Heat dissipation optimization method and system for air conditioner control panel

Abstract

The invention relates to a heat dissipation optimization method and system for an air conditioner control panel, and belongs to the technical field of energy-saving air conditioner heat dissipation. Comparing the real-time temperature data with a preset temperature threshold value, and determining a heat dissipation mode; when the real-time temperature data is lower than a first threshold value, a first-stage heat dissipation mode is started, a heat dissipation fan is controlled to operate at the lowest rotating speed, and a basic heat conduction path of a cooling fin is activated; when the real-time temperature data is between a first threshold value and a second threshold value, a second-stage heat dissipation mode is started, the rotating speed of a heat dissipation fan is adjusted to be in linear proportion to the real-time temperature data, and an expansion heat conduction path of a cooling fin is started to increase the heat dissipation area; and when the real-time temperature data exceeds a second threshold value, starting a three-stage heat dissipation mode, switching to phase change material auxiliary heat dissipation, controlling a heat dissipation fan to operate at the maximum rotating speed, dynamically adjusting the heat conduction efficiency of a heat dissipation sheet through a pulse width modulation signal, and triggering a temperature alarm signal.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving air conditioner heat dissipation, and particularly relates to a heat dissipation optimization method and system for an air conditioner control board. Background Art

[0002] Currently, the heat dissipation technology of air conditioner control boards generally adopts fixed threshold control of fan speed or single heat dissipation path design, resulting in problems such as imbalance between heat dissipation efficiency and energy consumption, and lag in dynamic response. For example, traditional solutions are prone to frequent switching of heat dissipation modes during temperature fluctuations, exacerbating mechanical losses and noise. Moreover, phase change materials only serve as passive heat storage units and do not form a collaborative control mechanism with active heat dissipation devices. Although multi-level fan control strategies have been proposed in existing patents, they lack the ability to adaptively adjust thresholds for environmental temperature disturbances. In addition, pulse width modulation signals mostly use static parameter control in optimizing heat conduction efficiency, making it difficult to cope with transient high heat flux density impacts. Summary of the Invention

[0003] To achieve the above object, the heat dissipation optimization method for an air conditioner control board provided by the present invention includes the following steps: Obtain real-time temperature data of the air conditioner control board; Compare the real-time temperature data with a preset temperature threshold to determine a heat dissipation mode; wherein, the heat dissipation mode includes a primary heat dissipation mode, a secondary heat dissipation mode, and a tertiary heat dissipation mode; When the real-time temperature data is lower than the first threshold, start the primary heat dissipation mode, control the heat dissipation fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink; When the real-time temperature data is between the first threshold and the second threshold, start the secondary heat dissipation mode, adjust the fan speed of the heat dissipation fan to be linearly proportional to the real-time temperature data, and open the extended heat conduction path of the heat sink to increase the heat dissipation area; When the real-time temperature data exceeds the second threshold, start the tertiary heat dissipation mode, switch to phase change material-assisted heat dissipation, control the heat dissipation fan to operate at the maximum speed, dynamically adjust the heat conduction efficiency of the heat sink through a pulse width modulation signal, and trigger a temperature alarm signal.

[0004] Further, the switching logic of the heat dissipation mode is realized by a temperature hysteresis comparison method, including: Establish a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting; If the predicted temperature is about to cross the first threshold or the second threshold, adjust the heat dissipation mode in advance and introduce a transition buffer stage, and smooth the fan speed change curve through a proportional-integral-derivative algorithm to avoid instantaneous power mutation.

[0005] Further, the steps of establishing a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting include: Collect historical temperature data for the previous N minutes, construct a temperature time series at an interval of N seconds, and perform sliding window mean filtering on outliers, where N is a positive integer greater than 1; Input the filtered data into the ARIMA model, and automatically select the optimal model order through the minimum information criterion, where the environmental temperature gradient is used as an exogenous variable to participate in model training; Based on the correlation between the historical rotation speed of the cooling fan and the temperature change rate, construct a rotation speed-temperature coupling coefficient matrix, and use the matrix as a dynamic correction factor for the ARIMA model; Update the model parameters every 5 seconds, verify the prediction accuracy through the sum of squared residuals, and trigger model retraining if the residuals exceed the set threshold.

[0006] Further, if it is predicted that the temperature is about to cross the first threshold or the second threshold, the steps of adjusting the cooling mode in advance and introducing a transition buffer stage, and smoothing the fan rotation speed change curve through the proportional integral derivative algorithm to avoid instantaneous power mutation include: When it is predicted that the temperature is about to cross the first threshold or the second threshold, determine the transition buffer time window , and its calculation formula is: ; Among them, is the predicted temperature, is the threshold temperature to be crossed, is the absolute value of the temperature change rate; Based on the transition buffer time window, generate a segmented continuous fan rotation speed target curve; Track the target curve through the proportional integral derivative algorithm, and the calculation formula of the proportional integral derivative algorithm is: ; Among them, the proportional term coefficient is dynamically adjusted according to the slope of the target curve, and the greater the slope, the higher, is the differential term coefficient; When the deviation between the actual rotation speed and the target curve exceeds the threshold, trigger an emergency smoothing instruction.

[0007] Further, the first threshold and the second threshold are dynamic parameters, which are adjusted in real time according to the environmental temperature change rate, and the environmental temperature is obtained together with the real-time temperature data; When the environmental temperature rise rate exceeds 0.5 °C / min, lower the first threshold by 3 °C - 5 °C and the second threshold by 5 °C - 8 °C; When the environmental temperature drop rate exceeds 0.3 °C / min, restore the initial threshold setting value, and perform noise cancellation on the real-time temperature data through a Kalman filter.

[0008] Furthermore, when the real-time temperature data is lower than the first threshold, start the first-level heat dissipation mode, control the cooling fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink. The steps include: Based on the current heat sink temperature of each area of the heat sink in the air conditioner, turn on the 3 main heat conduction paths with the lowest current heat sink temperature. The opening method includes starting the fan on the corresponding side of the heat sink; Real-time monitor the temperature difference gradient of the main heat conduction path. If the maximum gradient exceeds 2 °C / cm, automatically allocate additional heat conduction paths to form a parallel heat dissipation channel.

[0009] Furthermore, the steps of activating the heat conduction path of the heat sink include: The heat conduction path is that the heat sink is arranged on each control board of the air conditioner, and a fan is arranged on the side of the heat sink to cool the heat sink. By starting the fan, the heat transfer rate of each heat sink is conducted to form the heat conduction path.

[0010] Furthermore, the phase change material is a paraffin-based composite phase change material, its phase change temperature is 65 °C - 75 °C, and the phase change material is encapsulated in the sandwich cavity of the heat sink, and the liquid phase change material is driven by a micro pump to circulate to enhance heat absorption.

[0011] Furthermore, the steps of dynamically adjusting the heat conduction efficiency of the heat sink through a pulse width modulation signal include: Real-time obtain the temperature change rate, thermal resistance dynamic coefficient and fan speed feedback of the heat sink, and construct a multi-dimensional control variable set; Based on the reinforcement learning algorithm, dynamically generate the pulse width modulation reference duty cycle, and its objective function is: ; Wherein, is the target temperature of the current heat dissipation mode, is the dynamic weight coefficient of environmental humidity compensation; Through the reference duty cycle Dynamically adjust the heat conduction efficiency of the heat sink.

[0012] The present invention also proposes a heat dissipation optimization system for an air conditioner control board, including: An acquisition unit for acquiring real-time temperature data of the air conditioner control board; A comparison unit for comparing the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode; wherein, the heat dissipation mode includes a first-level heat dissipation mode, a second-level heat dissipation mode and a third-level heat dissipation mode; The first heat dissipation unit is used to start the first-level heat dissipation mode when the real-time temperature data is lower than the first threshold, control the cooling fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink; The second heat dissipation unit is used to start the second-level heat dissipation mode when the real-time temperature data is between the first threshold and the second threshold, adjust the rotation speed of the cooling fan to be linearly proportional to the real-time temperature data, and turn on the extended heat conduction path of the heat sink to increase the heat dissipation area; The third heat dissipation unit is used to start the third-level heat dissipation mode when the real-time temperature data exceeds the second threshold, switch to the phase change material-assisted heat dissipation, control the cooling fan to operate at the maximum speed, dynamically adjust the heat conduction efficiency of the heat sink through the pulse width modulation signal, and trigger a temperature alarm signal.

[0013] The heat dissipation optimization method and system for an air conditioner control board provided by the present invention have the following beneficial effects: Through the dynamic switching mechanism of the multi-level heat dissipation mode and the active cooperative control of the phase change material, the precise balance between the heat dissipation efficiency and energy consumption of the air conditioner control board is achieved. Based on the dynamic temperature prediction model and the adaptive threshold adjustment algorithm, the frequent mode switching and mechanical noise caused by temperature fluctuations in the traditional scheme are effectively suppressed. At the same time, through the pulse width modulation signal optimized by reinforcement learning and the phase change material failure prediction mechanism, the response speed to transient high heat flux density and the heat dissipation stability are significantly improved. Description of the Drawings

[0014] Figure 1 is a schematic flowchart of the heat dissipation optimization method for an air conditioner control board in an embodiment of the present invention; Figure 2 is a structural block diagram of the heat dissipation optimization system for an air conditioner control board in an embodiment of the present invention.

[0015] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0016] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0017] Refer to Figure 1 , which is a schematic flowchart of the heat dissipation optimization method for an air conditioner control board proposed by the present invention, and includes the following steps: Obtain the real-time temperature data of the air conditioner control board; Compare the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode; wherein, the heat dissipation mode includes a primary heat dissipation mode, a secondary heat dissipation mode, and a tertiary heat dissipation mode; When the real-time temperature data is lower than the first threshold, activate the primary heat dissipation mode, control the cooling fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink; When the real-time temperature data is between the first threshold and the second threshold, activate the secondary heat dissipation mode, adjust the speed of the cooling fan to be linearly proportional to the real-time temperature data, and turn on the extended heat conduction path of the heat sink to increase the heat dissipation area; When the real-time temperature data exceeds the second threshold, activate the tertiary heat dissipation mode, switch to phase change material assisted heat dissipation, control the cooling fan to operate at the maximum speed, dynamically adjust the heat conduction efficiency of the heat sink through a pulse width modulation signal, and trigger a temperature alarm signal.

[0018] In one embodiment, the execution subject of the present invention is the control board in an air conditioner; First, the temperature data of the air conditioner control board is collected in real time through a temperature sensor, and the heat dissipation mode is judged in combination with preset temperature thresholds (such as the first threshold of 50°C and the second threshold of 70°C). The control board automatically selects the primary, secondary, or tertiary heat dissipation mode according to the real-time temperature: Primary heat dissipation mode (low temperature state): When the temperature is lower than the first threshold, the system controls the cooling fan to operate at the lowest speed (such as 15% of the rated speed), and only turns on the basic copper heat conduction path of the heat sink to achieve low-power and silent heat dissipation.

[0019] Secondary heat dissipation mode (medium temperature state): When the temperature is between the first and second thresholds, the fan speed linearly increases with the temperature (such as the speed increases by 5% for every 1°C increase in temperature), and the extended graphene heat conduction path is activated to enhance the heat dissipation area.

[0020] Tertiary heat dissipation mode (high temperature state): When the temperature exceeds the second threshold, the system starts phase change material assisted heat dissipation, the fan switches to full speed operation, and at the same time, the heat conduction efficiency is optimized by dynamically adjusting the duty cycle of the PWM signal. If the temperature continues to exceed the standard, an alarm is triggered and an emergency protection strategy is executed.

[0021] In one embodiment, the switching logic of the heat dissipation mode is implemented by the temperature hysteresis comparison method, including: Establish a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting; If the predicted temperature is about to cross the first threshold or the second threshold, adjust the heat dissipation mode in advance and introduce a transition buffer stage, and smooth the fan speed change curve through a proportional integral derivative algorithm to avoid instantaneous power mutation.

[0022] In a specific embodiment, the steps of establishing a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting include: Collect historical temperature data for the previous N minutes, construct a temperature time series at an interval of N seconds, and perform moving window mean filtering on outliers, where N is a positive integer greater than 1; Input the filtered data into the ARIMA model, and automatically select the optimal model order through the minimum information criterion, where the environmental temperature gradient participates in the model training as an exogenous variable; Based on the correlation between the historical rotation speed of the cooling fan and the temperature change rate, construct a rotation speed-temperature coupling coefficient matrix, and use the matrix as the dynamic correction factor of the ARIMA model; Update the model parameters every 5 seconds, verify the prediction accuracy through the sum of squared residuals. If the residual exceeds the set threshold, trigger model retraining.

[0023] During the specific implementation process, the training process of the dynamic temperature prediction model is as follows: Collect historical temperature data for the previous 30 minutes at an interval of 1 second through a temperature sensor to form a time series data set. Use moving window mean filtering (window width 5 seconds) to smooth outliers. For example, when the temperature suddenly increases by more than 3°C in a certain second, replace it with the moving average of the data in the previous and next 2 seconds; Input the preprocessed temperature data into the autoregressive integrated moving average (ARIMA) model, and introduce the environmental temperature gradient as an exogenous variable at the same time. Automatically select the optimal model order through the minimum information criterion (AIC). The typical parameters are (p, d, q) = (3, 1, 2), indicating 3rd-order autoregression, 1st-order differencing, and 2nd-order moving average. During model training, the historical rotation speed data of the cooling fan is encoded as a 0-1 standardized feature vector to participate in the calculation; Based on the correlation analysis between the rotation speed of the cooling fan and the temperature change rate, construct a rotation speed-temperature coupling coefficient matrix. The specific method is: statistically calculate the average temperature change rate corresponding to the rotation speed interval (such as 0-30%, 30-70%, 70-100%) every 5 minutes to form a 3×3 coefficient matrix. This matrix serves as the state correction factor of the ARIMA model to adjust the prediction value weight in real time; The system recalculates the ARIMA model parameters every 5 seconds and evaluates the prediction accuracy through the sum of squared residuals (RSS). Set the residual threshold to 0.5°C². When the RSS exceeds the threshold for 3 consecutive iterations, automatically trigger model retraining: clear the historical data buffer, and re-execute the modeling process from step one to step three.

[0024] Example 1, the performance verification of the dynamic temperature prediction model uses the initial temperature of the air conditioner control board: 45°C; environmental temperature change: 30°C → 38°C; test duration: 60 minutes; Test results:

[0025] Conclusion: The model prediction error ≤ 1.5 °C. When the residual exceeds the threshold, automatic retraining ensures long-term reliability; during the environmental mutation stage (30 - 45 minutes), after model correction, the prediction accuracy is restored to within ±0.5 °C.

[0026] Example 2, comparison of the effectiveness of multi-level heat dissipation modes: The test scenarios are as follows: Scenario 1: Steady-state low temperature (ambient 25 °C, control board load 20%) Scenario 2: Dynamic medium temperature (ambient 35 °C, load fluctuating between 50% - 80%) Scenario 3: Transient high temperature (ambient 40 °C, load 100% continuous impact)

[0027]

[0028] Example 3, test of the effectiveness of pulse-width modulation dynamic regulation is as follows. The test parameters are: target temperature: 70 °C (switching point between the second and third modes); heat sink thermal resistance change range: 0.8 → 1.5 K / W; test algorithm: traditional PID vs the reinforcement learning optimization of the present invention; Comparison of adjustment effects:

[0029] Conclusion: Through dynamic weight optimization (α, β, γ), the present invention reduces the duty cycle fluctuation by 40% under the same heat dissipation efficiency; the temperature overshoot is reduced from 3.2 °C of the traditional PID to within 0.5 °C.

[0030] In one embodiment, if the predicted temperature is about to cross the first threshold or the second threshold, steps of adjusting the heat dissipation mode in advance and introducing a transition buffer stage, and smoothing the fan speed change curve through the proportional integral derivative algorithm to avoid instantaneous power mutation, include: When the predicted temperature is about to cross the first threshold or the second threshold, determine the transition buffer time window , and its calculation formula is: ; wherein, is the predicted temperature, is the threshold temperature to be crossed, is the absolute value of the temperature change rate; Generate a segmented continuous fan speed target curve based on the transition buffer time window; Track the target curve through the proportional integral derivative algorithm, and the calculation formula of the proportional integral derivative algorithm is: ; where the proportional term coefficient Dynamically adjust according to the slope of the target curve. The greater the slope, the higher, which is the differential coefficient; When the deviation between the actual rotational speed and the target curve exceeds the threshold, an emergency smoothing instruction is triggered.

[0031] In the specific implementation process, the method of generating a piecewise continuous fan rotational speed target curve is as follows: Based on Generate a three-segment target rotational speed curve, as shown in the following table

[0032] Example parameters: The initial rotational speed n0 = 1200 rpm, the target rotational speed n target = 2400 rpm; T buffer = 2 s, then the segmented intervals are: 0 - 0.6 s (quadratic acceleration), 0.6 - 1.4 s (linear), 1.4 - 2 s (exponential decay).

[0033] In the above step of triggering the emergency smoothing instruction when the deviation between the actual rotational speed and the target curve exceeds the threshold, when the difference between the actual rotational speed nreal and the target curve is: Reverse-correct the buffer time window , where is the deviation between the predicted temperature and the actual temperature Inject the feedforward compensation amount , where K ff Is optimized by the online gradient descent method, and the initial value is set to 0.8.

[0034] The smoothing instruction test example is:

[0035] In one embodiment, the first threshold and the second threshold are dynamic parameters, which are adjusted in real time according to the environmental temperature change rate, and the environmental temperature is acquired together with the real-time temperature data; When the environmental temperature rising rate exceeds 0.5 °C / min, reduce the first threshold by 3 °C - 5 °C and the second threshold by 5 °C - 8 °C; When the environmental temperature falling rate exceeds 0.3 °C / min, restore the initial threshold setting value, and perform noise elimination on the real-time temperature data through a Kalman filter.

[0036] In the specific implementation process, when the environmental temperature falling rate triggers the threshold restoration, a Kalman filter is used to perform noise reduction processing on the real-time temperature data. The filtering requirements are:

[0037] In one embodiment, when the real-time temperature data is lower than the first threshold, the first-level heat dissipation mode is started, and the steps of controlling the cooling fan to operate at the lowest speed and activating the basic heat conduction path of the heat sink include: Based on the current heat sink temperature of each area of the heat sink in the air conditioner, the 3 main heat conduction paths with the lowest current heat sink temperature are opened, and the opening method includes starting the fan on the axial side of the corresponding heat sink; The temperature difference gradient of the main heat conduction path is monitored in real time. If the maximum gradient exceeds 2 °C / cm, additional heat conduction paths are automatically allocated to form a parallel heat dissipation channel.

[0038] In a specific embodiment, when the real-time temperature of the air conditioner control board is lower than the first threshold (such as 50 °C), the system enters the first-level heat dissipation mode. The core of this mode lies in the combination of low-power operation and local precise heat dissipation; first, scan the real-time temperature of each area on the heat sink, and select the 3 main heat conduction paths with the lowest temperature to be opened first. For example, if the heat sink is divided into left, middle, and right areas, and the left side is detected to have the lowest temperature, the corresponding heat conduction channel on the left side is preferentially activated; by starting the micro fan on the axial side of the heat sink (such as an axial flow fan with a diameter of 40 mm), the forced air flow passes through the selected heat conduction path to form a basic heat dissipation circuit; The temperature distribution on the activated heat conduction path is monitored in real time, and the temperature difference gradient per unit length (per centimeter) is calculated; if the temperature difference gradient of a certain path exceeds 2 °C / cm (for example, the temperature at one end is 48 °C and the temperature at the other end rises to 52 °C), it indicates insufficient local heat dissipation, and the system automatically allocates additional heat conduction paths (such as adjacent auxiliary heat dissipation fins) to be connected in parallel with the original path to form a more uniform heat dissipation network.

[0039] In one embodiment, the steps of activating the heat conduction path of the heat sink include: The heat conduction path is that the heat sink is arranged on each control board of the air conditioner, and a fan is arranged on the axial side of the heat sink to cool the heat sink. By starting the fan, the heat transfer rate of each heat sink is conducted to form the heat conduction path.

[0040] In one embodiment, the phase change material is a paraffin-based composite phase change material, the upper limit of its phase change temperature is 65 °C - 75 °C, and the phase change material is encapsulated in the sandwich cavity of the heat sink, and the liquid phase change material is driven to circulate by a micro pump to enhance heat absorption.

[0041] In one embodiment, the steps of dynamically adjusting the heat conduction efficiency of the heat sink through a pulse width modulation signal include: Obtain the temperature change rate, thermal resistance dynamic coefficient and fan speed feedback amount of the heat sink in real time, and construct a multi-dimensional control variable set; Dynamically generate the PWM reference duty cycle based on the reinforcement learning algorithm, and its objective function is: ; Wherein, is the target temperature of the current heat dissipation mode, is the dynamic weight coefficient for environmental humidity compensation; Dynamically adjust the heat conduction efficiency of the heat sink through the reference duty cycle .

[0042] In a specific embodiment, three key parameters including the temperature change rate (the speed of temperature rise and fall per unit time), the dynamic coefficient of thermal resistance (reflecting the change in the heat transfer ability of the heat sink), and the fan speed feedback amount (the difference between the actual speed and the target speed) are collected in real time to construct a dynamic control variable set. For example, when the temperature change rate exceeds 0.5 °C / s, it is marked as the "high heat load state".

[0043] Adopt the Deep Deterministic Policy Gradient (DDPG) algorithm to dynamically generate the PWM reference duty cycle. The algorithm takes the following objective function as the optimization core: ; Obtain the environmental humidity data through the humidity sensor and dynamically adjust the weight coefficient in the objective function: High humidity environment (>70% RH): Increase the value of α (temperature control priority), reduce β (allow slightly higher energy consumption). Low humidity environment (<30% RH): Increase the value of γ (thermal resistance stability priority).

[0044] Refer to Appendix Figure 2 This is the structural block diagram of a heat dissipation optimization system for an air conditioner control board proposed by the present invention, which includes: An acquisition unit for acquiring the real-time temperature data of the air conditioner control board; A comparison unit for comparing the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode; wherein, the heat dissipation mode includes a primary heat dissipation mode, a secondary heat dissipation mode, and a tertiary heat dissipation mode; A first heat dissipation unit for starting the primary heat dissipation mode when the real-time temperature data is lower than the first threshold, controlling the heat dissipation fan to operate at the lowest speed, and activating the basic heat conduction path of the heat sink; A second heat dissipation unit for starting the secondary heat dissipation mode when the real-time temperature data is between the first threshold and the second threshold, adjusting the heat dissipation fan speed to be linearly proportional to the real-time temperature data, and opening the extended heat conduction path of the heat sink to increase the heat dissipation area; A third heat dissipation unit for starting the tertiary heat dissipation mode when the real-time temperature data exceeds the second threshold, switching to phase change material assisted heat dissipation, controlling the heat dissipation fan to operate at the maximum speed, dynamically adjusting the heat conduction efficiency of the heat sink through the pulse width modulation signal, and triggering a temperature alarm signal.

[0045] In summary, the present invention obtains the real-time temperature data of the air conditioner control board; compares the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode; when the real-time temperature data is lower than the first threshold, activates the primary heat dissipation mode, controls the heat dissipation fan to operate at the lowest speed, and activates the basic heat conduction path of the heat sink; when the real-time temperature data is between the first threshold and the second threshold, activates the secondary heat dissipation mode, adjusts the rotation speed of the heat dissipation fan to be linearly proportional to the real-time temperature data, and turns on the extended heat conduction path of the heat sink to increase the heat dissipation area; when the real-time temperature data exceeds the second threshold, activates the tertiary heat dissipation mode, switches to phase change material-assisted heat dissipation, controls the heat dissipation fan to operate at the maximum speed, dynamically adjusts the heat conduction efficiency of the heat sink through a pulse width modulation signal, and triggers a temperature alarm signal. Through the dynamic switching mechanism of the multi-level heat dissipation mode and the active collaborative control of the phase change material, the precise balance between the heat dissipation efficiency and energy consumption of the air conditioner control board is achieved. Based on the dynamic temperature prediction model and the adaptive threshold adjustment algorithm, the frequent mode switching and mechanical noise caused by temperature fluctuations in the traditional solution are effectively suppressed. At the same time, through the pulse width modulation signal optimized by reinforcement learning and the phase change material failure prediction mechanism, the response speed to transient high heat flux density and the heat dissipation stability are significantly improved.

[0046] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A heat dissipation optimization method for an air conditioner control board, characterized in that, It includes the following steps: Obtain the real-time temperature data of the air conditioner control board; Compare the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode; wherein, the heat dissipation mode includes a primary heat dissipation mode, a secondary heat dissipation mode, and a tertiary heat dissipation mode; When the real-time temperature data is lower than the first threshold, start the primary heat dissipation mode, control the heat dissipation fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink; When the real-time temperature data is between the first threshold and the second threshold, start the secondary heat dissipation mode, adjust the rotation speed of the heat dissipation fan to be linearly proportional to the real-time temperature data, and turn on the extended heat conduction path of the heat sink to increase the heat dissipation area; When the real-time temperature data exceeds the second threshold, start the tertiary heat dissipation mode, switch to phase change material assisted heat dissipation, control the heat dissipation fan to operate at the maximum speed, dynamically adjust the heat conduction efficiency of the heat sink through a pulse width modulation signal, and trigger a temperature alarm signal.

2. The heat dissipation optimization method for an air conditioner control board according to claim 1, characterized in that, The switching logic of the heat dissipation mode is realized by the temperature hysteresis comparison method, including: Establish a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting; If the predicted temperature is about to cross the first threshold or the second threshold, adjust the heat dissipation mode in advance and introduce a transition buffer stage, and smooth the fan speed change curve through a proportional integral derivative algorithm to avoid instantaneous power mutation.

3. The heat dissipation optimization method for an air conditioner control board according to claim 2, characterized in that, The step of establishing a dynamic temperature prediction model based on historical temperature data to predict the temperature change trend within a future setting includes: Collect the historical temperature data of the previous N minutes, construct a temperature time series at an interval of N seconds, and perform sliding window mean filtering on the outliers, where N is a positive integer greater than 1; Input the filtered data into the ARIMA model, and automatically select the optimal model order through the minimum information criterion, where the environmental temperature gradient participates in the model training as an exogenous variable; Based on the correlation between the historical rotation speed of the heat dissipation fan and the temperature change rate, construct a rotation speed-temperature coupling coefficient matrix, and use the matrix as a dynamic correction factor for the ARIMA model; Update the model parameters every 5 seconds, verify the prediction accuracy through the sum of squared residuals, and trigger model retraining if the residuals exceed the set threshold.

4. The heat dissipation optimization method for an air conditioner control board according to claim 2, wherein, The step of adjusting the heat dissipation mode in advance and introducing a transition buffer stage, and smoothing the fan speed change curve through a proportional integral derivative algorithm to avoid instantaneous power mutation if the predicted temperature is about to cross the first threshold or the second threshold includes: Determine a transition buffer time window when it is predicted that the temperature is about to cross the first threshold or the second threshold , and its calculation formula is: ; Among them, is the predicted temperature, is the threshold temperature to be crossed, is the absolute value of the temperature change rate; Generate a piecewise continuous fan speed target curve based on the transition buffer time window; Tracking the target curve through a proportional-integral-derivative algorithm, where the calculation formula of the proportional-integral-derivative algorithm is: ; Among them, the proportional term coefficient is dynamically adjusted according to the slope of the target curve. The greater the slope, the higher, which is the differential term coefficient; When the deviation between the actual rotation speed and the target curve exceeds the threshold, trigger an emergency smoothing instruction.

5. The heat dissipation optimization method for an air conditioner control board according to claim 1, characterized in that, The first threshold and the second threshold are dynamic parameters, which are adjusted in real time according to the environmental temperature change rate, wherein the environmental temperature is obtained together with the real-time temperature data; When the environmental temperature rising rate exceeds 0.5℃ / min, lower the first threshold by 3℃ - 5℃ and the second threshold by 5℃ - 8℃; When the environmental temperature falling rate exceeds 0.3℃ / min, restore the initial threshold setting value, and perform noise elimination on the real-time temperature data through a Kalman filter.

6. The heat dissipation optimization method for an air conditioner control board according to claim 1, wherein When the real-time temperature data is lower than the first threshold, start the first-level heat dissipation mode, control the cooling fan to operate at the lowest speed, and activate the basic heat conduction path of the heat sink. The steps include: Based on the current heat sink temperature of each area of the heat sink in the air conditioner, turn on the 3 main heat conduction paths with the lowest current heat sink temperature. The turning-on method includes starting the fans on the shaft side of the corresponding heat sink; Real-time monitor the temperature difference gradient of the main heat conduction path. If the maximum gradient exceeds 2 °C / cm, automatically allocate additional heat conduction paths to form a parallel heat dissipation channel.

7. The heat dissipation optimization method for an air conditioner control board according to claim 6, characterized in that, The steps of activating the heat conduction path of the heat sink include: The heat conduction path is that the heat sink is arranged on each control board of the air conditioner, and a fan is arranged on the shaft side of the heat sink to cool the heat sink. By starting the fan, the heat transfer rate of each heat sink is conducted to form the heat conduction path.

8. The heat dissipation optimization method for an air conditioner control board according to claim 1, wherein, The phase change material is a paraffin-based composite phase change material, whose phase change temperature is 65 °C - 75 °C, and the phase change material is encapsulated in the sandwich cavity of the heat sink, and the liquid phase change material is driven to circulate by a micro pump to enhance heat absorption.

9. The heat dissipation optimization method for an air conditioner control board according to claim 1, wherein The steps of dynamically adjusting the heat conduction efficiency of the heat sink through a pulse width modulation signal include: Real-time obtain the temperature change rate, thermal resistance dynamic coefficient and fan speed feedback amount of the heat sink, and construct a multi-dimensional control variable set; Dynamically generate the pulse width modulation reference duty cycle based on the reinforcement learning algorithm, and its objective function is: ; Among them, is the target temperature of the current heat dissipation mode, is the dynamic weight coefficient for environmental humidity compensation; By means of the reference duty cycle Dynamically adjust the heat conduction efficiency of the heat sink.

10. A heat dissipation optimization system for an air conditioner control board, characterized in that, including: An acquisition unit for acquiring the real-time temperature data of the air conditioner control board; A comparison unit for comparing the real-time temperature data with a preset temperature threshold to determine the heat dissipation mode. Among them, the heat dissipation mode includes a first-level heat dissipation mode, a second-level heat dissipation mode and a third-level heat dissipation mode; A first heat dissipation unit for starting the first-level heat dissipation mode when the real-time temperature data is lower than the first threshold, controlling the cooling fan to operate at the lowest speed, and activating the basic heat conduction path of the heat sink; A second heat dissipation unit for starting the second-level heat dissipation mode when the real-time temperature data is between the first threshold and the second threshold, adjusting the cooling fan speed to be linearly proportional to the real-time temperature data, and turning on the extended heat conduction path of the heat sink to increase the heat dissipation area; A third heat dissipation unit for starting the third-level heat dissipation mode when the real-time temperature data exceeds the second threshold, switching to phase change material-assisted heat dissipation, controlling the cooling fan to operate at the maximum speed, dynamically adjusting the heat conduction efficiency of the heat sink through a pulse width modulation signal, and triggering a temperature alarm signal.

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