Smart screen, smart screen environment temperature measurement method and program product

Abstract

The present disclosure provides a smart screen, a smart screen environment temperature measurement method and a program product, and relates to the field of smart screens. The smart screen environment temperature measurement method comprises the following steps: receiving a plurality of temperature values; determining an environment mode in which the smart screen is located according to the environment temperature value; calculating the reliability of each temperature value; determining the reliability adjustment factor of each temperature value according to the reliability of each temperature value; determining the environment mode adjustment factor of each temperature value according to the environment mode; calculating the first weight of each temperature value; weighting and fusing each temperature value based on the first weight to obtain a fused temperature value; compensating the fused temperature value according to a temperature compensation value to obtain a compensated temperature value; and outputting the compensated temperature value as a measured value of the environment temperature.

Description

Technical Field

[0001] This disclosure relates to the field of smart screens, specifically to a smart screen, a method for measuring the ambient temperature of a smart screen, and a software product. Background Technology

[0002] With the widespread application of smart display devices, the accuracy of ambient temperature sensing in smart screens (intelligent displays) directly affects their operational stability, power consumption management, and user experience. However, existing temperature measurement technologies face challenges such as severe internal heat source interference, variable external environments, and limited sensor placement under complex operating conditions. This makes ambient temperature sensor readings susceptible to external factors such as chip heating, PCB temperature rise, screen backlight heat, and direct sunlight from air conditioners, fans, and sunlight, causing measurement results to deviate from the true ambient temperature. Currently used fixed compensation or single-sensor linear correction methods suffer from poor adaptability and limited accuracy. Furthermore, they lack the ability to comprehensively model and dynamically optimize multi-source interference, making it difficult to meet the demands for high-precision, adaptive, and intelligent temperature sensing. Therefore, there is an urgent need for an ambient temperature compensation scheme that can integrate multi-sensor information to improve the temperature sensing accuracy and system reliability of smart screens in diverse usage scenarios. Summary of the Invention

[0003] This disclosure provides a smart screen, a method and program for measuring the ambient temperature of the smart screen.

[0004] According to one aspect of this disclosure, a method for measuring the ambient temperature of a smart screen is provided, comprising: receiving multiple temperature values, including a chip temperature value, a circuit board temperature value, and an ambient temperature value; determining the environmental mode of the smart screen based on the ambient temperature values; calculating the reliability of the chip temperature value, the circuit board temperature value, and the ambient temperature value; determining a reliability adjustment factor for each temperature value based on the reliability of each temperature value; determining an environmental mode adjustment factor for each temperature value based on the environmental mode; and calculating a first weight for each temperature value, wherein the first weight is the product of the reliability adjustment factor, the basic weight of the temperature value, and the environmental mode adjustment factor. Based on the first weight, the temperature values ​​are weighted and fused to obtain a fused temperature value; the fused temperature value is compensated according to the temperature compensation value to obtain a compensated temperature value, wherein the temperature compensation value includes a chip heating compensation value, a heat conduction compensation value, and an environmental compensation value. The chip heating compensation value is obtained by weighting and scaling the difference between the chip temperature value and the chip reference temperature value through a coupling coefficient and then superimposing a nonlinear attenuation factor; the heat conduction compensation value is determined based on a first-order thermal response model of steady-state temperature rise; the environmental compensation value is determined by calling a compensation model according to the environmental mode; and the compensated temperature value is output as a measurement of the ambient temperature.

[0005] According to one technical solution, by receiving multi-source temperature data such as chip temperature, circuit board temperature, and ambient temperature, the internal thermal state and external environmental changes of the smart screen can be comprehensively perceived. By determining the environmental mode of the smart screen based on the changing characteristics of the ambient temperature, the type of external interference can be intelligently identified and corresponding compensation strategies can be activated. By calculating the reliability of each temperature value and dynamically allocating weights by combining reliability adjustment factors and environmental mode adjustment factors, the robustness of data fusion can be improved, effectively suppressing the influence of abnormal sensors. By weighted fusion of multiple temperature values ​​based on a first weight, a fused temperature value closer to the real ambient temperature can be obtained. By introducing a comprehensive temperature compensation mechanism that includes chip heating compensation value, heat conduction compensation value, and environmental compensation value, internal heat source interference and external environmental disturbances can be systematically eliminated. Specifically, by weighting and scaling the difference between the chip temperature value and the chip reference temperature value using a coupling coefficient and then superimposing a nonlinear attenuation factor, the instantaneous impact and time delay characteristics of chip heating can be accurately modeled, improving the compensation accuracy under high-temperature loads. By determining the heat conduction compensation value based on a first-order thermal response model of steady-state temperature rise, the gradual process of heat transfer from the smart screen to environmental sensors can be dynamically simulated, avoiding overestimation or underestimation of temperature rise. By calling the corresponding compensation model according to the environmental mode to determine the environmental compensation value, measurement deviations caused by direct airflow from air conditioners, airflow disturbances, or solar heating can be specifically corrected. Finally, by performing multi-dimensional compensation on the fused temperature value and outputting the compensated temperature value, the accuracy of environmental temperature sensing and long-term operational stability of the smart screen under wide temperature ranges and varied usage scenarios can be significantly improved.

[0006] According to at least one embodiment of this disclosure, determining the environmental mode of the smart screen based on the ambient temperature value includes: calculating temperature characteristic values ​​based on multiple ambient temperature values, the temperature characteristic values ​​including: temperature change rate, temperature standard deviation, temperature mean, maximum instantaneous change rate, and dominant frequency; and determining the environmental mode of the smart screen based on the temperature characteristic values.

[0007] According to the technical solution of this embodiment, the environment mode of the smart screen can be accurately identified based on multi-dimensional characteristics such as temperature change rate, standard deviation, mean, maximum instantaneous change rate and dominant frequency, so as to realize intelligent judgment of complex working conditions such as directional airflow cooling, non-directional airflow disturbance, external radiation heating and steady-state thermal environment.

[0008] According to at least one embodiment of this disclosure, determining the environmental mode of the smart screen based on the temperature characteristic values ​​includes: determining that the smart screen is in a directional airflow cooling mode when the temperature change rate is less than a first change rate threshold, the temperature standard deviation is greater than a first fluctuation threshold, and the dominant frequency is within a first frequency range; determining that the smart screen is in a non-directional airflow disturbance mode when the temperature change rate is less than a second change rate threshold, the temperature standard deviation is greater than a second fluctuation threshold, and the maximum instantaneous change rate is greater than a first rate threshold; determining that the smart screen is in an external radiation heating mode when the temperature change rate is greater than a third change rate threshold, the maximum instantaneous change rate is greater than a second rate threshold, and the average temperature is greater than a first temperature threshold; and determining that the smart screen is in a steady-state thermal environment mode when the temperature standard deviation is less than a third fluctuation threshold, the absolute value of the temperature change rate is less than a third change rate threshold, and the average temperature is within a first temperature range.

[0009] According to the technical solution of this embodiment, by setting a combination of multi-dimensional temperature characteristics, it is possible to accurately distinguish typical operating conditions such as directional airflow cooling, non-directional airflow disturbance, external radiation heating, and steady-state thermal environment. When the temperature change rate is low, the fluctuation is large, and there is a specific dominant frequency, it can identify direct air conditioning blowing. When the temperature fluctuates rapidly and changes drastically in an instant, it can identify fan interference. When the temperature rises continuously and rapidly and is accompanied by a high average temperature, it can confirm direct sunlight. When the temperature is stable and the change is slight, it can be determined as an indoor steady-state environment. This enables highly reliable pattern recognition for complex thermal interference scenarios and provides an accurate basis for the dynamic switching of compensation strategies.

[0010] According to at least one embodiment of this disclosure, weighted fusion of each temperature value based on the first weight to obtain a fused temperature value includes: normalizing the first weight to obtain a second weight; smoothing the second weight to obtain a third weight; and weighted fusion of each temperature value based on the third weight to obtain a fused temperature value.

[0011] According to the technical solution of this embodiment, by normalizing and smoothing the first weight, the weight oscillation caused by sensor data mutation and environmental interference can be effectively suppressed, and the stable weighted fusion of chip temperature, circuit board temperature and ambient temperature can be achieved, thereby obtaining a fusion temperature value with high robustness and high accuracy.

[0012] According to at least one embodiment of this disclosure, the reliability of the temperature value is the product of the reliability of the numerical range of the temperature value, the reliability of the rate of change, the reliability of consistency, and the reliability of long-term drift.

[0013] According to the technical solution of this embodiment, the reliability of the sensor can be comprehensively reflected in four dimensions: amplitude rationality, dynamic response, short-term stability and long-term trend. It can effectively identify and suppress abnormal readings and improve the intelligence level and anti-interference capability of multi-sensor fusion.

[0014] According to at least one embodiment of this disclosure, the method for determining the reliability of the numerical range includes: determining the reliability of the numerical range as a when the temperature value is within the temperature range; determining the reliability of the numerical range as b when the temperature value is not within the temperature range and the difference between the temperature value and the boundary temperature of the temperature range is less than or equal to a difference threshold; and determining the reliability of the numerical range as c when the temperature value is not within the temperature range and the difference between the temperature value and the boundary temperature of the temperature range is greater than a difference threshold, wherein a>b>c.

[0015] According to the technical solution of this embodiment, by assigning the highest confidence level to readings within the normal working range, retaining partial confidence level for readings that slightly exceed the limit, and significantly reducing the weight of data that seriously exceed the limit, it is possible to achieve hierarchical identification of sensor abnormal or failure states, effectively improving the robustness and fault tolerance of the system under sensor failure or extreme interference.

[0016] According to at least one embodiment of this disclosure, the method for determining the rate of change reliability includes: reducing the rate of change reliability when the rate of change of the temperature value is greater than a rate of change threshold.

[0017] According to the technical solution of this embodiment, it is possible to effectively identify abnormal temperature rises or drops caused by sensor interference, poor contact or local thermal disturbances, suppress the impact of instantaneous and violent fluctuations on the fusion results, thereby improving the stability and noise resistance of the system in dynamic environments.

[0018] According to at least one embodiment of this disclosure, the process of determining the consistency reliability includes: calculating the standard deviation of n temperature values; and determining the consistency reliability based on the standard deviation using an exponential decay function.

[0019] According to the technical solution of this embodiment, it can effectively reflect the short-term stability of sensor readings, assign high confidence to data with small fluctuations and good repeatability, and significantly reduce the weight of abnormal sequences with large dispersion, thereby enhancing the system's ability to identify and suppress sensor noise and intermittent interference.

[0020] According to at least one embodiment of this disclosure, the process for determining the long-term drift reliability includes: calculating the average of m temperature values ​​to obtain a first average; calculating... l The second average is obtained by averaging the temperature values. l>m; calculate the difference between the first average value and the second average value; and determine the long-term drift reliability based on the difference using an exponential decay function.

[0021] According to the technical solution of this embodiment, by evaluating the long-term stability of the sensor based on the difference between short-term and long-term temperature averages and combining it with an exponential decay function, the reliability of slowly drifting readings can be reduced, while retaining high weight for stable data. This effectively suppresses measurement deviations caused by device aging or gradual environmental changes, and improves the reliability and accuracy of the system during continuous operation.

[0022] According to at least one embodiment of this disclosure, determining a reliability adjustment factor for each temperature value based on the reliability of each temperature value includes: reducing the reliability adjustment factor when the reliability decreases.

[0023] According to the technical solution of this embodiment, the reliability adjustment factor can be reduced accordingly as the reliability of each temperature value decreases, so that the influence of abnormal or unstable sensors is weakened as the data reliability decreases, thereby dynamically optimizing the weight allocation of multi-sensor fusion and improving the robustness and measurement accuracy of the system under complex working conditions.

[0024] According to at least one embodiment of this disclosure, the environmental modes include: a directional airflow cooling mode, a non-directional airflow disturbance mode, an external radiation heating mode, and a steady-state thermal environment mode. Determining the environmental mode adjustment factor for each temperature value according to the environmental mode includes: in the steady-state thermal environment mode, setting the environmental mode adjustment factor for each temperature value to an initial value; in the directional airflow cooling mode, increasing the environmental mode adjustment factor for the ambient temperature value and decreasing the environmental mode adjustment factor for the chip temperature value; in the non-directional airflow disturbance mode, increasing the environmental mode adjustment factor for the circuit board temperature value and decreasing the environmental mode adjustment factor for the ambient temperature value; and in the external radiation heating mode, increasing the environmental mode adjustment factor for the chip temperature value and decreasing the environmental mode adjustment factor for the ambient temperature value.

[0025] According to the technical solution of this embodiment, the environmental mode adjustment factor of each temperature value can be dynamically adjusted according to different environmental modes. In the steady-state thermal environment mode, the weight balance is maintained. In the directional airflow cooling mode, the contribution of the ambient temperature value is enhanced and the chip thermal interference is suppressed. In the non-directional airflow disturbance mode, the reference value of the circuit board temperature is improved and the weight of the ambient temperature which is greatly affected by airflow is reduced. In the external radiation heating mode, the responsiveness of the chip temperature is enhanced and the distortion of the environmental sensor caused by sunlight conduction is reduced. Thus, the intelligent adaptive adjustment of the compensation weight is realized, which significantly improves the accuracy of temperature measurement and the robustness of the system in complex environments.

[0026] According to at least one embodiment of this disclosure, the fusion temperature value is compensated based on the temperature compensation value, including: determining whether the smart screen is in a high temperature range and a low temperature range based on the fusion temperature value; when the smart screen is in a high temperature range, the temperature compensation value also includes a high temperature compensation value; when the smart screen is in a low temperature range, the temperature compensation value also includes a low temperature compensation value, the low temperature compensation value including: a low temperature correction item, a circuit temperature drift compensation item, and a humidity condensation compensation item.

[0027] According to the technical solution of this embodiment, the ability to correct the thermal saturation effect of the chip can be enhanced at high temperatures, and the measurement deviation caused by sensor response hysteresis, circuit parameter drift and moisture condensation can be corrected at low temperatures, thereby achieving high-precision temperature compensation in the entire temperature range and improving the perception accuracy and operational reliability of the smart screen in extreme environments.

[0028] According to at least one embodiment of this disclosure, the method further includes: filtering the compensated temperature value to obtain a filtered temperature value; and outputting the filtered temperature value as a measurement of the ambient temperature.

[0029] According to the technical solution of this embodiment, residual fluctuations and measurement noise can be effectively suppressed, and a smooth and stable filtered temperature value can be output, thereby improving the continuity, reliability and user experience of the readings.

[0030] According to at least one embodiment of this disclosure, the method further includes: performing Kalman filtering on the compensated temperature value to obtain a filtered temperature value; correcting the filtered temperature value to obtain a corrected temperature value; and outputting the corrected temperature value as a measurement of the ambient temperature.

[0031] According to the technical solution of this embodiment, not only can noise be effectively suppressed and dynamic response accuracy improved, but also residual system errors can be further eliminated through adaptive correction, thereby outputting high-precision and high-stability temperature values.

[0032] According to at least one embodiment of this disclosure, correcting a filtered temperature value to obtain a corrected temperature value includes: determining a learning rate used in the correction process based on the environmental mode; determining a correction term using a recursive update function based on the learning rate; and correcting the filtered temperature value based on the correction term to obtain the corrected temperature value.

[0033] According to the technical solution of this embodiment, the learning rate can be adaptively determined according to the environmental mode, and a dynamic correction term can be generated through a recursive update function to correct the filtered temperature value online, effectively compensating for model bias and residual errors caused by gradual environmental changes, thereby improving the system's self-learning ability and long-term measurement accuracy under different operating conditions.

[0034] According to another aspect of this disclosure, a smart screen is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to execute a smart screen ambient temperature measurement method according to any embodiment of this disclosure.

[0035] According to another aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement the smart screen ambient temperature measurement method of any embodiment of this disclosure.

[0036] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a smart screen ambient temperature measurement method according to any embodiment of this disclosure. Attached Figure Description

[0037] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0038] Figure 1 This is a schematic diagram illustrating an application scenario of a smart screen ambient temperature measurement method according to one embodiment of the present disclosure.

[0039] Figure 2 This is a flowchart illustrating a smart screen ambient temperature measurement method according to one embodiment of the present disclosure.

[0040] Figure 3 This is a flowchart illustrating a method for determining a thermal conductivity compensation value according to one embodiment of the present disclosure.

[0041] Figure 4 This is a flowchart illustrating a smart screen ambient temperature measurement method according to another embodiment of the present disclosure.

[0042] Figure 5 This is a flowchart illustrating a modified method according to one embodiment of the present disclosure.

[0043] Figure 6 This is a schematic block diagram of the structure of a smart screen ambient temperature measuring device according to one embodiment of the present disclosure.

[0044] Figure 7 This is a schematic structural block diagram of a smart screen using a processor-based hardware implementation according to one embodiment of the present disclosure. Detailed Implementation

[0045] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0046] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] Existing temperature measurement technologies for smart screen applications suffer from problems such as severe internal heat source interference, variable external environment, and limited sensor placement. This leads to distorted ambient temperature readings due to chip heating, circuit board temperature rise, and the influence of air conditioning, fans, and direct sunlight. Commonly used fixed compensation or single-sensor linear correction methods have poor adaptability and limited accuracy, lacking the ability to comprehensively model and dynamically optimize multi-source interference, making it difficult to achieve high-precision, adaptive ambient temperature sensing.

[0048] To this end, the present disclosure proposes the following technical solution, in which the chip, circuit board and ambient temperature values ​​are fused, and environmental pattern recognition and multi-dimensional reliability assessment are combined to dynamically weightedly fuse and obtain a high-confidence fused temperature. Furthermore, a comprehensive compensation mechanism for chip heating, heat conduction and environmental interference is introduced to effectively eliminate the influence of internal and external heat sources and significantly improve the accuracy, stability and adaptability of temperature measurement under complex working conditions.

[0049] To facilitate description and make the technical solutions of this disclosure easier to understand, the terminology of this disclosure will be explained before describing the technical solutions of this disclosure.

[0050] The environmental mode refers to the operating condition category identified based on the dynamic characteristics of the external thermal environment in which the smart screen is located. It is used to characterize whether the smart screen is subject to typical interferences such as direct air conditioning, fan disturbance, or sunlight.

[0051] The environmental model adjustment factor is a correction coefficient that dynamically adjusts the weight of each temperature value based on the currently identified environmental model.

[0052] Reliability refers to the degree of credibility of the sensor's output data.

[0053] The reliability adjustment factor maps the reliability of a sensor to an intermediate parameter in the weighting process, and is used to dynamically adjust the contribution of each sensor in data fusion.

[0054] Figure 1 A schematic diagram illustrating an application scenario of a smart screen ambient temperature measurement method according to one embodiment of this disclosure is shown. Figure 1The diagram shows a front view, side view, and rear view of the smart screen 100. Three types of temperature sensors are installed in the smart screen 100: a chip temperature sensor 200, a circuit board temperature sensor 300, and an ambient temperature and humidity sensor 400. The chip temperature sensor, installed inside the core processor (such as the CPU) 500, is used to collect the operating temperature of the core processor 500 in the smart screen 100. The circuit board temperature sensor 300, installed on the circuit board, is used to collect the temperature value of the circuit board in the smart screen 100. The ambient temperature and humidity sensor 400, installed near the ventilation opening 600 on the side of the smart screen 100, is used to collect the temperature of the environment in which the smart screen is located.

[0055] Figure 2 A schematic flowchart illustrating the overall process of a smart screen ambient temperature measurement method according to one embodiment of this disclosure is shown. Figure 2 The method shown includes steps S110 to S190.

[0056] In step S110, multiple temperature values ​​are received, including chip temperature value, circuit board temperature value and ambient temperature value.

[0057] The chip temperature value reflects the operating temperature of the smart screen's core processor and is used to assess the heating status of the main heat sources inside the smart screen and their conductive impact on ambient temperature measurement. For example, the chip temperature value is collected by a chip temperature sensor installed inside the core processor chip.

[0058] The circuit board temperature value reflects the overall temperature rise of the smart screen's circuit board, characterizing the comprehensive impact of the circuit system's heat generation on ambient temperature measurement. For example, the circuit board temperature value is collected by circuit board temperature sensors installed at key locations on the smart screen's circuit board. It should be noted that key locations are those that can represent the overall thermal state or key heat source areas. These locations should possess characteristics such as high thermal sensitivity, strong representativeness, and distance from local interference to ensure that the collected temperature data not only reflects the system-level temperature rise but can also be used for subsequent temperature compensation algorithms. As one possible implementation, key locations may be: near the power management module, near the main control chip (SOC), near the screen driver circuit, or in the central area of ​​the motherboard.

[0059] Ambient temperature values ​​reflect the thermal state of the actual environment in which the smart screen is located and are a core reference quantity for measuring the ambient temperature of the smart screen. For example, ambient temperature values ​​are collected using temperature and humidity sensors installed near the side ventilation openings of the smart screen. This method effectively obtains temperature data that closely approximates the actual external environment, reducing the impact of direct radiation from internal heat sources within the smart screen. Simultaneously, the ventilation airflow can be utilized to improve response speed, thereby enhancing the accuracy, representativeness, and dynamic tracking capabilities of the ambient temperature measurement.

[0060] In step S120, the environmental mode of the smart screen is determined based on the ambient temperature value. For example, four environmental modes are set: directional airflow cooling mode, non-directional airflow disturbance mode, external radiant heating mode, and steady-state thermal environment mode. The directional airflow cooling mode corresponds to scenarios where directional cold air is directly blown by devices such as air conditioners or evaporative coolers. Its temperature changes slowly but continuously, with large overall fluctuations, and exhibits low-frequency characteristics (0.01–0.05Hz) due to the periodic airflow. The non-directional airflow disturbance mode corresponds to scenarios with unstable airflow interference such as fans or vent turbulence. The airflow is irregular and frequently disturbed, leading to rapid temperature fluctuations and drastic instantaneous changes. The external radiant heating mode corresponds to scenarios where sunlight or strong heat source radiation causes the smart screen surface to heat up rapidly. The temperature rises rapidly, with a high instantaneous change rate, and the overall average temperature increases significantly (>35℃). The steady-state thermal environment mode corresponds to indoor environments with no significant thermal interference and stable temperature. Temperature fluctuations are small and gradual, falling within the typical room temperature range (20–30℃).

[0061] As one possible implementation, determining the environmental mode of the smart screen based on ambient temperature values ​​includes: calculating temperature characteristic values ​​based on multiple ambient temperature values; and determining the environmental mode of the smart screen based on the temperature characteristic values.

[0062] Temperature feature values ​​are key statistical parameters extracted from environmental temperature sequences through time-domain and frequency-domain analysis. They are used to characterize the dynamic characteristics of temperature changes, such as trends, fluctuations, extreme values, and periodicity, providing a quantitative basis for environmental pattern recognition.

[0063] For example, the temperature characteristic value includes: the rate of temperature change. rate Temperature standard deviation Average temperature T mean Maximum instantaneous rate of change (max) rate and dominant frequency f dominant .

[0064] The rate of temperature change represents the average speed at which temperature changes per unit time, reflecting the strength of a sustained upward or downward trend in temperature. The formula for calculating the rate of temperature change is:

[0065]

[0066] in, Indicates the first i Temperature values ​​at each sampling point Indicates the first i - j Temperature values ​​at each sampling point i and j Both represent the serial numbers of the sampling points. This indicates the time interval between two adjacent sampling points.

[0067] The standard deviation of temperature reflects the dispersion of temperature fluctuations over a period of time; a larger value indicates greater temperature instability. The temperature mean represents the average temperature level during the monitoring period and is used to determine the overall thermal state of the environment in which the smart screen is located. The formula for calculating the standard deviation of temperature is:

[0068]

[0069] Where N represents the number of sampling points within 1 minute, Indicates the first i Temperature values ​​at each sampling point.

[0070] The maximum instantaneous rate of change describes the maximum temperature change between adjacent sampling points and is used to capture sudden temperature jumps. The formula for calculating the maximum instantaneous rate of change is:

[0071]

[0072] Where max represents finding the maximum value. This indicates finding the absolute value. Indicates the first i +1 temperature value at sampling point.

[0073] The dominant frequency is obtained through frequency domain analysis and represents the most periodic frequency component in the temperature change. To solve for the dominant frequency, a Fast Fourier Transform (FFT) is first performed on the temperature series to convert the time-domain signal into a frequency-domain signal, obtaining the complex representation of each frequency component. Next, the power spectral density (PSD) of the frequency-domain signal is calculated, which is the square of the amplitude of each frequency component. Finally, the frequency component with the highest energy in the power spectrum is identified as the dominant frequency.

[0074] Using temperature change rate, temperature standard deviation, temperature mean, maximum instantaneous change rate, and dominant frequency as temperature feature values, this method comprehensively characterizes the dynamic properties of ambient temperature from five dimensions: trend, fluctuation, horizontal benchmark, transient response, and periodicity, effectively distinguishing different thermal interference patterns. The synergistic effect of these five factors significantly improves the accuracy, robustness, and anti-interference capability of environmental pattern recognition, providing a reliable basis for adaptive temperature compensation.

[0075] In one example, determining the environmental mode of the smart screen based on temperature characteristic values ​​includes: determining that the smart screen is in a directional airflow cooling mode when the temperature change rate is less than a first change rate threshold, the temperature standard deviation is greater than a first fluctuation threshold, and the dominant frequency is within a first frequency range. Determining that the smart screen is in a non-directional airflow disturbance mode when the temperature change rate is less than a second change rate threshold, the temperature standard deviation is greater than a second fluctuation threshold, and the maximum instantaneous change rate is greater than a first rate threshold. Determining that the smart screen is in an external radiation heating mode when the temperature change rate is greater than a third change rate threshold, the maximum instantaneous change rate is greater than a second rate threshold, and the average temperature is greater than a first temperature threshold. And determining that the smart screen is in a steady-state thermal environment mode when the temperature standard deviation is less than a third fluctuation threshold, the absolute value of the temperature change rate is less than a third change rate threshold, and the average temperature is within a first temperature range. For example, Table 1 illustrates the judgment conditions for determining the environmental mode of the smart screen based on temperature characteristic values.

[0076] Table 1

[0077]

[0078] Based on Table 1, the first rate of change threshold is -0.5℃ / min, the first fluctuation threshold is 1.5℃, the first frequency range is (0.01Hz, 0.5Hz), the second rate of change threshold is -0.5℃ / min, the second fluctuation threshold is 1.5℃, the first rate threshold is 1.0℃ / min, the third rate of change threshold is 0.8℃ / min, the second rate threshold is 2.0℃ / min, the first temperature threshold is 35℃, the third fluctuation threshold is 0.5℃, the third rate of change threshold is 0.1℃ / min, and the first temperature range is [20℃, 30℃].

[0079] In step S130, the reliability of the chip temperature value, the circuit board temperature value, and the ambient temperature value is calculated. The reliability of the temperature value is the product of the reliability of the temperature value's numerical range, the reliability of its rate of change, the reliability of its consistency, and the reliability of its long-term drift.

[0080] Numerical range reliability reflects the degree of confidence in whether a temperature value is within the reasonable operating range of the sensor. It can effectively identify abnormal situations such as sensor failure, disconnection, or severe offset, preventing extreme erroneous data from affecting the fusion results. As one possible implementation, the method for determining numerical range reliability includes: determining the numerical range reliability as 'a' when the temperature value is within the temperature range; determining the numerical range reliability as 'b' when the temperature value is not within the temperature range and the difference between it and the boundary temperature of the temperature range is less than or equal to a difference threshold; and determining the numerical range reliability as 'c' when the temperature value is not within the temperature range and the difference between it and the boundary temperature of the temperature range is greater than a difference threshold, where a>b>c. For example, the temperature range of the chip temperature value is 30℃ to 100℃, the temperature range of the circuit board temperature value is 15℃ to 65℃, and the temperature range of the ambient temperature value is -10℃ to 50℃, with a=1.0, b=0.5, and c=0.1.

[0081] Rate of change reliability is used to assess the drasticness of temperature changes and can improve the system's immunity to dynamic noise. As one possible implementation, the method for determining rate of change reliability includes reducing the reliability when the rate of change of the temperature value exceeds a rate of change threshold. For example, the rate of change threshold is set to 5°C / min.

[0082] Consistency reliability measures the measurement stability of a sensor over a short period, assigning high confidence to data with small reading fluctuations and good repeatability, and reducing the impact of random noise on weight allocation. As one possible implementation, the process of determining consistency reliability includes: calculating the standard deviation of n temperature values; and determining the consistency reliability R based on the standard deviation using an exponential decay function. The n temperature values ​​are the most recent n received temperature values; exemplarily, n=10. The consistency reliability R is determined using an exponential decay function. consistency The calculation formula is: R consistency =exp(-σ recent / 3), where σ recent This represents the standard deviation of n temperature values.

[0083] Long-term drift reliability is used to detect the slow drift trend of a sensor over time, identifying performance degradation caused by aging, contamination, or temperature drift. As one possible implementation, the process for determining long-term drift reliability includes: calculating the average of m temperature values ​​to obtain a first average; calculating... l The second average is obtained by averaging the temperature values. l >m; calculate the difference between the first average and the second average; and based on the difference, determine the long-term drift reliability using an exponential decay function. The above m temperature values ​​are the temperature values ​​from the most recent m received temperatures. l The temperature value is in the historical processl The temperature value received at the first time. The long-term drift reliability R is determined using an exponential decay function. drift The calculation formula is: R drift =exp(-|T1 avg –T2 avg | / 2), where T1 avg T2 represents the average of m temperature values. avg express l The average of several temperature values.

[0084] In step S140, the reliability adjustment factor for each temperature value is determined based on the reliability of each temperature value.

[0085] As one possible implementation, a reliability adjustment factor is determined for each temperature value based on the reliability of each temperature value, including reducing the reliability adjustment factor when the reliability decreases. This method effectively suppresses the impact of abnormal or unstable data on the fusion results, improving system robustness and temperature measurement accuracy.

[0086] For example, when the reliability is greater than 0.9, the reliability adjustment factor is determined to be 2.0. When the reliability is greater than 0.7 and less than or equal to 0.9, the reliability adjustment factor is determined to be 1.5. When the reliability is greater than 0.5 and less than or equal to 0.7, the reliability adjustment factor is determined to be 1.0. When the reliability is greater than 0.3 and less than or equal to 0.5, the reliability adjustment factor is determined to be 0.5. When the reliability is less than or equal to 0.3, the reliability adjustment factor is determined to be 0.2.

[0087] In step S150, the environmental mode adjustment factor for each temperature value is determined according to the environmental mode.

[0088] As one possible implementation, determining the environmental mode adjustment factor for each temperature value based on the environmental mode includes: setting the environmental mode adjustment factor for each temperature value to an initial value under a steady-state thermal environment mode; increasing the environmental mode adjustment factor for the ambient temperature value and decreasing the environmental mode adjustment factor for the chip temperature value under a directional airflow cooling mode; increasing the environmental mode adjustment factor for the circuit board temperature value and decreasing the environmental mode adjustment factor for the ambient temperature value under a non-directional airflow disturbance mode; and increasing the environmental mode adjustment factor for the chip temperature value and decreasing the environmental mode adjustment factor for the ambient temperature value under an external radiation heating mode. This method can suppress the contribution of interfered sensors and enhance the weight of reliable sensors, significantly improving the accuracy of temperature fusion and the system's adaptive capability under complex environments.

[0089] For example, in steady-state thermal environment mode, the environmental mode adjustment factor for each temperature value is set to an initial value of 1.0. In directional airflow cooling mode, the environmental mode adjustment factor for ambient temperature value is increased to 1.5, and the environmental mode adjustment factor for chip temperature value is decreased to 0.8. In non-directional airflow disturbance mode, the environmental mode adjustment factor for circuit board temperature value is increased to 1.2, and the environmental mode adjustment factor for ambient temperature value is decreased to 0.7. In external radiation heating mode, the environmental mode adjustment factor for chip temperature value is increased to 1.3, and the environmental mode adjustment factor for ambient temperature value is decreased to 0.8.

[0090] In step S160, the first weight of each temperature value is calculated. The first weight is the product of the reliability adjustment factor, the basic weight of the temperature value, and the environmental mode adjustment factor.

[0091] The basic weight of the temperature value is an initial weight that is preset based on the relative reliability of each sensor in measuring ambient temperature under normal operating conditions. It is used to reasonably allocate the fusion ratio of chip temperature value, circuit board temperature value and ambient temperature value under interference-free conditions.

[0092] In step S170, the temperature values ​​are weighted and fused based on the first weight to obtain the fused temperature value.

[0093] As one possible implementation, weighted fusion involves multiplying each temperature value by its corresponding first weight and then summing the results. According to a further implementation, weighted fusion involves multiplying each temperature value by its corresponding first weight and a confidence level, and then summing the results. The confidence level is a static or semi-static evaluation parameter reflecting the sensor's hardware performance, the rationality of its installation location, and the stability of its historical performance. Its value typically ranges from 0.8 to 1.0, and it characterizes the sensor's ability to provide accurate data under ideal conditions. For example, the confidence level of a temperature and humidity sensor is set to 1.0, while the confidence level of a chip temperature sensor, which is significantly affected by heat sources, can be set to 0.85. By introducing a confidence level, the system can effectively distinguish the inherent performance differences between different sensors, preventing low-confidence sensors from dominating the fusion result when their weights suddenly increase, thus achieving more refined and intelligent multi-source temperature fusion.

[0094] As a further implementation, the process of weighting and fusing each temperature value based on the first weight to obtain the fused temperature value includes: normalizing the first weight to obtain the second weight; smoothing the second weight to obtain the third weight; and weighting and fusing each temperature value based on the third weight to obtain the fused temperature value.

[0095] Normalization involves dividing the first weight of each temperature value by the sum of the first weights of all temperature values ​​to obtain the second weight, ensuring that the sum of all second weights is 1. This process ensures the mathematical validity of the weighted fusion calculation, avoids excessive dominance of any one sensor due to biases in the absolute value of the weights, and thus achieves a relatively fair distribution of contributions from each sensor.

[0096] Smoothing refers to the exponentially weighted average of the normalized second weight at time t and the third weight obtained at time t-1 to obtain the third weight at time t. The calculation formula is as follows:

[0097] W3 t =λ×W2 t +(1-λ)×W3 t-1

[0098] Where λ represents the smoothing factor, typically taken as 0.3, W3 t W3 represents the third weight at time t. t-1 W2 represents the third weight at time t-1. t This represents the second weight at time t. This process suppresses drastic changes in weights within a short period, preventing oscillations in the fusion results caused by sudden environmental changes or instantaneous sensor fluctuations, thus making weight changes more continuous and stable.

[0099] Normalization ensures a reasonable relative proportion of fusion weights, improving the accuracy of multi-sensor data fusion. Smoothing effectively reduces the system's sensitivity to noise and transient interference, enhancing the continuity and robustness of the fusion results, thus obtaining more stable and reliable fused temperature values ​​suitable for high-precision temperature measurement in complex dynamic environments.

[0100] In step S180, the fusion temperature value is compensated according to the temperature compensation value to obtain the compensated temperature value. The temperature compensation value includes the chip heat generation compensation value, the thermal conduction compensation value, and the environmental compensation value.

[0101] The chip heat compensation value is obtained by weighting and scaling the difference between the chip temperature value and the chip reference temperature value using a coupling coefficient, and then superimposing a nonlinear attenuation factor. The calculation formula is as follows: ,in, This indicates the chip's heat compensation value. Indicates the chip temperature value. α 1 represents the coupling coefficient. Indicates the chip's reference temperature value. Indicates the attenuation coefficient. This represents the nonlinear decay factor. For example, α 1 = 0.12 =25℃, β =0.02s-1 .

[0102] The heat conduction compensation value is determined based on a first-order thermal response model of steady-state temperature rise. Figure 3 A flowchart illustrating a method for determining thermal conductivity compensation values ​​according to an embodiment of this disclosure is shown. Figure 3 The method shown includes steps S210 to S250.

[0103] In step S201, the total power consumption of the smart screen is calculated. Based on the operating status of the smart screen, the power consumption of the main heat sources is acquired or estimated in real time, including base power consumption, core processor power consumption, and screen display power consumption. The sum of these three constitutes the total power consumption of the smart screen. P total .

[0104] The base power consumption is usually determined based on the model of the smart screen.

[0105] The power consumption of the core processor has a non-linear relationship with the usage rate of the smart screen. The specific calculation formula is as follows:

[0106]

[0107] in, Indicates the core processor power consumption. This represents the baseline power consumption when the core processor is fully loaded. This indicates the current CPU utilization (0-100%), and the exponent of 1.5 reflects the non-linear characteristics of voltage dynamic increase under high load. This represents the frequency scaling factor (typically 0.8-1.2). This indicates the power consumption of the core processor when it is idle.

[0108] Screen power consumption is directly proportional to brightness. The specific calculation formula is as follows:

[0109]

[0110] in, This indicates the power consumption of the screen display. This indicates the maximum power consumption of the smart screen's backlight. Indicates screen brightness (0-100%). This indicates the backlight efficiency factor (e.g., 0.85). This indicates the fixed power consumption of the smart screen's driving circuit.

[0111] In step S202, the equivalent resistance is determined. Based on the thermal conductivity characteristics of the smart screen's structural materials (such as circuit boards, casings, thermally conductive adhesives, etc.), the equivalent thermal resistance from the heat source to the location of the environmental sensor is obtained through experimental calibration or simulation. R thermal(The unit is ℃ / W, which represents the temperature rise caused by each watt of power consumption).

[0112] For example, the determination of equivalent thermal resistance is based on the physical mechanism of heat conduction, combined with the relationship between the structural parameters and heat flow of the actual smart screen, and is accomplished through a combination of theoretical modeling and experimental calibration. Specifically, a basic heat transfer model is first established based on Fourier's law of heat conduction: Q = k 1× A ×Δ T / d .in, Q Indicates heat flow (unit: W). k 1 represents the overall thermal conductivity of the material (unit: W / m·K). A Indicates the effective heat transfer area (unit: m²). d Δ represents the equivalent heat transfer distance (unit: m). T This represents the temperature difference (in Kelvin) between the heat source and the sensor. The equivalent thermal resistance is then determined based on the heat flow. In one example, the overall thermal conductivity of the material is... k 1 = 2.5 W / m·K (considering the combined thermal resistance of the circuit board, casing, air, etc.), effective heat transfer area A =0.02m² (based on the geometric dimensions of the smart screen), equivalent heat transfer distance d =0.05m (average distance from temperature and humidity sensors to each heat source), equivalent thermal resistance R thermal =2.5℃ / W.

[0113] In step S203, the steady-state temperature rise is calculated. The steady-state temperature rise represents the maximum additional temperature rise if all heat is conducted to the sensor location and thermal equilibrium is reached. Using the product of total power consumption and equivalent thermal resistance, the theoretical temperature rise (i.e., steady-state temperature rise) that the smart screen eventually stabilizes under continuous operation is obtained: Δ T steady = P total × R thermal ,in, P total Δ represents the total power consumption of the smart screen. T steady This indicates a steady-state temperature rise.

[0114] In step S204, a first-order thermal response model (first-order exponential response model) is constructed. Considering the time delay and inertia of heat transfer, a first-order thermal response model is used to simulate the dynamic change of temperature rise over time: Δ T thermal ( t )=Δ T steady ×(1- e−t / τ ), where Δ T thermal ( t The ) represents the thermal conductivity compensation value. τ = R thermal × C thermal , τ Represents the thermal time constant. C thermal This indicates the equivalent heat capacity of the device. For example, C thermal =150J / ℃, τ=2.5×150=375 seconds (approximately 6.25 minutes).

[0115] In step S205, the heat conduction compensation value is output. The current running time is then set. t Substituting into the first-order thermal response model, the real-time heat conduction compensation value Δ is calculated. T thermal ( t This value gradually approaches a steady-state temperature rise from 0 over time, and is used to subsequently correct the fusion temperature value in reverse.

[0116] The environmental compensation value is determined by calling the corresponding compensation model based on the environmental model. As one possible implementation, a mapping table between environmental models and environmental compensation values ​​can be pre-built, and then the environmental compensation value corresponding to the environmental model can be found by looking up the table.

[0117] In step S190, the compensated temperature value is output as the measured value of the ambient temperature.

[0118] Based on the implementation of steps S110 to S190 above, through multi-source temperature sensing, dynamic weight fusion and environmental pattern-based composite compensation mechanism, internal heat source interference and external environmental disturbances can be effectively eliminated, significantly improving the accuracy, stability and adaptability of the smart screen in environmental temperature measurement under complex working conditions.

[0119] Please combine Figure 4 As one possible implementation, step S190 may include steps S191, S192 and S193.

[0120] In step S191, the compensated temperature value is filtered to obtain a filtered temperature value. As one possible implementation, the filtering process employs an adaptive Kalman filter. The adaptive Kalman filter uses a recursive approach to optimally estimate the temperature state, alternating between prediction and update steps. By combining the system's dynamic model with actual observation data, it continuously outputs smooth and accurate filtered temperature values.

[0121] Specifically, the compensated temperature value is used as the observation input for the Kalman filter. The system state vector is defined as a two-dimensional vector X = [T, dT / dt]ᵀ, where... T dT / dt represents the current temperature estimate, and dT / dt represents the rate of temperature change, used to characterize the dynamic trend of temperature. The system state is predicted using the state transition equation: .in, k Indicates time, This represents the state estimate. Indicates at time k Based on the time k The state estimate predicted by all observation information of -1, Indicates at time k -1, based on time... k The optimal state estimate obtained from all observations of -1, where F represents the state transition matrix, typically determined based on the sampling interval Δ. t Designed as follows: This indicates that the temperature evolves linearly with respect to the rate of change. The observation equation is: Z( k )=H×X( k )+v( k ), where Z( k ) represents the compensated temperature value at the current moment, H=

[10] represents the observation matrix, v( k () indicates measurement noise. After completing the state prediction, the prediction results are updated using the actual observations: ,in, This represents the information, specifically the difference between the observed and predicted values. This represents the Kalman gain, calculated from the prediction error covariance and noise statistics.

[0122] In the aforementioned adaptive Kalman filtering process, the process noise covariance is dynamically adjusted by analyzing the statistical characteristics of the innovation in real time. Q and measurement noise covariance R When the information remains excessively large, the system automatically increases the process noise covariance. Q Or measure noise covariance R This enhances the filter's response to sudden changes. When the information is stable, the noise assumption is reduced, improving the smoothness of the filter. This adaptive mechanism enables the filter to adapt to drastic environmental changes such as air conditioning start-up and shutdown, and sudden sunlight, avoiding the hysteresis or oscillation problems caused by fixed noise parameters in traditional Kalman filters.

[0123] In step S192, the filtered temperature value is corrected to obtain the corrected temperature value. This correction further eliminates residual errors and improves long-term measurement accuracy.

[0124] As one possible implementation, the correction process is based on an error feedback mechanism and an online learning algorithm to achieve dynamic optimization of the correction parameters. Accordingly, please combine... Figure 5 Step S192 may include steps S1921 to S1923, which realizes automatic optimization of temperature compensation accuracy under different usage scenarios, significantly improving the smart screen's ability to sense ambient temperature in complex environments and the level of product intelligence.

[0125] In step S1921, the learning rate used in the correction process is determined based on the environmental mode. Since temperature change characteristics differ significantly under different environmental modes, a fixed learning rate can easily lead to overshooting or slow response. Therefore, the learning rate is dynamically set during the correction process based on the currently identified environmental mode of the smart screen. α It can implement differentiated response strategies under different environmental modes.

[0126] As one possible implementation, different learning rate values ​​or adjustment strategies are configured for different environmental modes. In a steady-state thermal environment mode, the ambient temperature is stable, and the error mainly comes from zero-point drift or long-term temperature drift of the sensor. In this case, a smaller learning rate (e.g., ...) is used. α =0.01), achieving slow convergence and avoiding erroneous corrections caused by noise interference. In directional airflow cooling mode, the temperature continuously decreases and changes predictably, so appropriately increasing the learning rate (e.g.) α =0.03), accelerating the tracking capability for negative deviations. In non-directional airflow disturbance mode, temperature fluctuations are frequent but without a trend; reducing the learning rate or pausing learning prevents instantaneous disturbances from being misjudged as system deviations. In external radiation heating mode, the smart screen surface heats up rapidly and is conducted to the sensor; enabling a moderately high learning rate (e.g., =0.03) accelerates the tracking capability for negative deviations. α =0.04), and collaborative correction is performed using a heat conduction model. Optionally, the learning rate can be further weighted and adjusted based on the confidence level of environmental pattern recognition. When the confidence level of environmental pattern recognition is high, a larger update range is allowed. When the confidence level of environmental pattern recognition is low, conservative updates or stopping learning are adopted.

[0127] In step S1922, the correction term Δ is determined by a recursive update function based on the learning rate. T adaptive .

[0128] As one possible implementation, the following recursive update function is executed: Δ T adaptive ( k )=Δ T adaptive ( k -1)+ α × e ( k ),in, e (k )= T reference - T filtered ( k ) indicates the current time ( k Temperature difference at time (time) T reference Indicates reference temperature. T filtered ( k ) indicates the current time ( k The filtered temperature value at time Δ T adaptive ( k -1) indicates the previous time ( k The correction term at time -1, α This represents the learning rate (which can be initially set to 0). The reference temperature can be from an external high-precision sensor, user calibration input, or a multi-device collaborative averaging result. The initial value of the correction term is Δ. T adaptive (0) can be set to 0. This recursive update function supports online real-time updates without storing a large amount of historical data, and can be combined with the learning rate to achieve fine-tuning of the response intensity to different environments.

[0129] In step S1923, the filtered temperature value is corrected according to the correction term to obtain the corrected temperature value. The obtained correction term is then superimposed on the filtered temperature value to complete the final accuracy correction and obtain the corrected temperature value.

[0130] Optionally, one or more of the following constraints can be applied to the correction term before superposition. Constraint 1: Amplitude limitation, i.e., the absolute value of the correction term is less than or equal to a set correction term threshold (e.g., 3℃) to avoid overcorrection. Constraint 2: Single-step rate of change limitation, i.e., the difference between the correction values ​​of two adjacent moments (e.g., the current moment and the previous moment) is less than or equal to a set difference threshold. Constraint 3: Correction is stopped under abnormal operating conditions (e.g., low confidence in environmental pattern recognition, loss or invalidity of reference temperature signal, temperature sudden change exceeding the physically reasonable range, during smart screen start-up / shutdown or power fluctuation, etc.) to prevent erroneous correction.

[0131] As a further implementation, the above correction process can be performed based on multiple time scales. For example, it can be divided into three time scales: short-term (seconds), medium-term (minutes), and long-term (hours or days). At the short-term time scale, a recursive update mechanism with a high learning rate and short memory window is used to quickly introduce small corrections within seconds, which then decay rapidly to avoid long-term effects. At the medium-term time scale, the learning rate and correction direction are dynamically adjusted based on the environmental pattern recognition results, continuously accumulating moderate correction terms during pattern switching for several minutes to tens of minutes. At the long-term time scale, an extremely low learning rate (e.g., ...) is used. α =0.001), and the long memory window accumulation mechanism, which slowly updates the long-term correction baseline only under high confidence conditions (such as being in a steady-state thermal environment mode for several consecutive days).

[0132] In step S193, the corrected temperature value is output as the measured ambient temperature. By performing adaptive correction based on error feedback on the filtered temperature value, residual system deviations can be dynamically compensated, eliminating long-term errors caused by environmental changes, device aging, and other factors. The final output corrected temperature value combines high precision, strong robustness, and good dynamic response characteristics, significantly improving the accuracy and reliability of ambient temperature measurement for the smart screen under complex and variable operating conditions.

[0133] According to a further embodiment of this disclosure, a method for measuring the ambient temperature of a smart screen is also provided. The method includes, in the process of compensating the fused temperature value based on the temperature compensation value, determining whether the smart screen is in a high-temperature range (e.g., greater than 35°C) or a low-temperature range (less than 10°C) based on the fused temperature value. When the smart screen is in a high-temperature range, the exponent used in the core processor power consumption calculation formula is increased (e.g., the exponent is increased from 1.5 to 2.2), the overall thermal conductivity of the material is increased (considering the decrease in heat dissipation efficiency at high temperatures), and a high-temperature compensation value (linear correction term) is added to the temperature compensation value. When the smart screen is in a low-temperature range, the proportion of the chip heat dissipation compensation value is reduced (e.g., ×0.8), the proportion of the heat conduction compensation value is increased (e.g., ×1.2), and a low-temperature compensation value is added to the temperature compensation value. Exemplarily, the low-temperature compensation value includes: a low-temperature correction term, a circuit temperature drift compensation term, and a humidity condensation compensation term. The low-temperature correction term, based on the nonlinear characteristics of the sensor in the low-temperature region, uses a power function-based correction formula to dynamically correct the sensor's temperature drift. The circuit temperature drift compensation item monitors the circuit board temperature and models the accuracy drift of the analog-to-digital converter (ADC), reference voltage offset, and operational amplifier zero-point drift, respectively, and converts them into equivalent temperature errors for compensation. The humidity condensation compensation item combines ambient humidity data, uses the Magnus formula to calculate the dew point temperature, assesses the risk of condensation, and introduces a graded compensation factor based on the coupling relationship between temperature and humidity to correct the impact of changes in heat transfer characteristics on sensor readings under high humidity conditions. At the same time, it activates early warning and protection mechanisms when approaching condensation conditions to ensure the accuracy of temperature measurement and the reliability of the system under low temperature and high humidity conditions.

[0134] Based on the above-described embodiments of this disclosure, comparative tests were conducted with traditional methods under different application scenarios, and the results are shown in Table 2. The test results in various application scenarios demonstrate that the method of this disclosure can significantly reduce ambient temperature measurement errors, with an improvement of 72%–80% compared to traditional methods, effectively enhancing the temperature measurement accuracy and system reliability of smart screens under complex operating conditions.

[0135] Table 2

[0136]

[0137] According to any of the above embodiments, this disclosure also provides a smart screen ambient temperature measuring device 700. Figure 6 This is a schematic block diagram of a smart screen ambient temperature measuring device 700 according to one embodiment of this disclosure. Figure 6 As shown, the smart screen ambient temperature measurement device 700 includes a temperature receiving module 710, an environmental pattern recognition module 720, a temperature fusion module 730, a temperature compensation module 740, and a temperature output module 750.

[0138] The temperature receiving module 710 receives multiple temperature values, including chip temperature, circuit board temperature, and ambient temperature. The environmental pattern recognition module 720 determines the environmental mode of the smart screen based on the ambient temperature values. The temperature fusion module 730 calculates the reliability of the chip temperature, circuit board temperature, and ambient temperature values. Based on the reliability of each temperature value, it determines a reliability adjustment factor. Based on the environmental mode, it determines an environmental mode adjustment factor for each temperature value. It calculates a first weight for each temperature value, which is the product of the reliability adjustment factor, the base weight of the temperature value, and the environmental mode adjustment factor. Based on the first weight, it performs weighted fusion of the temperature values ​​to obtain a fused temperature value. The temperature compensation module 740 compensates the fused temperature value based on the temperature compensation value to obtain a compensated temperature value. The temperature compensation value includes a chip heating compensation value, a heat conduction compensation value, and an environmental compensation value. Specifically, the chip heating compensation value is obtained by weighting and scaling the difference between the chip temperature value and the chip reference temperature value using a coupling coefficient, and then superimposing a nonlinear attenuation factor. The heat conduction compensation value is determined based on a first-order thermal response model of steady-state temperature rise. The environmental compensation value is determined by calling the compensation model based on the environmental model. The temperature output module 750 is used to output the compensated temperature value as the measured value of the ambient temperature.

[0139] According to a further embodiment of this disclosure, a smart screen is also provided. Figure 7This diagram illustrates a schematic block diagram of a smart screen using a processor-based hardware implementation according to an embodiment of the present disclosure. The hardware structure of the smart screen can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the hardware. Bus 1100 connects various circuits including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400 such as peripheral devices, voltage regulators, power management circuits, external antennas, etc. Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one connecting line is used in this figure, but this does not indicate that there is only one bus or one type of bus. The memory 1300 stores a computer program. When the processor 1200 executes the computer program, the processor 1200 can perform the following processes: Receive multiple temperature values, including chip temperature, circuit board temperature, and ambient temperature. Determine the environmental mode of the smart screen based on the ambient temperature value. Calculate the reliability of the chip temperature, circuit board temperature, and ambient temperature value. Determine the reliability adjustment factor for each temperature value based on its reliability. Determine the environmental mode adjustment factor for each temperature value based on the environmental mode. Calculate the first weight for each temperature value, which is the product of the reliability adjustment factor, the base weight of the temperature value, and the environmental mode adjustment factor. Perform weighted fusion on the temperature values ​​based on the first weight to obtain a fused temperature value. Compensate the fused temperature value based on the temperature compensation value to obtain a compensated temperature value. The temperature compensation value includes a chip heating compensation value, a heat conduction compensation value, and an environmental compensation value. Specifically, the chip heating compensation value is obtained by weighting and scaling the difference between the chip temperature value and the chip reference temperature value using a coupling coefficient, and then superimposing a nonlinear attenuation factor. The heat conduction compensation value is determined based on a first-order thermal response model of steady-state temperature rise. The environmental compensation value is determined by calling the compensation model based on the environmental pattern. The compensated temperature value is then output as the measured value of the ambient temperature.

[0140] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0141] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0142] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0143] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0148] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for measuring ambient temperature on a smart screen, characterized in that, include: Receive multiple temperature values, including chip temperature value, circuit board temperature value and ambient temperature value; The ambient temperature value is used to determine the environment mode of the smart screen. Calculate the reliability of chip temperature values, circuit board temperature values, and ambient temperature values; Determine the reliability adjustment factor for each temperature value based on the reliability of each temperature value; Based on the environmental model, determine the environmental model adjustment factor for each temperature value; Calculate the first weight of each temperature value, where the first weight is the product of the reliability adjustment factor, the basic weight of the temperature value, and the environmental mode adjustment factor. Based on the first weight, the temperature values ​​are weighted and fused to obtain the fused temperature value; The fusion temperature value is compensated based on the temperature compensation value to obtain the compensated temperature value. The temperature compensation value includes chip heating compensation value, heat conduction compensation value, and environmental compensation value. Specifically, the chip heating compensation value is obtained by weighting and scaling the difference between the chip temperature value and the chip reference temperature value through a coupling coefficient and then superimposing a nonlinear attenuation factor. The heat conduction compensation value is determined based on a first-order thermal response model of steady-state temperature rise. The environmental compensation value is determined by calling a compensation model according to the environmental mode. Output the compensated temperature value as the measured ambient temperature.

2. The method as described in claim 1, characterized in that, Based on the ambient temperature value, the environmental mode of the smart screen is determined, including: Based on multiple ambient temperature values, temperature characteristic values ​​are calculated, including: temperature change rate, temperature standard deviation, temperature mean, maximum instantaneous change rate, and dominant frequency; and Based on the temperature characteristic values, the environmental mode of the smart screen is determined.

3. The method as described in claim 2, characterized in that, Determining the environmental mode of the smart screen based on the temperature characteristic values ​​includes: When the temperature change rate is less than the first change rate threshold, the temperature standard deviation is greater than the first fluctuation threshold, and the dominant frequency is within the first frequency range, the smart screen is determined to be in directional airflow cooling mode. If the temperature change rate is less than the second change rate threshold, the temperature standard deviation is greater than the second fluctuation threshold, and the maximum instantaneous change rate is greater than the first rate threshold, the smart screen is determined to be in a non-directional airflow disturbance mode. When the temperature change rate is greater than the third change rate threshold, the maximum instantaneous change rate is greater than the second rate threshold, and the average temperature is greater than the first temperature threshold, the smart screen is determined to be in external radiation heating mode; and When the temperature standard deviation is less than the third fluctuation threshold, the absolute value of the temperature change rate is less than the third change rate threshold, and the temperature mean is within the first temperature range, the smart screen is determined to be in a steady-state thermal environment mode.

4. The method as described in claim 1, characterized in that, The reliability of a temperature value is the product of the reliability of its numerical range, the reliability of its rate of change, the reliability of its consistency, and the reliability of its long-term drift.

5. The method as described in claim 1, characterized in that: The environmental modes include: directional airflow cooling mode, non-directional airflow disturbance mode, external radiation heating mode, and steady-state thermal environment mode. The environmental mode adjustment factors for each temperature value determined based on the environmental modes include: In steady-state thermal environment mode, the environmental mode adjustment factor for each temperature value is set to the initial value; In directional airflow cooling mode, increase the environmental mode adjustment factor for ambient temperature and decrease the environmental mode adjustment factor for chip temperature. Under non-directional airflow disturbance mode, increase the environmental mode adjustment factor for circuit board temperature and decrease the environmental mode adjustment factor for ambient temperature; and In external radiation heating mode, increase the environmental mode adjustment factor for chip temperature value and decrease the environmental mode adjustment factor for ambient temperature value.

6. The method as described in claim 1, characterized in that: The fusion temperature value is compensated based on the temperature compensation value, including: Based on the fusion temperature value, it is determined whether the smart screen is in a high temperature range or a low temperature range. When the smart screen is in a high temperature range, the temperature compensation value also includes a high temperature compensation value. When the smart screen is in a low temperature range, the temperature compensation value also includes a low temperature compensation value. The low temperature compensation value includes: a low temperature correction item, a circuit temperature drift compensation item, and a humidity condensation compensation item.

7. The method as described in claim 1, characterized in that, Also includes: The compensated temperature value is filtered to obtain the filtered temperature value. The filtered temperature value is corrected to obtain the corrected temperature value, and Output the corrected temperature value as the measured ambient temperature.

8. The method as described in claim 7, characterized in that, The filtered temperature value is corrected to obtain the corrected temperature value, including: Based on the described environment pattern, determine the learning rate to be used during the correction process; Based on the learning rate, the correction term is determined through a recursive update function; and The filtered temperature value is corrected according to the correction term to obtain the corrected temperature value.

9. A smart screen, characterized in that, include: The memory stores execution instructions; as well as A processor that executes the execution instructions stored in the memory, causing the processor to perform the smart screen ambient temperature measurement method according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the smart screen ambient temperature measurement method according to any one of claims 1 to 8.

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