A temperature compensation method and system for single-pixel infrared temperature measurement

Abstract

The application relates to the technical field of sensor temperature compensation, and discloses a temperature compensation method and system for single-pixel infrared temperature measurement, which models the angular sensitivity of a sensor as an axisymmetric Gaussian distribution and determines a standard deviation; based on the area integration of a circular target by a Gaussian function in polar coordinates, a closed-form analytical solution of an effective filling ratio is derived; background temperature values are determined according to a three-level background temperature priority rule, and background radiation emittance is calculated; a functional relationship between total radiation emittance and target and background radiation emittance is constructed, and a real target temperature is obtained through algebraic back solution. The application can effectively eliminate the influence of the non-uniformity of angular sensitivity, target filling ratio change and background radiation interference on temperature measurement accuracy, and has high calculation efficiency.

Description

Technical Field

[0001] This invention relates to the field of sensor temperature compensation technology, and more specifically, to a temperature compensation method and system for single-pixel infrared temperature measurement. Background Technology

[0002] Infrared radiation thermometry technology achieves non-contact temperature measurement by receiving infrared electromagnetic radiation from the target within a specific wavelength range. Single-pixel thermopile infrared sensors are widely used in portable temperature measurement products, smart homes, and industrial inspections due to their advantages of low cost, small size, and low power consumption. However, these sensors face a series of technical problems in practical applications, limiting their temperature measurement accuracy and applicable scenarios. For example: 1. Systemic distortion in small target temperature measurement: When the geometric size of the target is smaller than the sensor's field of view spot, the radiation energy received by the sensor does not all come from the target itself, but is a mixture of target radiation and background radiation. This physical limitation causes the apparent temperature output by the sensor to be significantly lower than the target's true temperature; the smaller the target's proportion in the field of view, the greater the measurement deviation. Existing conventional solutions lack effective quantitative correction methods, resulting in severely insufficient temperature measurement accuracy for single-pixel thermopile sensors when measuring targets with sub-spot sizes. 2. Precision defects in traditional fill ratio calculation methods: Existing solutions generally use the assumption of a uniform area ratio of "target area / spot area" to calculate the fill ratio, assuming that the sensitivity is consistent throughout the field of view. However, the angular sensitivity characteristics of thermopile sensors actually exhibit a Gaussian distribution with a "high center and smooth attenuation at the edges," and the uniform area ratio assumption deviates significantly from the actual physical response characteristics of the device. This defect leads to an overestimation of the radiation weight of the background region at the edge of the field of view and an underestimation of the radiation contribution of the central target region. In small target measurement scenarios, the calculation error of the fill ratio can reach more than 20%, directly degrading the accuracy of the temperature inverse solution. Third, high-cost alternatives are unsuitable for mass-market products: Focal plane array temperature measurement solutions require multi-pixel readout circuits and image processing units, with hardware costs 5-10 times that of single-pixel solutions. The size and power consumption increase significantly, making them unsuitable for the needs of low-cost, miniaturized portable temperature measurement products. Mechanical chopper solutions require the addition of motors, chopper blades, and drive circuits, introducing moving parts, which not only significantly increases size and power consumption but also presents wear-life limitations and reliability issues. None of the above solutions are suitable for mass-market temperature measurement products with strict requirements for cost, size, and reliability. IV. Insufficient Robustness of Background Tracking: In dynamic measurement environments, the background temperature is not constant. Factors such as ambient temperature drift, airflow, and changes in the sensor's own thermal equilibrium can all cause slow fluctuations in background radiation. Traditional solutions use fixed thresholds to determine background transitions, which cannot adapt to the noise differences of different individual sensors and the changing ambient temperature conditions. This easily leads to two failure modes: one is false triggering, where the background follows the target transition, causing correction failure; the other is missed triggering, where background drift cannot be updated in time, resulting in poor inverse temperature stability.

[0003] Furthermore, a search revealed that existing related patents also fail to effectively address the aforementioned issues. CN102661799A discloses an environmental temperature compensation method, but its fill ratio calculation is still based on the assumption of a uniform area ratio and does not model the sensor's angular sensitivity curve. CN122192521A discloses a laser heating temperature measurement calibration system that uses a Physical Information Neural Network (PINN) to embed constraints in the hidden layer to handle background interference, but this method relies on a large amount of labeled data for training, has high computational requirements, and suffers from poor black-box interpretability of the model, making it difficult to deploy on resource-constrained embedded platforms. In addition, while digital compensation methods based on environmental radiation separation modeling have been explored to some extent, a complete solution implemented entirely in embedded software and suitable for single-pixel thermopile sensors has not yet been formed.

[0004] Therefore, there is an urgent need for a method to achieve high-precision temperature measurement in small target scenarios based on a single-pixel thermopile sensor without increasing hardware costs, while ensuring the deployability and robustness of the algorithm on resource-constrained embedded platforms. Summary of the Invention

[0005] In view of this, the present invention proposes a temperature compensation method and system for single-pixel infrared thermometry, aiming to solve one or more of the above-mentioned problems.

[0006] On one hand, this invention proposes a temperature compensation method for single-pixel infrared thermometry, comprising: The angular sensitivity function of the single-pixel thermopile infrared temperature sensor is modeled as an axisymmetric Gaussian distribution, and the standard deviation of the Gaussian distribution is determined based on the nominal half-field angle of the sensor. Based on the area integral of the Gaussian function over a circular target in polar coordinates, a closed-form analytical solution for the effective filling ratio is derived, and the effective filling ratio is obtained based on the target half-angle and the standard deviation of the Gaussian distribution. The background temperature value is determined according to the three-level background temperature priority rule, and the background radiative exitance is determined based on the simplified Stefan-Boltzmann thermal radiation law according to the background temperature value. The total radiative exitance measured by the sensor is obtained, and a functional relationship is constructed between the total radiative exitance, the target's true radiative exitance, and the background radiative exitance. The target's true radiative exitance is obtained through inverse algebraic solution, and the target's true temperature is obtained based on the target's true radiative exitance.

[0007] Preferably, when determining the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor, the standard deviation of the Gaussian distribution is determined according to the following formula: in, σ represents the nominal half field of view of the sensor, and σ represents the standard deviation of the Gaussian distribution. The expression for the Gaussian distribution is: θ represents the sensitivity, and θ represents the off-axis angle of the sensor.

[0008] Preferably, the expression for the closed-form analytical solution of the effective filling ratio is: in, Indicates the effective fill ratio. Let σ represent the target half-angle, and σ represent the standard deviation of the Gaussian distribution.

[0009] Preferably, the target half-angle is determined according to the following formula: in, Indicates the target half-angle. d represents the radius of the target being measured, and d represents the measurement distance between the sensor and the target being measured.

[0010] Preferably, the three-level background temperature priority rule is as follows: The first level is a manual lock value, which is set to the highest priority by storing the current ambient temperature reading as the background temperature value through button press or host computer command. The second level is the adaptive exponential moving average tracking value, which uses the output baseline of the background tracker tracked by the slow exponential moving average as the background temperature value, and is set as the default priority. The third level uses the reading from the sensor's built-in ambient temperature channel as the background temperature value, and sets it to the lowest priority. The three-level background temperature priority rules automatically switch from high to low priority levels.

[0011] Preferably, determining the adaptive exponential moving average tracking value further includes: Obtain the median absolute deviation estimate, and determine the jump threshold based on the median absolute deviation estimate, wherein the jump threshold = k × median absolute deviation estimate; If the deviation between the current measured temperature value and the baseline value output by the background tracker exceeds the jump threshold, it is determined that the target being measured has entered the market, and the background tracker is immediately stopped; if the deviation between the current measured temperature value and the baseline value output by the background tracker falls back to the jump threshold, the background tracker automatically resumes background tracking.

[0012] Preferably, the exponential moving average iterative formula for the median absolute deviation estimate is: in, This represents the updated estimate of the absolute deviation of the median. This represents the estimated absolute deviation of the median before the update. This indicates the current measured temperature value. This represents the baseline value output by the background tracker, and α represents the smoothing coefficient.

[0013] Preferably, when constructing the functional relationship between the total radiative exitance and the target's true radiative exitance and the background radiative exitance, the expression of the functional relationship is: in, Indicates total radiative exitance. Indicates the effective fill ratio. Indicates the target's true radiative exitance. This represents the background radiative exitance.

[0014] Preferably, when the true radiative exitance of the target is obtained through inverse algebraic solution, and the true temperature of the target is obtained based on the true radiative exitance, the inverse algebraic expression for the true radiative exitance of the target is: The target true temperature is determined according to the following formula: The target true temperature is determined based on the simplified Stefan-Boltzmann thermal radiation law, which is expressed as follows: Where M represents radiative exitance and T represents temperature.

[0015] Compared with existing technologies, the advantages of this invention are as follows: This invention models the angular sensitivity function of a single-pixel thermopile infrared temperature sensor as an axisymmetric Gaussian distribution, and directly derives the standard deviation of the Gaussian distribution based on the sensor's nominal half-field of view. Based on this, a closed-form analytical solution for the effective fill ratio is derived by integrating the Gaussian function over the area of ​​a circular target in polar coordinates. This solution is directly calculated using the target's half-angle and the Gaussian standard deviation, without any numerical iteration. The calculation accuracy of the effective fill ratio is improved by more than 20% compared to the traditional uniform area ratio scheme, fundamentally eliminating the systematic error introduced by ignoring the sensor's Gaussian angular sensitivity characteristics. The computational complexity of the closed-form analytical solution is comparable to that of the traditional simple area ratio scheme, requiring no numerical iteration. It can be run in real-time on a standard 8-bit / 32-bit microcontroller with extremely low computational overhead. All model parameters have clear physical meanings (sensor field of view, target size, measurement distance), are highly interpretable, and require no training or calibration process.

[0016] This invention is based on the Stefan-Boltzmann fourth law of thermal radiation. It expresses the total radiative exitance measured by the sensor as a weighted superposition of the target's true radiative exitance and the background radiative exitance. Through inverse algebraic solution, and then by taking the fourth root and converting units, the true target temperature is obtained. A rigorous radiative energy superposition model is established based on the physical laws of thermal radiation, rather than empirical fitting or black-box mapping, ensuring the theoretical rigor and generalization ability of the inverse solution results. By accurately subtracting the background radiation component, the contamination of temperature measurement results by background radiation in small target measurement scenarios is effectively eliminated, significantly improving the temperature measurement accuracy of sub-spot targets. When the effective fill ratio is ≥0.99, the system automatically determines that the target completely fills the field of view and the background radiation ratio is less than 1%, skipping the inverse calculation, thus reducing computational overhead while ensuring accuracy.

[0017] This invention establishes a three-level priority background temperature determination mechanism: the first level is a manually locked value (set by the user via button or host computer command, highest priority); the second level is an adaptive exponential moving average tracking value (default priority); and the third level is the sensor's built-in ambient temperature channel reading (lowest priority). The three levels automatically switch according to priority. The manual locking level is suitable for high-precision measurement scenarios with a fixed background, allowing users to actively intervene to ensure background temperature accuracy. The adaptive EMA tracking level is suitable for dynamic scenarios with slowly changing backgrounds, automatically tracking environmental baseline drift without manual intervention. The sensor's ambient temperature channel fallback level ensures the system's basic availability under any extreme conditions. This three-level mechanism covers all scenario requirements from fixed to dynamically changing backgrounds, significantly improving the method's applicability and engineering reliability.

[0018] On the other hand, this application also provides a temperature compensation system for single-pixel infrared thermometry, used to apply the above-mentioned temperature compensation method for single-pixel infrared thermometry, including: The Gaussian angular sensitivity modeling unit is configured to model the angular sensitivity function of a single-pixel thermopile infrared temperature sensor as an axisymmetric Gaussian distribution, and determine the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor. The effective filling ratio determination unit is configured to derive a closed-form analytical solution for the effective filling ratio based on the area integral of the Gaussian function over the circular target in polar coordinates, and to obtain the effective filling ratio based on the target half-angle and the standard deviation of the Gaussian distribution. The three-level background temperature management unit is configured to determine the background temperature value according to the three-level background temperature priority rule, and to determine the background radiative output based on the background temperature value according to the simplified Stefan-Boltzmann thermal radiation law. The temperature compensation inverse kinematics unit is configured to acquire the total radiative exitance measured by the sensor, construct a functional relationship between the total radiative exitance and the target true radiative exitance and the background radiative exitance, obtain the target true radiative exitance through inverse kinematics, and obtain the target true temperature based on the target true radiative exitance.

[0019] It is understood that the temperature compensation method and system for single-pixel infrared thermometry provided in this application have the same beneficial effects, and will not be described in detail here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart of a temperature compensation method for single-pixel infrared thermometry provided in an embodiment of the present invention; Figure 2 This is a functional block diagram of a single-pixel infrared temperature compensation system provided in an embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] In some embodiments of this application, such as Figure 1 As shown, this embodiment provides a temperature compensation method for single-pixel infrared thermometry, including: The angular sensitivity function of the single-pixel thermopile infrared temperature sensor is modeled as an axisymmetric Gaussian distribution, and the standard deviation of the Gaussian distribution is determined based on the nominal half-field angle of the sensor. Based on the area integral of the Gaussian function over a circular target in polar coordinates, a closed-form analytical solution for the effective filling ratio is derived, and the effective filling ratio is obtained based on the target half-angle and the standard deviation of the Gaussian distribution. The background temperature value is determined according to the three-level background temperature priority rule, and the background radiative exitance is determined based on the simplified Stefan-Boltzmann thermal radiation law according to the background temperature value. The total radiative exitance measured by the sensor is obtained, and a functional relationship is constructed between the total radiative exitance, the target's true radiative exitance, and the background radiative exitance. The target's true radiative exitance is obtained through inverse algebraic solution, and the target's true temperature is obtained based on the target's true radiative exitance.

[0023] This invention models the angular sensitivity function as an axisymmetric Gaussian distribution and derives a closed-form analytical solution for the effective fill ratio, eliminating the need for numerical integration calculations and effectively reducing computational complexity. This makes it suitable for low-cost embedded temperature measurement devices with limited computing power. By determining the background temperature through a three-level background temperature priority rule, it can adapt to different actual temperature measurement scenarios, simplifying the operational process in practical applications while ensuring compensation accuracy. Compared to traditional fill ratio calculation methods that do not consider the influence of angular sensitivity distribution, this method yields a smaller target temperature error and more stable compensation effect, effectively improving the measurement accuracy of single-pixel thermopile infrared temperature sensors in small-target, long-distance temperature measurement scenarios.

[0024] In some embodiments of this application, when determining the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor, the standard deviation of the Gaussian distribution is determined according to the following formula: in, σ represents the nominal half field of view of the sensor, and σ represents the standard deviation of the Gaussian distribution. The expression for the Gaussian distribution is: θ represents the sensitivity, and θ represents the off-axis angle of the sensor.

[0025] In this embodiment, after the device is powered on, the microcontroller first executes the system initialization routine. The initialization includes: reading the sensor's nominal parameters from the non-volatile memory, including the half-angle of the sensor's nominal field of view (i.e., the angle corresponding to when the sensitivity drops to 50% of the center value); configuring the analog-to-digital converter's sampling rate and resolution; and initializing the initial values ​​of each state variable and filter.

[0026] Understandably, this embodiment quantifies the sensor's angular sensitivity characteristics into a precise mathematical model. The sensitivity of the thermopile sensor decreases as the off-axis angle increases, and this decrease directly affects the temperature measurement accuracy. By modeling with a Gaussian distribution, the sensor's response characteristics at different off-axis angles can be accurately described.

[0027] Modeling using an axisymmetric Gaussian distribution offers higher fitting accuracy and better mathematical tractability compared to traditional linear approximations or table lookup methods. The Gaussian distribution requires only one parameter (standard deviation σ) to fully describe the angular sensitivity characteristics, resulting in fewer parameters and lower computational cost, making it suitable for implementation in resource-constrained embedded systems. Furthermore, the method of directly calculating the standard deviation based on the nominal half-field of view eliminates the need for additional calibration experiments, facilitating rapid deployment in mass production.

[0028] In some embodiments of this application, the expression for the closed-form analytical solution of the effective fill ratio is: in, Indicates the effective fill ratio. Let σ represent the target half-angle, and σ represent the standard deviation of the Gaussian distribution.

[0029] In some embodiments of this application, when the effective fill ratio is ≥0.99, it is determined that the target being measured completely fills the sensor field of view and the background radiation ratio is less than 1%, and the correction calculation is skipped, thereby reducing the computing power overhead while ensuring accuracy.

[0030] In some embodiments of this application, the target half-angle is determined according to the following formula: in, Indicates the target half-angle. d represents the radius of the target being measured, and d represents the measurement distance between the sensor and the target being measured.

[0031] In this embodiment, if the target being measured is not circular (such as rectangular, elliptical, etc.), its equivalent radius can be calculated first and then substituted into the above formula. The measurement distance d can be obtained through a laser ranging module, an ultrasonic ranging module, or a pre-set fixed working distance.

[0032] Understandably, this quantifies the actual degree to which the target fills the sensor's field of view. Since a single-pixel sensor lacks imaging capabilities, its output is a weighted average of all radiation within the field of view (including the target and background). The effective fill ratio F reflects the weight of the target's radiation in the total measurement signal and is a key parameter for subsequently resolving the target's true temperature from the mixed signal.

[0033] Calculating the effective fill ratio using a closed-form analytical solution instead of a numerical integration method offers significant advantages, including fast computation speed, no need for iteration, and controllable accuracy. The closed-form solution is concise and can be completed with only one exponential operation in embedded systems, resulting in excellent real-time performance. Furthermore, this analytical solution is based on rigorous mathematical derivation, avoiding truncation and cumulative errors that may be introduced by numerical methods, thus ensuring consistent compensation accuracy.

[0034] In some embodiments of this application, the three-level background temperature priority rule is specifically as follows: The first level is a manual lock value, which is set to the highest priority by storing the current ambient temperature reading as the background temperature value through button press or host computer command. The second level is the adaptive exponential moving average tracking value, which uses the output baseline of the background tracker tracked by the slow exponential moving average as the background temperature value, and is set as the default priority. The third level uses the reading from the sensor's built-in ambient temperature channel as the background temperature value, and sets it to the lowest priority. The three-level background temperature priority rules automatically switch from high to low priority levels.

[0035] In some embodiments of this application, determining the adaptive exponential moving average tracking value further includes: Obtain the median absolute deviation estimate, and determine the jump threshold based on the median absolute deviation estimate, wherein the jump threshold = k × median absolute deviation estimate; If the deviation between the current measured temperature value and the baseline value output by the background tracker exceeds the jump threshold, it is determined that the target being measured has entered the market, and the background tracker is immediately stopped; if the deviation between the current measured temperature value and the baseline value output by the background tracker falls back to the jump threshold, the background tracker automatically resumes background tracking.

[0036] In some embodiments of this application, the exponential moving average iterative formula for the median absolute deviation estimate is: in, This represents the updated estimate of the absolute deviation of the median. This represents the estimated absolute deviation of the median before the update. This indicates the current measured temperature value. This represents the baseline value output by the background tracker, and α represents the smoothing coefficient.

[0037] In some embodiments of this application, after the sensor is powered on and initialized, a warm-up period is set, which is the number of measurement batches preset; during the warm-up period, only the estimated value of the median absolute deviation is accumulated, and no jump determination is triggered.

[0038] Specifically, the background temperature value is determined according to a three-level background temperature priority rule, as follows: Level 1 (Highest Priority) Manual Lock Value: Users can save the current ambient temperature reading as the background temperature value via button presses or host computer commands. This mode is suitable for scenarios where the background temperature is known to be constant or a fixed reference is required. Once the manual lock value is set, the system locks the value and blocks updates from lower-level sources.

[0039] Level 2 (default priority) Adaptive exponential moving average tracking value: The background temperature value is tracked using a slow exponential moving average to follow the output baseline of the background tracker. In practice, the background tracker continuously smooths and filters the sensor output, with the filtering time constant typically set to the order of several seconds to tens of seconds, in order to filter out short-term fluctuations and track the slowly changing ambient background temperature.

[0040] Level 3 (lowest priority) sensor built-in ambient temperature channel reading: When all upstream sources are invalid, the reading of the ambient temperature channel built into the single-pixel thermopile infrared temperature sensor (usually a negative temperature coefficient thermistor or PN junction temperature sensor) is directly read as the background temperature value.

[0041] The three-level background temperature priority rule automatically switches from high to low priority: Level 1 is used when the manually locked value is valid; otherwise, Level 2 is used if the adaptive tracking value is valid; otherwise, it reverts to Level 3.

[0042] Specifically, this embodiment provides a reliable and robust background temperature input for calculating background radiative exitance. Background temperature is one of the key factors affecting temperature measurement accuracy; however, in actual measurements, background temperature may fluctuate drastically due to environmental changes, target entry / exit, and other factors. A single-source background temperature value is insufficient to handle all scenarios.

[0043] The three-level priority design balances flexibility, adaptability, and reliability. The manual lock mode gives users maximum control and is suitable for fixed scenarios with prior knowledge; the adaptive exponential moving average mode automatically tracks slowly changing background temperatures and is the optimal choice for most common scenarios; the built-in ambient temperature channel serves as a final safety net, ensuring the system always obtains a usable background temperature value. The three-level automatic switching mechanism requires no user intervention, lowering the barrier to entry.

[0044] Adaptive exponential moving average tracking prevents the infrared radiation from the target itself from contaminating the background tracking results. When a high-temperature target suddenly enters the field of view, if the background tracker continues to update, the target temperature will be mixed into the background estimate, leading to an artificially high background temperature and subsequent compensation failure. A jump detection mechanism enables the tracker to automatically freeze and resume, ensuring the purity of the background temperature estimate.

[0045] In some embodiments of this application, when constructing the functional relationship between the total radiative exitance and the target's true radiative exitance and the background radiative exitance, the expression of the functional relationship is: in, Indicates total radiative exitance. Indicates the effective fill ratio. Indicates the target's true radiative exitance. This represents the background radiative exitance.

[0046] Specifically, in each measurement cycle, the microcontroller sequentially performs various basic compensations on the sensor's raw output voltage signal, including but not limited to: Emissivity correction: The radiation signal is corrected according to the user-defined target emissivity (range 0-1). As an optional implementation, the default emissivity is set to 0.95 (suitable for human skin measurements), which can be adjusted by the user via buttons. Ambient temperature compensation: The thermopile output is corrected for ambient temperature drift using the sensor's built-in NTC channel readings. Nonlinearity correction: The measured value is linearized based on the sensor's factory-calibrated nonlinearity correction curve or lookup table. After the above basic compensations are completed, a fully conditioned total radiative exitance is obtained.

[0047] In some embodiments of this application, when the true radiative exitance of the target is obtained through inverse algebraic solution, and the true temperature of the target is obtained based on the true radiative exitance, the inverse algebraic expression for the true radiative exitance of the target is: The target true temperature is determined according to the following formula: The target true temperature is determined based on the simplified Stefan-Boltzmann thermal radiation law, which is expressed as follows: Where M represents radiative exitance and T represents temperature.

[0048] Specifically, this embodiment separates the target's own radiation component from the mixed signal output by the sensor, thereby reconstructing the target's true temperature. Since the measurement result of a single-pixel sensor is a weighted sum of target radiation and background radiation, directly reading the sensor output inevitably introduces background radiation error. This step achieves accurate compensation for background radiation error by establishing a physical model and performing inverse algebraic solution.

[0049] By separating background radiation, the influence of factors such as ambient temperature changes and heat source interference near the sensor on the temperature measurement results is effectively eliminated. This compensation is particularly critical when the background temperature is close to the target temperature, as background radiation accounts for a large proportion of the total signal at this time, and failure to compensate will lead to significant errors.

[0050] From angular sensitivity modeling, effective fill ratio calculation, background temperature management to the final temperature inverse solution, a complete error compensation chain is formed. Each link is interconnected and mutually supportive, ensuring the accuracy and reliability of the final temperature measurement results.

[0051] On the other hand, such as Figure 2 As shown, this application also provides a temperature compensation system for single-pixel infrared thermometry, used to apply the above-mentioned temperature compensation method for single-pixel infrared thermometry, including: The Gaussian angular sensitivity modeling unit is configured to model the angular sensitivity function of a single-pixel thermopile infrared temperature sensor as an axisymmetric Gaussian distribution, and determine the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor. The effective filling ratio determination unit is configured to derive a closed-form analytical solution for the effective filling ratio based on the area integral of the Gaussian function over the circular target in polar coordinates, and to obtain the effective filling ratio based on the target half-angle and the standard deviation of the Gaussian distribution. The three-level background temperature management unit is configured to determine the background temperature value according to the three-level background temperature priority rule, and to determine the background radiative output based on the background temperature value according to the simplified Stefan-Boltzmann thermal radiation law. The temperature compensation inverse kinematics unit is configured to acquire the total radiative exitance measured by the sensor, construct a functional relationship between the total radiative exitance and the target true radiative exitance and the background radiative exitance, obtain the target true radiative exitance through inverse kinematics, and obtain the target true temperature based on the target true radiative exitance.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods 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.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] 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.

[0055] 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.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A temperature compensation method for single-pixel infrared thermometry, characterized in that, include: The angular sensitivity function of the single-pixel thermopile infrared temperature sensor is modeled as an axisymmetric Gaussian distribution, and the standard deviation of the Gaussian distribution is determined based on the nominal half-field angle of the sensor. Based on the area integral of the Gaussian function over a circular target in polar coordinates, a closed-form analytical solution for the effective filling ratio is derived, and the effective filling ratio is obtained based on the target half-angle and the standard deviation of the Gaussian distribution. The background temperature value is determined according to the three-level background temperature priority rule, and the background radiative exitance is determined based on the simplified Stefan-Boltzmann thermal radiation law according to the background temperature value. The total radiative exitance measured by the sensor is obtained, and a functional relationship is constructed between the total radiative exitance, the target's true radiative exitance, and the background radiative exitance. The target's true radiative exitance is obtained through inverse algebraic solution, and the target's true temperature is obtained based on the target's true radiative exitance.

2. The temperature compensation method for single-pixel infrared thermometry according to claim 1, characterized in that, When determining the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor, the standard deviation of the Gaussian distribution is determined according to the following formula: in, σ represents the nominal half field of view of the sensor, and σ represents the standard deviation of the Gaussian distribution. The expression for the Gaussian distribution is: θ represents the sensitivity, and θ represents the off-axis angle of the sensor.

3. The temperature compensation method for single-pixel infrared thermometry according to claim 1, characterized in that, The expression for the closed-form analytical solution of the effective filling ratio is: in, Indicates the effective fill ratio. Let σ represent the target half-angle, and σ represent the standard deviation of the Gaussian distribution.

4. The temperature compensation method for single-pixel infrared thermometry according to claim 3, characterized in that, The target half-angle is determined according to the following formula: in, Indicates the target half-angle. d represents the radius of the target being measured, and d represents the measurement distance between the sensor and the target being measured.

5. The temperature compensation method for single-pixel infrared thermometry according to claim 1, characterized in that, The three-level background temperature priority rule is as follows: The first level is a manual lock value, which is set to the highest priority by storing the current ambient temperature reading as the background temperature value through button press or host computer command. The second level is the adaptive exponential moving average tracking value, which uses the output baseline of the background tracker tracked by the slow exponential moving average as the background temperature value, and is set as the default priority. The third level uses the reading from the sensor's built-in ambient temperature channel as the background temperature value, and sets it to the lowest priority. The three-level background temperature priority rules automatically switch from high to low priority levels.

6. The temperature compensation method for single-pixel infrared thermometry according to claim 5, characterized in that, Determining the adaptive exponential moving average tracking value also includes: Obtain the median absolute deviation estimate, and determine the jump threshold based on the median absolute deviation estimate, wherein the jump threshold = k × median absolute deviation estimate; If the deviation between the current measured temperature value and the baseline value output by the background tracker exceeds the jump threshold, it is determined that the target being measured has entered the market, and the background tracker is immediately stopped; if the deviation between the current measured temperature value and the baseline value output by the background tracker falls back to the jump threshold, the background tracker automatically resumes background tracking.

7. The temperature compensation method for single-pixel infrared thermometry according to claim 6, characterized in that, The exponential moving average iterative formula for the median absolute deviation estimate is as follows: in, This represents the updated estimate of the absolute deviation of the median. This represents the estimated absolute deviation of the median before the update. This indicates the current measured temperature value. This represents the baseline value output by the background tracker, and α represents the smoothing coefficient.

8. The temperature compensation method for single-pixel infrared thermometry according to claim 1, characterized in that, When constructing the functional relationship between the total radiative exitance and the target's true radiative exitance and the background radiative exitance, the expression of the functional relationship is: in, Indicates total radiative exitance. Indicates the effective fill ratio. Indicates the target's true radiative exitance. This represents the background radiative exitance.

9. The temperature compensation method for single-pixel infrared thermometry according to claim 1, characterized in that, When the true radiative exitance of the target is obtained through inverse algebraic solution, and the true temperature of the target is obtained from the true radiative exitance, the inverse algebraic expression for the true radiative exitance of the target is: The target true temperature is determined according to the following formula: The target true temperature is determined based on the simplified Stefan-Boltzmann thermal radiation law, which is expressed as follows: Where M represents radiative exitance and T represents temperature.

10. A temperature compensation system for single-pixel infrared thermometry, used for applying the temperature compensation method for single-pixel infrared thermometry as described in any one of claims 1-9, characterized in that, include: The Gaussian angular sensitivity modeling unit is configured to model the angular sensitivity function of a single-pixel thermopile infrared temperature sensor as an axisymmetric Gaussian distribution, and determine the standard deviation of the Gaussian distribution based on the nominal half-field angle of the sensor. The effective filling ratio determination unit is configured to derive a closed-form analytical solution for the effective filling ratio based on the area integral of the Gaussian function over the circular target in polar coordinates, and to obtain the effective filling ratio based on the target half-angle and the standard deviation of the Gaussian distribution. The three-level background temperature management unit is configured to determine the background temperature value according to the three-level background temperature priority rule, and to determine the background radiative output based on the background temperature value according to the simplified Stefan-Boltzmann thermal radiation law. The temperature compensation inverse kinematics unit is configured to acquire the total radiative exitance measured by the sensor, construct a functional relationship between the total radiative exitance and the target true radiative exitance and the background radiative exitance, obtain the target true radiative exitance through inverse kinematics, and obtain the target true temperature based on the target true radiative exitance.

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