process of performing step 200 may specifically include the following steps: The voltage signal output by the NTC temperature sensor is acquired, and the voltage signal is differentially amplified by an operational amplifier and then converted into a high-frequency analog-to-digital converter to obtain the sampling temperature of the soldering iron tip. The difference between the target temperature and the sampled temperature is calculated to obtain the instantaneous error; The error rate of change is calculated based on the instantaneous error of the current sampling period and the instantaneous error of the previous sampling period, and the cumulative error is obtained by summing the instantaneous errors of multiple consecutive sampling periods.

[0026] Specifically, an analog voltage signal is extracted from an NTC temperature sensor located near the soldering iron tip. The NTC sensor, based on a negative temperature coefficient material, exhibits a non-linear decreasing trend in output voltage with temperature; that is, its resistance decreases as temperature rises, leading to a decrease in the voltage signal. The NTC sensor is connected in series in a voltage divider circuit, and a high-input-impedance instrumentation-grade operational amplifier is placed between the main control chip and the sensor to ensure accurate transmission of the acquired signal without affecting the thermal response characteristics of the NTC element. The operational amplifier operates in differential mode, linearly amplifying the small voltage difference between the sensor output and the reference level, enhancing the voltage change amplitude caused by minute temperature variations, and improving the sensitivity of the entire temperature measurement system to minor thermal disturbances. The amplified voltage signal enters the analog-to-digital converter (ADC) module, configured in the main control chip as a high-speed continuous sampling mode, performing thousands of samples per second to ensure timely response to drastic temperature changes during rapid heating or cooling. The ADC module converts the analog signal into digital data according to the set quantization precision and maps it to a temperature range using a lookup table or fitting function to obtain the sampling temperature for the current sampling period. The difference between the sampled temperature and the preset target temperature is calculated to obtain the instantaneous error, i.e., error 1 = T. t -T s T t For target temperature, T s The instantaneous error, representing the current sampled temperature, reflects the absolute degree to which the heating system deviates from the control target. Simultaneously, combining the instantaneous error value recorded in the previous control cycle, a differential operation is performed to calculate the error change rate Δ = error1 - error0, used to determine whether the temperature deviation trend is worsening or mitigating. Within each cycle, the latest instantaneous error value is accumulated with the historical error sequence to form the cumulative error.

[0027] In one specific embodiment, the process of performing step 300 may specifically include the following steps: The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. A first adjustment value for the proportional gain, a second adjustment value for the integral gain, and a third adjustment value for the derivative gain are generated by pre-setting fuzzy rules, and the control output parameters of the fuzzy PID controller are calculated based on the first adjustment value, the second adjustment value, and the third adjustment value. The compensation factor and the control output parameter are used to perform correction calculations to obtain the correction result. The correction result is then added to the pulse width modulation duty cycle of the previous control cycle to obtain the pulse width modulation duty cycle command of the alloy resistance heater.

[0028] Specifically, the instantaneous error, error rate of change, and cumulative error are input into a BP neural network model that has been trained offline and deployed in the main control chip. The BP neural network has a multi-layer feedforward structure and achieves nonlinear mapping capability through activation functions and weight connections. During the forward propagation process, the three-dimensional input passes through the input layer, hidden layer, and output layer in sequence. Matrix multiplication, weighted summation, and activation transformation are performed in each layer, and a floating-point compensation factor is output. The value of the compensation factor reflects the tendency of the neural network to correct the response capability of the control system under the current error characteristics. That is, it is a factor for global dynamic compensation of the fuzzy controller output, used to eliminate the influence of nonlinear interference such as non-modeling errors, thermal inertia disturbances, and power fluctuations in the temperature control object. Simultaneously, the fuzzy PID controller remaps the error and error rate of change to the fuzzy domain. It calculates the membership values ​​of each error in multiple preset fuzzy sets using triangular membership functions and performs fuzzy inference based on a fuzzy rule table constructed from expert experience, forming three fuzzy control quantities: proportional gain adjustment, integral gain adjustment, and derivative gain adjustment. These three control quantities are numerically superimposed with their respective PID reference gains to obtain a set of corrected PID gain coefficients, denoted as P′, I′, and D′. The corrected PID gain is then weighted and multiplied with the instantaneous error, cumulative error, and error rate of change, and summed to generate the control output parameters of the fuzzy PID controller, which is the ideal control quantity calculated for the current temperature state. Considering the time delay in actual thermal response, the nonlinearity of the resistance heating element power, and the non-uniform amplitude effect of PWM duty cycle changes on heating power, the fuzzy PID control output is multiplied and corrected with the compensation factor of the BP neural network output to generate the corrected control quantity. The corrected control quantity is accumulated with the PWM duty cycle that has taken effect in the previous cycle to obtain the PWM duty cycle instruction that should be applied to the alloy resistor heating element in the new cycle. The PWM duty cycle instruction drives the power MOSFET or IGBT device in the form of a high-speed timer output at the hardware level, thereby controlling the on and off time ratio of the alloy resistor and indirectly regulating the heat power released per unit time.

[0029] In one specific embodiment, the process of generating a first adjustment value for the proportional gain, a second adjustment value for the integral gain, and a third adjustment value for the derivative gain using preset fuzzy rules, and calculating the control output parameters of the fuzzy PID controller based on the first adjustment value, the second adjustment value, and the third adjustment value, can specifically include the following steps: The instantaneous error and the rate of change of error are divided into multiple preset fuzzy sets, and the membership values ​​of the instantaneous error and the rate of change of error in each fuzzy set are calculated by the triangular membership function. Calculate the first adjustment amount of the proportional gain, the second adjustment amount of the integral gain, and the third adjustment amount of the derivative gain based on the membership values. The proportional gain reference value is superimposed with the first adjustment amount to obtain the corrected proportional gain; the integral gain reference value is superimposed with the second adjustment amount to obtain the corrected integral gain; and the differential gain reference value is superimposed with the third adjustment amount to obtain the corrected differential gain. The control output parameters of the fuzzy PID controller are obtained by weighted summing of the instantaneous error, the cumulative error, and the error change rate based on the corrected proportional gain, the corrected integral gain, and the corrected derivative gain.

[0030] Specifically, a gain adjustment mechanism based on fuzzy set partitioning and membership function inference is established within the fuzzy PID controller. The two input variables, instantaneous error and error rate of change, are divided into multiple preset fuzzy sets, such as seven symmetrically distributed fuzzy linguistic variables: negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB). Each fuzzy set corresponds to a domain interval and is covered by a set of triangular membership functions with equal or overlapping widths. After obtaining the values ​​of instantaneous error and error rate of change in each control cycle, these values ​​are substituted into their respective membership functions for calculation, yielding their membership values ​​within their respective fuzzy sets. These membership values ​​indicate the confidence level of the current error falling into a particular fuzzy set. The membership values ​​serve as input conditions for the fuzzy inference rule table. Combined with an empirically designed rule table, each pair of fuzzy sets is mapped to a set of three control outputs: the adjustment amount of the proportional gain, the adjustment amount of the integral gain, and the adjustment amount of the derivative gain. The rule table is designed in the form of a two-dimensional matrix. Each matrix element embeds a set of output adjustment strategies defined by the expert system, and multiple output results are fused using either the maximum membership method or the weighted average method. The three adjustment variables are added to their corresponding PID baseline gain values ​​to form the corrected proportional gain, integral gain, and derivative gain, denoted as K. p ′、K i ′ and K d The corrected gain set reflects the adaptive adjustment strategy that the system should adopt under the current error state. The control logic uses the corrected gain to perform a multiplicative weighted operation on the corresponding error signal, and sets K... p Multiply by the instantaneous error, K i Multiply by the cumulative error, K d Multiply the result by the error rate of change and sum the three results to obtain the control output parameters of the fuzzy PID controller.

[0031] In one specific embodiment, it further includes: The real-time temperature of a continuous sampling period is differentially calculated to obtain the temperature drop rate, and the temperature drop rate is compared with a preset load threshold. When the temperature drop rate exceeds the preset load threshold, the zero-crossing moment of the AC voltage waveform is detected by the optocoupler circuit. At the zero-crossing moment, the power supply circuit of the alloy resistance heating element is connected through the bidirectional thyristor, and the pulse width modulation duty cycle command is increased to the preset boost value. The pulse width modulation duty cycle command is dynamically adjusted according to the temperature recovery rate of the soldering iron tip until the temperature recovers to the target temperature range.

[0032] Specifically, in the continuous control cycle, the real-time temperature value of the soldering iron tip collected in each cycle is differentially calculated. That is, the temperature of the current cycle is subtracted from the temperature of the previous cycle to calculate the rate of temperature change per unit time, which is the temperature drop rate. In the software implementation, the temperature values ​​of the two or more most recent sampling cycles are stored through a sliding window, and the difference is updated in real time to construct a temperature change rate sequence. The control algorithm compares the current temperature drop rate with a preset load disturbance judgment threshold. The load disturbance judgment threshold is determined through experiments or thermal model analysis and is used to identify whether the soldering iron tip is encountering strong load heat absorption during the soldering process, such as contact with high-power solder joints or metal parts with high thermal conductivity. When the temperature drop rate is greater than the threshold, it is determined that the current stage is in the thermal load impact stage, and the rapid temperature rise compensation mode is entered. At this time, the main control chip detects the voltage waveform of the 220V AC power supply through a dedicated optocoupler isolation circuit and extracts the zero crossing point of the positive and negative half-cycles of AC, that is, the zero crossing point. This moment has the minimum voltage difference characteristic, which is suitable for performing the turn-on operation to reduce electromagnetic interference. At the zero-crossing point, the main control logic activates the bidirectional thyristor, connecting the power circuit for the alloy resistance heater. Under grid synchronization, a high-power heating process is initiated, and the PWM duty cycle command is instantaneously increased to a preset boost value. This boost value exceeds the duty cycle setting range for the normal constant-temperature phase to compensate for rapid heat loss and accelerate temperature recovery. While continuously monitoring the temperature rise, the duty cycle command is dynamically adjusted based on the real-time temperature rise rate and the distance to the target temperature, gradually decreasing from an initial high value to the normal constant-temperature control range until the temperature returns to the target temperature range.

[0033] In one specific embodiment, it further includes: Start an idle timer to accumulate standby time. When the standby time reaches a preset time and the temperature drop rate of the soldering iron tip does not exceed a preset drop threshold, reduce the pulse width modulation duty cycle of the alloy resistance heating element to zero and adjust the pulse width modulation duty cycle of the PTC ceramic heating element to the first sleep duty cycle. When the temperature of the soldering iron tip drops to the first sleep temperature threshold, the pulse width modulation duty cycle of the PTC ceramic heating element is adjusted to the second sleep duty cycle; When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain the low-power heat preservation state at the preset sleep temperature.

[0034] Specifically, an idle timer logic unit is embedded in the idle detection module. It continuously counts from the moment user inactivity is detected, accumulating the duration of inactivity in each control cycle. Simultaneously, by dynamically monitoring temperature changes, the temperature drop rate of the soldering iron tip is continuously analyzed to determine if there is a thermal load disturbance. If the measured temperature change rate remains below the set drop threshold within a continuous sampling period, the soldering iron is presumed to be in a non-working state. When the accumulated standby time exceeds the preset idle time threshold, and the temperature change rate still does not exceed the drop threshold, the standby state confirmation condition is met. At this point, the main control logic executes an energy consumption switching strategy, setting the pulse width modulation duty cycle of the alloy resistance heating element to 0%, completely cutting off its power supply. The PWM control signal for the PTC ceramic heating element is adjusted to a preset low-power sleep duty cycle, i.e., the first sleep duty cycle is 15%, to maintain a slow temperature decrease and provide basic thermal stability support. As the system enters sleep mode, the temperature control algorithm tracks the hot-end temperature change of the soldering iron tip in real time using an NTC temperature sensor. When the temperature is detected to have dropped to the first sleep temperature threshold, the PWM duty cycle of the PTC heating element is lowered from the first sleep value to a lower second sleep duty cycle of 10% to adapt to the thermal balance requirements during the temperature drop and reduce buffer deviations caused by thermal inertia. When the temperature continues to drop and further reaches the set minimum sleep temperature, the PWM duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain a constant temperature for the soldering iron tip under low power conditions.

[0035] In one specific embodiment, the process of stabilizing the pulse width modulation duty cycle of the PTC ceramic heating element at a third sleep duty cycle to maintain the low-power heat preservation state at the preset sleep temperature when the temperature of the soldering iron tip continues to drop to the preset sleep temperature can specifically include the following steps: When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is locked to the third sleep duty cycle; The temperature of the soldering iron tip is continuously monitored by an NTC temperature sensor. When the temperature of the soldering iron tip deviates from the preset sleep temperature, the third sleep duty cycle is finely adjusted to compensate, so that the output power of the PTC ceramic heating element and the heat dissipation power of the environment are dynamically balanced, so as to maintain the constant temperature and heat preservation state of the preset sleep temperature.

[0036] Specifically, a low-power constant-temperature maintenance mechanism is preset in the main control logic. As the soldering tip temperature continues to decrease, it dynamically determines whether it has reached a preset sleep temperature threshold, for example, set to 150℃, to meet the thermal inertia requirements for material oxidation control and rapid wake-up. When continuous sampling results from the NTC temperature sensor indicate that the actual temperature of the soldering tip has stabilized and dropped to near the sleep temperature, the main control chip automatically locks the pulse width modulation duty cycle of the PTC ceramic heating element to the system's preset third sleep duty cycle, for example, 6%. At this time, the PTC heating element maintains the hot-end temperature at extremely low power output to prevent further decrease and temporarily enters a low-power constant-temperature mode. The NTC temperature sensor continuously collects the real-time temperature of the soldering tip and compares the current temperature with the preset sleep temperature. When a positive or negative deviation in the actual temperature is detected, the main control chip makes a small adjustment to the third sleep duty cycle, with an adjustment step size of ±0.5% and a maximum deviation limited to ±2%, to prevent system oscillation during fine-tuning. If the ambient temperature rises, causing a decrease in heat loss from the soldering iron tip, the duty cycle will be automatically lowered slightly to prevent the temperature from rising. If external airflow or heat conduction conditions enhance heat dissipation, the duty cycle will be automatically raised slightly to compensate for the lost heat, ensuring that the heat output of the PTC heating element and the heat dissipation power of the soldering iron tip to the environment are always in dynamic balance.

[0037] In one specific embodiment, it further includes: Calculate the temperature fluctuation variance of the soldering iron tip, and construct a comprehensive feature vector based on the instantaneous error, the error change rate, the cumulative error, and the temperature fluctuation variance; The loss function value is calculated based on the comprehensive feature vector, and the connection weights of the BP neural network are incrementally updated according to the loss function value to obtain the updated BP neural network.

[0038] Specifically, in each control cycle, a time series window is constructed using multi-cycle continuous temperature data collected by the NTC temperature sensor. The temperature fluctuation variance is calculated based on all temperature sampling points within the window. The average of the squared differences between the n temperature values ​​in the current cycle and their mean is calculated to quantify the local stability index in the temperature control process. A smaller variance indicates a better temperature control effect, while a larger variance indicates fluctuations or uncontrolled disturbances in the control process. The temperature fluctuation variance, along with the instantaneous error, error rate of change, and cumulative error obtained simultaneously, forms a four-dimensional comprehensive feature vector, reflecting control performance indicators such as the degree of control deviation, disturbance trend, long-term deviation accumulation, and system output stability. The comprehensive feature vector is input into the loaded BP neural network model, performing one forward propagation to generate the expected compensation factor output. Simultaneously, the loss function value is calculated. The loss function is designed as the mean square error between the target duty cycle and the actual output, or as a combined error function with temperature control error as the core, for example, using the following form: L = α·(instantaneous error)2 ) + β·(variance) + γ·(error rate of change) 2 ), where α, β, and γ are weighting coefficients used to balance the contributions of various factors to system stability. After the loss function is calculated, the control algorithm executes a weight update process based on the gradient of the loss function. That is, the connection weights of each layer of the network are adjusted by a small amount through the error backpropagation algorithm. This process is based on the partial derivative of the current loss value with respect to the weights, combined with the learning rate parameter η, and is incrementally adjusted in the manner of ΔW = -η·∂L / ∂W. This allows the network structure to be corrected in real time without retraining and gradually converge, resulting in the updated BP neural network.

[0039] The working method in the embodiments of the present invention has been described above. The following describes the high-efficiency temperature-controlled soldering iron in the embodiments of the present invention. Please refer to [link / reference]. Figure 2 The soldering iron casing consists of a left cover, a right cover, a top cover, and front and rear end covers. The front end cover features a 0.96-inch OLED screen for displaying temperature and operating status, an encoder switch (22mm diameter) for temperature adjustment and function selection, and a handle (32mm diameter) for mounting the soldering tip. Internally, it integrates a PTC ceramic heating element, an alloy resistance heating element, and an NTC temperature sensor. The rear end cover has a power socket (24mm×24mm) for 220V AC power input and a cooling fan (56mm diameter) for forced air cooling of the main control chip and power components. A power switch (13mm×11mm) is located below the right cover to control the power supply. The dimensions of each cover are clearly marked: the top and right covers are both 150mm long and 78mm wide, while the front and rear end covers are 78mm wide and 50mm high. The overall structure is compact and easy to hold. One embodiment of the high-efficiency temperature-controlled soldering iron in this invention includes: The heating module is used to rapidly heat the PTC ceramic heating element in the soldering iron tip. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating. The calculation module is used to obtain the sampling temperature of the soldering iron tip, and calculate the instantaneous error, error change rate and cumulative error based on the target temperature and the sampling temperature; The compensation module is used to input the instantaneous error, the error change rate, and the cumulative error into a BP neural network for compensation calculation to obtain a compensation factor, and then use a fuzzy PID controller to adjust the pulse width modulation duty cycle command of the alloy resistance heating element according to the compensation factor.

[0040] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0042] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A working method, characterized in that, include: The PTC ceramic heating element in the soldering iron tip is rapidly heated. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating. The sampling temperature of the soldering iron tip is obtained, and the instantaneous error, error change rate, and cumulative error are calculated based on the target temperature and the sampling temperature. The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. The pulse width modulation duty cycle command of the alloy resistance heater is then adjusted by a fuzzy PID controller according to the compensation factor.

2. The working method according to claim 1, characterized in that, The PTC ceramic heating element in the soldering iron tip is rapidly heated. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating, including: The main control chip receives the power-on command and applies a pulse width modulation signal to the PTC ceramic heating element, and monitors the real-time temperature of the soldering iron tip through the NTC temperature sensor; Compare the real-time temperature with a preset switching temperature threshold; When the real-time temperature reaches the preset switching temperature threshold, the power supply current of the PTC ceramic heating element is adjusted by the solid-state relay and the alloy resistance heating element is activated, and the pulse width modulation duty cycle of the alloy resistance heating element is adjusted to the preset constant temperature power duty cycle.

3. The working method according to claim 2, characterized in that, The sampling temperature of the soldering iron tip is obtained, and the instantaneous error, error rate of change, and cumulative error are calculated based on the target temperature and the sampling temperature, including: The voltage signal output by the NTC temperature sensor is acquired, and the voltage signal is differentially amplified by an operational amplifier and then converted into a high-frequency analog-to-digital converter to obtain the sampling temperature of the soldering iron tip. The difference between the target temperature and the sampled temperature is calculated to obtain the instantaneous error; The error rate of change is calculated based on the instantaneous error of the current sampling period and the instantaneous error of the previous sampling period, and the cumulative error is obtained by summing the instantaneous errors of multiple consecutive sampling periods.

4. The working method according to claim 1, characterized in that, The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. A fuzzy PID controller then adjusts the pulse width modulation duty cycle of the alloy resistance heater according to the compensation factor, including: The instantaneous error, the rate of change of error, and the cumulative error are input into a BP neural network for compensation calculation to obtain a compensation factor. A first adjustment value for the proportional gain, a second adjustment value for the integral gain, and a third adjustment value for the derivative gain are generated by pre-setting fuzzy rules, and the control output parameters of the fuzzy PID controller are calculated based on the first adjustment value, the second adjustment value, and the third adjustment value. The compensation factor and the control output parameter are used to perform correction calculations to obtain the correction result. The correction result is then added to the pulse width modulation duty cycle of the previous control cycle to obtain the pulse width modulation duty cycle command of the alloy resistance heater.

5. The working method according to claim 4, characterized in that, A first adjustment value for the proportional gain, a second adjustment value for the integral gain, and a third adjustment value for the derivative gain are generated using preset fuzzy rules. Based on these adjustments, the control output parameters of the fuzzy PID controller are calculated, including: The instantaneous error and the rate of change of error are divided into multiple preset fuzzy sets, and the membership values ​​of the instantaneous error and the rate of change of error in each fuzzy set are calculated by the triangular membership function. Calculate the first adjustment amount of the proportional gain, the second adjustment amount of the integral gain, and the third adjustment amount of the derivative gain based on the membership values. The proportional gain reference value is superimposed with the first adjustment amount to obtain the corrected proportional gain; the integral gain reference value is superimposed with the second adjustment amount to obtain the corrected integral gain; and the differential gain reference value is superimposed with the third adjustment amount to obtain the corrected differential gain. The control output parameters of the fuzzy PID controller are obtained by weighted summing of the instantaneous error, the cumulative error, and the error change rate based on the corrected proportional gain, the corrected integral gain, and the corrected derivative gain.

6. The working method according to claim 1, characterized in that, Also includes: The real-time temperature of a continuous sampling period is differentially calculated to obtain the temperature drop rate, and the temperature drop rate is compared with a preset load threshold. When the temperature drop rate exceeds the preset load threshold, the zero-crossing moment of the AC voltage waveform is detected by the optocoupler circuit. At the zero-crossing moment, the power supply circuit of the alloy resistance heating element is connected through the bidirectional thyristor, and the pulse width modulation duty cycle command is increased to the preset boost value. The pulse width modulation duty cycle command is dynamically adjusted according to the temperature recovery rate of the soldering iron tip until the temperature recovers to the target temperature range.

7. The working method according to claim 6, characterized in that, Also includes: Start an idle timer to accumulate standby time. When the standby time reaches a preset time and the temperature drop rate of the soldering iron tip does not exceed a preset drop threshold, reduce the pulse width modulation duty cycle of the alloy resistance heating element to zero and adjust the pulse width modulation duty cycle of the PTC ceramic heating element to the first sleep duty cycle. When the temperature of the soldering iron tip drops to the first sleep temperature threshold, the pulse width modulation duty cycle of the PTC ceramic heating element is adjusted to the second sleep duty cycle; When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain the low-power heat preservation state at the preset sleep temperature.

8. The working method according to claim 7, characterized in that, When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is stabilized at the third sleep duty cycle to maintain a low-power heat preservation state at the preset sleep temperature, including: When the temperature of the soldering iron tip continues to drop to the preset sleep temperature, the pulse width modulation duty cycle of the PTC ceramic heating element is locked to the third sleep duty cycle; The temperature of the soldering iron tip is continuously monitored by an NTC temperature sensor. When the temperature of the soldering iron tip deviates from the preset sleep temperature, the third sleep duty cycle is finely adjusted to compensate, so that the output power of the PTC ceramic heating element and the heat dissipation power of the environment are dynamically balanced, so as to maintain the constant temperature and heat preservation state of the preset sleep temperature.

9. The working method according to claim 1, characterized in that, Also includes: Calculate the temperature fluctuation variance of the soldering iron tip, and construct a comprehensive feature vector based on the instantaneous error, the error change rate, the cumulative error, and the temperature fluctuation variance; The loss function value is calculated based on the comprehensive feature vector, and the connection weights of the BP neural network are incrementally updated according to the loss function value to obtain the updated BP neural network.

10. A high-efficiency temperature-controlled soldering iron, characterized in that, For performing the working method as described in any one of claims 1-9, comprising: The heating module is used to rapidly heat the PTC ceramic heating element in the soldering iron tip. When the real-time temperature of the soldering iron tip reaches the preset switching temperature threshold, it switches to the alloy resistance heating element for constant temperature heating. The calculation module is used to obtain the sampling temperature of the soldering iron tip, and calculate the instantaneous error, error change rate and cumulative error based on the target temperature and the sampling temperature; The compensation module is used to input the instantaneous error, the error change rate, and the cumulative error into a BP neural network for compensation calculation to obtain a compensation factor, and then use a fuzzy PID controller to adjust the pulse width modulation duty cycle command of the alloy resistance heating element according to the compensation factor.

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