An adaptive intelligent temperature control method and a cooling wireless charger

Abstract

This invention relates to the field of wireless charging temperature control technology, and particularly to an adaptive intelligent temperature control method and a cooling wireless charger. This invention quantifies the inherent heat generation tendency of mobile devices through initial charging data to obtain a heat generation index, and then obtains a basic temperature threshold range based on the heat generation index. Furthermore, by introducing real-time charging environment data to obtain personalized temperature thresholds, it addresses the problem of unreasonable threshold settings caused by differences in battery characteristics between new and old devices and the varying heat generation characteristics of different models. Next, by capturing charging process data from the mobile device and combining it with threshold judgment, it can provide early warnings and trigger cooling in the early stages of anomalies. The temperature control demand intensity value calculated by analyzing the charging process data can accurately describe the urgency of the current cooling demand. Combined with the preset control parameter group of the cooling wireless charger, it dynamically generates precise temperature control adjustment parameters, directly controlling the charging and cooling components, which helps improve the temperature control response speed.

Description

Technical Field

[0001] This invention relates to the field of wireless charging temperature control technology, and in particular to an adaptive intelligent temperature control method and a cooling wireless charger. Background Technology

[0002] A cooled wireless charger is a wireless charging device that integrates cooling and heat dissipation functions. Its core function is to solve the heat generation problem caused by energy conversion and electromagnetic induction during wireless charging, ensuring the safety, stability, and efficiency of the charging process. This device achieves wireless charging based on electromagnetic induction or magnetic resonance principles, while simultaneously monitoring the charging device and ambient temperature in real time using a temperature sensor. When the detected temperature exceeds a preset threshold, the built-in cooling module (such as a thermoelectric cooler, fan, or heat pipe) will automatically activate, reducing the temperature through an active heat dissipation mechanism. Once the temperature drops to a safe range, the cooling module automatically reduces power or shuts down to achieve energy-saving operation. This effectively avoids performance degradation, battery damage, or safety hazards caused by excessively high temperatures, making it particularly suitable for high-power wireless charging, long-term charging, or high-temperature environments.

[0003] The existing temperature control strategies of wireless chargers have a rigidity problem. The root cause is the use of a fixed threshold control mode. By setting a uniform temperature threshold, the heat characteristics of different devices and the differences in batteries between new and old devices are not taken into account. This causes the threshold setting to deviate from the actual needs, either too high or too low, ultimately resulting in poor temperature control. Summary of the Invention

[0004] The main objective of this invention is to provide an adaptive intelligent temperature control method and a cooling wireless charger, aiming to solve the technical problems in the prior art.

[0005] This invention proposes an adaptive intelligent temperature control method applicable to a cooling wireless charger, wherein the cooling wireless charger is connected to a mobile device to power the mobile device, comprising:

[0006] Acquire charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data, and charging environment data;

[0007] The heat index of the mobile device is obtained based on the initial charging data and the basic device data, and the basic temperature threshold range is obtained based on the heat index.

[0008] The personalized temperature threshold corresponding to the mobile device is obtained based on the charging environment data and the basic temperature threshold range.

[0009] Determine whether cooling is needed based on the charging process data and the personalized temperature threshold.

[0010] If cooling is required, the temperature control demand intensity value is obtained based on the charging process data;

[0011] Obtain the control parameter set of the cooling wireless charger, and obtain the temperature control adjustment parameters according to the control parameter set and the temperature control demand intensity value, wherein the temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters;

[0012] Cooling is regulated according to the temperature control parameters.

[0013] Preferably, the step of obtaining the heat index of the mobile device based on the initial charging data and the device basic data, and obtaining the basic temperature threshold range based on the heat index, includes:

[0014] Based on the device's basic data, the battery health, aging impact coefficient, and ideal operating temperature are obtained; and based on the initial charging data, the real-time surface temperature value and initial charging power of the mobile device are obtained.

[0015] Obtain the ambient temperature value based on the charging environment data;

[0016] The initial output power of the cooling wireless charger is obtained, and the real-time thermal resistance of the mobile device is obtained based on the real-time surface temperature value, ambient temperature value, initial output power and initial charging power.

[0017] The aging compensation value of the mobile device is obtained based on the battery health and aging impact coefficient, and the heat generation index is obtained based on the aging compensation value, initial output power, initial charging power and real-time thermal resistance.

[0018] Obtain a heat threshold mapping table, and obtain the basic temperature threshold range based on the heat threshold mapping table and the calorific value.

[0019] Preferably, the step of obtaining the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range includes:

[0020] Environmental characteristic impact data are obtained based on the charging environment data, wherein the environmental characteristic impact data includes comprehensive temperature impact value, comprehensive humidity impact value, air flow impact value, and space limitation impact value;

[0021] The environmental heat dissipation efficiency index is obtained based on the comprehensive influence value of temperature, the influence value of air flow, and the influence value of space limitation; and the environmental severity index is obtained based on the comprehensive influence value of temperature, the comprehensive influence value of humidity, and the influence value of air flow.

[0022] Obtain the environmental adaptive threshold correlation table, and obtain environmental stress data based on the environmental adaptive threshold correlation table;

[0023] The heat dissipation-temperature response curve and the severity-temperature response curve are obtained based on the environmental stress data, and the efficiency compensation value is obtained based on the environmental heat dissipation efficiency index and the heat dissipation-temperature response curve.

[0024] The safety compensation value is obtained based on the environmental severity index and the severity-temperature response curve;

[0025] A personalized temperature threshold is obtained based on the safety compensation value, efficiency compensation value, and basic temperature threshold range.

[0026] Preferably, the step of determining whether cooling is needed based on the charging process data and the personalized temperature threshold includes:

[0027] Based on the charging process data, obtain charging power fluctuation data, equivalent impedance change data, and surface thermal field distribution data;

[0028] A three-dimensional temperature field is obtained based on the surface thermal field distribution data, and multiple core heating regions and core region temperature values ​​are obtained based on the three-dimensional temperature field.

[0029] The weight parameters of the core heating region are obtained by principal component analysis, and the comprehensive temperature value is obtained based on the temperature value of the core region and the corresponding weight parameters.

[0030] The instantaneous thermal shock intensity is obtained based on the charging power fluctuation data, and the impedance temperature rise coefficient is obtained based on the equivalent impedance change data.

[0031] The temperature compensation value is obtained based on the instantaneous thermal shock intensity, and the final temperature value is obtained based on the temperature compensation value and the comprehensive temperature value.

[0032] Determine whether the final temperature value is greater than the personalized temperature threshold:

[0033] If the final temperature value is not greater than the personalized temperature threshold, it is determined that the cooling wireless charger does not need to perform cooling.

[0034] If the final temperature value is greater than the personalized temperature threshold, it is determined that the cooling wireless charger needs to be cooled.

[0035] Preferably, the step of obtaining the temperature control demand intensity value based on the charging process data includes:

[0036] The charging power fluctuation data is subjected to a fast Fourier transform to obtain the high-frequency disturbance characteristic frequency band, and the disturbance energy ratio is obtained based on the high-frequency disturbance characteristic frequency band.

[0037] Dynamic correlation data is obtained based on the equivalent impedance change data and the surface thermal field distribution data, and the temperature rise response coefficient is obtained based on the dynamic correlation data.

[0038] The coupling influence value is obtained based on the temperature rise response coefficient and the disturbance energy ratio, and the aging state correction coefficient is obtained based on the weight parameter of the core heat-generating area and the battery health.

[0039] The temperature control requirement intensity value is obtained based on the coupling effect value and the aging state correction coefficient.

[0040] Preferably, the step of obtaining the temperature control adjustment parameters based on the control parameter set and the temperature control demand intensity value includes:

[0041] The temperature deviation value is obtained based on the final temperature value and the personalized temperature threshold, and the power adjustment amount is obtained based on the control parameter group, the temperature deviation value, and the temperature control demand intensity value.

[0042] Obtain power derating limit data, and obtain the maximum charging power and maximum derating ratio based on the power derating limit data;

[0043] The charging power adjustment parameters are obtained based on the maximum charging power, the maximum reduction ratio, and the power adjustment amount, and the cooling chip current adjustment parameters are obtained based on the charging power adjustment parameters and the temperature control demand intensity value.

[0044] The temperature control requirement level is obtained based on the temperature control requirement intensity value and the ambient temperature value;

[0045] Obtain the speed parameter reference table, and obtain the fan speed adjustment parameters based on the speed parameter reference table and the temperature control requirement level.

[0046] Preferably, the step of adjusting the cooling according to the temperature control parameters includes:

[0047] The cooling efficiency index is obtained based on the charging power adjustment parameters, the cooling chip current adjustment parameters, and the fan speed adjustment parameters.

[0048] Obtain a multi-parameter collaborative optimization table, and obtain refrigeration adjustment data based on the multi-parameter collaborative optimization table and the refrigeration efficiency index;

[0049] Based on the cooling adjustment data, the charging power is adjusted, the cooling chip current is controlled, and the fan speed is adjusted to obtain real-time operating parameters;

[0050] The cooling performance evaluation value is obtained based on the real-time operating parameters, and it is determined whether the cooling performance evaluation value exceeds a preset threshold.

[0051] If the value is not exceeded, an adjustment correction coefficient is obtained based on the cooling efficiency evaluation value and the preset threshold. The charging power adjustment parameter, the cooling chip current adjustment parameter, and the fan speed adjustment parameter are then adjusted based on the adjustment correction coefficient to obtain dynamic adjustment parameters.

[0052] The dynamic adjustment parameters are returned as the temperature control adjustment parameters to the step of obtaining the cooling performance index until the cooling performance evaluation value exceeds the preset threshold.

[0053] This application also provides a cooled wireless charger, comprising:

[0054] The first acquisition module is used to acquire charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data and charging environment data;

[0055] The second acquisition module is used to acquire the heat index of the mobile device based on the initial charging data and the basic device data, and to acquire the basic temperature threshold range based on the heat index.

[0056] The third acquisition module is used to acquire the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range.

[0057] The judgment module is used to determine whether cooling is needed based on the charging process data and the personalized temperature threshold.

[0058] The fourth acquisition module is used to acquire the temperature control demand intensity value based on the charging process data if cooling is required.

[0059] The fifth acquisition module is used to acquire the control parameter group of the cooling wireless charger, and acquire the temperature control adjustment parameters according to the control parameter group and the temperature control demand intensity value, wherein the temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters;

[0060] The adjustment module is used to adjust the cooling according to the temperature control parameters.

[0061] Preferably, the determination module includes:

[0062] The first acquisition unit is used to acquire charging power fluctuation data, equivalent impedance change data and surface thermal field distribution data based on the charging process data.

[0063] The second acquisition unit is used to acquire a three-dimensional temperature field based on the surface thermal field distribution data, and to acquire multiple core heating regions and core region temperature values ​​based on the three-dimensional temperature field.

[0064] The third acquisition unit is used to acquire the weight parameters of the core heating region through principal component analysis, and to acquire the comprehensive temperature value based on the temperature value of the core region and the corresponding weight parameters.

[0065] The fourth acquisition unit is used to acquire the instantaneous thermal shock intensity based on the charging power fluctuation data, and to acquire the impedance temperature rise coefficient based on the equivalent impedance change data.

[0066] The fifth acquisition unit is used to acquire a temperature compensation value based on the instantaneous thermal shock intensity and the instantaneous thermal shock intensity, and to acquire a final temperature value based on the temperature compensation value and the comprehensive temperature value.

[0067] The judgment unit is used to determine whether the final temperature value is greater than the personalized temperature threshold.

[0068] If the final temperature value is not greater than the personalized temperature threshold, it is determined that the cooling wireless charger does not need to perform cooling.

[0069] If the final temperature value is greater than the personalized temperature threshold, it is determined that the cooling wireless charger needs to be cooled.

[0070] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described adaptive intelligent temperature control method.

[0071] The beneficial effects of this invention are as follows: By acquiring charging status data and device basic data, this invention can establish a three-dimensional data model of "basic data + dynamic status + environmental perception," and quantify the inherent heat generation tendency of mobile devices through initial charging data to obtain a heat generation index. Based on the heat generation index, a basic temperature threshold range is obtained. This method solves the problem of unreasonable threshold settings caused by "differences in batteries between new and old devices and significant differences in heat generation characteristics between different models." Furthermore, this solution effectively solves the problem of "uniform thresholds failing in extreme environments" by introducing real-time intervention of charging environment data to obtain personalized temperature thresholds. Then, by capturing charging process data of mobile devices and combining it with threshold judgment, abnormal... The system provides early warnings and triggers cooling to prevent problems from worsening. It also addresses the issue of poor temperature control caused by the "reaction lag" of fixed thresholds through a dual judgment mechanism of "real-time data + dynamic thresholds." When cooling is deemed necessary, the system calculates the intensity of temperature control demand by quantitatively analyzing charging process data to accurately describe the urgency and intensity of the current cooling requirement. Finally, by integrating the preset control parameter group of the cooling wireless charger with the real-time calculated intensity of temperature control demand, it dynamically generates precise temperature control adjustment parameters to directly control the charging and cooling components. This multi-component collaborative adjustment method forms a dual mechanism of "heat generation control + active heat dissipation," which helps improve the temperature control response speed. Attached Figure Description

[0072] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0073] Figure 2 This is a schematic diagram of the device structure according to an embodiment of the present invention.

[0074] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0075] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0076] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0077] like Figure 1 As shown, this application provides an adaptive intelligent temperature control method applicable to a cooling wireless charger, wherein the cooling wireless charger is connected to a mobile device to power the mobile device, comprising:

[0078] S1. Obtain charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data and charging environment data;

[0079] S2. Obtain the heat generation index of the mobile device based on the initial charging data and the basic device data, and obtain the basic temperature threshold range based on the heat generation index;

[0080] S3. Obtain the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range;

[0081] S4. Determine whether cooling is required based on the charging process data and the personalized temperature threshold.

[0082] S5. If cooling is required, obtain the temperature control requirement intensity value based on the charging process data.

[0083] S6. Obtain the control parameter group of the cooling wireless charger, and obtain the temperature control adjustment parameters according to the control parameter group and the temperature control demand intensity value to perform cooling adjustment. The temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters.

[0084] S7. Adjust the cooling according to the temperature control parameters.

[0085] As described in steps S1-S7 above, this invention can establish a three-dimensional data model of "basic data + dynamic status + environmental perception" by acquiring charging status data and device basic data. Charging status data refers to various status data of mobile devices (such as mobile phones and smartwatches) during the charging process, collected through sensors, protocol interactions, and device-level interfaces. This includes initial charging data, charging process data, and charging environment data. Initial charging data refers to device status data collected in the initial charging stage (such as the first 30 seconds after connecting the charger), such as initial charging power. Charging process data refers to continuously collected dynamic data during the charging process, reflecting the real-time energy state and temperature change trends of the device, such as real-time battery level. The charging environment data refers to external environmental parameters collected by environmental sensors (such as temperature, humidity, and light sensors), which are located inside the wireless cooling unit. Device basic data refers to the inherent attribute data of the mobile device, such as battery health. Because traditional temperature control solutions do not consider the heating characteristics of different devices and rely solely on a uniform threshold (such as a 42°C alarm), this uniform threshold may lead to the actual temperature of the device far exceeding the safe range without triggering an alarm. Furthermore, the device may frequently activate cooling in low-temperature environments due to an overly conservative threshold, wasting energy. Therefore, this solution quantifies the inherent heating tendency of the mobile device using initial charging data to obtain a heating index. The heating index is used to evaluate the device's performance in a standard environment. The inherent heat dissipation tendency value is used to obtain a basic temperature threshold range based on the heat dissipation index. This basic temperature threshold range refers to the temperature safety range dynamically set for each individual device based on the heat dissipation index. This method addresses the problem of unreasonable threshold settings caused by differences in battery characteristics between new and old devices and significant variations in heat dissipation characteristics between different models. Furthermore, traditional solutions do not consider the influence of environmental factors. However, in real-world scenarios, environmental variables (such as indoor-outdoor temperature differences and direct sunlight during vehicle charging) significantly impact device heat dissipation. Therefore, this solution introduces real-time charging environment data to obtain personalized temperature thresholds. Personalized temperature thresholds refer to real-time thresholds that are further corrected based on the basic temperature threshold range and combined with charging environment data, thereby effectively solving the problem of inconsistent... To address the issue of threshold failure in extreme environments (such as a fixed threshold not being lowered in high-temperature environments leading to overheating and slowdown, or a threshold being too strict in low-temperature environments resulting in wasted charging efficiency), this solution captures charging process data from mobile devices and combines it with threshold judgment. This allows for early warning and triggering of cooling in the early stages of anomalies (such as a rapid temperature rise before reaching the traditional threshold), preventing the problem from worsening. Through a dual judgment mechanism of "real-time data + dynamic threshold," it solves the problem of poor temperature control caused by the "lag" of fixed thresholds. When cooling is deemed necessary, the solution calculates the temperature control demand intensity value by quantitatively analyzing the charging process data. This temperature control demand intensity value reflects the degree to which the current device temperature deviates from the personalized threshold and the urgency of heat dissipation.To accurately describe the urgency and intensity of the current cooling demand, the system integrates a pre-set control parameter set from the wireless charger. This control parameter set refers to the set of control parameters supported by the wireless charger hardware, such as the supported charging power range, cooling chip current range, and fan speed settings. Combined with real-time calculated temperature control demand intensity values, precise temperature control adjustment parameters are dynamically generated. These temperature control adjustment parameters are specific execution parameters dynamically generated based on the temperature control demand intensity value and the control parameter set, directly controlling the charging and cooling components (e.g., reducing charging power to 15W, adjusting the cooling chip current to 1.2A, and increasing the fan speed to 2500 rpm). This multi-component collaborative adjustment method solves the problem of "low efficiency of single actuator adjustment," forming a dual mechanism of "heat generation control + active heat dissipation," which is beneficial for improving temperature control response speed.

[0086] In one embodiment, step S2, which involves obtaining the heat index of the mobile device based on the initial charging data and the device's basic data, and obtaining a basic temperature threshold range based on the heat index, includes:

[0087] S21. Obtain battery health, aging impact coefficient and ideal operating temperature based on the device basic data, and obtain the real-time surface temperature value and initial charging power of the mobile device based on the initial charging data.

[0088] S22. Obtain the ambient temperature value based on the charging environment data;

[0089] S23. Obtain the initial output power of the cooling wireless charger, and obtain the real-time thermal resistance of the mobile device based on the real-time surface temperature value, ambient temperature value, initial output power and initial charging power.

[0090] S24. Obtain the aging compensation value of the mobile device based on the battery health and aging impact coefficient, and obtain the heat generation index based on the aging compensation value, initial output power, initial charging power and real-time thermal resistance.

[0091] S25. Obtain the heat threshold mapping table, and obtain the basic temperature threshold range based on the heat threshold mapping table and the heating index.

[0092] As described in steps S21-S25 above, this invention identifies the aging state and inherent characteristics of mobile devices by acquiring the battery health, aging impact coefficient, and ideal operating temperature. By introducing battery health and aging impact coefficient, the battery status of new and old devices can be distinguished. This allows the temperature control strategy to be adjusted according to the age of the device's battery, solving the problem that traditional temperature control methods ignore the aging differences of mobile devices. Next, the real-time surface temperature and initial charging power of the mobile device, as well as the ambient temperature of the mobile device, are acquired. The real-time thermal resistance is then calculated using the formula "Real-time thermal resistance = (Real-time surface temperature - Ambient temperature) / (Initial output power - Initial charging power)". Thermal resistance, specifically real-time thermal resistance, refers to the resistance of a mobile device in its current charging state to conduct internal heat to the external environment. It reflects the relationship between heat generation and temperature changes in the current state. Traditional temperature control methods assume a fixed thermal resistance, but in practice, thermal resistance changes continuously with the device's state. Therefore, dynamically calculating thermal resistance can represent the heat dissipation characteristics of different devices. The aging compensation value is then calculated using the formula: "Aging Compensation Value = 1 + (1 - Battery Health) * Aging Influence Coefficient." This aging compensation value is a compensation coefficient that corrects for overheating risks based on the device's battery health and aging level. By introducing an aging compensation mechanism, the problems associated with older devices can be addressed. The issue of altered heat generation patterns due to battery degradation is addressed by calculating the heat index using the formula: "Heat Index = (Initial Output Power - Initial Charging Power) * Aging Compensation Value / [(1 / Real-time Thermal Resistance) * Ideal Operating Temperature]". Essentially, the heat index is the ratio of "actual heat generation" to "theoretical heat dissipation requirement". A high heat index indicates excessive heat generation or poor heat dissipation, necessitating a stricter temperature threshold for earlier cooling. Conversely, a low heat index indicates low heat generation or good heat dissipation, requiring a more relaxed temperature threshold to avoid unnecessary cooling. The heat index provides a direct comparison of heat generation under different devices and conditions, offering accurate data for subsequent temperature control decisions and ultimately leading to the determination of the heat threshold. The mapping table, specifically the heat threshold mapping table, refers to a pre-established mapping relationship between the heat index and the basic temperature threshold range. Essentially, it's a "threshold database" based on the device's heat generation characteristics. The corresponding basic temperature threshold range is matched from the heat index in the heat threshold mapping table. This method allows for better application to different new and old devices, avoiding a "one-size-fits-all" approach of using fixed temperature thresholds to control all devices. Instead, it dynamically obtains a reasonable temperature threshold range based on the real-time heat generation and dissipation capabilities of the mobile device. Different devices have different heat indices, resulting in different heat levels and basic temperature threshold ranges, avoiding the problem of traditional fixed thresholds being either too high or too low, thus laying the foundation for precise temperature control.

[0093] In one embodiment, step S3, which involves obtaining the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range, includes:

[0094] S31. Obtain environmental characteristic impact data based on the charging environment data, wherein the environmental characteristic impact data includes comprehensive temperature impact value, comprehensive humidity impact value, air flow impact value, and space limitation impact value;

[0095] S32. Obtain the environmental heat dissipation efficiency index based on the comprehensive temperature influence value, air flow influence value, and space limitation influence value, and obtain the environmental severity index based on the comprehensive temperature influence value, comprehensive humidity influence value, and air flow influence value.

[0096] S33. Obtain the environmental adaptive threshold association table, and obtain the environmental stress data according to the environmental adaptive threshold association table;

[0097] S34. Obtain the heat dissipation-temperature response curve and the severity-temperature response curve based on the environmental stress data, and obtain the efficiency compensation value based on the environmental heat dissipation efficiency index and the heat dissipation-temperature response curve.

[0098] S35. Obtain the safety compensation value based on the environmental severity index and the severity-temperature response curve;

[0099] S36. Obtain a personalized temperature threshold based on the safety compensation value, efficiency compensation value, and basic temperature threshold range.

[0100] As described in steps S31-S36 above, this invention normalizes charging environment data to obtain environmental characteristic impact data. This environmental characteristic impact data refers to quantifying the core physical characteristic parameters affecting device heat dissipation and temperature safety in the charging environment, including comprehensive temperature impact value, comprehensive humidity impact value, airflow impact value, and space constraint impact value. This transforms complex environmental information into quantifiable and analyzable key indicators, providing fundamental data for subsequent assessment of the environment's impact on charging device temperature. This overcomes the limitations of traditional unified threshold temperature control that does not consider environmental factors. By quantifying environmental characteristics, the temperature control strategy can perceive environmental changes. For example, different usage scenarios (indoor / outdoor, enclosed / open space) have a significant impact on device heat dissipation. This step enables the temperature control system to have environmental adaptability, thereby setting temperature thresholds more accurately. Since wind speed and convective heat dissipation efficiency are not linearly related, and considering the boundary layer theory in fluid mechanics, the inhibitory effect of space constraints on heat dissipation exhibits an inverse proportional relationship. This can be achieved through formulas... The environmental heat dissipation efficiency index was calculated, where, Indicates the environmental heat dissipation efficiency index. This indicates a nonlinear transformation of the influence value on airflow to the power of 0.8. Indicates the impact value of space constraints. The environmental heat dissipation efficiency index, representing the overall impact of temperature, is a comprehensive indicator that measures the degree to which the charging environment promotes or inhibits the heat dissipation capacity of the equipment. A higher value indicates a more favorable environment for equipment heat dissipation. To highlight the severity of high-temperature environments and make the temperature control strategy more sensitive to high temperatures, a superlinear growth model is used to simulate the accelerated aging effect of high temperatures on the equipment. Furthermore, a logarithmic function is used to control the penalty in high humidity ranges, avoiding the overreaction to high humidity seen in traditional methods and better reflecting reality. Humidity primarily affects heat dissipation indirectly through condensation and corrosion, and the mitigating effect of airflow is inversely proportional to the square root of wind speed, as shown by the formula... The environmental severity index was calculated, where, Indicates the severity index of the environment. This indicates a superlinear transformation of the overall temperature effect value to the power of 1.2. This represents the overall impact value of humidity. The environmental severity index, representing the impact of airflow, is a comprehensive indicator assessing the threat posed by the charging environment to equipment safety. A higher index indicates harsher environmental conditions and higher requirements for equipment temperature control. The environmental heat dissipation efficiency index and environmental severity index, calculated using specific formulas, further integrate multiple environmental characteristic impact data into a comprehensive index that intuitively reflects the environment's heat dissipation capacity and severity, providing a quantitative basis for subsequent temperature threshold adjustments. Traditional temperature control does not systematically quantify environmental conditions; this step, by constructing indices, directly links environmental factors to equipment temperature control requirements. For example, a low environmental heat dissipation efficiency index indicates difficulty in heat dissipation, necessitating a corresponding reduction in the temperature threshold to ensure equipment safety. Conversely, a high environmental severity index signifies a significant threat to the equipment, requiring threshold adjustments to strengthen protection. This effectively addresses the problem of a uniform threshold failing to adapt to environmental changes. Subsequently, an environmental adaptive threshold correlation table is obtained based on extensive experiments and experience. This table refers to a pre-generated "environmental feature combination and threshold adjustment rule." The system first establishes a mapping relationship table and then obtains environmental stress data based on an environmental adaptive threshold association table. This environmental stress data refers to a set of parameters describing the intensity of the physical effects of environmental factors on equipment temperature safety and the threshold adjustment logic. Next, it obtains heat dissipation-temperature response curves and severity-temperature response curves based on the environmental stress data. The heat dissipation-temperature response curve is a function curve reflecting the relationship between the environmental heat dissipation efficiency index and the temperature threshold adjustment amount, while the severity-temperature response curve is a function curve reflecting the relationship between the environmental severity index and the temperature threshold adjustment amount. Finally, it obtains efficiency compensation values ​​based on the environmental heat dissipation efficiency index and the heat dissipation-temperature response curves. These efficiency compensation values ​​refer to the impact of environmental heat dissipation efficiency on equipment charging efficiency. The compensation adjustment amount for the temperature threshold is obtained based on the environmental severity index and the severity-temperature response curve to obtain the safety compensation coefficient. The safety compensation coefficient refers to the mandatory safety margin adjustment amount of the temperature threshold based on the impact of environmental severity on equipment safety risks. Through these two coefficients, environmental factors are transformed into adjustment parameters for the temperature threshold. The base temperature threshold range includes the upper value of the base temperature and the lower value of the base temperature. Therefore, the personalized temperature threshold = [(upper value of base temperature + efficiency compensation value) + (lower value of base temperature + safety compensation coefficient)] / 2. The above method transforms equipment characteristics and environmental stress into calculable compensation parameters through a multi-factor fusion mathematical model, forming a hierarchical control framework of "benchmark + correction", breaking through the limitations of the traditional single threshold.

[0101] In one embodiment, step S4, which determines whether cooling is needed based on the charging process data and the personalized temperature threshold, includes:

[0102] S41. Obtain charging power fluctuation data, equivalent impedance change data, and surface thermal field distribution data based on the charging process data;

[0103] S42. Obtain a three-dimensional temperature field based on the surface thermal field distribution data, and obtain multiple core heating regions and core region temperature values ​​based on the three-dimensional temperature field.

[0104] S43. Obtain the weight parameters of the core heating region through principal component analysis, and obtain the comprehensive temperature value based on the temperature value of the core region and the corresponding weight parameters;

[0105] S44. Obtain the instantaneous thermal shock intensity based on the charging power fluctuation data, and obtain the impedance temperature rise coefficient based on the equivalent impedance change data;

[0106] S45. Obtain a temperature compensation value based on the instantaneous thermal shock intensity and the instantaneous thermal shock intensity, and obtain a final temperature value based on the temperature compensation value and the comprehensive temperature value;

[0107] S46. Determine whether the final temperature value is greater than the personalized temperature threshold:

[0108] If the final temperature value is not greater than the personalized temperature threshold, it is determined that the cooling wireless charger does not need to perform cooling.

[0109] If the final temperature value is greater than the personalized temperature threshold, it is determined that the cooling wireless charger needs to be cooled.

[0110] As described in steps S41-S46 above, this invention comprehensively depicts the device's heating state by acquiring multi-dimensional data (electrical parameters, thermal parameters) to solve the problem of "fixed thresholds failing to detect dynamic thermal shocks." For example, when power fluctuations are large but the instantaneous temperature does not exceed the threshold, a potential overheating risk is warned in advance. Then, based on the surface thermal field distribution data, the two-dimensional surface temperature distribution is reconstructed into a three-dimensional temperature field. The three-dimensional temperature field refers to a three-dimensional spatial model of the temperature distribution at various points inside and on the surface of the mobile device during charging, usually presented as a temperature gradient cloud map or grid. Next, the core heating area inside the device is identified based on the three-dimensional temperature field, and the temperature value of the core area is obtained. The core heating area refers to the area where the temperature is significantly higher than the average temperature of the device in the three-dimensional temperature field. The core area temperature, which is the most critical local area for device safety or performance (such as the battery compartment or near the charging chip), refers to the real-time temperature measurement within the core heat-generating area. This avoids misjudgments based solely on surface temperature (e.g., the outer shell is low but the interior is overheated). This method uses 3D modeling to reverse-calculate the "surface-to-interior" heat conduction, solving the problem of "missed detection of internal overheating." It is particularly suitable for wireless charging devices with sealed structures. Principal component analysis (PCA) is then introduced to quantify the thermal importance of different areas, addressing the issue of "ignoring the differences in contributions from multiple heat sources." For example, when the battery core area temperature is close to a threshold while the outer shell temperature is low, traditional strategies may not trigger cooling, while this method prioritizes response through weighted allocation. For high-risk areas, the contribution weight of each area to overall thermal safety is calculated (e.g., the battery area has a higher weight than the casing) to avoid neglecting the risks of key areas due to averaging. Then, a comprehensive temperature value is calculated based on the temperature of the core heat-generating areas and weight parameters. The comprehensive temperature value refers to the overall temperature index obtained by weighting the temperature values ​​of each core heat-generating area through principal component analysis (PCA). Next, the power change and time interval are obtained based on charging power fluctuation data, and the instantaneous thermal shock intensity is obtained based on the ratio of the power change to the time interval. The instantaneous thermal shock intensity refers to the heat generated by the device in a short period of time due to sudden fluctuations in charging power (such as fast charging protocol switching or load change) during the charging process. The rate of change is then determined. Next, the impedance change and temperature change are obtained based on the equivalent impedance change data. The impedance temperature rise coefficient is then calculated based on the ratio of the impedance change to the temperature change. This impedance temperature rise coefficient refers to the sensitivity of the mobile device's internal equivalent impedance (such as battery internal resistance or line impedance) to temperature increases. In addition, considering power surges and impedance feedback, an extra temperature compensation value is obtained. This temperature compensation value is a calculated additional temperature increment used to correct the real-time temperature measurement. Finally, the sum of the comprehensive temperature value and the temperature compensation value is used to obtain the final temperature value. This final temperature value is the temperature index obtained by superimposing the comprehensive temperature value and the temperature compensation value. The final temperature value is then compared with a personalized temperature threshold to determine whether cooling should be triggered.The above method replaces the traditional "black and white" judgment based on a single threshold by using comprehensive temperature criteria. For example, if the comprehensive temperature is close to the threshold but the compensation value shows a potential warming trend, low-intensity cooling can be initiated in advance even if the threshold is not exceeded, which is beneficial for achieving "preventive temperature control."

[0111] In one embodiment, step S5, which involves obtaining the temperature control demand intensity value based on the charging process data, includes:

[0112] S51. Perform a fast Fourier transform on the charging power fluctuation data to obtain the high-frequency disturbance characteristic frequency band, and obtain the disturbance energy ratio based on the high-frequency disturbance characteristic frequency band.

[0113] S52. Obtain dynamic correlation data based on the equivalent impedance change data and the surface thermal field distribution data, and obtain the temperature rise response coefficient based on the dynamic correlation data;

[0114] S53. Obtain the coupling influence value based on the temperature rise response coefficient and the disturbance energy ratio, and obtain the aging state correction coefficient based on the weight parameter of the core heat-generating area and the battery health.

[0115] S54. Obtain the temperature control demand intensity value based on the coupling influence value and the aging state correction coefficient.

[0116] As described in steps S51-S54 above, this invention converts the time-domain signal into a frequency-domain signal by performing a Fourier transform on the charging power fluctuation data, identifying the high-frequency bands with concentrated energy as high-frequency disturbance characteristic bands. These high-frequency disturbance characteristic bands refer to frequency ranges with significant high-frequency fluctuation characteristics. The proportion of energy within this frequency band in the total energy is calculated based on the high-frequency disturbance characteristic bands to obtain the disturbance energy proportion. This disturbance energy proportion refers to the ratio of energy within the high-frequency disturbance characteristic bands to the total charging power energy. Next, dynamic correlation data is obtained based on equivalent impedance change data and surface thermal field distribution data. This dynamic correlation data refers to the dynamic mapping relationship established by analyzing the spatiotemporal correlation between equivalent impedance change data and surface thermal field distribution data. A dynamic correlation model is then fitted based on the dynamic correlation data, and points where impedance changes abruptly are found in the dynamic correlation model. The calculation of this... The temperature rise corresponding to a unit impedance change at a given point is used to obtain the temperature rise response coefficient. This coefficient characterizes the temperature rise caused by a unit impedance change in dynamic correlation data, reflecting the sensitivity of the battery's internal temperature changes to impedance. Next, considering the correlation between high-frequency power disturbances and battery temperature changes, the temperature rise response coefficient is combined with the disturbance energy ratio to obtain a coupling effect value. This value reflects the synergistic effect of high-frequency disturbances and impedance changes on temperature. Further considering the impact of battery aging on temperature control requirements, an aging state correction coefficient is obtained by combining the weight parameters of the core heat-generating area and battery health. Finally, the coupling effect value and the aging state correction coefficient are fused to obtain the temperature control demand intensity value, providing a clear basis for determining subsequent temperature control adjustment parameters by intuitively quantifying the current temperature control demand intensity.

[0117] In one embodiment, step S6, which involves obtaining the temperature control adjustment parameters based on the control parameter group and the temperature control demand intensity value, includes:

[0118] S61. Obtain the temperature deviation value based on the final temperature value and the personalized temperature threshold, and obtain the power adjustment amount based on the control parameter group, the temperature deviation value and the temperature control demand intensity value;

[0119] S62. Obtain power reduction limit data, and obtain the maximum charging power and maximum reduction ratio based on the power reduction limit data;

[0120] S63. Obtain charging power adjustment parameters based on the maximum charging power, maximum reduction ratio, and power adjustment amount, and obtain cooling chip current adjustment parameters based on the charging power adjustment parameters and the temperature control demand intensity value.

[0121] S64. Obtain the temperature control requirement level based on the temperature control requirement intensity value and the ambient temperature value;

[0122] S65. Obtain the speed parameter comparison table, and obtain the fan speed adjustment parameters according to the speed parameter comparison table and the temperature control requirement level.

[0123] As described in steps S61-S65 above, this invention uses a PID control algorithm to obtain the power adjustment amount based on the control parameter set, temperature deviation value, and temperature control demand intensity value. The power adjustment amount refers to the charging power adjustment amount calculated by the algorithm. Next, power reduction limit data is obtained, which refers to pre-set charging power reduction constraints, including the maximum charging power and the maximum reduction ratio. By clarifying the adjustable range and safe reduction limit of the charging power through the power reduction limit data, the power adjustment can be prevented from exceeding the equipment hardware's tolerance. Charging power adjustment parameters are obtained based on the maximum charging power, the maximum reduction ratio, and the power adjustment amount. These charging power adjustment parameters refer to the finally determined actual charging power adjustment value. This method can solve the equipment damage risk caused by the "unbounded parameter adjustment" in traditional strategies. Furthermore, since cooling and power adjustment operate independently in traditional temperature control, this method combines... By combining the charging power adjustment parameters and the temperature control demand intensity value, the cooling chip current adjustment parameters are dynamically adjusted. The cooling chip current adjustment parameter refers to the adjustment amount of the cooling chip's operating current calculated based on the charging power adjustment parameters and the temperature control demand intensity value, thereby achieving coordinated control of "power derating + active cooling" to avoid the limitations of a single adjustment method. For example, in high-temperature environments, power can be reduced and cooling enhanced simultaneously to improve temperature control efficiency. Finally, the temperature control demand intensity value and ambient temperature are discretized into temperature control demand levels. The temperature control demand level refers to the level of urgency of temperature control (such as low, medium, high, and urgent) divided by the temperature control demand intensity value (reflecting the thermal stress level) and the ambient temperature value. The obtained speed parameter reference table is a pre-established mapping table between temperature control demand levels and fan speed. Through the preset speed parameter reference table, the temperature control demand level is converted into the actual fan speed value, thereby achieving step-by-step adjustment of heat dissipation capacity.

[0124] In one embodiment, step S7, which involves adjusting the cooling system according to the temperature control parameters, includes:

[0125] S71. Obtain the cooling efficiency index based on the charging power adjustment parameters, the cooling chip current adjustment parameters, and the fan speed adjustment parameters.

[0126] S72. Obtain the multi-parameter collaborative optimization table, and obtain refrigeration adjustment data based on the multi-parameter collaborative optimization table and the refrigeration efficiency index;

[0127] S73. Based on the cooling adjustment data, perform charging power adjustment, cooling chip current control and fan speed adjustment to obtain real-time operating parameters;

[0128] S74. Obtain the cooling performance evaluation value based on the real-time operating parameters, and determine whether the cooling performance evaluation value exceeds a preset threshold.

[0129] S75. If not exceeded, an adjustment correction coefficient is obtained based on the cooling efficiency evaluation value and the preset threshold, and the charging power adjustment parameter, the cooling chip current adjustment parameter and the fan speed adjustment parameter are corrected based on the adjustment correction coefficient to obtain dynamic adjustment parameters;

[0130] S76. The dynamic adjustment parameter is returned as the temperature control adjustment parameter to the step of obtaining the cooling performance index until the cooling performance evaluation value exceeds the preset threshold.

[0131] As described in steps S71-S76 above, this invention uses a neural network model to transform the charging power adjustment parameters, the cooling chip current adjustment parameters, and the fan speed adjustment parameters into a comprehensive index that reflects the nonlinear characteristics of the actual cooling efficiency, thus obtaining a cooling efficiency index. The cooling efficiency index refers to a quantitative value that comprehensively reflects the overall cooling capacity of multiple input parameters, including the charging power adjustment parameters, the cooling chip current adjustment parameters, and the fan speed adjustment parameters. A multi-parameter collaborative optimization table is also obtained. This table refers to a preset optimized combination of parameters such as charging power, cooling chip current, and fan speed with cooling effect and energy efficiency. Based on the multi-parameter collaborative optimization table, the cooling efficiency index is transformed into specific adjustment commands. The cooling adjustment data, which refers to specific adjustment commands, includes a charging power step-down sequence, a cooling chip pulse current waveform, and a fan speed cycle. This cooling adjustment data is then converted into actual control signals to drive actuators such as the charging module, cooling chip, and fan. For example, the wireless charging output power is adjusted according to the charging power step-down sequence to obtain the real-time charging power; the intermittent operation mode of the semiconductor cooling chip is controlled according to the cooling chip pulse current waveform to obtain the real-time cooling chip current; and the fan speed cycle is adjusted to obtain the real-time fan speed. This method allows for simultaneous adjustment of charging power and cooling equipment, avoiding the temperature control lag caused by "adjusting cooling without adjusting charging" or the efficiency sacrifice caused by "adjusting charging only," thus achieving power-to-temperature control. Temperature dynamic coupling control is implemented, and then, based on real-time operating parameters after execution (such as real-time charging power, real-time current of the cooling chip, and real-time fan speed), a comprehensive evaluation model is used to calculate the cooling efficiency evaluation value. This evaluation value measures the actual efficiency of the current cooling system. The evaluation value is compared with a preset threshold to determine if further parameter adjustments are needed, thus establishing a termination condition for closed-loop control. If the cooling efficiency evaluation value does not exceed the preset threshold, a PID control algorithm is used to obtain an adjustment correction coefficient based on the evaluation value and the preset threshold. This adjustment correction coefficient is used to quantify... The values ​​of parameters such as charging power, cooling chip current, and fan speed are adjusted, and the charging power adjustment parameters, cooling chip current adjustment parameters, and fan speed adjustment parameters are corrected according to the adjustment correction coefficient to obtain dynamic adjustment parameters. The dynamic adjustment parameters refer to the new parameter combination obtained after correcting the original temperature control adjustment parameters by adjusting the correction coefficient. Finally, the corrected parameters are returned to the initial evaluation step to form a closed-loop adjustment until the performance meets the standard. This adjustment method can achieve a balance between cooling performance and adjustment efficiency. By limiting the number of cycles or the error range, it ensures that the system reaches the optimal state within a reasonable time.

[0132] This application also provides a cooled wireless charger, comprising:

[0133] The first acquisition module is used to acquire charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data and charging environment data;

[0134] The second acquisition module is used to acquire the heat index of the mobile device based on the initial charging data and the basic device data, and to acquire the basic temperature threshold range based on the heat index.

[0135] The third acquisition module is used to acquire the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range.

[0136] The judgment module is used to determine whether cooling is needed based on the charging process data and the personalized temperature threshold.

[0137] The fourth acquisition module is used to acquire the temperature control demand intensity value based on the charging process data if cooling is required.

[0138] The fifth acquisition module is used to acquire the control parameter group of the cooling wireless charger, and acquire the temperature control adjustment parameters according to the control parameter group and the temperature control demand intensity value, wherein the temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters;

[0139] The adjustment module is used to adjust the cooling according to the temperature control parameters.

[0140] In one embodiment, the determining module includes:

[0141] The first acquisition unit is used to acquire charging power fluctuation data, equivalent impedance change data and surface thermal field distribution data based on the charging process data.

[0142] The second acquisition unit is used to acquire a three-dimensional temperature field based on the surface thermal field distribution data, and to acquire multiple core heating regions and core region temperature values ​​based on the three-dimensional temperature field.

[0143] The third acquisition unit is used to acquire the weight parameters of the core heating region through principal component analysis, and to acquire the comprehensive temperature value based on the temperature value of the core region and the corresponding weight parameters.

[0144] The fourth acquisition unit is used to acquire the instantaneous thermal shock intensity based on the charging power fluctuation data, and to acquire the impedance temperature rise coefficient based on the equivalent impedance change data.

[0145] The fifth acquisition unit is used to acquire a temperature compensation value based on the instantaneous thermal shock intensity and the instantaneous thermal shock intensity, and to acquire a final temperature value based on the temperature compensation value and the comprehensive temperature value.

[0146] The judgment unit is used to determine whether the final temperature value is greater than the personalized temperature threshold.

[0147] If the final temperature value is not greater than the personalized temperature threshold, it is determined that the cooling wireless charger does not need to perform cooling.

[0148] If the final temperature value is greater than the personalized temperature threshold, it is determined that the cooling wireless charger needs to be cooled.

[0149] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described adaptive intelligent temperature control method.

[0150] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0152] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive intelligent temperature control method, applicable to a cooling wireless charger, wherein the cooling wireless charger is connected to a mobile device to power the mobile device, characterized in that, include: Acquire charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data, and charging environment data; The heat index of the mobile device is obtained based on the initial charging data and the basic device data, and the basic temperature threshold range is obtained based on the heat index. The personalized temperature threshold corresponding to the mobile device is obtained based on the charging environment data and the basic temperature threshold range. The process involves determining whether cooling is required based on the charging process data and the personalized temperature threshold. This determination includes: acquiring charging power fluctuation data, equivalent impedance change data, and surface thermal field distribution data based on the charging process data; acquiring a three-dimensional temperature field based on the surface thermal field distribution data, and acquiring multiple core heating regions and their temperature values ​​based on the three-dimensional temperature field; acquiring weight parameters for the core heating regions through principal component analysis, and acquiring a comprehensive temperature value based on the core region temperature value and the corresponding weight parameters; acquiring instantaneous thermal shock intensity based on the charging power fluctuation data, and acquiring an impedance temperature rise coefficient based on the equivalent impedance change data; acquiring a temperature compensation value based on the instantaneous thermal shock intensity and the comprehensive temperature value; and determining whether the final temperature value is greater than the personalized temperature threshold. If the final temperature value is not greater than the personalized temperature threshold, the wireless charger is deemed not to require cooling; if the final temperature value is greater than the personalized temperature threshold, the wireless charger is deemed to require cooling. If cooling is required, the temperature control demand intensity value is obtained based on the charging process data; Obtain the control parameter set of the cooling wireless charger, and obtain the temperature control adjustment parameters according to the control parameter set and the temperature control demand intensity value, wherein the temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters; Cooling is regulated according to the temperature control parameters.

2. The adaptive intelligent temperature control method according to claim 1, characterized in that, The step of obtaining the heat index of the mobile device based on the initial charging data and the device basic data, and obtaining the basic temperature threshold range based on the heat index, includes: Based on the device's basic data, the battery health, aging impact coefficient, and ideal operating temperature are obtained; and based on the initial charging data, the real-time surface temperature value and initial charging power of the mobile device are obtained. Obtain the ambient temperature value based on the charging environment data; The initial output power of the cooling wireless charger is obtained, and the real-time thermal resistance of the mobile device is obtained based on the real-time surface temperature value, ambient temperature value, initial output power and initial charging power. The aging compensation value of the mobile device is obtained based on the battery health and aging impact coefficient, and the heat generation index is obtained based on the aging compensation value, initial output power, initial charging power and real-time thermal resistance. Obtain a heat threshold mapping table, and obtain the basic temperature threshold range based on the heat threshold mapping table and the calorific value.

3. The adaptive intelligent temperature control method according to claim 1, characterized in that, The step of obtaining the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range includes: Environmental characteristic impact data are obtained based on the charging environment data, wherein the environmental characteristic impact data includes comprehensive temperature impact value, comprehensive humidity impact value, air flow impact value, and space limitation impact value; The environmental heat dissipation efficiency index is obtained based on the comprehensive influence value of temperature, the influence value of air flow, and the influence value of space limitation; and the environmental severity index is obtained based on the comprehensive influence value of temperature, the comprehensive influence value of humidity, and the influence value of air flow. Obtain the environmental adaptive threshold correlation table, and obtain environmental stress data based on the environmental adaptive threshold correlation table; The heat dissipation-temperature response curve and the severity-temperature response curve are obtained based on the environmental stress data, and the efficiency compensation value is obtained based on the environmental heat dissipation efficiency index and the heat dissipation-temperature response curve. The safety compensation value is obtained based on the environmental severity index and the severity-temperature response curve; A personalized temperature threshold is obtained based on the safety compensation value, efficiency compensation value, and basic temperature threshold range.

4. The adaptive intelligent temperature control method according to claim 1, characterized in that, The step of obtaining the temperature control demand intensity value based on the charging process data includes: The charging power fluctuation data is subjected to a fast Fourier transform to obtain the high-frequency disturbance characteristic frequency band, and the disturbance energy ratio is obtained based on the high-frequency disturbance characteristic frequency band. Dynamic correlation data is obtained based on the equivalent impedance change data and the surface thermal field distribution data, and the temperature rise response coefficient is obtained based on the dynamic correlation data. The coupling influence value is obtained based on the temperature rise response coefficient and the disturbance energy ratio, and the aging state correction coefficient is obtained based on the weight parameter of the core heat-generating area and the battery health. The temperature control requirement intensity value is obtained based on the coupling effect value and the aging state correction coefficient.

5. The adaptive intelligent temperature control method according to claim 1, characterized in that, The step of obtaining the temperature control adjustment parameters based on the control parameter group and the temperature control demand intensity value includes: The temperature deviation value is obtained based on the final temperature value and the personalized temperature threshold, and the power adjustment amount is obtained based on the control parameter group, the temperature deviation value, and the temperature control demand intensity value. Obtain power derating limit data, and obtain the maximum charging power and maximum derating ratio based on the power derating limit data; The charging power adjustment parameters are obtained based on the maximum charging power, the maximum reduction ratio, and the power adjustment amount, and the cooling chip current adjustment parameters are obtained based on the charging power adjustment parameters and the temperature control demand intensity value. The temperature control requirement level is obtained based on the temperature control requirement intensity value and the ambient temperature value. Obtain the speed parameter reference table, and obtain the fan speed adjustment parameters based on the speed parameter reference table and the temperature control requirement level.

6. The adaptive intelligent temperature control method according to claim 1, characterized in that, The step of adjusting the cooling according to the temperature control parameters includes: The cooling efficiency index is obtained based on the charging power adjustment parameters, the cooling chip current adjustment parameters, and the fan speed adjustment parameters. Obtain a multi-parameter collaborative optimization table, and obtain refrigeration adjustment data based on the multi-parameter collaborative optimization table and the refrigeration efficiency index; Based on the cooling adjustment data, the charging power is adjusted, the cooling chip current is controlled, and the fan speed is adjusted to obtain real-time operating parameters; The cooling performance evaluation value is obtained based on the real-time operating parameters, and it is determined whether the cooling performance evaluation value exceeds a preset threshold. If the value is not exceeded, an adjustment correction coefficient is obtained based on the cooling efficiency evaluation value and the preset threshold. The charging power adjustment parameter, the cooling chip current adjustment parameter, and the fan speed adjustment parameter are then adjusted based on the adjustment correction coefficient to obtain dynamic adjustment parameters. The dynamic adjustment parameters are returned as the temperature control adjustment parameters to the step of obtaining the cooling performance index until the cooling performance evaluation value exceeds the preset threshold.

7. A cooling wireless charger, characterized in that, include: The first acquisition module is used to acquire charging status data and basic device data of the mobile device, wherein the charging status data includes initial charging data, charging process data and charging environment data; The second acquisition module is used to acquire the heat index of the mobile device based on the initial charging data and the basic device data, and to acquire the basic temperature threshold range based on the heat index. The third acquisition module is used to acquire the personalized temperature threshold corresponding to the mobile device based on the charging environment data and the basic temperature threshold range. A judgment module is used to determine whether cooling is needed based on the charging process data and the personalized temperature threshold. Specifically, the judgment module includes: a first acquisition unit, used to acquire charging power fluctuation data, equivalent impedance change data, and surface thermal field distribution data based on the charging process data; a second acquisition unit, used to acquire a three-dimensional temperature field based on the surface thermal field distribution data, and acquire multiple core heating regions and core region temperature values ​​based on the three-dimensional temperature field; a third acquisition unit, used to acquire weight parameters of the core heating regions through principal component analysis, and acquire a comprehensive temperature value based on the core region temperature values ​​and the corresponding weight parameters; The fourth acquisition unit is used to acquire the instantaneous thermal shock intensity based on the charging power fluctuation data, and to acquire the impedance temperature rise coefficient based on the equivalent impedance change data; the fifth acquisition unit is used to acquire the temperature compensation value based on the instantaneous thermal shock intensity and the temperature compensation value, and to acquire the final temperature value based on the comprehensive temperature value; the judgment unit is used to determine whether the final temperature value is greater than the personalized temperature threshold: if the final temperature value is not greater than the personalized temperature threshold, it is determined that the cooling wireless charger does not need to cool; if the final temperature value is greater than the personalized temperature threshold, it is determined that the cooling wireless charger needs to cool. The fourth acquisition module is used to acquire the temperature control demand intensity value based on the charging process data if cooling is required. The fifth acquisition module is used to acquire the control parameter group of the cooling wireless charger, and acquire the temperature control adjustment parameters according to the control parameter group and the temperature control demand intensity value, wherein the temperature control adjustment parameters include charging power adjustment parameters, cooling chip current adjustment parameters and fan speed adjustment parameters; The adjustment module is used to adjust the cooling according to the temperature control parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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