Self-adaptive heat dissipation control method, system, device and medium for photochromic material

Abstract

The present application relates to a method, system, device and medium for self-adaptive heat dissipation control of photochromic materials. [Solution] In this application, through deep integration of material properties and control strategies, faster temperature response is achieved, and at the same time, heat dissipation adjustment is accurately performed based on phase transition signals, significantly improving the heat dissipation efficiency and intelligence level of the system.

Description

[Technical Field]

[0001] The present application relates to the technical field of heat dissipation control, and in particular to a method, system, device and medium for self-adaptive heat dissipation control using photochromic materials. [Background technology]

[0002] As electronic devices become more powerful and smaller, heat dissipation becomes an increasingly prominent issue. Traditional heat dissipation systems always operate in a continuous mode, which not only wastes energy but also accelerates system aging. Therefore, the development of intelligent, self-adaptive heat dissipation control technology has become a hot research topic.

[0003] Currently, commonly used heat dissipation control solutions are mainly based on temperature sensor detection and thermostat feedback. These systems trigger the heat dissipation device based on a fixed temperature threshold, and use simple water-chamber control or multi-stage adjustment methods to achieve the heat dissipation function. However, due to the lack of real-time response capabilities and intelligent prediction mechanisms, they are difficult to apply to complex and changeable working environments. This situation needs further improvement. Summary of the Invention [Problem to be solved by the invention]

[0004] To solve the problem that traditional heat dissipation control solutions lack real-time response capability and intelligent prediction mechanisms, making them difficult to apply to complex and changeable working environments, the present application provides a self-adaptive heat dissipation control method, system, device and medium for photochromic materials using the following technical solutions: [Means for solving the problem]

[0005] In a first aspect, the present application provides a self-adaptive heat dissipation control method for a heat dissipation channel having temperature-responsive characteristics, the inner wall of the heat dissipation channel being coated with a heat-sensitive photochromic material, the method comprising: monitoring the temperature of the heat-generating member in real time using a near-infrared sensor according to the temperature distribution of the heat dissipation channel to obtain real-time temperature data; determining whether a preset temperature threshold is exceeded based on the real-time temperature data, and if so, a phase transition occurs in the heat-sensitive photochromic material, thereby obtaining a phase transition signal; and calculating and adjusting an operating parameter of the heat dissipation component based on the phase transition signal.

[0006] In order to solve the problem of high response hysteresis and high energy consumption of the traditional heat dissipation system by using the above technical solution, some researches have adopted the solution of temperature sensor detection and thermostat feedback. However, in practical application, when the load of the electronic device suddenly increases, the delay between temperature detection and heat dissipation execution will always cause the temperature of the device to instantly exceed the safety threshold, further resulting in performance degradation. In this application, first, the inner wall of the heat dissipation channel is coated with a thermosensitive photochromic material, and a heat dissipation channel with temperature response characteristics is constructed, and the temperature of the heat dissipation channel changes. The near-infrared sensor collects the temperature data of the heat-generating components in real time, and the system determines whether the temperature exceeds the preset temperature threshold based on the collected real-time temperature data. Once the temperature exceeds the threshold, the thermosensitive photochromic material will immediately undergo a phase change and generate a phase change signal. The control system will then immediately calculate the optimal heat dissipation parameters based on the phase change signal and correspondingly adjust the working status of the heat dissipation components. Through the deep integration of material properties and control strategy, a faster temperature response is achieved, and at the same time, heat dissipation can be accurately adjusted based on the phase change signal, significantly improving the heat dissipation efficiency and intelligence level of the system.

[0007] Optionally, said heat-sensitive photochromic material is tungsten-doped vanadium dioxide.

[0008] By using the above technical solution, the present application dopes the vanadium dioxide lattice with an appropriate amount of tungsten to form a stable solid solution structure, allowing the material to maintain fast phase transition properties while continuously adjusting the phase transition temperature within the range of 40-70°C. The doped modified material has better structural stability and cycle durability, which not only overcomes the limitation of a single phase transition temperature, but also improves the material performance through optimized doping, and allows the phase transition temperature to be flexibly designed according to the heat dissipation needs of different devices.

[0009] Optionally, the method further comprises: collecting historical equipment execution data to obtain a historical data set; analyzing the historical data set based on a machine learning algorithm to obtain data characteristics; predicting a future high load period based on the data characteristics and obtaining a prediction time point; and calculating a preheating parameter based on the predicted time point and controlling the parameter to preheat the heat-sensitive photochromic material to a temperature close to the phase transition temperature.

[0010] 2. Description of the Related Art When using the above technical solution, due to the complex and changeable usage conditions of the equipment, the preset time does not match the actual high-load period, resulting in inaccurate heat dissipation control, inability to prevent temperature spikes in time, and unnecessary energy consumption. In the present application, the running data of the equipment, including multi-dimensional information such as load changes, temperature fluctuations, and environmental parameters, is continuously collected and stored to form a complete historical data set. Then, a machine learning algorithm is used to perform a detailed analysis of the historical data to extract the time pattern of equipment use, load characteristics, and temperature change trends. Based on the extracted data characteristics, the system can accurately predict possible high-load periods in the future and generate a specific prediction time point. Before the predicted time point arrives, the control system calculates the optimal pre-heating parameters in advance to control the thermo-sensitive photochromic material to gradually heat up to a state close to the phase transition temperature, ensuring that the phase transition heat dissipation is immediately triggered under high load. By combining data analysis and predictive control, the heat dissipation system can be transformed from a reactive to a proactive prevention, significantly improving the timeliness and accuracy of heat dissipation while avoiding unnecessary pre-heating processes.

[0011] Optionally, the step of calculating a preheating parameter based on the predicted time point and controlling the preheating of the heat-sensitive photochromic material to near the phase transition temperature specifically includes: determining a preheating start time and a preheating end time based on the predicted time point, and obtaining a preheating control period; extracting a temperature rise stage and a temperature keeping stage from the preheating control period and acquiring preheating timing parameters, the temperature rise stage including a temperature rise time and a temperature rise target temperature, and the temperature keeping stage including a temperature keeping time and a temperature keeping target temperature; calculating a temperature rise rate based on a difference between the current temperature and the target temperature to obtain a temperature control curve; and associating the preheat timing parameter with the temperature control curve to obtain a preheat control strategy.

[0012] In order to solve the problem of the conventional pre-heating control solution having rough parameter settings and a single temperature rise curve by using the above technical solution, the present application first determines the optimal pre-heating start and end times based on the high-load time points obtained by system prediction, and specifies the overall period of pre-heating control. Then, the pre-heating process is divided into two stages: heating and heat-keeping, and the time parameters and temperature targets of each stage are set respectively. In the heating stage, the heating time and target temperature are specified, and in the heat-keeping stage, the heat-keeping duration and temperature level are specified. The system calculates the deviation between the current temperature and the target temperature in real time, and thereby dynamically adjusts the heating rate and generates an optimal temperature control curve. Finally, the timing parameters of each pre-heating stage are associated and matched with the temperature curve to form a complete pre-heating control strategy. Through sophisticated stage control and dynamic optimization, stable control of the pre-heating process is achieved, and sudden temperature fluctuations are effectively avoided, while pre-heating accuracy and energy utilization efficiency are improved.

[0013] Optionally, the temperature increase step includes a fast temperature increase step and a slow temperature increase step, and the step of calculating a temperature increase rate based on a difference between the current temperature and the target temperature specifically includes: determining a temperature rise rate for the rapid temperature rise stage and a temperature rise rate for the slow temperature rise stage based on a difference between a current temperature and the target temperature; calculating a total heating time based on the durations of the rapid heating stage and the slow heating stage; and obtaining a preheat complete period based on the total heat-up time and the warm-up time.

[0014] The above technical solution is a general solution that always heats up at a fixed rate. However, in practical application, this method is prone to overshoot when the temperature is close to the target value, and at the same time, the temperature tolerance range at the beginning of the temperature rise cannot be fully utilized, resulting in time waste. In this application, first, based on the difference range between the current temperature and the target temperature, the temperature rise stage is divided into a fast temperature rise stage and a slow temperature rise stage, and different temperature rise rates are set respectively. When the temperature difference is large, the temperature is rapidly increased at a high temperature rise rate, and when the temperature is close to the target value, the temperature rise rate is reduced. This ensures a smooth temperature change, and calculates the times required for the rapid heating stage and the slow heating stage based on the heating rates and temperature sections of each of the two stages, and adds them together to obtain the total duration of the entire heating stage. Finally, the calculated total heating time is combined with the duration of the warming stage to determine the complete period of the preheating process. The rational speed distribution and time planning not only ensure that the heating process is fast and efficient, but also avoid sudden temperature fluctuations. At the same time, the accurate time management ensures the consistency and controllability of the entire preheating process, and improves the overall performance of preheating control.

[0015] Optionally, the method further comprises: acquiring environmental temperature data to obtain real-time environmental parameters; Calculating a heat dissipation adjustment coefficient based on the real-time environmental parameters to obtain an optimization parameter; and determining start-up and shutdown conditions for the heat dissipation components based on the optimization parameters to obtain a dynamic control strategy.

[0016] By using the above technical solution, the present application continuously collects environmental temperature data to obtain real-time environmental parameter information, and based on the collected environmental parameters, combines with preset standard working conditions to calculate the heat dissipation adjustment coefficient for the current environment, and generates optimization parameters for heat dissipation control, and based on the calculated optimization parameters, the system dynamically adjusts the activation threshold and shutdown conditions of the heat dissipation components, and forms a control strategy that adapts to environmental changes. Through environmental sensing and parameter self-adaptation, the environment response ability of the heat dissipation control is realized, and in addition to ensuring the stability of the heat dissipation effect, unnecessary energy consumption is avoided. At the same time, the adaptability of the system to different environmental conditions is improved, making the cooling control more intelligent and efficient.

[0017] Optionally, the step of calculating a heat dissipation adjustment coefficient to obtain an optimization parameter based on the real-time environmental parameter specifically includes: Obtaining the indoor / outdoor temperature difference and relative humidity to obtain basic environmental parameters; calculating an environmental impact factor based on the deviation between the environmental basic parameters and the preset standard working environment parameters; obtaining a temperature correction value based on the environmental impact factor and a seasonal correction factor; multiplying the temperature correction value by a heat dissipation benchmark coefficient to obtain a real-time heat dissipation adjustment coefficient; Calculating a heat-dissipating component power correction ratio based on the real-time heat-dissipating adjustment coefficient to obtain the optimization parameter.

[0018] By using the above technical solution, in order to solve the problem that only a single environmental parameter is considered in the traditional adjustment solution and the adjustment is not accurate, the general technology simply adjusts based on the environmental temperature alone, but in actual application, the influence of humidity change and seasonal difference is ignored, so there will be a large deviation between the adjustment result and the actual heat dissipation demand, which will affect the system performance. In this application, first, indoor and outdoor temperature and relative humidity data are collected simultaneously to establish a complete environmental basic parameter set, and the collected environmental parameters are compared with the system's preset standard working environment parameters to calculate the temperature difference and humidity deviation, and obtain the environmental impact factors, and then the current seasonal According to the environmental impact factor, a corresponding seasonal correction coefficient is introduced, and a temperature correction value is calculated in combination with the environmental impact factor. The temperature correction value is multiplied by the system's preset heat dissipation benchmark coefficient to obtain a real-time heat dissipation adjustment coefficient that adapts to the current environment. Finally, the required power adjustment proportion for the heat dissipation components is calculated based on the adjustment coefficient, and the final optimization parameters are generated. Through the collaborative calculation and accurate adjustment of multiple environmental factors, all-dimensional optimization of heat dissipation adjustment is realized. In addition to ensuring the accuracy of the adjustment, the system's adaptability to complex environments is also improved. At the same time, seasonal adjustment ensures year-round running stability and significantly improves the intelligent level of heat dissipation control.

[0019] In a second aspect, the present application provides a photochromic material self-adaptive heat dissipation control system, comprising a heat dissipation channel having a temperature-responsive characteristic, and an inner wall of the heat dissipation channel is coated with a heat-sensitive photochromic material, the system comprising: a real-time temperature data acquisition module for monitoring the temperature of the heat-generating component in real time using a near-infrared sensor according to the temperature distribution of the heat dissipation channel and obtaining real-time temperature data; a phase transition signal acquisition module for determining whether a preset temperature threshold is exceeded based on the real-time temperature data, and if so, a phase transition occurs in the heat-sensitive photochromic material, thereby obtaining a phase transition signal; and a heat dissipation component operating parameter acquisition module for calculating and adjusting operating parameters of the heat dissipation component according to the phase transition signal.

[0020] In a third aspect, the present application provides an electronic device including a computer program stored in a memory and executable by a processor, the computer program implementing the steps of the self-adaptive heat dissipation control method for photochromic materials when the processor executes the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program implementing the steps of the method for self-adaptive heat dissipation control of a photochromic material when executed by a processor. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 is a schematic flow chart of a self-adaptive heat dissipation control method for photochromic materials in an embodiment of the present application. [Figure 2] FIG. 2 is a schematic flow chart of the prediction point in the self-adaptive heat dissipation control method of the photochromic material in the embodiment of the present application. [Figure 3] FIG. 3 is a schematic flowchart of step S240 in the self-adaptive heat dissipation control method for photochromic materials in an embodiment of the present application. [Figure 4] FIG. 4 is a schematic flowchart of step S243 in the self-adaptive heat dissipation control method for photochromic materials according to an embodiment of the present application. [Figure 5] FIG. 5 is a schematic flow chart of obtaining a dynamic control strategy in the self-adaptive heat dissipation control method of photochromic material in an embodiment of the present application. [Figure 6] FIG. 6 is a schematic flowchart of step S520 in the self-adaptive heat dissipation control method for photochromic materials in an embodiment of the present application. [Figure 7]FIG. 7 is a module schematic diagram of a self-adaptive heat dissipation control system of photochromic material in an embodiment of the present application. [Figure 8] FIG. 8 is a diagram showing the internal configuration of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, the embodiments of the present application will be described in more detail with reference to the specification and drawings.

[0024] In a first aspect, the present application provides a self-adaptive heat dissipation control method for a photochromic material, which is applied to a heat dissipation channel having temperature-responsive characteristics, and the inner wall of the heat dissipation channel is coated with a heat-sensitive photochromic material. Referring to FIG. 1, the method includes the following steps:

[0025] S110: According to the temperature distribution of the heat dissipation channel, the temperature of the heat-generating component is monitored in real time by the near-infrared sensor to obtain real-time temperature data.

[0026] S120: Based on the real-time temperature data, determine whether the preset temperature threshold is exceeded, and if so, a phase transition occurs in the heat-sensitive photochromic material, thereby obtaining a phase transition signal.

[0027] Here, the heat-sensitive photochromic material is tungsten-doped vanadium dioxide.

[0028] In this embodiment, the heat dissipation channel refers to a pipe structure for heat transfer inside the device, and the heat-sensitive photochromic material coated on its inner wall undergoes a phase transition at a specific temperature, thereby changing its optical and thermal properties. The near-infrared sensor is a non-contact temperature measurement device that can detect the temperature of an object and determines its surface temperature by detecting infrared rays emitted from the object. The heat-generating component refers to a core component that generates heat during device operation, and in this embodiment, refers to the processor and power module. The heat-dissipating component includes a forced heat dissipation component such as a fan or water.

[0029] Specifically, the system first places a near-infrared sensor array at key locations in the heat dissipation channel and creates a spatial distribution mapping map of temperature detection points. The coordinate information of each detection point and the corresponding temperature threshold are stored in the system database. In actual operation, the near-infrared sensor continuously collects the surface temperature of the heat-generating component at a preset sampling frequency and records the data in a temperature monitoring log. The system queries the mapping map to determine whether the real-time temperature of each detection point is within the normal range. When the temperature of a certain point exceeds the preset threshold (for example, the temperature threshold may be set to 75°C in the processor), the system immediately sends a phase transition trigger signal.

[0030] S130: Calculate and adjust the operating parameters of the heat dissipation component based on the phase transition signal.

[0031] In this embodiment, the temperature threshold is the critical temperature point determined by the phase transition characteristics of the thermo-sensitive photochromic material. The phase transition signal includes the location information of the temperature anomaly and the degree to which the temperature exceeds the threshold. The operating parameters mainly refer to adjustable quantities such as the rotation speed and power of the heat dissipation component.

[0032] Specifically, after receiving a phase transition signal, the system first queries a pre-created heat dissipation parameter comparison table, which records standard heat dissipation parameters corresponding to different temperature ranges. Based on the degree to which the current temperature exceeds the threshold (e.g., 5°C, 10°C, etc.), the system selects a corresponding parameter combination from the comparison table. For example, if the temperature exceeds the threshold by 5°C, the fan speed is increased to 80% of the rated speed; if it exceeds by 10°C, it is increased to 100%. This hierarchical adjustment method avoids overheating and ensures effective heat dissipation. The system records the temperature change trend during adjustment to verify the adjustment effect.

[0033] In one embodiment, referring to FIG. 2, the method further includes the following steps:

[0034] S210: Collect historical equipment execution data to obtain a historical data set.

[0035] In this embodiment, the historical execution data refers to the execution parameters recorded by the equipment during past execution, including the equipment load factor, temperature change, and execution period. The historical data set refers to the valid historical data that has undergone initial screening and pre-processing, and these data are stored in chronological order and are marked with corresponding time stamps.

[0036] Specifically, the system creates a single execution log database to store daily device execution data. Data collection occurs every 15 minutes, recording important parameters such as current CPU usage, memory usage, and temperature. The database retains execution records from the past three months, and expired data is archived at the beginning of each month.

[0037] S220: Based on a machine learning algorithm, analyze the historical data set and obtain data characteristics.

[0038] In this embodiment, the data characteristics refer to the regular load patterns and temperature change characteristics extracted from the historical data. The machine learning algorithm mainly uses time series analysis methods to recognize periodic patterns in the data using statistical techniques.

[0039] Specifically, the system first performs a time-based analysis of historical data to create a load characteristic table. The table is divided into "day of the week-period" categories, and statistics are compiled for the average load level and load fluctuation range for each period. By calculating the variance and average, time periods with high loads are identified.

[0040] S230: Based on the data characteristics, a future high load period is predicted, and a prediction time point is obtained.

[0041] S240: Based on the predicted time point, a preheating parameter is calculated and controlled to preheat the heat-sensitive photochromic material to near the phase transition temperature.

[0042] In this embodiment, the predicted time refers to the time when the equipment will enter a high-load state, as predicted by the system. The preheating parameters include a preheating start time and a target temperature.

[0043] Specifically, the system creates a predictive rule base to identify possible periods of high load based on statistically derived load characteristics. For highly regular scenarios, such as servers in an office environment, the system initiates preheating 30 minutes before the high load arrives. A gradual promotion scheme is adopted to set the preheat temperature, first increasing the temperature to 90% of the phase transition temperature and then maintaining that temperature until the actual load increases. For example, if the phase transition temperature is 68°C, the preheat target temperature is set to 61°C. This preheating strategy not only ensures a fast system response but also avoids unnecessary energy consumption.

[0044] In one embodiment, referring to FIG. 3, in step S240, a pre-heating parameter is calculated based on the predicted time point, and controlled to pre-heat the heat-sensitive photochromic material to near the phase transition temperature, which specifically includes the following steps:

[0045] S241: Based on the predicted time, the pre-heating start time and the pre-heating end time are identified, and a pre-heating control period is obtained.

[0046] In this embodiment, the preheating control period refers to the entire period from the start of preheating to the predicted load increase. The preheating timing parameters include specific control parameters for both the temperature rise and temperature maintenance stages. The temperature control curve describes the trajectory of temperature change over time during preheating. The preheating control strategy is a complete control solution that combines two dimensions: time and temperature.

[0047] Specifically, the system maintains a prediction time setting table and performs preheating planning according to the preheating demand time in different scenarios. The system references the table to determine the preheating progress. For example, in an office scenario, preheating typically begins 45 minutes in advance, including a 30-minute warm-up phase and a 15-minute warm-up phase. In the system, if the predicted time is denoted as t, the preheating start time is (t-45) minutes and the preheating end time is t, thereby accurately dividing the preheating control period.

[0048] S242: The temperature rising stage and the temperature keeping stage are extracted from the preheating control period, and preheating timing parameters are obtained.

[0049] Here, the temperature increase stage includes a temperature increase time and a target temperature, and the heat retention stage includes a heat retention time and a target temperature.

[0050] S243: Based on the difference between the current temperature and the target temperature, calculate the temperature rise rate and obtain a temperature control curve.

[0051] In this embodiment, the important parameters of the heating stage include the target temperature and the duration of heating. During the warming stage, the temperature must be maintained within the target range. The heating rate refers to the amount of temperature rise per unit time, which directly affects the preheating effect and energy consumption.

[0052] Specifically, the system creates a step-by-step heating parameter table and divides the heating process into two stages: fast heating and slow heating. When the temperature difference is greater than 20°C, a heating rate of 3°C / min is used, and when the temperature difference is less than 20°C, the rate is reduced to 1°C / min to avoid temperature overshoot.

[0053] S244: Associate the preheating timing parameters with the temperature control curve to obtain a preheating control strategy.

[0054] In this embodiment, the temperature control curve describes the temperature change pattern of the entire preheating process by a piecewise function, and the preheating control strategy integrates the timing parameters and the temperature curve into a complete control command sequence.

[0055] Specifically, the system uses a table lookup method to generate a control strategy. First, a temperature-time table is created to divide the preheating process into several control time points, each corresponding to a target temperature. The system then uses linear interpolation to calculate the temperature setpoint at any given time, forming a continuous control curve.

[0056] In one embodiment, the heating stage includes a rapid heating stage and a slow heating stage. Referring to FIG. 4, in step S243, calculating a heating rate based on the difference between the current temperature and the target temperature specifically includes the following steps:

[0057] S2431: The heating rate in the rapid heating stage and the heating rate in the slow heating stage are determined based on the difference between the current temperature and the target heating temperature.

[0058] S2432: Calculate the total heating time based on the duration of the rapid heating stage and the slow heating stage.

[0059] In this embodiment, the rapid heating stage refers to the initial heating stage when the temperature difference is large, and a high heating rate is used to improve efficiency. The slow heating stage refers to the fine-tuning stage when the target temperature is close, and a slow heating rate is used to avoid temperature overshoot. The heating rate refers to the amount of temperature change per unit time, and the total heating time refers to the time from the start of heating to reaching the target temperature.

[0060] Specifically, the system creates a temperature difference step control table that selects different heating strategies depending on the temperature difference. When the temperature difference is greater than 25°C, the system enters the rapid heating stage, using a heating rate of 3°C / min. When the temperature difference drops below 25°C, the system enters the slow heating stage, slowing the heating rate to 1°C / min. For example, if the starting temperature is 20°C and the target temperature is 60°C, the system will use the rapid heating stage for a temperature difference of 25°C and the slow heating stage for a temperature difference of 15°C. This step control strategy not only ensures heating efficiency but also prevents sudden temperature fluctuations.

[0061] S2433: Obtain a complete preheating period based on the total heating time and the warming time.

[0062] In this embodiment, the cumulative effect of the two heating stages needs to be considered when determining the total heating time. The complete preheating period includes two parts: the total heating time and the warming time, and is the time span of the entire preheating process.

[0063] Specifically, the system uses a simple time calculation table to determine the duration of each stage. For the rapid heating stage, the system calculates the required time based on the temperature difference and heating rate. For example, a temperature difference of 25°C and a heating rate of 3°C / min requires approximately 8.3 minutes. The slow heating stage uses the same method. For example, a temperature difference of 15°C and a heating rate of 1°C / min requires 15 minutes. The total heating time for the two stages is approximately 23.3 minutes. Multiplying this by a preset warming time (e.g., 15 minutes), the complete preheating cycle takes approximately 38.3 minutes. The system rounds the calculation result, adding up the time margin during control to ensure effective preheating.

[0064] Furthermore, to avoid energy waste during preheating, this embodiment employs real-time load assessment and a dynamic exit mechanism. During the preheating phase, the system performs sampling assessment of the actual load of the equipment every five minutes and maintains a load assessment judgment table. Early termination of the preheating state is triggered when the load level of three consecutive samples is below 50% of the expected load, when the load of similar equipment in the current time period is below 60% of the load for the same period, or when business schedule information is received indicating that there are no large-scale computing tasks in the near future. For example, if a server is scheduled to start running a high load from 9:00, the system preheats the server from 8:15. If the actual load remains below 30% at 8:45 and shows no obvious upward trend, the system will terminate the preheating process and switch to normal temperature control mode. At the same time, the system stores the current prediction bias in a database to optimize future load forecast accuracy.

[0065] In one embodiment, referring to FIG. 5, the method further includes the following steps:

[0066] S510: Obtain environmental temperature data to obtain real-time environmental parameters.

[0067] In this embodiment, the environmental temperature data includes indoor and outdoor temperatures and environmental humidity. The real-time environmental parameters refer to the effective environmental data after the initial processing. The heat dissipation adjustment coefficient is a heat dissipation capacity correction value dynamically calculated based on the environmental conditions. The optimization parameters are used to guide the real-time control of the heat dissipation components.

[0068] Specifically, the system is equipped with environmental parameter collection nodes that collect data in real time via a temperature and humidity sensor network. Collection points are placed at strategic locations around the equipment, such as air intakes and exhausts, with a sampling interval of 5 minutes. The system also creates an environmental data cache table that stores environmental parameter records from the past hour to calculate environmental change trends.

[0069] S520: Calculate the heat dissipation adjustment coefficient based on the real-time environmental parameters to obtain the optimization parameters.

[0070] In this embodiment, the optimization parameters include, but are not limited to, the fan rotation speed and the heat dissipation power. The dynamic control strategy is a complete heat dissipation control command sequence.

[0071] Specifically, the system maintains an environmental impact factor table and determines adjustment coefficients based on different environmental parameter combinations. When the indoor / outdoor temperature difference is greater than 10°C, more natural heat dissipation is used; when the environmental humidity is high, the proportion of forced heat dissipation is increased. The system obtains the baseline adjustment coefficients through a table lookup method and then fine-tunes them based on real-time conditions. For example, in an industrial environment, if the outdoor temperature is 15°C lower than the indoor temperature, the system will reduce the fan speed to 70% of the standard value to achieve the energy-saving goal.

[0072] S530: Identify the start / stop conditions of the heat dissipation components based on the optimization parameters, and obtain the dynamic control strategy.

[0073] In this embodiment, the activation and deactivation conditions of the heat dissipation components include activation thresholds and deactivation thresholds. The dynamic control strategy needs to balance the heat dissipation effect and energy consumption.

[0074] Specifically, the system creates a heat dissipation control rule table and dynamically adjusts the start and stop thresholds based on the optimization parameters. The start condition is usually set to the temperature threshold after environmental compensation, and a certain amount of hysteresis must be considered to avoid frequent start and stop. For example, when the ambient temperature is low, the system appropriately increases the start threshold and extends the natural heat dissipation time. When the ambient temperature rises, the system correspondingly decreases the start threshold and enters forced heat dissipation mode in advance.

[0075] In one embodiment, referring to FIG. 6, in step S520, calculating the heat dissipation adjustment coefficient and obtaining the optimization parameters based on the real-time environmental parameters specifically includes the following steps:

[0076] S521: Obtain the indoor / outdoor temperature difference and relative humidity to obtain basic environmental parameters.

[0077] S522: Calculate the environmental impact factor based on the deviation between the environmental basic parameters and the preset standard working environment parameters.

[0078] S523: Obtain the temperature correction value based on the environmental impact factor and the seasonal correction factor.

[0079] In this embodiment, the environmental basic parameters refer to the main environmental indicators that directly affect the heat dissipation effect. The standard working environment parameters are a set of optimal working condition parameters predefined by the system. The environmental impact factor reflects the influence of the current environment on the heat dissipation effect. The seasonal correction coefficient is used to adapt to the heat dissipation characteristics of different seasons.

[0080] Specifically, the system creates an environmental parameter reference table, collects indoor and outdoor temperature data through a temperature sensor network, and deploys humidity sensors to monitor environmental humidity.

[0081] S524: Multiply the temperature correction value and the heat dissipation benchmark coefficient to obtain a real-time heat dissipation adjustment coefficient.

[0082] In this embodiment, when comparing the environmental basic parameters with the standard working environment parameters, it is necessary to consider bias in multiple dimensions. The temperature correction value is a quantitative adjustment to the heat dissipation capacity. The heat dissipation benchmark coefficient is a basic heat dissipation coefficient determined under standard conditions.

[0083] Specifically, the system maintains an environmental deviation calculation table, with a temperature difference of 20°C and a relative humidity of 50% as the standard operating environment. When the deviation between the actual temperature difference and the standard temperature difference is within a ±5°C range, the environmental impact factor is adjusted to 0.9-1.1; when the relative humidity deviation is within a ±10% range, the impact factor is adjusted to 0.95-1.05. The system selects the corresponding correction factor according to seasonal characteristics: 1.2 for summer, 0.8 for winter, and 1.0 for spring and autumn. The final temperature correction value is obtained by multiplying the environmental impact factor by the seasonal correction factor.

[0084] S525: Calculate the heat dissipation component power correction ratio based on the real-time heat dissipation adjustment coefficient to obtain the optimization parameters.

[0085] In this embodiment, the real-time heat dissipation adjustment coefficient represents the actual heat dissipation capacity under the current conditions, and the heat dissipation component power correction ratio is used to guide the specific operating parameter adjustment of the heat dissipation device.

[0086] Specifically, the system creates a power adjustment comparison table and multiplies the temperature offset value by a preset heat dissipation benchmark coefficient to obtain a real-time heat dissipation adjustment coefficient. For example, if the temperature offset value is 1.2, the real-time heat dissipation adjustment coefficient is also 1.2, indicating that the heat dissipation capacity needs to be improved. The system calculates the power adjustment amount of the heat dissipation component based on the coefficient. That is, if the adjustment coefficient is greater than 1, the power is increased proportionally; if it is less than 1, the power is correspondingly decreased. For example, if the adjustment coefficient is 1.2, the fan power is increased to 120% of the reference power.

[0087] In a second aspect, the present application provides a self-adaptive heat dissipation control system for photochromic materials, and the self-adaptive heat dissipation control system for photochromic materials of the present application will be described below with reference to the above-mentioned self-adaptive heat dissipation control method for photochromic materials.

[0088] Referring to FIG. 7, a self-adaptive heat dissipation control system of a photochromic material includes a heat dissipation channel having a temperature-responsive characteristic, and the inner wall of the heat dissipation channel is coated with a heat-sensitive photochromic material, and the system includes: a real-time temperature data acquisition module for monitoring the temperature of the heat-generating component in real time using a near-infrared sensor according to the temperature distribution of the heat dissipation channel and obtaining real-time temperature data; a phase transition signal acquisition module for determining whether a preset temperature threshold is exceeded based on the real-time temperature data, and if so, a phase transition occurs in the heat-sensitive photochromic material, thereby obtaining a phase transition signal; and a heat dissipation component operating parameter acquisition module for calculating and adjusting the operating parameters of the heat dissipation component based on the phase transition signal.

[0089] In one embodiment, the present application provides an electronic device, which may be a server, the internal configuration of which is shown in Figure 8. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control functions. The memory of the electronic 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 operating system and the computer program in the non-volatile storage medium to run. The database of the electronic device is used to store data. The network interface of the electronic device is connected to and communicates with an external terminal via a network. When the computer program is executed by the processor, a self-adaptive heat dissipation control method for a photochromic material is realized.

[0090] In one embodiment, there is further provided an electronic device including a memory in which a computer program is stored, and a processor that, when executing the computer program, implements the steps in each of the method embodiments above.

[0091] Those skilled in the art will understand that all or part of the steps in the above-mentioned method embodiments can be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium, which can include the steps of each of the above-mentioned method embodiments when executed. All of the above are preferred embodiments of the present application and do not limit the scope of protection of the present application. Therefore, any equivalent modifications made based on the structure, shape, and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for self-adaptive heat dissipation control of a photochromic material, comprising: The method is applied to a heat dissipation channel having temperature-responsive characteristics, and the inner wall of the heat dissipation channel is coated with a heat-sensitive photochromic material, and the method includes: measuring the temperature of the heat-generating component in real time using a near-infrared sensor according to the temperature distribution of the heat dissipation channel to obtain real-time temperature data; When the temperature measured by the near-infrared sensor exceeds a preset temperature threshold, a phase transition occurs in the heat-sensitive photochromic material, and a phase transition signal is obtained; calculating and adjusting an operating parameter of the heat dissipation component based on the phase transition signal; A self-adaptive heat dissipation control method for a photochromic material, characterized in that:

2. The heat-sensitive photochromic material is tungsten-doped vanadium dioxide The self-adaptive heat dissipation control method for photochromic materials according to claim 1 .

3. The method comprises: collecting historical equipment execution data to obtain a historical data set; analyzing the historical data set based on a machine learning algorithm to obtain data characteristics; predicting a future high load period based on the data characteristics and obtaining a prediction time point; and calculating a preheating parameter based on the predicted time point and controlling the preheating parameter to preheat the heat-sensitive photochromic material to a temperature close to the phase transition temperature. The self-adaptive heat dissipation control method for photochromic materials according to claim 1 .

4. The step of calculating a preheating parameter based on the predicted time point and controlling the preheating of the heat-sensitive photochromic material to a temperature close to the phase transition temperature specifically includes: determining a preheating start time and a preheating end time based on the predicted time point, and obtaining a preheating control period; extracting a temperature rise stage and a temperature keeping stage from the preheating control period and acquiring preheating timing parameters, the temperature rise stage including a temperature rise time and a temperature rise target temperature, and the temperature keeping stage including a temperature keeping time and a temperature keeping target temperature; calculating a temperature rise rate based on a difference between the current temperature and the target temperature to obtain a temperature control curve; and associating the preheat timing parameter with the temperature control curve to obtain a preheat control strategy. The self-adaptive heat dissipation control method for photochromic materials according to claim 3 .

5. The temperature increase step includes a rapid temperature increase step and a slow temperature increase step, and the step of calculating the temperature increase rate based on the difference between the current temperature and the target temperature specifically includes: determining a temperature rise rate for the rapid temperature rise stage and a temperature rise rate for the slow temperature rise stage based on a difference between a current temperature and the target temperature; calculating a total heating time based on the durations of the rapid heating stage and the slow heating stage; and obtaining a preheat complete period based on the total warm-up time and the warm-up time. The self-adaptive heat dissipation control method for photochromic materials according to claim 4 .

6. The method comprises: acquiring environmental temperature data to obtain real-time environmental parameters; Calculating a heat dissipation adjustment coefficient based on the real-time environmental parameters to obtain an optimization parameter; and determining activation and deactivation conditions of the heat dissipation components based on the optimization parameters to obtain a dynamic control strategy. The self-adaptive heat dissipation control method for photochromic materials according to claim 1 .

7. The step of calculating the heat dissipation adjustment coefficient based on the real-time environmental parameters to obtain the optimization parameters specifically includes: Obtaining the indoor / outdoor temperature difference and relative humidity to obtain basic environmental parameters; calculating an environmental impact factor based on the deviation between the environmental basic parameters and the preset standard working environment parameters; obtaining a temperature correction value based on the environmental impact factor and a seasonal correction factor; multiplying the temperature correction value by a heat dissipation benchmark coefficient to obtain a real-time heat dissipation adjustment coefficient; calculating a heat-dissipating component power correction ratio based on the real-time heat-dissipation adjustment coefficient to obtain the optimization parameter; The self-adaptive heat dissipation control method for photochromic materials according to claim 6.

8. A photochromic material self-adaptive heat dissipation control system, comprising: The system includes a heat dissipation channel having a temperature responsive characteristic, and an inner wall of the heat dissipation channel is coated with a heat-sensitive photochromic material, and the system further comprises: a real-time temperature data acquisition module for measuring the temperature of the heat-generating component in real time using a near-infrared sensor according to the temperature distribution of the heat dissipation channel to obtain real-time temperature data; a phase transition signal acquisition module for detecting a phase transition in the heat-sensitive photochromic material when the temperature measured by the near-infrared sensor exceeds a preset temperature threshold, and obtaining a phase transition signal; and a heat dissipation component operation parameter acquisition module for calculating and adjusting operation parameters of the heat dissipation component based on the phase transition signal. A self-adaptive heat dissipation control system for photochromic materials, characterized by:

9. An electronic device, A method for self-adaptive heat dissipation control of a photochromic material according to claim 1, comprising: a memory; a processor; and a computer program stored in the memory and executable by the processor, the method implementing the steps of the method for self-adaptive heat dissipation control of a photochromic material according to claim 1 when the processor executes the computer program. An electronic device characterized by:

10. A computer-readable storage medium on which a computer program is stored, When the computer program is executed by a processor, it realizes the steps of the method for self-adaptive heat dissipation control of photochromic materials according to claim 1. A computer-readable storage medium comprising:

Images