Temperature measurement compensation methods, devices, equipment and media

Abstract

This disclosure presents a temperature measurement compensation method, relating to the field of temperature compensation. The method includes: first, performing feedforward prediction based on the operating status and historical temperature of the target device to generate a predicted value that instantly reflects the temperature change trend, thereby directly compensating for the inherent response lag of the sensor; second, performing Kalman filtering on the sensor measurement value to obtain an optimal estimate that effectively suppresses noise and has higher accuracy; then, dynamically determining the fusion weight based on the deviation between the feedforward prediction and the real-time measurement, and finally fusing the two according to their weights for output. This embodiment organically combines the speed of feedforward prediction with the accuracy of Kalman filtering through a deviation-based dynamic weight allocation, thereby outputting a compensated temperature that is both responsive and stable in complex dynamic scenarios such as the switching between hot and cold temperatures of the target device. This improves temperature control accuracy and enhances the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of temperature compensation technology, and in particular to a temperature measurement and compensation method, apparatus, equipment and medium. Background Technology

[0002] In personal care appliances such as hair dryers and hot air combs, as well as various small household appliances, non-contact temperature detection technology, especially far-infrared sensors, has been widely used to achieve precise temperature control and a comfortable experience. Compared with traditional contact temperature sensing elements, far-infrared sensors have significant advantages such as fast response, no contact with heat sources, and ease of use. However, the operating environment of these devices is usually very dynamic. For example, hair dryers need to frequently switch between cold and hot air settings, resulting in drastic and rapid changes in the temperature of the air outlet.

[0003] Currently, existing technologies cannot effectively handle measurement deviations under dynamic temperature changes in the application of far-infrared sensors, which present measurement errors. This makes it impossible for current methods to provide both fast and accurate temperature measurements in critical scenarios such as rapid switching between hot and cold air in devices like hair dryers, thus limiting the improvement of temperature control accuracy and resulting in a poor user experience. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a temperature measurement compensation method, apparatus, equipment, and medium.

[0005] This disclosure provides a temperature measurement compensation method, the method comprising: Acquire the operating status parameters of the target device and the measured temperature from the infrared sensor; Based on the operating status parameters and the measured temperature, calculate the feedforward predicted temperature of the target device; The measured temperature is processed by Kalman filtering to obtain the Kalman estimated temperature; Based on the deviation between the feedforward predicted temperature and the measured temperature, determine the first weight of the feedforward predicted temperature and the second weight of the Kalman estimated temperature; Based on the first and second weights, the feedforward predicted temperature and the Kalman estimated temperature are fused to output the compensated temperature.

[0006] In this way, the speed of prediction and the accuracy of statistical filtering are organically combined through an adaptive mechanism, so as to output a compensation temperature that is both responsive and stable and reliable in complex dynamic scenarios such as the switching between hot and cold on the target device, thereby improving the temperature control accuracy and enhancing the user experience.

[0007] This disclosure also provides a temperature measurement compensation device, the device comprising: The acquisition unit is used to acquire the operating status parameters of the target device and the measured temperature of the infrared sensor; The calculation unit is used to calculate the feedforward predicted temperature of the target device based on the operating status parameters and the measured temperature. The processing unit is used to perform Kalman filtering on the measured temperature to obtain the Kalman estimated temperature; The determining unit is configured to determine a first weight for the feedforward predicted temperature and a second weight for the Kalman estimated temperature based on the deviation between the feedforward predicted temperature and the measured temperature. The fusion unit is used to fuse the feedforward predicted temperature and the Kalman estimated temperature based on the first weight and the second weight, and output the compensated temperature.

[0008] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the temperature measurement compensation method provided in this disclosure.

[0009] This disclosure also provides a computer-readable storage medium storing a computer program for performing the temperature measurement compensation method provided in this disclosure. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 A schematic flowchart of a temperature measurement compensation method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a temperature measurement compensation device provided in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this disclosure. Detailed Implementation

[0012] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0013] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0014] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0016] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0018] In personal care appliances such as hair dryers and hot air combs, as well as various small household appliances, non-contact temperature detection technology, especially far-infrared sensors, has been widely used to achieve precise temperature control and a comfortable experience. Compared with traditional contact temperature sensing elements, far-infrared sensors have significant advantages such as fast response, no contact with heat sources, and ease of use. However, the operating environment of these devices is usually very dynamic. For example, hair dryers need to frequently switch between cold and hot air settings, resulting in drastic and rapid changes in the temperature of the air outlet.

[0019] Currently, the industry commonly uses two main compensation methods to address measurement errors in far-infrared sensors. One is static calibration, which involves calibration under constant temperature conditions before the product leaves the factory, establishing a fixed error correction value or lookup table to uniformly correct for all measurement results. The other is dynamic filtering, which primarily uses algorithms such as low-pass filters to smooth the real-time output temperature signal from the sensor, suppressing random noise. These methods are common techniques for improving FIR measurement accuracy in existing technologies.

[0020] However, the aforementioned existing technical solutions share a common problem: they cannot effectively handle measurement deviations under dynamic temperature change conditions.

[0021] Specifically, static calibration cannot correct real-time errors introduced by dynamic factors such as sensor thermal response hysteresis and changes in ambient temperature gradients. While simple low-pass filtering smooths noise, it further exacerbates the hysteresis of the sensor output signal relative to actual temperature changes, even leading to significant overshoot or dropout during rapid temperature changes. This makes it impossible for existing methods to provide both fast and accurate temperature measurement in critical scenarios such as rapid switching between hot and cold air in hair dryers, thus limiting the improvement of temperature control accuracy and resulting in a poor user experience.

[0022] In view of this, this application provides a temperature measurement compensation method, comprising: firstly, performing feedforward prediction based on the operating status and historical temperature of the target device to generate a predicted value that can instantly reflect the temperature change trend, thereby directly compensating for the inherent response lag of the sensor; secondly, performing Kalman filtering on the sensor measurement value to obtain an optimal estimate that can effectively suppress noise and has higher accuracy; then, dynamically determining the fusion weight based on the deviation between the feedforward prediction and the real-time measurement, thus organically combining the speed of prediction with the accuracy of statistical filtering through an adaptive mechanism, thereby outputting a compensation temperature that is both fast-responding and stable and reliable in complex dynamic scenarios such as the switching between hot and cold temperatures of the target device, thereby improving the temperature control accuracy and enhancing the user experience.

[0023] To address the aforementioned problems, this disclosure provides a temperature measurement compensation method, which will be described below with reference to specific embodiments.

[0024] Figure 1 This is a flowchart illustrating a temperature measurement compensation method provided in an embodiment of this disclosure. The method can be executed by a temperature measurement compensation device, which can be implemented using software and / or hardware, and is generally integrated into a computing device, such as a microprocessor or control unit within a hair dryer or hot air comb. Figure 1 As shown, the method includes: S101: Obtain the operating status parameters of the target device and the measured temperature of the sensor.

[0025] The computing device can acquire the operating status parameters of the target device and the measured temperature from sensors (e.g., infrared sensors). Here, the target device specifically refers to a temperature-sensitive device (such as a hair dryer or hot air comb) using this method. The operating status parameters may include key control parameters and operating variables that directly affect the outlet air temperature of the device, such as: temperature setting (to determine heating power), fan speed setting (to determine airflow intensity), and hot / cold air switching status. These parameters are set and known by the main controller of the target device, and together they define the current operating point, serving as the core basis for feedforward prediction.

[0026] The infrared sensor measures the raw, uncompensated temperature value as a real-time output. In hair dryer applications, this measurement may lag behind or deviate from the actual temperature in the air duct due to factors such as sensor response lag, environmental airflow disturbances (e.g., strong winds), and thermal shocks, resulting in dynamic deviations.

[0027] S102: Calculate the feedforward predicted temperature of the target device based on the operating status parameters and the measured temperature.

[0028] The computing device can calculate the feedforward predicted temperature of the target device based on operating status parameters and measured temperature.

[0029] For example, the computing device can calculate the temperature change trend based on the measured temperature and historical temperature data; The feedforward predicted temperature is determined based on the temperature change trend and operating status parameters.

[0030] The computing device first uses the measured temperature from the infrared sensor and its stored historical temperature data to calculate the current temperature change trend through algorithms (such as differential calculation). This temperature change trend characterizes the dynamic direction and rate of temperature change in the system. Subsequently, the computing device combines this temperature change trend with acquired operating status parameters (such as temperature setting and fan speed setting) to ultimately calculate the future feedforward predicted temperature. This process is an open-loop prediction method based on current temperature conditions and historical behavior, aiming to estimate temperature trends in advance to compensate for the dynamic lag in sensor measurements.

[0031] In some possible implementations, the computing device can determine the corresponding correction factor based on the operating state parameters; The temperature change trend is corrected based on the correction factor to obtain the corrected temperature change trend; The feedforward predicted temperature is determined based on the corrected temperature change trend and the measured temperature.

[0032] The computing device can first determine the corresponding correction factor based on the operating state parameters. The device can pre-store a mapping relationship (e.g., obtained through experimental calibration or thermal model derivation), which maps different combinations of operating states to a specific correction factor K_correction. The technical function of this correction factor is to adapt the calculated general temperature change trend ΔT / Δt to the specific heating power and heat dissipation conditions of the target device, because the same temperature change trend will predict different future temperature trends under different power and airflow conditions.

[0033] Subsequently, the computing device corrects the temperature change trend based on a correction factor. Specifically, the correction factor K_correction obtained in the previous step is multiplied by the previously calculated original temperature change trend ΔT / Δt to obtain the corrected temperature change trend, which is mathematically expressed as: (ΔT / Δt)_corrected = K_correction * (ΔT / Δt).

[0034] This step essentially involves using operating status parameters to physically model and correct the temperature change trend, making the trend prediction more closely match the actual dynamic characteristics.

[0035] Finally, the computing device determines the feedforward predicted temperature based on the corrected temperature change trend and the measured temperature. The computing device uses the measured temperature T_measure(t) of the infrared sensor at the current moment as a reference, and adds the predicted temperature change (ΔT / Δt)_corrected generated within one prediction step Δt (usually equal to the sampling period) to calculate the feedforward predicted temperature T_ff(t+1) for the next moment. This calculation follows the formula: T_ff(t+1) = T_measure(t) + (ΔT / Δt)_corrected * Δt.

[0036] The entire process achieves open-loop feedforward prediction based on the current temperature state and the model, aiming to predict temperature changes in advance to compensate for the lag in sensor measurements.

[0037] S103: Perform Kalman filtering on the measured temperature to obtain the Kalman estimated temperature.

[0038] The computing device can perform Kalman filtering on the measured temperature to obtain a Kalman estimated temperature. Kalman filtering is a recursive optimal estimation algorithm based on a state-space model. The Kalman estimated temperature is the optimal estimate obtained after processing the measured temperature using Kalman filtering. It is a temperature value that has been smoothed and corrected by the algorithm.

[0039] For example, the computing device can calculate the predicted temperature value and the Kalman gain; The difference between the measured temperature and the predicted temperature is calculated using Kalman gain. Multiply the difference by the Kalman gain to obtain the correction amount; The correction amount is added to the predicted temperature value to obtain the Kalman estimated temperature.

[0040] For example, the computing device can first perform a prediction step, calculating the current temperature prediction T_pred(t) and the current Kalman gain K(t). This Kalman gain can be a weight between 0 and 1. Subsequently, the computing device can proceed to an update step: it calculates the difference between the current measured temperature T_measure(t) and the temperature prediction T_pred(t) obtained in the previous step (i.e., T_measure(t) - T_pred(t). Multiplying this difference by the calculated Kalman gain K(t) yields the correction amount K(t)*(T_measure(t) - T_pred(t)). Finally, the computing device adds this correction amount to the temperature prediction T_pred(t) to obtain the optimally fused Kalman estimated temperature T_kalman(t) for the current moment, calculated using the following formula: T_kalman(t) = T_pred(t) + K(t) * (T_measure(t) - T_pred(t)).

[0041] This process uses recursive iteration to continuously refine the model predictions using new observations, ultimately outputting an optimal, smooth temperature estimate that effectively suppresses measurement noise.

[0042] In some possible implementations, the computing device can use the Kalman estimate of the temperature at the previous moment as the temperature prediction for the current moment; The sum of the prediction error covariance at the current moment and the preset measurement noise covariance is determined, and the Kalman gain at the current moment is determined based on the ratio of the prediction error covariance to the sum.

[0043] For example, the computing device can first perform time prediction: it directly uses the optimally estimated Kalman temperature T_kalman(k-1) from the previous filtering cycle as the predicted temperature T_pred(k) for the current time k. This operation is based on the assumption of continuous temperature change and is the simplest prediction model, assuming that the temperature will remain at the optimal estimate from the previous time step for a short period. Furthermore, the computing device needs to update the prediction uncertainty, i.e., calculate the prediction error covariance P_pred(k) for the current time step. This covariance is determined by the estimation error covariance from the previous time step and the process noise covariance Q, which reflects the uncertainty of the system's dynamic model (for example, Q needs to be set larger during hot / cold air switching to characterize the high uncertainty of drastic temperature changes).

[0044] Subsequently, the computing device determines the critical Kalman gain K(k). This step first calculates the sum of the current prediction uncertainty (P_pred(k)) and the infrared sensor measurement uncertainty (i.e., the preset measurement noise covariance R), P_pred(k) + R. The value of R reflects the degree of confidence in the measured temperature (R needs to increase when the infrared sensor is interfered with). Next, the computing device calculates the Kalman gain at the current moment according to the formula K(k) = P_pred(k) / (P_pred(k) + R). The physical meaning of this formula is that the Kalman gain is the ratio of the prediction error covariance to the total uncertainty. If the prediction is highly uncertain (P_pred(k) is large), the gain K(k) approaches 1, and the algorithm will have more confidence in the current measured temperature; conversely, if the measurement noise is large (R is large), the gain K(k) approaches 0, and the algorithm will have more confidence in the temperature prediction value. This calculation of dynamic weights is the core of Kalman filtering's ability to optimally fuse prediction and measurement.

[0045] S104: Determine the first weight of the feedforward predicted temperature and the second weight of the Kalman estimated temperature based on the deviation between the feedforward predicted temperature and the measured temperature.

[0046] The computing device can determine a first weight for the feedforward predicted temperature and a second weight for the Kalman estimated temperature based on the deviation between the feedforward predicted temperature and the measured temperature.

[0047] For example, the computing device can obtain a first initial weight for the feedforward predicted temperature and a second initial weight for the Kalman estimated temperature, calculate the residual value between the feedforward predicted temperature and the measured temperature, and adjust the first initial weight and / or the second initial weight according to the relationship between the residual value and a preset temperature threshold to obtain the first weight and the second weight.

[0048] In some possible implementations, the computing device can adjust the first initial weight and / or the second initial weight based on the relationship between the residual value and a preset temperature threshold. Specifically, this can be achieved by: If the residual value is greater than the preset threshold, then according to the preset weight adjustment rules, the first weight is increased and / or the second weight is decreased. If the residual value is less than or equal to the preset threshold, then according to the preset weight adjustment rules, the first weight is reduced and / or the second weight is increased.

[0049] For example, a computing device can determine the severity of temperature changes in the current target device and the reliability of the infrared sensor measurement by comparing the residual between the feedforward predicted temperature and the measured temperature with a preset temperature threshold, and adjust the trust level (i.e., weight) of the two temperature estimation sources accordingly. The preset temperature threshold can be a pre-set threshold value based on the specific application scenario (such as the control accuracy requirements of a hair dryer's outlet temperature) and the characteristics of the infrared sensor. Its adjustment rules can specifically be as follows: If the residual value is greater than the preset threshold, it indicates that the actual temperature change at the current moment may be quite drastic, and the measured temperature fails to keep up due to the inherent thermal response lag of the infrared sensor, resulting in a large deviation between the feedforward predicted temperature and the current measured value. In this case, the computing device determines that it should rely more on the feedforward predicted temperature, which can proactively respond to dynamic changes. Therefore, the computing device can increase the first weight (i.e., give the feedforward predicted temperature a higher fusion weight) and / or correspondingly decrease the second weight (i.e., reduce the weight of the Kalman estimated temperature) according to preset rules (e.g., by a fixed step size or based on the proportion of the residual) to ensure that the fused compensated temperature can quickly track the actual temperature change. Conversely, if the residual value is less than or equal to the preset threshold, it indicates that the current temperature change is relatively gradual, the lag effect of the measured temperature is not obvious, and its value is relatively reliable. In this case, the computing device can determine that it should prefer to utilize the noise immunity and smoothing filtering characteristics of the Kalman estimated temperature to obtain a high-precision steady-state estimate. Therefore, the computing device can decrease the first weight and / or increase the second weight according to preset rules, so that the fusion output focuses more on the Kalman estimated temperature, thereby obtaining a more stable and accurate temperature value under steady-state or small-change conditions. This dynamic weight adjustment mechanism is the core of achieving complementary advantages between feedforward predicted temperature (superior in fast dynamic response) and Kalman estimated temperature (superior in high steady-state accuracy), enabling the final output compensated temperature to adapt to different operating conditions, combining speed and accuracy.

[0050] S105: Based on the first weight and the second weight, the feedforward predicted temperature and the Kalman estimated temperature are fused to output the compensated temperature.

[0051] The computing device can fuse the feedforward predicted temperature and the Kalman estimated temperature based on the first weight and the second weight, and output the compensated temperature, which is the final measured temperature.

[0052] For example, the sum of the first weight and the second weight is 1, and the computing device can determine the first product of the first weight and the feedforward predicted temperature and the second product of the second weight and the Kalman estimated temperature; The compensated temperature is output based on the sum of the first product and the second product.

[0053] For example, the sum of the first and second weights can be 1. This ensures that the combined contribution of the feedforward predicted temperature and the Kalman estimated temperature is 100% during fusion, making the output compensated temperature a convex combination of the two based on confidence. The result will fall within the interval formed by the two input temperature values, guaranteeing the numerical stability of the fusion process. The computing device can then perform a weighted fusion calculation: First, it multiplies the dynamically adjusted first weight (denoted as w1) with the calculated feedforward predicted temperature (denoted as T_ff), obtaining a first product w1 * T_ff; simultaneously, it multiplies the second weight (denoted as w2, satisfying w2 = 1 - w1) with the obtained Kalman estimated temperature (denoted as T_kalman), obtaining a second product w2 * T_kalman. These two products represent the contribution of the two temperature estimates to the final result based on their current confidence level (weight), respectively. Finally, the computing device adds the first and second products, and the sum is the final output compensated temperature (denoted as T_comp). This process can be clearly expressed by the formula: T_comp = w1 * T_ff + w2 * T_kalman. Through this weighted averaging operation, the computing device can combine the advantages of fast dynamic response of feedforward temperature prediction with the advantages of high steady-state accuracy and strong noise resistance of Kalman estimation, thereby outputting an optimized temperature value that can adapt to different operating conditions and has both speed and accuracy for precise control of the target equipment.

[0054] This application provides a temperature measurement compensation method, comprising: firstly, performing feedforward prediction based on the operating status and historical temperature of the target device to generate a predicted value that instantly reflects the temperature change trend, thereby directly compensating for the inherent response lag of the sensor; secondly, performing Kalman filtering on the sensor measurement value to obtain an optimal estimate that can effectively suppress noise and has higher accuracy; most importantly, dynamically determining the fusion weight based on the deviation between the feedforward prediction and the real-time measurement, increasing the feedforward weight for rapid tracking when the temperature changes drastically (large deviation), and increasing the Kalman weight for improved accuracy when the temperature is stable (small deviation), and finally fusing the two according to their weights for output. In the embodiments of this application, when switching between hot and cold, the actual temperature changes drastically, and the infrared sensor response lags significantly, resulting in a significant increase in residuals exceeding the threshold. At this time, the computing device can increase the weight of the feedforward prediction, so that the fused output closely follows the rapid change trend of the feedforward prediction, ensuring a rapid response. When the operation tends to be stable, the infrared sensor measurement can keep up with the actual temperature, the residuals decrease and fall below the threshold, at which point the computing device can increase the weight of the Kalman estimation, so that the fused output relies on the smoothing and noise reduction capabilities of the Kalman filter, ensuring stability and reliability. Ultimately, the compensated temperature is the weighted sum of the two values ​​calculated according to this dynamic weight. This value is dominated by feedforward prediction during drastic changes, achieving rapid tracking; and dominated by Kalman estimation during stable conditions, achieving high-precision constancy. Thus, in complex dynamic scenarios such as the switching between hot and cold temperatures of the target device, a compensated temperature that is both responsive and reliable is output, thereby improving temperature control accuracy and enhancing the user experience.

[0055] To achieve the above embodiments, this disclosure also proposes a temperature measurement compensation device.

[0056] Figure 2 This is a schematic diagram of a temperature measurement compensation device provided in an embodiment of this disclosure. The device can be implemented by software and / or hardware, and is generally integrated into an electronic device. Figure 2 As shown, the device includes: The acquisition unit 210 is used to acquire the operating status parameters of the target device and the measured temperature of the infrared sensor; The calculation unit 220 is used to calculate the feedforward predicted temperature of the target device based on the operating status parameters and the measured temperature; Processing unit 230 is used to perform Kalman filtering on the measured temperature to obtain the Kalman estimated temperature; The determining unit 240 is configured to determine a first weight of the feedforward predicted temperature and a second weight of the Kalman estimated temperature based on the deviation between the feedforward predicted temperature and the measured temperature. The fusion unit 250 is used to fuse the feedforward predicted temperature and the Kalman estimated temperature based on the first weight and the second weight, and output a compensated temperature.

[0057] Optionally, the determining unit is specifically used for: Obtain the first initial weight of the feedforward predicted temperature and the second initial weight of the Kalman estimated temperature; Calculate the residual between the feedforward predicted temperature and the measured temperature; Based on the relationship between the residual value and the preset temperature threshold, the first initial weight and / or the second initial weight are adjusted to obtain the first weight and the second weight.

[0058] Optionally, the determining unit is specifically used for: If the residual value is greater than the preset threshold, then according to the preset weight adjustment rule, the first weight is increased and / or the second weight is decreased; If the residual value is less than or equal to the preset threshold, then according to the preset weight adjustment rules, the first weight is reduced and / or the second weight is increased.

[0059] Optional, computational unit, specifically used for: Based on the measured temperature and historical temperature data, calculate the temperature change trend; The feedforward predicted temperature is determined based on the temperature change trend and the operating status parameters.

[0060] Optional, computational unit, specifically used for: Determine the corresponding correction factor based on the operating status parameters; The temperature change trend is corrected based on the correction factor to obtain the corrected temperature change trend; The feedforward predicted temperature is determined based on the corrected temperature change trend and the measured temperature.

[0061] Optional, processing unit, specifically used for: Calculate the predicted temperature and Kalman gain; Using the Kalman gain, the difference between the measured temperature and the predicted temperature is calculated; Multiply the difference by the Kalman gain to obtain the correction amount; The correction amount is added to the predicted temperature value to obtain the Kalman estimated temperature.

[0062] Optionally, the processing unit is specifically used for: The Kalman-estimated temperature from the previous moment is used as the predicted temperature for the current moment. The sum of the prediction error covariance at the current moment and the preset measurement noise covariance is determined, and the Kalman gain at the current moment is determined based on the ratio of the prediction error covariance to the sum.

[0063] Optionally, the sum of the first weight and the second weight is 1, and the fusion unit is specifically used for: Determine the first product of the first weight and the feedforward predicted temperature, and the second product of the second weight and the Kalman estimated temperature; The compensated temperature is output based on the sum of the first product and the second product.

[0064] The temperature measurement compensation device provided in this disclosure can execute the temperature measurement compensation method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of executing the method.

[0065] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implements the temperature measurement compensation method described above.

[0066] Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this disclosure.

[0067] The following is a detailed reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing the terminal device 300 in the embodiments of this disclosure. The terminal device 300 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0068] like Figure 3 As shown, the terminal device 300 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the terminal device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0069] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows terminal device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 A terminal device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0070] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from memory 308, or installed from ROM 302. When the computer program is executed by processor 301, it performs the functions defined in the temperature measurement compensation method of embodiments of this disclosure.

[0071] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0072] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0073] The aforementioned computer-readable medium may be included in the aforementioned terminal device; or it may exist independently and not assembled into the terminal device.

[0074] The aforementioned computer-readable medium carries one or more programs, which, when executed by the terminal device, cause the terminal device to perform the aforementioned temperature measurement compensation method.

[0075] The terminal device may be written in one or more programming languages ​​or a combination thereof to perform computer program code for the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0077] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0078] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0079] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0081] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0082] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A temperature measurement compensation method, characterized in that, include: Acquire the operating status parameters of the target device and the measured temperature of the sensor; Based on the operating status parameters and the measured temperature, the feedforward predicted temperature of the target device is calculated; The measured temperature is processed by Kalman filtering to obtain the Kalman estimated temperature; Based on the deviation between the feedforward predicted temperature and the measured temperature, a first weight for the feedforward predicted temperature and a second weight for the Kalman estimated temperature are determined. Based on the first weight and the second weight, the feedforward predicted temperature and the Kalman estimated temperature are fused to output a compensated temperature.

2. The method according to claim 1, characterized in that, The step of determining a first weight for the feedforward predicted temperature and a second weight for the Kalman estimated temperature based on the deviation between the feedforward predicted temperature and the measured temperature includes: Obtain the first initial weight of the feedforward predicted temperature and the second initial weight of the Kalman estimated temperature; Calculate the residual between the feedforward predicted temperature and the measured temperature; Based on the relationship between the residual value and the preset temperature threshold, the first initial weight and / or the second initial weight are adjusted to obtain the first weight and the second weight.

3. The method according to claim 2, characterized in that, The step of adjusting the first initial weight and / or the second initial weight based on the relationship between the residual value and the preset temperature threshold includes: If the residual value is greater than the preset threshold, then according to the preset weight adjustment rule, the first weight is increased and / or the second weight is decreased; If the residual value is less than the preset threshold, then according to the preset weight adjustment rules, the first weight is reduced and / or the second weight is increased.

4. The method according to claim 1, characterized in that, Based on the operating status parameters and the measured temperature, the feedforward predicted temperature of the target device is calculated, including: Based on the measured temperature and historical temperature data, calculate the temperature change trend; The feedforward predicted temperature is determined based on the temperature change trend and the operating status parameters.

5. The method according to claim 4, characterized in that, The process of determining the feedforward predicted temperature based on the temperature change trend and the operating status parameters includes: Determine the corresponding correction factor based on the operating status parameters; The temperature change trend is corrected based on the correction factor to obtain the corrected temperature change trend; The feedforward predicted temperature is determined based on the corrected temperature change trend and the measured temperature.

6. The method according to claim 1, characterized in that, The step of performing Kalman filtering on the measured temperature to obtain the Kalman estimated temperature includes: Calculate the predicted temperature and Kalman gain; Using the Kalman gain, the difference between the measured temperature and the predicted temperature is calculated; Multiply the difference by the Kalman gain to obtain the correction amount; The correction amount is added to the predicted temperature value to obtain the Kalman estimated temperature.

7. The method according to claim 6, characterized in that, The calculation of the predicted temperature and Kalman gain includes: The Kalman-estimated temperature from the previous moment is used as the predicted temperature for the current moment. The sum of the prediction error covariance at the current moment and the preset measurement noise covariance is determined, and the Kalman gain at the current moment is determined based on the ratio of the prediction error covariance to the sum.

8. The method according to any one of claims 1-7, characterized in that, The sum of the first weight and the second weight is 1. The step of fusing the feedforward predicted temperature and the Kalman estimated temperature based on the first weight and the second weight to output a compensated temperature includes: Determine the first product of the first weight and the feedforward predicted temperature, and the second product of the second weight and the Kalman estimated temperature; The compensated temperature is output based on the sum of the first product and the second product.

9. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores a computer program that, when executed by the processor, performs the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-8.

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