Infrared thermal imaging sensor and infrared temperature measurement method
By integrating a multispectral infrared detector array, an adaptive blackbody reference, and multimodal information fusion, the accuracy and stability issues of traditional infrared temperature measurement systems have been resolved, achieving high-precision temperature and emissivity distribution measurements.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BAODING DEYOU ELECTRICAL EQUIP MFG CO LTD
- Filing Date
- 2026-05-26
- Publication Date
- 2026-07-10
AI Technical Summary
Traditional infrared temperature measurement systems suffer from problems such as emissivity uncertainty, detector non-uniformity, thermal drift effect, lens contamination, and environmental radiation interference, which limit the accuracy and stability of temperature measurement and lack the ability to perform real-time online correction and multi-modal information fusion.
It employs a multispectral infrared detector array, an adaptive reference blackbody, an internal temperature monitoring module, an online lens contamination monitoring module, and an active multispectral excitation unit, combined with a signal processing and computing unit, to achieve real-time thermal drift compensation, non-uniformity correction, spectral distortion correction, and multimodal information fusion.
It improves the accuracy and stability of temperature measurement, enhances the robustness and adaptability of the system in complex environments, and achieves high-precision temperature and emissivity distribution measurement.
Smart Images
Figure CN122360700A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of infrared thermal imaging sensor technology, and more specifically, to an infrared thermal imaging sensor and an infrared temperature measurement method. Background Technology
[0002] Infrared thermal imaging technology has been widely used in industrial inspection, security monitoring, medical diagnosis, and scientific research. Its core principle is to invert surface temperature distribution by detecting the infrared radiation intensity of a target. Traditional infrared temperature measurement systems are mostly based on single-band detectors, and their temperature inversion relies on prior assumptions or estimates of the target's emissivity. However, in practical applications, the target emissivity is affected by many factors such as material, surface condition, wavelength, and viewing angle, often resulting in uncertainties and limiting measurement accuracy. Furthermore, issues such as the detector's non-uniform response, thermal drift effect, lens contamination, and environmental radiation interference can significantly affect the stability and reliability of long-term measurements.
[0003] While existing technologies have developed dual-band or multi-band temperature measurement methods to reduce emissivity dependence, they still suffer from the following shortcomings: First, they lack a real-time online dynamic reference source, making it difficult to achieve continuous compensation for non-uniformity and thermal drift; second, they do not fully consider the impact of internal temperature gradient changes on detector performance; third, they lack an online monitoring and correction mechanism for spectral distortion caused by lens contamination; and fourth, they lack multimodal information fusion and intelligent decision-making capabilities in complex scenarios, resulting in weak system adaptability and rigid correction triggering strategies.
[0004] Based on this, the present invention designs an infrared thermal imaging sensor and an infrared temperature measurement method to solve the above problems. Summary of the Invention
[0005] The purpose of this invention is to provide an infrared thermal imaging sensor and an infrared temperature measurement method to solve the problems mentioned in the background art.
[0006] An infrared thermal imaging sensor, comprising: Infrared optical lens, used to receive and focus infrared radiation from within the field of view; A multispectral infrared detector array, positioned at the focal plane of the infrared optical lens, is used to convert converged infrared radiation into electrical signals. The multispectral infrared detector array includes at least a first subset of pixels sensitive to a first infrared band and a second subset of pixels sensitive to a second infrared band. The internal temperature monitoring module includes at least two high-precision temperature sensors distributed on key thermal nodes inside the sensor housing, for real-time monitoring of the temperature gradient distribution inside the sensor. An adaptive reference blackbody is set in the edge field of view of the infrared optical lens or can be moved into the main optical path. The adaptive reference blackbody includes a target surface with precisely adjustable temperature and a blackbody temperature probe for measuring the true temperature of the target surface. The signal processing and computing unit is electrically connected to the multispectral infrared detector array, the internal temperature monitoring module, and the adaptive reference blackbody. The signal processing and computation unit stores an algorithm program for performing the following operations: Real-time thermal drift compensation is performed on the detector array based on data from the internal temperature monitoring module; online non-uniformity correction is performed based on the dynamic reference temperature value provided by the adaptive blackbody; and the response signals of the first and second pixel subsets are fused to calculate the true temperature and emissivity distribution of the target within the field of view.
[0007] Preferred options also include: The lens contamination online monitoring module includes a broadband diagnostic light source disposed inside the sensor housing and pointing towards the center region of the infrared optical lens, and a built-in reference detector for receiving the broadband diagnostic light source signal reflected back from the inner surface of the infrared optical lens and being able to distinguish its spectral composition. An active multispectral excitation unit includes at least one modulated output auxiliary infrared light source whose emitted light spot can cover a specific target area in the sensor's field of view. The emission spectrum of the auxiliary infrared light source includes at least one third characteristic band in addition to the first and second infrared bands. The multimodal fusion and confidence assessment module, integrated into the signal processing and computing unit, is used to receive and fuse signals from the multispectral infrared detector array, the lens contamination online monitoring module, and the active multispectral excitation unit, calculate the confidence of different measurement modes, and manage the correction triggering strategy. The signal processing and computing unit is electrically connected to the lens contamination online monitoring module, the active multispectral excitation unit, and the multimodal fusion and confidence assessment module.
[0008] An infrared temperature measurement method using an infrared thermal imaging sensor includes the following steps: S1: System initialization, control the adaptive reference blackbody to start and stabilize to a preset initial temperature T_ref, and collect the calibration image data of the reference blackbody on the multispectral infrared detector array at this time; S2: Online dynamic calibration. During the temperature measurement process, the target surface temperature of the adaptive reference blackbody is periodically adjusted to another stable temperature point or according to preset triggering conditions. New calibration data is collected and combined with the data of the internal temperature monitoring module to update the non-uniformity correction coefficient and thermal drift compensation model of the detector in real time. S3: Target image acquisition and processing. The adaptive reference blackbody is moved out of the main optical path, and an infrared image of the scene containing the target is acquired. The original image data is processed using the correction and compensation model updated in step S2 to obtain the corrected first-band image. With second band image ; S4: Temperature and emissivity inversion, targeting a specific pixel in the image, using its value in the corrected first band image. With second band image response value Hehe By combining Planck's radiation law, a dual-band equation system is established to simultaneously solve for the true temperature T and spectral emissivity corresponding to the pixel. approximation.
[0009] Preferably, in step S4, the method for simultaneously solving for the approximate values of the true temperature T and spectral emissivity ε(λ) corresponding to the pixel is as follows: S4.1: Set the emissivity ratio of the target in two adjacent bands to a constant k, i.e., ε(λ1) / ε(λ2)≈k; S4.2: Using the ratio of the dual-band radiation measurement equations, an equation mainly dependent on temperature T and ratio k is constructed, and the true temperature T of the target is directly obtained through iterative calculation; S4.3: Substitute the obtained T back into the original radiation equation to calculate the emissivity values ε(λ1) and ε(λ2) for each band.
[0010] Preferably, the preset triggering condition mentioned in step S2 includes an internal temperature triggering condition, specifically: S2.1: Real-time monitoring of data from the internal temperature monitoring module; S2.2: When the temperature difference between any two key thermal nodes exceeds the first preset threshold, or the temperature change rate of any node exceeds the second preset range, a dynamic correction process for the adaptive reference blackbody is automatically triggered. S2.3: During the calibration triggering and execution process, the temperature measurement data is interpolated and compensated based on the established model to achieve uninterrupted temperature measurement.
[0011] Preferably, before step S3, an online lens contamination correction step is also included: S3a: The lens contamination online monitoring module acquires real-time reflectance spectral data emitted by the broadband diagnostic light source and reflected by the inner surface of the lens; S3b: Compare the real-time reflectance spectral data with the pre-stored clean lens reflectance spectral reference, and identify whether there is spectral selective distortion caused by a uniform contamination film by analyzing the spectral oscillation characteristics; S3c: If spectral distortion is detected, an optical thin film model of the contamination layer is established, and a wavelength-dependent correction matrix is calculated; S3d: Using the correction matrix, the original radiation signal of the target received by the multispectral infrared detector array in step S3 is preprocessed to eliminate spectral distortion caused by thin-film interference.
[0012] Preferably, it also includes an active excitation-enhanced measurement step for accurate measurement of fixed or slowly varying targets: S5: Control the active multispectral excitation unit to turn its auxiliary infrared light source on and off at a specific frequency. S6: Collect image sequences of the target area when the light source is turned on and off respectively, and extract the differential signal of the target's reflection and thermal response to active excitation in the third characteristic band through image differential processing; S7: Using the differential signal, construct or modify the emissivity model of the target in the third characteristic band, and use this information as a supplementary constraint condition, substitute it into the dual-band equation system in step S4, and jointly solve for the target's true temperature and more accurate emissivity spectral distribution.
[0013] Preferably, it also includes intelligent correction triggering and mode management steps: S8: Through the multimodal fusion and confidence assessment module, the temperature change trend, active excitation differential signal intensity and dual-band radiation signal of the target area are analyzed in real time; S9: Based on the analysis in step S8, calculate the confidence level of the effectiveness of the active incentive mode in real time and evaluate the rationality of the correction trigger. S10: Make dynamic decisions based on the evaluation results: When the validity confidence level is lower than the first set threshold, discard the active excitation data, switch to the pure passive dual-band temperature measurement mode, and mark the uncertainty range in the output data; when the system abnormal trigger frequency is associated with a continuous low confidence state, automatically start the adaptive learning and adjustment process of the internal trigger rule parameters.
[0014] Preferably, the preset triggering condition mentioned in step S2 further includes an external event triggering condition, specifically: S2.4: Through the multimodal fusion and confidence assessment module, continuously determine whether the target area exhibits preset abnormal thermal characteristics; S2.5: When abnormal thermal characteristics are detected, a high-precision event correction and measurement involving the active multispectral excitation unit is automatically triggered to obtain reliable data at the abnormal moment.
[0015] Compared with the prior art, the advantages of this invention are: 1. This invention integrates a multispectral infrared detector array with an adaptive blackbody and combines it with an internal temperature monitoring module to achieve real-time online dynamic correction of detector non-uniformity response and thermal drift effect, significantly improving the accuracy and long-term stability of temperature measurement.
[0016] 2. By introducing an online lens contamination monitoring module and an active multispectral excitation unit, this invention can identify and correct spectral distortion caused by lens contamination in real time. At the same time, it uses active excitation to enhance the constraint on the target emissivity, thereby improving the temperature measurement accuracy and reliability of fixed or slowly changing targets.
[0017] 3. This invention achieves intelligent fusion and mode management of multiple measurement signals through a multimodal fusion and confidence assessment module. It can adaptively adjust the correction strategy and working mode according to changes in the scene, thereby enhancing the robustness and adaptability of the system in complex environments.
[0018] 4. This invention employs a dual-band radiation measurement and emissivity synchronous inversion algorithm, which effectively overcomes the error introduced by the emissivity assumption in traditional single-band temperature measurement, and realizes high-precision temperature and emissivity distribution measurement without prior emissivity information. Attached Figure Description
[0019] Figure 1 This is a circuit diagram of an infrared thermal imaging sensor proposed in this invention; Figure 2 This is a flowchart illustrating the infrared temperature measurement method of an infrared thermal imaging sensor proposed in this invention. Detailed Implementation
[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Please see Figures 1-2 An infrared thermal imaging sensor, comprising: Infrared optical lenses, made of chalcogenide glass or germanium, are coated with an anti-reflective film to receive and focus infrared radiation from within the field of view; the field of view can be designed according to the application scenario. to scope.
[0022] A multispectral infrared detector array, employing an uncooled microbolometer or a cooled photon detector, is positioned at the focal plane of the infrared optical lens, with a pixel size that can be [missing information]. The array size is The multispectral infrared detector array, used to convert converged infrared radiation into electrical signals, comprises at least a first subset of pixels sensitive to a first infrared band and a second subset of pixels sensitive to a second infrared band; wherein the response band of the first subset is... The response band of the second pixel subset is .
[0023] The internal temperature monitoring module includes at least two high-precision temperature sensors distributed on key thermal nodes inside the sensor housing. Specifically, they are arranged around the detector array, on the surface of the signal processing chip, on the lens mount, and other key thermal nodes. The PT1000 type temperature sensor can be used, with a sampling frequency of not less than 10Hz, to monitor the temperature gradient distribution inside the sensor in real time. An adaptive reference blackbody, positioned at the edge of the infrared optical lens's field of view or movable into the main optical path, employs a miniature MEMS blackbody structure with a target surface size of [missing information]. Temperature control range is Accuracy can reach It can be moved along the guide rail to the main optical path or placed in the edge field of view. The adaptive reference blackbody includes a target surface with a precisely adjustable temperature and a blackbody temperature probe for measuring the true temperature of the target surface. The signal processing and computing unit is electrically connected to the multispectral infrared detector array, the internal temperature monitoring module and the adaptive reference blackbody. It adopts an FPGA+ARM architecture and has built-in non-uniformity correction, thermal drift compensation and dual-band fusion algorithms to output temperature and emissivity distribution maps in real time. The signal processing and computation unit stores algorithm programs used to perform the following operations: Real-time thermal drift compensation is performed on the detector array based on data from the internal temperature monitoring module; online non-uniformity correction is performed based on the dynamic reference temperature value provided by the adaptive blackbody; and the response signals of the first and second pixel subsets are fused to calculate the true temperature and emissivity distribution of the target within the field of view.
[0024] Also includes: The lens contamination online monitoring module includes a broadband diagnostic light source (using an LED array, covering a wavelength range) located inside the sensor housing and pointing towards the center of the infrared optical lens. ( ), and an internal reference detector for receiving broadband diagnostic light source signals reflected back from the inner surface of the infrared optical lens and for distinguishing their spectral components, the internal reference detector being an InGaAs array. The active multispectral excitation unit includes at least one modulated output auxiliary infrared light source, whose emitted light spot can cover a specific target area in the sensor's field of view. The auxiliary infrared light source is a tunable quantum cascade laser, and its output wavelength includes... (CO2 absorption characteristic band) and (Ozone characteristic band), the spot size can be adjusted by the lens group, and the emission spectrum of the auxiliary infrared light source includes at least one third characteristic band in addition to the first infrared band and the second infrared band. The multimodal fusion and confidence assessment module, integrated into the signal processing and computing unit, uses Kalman filtering and Bayesian inference algorithms to receive and fuse signals from the multispectral infrared detector array, the lens contamination online monitoring module, and the active multispectral excitation unit, calculate the confidence of different measurement modes, and manage the correction triggering strategy. The signal processing and computing unit is electrically connected to the lens contamination online monitoring module, the active multispectral excitation unit, and the multimodal fusion and confidence assessment module.
[0025] An infrared temperature measurement method using an infrared thermal imaging sensor includes the following steps: S1: System initialization, control the adaptive reference blackbody to start and stabilize to a preset initial temperature T_ref, and collect the calibration image data of the reference blackbody on the multispectral infrared detector array at this time; S2: Online dynamic calibration. During the temperature measurement process, the target surface temperature of the adaptive reference blackbody is periodically adjusted to another stable temperature point or according to preset trigger conditions. New calibration data is collected and combined with the data from the internal temperature monitoring module to update the non-uniformity correction coefficient and thermal drift compensation model of the detector in real time. S3: Target image acquisition and processing. The adaptive reference blackbody is moved out of the main optical path, and an infrared image of the scene containing the target is acquired. The original image data is processed using the correction and compensation model updated in step S2 to obtain the corrected first-band image. With second band image ; S4: Temperature and emissivity inversion, targeting a specific pixel in the image, using its value in the corrected first band image. With second band image response value Hehe By combining Planck's radiation law, a dual-band equation system is established to simultaneously solve for the true temperature T and spectral emissivity corresponding to the pixel. The approximate value, specifically the dual-band equation system, is as follows: ; ; in This is the blackbody radiation spectrum. For the environmental radiation term, the emissivity ratio of the two bands is set. , construct about temperature The equation is solved using Newton's iterative method. , then go back and ask for and .
[0026] In step S4, the method for simultaneously solving for the approximate values of the true temperature T and spectral emissivity ε(λ) corresponding to the pixel is as follows: S4.1: Set the emissivity ratio of the target in two adjacent bands to a constant k, i.e., ε(λ1) / ε(λ2)≈k; S4.2: Using the ratio of the dual-band radiation measurement equations, an equation mainly dependent on temperature T and ratio k is constructed, and the true temperature T of the target is directly obtained through iterative calculation; S4.3: Substitute the obtained T back into the original radiation equation to calculate the emissivity values ε(λ1) and ε(λ2) for each band.
[0027] The preset triggering conditions in step S2 include the internal temperature triggering condition, specifically: S2.1: Real-time monitoring of data from the internal temperature monitoring module; S2.2: When the temperature difference between any two key thermal nodes exceeds the first preset threshold, or the temperature change rate of any node exceeds the second preset range, a dynamic correction process for the adaptive reference blackbody is automatically triggered. S2.3: During the calibration triggering and execution process, the temperature measurement data is interpolated and compensated based on the established model to achieve uninterrupted temperature measurement.
[0028] Prior to step S3, an online lens contamination correction step is also included: S3a: The lens contamination online monitoring module acquires real-time reflectance spectral data emitted by a broadband diagnostic light source and reflected by the inner surface of the lens; S3b: Compare the real-time reflectance spectral data with the pre-stored clean lens reflectance spectral reference, and identify whether there is spectral selective distortion caused by a uniform contamination film by analyzing the spectral oscillation characteristics; S3c: If spectral distortion is detected, an optical thin film model of the contamination layer is established, and a wavelength-dependent correction matrix is calculated; S3d: Using the correction matrix, the original radiation signal of the target received by the multispectral infrared detector array in step S3 is preprocessed to eliminate spectral distortion caused by thin-film interference.
[0029] It also includes an active stimulus-enhanced measurement step for accurate measurement of fixed or slowly varying targets: S5: Control the active multispectral excitation unit to turn its auxiliary infrared light source on and off at a specific frequency; S6: Collect image sequences of the target area when the light source is turned on and off respectively, and extract the differential signal of the target's reflection and thermal response to active excitation in the third characteristic band through image differential processing; S7: Using the differential signal, construct or modify the emissivity model of the target in the third characteristic band, and use this information as a supplementary constraint condition, substitute it into the dual-band equation system in step S4, and jointly solve for the target's true temperature and a more accurate emissivity spectral distribution.
[0030] It also includes intelligent correction triggering and mode management steps: S8: Through the multimodal fusion and confidence assessment module, the temperature change trend, active excitation differential signal intensity and dual-band radiation signal of the target area are analyzed in real time; S9: Based on the analysis in step S8, calculate the confidence level of the effectiveness of the active incentive mode in real time and evaluate the rationality of the correction trigger. S10: Make dynamic decisions based on the evaluation results: When the confidence level of effectiveness is lower than the first set threshold, discard the active excitation data, switch to the pure passive dual-band temperature measurement mode, and mark the uncertainty range in the output data; when the frequency of abnormal system triggering is associated with a continuous low confidence state, automatically start the adaptive learning and adjustment process of the internal triggering rule parameters.
[0031] The preset triggering conditions in step S2 further include external event triggering conditions, specifically: S2.4: Through the multimodal fusion and confidence assessment module, continuously determine whether the target area exhibits preset abnormal thermal characteristics; S2.5: When abnormal thermal characteristics are detected, a high-precision event correction and measurement involving an active multispectral excitation unit is automatically triggered to obtain reliable data at the time of the abnormality.
[0032] Working principle of the invention: When the system is working, the infrared optical lens first receives and converges the infrared radiation from the target in the field of view, focusing it onto a multispectral infrared detector array. This array contains at least two sensitive pixel subsets that can simultaneously acquire the radiation information of the target in different bands. The internal temperature monitoring module collects temperature data of multiple key thermal nodes inside the sensor housing in real time to monitor changes in the internal temperature gradient and provide a basis for thermal drift compensation. The adaptive reference blackbody can periodically or triggerically move to the main optical path or edge field of view during the temperature measurement process. Through its precisely temperature-adjustable target surface and matching temperature probe, it provides a dynamic and accurate reference radiation signal for online updating of the non-uniformity correction coefficient.
[0033] The signal processing and computing unit integrates data from the detector array, internal temperature monitoring module and adaptive reference blackbody, performs real-time thermal drift compensation and non-uniformity correction to improve data accuracy. In dual-band temperature measurement mode, by fusing the response signals of the two bands and establishing a set of equations based on Planck's radiation law, the true temperature and spectral emissivity distribution of the target are simultaneously inverted, effectively overcoming the error introduced by the emissivity assumption in traditional single-band temperature measurement.
[0034] The system can also integrate an online lens contamination monitoring module, which monitors the contamination status of the lens surface in real time through a broadband diagnostic light source and a built-in reference detector. It models and corrects spectral distortion caused by contamination, ensuring measurement stability for long-term use. The active multispectral excitation unit can emit modulated infrared light of a specific band to the target when needed. By differentially extracting the reflection and thermal response information of the target in the characteristic band, it further constrains the emissivity model and improves the temperature measurement accuracy and reliability for fixed or slowly changing targets.
[0035] The multimodal fusion and confidence assessment module analyzes signals from different modules in real time, assesses the confidence of each measurement mode, and intelligently manages the correction trigger strategy and working mode switching to achieve an adaptive and highly robust infrared temperature measurement process. The entire system achieves high-precision and high-reliability temperature and emissivity measurement under complex environments and long-term operating conditions through multi-sensor fusion, online correction and intelligent decision-making.
[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. An infrared thermal imaging sensor, characterized in that, include: Infrared optical lens, used to receive and focus infrared radiation from within the field of view; A multispectral infrared detector array, positioned at the focal plane of the infrared optical lens, is used to convert converged infrared radiation into electrical signals. The multispectral infrared detector array includes at least a first subset of pixels sensitive to a first infrared band and a second subset of pixels sensitive to a second infrared band. The internal temperature monitoring module includes at least two high-precision temperature sensors distributed on key thermal nodes inside the sensor housing, for real-time monitoring of the temperature gradient distribution inside the sensor. An adaptive reference blackbody is set in the edge field of view of the infrared optical lens or can be moved into the main optical path. The adaptive reference blackbody includes a target surface with precisely adjustable temperature and a blackbody temperature probe for measuring the true temperature of the target surface. The signal processing and computing unit is electrically connected to the multispectral infrared detector array, the internal temperature monitoring module, and the adaptive reference blackbody. The signal processing and computation unit stores an algorithm program for performing the following operations: Real-time thermal drift compensation is performed on the detector array based on data from the internal temperature monitoring module; online non-uniformity correction is performed based on the dynamic reference temperature value provided by the adaptive blackbody; and the response signals of the first and second pixel subsets are fused to calculate the true temperature and emissivity distribution of the target within the field of view.
2. The infrared thermal imaging sensor according to claim 1, characterized in that, Also includes: The lens contamination online monitoring module includes a broadband diagnostic light source disposed inside the sensor housing and pointing towards the center region of the infrared optical lens, and a built-in reference detector for receiving the broadband diagnostic light source signal reflected back from the inner surface of the infrared optical lens and being able to distinguish its spectral composition. An active multispectral excitation unit includes at least one modulated output auxiliary infrared light source whose emitted light spot can cover a specific target area in the sensor's field of view. The emission spectrum of the auxiliary infrared light source includes at least one third characteristic band in addition to the first and second infrared bands. The multimodal fusion and confidence assessment module, integrated into the signal processing and computing unit, is used to receive and fuse signals from the multispectral infrared detector array, the lens contamination online monitoring module, and the active multispectral excitation unit, calculate the confidence of different measurement modes, and manage the correction triggering strategy. The signal processing and computing unit is electrically connected to the lens contamination online monitoring module, the active multispectral excitation unit, and the multimodal fusion and confidence assessment module.
3. An infrared temperature measurement method using an infrared thermal imaging sensor, characterized in that, Includes the following steps: S1: System initialization, control the adaptive reference blackbody to start and stabilize to a preset initial temperature T_ref, and collect the calibration image data of the reference blackbody on the multispectral infrared detector array at this time; S2: Online dynamic calibration. During the temperature measurement process, the target surface temperature of the adaptive reference blackbody is periodically adjusted to another stable temperature point or according to preset triggering conditions. New calibration data is collected and combined with the data of the internal temperature monitoring module to update the non-uniformity correction coefficient and thermal drift compensation model of the detector in real time. S3: Target image acquisition and processing. The adaptive reference blackbody is moved out of the main optical path, and an infrared image of the scene containing the target to be measured is acquired. The original image data is processed using the correction and compensation model updated in step S2 to obtain the corrected first wave image. With second band image ; S4: Temperature and emissivity inversion, targeting a specific pixel in the image, using its value in the corrected first band image. With the second Band image response value Hehe By combining Planck's radiation law, a dual-band equation system is established to simultaneously solve for the true temperature T and spectral emissivity corresponding to the pixel. approximation.
4. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 3, characterized in that, In step S4, the method for simultaneously solving for the approximate values of the true temperature T and spectral emissivity ε(λ) corresponding to the pixel is as follows: S4.1: Set the emissivity ratio of the target in two adjacent bands to a constant k, i.e., ε(λ1) / ε(λ2)≈k; S4.2: Using the ratio of the dual-band radiation measurement equations, an equation mainly dependent on temperature T and ratio k is constructed, and the true temperature T of the target is directly obtained through iterative calculation; S4.3: Substitute the obtained T back into the original radiation equation to calculate the emissivity values ε(λ1) and ε(λ2) for each band.
5. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 3, characterized in that, The preset triggering conditions mentioned in step S2 include internal temperature triggering conditions, specifically: S2.1: Real-time monitoring of data from the internal temperature monitoring module; S2.2: When the temperature difference between any two key thermal nodes exceeds the first preset threshold, or the temperature change rate of any node exceeds the second preset range, a dynamic correction process for the adaptive reference blackbody is automatically triggered. S2.3: During the calibration triggering and execution process, the temperature measurement data is interpolated and compensated based on the established model to achieve uninterrupted temperature measurement.
6. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 3, characterized in that, Prior to step S3, an online lens contamination correction step is also included: S3a: The lens contamination online monitoring module acquires real-time reflectance spectral data emitted by the broadband diagnostic light source and reflected by the inner surface of the lens; S3b: Compare the real-time reflectance spectral data with the pre-stored clean lens reflectance spectral reference, and identify whether there is spectral selective distortion caused by a uniform contamination film by analyzing the spectral oscillation characteristics; S3c: If spectral distortion is detected, an optical thin film model of the contamination layer is established, and a wavelength-dependent correction matrix is calculated; S3d: Using the correction matrix, the original radiation signal of the target received by the multispectral infrared detector array in step S3 is preprocessed to eliminate spectral distortion caused by thin-film interference.
7. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 3, characterized in that, It also includes an active stimulus-enhanced measurement step for accurate measurement of fixed or slowly varying targets: S5: Control the active multispectral excitation unit to turn its auxiliary infrared light source on and off at a specific frequency. S6: Collect image sequences of the target area when the light source is turned on and off respectively, and extract the differential signal of the target's reflection and thermal response to active excitation in the third characteristic band through image differential processing; S7: Using the differential signal, construct or modify the emissivity model of the target in the third characteristic band, and use this information as a supplementary constraint condition, substitute it into the dual-band equation system in step S4, and jointly solve for the target's true temperature and more accurate emissivity spectral distribution.
8. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 7, characterized in that, It also includes intelligent correction triggering and mode management steps: S8: Through the multimodal fusion and confidence assessment module, the temperature change trend, active excitation differential signal intensity and dual-band radiation signal of the target area are analyzed in real time; S9: Based on the analysis in step S8, calculate the confidence level of the effectiveness of the active incentive mode in real time and evaluate the rationality of the correction trigger. S10: Make dynamic decisions based on the evaluation results: When the validity confidence level is lower than the first set threshold, discard the active excitation data, switch to the pure passive dual-band temperature measurement mode, and mark the uncertainty range in the output data; when the system abnormal trigger frequency is associated with a continuous low confidence state, automatically start the adaptive learning and adjustment process of the internal trigger rule parameters.
9. The infrared temperature measurement method of the infrared thermal imaging sensor according to claim 8, characterized in that, The preset triggering condition mentioned in step S2 further includes an external event triggering condition, specifically: S2.4: Through the multimodal fusion and confidence assessment module, continuously determine whether the target area exhibits preset abnormal thermal characteristics; S2.5: When abnormal thermal characteristics are detected, a high-precision event correction and measurement involving the active multispectral excitation unit is automatically triggered to obtain reliable data at the abnormal moment.