Heat dissipation control method, device, computer equipment and storage medium of a fan light

Abstract

The present application relates to a heat dissipation control method, device, computer device and storage medium for a fan light, including: when the fan light is working, real-time monitoring the working state of the fan light; when the temperature data of the fan light exceeds a preset temperature threshold, triggering a fan light heat dissipation message, and obtaining the difference between the temperature data of the fan light and the preset temperature threshold, that is, the fan light temperature difference; transmitting the temperature difference to a trained neural network PID model to obtain a fan light adjustment parameter; and performing corresponding adjustment on the fan light according to the fan light adjustment parameter. The present application has the effect of prolonging the service life of the fan light.

Description

Technical Field

[0001] The present invention relates to the technical field of fan lights, and in particular, to a heat dissipation control method, device, computer device, and storage medium for a fan light. Background Art

[0002] Currently, as a common lighting and heat dissipation device, the heat dissipation effect of a fan light directly affects the service life and performance of the device. At present, most of the heat dissipation control methods for fan lights on the market adopt fixed heat dissipation strategies and cannot dynamically adjust the heat dissipation intensity according to the actual situation, resulting in poor heat dissipation effect and high energy consumption. Traditional heat dissipation control methods for fan lights usually adopt fixed fan speeds or heat sink designs to achieve heat dissipation. This method cannot dynamically adjust the heat dissipation strategy according to the actual working temperature and ambient temperature of the fan light, resulting in an unsatisfactory heat dissipation effect.

[0003] The above-mentioned prior art solutions have the following defects: Traditional heat dissipation control methods for fan lights have problems such as poor heat dissipation effect and high energy consumption, and cannot meet the requirements of modern electronic devices for efficient and energy-saving heat dissipation. Therefore, there is room for improvement. Summary of the Invention

[0004] To extend the service life of a fan light, the present application provides a heat dissipation control method, device, computer device, and storage medium for a fan light.

[0005] The first invention object of the present application is achieved through the following technical solutions:

[0006] A heat dissipation control method for a fan light, the heat dissipation control method for the fan light includes:

[0007] When the fan light is working, real-time monitor the working state of the fan light;

[0008] When the temperature data of the fan light exceeds a preset temperature threshold, trigger a heat dissipation message for the fan light, and obtain the difference between the temperature data of the fan light and the preset temperature threshold, that is, the fan light temperature difference;

[0009] Transmit the temperature difference to a trained neural network PID model to obtain a fan light adjustment parameter;

[0010] According to the fan light adjustment parameter, perform corresponding adjustment on the fan light.

[0011] By adopting the above technical solutions, when the fan light is working, sensors are used to monitor the fan light in the working state in real time, monitoring temperature data, rotation speed data, current data, power data, etc. of the fan light during operation. By monitoring the working state of the fan light in real time, information such as operation parameters, temperature, and power consumption of the fan light can be obtained in a timely manner, so that the operation of the device can be monitored in real time, abnormal situations can be detected in a timely manner, and measures can be taken to adjust or repair them, ensuring the safety and stability of the device, improving the reliability and service life of the device. Through the analysis and processing of these data, it can be judged whether there is an abnormality in the fan light, and preventive maintenance can also be carried out on the device, and worn parts can be replaced in a timely manner, thereby reducing downtime and maintenance costs, and improving the overall operation efficiency and performance of the device; when the temperature data of the fan light exceeds the pre-set temperature threshold, the system will trigger a heat dissipation message, that is, adjust the fan light device to make the fan light dissipate heat, reduce the working temperature of the fan light, and obtain the current temperature data of the fan light, and calculate the difference between the actual temperature and the pre-set temperature threshold, that is, the temperature difference of the fan light, so that the real-time monitoring and control of the fan light temperature can be realized. When the temperature exceeds the set threshold, it can quickly respond and take heat dissipation measures to protect the device from overheating damage. It also helps to understand the operation status of the device, detect the situation of too high temperature in a timely manner, give early warnings and take measures to deal with it, so as to ensure the safe operation of the device and extend the service life of the device; by obtaining the temperature difference of the fan light, that is, the difference between the actual temperature and the pre-set temperature threshold, then, this temperature difference is used as input data and transmitted to the trained neural network PID model. The trained neural network PID model will calculate the corresponding fan light adjustment parameters according to the input temperature difference, as well as the parameters and network structure learned by the model before. Finally, the obtained fan light adjustment parameters can be used to adjust the rotation speed of the fan, the brightness of the light or other relevant parameters to achieve the purpose of controlling the temperature of the fan light. The neural network PID model can dynamically adjust the working state of the fan light according to the real-time temperature difference, realize the stable control of the device temperature, and maximize the performance and efficiency of the fan light system; by calculating the corresponding fan light adjustment parameters through the neural network PID model, the operation state of the fan light is adjusted according to the adjustment parameters to achieve the temperature control of the device. For example, if the fan light adjustment parameters indicate that heat dissipation measures need to be increased, then the system may accelerate the rotation speed of the fan to enhance the heat dissipation effect; on the contrary, if the adjustment parameters indicate that the temperature needs to be reduced, the system may reduce the rotation speed of the fan or adjust the brightness of the light to reduce the heat generation. By actually adjusting according to the fan light adjustment parameters obtained from the neural network PID model, the fan light system can be made more intelligent and automated, and can dynamically adjust the operation states of the fan and the light according to the real-time temperature change, so as to keep the device working within a suitable temperature range, ensure the stability and safety of the device, extend the service life of the device, and improve the work efficiency.

[0012] In a preferred example, the present application can be further configured as follows: When the fan light is working, the real-time monitoring of the working state of the fan light includes:

[0013] When the fan light is working, trigger a sensor detection message;

[0014] Through the sensor detection message, the fan light during operation is monitored in real time to obtain the working state of the fan light, and the working state of the fan light includes temperature data, rotation speed data, and power data.

[0015] By adopting the above technical solution, when the fan light product is in an operating state, the system will trigger a sensor detection message, and the system will activate the sensor device to monitor the operation of the fan light. The sensor can be used to detect various parameters, such as temperature, humidity, pressure, etc., to obtain real-time data on the working state of the fan light. By triggering the sensor detection message, relevant monitoring data can be obtained in a timely manner when the fan light is working, including information such as the environmental state and the device state. This helps to monitor the operation status of the device in a timely manner, detect possible problems or abnormalities, so as to take measures in a timely manner to ensure the normal operation and safety of the fan light product. The triggering of the sensor detection message can improve the intelligence and real-time monitoring ability of the device, ensuring that the device operates in the best state; when the sensor detection message is triggered, the system will activate the sensor device to monitor the working condition of the fan light, and the sensor will measure and obtain the temperature data of the fan light in real time to understand the working temperature of the device. At the same time, the sensor will also record the rotation speed data of the fan to display the rotation speed of the fan. In addition, the sensor can also obtain the power data of the fan light, that is, the energy consumption of the device. By obtaining the temperature data, rotation speed data, and power data of the fan light, the system can monitor the working state of the fan light in real time, detect whether the device is operating normally, and whether there are problems such as overheating, overload, or other problems, which helps to discover potential faults or abnormal conditions in a timely manner, thereby strengthening the monitoring and management of the device and ensuring the normal operation of the fan light product in a safe and efficient state.

[0016] In a preferred example, the present application can be further configured as follows: Before transmitting the temperature difference to the trained neural network PID model to obtain the fan light adjustment parameter, obtaining the trained neural network PID model specifically includes: obtaining abnormal fan light temperature data, calculating the difference between the abnormal fan light temperature data and the preset temperature threshold to obtain abnormal temperature difference data;

[0017] Obtain the fan light adjustment parameter data set corresponding to the abnormal temperature difference data, and train the neural network PID model with the abnormal temperature difference data and the fan light adjustment parameter data set to obtain the trained neural network PID model.

[0018] By adopting the above technical solution, the system will monitor the temperature data of the fan light. When the temperature data is abnormal, it indicates that there may be problems such as device failure or overheating. Then, the system will compare the abnormal fan light temperature data with the preset temperature threshold, and calculate the difference between the actual temperature and the preset temperature threshold, that is, the abnormal temperature difference data. By calculating the abnormal temperature difference data, the system can more clearly understand the working state of the device, detect the temperature abnormality in time, so as to take corresponding measures to handle the problem in time. The acquisition of the abnormal temperature difference data helps to monitor and diagnose the device in real time, improve the perception ability of the device abnormal situation, so as to ensure the safe operation of the device and extend the life of the device; collect and save the abnormal temperature difference data and the corresponding fan light adjustment parameter data set. The abnormal temperature difference data reflects the abnormal situation in the device working state, while the fan light adjustment parameter data set contains the adjustment measures taken by the system under different temperature difference conditions. The abnormal temperature difference data and the fan light adjustment parameter data set are used as training data to train the neural network PID model. By training the neural network PID model, the system can learn how to adjust the control parameters of the fan light according to the difference between the abnormal temperature data and the corresponding adjustment parameter data set to better maintain the device in the ideal working state. Finally, the trained neural network PID model can adjust the fan light in real time according to the abnormal temperature difference data and make appropriate control actions according to the previously learned adjustment parameter data set to ensure that the device runs within a stable and safe working range, which helps to improve the response ability to abnormal situations, thus improving the performance and reliability of the device.

[0019] In a preferred example of the present application, it can be further configured as: the process of transmitting the fan light temperature difference to the trained neural network PID model to obtain the fan light adjustment parameter includes:

[0020] Analyze the fan light temperature difference through the trained neural network PID model to obtain the fan light abnormal cause information; find the corresponding fan light adjustment mode through the fan light abnormal cause information to obtain the fan light adjustment parameter.

[0021] By adopting the above technical solution, the trained neural network PID model is used to analyze the temperature difference of the fan light obtained in real time. The neural network PID model has been trained using the abnormal temperature difference data and the fan light adjustment parameter dataset before, so as to adjust the control parameters of the fan light according to the temperature difference. By analyzing the temperature difference of the fan light, the neural network PID model can judge whether the working state of the fan light is normal or abnormal. According to the trained PID model, the possible abnormal reasons can be inferred, such as whether the temperature is too high, the fan does not work or the power is insufficient, etc., so as to provide information on the abnormal reasons of the fan light, which helps to timely identify and analyze the abnormal situation of the fan light, give appropriate countermeasures, and avoid failures or damages caused by abnormal situations; when the system detects an abnormality in the fan light, it will search for the corresponding fan light adjustment mode according to the abnormal reason information. This adjustment mode is summarized and obtained based on previous abnormal adjustment experiences. Each abnormal reason may correspond to a different adjustment mode and corresponding fan light adjustment parameters. By searching for the corresponding fan light adjustment mode, the system can obtain appropriate fan light adjustment parameters to adjust the operating state of the device to solve the abnormal situation or optimize it to ensure that the fan light always operates within a safe and efficient working range. By matching the abnormal reason information with the fan light adjustment mode, the system can quickly respond to the abnormal situation and adjust according to the specific situation, thereby improving the performance, stability and safety of the fan light.

[0022] In a preferred example of the present application, it can be further configured that: the corresponding adjustment of the fan light according to the fan light adjustment parameters includes:

[0023] The fan light adjustment parameters include the fan light speed adjustment amount and the fan light power adjustment amount;

[0024] The fan light is correspondingly adjusted by the fan light speed adjustment amount and the fan light power adjustment amount.

[0025] By adopting the above technical solutions, the adjustment parameters of the fan light include the fan light rotation speed adjustment amount and the fan light power adjustment amount. By adjusting the rotation speed of the fan, the heat dissipation effect and the air volume of the fan can be controlled. Increasing the rotation speed of the fan can improve the heat dissipation effect and reduce the temperature of the device, while decreasing the rotation speed of the fan may reduce noise and energy consumption. By adjusting the power of the fan light, the brightness of the light and the power consumption of the fan can be controlled. Increasing the power usually leads to a stronger lighting effect and a greater heat dissipation capacity, thus helping to optimize the performance, energy consumption and usage experience of the device, and keeping the fan light in a suitable state during operation; by adjusting the rotation speed and power of the fan light, the control of aspects such as the brightness, air volume and energy efficiency of the fan light can be achieved. Specifically, the rotation speed adjustment amount can control the rotation speed of the fan, thereby affecting the air volume and heat dissipation effect of the fan. By adjusting these two parameters, the working state of the fan light can be optimized according to actual needs and environmental conditions. For example, when it is necessary to increase the heat dissipation effect or improve the lighting brightness, the rotation speed and power of the fan can be appropriately increased, while in the case of requiring energy conservation or reducing noise, the rotation speed and power of the fan can be appropriately decreased. Therefore, by correspondingly adjusting the rotation speed and power of the fan light, the fine control of the fan light in different scenarios can be achieved, and the performance and efficiency of the device can be improved.

[0026] In a preferred example of the present application, it can be further configured that: the heat dissipation control method of the fan light further includes:

[0027] Through the weight formula: Calculate the fan light rotation speed adjustment amount and the fan light power adjustment amount to obtain a weight score, where α and β are weight coefficients, C is the weight score, V is the fan light rotation speed adjustment amount, V0 is the standard rotation speed of the fan light, W is the fan light power adjustment amount, W1 is the circuit load power, and W0 is the standard power of the fan light;

[0028] When the weight score is higher than the preset weight score, trigger a fan light abnormal message and generate fan light maintenance suggestion information.

[0029] By adopting the above technical solutions, through the weight formula: Calculate the adjustment amount of the fan light rotation speed and the adjustment amount of the fan light power to obtain the weight score. By calculating the difference between the fan light power adjustment amount and the circuit load power, in order to reduce the error in the adjustment of the fan light caused by the loss in the circuit, using α and β as weight coefficients can adjust the ratio of the rotation speed and power according to the specific situation and requirements, realizing a more intelligent and accurate adjustment of the fan light; calculate the weight scores of the fan light rotation speed adjustment amount and the fan light power adjustment amount through the weight formula, and compare these weight scores with the preset weight scores. If the calculated weight score is higher than the preset weight score, it means that the adjustment amount of the fan light has exceeded the preset range, and an abnormal situation may occur. In this case, the system will trigger a fan light abnormal message to prompt the user or system operator to pay attention to the abnormal situation of the fan light. At the same time, the system will also generate fan light maintenance suggestion information to guide the user or technician to take corresponding maintenance measures for the abnormal situation of the fan light, such as checking whether parts need to be replaced, cleaning the fan, etc. In this way, the system can detect and handle the abnormal situation of the fan light in time, prevent equipment failure or safety problems caused by abnormal situations, and generating fan light maintenance suggestion information can guide users and maintenance personnel to effectively carry out maintenance and upkeep, improving the reliability and service life of the equipment. This automated abnormal handling mechanism helps to improve the intelligence and automation level of the system and enhance the efficiency and quality of equipment maintenance.

[0030] The second invention object of the present application is achieved by the following technical solutions:

[0031] A heat dissipation control device for a fan light, the heat dissipation control device for the fan light includes:

[0032] A monitoring module, used to monitor the working state of the fan light in real time when the fan light is working;

[0033] A temperature difference calculation module, used to trigger a fan light heat dissipation message when the fan light temperature data exceeds a preset temperature threshold, and obtain the difference between the fan light temperature data and the preset temperature threshold, that is, the fan light temperature difference;

[0034] A data processing module, used to transmit the temperature difference to a trained neural network PID model to obtain fan light adjustment parameters;

[0035] An adjustment module, used to perform corresponding adjustment on the fan light according to the fan light adjustment parameters.

[0036] By adopting the above technical solutions, when the fan light is working, sensors are used to monitor the fan light in the working state in real time, monitoring temperature data, rotation speed data, current data, power data, etc. of the fan light during operation. By monitoring the working state of the fan light in real time, information such as operation parameters, temperature, and power consumption of the fan light can be obtained in a timely manner, so that the operation of the device can be monitored in real time, abnormal situations can be detected in a timely manner and measures can be taken for adjustment or repair to ensure the safety and stability of the device, improve the reliability and service life of the device. Through the analysis and processing of these data, it can be judged whether there is an abnormality in the fan light, and preventive maintenance can also be carried out on the device, and worn parts can be replaced in a timely manner, thereby reducing downtime and maintenance costs, and improving the overall operation efficiency and performance of the device; when the temperature data of the fan light exceeds the pre-set temperature threshold, the system will trigger a heat dissipation message, that is, adjust the fan light device to make the fan light dissipate heat, reduce the working temperature of the fan light, and obtain the current temperature data of the fan light, and calculate the difference between the actual temperature and the preset temperature threshold, that is, the temperature difference of the fan light, so as to realize the real-time monitoring and control of the temperature of the fan light. When the temperature exceeds the set threshold, it can quickly respond and take heat dissipation measures to protect the device from overheating damage. It also helps to understand the operation status of the device, detect the situation of too high temperature in a timely manner, give early warnings and take measures to deal with it, so as to ensure the safe operation of the device and extend the service life of the device; by obtaining the temperature difference of the fan light, that is, the difference between the actual temperature and the preset temperature threshold, then, this temperature difference is used as input data and transmitted to the trained neural network PID model. The trained neural network PID model will calculate the corresponding fan light adjustment parameters according to the input temperature difference, as well as the parameters and network structure learned by the model before. Finally, the obtained fan light adjustment parameters can be used to adjust the rotation speed of the fan, the brightness of the light or other related parameters to achieve the purpose of controlling the temperature of the fan light. The neural network PID model can dynamically adjust the working state of the fan light according to the real-time temperature difference, realize the stable control of the device temperature, and maximize the performance and efficiency of the fan light system; by calculating the corresponding fan light adjustment parameters through the neural network PID model, the operation state of the fan light is adjusted according to the adjustment parameters to achieve the temperature control of the device. For example, if the fan light adjustment parameters indicate that heat dissipation measures need to be increased, then the system may accelerate the rotation speed of the fan to enhance the heat dissipation effect; on the contrary, if the adjustment parameters indicate that the temperature needs to be reduced, the system may reduce the rotation speed of the fan or adjust the brightness of the light to reduce the heat generation. By actually adjusting according to the fan light adjustment parameters obtained from the neural network PID model, the fan light system can be made more intelligent and automated, and can dynamically adjust the operation states of the fan and the light according to the real-time temperature change, so as to keep the device working within a suitable temperature range, ensure the stability and safety of the device, extend the service life of the device, and improve work efficiency.

[0037] The third above object of the present application is achieved by the following technical solutions:

[0038] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned heat dissipation control method of the fan lamp are implemented.

[0039] The fourth above object of the present application is achieved by the following technical solutions:

[0040] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned heat dissipation control method of the fan lamp are implemented.

[0041] In summary, the present application includes at least one of the following beneficial technical effects:

[0042] 1. When the fan light is working, use sensors to monitor the fan light in real time during operation, monitoring temperature data, rotation speed data, current data, power data, etc. of the fan light during operation. By monitoring the working state of the fan light in real time, information such as the operating parameters, temperature, and power consumption of the fan light can be obtained in a timely manner, so that the operation of the device can be monitored in real time, abnormal situations can be detected in a timely manner, and measures can be taken to adjust or repair, ensuring the safety and stability of the device, improving the reliability and service life of the device. Through the analysis and processing of these data, it can be judged whether there is an abnormality in the fan light, and preventive maintenance can also be carried out on the device, and worn parts can be replaced in a timely manner, thereby reducing downtime and maintenance costs, and improving the overall operating efficiency and performance of the device; when the temperature data of the fan light exceeds the pre-set temperature threshold, the system will trigger a heat dissipation message, that is, adjust the fan light device to make the fan light dissipate heat, reduce the working temperature of the fan light, and obtain the current temperature data of the fan light, and calculate the difference between the actual temperature and the preset temperature threshold, that is, the temperature difference of the fan light, so that the real-time monitoring and control of the fan light temperature can be realized. When the temperature exceeds the set threshold, it can quickly respond and take heat dissipation measures to protect the device from overheating damage. It also helps to understand the operating conditions of the device, detect the situation of too high temperature in a timely manner, give early warnings and take measures to deal with it, so as to ensure the safe operation of the device and extend the service life of the device; by obtaining the temperature difference of the fan light, that is, the difference between the actual temperature and the preset temperature threshold, then, this temperature difference is used as input data and transmitted to the trained neural network PID model. The trained neural network PID model will calculate the corresponding fan light adjustment parameters according to the input temperature difference, as well as the parameters and network structure learned by the model before. Finally, the obtained fan light adjustment parameters can be used to adjust the rotation speed of the fan, the brightness of the light or other relevant parameters to achieve the purpose of controlling the temperature of the fan light. The neural network PID model can dynamically adjust the working state of the fan light according to the real-time temperature difference, realize the stable control of the device temperature, and maximize the performance and efficiency of the fan light system; calculate the corresponding fan light adjustment parameters through the neural network PID model, and adjust the operating state of the fan light according to the adjustment parameters to achieve the temperature control of the device. For example, if the fan light adjustment parameters indicate that heat dissipation measures need to be increased, then the system may accelerate the rotation speed of the fan to enhance the heat dissipation effect; on the contrary, if the adjustment parameters indicate that the temperature needs to be reduced, the system may reduce the rotation speed of the fan or adjust the brightness of the light to reduce the heat generation. By actually adjusting according to the fan light adjustment parameters obtained from the neural network PID model, the fan light system can be made more intelligent and automated, and can dynamically adjust the operating states of the fan and the light according to the real-time temperature change, so as to keep the device working within a suitable temperature range, ensure the stability and safety of the device, extend the service life of the device, and improve work efficiency;

[0043] 2. When the ceiling fan light product is in the running state, the system will trigger a sensor detection message, and the system will activate the sensor device to monitor the operation of the ceiling fan light. The sensor can be used to detect various parameters, such as temperature, humidity, pressure, etc., to obtain real-time data on the working state of the ceiling fan light. By triggering the sensor detection message, relevant monitoring data can be obtained in a timely manner when the ceiling fan light is working, including information such as the environmental state and the device state. This helps to monitor the operation status of the device in a timely manner, detect possible problems or abnormalities, so as to take measures in a timely manner to ensure the normal operation and safety of the ceiling fan light product. The triggering of the sensor detection message can improve the intelligence and real-time monitoring ability of the device, ensuring that the device operates in the best state; when the sensor detection message is triggered, the system will activate the sensor device to monitor the working condition of the ceiling fan light. The sensor will measure and obtain the temperature data of the ceiling fan light in real time to understand the working temperature of the device. At the same time, the sensor will also record the rotation speed data of the fan to display the rotation speed of the fan. In addition, the sensor can also obtain the power data of the ceiling fan light, that is, the energy consumption of the device. By obtaining the temperature data, rotation speed data and power data of the ceiling fan light, the system can monitor the working state of the ceiling fan light in real time, detect whether the device is operating normally, and whether there are problems such as overheating, overload or other problems, which helps to discover potential faults or abnormal situations in a timely manner, thereby strengthening the monitoring and management of the device and ensuring the normal operation of the ceiling fan light product in a safe and efficient state;

[0044] 3. The system monitors the temperature data of the fan light. When the temperature data is abnormal, it indicates that there may be problems such as device failure or overheating. Then, the system compares the abnormal temperature data of the fan light with a preset temperature threshold, calculates the difference between the actual temperature and the preset temperature threshold, that is, the abnormal temperature difference data. By calculating the abnormal temperature difference data, the system can more clearly understand the working state of the device, detect the abnormal temperature situation in a timely manner, so as to take corresponding measures to handle the problem in a timely manner. The acquisition of the abnormal temperature difference data helps to monitor and diagnose the device in real time, improve the perception ability of device abnormalities, so as to ensure the safe operation of the device and extend the life of the device; collect and save the abnormal temperature difference data and the corresponding fan light adjustment parameter data set. The abnormal temperature difference data reflects the abnormal situation in the device working state, while the fan light adjustment parameter data set contains the adjustment measures taken by the system under different temperature difference conditions. Use the abnormal temperature difference data and the fan light adjustment parameter data set as training data to train the neural network PID model. By training the neural network PID model, the system can learn how to adjust the control parameters of the fan light according to the difference between the abnormal temperature data and the corresponding adjustment parameter data set to better maintain the device in an ideal working state. Finally, the trained neural network PID model can adjust the fan light in real time according to the abnormal temperature difference data and make appropriate control actions according to the previously learned adjustment parameter data set to ensure that the device operates within a stable and safe working range, which helps to improve the response ability to abnormal situations, thereby improving the performance and reliability of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of a heat dissipation control method for a fan light in an embodiment of the present application;

[0046] Figure 2 is an implementation flowchart of step S10 in the heat dissipation control method for a fan light in an embodiment of the present application;

[0047] Figure 3 is an implementation flowchart of step S20 in the heat dissipation control method for a fan light in an embodiment of the present application;

[0048] Figure 4 is an implementation flowchart of step S30 in the heat dissipation control method for a fan light in an embodiment of the present application;

[0049] Figure 5 is an implementation flowchart of step S40 in the heat dissipation control method for a fan light in an embodiment of the present application;

[0050] Figure 6 is an implementation flowchart of step S50 in the heat dissipation control method for a fan light in an embodiment of the present application;

[0051] Figure 7 It is a schematic block diagram of a heat dissipation control device for a fan light in an embodiment of the present application;

[0052] Figure 8 It is a schematic diagram of a device in an embodiment of the present application. Specific embodiments

[0053] The following further elaborates on the present application in conjunction with the accompanying drawings.

[0054] In one embodiment, as Figure 1 shown, the present application discloses a heat dissipation control method for a fan light, which specifically includes the following steps:

[0055] S10: When the fan light is operating, real-time monitor the operating state of the fan light.

[0056] In this embodiment, the operating state of the fan light refers to the state when the fan light is operating.

[0057] Specifically, by real-time monitoring the operating state of the fan light, information such as the operating parameters, temperature, power consumption, etc. of the fan light can be obtained in a timely manner. Thus, the operation of the device can be monitored in real time, abnormal situations can be detected in a timely manner and measures can be taken for adjustment or repair to ensure the safety and stability of the device, improve the reliability and service life of the device. Therefore, when the fan light is operating, sensors are used to real-time monitor the fan light in the operating state, monitor temperature data, rotation speed data, current data, power data, etc. of the fan light when it is operating. By analyzing and processing these data, it can be determined whether there is an abnormality in the fan light, and preventive maintenance can also be carried out on the device, and worn parts can be replaced in a timely manner, thereby reducing downtime and maintenance costs and improving the overall operating efficiency and performance of the device.

[0058] S20: When the temperature data of the fan light exceeds a preset temperature threshold, trigger a heat dissipation message for the fan light and obtain the difference between the temperature data of the fan light and the preset temperature threshold, that is, the temperature difference of the fan light.

[0059] In this embodiment, the temperature data of the fan light refers to the data of the temperature when the fan light is operating. The heat dissipation message for the fan light refers to a message for dissipating heat from the fan light.

[0060] Specifically, when the temperature data of the ceiling fan light exceeds a pre-set temperature threshold, the system will trigger a heat dissipation message, that is, adjust the ceiling fan light device to make the ceiling fan light dissipate heat, reduce the operating temperature of the ceiling fan light, obtain the current temperature data of the ceiling fan light, and calculate the difference between the actual temperature and the preset temperature threshold, that is, the temperature difference of the ceiling fan light, so as to realize the real-time monitoring and control of the temperature of the ceiling fan light. When the temperature exceeds the set threshold, it can quickly respond and take heat dissipation measures to protect the device from overheating damage. It also helps to understand the operating condition of the device, timely detect the situation of too high temperature, give an early warning and take measures to deal with it, so as to ensure the safe operation of the device and extend the service life of the device.

[0061] S30: Transmit the temperature difference to the trained neural network PID model to obtain the ceiling fan light adjustment parameters.

[0062] In this embodiment, the ceiling fan light adjustment parameter refers to the parameter for adjusting the ceiling fan light.

[0063] Specifically, by obtaining the temperature difference of the ceiling fan light, that is, the difference between the actual temperature and the preset temperature threshold, and then transmitting this temperature difference as input data to the trained neural network PID model. The trained neural network PID model will calculate the corresponding ceiling fan light adjustment parameters according to the input temperature difference, as well as the parameters and network structure learned by the model before. Finally, the obtained ceiling fan light adjustment parameters can be used to adjust the rotation speed of the fan, the brightness of the light or other related parameters to achieve the purpose of controlling the temperature of the ceiling fan light. The neural network PID model can dynamically adjust the working state of the ceiling fan light according to the real-time temperature difference, realize the stable control of the device temperature, and maximize the performance and efficiency of the ceiling fan light system.

[0064] S40: Adjust the ceiling fan light correspondingly according to the ceiling fan light adjustment parameters.

[0065] Specifically, calculate the corresponding ceiling fan light adjustment parameters through the neural network PID model, and adjust the operating state of the ceiling fan light according to the adjustment parameters to achieve the temperature control of the device. For example, if the ceiling fan light adjustment parameters indicate that heat dissipation measures need to be increased, then the system may accelerate the rotation speed of the fan to enhance the heat dissipation effect; conversely, if the adjustment parameters indicate that the temperature needs to be reduced, the system may reduce the rotation speed of the fan or adjust the brightness of the light to reduce the heat generation. By actually adjusting according to the ceiling fan light adjustment parameters obtained from the neural network PID model, the ceiling fan light system can be made more intelligent and automated, and can dynamically adjust the operating states of the fan and the light according to the real-time temperature change, so as to keep the device working within a suitable temperature range, ensure the stability and safety of the device, extend the service life of the device, and improve the work efficiency.

[0066] Through the above technical solutions, when the fan light is working, sensors are used to monitor the fan light in real time during operation, monitoring temperature data, rotation speed data, current data, power data, etc. of the fan light during operation. By monitoring the working state of the fan light in real time, information such as the operating parameters, temperature, and power consumption of the fan light can be obtained in a timely manner. Thus, the operation of the device can be monitored in real time, abnormal situations can be detected in a timely manner, and measures can be taken to adjust or repair them, ensuring the safety and stability of the device, improving the reliability and service life of the device. Through the analysis and processing of these data, it can be judged whether there is an abnormality in the fan light, and preventive maintenance can also be carried out on the device, and worn parts can be replaced in a timely manner, thereby reducing downtime and maintenance costs, and improving the overall operating efficiency and performance of the device; when the temperature data of the fan light exceeds the pre-set temperature threshold, the system will trigger a heat dissipation message, that is, adjust the fan light device to make the fan light dissipate heat, reduce the working temperature of the fan light, and obtain the current temperature data of the fan light, and calculate the difference between the actual temperature and the pre-set temperature threshold, that is, the temperature difference of the fan light, so as to realize the real-time monitoring and control of the temperature of the fan light. When the temperature exceeds the set threshold, it can quickly respond and take heat dissipation measures to protect the device from overheating damage, which also helps to understand the operating conditions of the device, detect the situation of too high temperature in a timely manner, give early warnings and take measures to deal with it, so as to ensure the safe operation of the device and extend the service life of the device; by obtaining the temperature difference of the fan light, that is, the difference between the actual temperature and the pre-set temperature threshold, then, this temperature difference is used as input data and transmitted to the trained neural network PID model. The trained neural network PID model will calculate the corresponding fan light adjustment parameters according to the input temperature difference, as well as the parameters and network structure learned by the model before. Finally, the obtained fan light adjustment parameters can be used to adjust the rotation speed of the fan, the brightness of the light or other relevant parameters to achieve the purpose of controlling the temperature of the fan light. The neural network PID model can dynamically adjust the working state of the fan light according to the real-time temperature difference, realize the stable control of the device temperature, and maximize the performance and efficiency of the fan light system; by calculating the corresponding fan light adjustment parameters through the neural network PID model, the operating state of the fan light is adjusted according to the adjustment parameters to achieve the temperature control of the device. For example, if the fan light adjustment parameters indicate that heat dissipation measures need to be increased, then the system may accelerate the rotation speed of the fan to enhance the heat dissipation effect; conversely, if the adjustment parameters indicate that the temperature needs to be reduced, the system may reduce the rotation speed of the fan or adjust the brightness of the light to reduce the heat generation. By actually adjusting according to the fan light adjustment parameters obtained from the neural network PID model, the fan light system can be made more intelligent and automated, and can dynamically adjust the operating states of the fan and the light according to the real-time temperature change, so as to keep the device working within a suitable temperature range, ensure the stability and safety of the device, extend the service life of the device, and improve the work efficiency.

[0067] In one embodiment, as Figure 2 shown, in step S10, that is, when the fan light is working, the working state of the fan light is monitored in real time, specifically including:

[0068] S11: When the fan light is working, trigger a sensor detection message.

[0069] In this embodiment, the sensor detection message refers to a message for controlling the sensor to detect the fan light.

[0070] Specifically, when the fan light product is in an operating state, the system will trigger a sensor detection message, and the system will start the sensor device to monitor the operation of the fan light. The sensor can be used to detect various parameters, such as temperature, humidity, pressure, etc., to obtain real-time data on the working state of the fan light. By triggering the sensor detection message, relevant monitoring data, including environmental status and device status information, can be obtained in a timely manner when the fan light is working. This helps to monitor the operation status of the device in a timely manner, detect possible problems or abnormalities, so as to take measures in a timely manner to ensure the normal operation and safety of the fan light product. The triggering of the sensor detection message can improve the intelligence and real-time monitoring ability of the device, ensuring that the device operates in the best state.

[0071] S12: Through the sensor detection message, the fan light during operation is monitored in real time, and the working state of the fan light is obtained. The working state of the fan light includes temperature data, rotation speed data, and power data.

[0072] Specifically, when the sensor detection message is triggered, the system will start the sensor device to monitor the operation of the fan light. The sensor will measure and obtain the temperature data of the fan light in real time to understand the working temperature of the device. At the same time, the sensor will also record the rotation speed data of the fan to display the rotation speed of the fan. In addition, the sensor can also obtain the power data of the fan light, that is, the energy consumption of the device. By obtaining the temperature data, rotation speed data, and power data of the fan light, the system can monitor the working state of the fan light in real time, detect whether the device is operating normally, and whether there are problems such as overheating, overload, or other problems, which helps to discover potential faults or abnormal situations in a timely manner, thereby strengthening the monitoring and management of the device and ensuring the normal operation of the fan light product in a safe and efficient state.

[0073] In one embodiment, as Figure 3As shown, before step S30, that is, before transmitting the temperature difference to the trained neural network PID model to obtain the fan light adjustment parameters, obtaining the trained neural network PID model specifically includes: S301: Obtain abnormal fan light temperature data, calculate the difference between the abnormal fan light temperature data and the preset temperature threshold to obtain abnormal temperature difference data.

[0074] In this embodiment, the abnormal fan light temperature data refers to the fan light temperature data that exceeds the preset temperature threshold.

[0075] Specifically, the system monitors the temperature data of the fan light. When the temperature data is abnormal, it indicates that there may be problems such as device failure or overheating. Then, the system compares the abnormal fan light temperature data with the preset temperature threshold and calculates the difference between the actual temperature and the preset temperature threshold, that is, the abnormal temperature difference data. By calculating the abnormal temperature difference data, the system can more clearly understand the working state of the device, detect the temperature abnormality in a timely manner, so as to take corresponding measures to handle the problem in a timely manner. The acquisition of the abnormal temperature difference data helps to monitor and diagnose the device in real time, improve the perception ability of device abnormalities, and thus ensure the safe operation of the device and extend the life of the device.

[0076] S302: Obtain the fan light adjustment parameter dataset corresponding to the abnormal temperature difference data, and train the neural network PID model with the abnormal temperature difference data and the fan light adjustment parameter dataset to obtain the trained neural network PID model.

[0077] Specifically, collect and save the abnormal temperature difference data and the corresponding fan light adjustment parameter dataset. The abnormal temperature difference data reflects the abnormal conditions that occur in the device working state, while the fan light adjustment parameter dataset contains the adjustment measures taken by the system under different temperature differences. Use the abnormal temperature difference data and the fan light adjustment parameter dataset as training data to train the neural network PID model. By training the neural network PID model, the system can learn how to adjust the control parameters of the fan light according to the difference between the abnormal temperature data and the corresponding adjustment parameter dataset to better maintain the device in an ideal working state. Finally, the trained neural network PID model can adjust the fan light in real time according to the abnormal temperature difference data and make appropriate control actions according to the previously learned adjustment parameter dataset to ensure that the device operates within a stable and safe working range, which helps to improve the response ability to abnormal situations and thus improve the performance and reliability of the device.

[0078] In one embodiment, such as Figure 4As shown, in step S30, the temperature difference is transmitted to the trained neural network PID model to obtain the fan light adjustment parameters, specifically including:

[0079] S31: Analyze the temperature difference of the fan light through the trained neural network PID model to obtain the fan light abnormal cause information.

[0080] In this embodiment, the fan light abnormal cause information refers to the information containing the reasons for the abnormality of the fan light.

[0081] Specifically, use the trained neural network PID model to analyze the temperature difference of the fan light obtained in real time. The neural network PID model has been trained through the previous abnormal temperature difference data and the fan light adjustment parameter dataset, so as to adjust the control parameters of the fan light according to the temperature difference. By analyzing the temperature difference of the fan light, the neural network PID model can judge whether the working state of the fan light is normal and whether there is an abnormality. According to the trained PID model, possible abnormal reasons can be inferred, such as whether the temperature is too high, the fan does not work or the power is insufficient, etc., so as to provide the information of the abnormal cause of the fan light, which helps to timely identify and analyze the abnormal situation of the fan light, give appropriate countermeasures, and avoid failures or damages caused by abnormal situations.

[0082] S32: Find the corresponding fan light adjustment mode through the fan light abnormal cause information to obtain the fan light adjustment parameters.

[0083] In this embodiment, the fan light adjustment mode refers to

[0084] Specifically, when the system detects an abnormality in the fan light, it will find the corresponding fan light adjustment mode according to the abnormal cause information. This adjustment mode is summarized and obtained based on previous abnormal adjustment experiences. Each abnormal cause may correspond to a different adjustment mode and the corresponding fan light adjustment parameters. By finding the corresponding fan light adjustment mode, the system can obtain appropriate fan light adjustment parameters to adjust the operating state of the device to solve the abnormal situation or optimize it to ensure that the fan light always operates within a safe and efficient working range. Through the matching of the abnormal cause information and the fan light adjustment mode, the system can quickly respond to the abnormal situation and adjust according to the specific situation, thereby improving the performance, stability and safety of the fan light.

[0085] In one embodiment, as Figure 5 shown, in step S40, that is, according to the fan light adjustment parameters, the fan light is adjusted correspondingly, specifically including:

[0086] S41: The fan light adjustment parameters include the fan light speed adjustment amount and the fan light power adjustment amount.

[0087] Specifically, the adjustment parameters of the fan light include the fan light speed adjustment amount and the fan light power adjustment amount. By adjusting the speed of the fan, the heat dissipation effect and the air volume of the fan can be controlled. Increasing the speed of the fan can improve the heat dissipation effect and reduce the temperature of the device, while decreasing the fan speed may reduce noise and energy consumption. By adjusting the power of the fan light, the brightness of the light and the power consumption of the fan can be controlled. Increasing the power usually results in a stronger lighting effect and a greater heat dissipation capacity, thus helping to optimize the performance, energy consumption, and usage experience of the device, and keeping the fan light in a suitable state during operation.

[0088] S42: Adjust the fan light correspondingly according to the fan light speed adjustment amount and the fan light power adjustment amount.

[0089] Specifically, by adjusting the speed and power of the fan light, the control of aspects such as the brightness, air volume, and energy efficiency of the fan light can be achieved. Specifically, the speed adjustment amount can control the speed of the fan, thereby affecting the air volume and heat dissipation effect of the fan. By adjusting these two parameters, the working state of the fan light can be optimized according to actual needs and environmental conditions. For example, when increased heat dissipation effect or higher lighting brightness is required, the fan speed and power can be appropriately increased, while in cases where energy conservation or noise reduction is required, the fan speed and power can be appropriately decreased. Therefore, by correspondingly adjusting the speed and power of the fan light, fine control of the fan light in different scenarios can be achieved, improving the performance and efficiency of the device.

[0090] In one embodiment, as Figure 6 shown, the heat dissipation control method of the fan light further includes:

[0091] S51: Calculate the fan light speed adjustment amount and the fan light power adjustment amount through the weight formula: to obtain a weight score, where α and β are weight coefficients, C is the weight score, V is the fan light speed adjustment amount, V0 is the standard speed of the fan light, W is the fan light power adjustment amount, W1 is the circuit load power, and W0 is the standard power of the fan light.

[0092] Specifically, through the weight formula: calculate the fan light speed adjustment amount and the fan light power adjustment amount to obtain a weight score. By calculating the difference between the fan light power adjustment amount and the circuit load power, in order to reduce the error in the adjustment of the fan light caused by the loss in the circuit, using α and β as weight coefficients is to adjust the ratio of speed and power according to specific situations and requirements, realizing more intelligent and precise adjustment of the fan light.

[0093] S52: When the weight score is higher than the preset weight score, trigger a fan light exception message and generate fan light maintenance advice information.

[0094] In this embodiment, the fan light anomaly message refers to the message triggered when the fan light has an abnormal condition. The fan light maintenance advice information refers to the information for advising the maintenance personnel on maintenance and upkeep.

[0095] Specifically, the weight scores of the fan light rotation speed adjustment amount and the fan light power adjustment amount are calculated through a weight formula. Then, the system will compare these weight scores with the preset weight scores. If the calculated weight scores are higher than the preset weight scores, it indicates that the adjustment amount of the fan light has exceeded the preset range and an abnormal situation may occur. In this case, the system will trigger a fan light anomaly message to prompt the user or system operator to pay attention to the abnormal situation of the fan light. At the same time, the system will also generate fan light maintenance advice information to guide the user or technician to take corresponding maintenance measures for the abnormal situation of the fan light, such as checking whether parts need to be replaced, cleaning the fan, etc.

[0096] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0097] In one embodiment, a heat dissipation control device for a fan light is provided, and the heat dissipation control device for the fan light corresponds one-to-one with the heat dissipation control method of the fan light in the above embodiment. As Figure 7 shown, the heat dissipation control device for the fan light includes a monitoring module, a temperature difference calculation module, a data processing module, and an adjustment module. The detailed description of each functional module is as follows:

[0098] The monitoring module is used to monitor the working state of the fan light in real time when the fan light is working;

[0099] The temperature difference calculation module is used to trigger a fan light heat dissipation message when the fan light temperature data exceeds the preset temperature threshold, and obtain the difference between the fan light temperature data and the preset temperature threshold, that is, the fan light temperature difference;

[0100] The data processing module is used to transmit the temperature difference to the trained neural network PID model to obtain the fan light adjustment parameter; the adjustment module is used to adjust the fan light correspondingly according to the fan light adjustment parameter.

[0101] Optionally, the monitoring module includes:

[0102] The sensor detection sub-module is used to trigger a sensor detection message when the fan light is working;

[0103] The working status acquisition sub-module is used to monitor the fan light in real time through sensor detection messages, obtain the working status of the fan light, and the working status of the fan light includes temperature data, rotation speed data, and power data.

[0104] Optionally, the heat dissipation control device of the fan light further includes:

[0105] The abnormal temperature difference acquisition module is used to obtain the abnormal fan light temperature data, calculate the difference between the abnormal fan light temperature data and the preset temperature threshold, and obtain the abnormal temperature difference data;

[0106] The training model module is used to obtain the fan light adjustment parameter data set corresponding to the abnormal temperature difference data, and train the neural network PID model through the abnormal temperature difference data and the fan light adjustment parameter data set to obtain the trained neural network PID model.

[0107] The weight calculation module is used to use the weight formula: Calculate the fan light rotation speed adjustment amount and the fan light power adjustment amount to obtain a weight score, where α and β are weight coefficients, C is the weight score, V is the fan light rotation speed adjustment amount, V0 is the standard rotation speed of the fan light, W is the fan light power adjustment amount, W1 is the circuit load power, and W0 is the standard power of the fan light;

[0108] The suggestion module is used to trigger the fan light abnormal message and generate the fan light maintenance suggestion information when the weight score is higher than the preset weight score.

[0109] Optionally, the data processing module includes:

[0110] The cause analysis sub-module is used to analyze the fan light temperature difference through the trained neural network PID model to obtain the fan light abnormal cause information;

[0111] The parameter obtaining sub-module is used to find the corresponding fan light adjustment mode through the fan light abnormal cause information to obtain the fan light adjustment parameters.

[0112] Optionally, the adjustment module includes:

[0113] The adjustment quantum sub-module, where the fan light adjustment parameters include the fan light rotation speed adjustment amount and the fan light power adjustment amount;

[0114] The adjustment execution sub-module is used to perform corresponding adjustments on the fan light through the fan light rotation speed adjustment amount and the fan light power adjustment amount.

[0115] For the specific limitations of the heat dissipation control device of the fan light, reference can be made to the limitations of the heat dissipation control method of the fan light in the above text, which will not be elaborated here. Each module in the above heat dissipation control device of the fan light can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0116] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a heat dissipation control method for a fan light.

[0117] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0118] When the fan light is working, monitor the working state of the fan light in real time;

[0119] When the temperature data of the fan light exceeds a preset temperature threshold, trigger a heat dissipation message for the fan light, and obtain the difference between the temperature data of the fan light and the preset temperature threshold, that is, the temperature difference of the fan light;

[0120] Transmit the temperature difference to the trained neural network PID model to obtain the fan light adjustment parameter;

[0121] Adjust the fan light correspondingly according to the fan light adjustment parameter.

[0122] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are implemented:

[0123] When the fan light is working, monitor the working state of the fan light in real time;

[0124] When the temperature data of the fan light exceeds a preset temperature threshold, trigger a heat dissipation message for the fan light, and obtain the difference between the temperature data of the fan light and the preset temperature threshold, that is, the temperature difference of the fan light;

[0125] Transfer the temperature difference to the trained neural network PID model to obtain the fan light adjustment parameters;

[0126] Adjust the fan light correspondingly according to the fan light adjustment parameters.

[0127] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0128] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0129] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A heat dissipation control method for a fan light, characterized in that The heat dissipation control method of the fan light includes: When the fan light is working, the working state of the fan light is monitored in real time; When the temperature data of the fan light exceeds the preset temperature threshold, a fan light heat dissipation message is triggered, and the difference between the temperature data of the fan light and the preset temperature threshold is obtained, that is, the fan light temperature difference; Abnormal fan light temperature data is obtained, and the difference between the abnormal fan light temperature data and the preset temperature threshold is calculated to obtain abnormal temperature difference data; The fan light adjustment parameter data set corresponding to the abnormal temperature difference data is obtained, and the neural network PID model is trained through the abnormal temperature difference data and the fan light adjustment parameter data set to obtain a trained neural network PID model; the temperature difference is transmitted to the trained neural network PID model to obtain fan light adjustment parameters; The step of transmitting the fan light temperature difference to the trained neural network PID model to obtain fan light adjustment parameters includes: analyzing the fan light temperature difference through the trained neural network PID model to obtain fan light abnormal cause information; finding the corresponding fan light adjustment mode through the fan light abnormal cause information to obtain the fan light adjustment parameters; The fan light is adjusted correspondingly according to the fan light adjustment parameters; Adjusting the fan light correspondingly according to the fan light adjustment parameters includes: The fan light adjustment parameters include the fan light speed adjustment amount and the fan light power adjustment amount; The fan light is adjusted correspondingly through the fan light speed adjustment amount and the fan light power adjustment amount.

2. The heat dissipation control method of the fan lamp according to claim 1, characterized in that, The step of monitoring the working state of the fan light in real time when the fan light is working includes: When the fan light is working, a sensor detection message is triggered; The working fan light is monitored in real time through the sensor detection message to obtain the working state of the fan light, and the working state of the fan light includes temperature data, speed data, and power data.

3. The heat dissipation control method of the fan lamp according to claim 1, characterized in that, The heat dissipation control method of the fan light further includes: Through the weight formula: calculate the adjustment amount of the fan light rotation speed and the adjustment amount of the fan light power to obtain a weight score, where α and β are weight coefficients, C is the weight score, V is the adjustment amount of the fan light rotation speed, V0 is the standard rotation speed of the fan light, W is the adjustment amount of the fan light power, W1 is the circuit load power, and W0 is the standard power of the fan light; When the weight score is higher than the preset weight score, a fan light abnormal message is triggered, and fan light maintenance suggestion information is generated.

4. A heat dissipation control device for a fan light, characterized in that, The heat dissipation control device of the fan light includes: A monitoring module for monitoring the working state of the fan light in real time when the fan light is working; A temperature difference calculation module for triggering a fan light heat dissipation message when the temperature data of the fan light exceeds the preset temperature threshold, and obtaining the difference between the temperature data of the fan light and the preset temperature threshold, that is, the fan light temperature difference; An abnormal temperature difference acquisition module for obtaining abnormal fan light temperature data and calculating the difference between the abnormal fan light temperature data and the preset temperature threshold to obtain abnormal temperature difference data; A training model module for obtaining the fan light adjustment parameter data set corresponding to the abnormal temperature difference data, and training the neural network PID model through the abnormal temperature difference data and the fan light adjustment parameter data set to obtain a trained neural network PID model; A data processing module for transmitting the temperature difference to the trained neural network PID model to obtain fan light adjustment parameters; An analysis cause sub-module, configured to analyze the temperature difference of the fan light through the trained neural network PID model to obtain fan light abnormal cause information; A parameter obtaining sub-module, configured to find a corresponding fan light adjustment mode according to the fan light abnormal cause information to obtain the fan light adjustment parameter; An adjustment module, configured to perform corresponding adjustment on the fan light according to the fan light adjustment parameter; An adjustment quantity sub-module, wherein the fan light adjustment parameter includes a fan light rotation speed adjustment quantity and a fan light power adjustment quantity; An adjustment execution sub-module, configured to perform corresponding adjustment on the fan light through the fan light rotation speed adjustment quantity and the fan light power adjustment quantity.

5. The heat dissipation control device of the fan light according to claim 4, characterized in that, The heat dissipation control device of the fan light further includes: A weight score calculation module, which is used to calculate the weight score through the weight formula: calculate the fan light rotation speed adjustment amount and power adjustment amount to obtain a weight score, where α and β are weight coefficients, C is the weight score, V is the fan light rotation speed adjustment amount, V0 is the standard rotation speed of the fan light, W is the fan light power adjustment amount, W1 is the circuit load power, and W0 is the standard power of the fan light; A suggestion information generation module, configured to trigger a fan light abnormal message and generate fan light maintenance suggestion information when the weight score is higher than a preset weight score.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the heat dissipation control method of the fan light according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the heat dissipation control method of the fan light according to any one of claims 1 to 3 are implemented.

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