A temperature calibration method and calibration system for area array infrared data

Abstract

This application discloses a temperature calibration method and system for area array infrared data. The calibration method includes: simultaneously acquiring reference temperature data output by a dot matrix temperature measurement unit and raw infrared grayscale data output by an infrared external array imaging unit; aligning the acquisition sequence of the reference temperature data and the raw infrared grayscale data; determining the central region in the raw infrared grayscale data corresponding to the temperature measurement field of view of the dot matrix temperature measurement unit based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit; constructing a calibration reference pair by comparing the average grayscale value of the central region with the reference temperature data; dividing the imaging area of ​​the raw infrared grayscale data into several calibration units using the pixel area of ​​the central region as the standard unit; and converting the average grayscale value of each calibration unit into the corresponding temperature value based on the calibration reference pair. This application can ensure the calibration consistency of temperature in each region of the area array and maintain stable calibration accuracy even in high-temperature dynamic scenarios.

Description

Technical Field

[0001] This application belongs to the field of temperature calibration technology, specifically relating to a temperature calibration method and calibration system for area array infrared data. Background Technology

[0002] Area array infrared thermometry technology has been widely used in industrial high-temperature monitoring and equipment thermal fault diagnosis because it can reveal the spatial temperature distribution characteristics of the measured target. However, the accuracy of traditional area array infrared thermometry has always been hampered by insurmountable bottlenecks, falling far short of the precision level of unit array thermometry.

[0003] Currently, the calibration method for area array infrared temperature measurement is mainly a pure algorithm correction scheme. The pure algorithm correction scheme only relies on the data collected by the area array detector itself and makes corrections through environmental temperature compensation, blackbody single-point calibration, etc. This type of method lacks the closed-loop participation of an external high-precision temperature measurement reference, making it difficult to meet the high-precision temperature measurement requirements in high-temperature environments.

[0004] To address this need, related technologies have proposed a unit-area array fusion temperature measurement scheme. However, such schemes can usually only achieve simple single-point value replacement, making it difficult to guarantee an effective correspondence between the unit array reference and the area array data. The calibration effect is unstable in high-temperature dynamic scenarios. At the same time, the correction of the entire area temperature measurement data is relatively simplified, and the correction of the entire area data is completed by simply scaling the scale. This results in poor temperature calibration consistency in different areas of the area array, and cannot ensure the accuracy and reliability of the area array temperature measurement data. Summary of the Invention

[0005] This application provides a temperature calibration method and system for area array infrared data, which can ensure the calibration consistency of temperature in each area of ​​the area array and maintain stable calibration accuracy under high temperature dynamic scenarios.

[0006] To address the aforementioned technical problems, this application provides a temperature calibration method for area array infrared data, comprising the following steps: Simultaneously acquire the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit; The acquisition timing of the reference temperature data and the raw infrared grayscale data is aligned to control the timing synchronization error within a preset error. Based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit, the central region in the infrared raw grayscale data corresponding to the temperature measurement field of view of the dot matrix temperature measurement unit is determined. The gray values ​​of each pixel in the central region are extracted from the raw infrared gray data, and the average gray value of the central region is used to construct a calibration reference pair with the reference temperature data. Using the pixel area of ​​the central region as the standard unit, the imaging area of ​​the infrared raw grayscale data is divided into several calibration units; Calculate the average gray value of each calibration unit, and convert the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair to generate calibrated area array infrared data.

[0007] As a further improvement to this application, aligning the acquisition timing of the reference temperature data with that of the raw infrared grayscale data to control the timing synchronization error within a preset error includes: The first timestamp when the dot matrix temperature measurement unit collects the reference temperature data and the second timestamp when the infrared external array imaging unit collects the infrared raw grayscale data are obtained. The first timestamp is matched with the second timestamp so that the timing synchronization error between the matched reference temperature data and the original infrared grayscale data is ≤10ms.

[0008] As a further improvement to this application, the step of determining the central region in the original infrared grayscale data corresponding to the temperature measurement field of view of the dot matrix temperature measurement unit based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit includes: Based on the temperature measurement field of view of the dot matrix temperature measurement unit and the pixel resolution of the infrared external array imaging unit, a spatial coordinate mapping relationship is established between the temperature measurement field of view and the position of each pixel in the infrared raw grayscale data. By using the spatial coordinate mapping relationship, the pixel region corresponding to the light spot of the dot matrix temperature measuring unit in the original infrared grayscale data is determined as the central region.

[0009] As a further improvement to this application, the step of dividing the imaging area of ​​the raw infrared grayscale data into several calibration units, using the pixel area of ​​the central region as the standard unit, includes: Obtain the pixel area of ​​the central region; Using the pixel area as a standard unit, the imaging region is divided into equal grids, so that the pixel area of ​​each calibration unit is equal to the pixel area of ​​the central region.

[0010] As a further improvement to this application, after converting the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair, the method further includes: The temperature values ​​converted by each calibration unit are smoothed by an interpolation algorithm, so that the temperature value of the central region in the normalized and corrected area array infrared data deviates from the reference temperature data output by the dot matrix temperature measurement unit by ≤±0.5℃.

[0011] As a further improvement to this application, after generating the calibrated area array infrared data, the method further includes: Based on the calibrated area array infrared data, a corresponding temperature gradient distribution map is generated using a bilinear interpolation algorithm; and isotherms are calibrated in the temperature gradient distribution map using an isotherm fitting algorithm. The temperature gradient distribution map includes the spatial distribution of the temperature field of the target being measured, the location of hot spots, and the temperature difference range.

[0012] As a further improvement to this application, after generating the calibrated area array infrared data, the method further includes: The temperature characteristic parameters of the target under test are extracted from the calibrated area array infrared data; When the extracted temperature feature parameters exceed the corresponding preset threshold, an alarm message is generated; The temperature characteristic parameters include the highest temperature, lowest temperature, maximum temperature difference, and location of hotspot areas of the target being measured.

[0013] As a further improvement to this application, after generating the calibrated area array infrared data, the method further includes: The calibrated area array infrared data, the temperature gradient distribution map, and / or the alarm information are transmitted to an external terminal; wherein the external terminal includes one or more of an industrial MES system, a remote monitoring platform, or a smart terminal.

[0014] As a further improvement of this application, the dot matrix temperature measuring unit is a unit dot matrix temperature measuring instrument, and the infrared array imaging unit is an organic infrared array camera, a quantum dot infrared array camera, or an indium gallium arsenide infrared array camera. The dot matrix temperature measuring unit and the infrared array imaging unit are coaxially fixedly arranged.

[0015] As a further improvement to this application, this application also provides a temperature calibration system for area array infrared data, comprising: The acquisition module is used to synchronously acquire the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit. The alignment module is used to align the acquisition timing of the reference temperature data with that of the infrared raw grayscale data, so as to control the timing synchronization error within a preset error. The mapping module is used to determine the central region in the infrared raw grayscale data that corresponds to the temperature measurement field of view of the dot matrix temperature measurement unit based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit. An extraction module is used to extract the gray values ​​of each pixel in the central region from the raw infrared grayscale data, and construct a calibration reference pair by combining the average gray value of the central region with the reference temperature data. The partitioning module is used to divide the imaging area of ​​the infrared raw grayscale data into several calibration units, using the pixel area of ​​the central region as the standard unit. The calibration module is used to calculate the average gray value of each calibration unit, convert the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair, and generate calibrated area array infrared data.

[0016] This application provides a temperature calibration method and system for area array infrared data. It simultaneously acquires the reference temperature data of the dot matrix temperature measurement unit and the raw infrared grayscale data of the infrared area array imaging unit. The timing synchronization error between the reference temperature data and the raw infrared grayscale data is controlled within a preset error range, achieving precise matching between the reference temperature data and the raw infrared grayscale data in the time dimension. Based on the spatial coordinate mapping relationship, a central region corresponding to the dot matrix temperature measurement field of view is determined, ensuring that the reference temperature data and the raw infrared grayscale data accurately correspond to the same physical location of the target being measured. Subsequently, the grayscale values ​​of each pixel within the central region are extracted and the average grayscale value is calculated. A calibration reference pair is constructed with the reference temperature data, mitigating the deviation caused by single-pixel anomalies.

[0017] Furthermore, the imaging area is divided into several calibration units using the pixel area of ​​the central region as the standard unit, avoiding the problem of inconsistent calibration standards caused by differences in regional area. Finally, the average gray value of each calibration unit is calculated, converted into the corresponding temperature value based on the calibration reference, and calibrated area array infrared data is generated. This ensures that the temperature value of each calibration unit is corrected based on the high-precision temperature value of the central region, avoiding the problem of poor overall calibration consistency caused by only single-point replacement or linear scaling in traditional fusion schemes. This ensures the temperature calibration consistency of each region of the area array and maintains stable calibration accuracy even in high-temperature dynamic scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only a part of the embodiments of this application, and not all of the embodiments. For those skilled in the art, other drawings obtained from these drawings without creative effort are all within the scope of protection of this application.

[0019] Figure 1 A flowchart of a method for calibrating area array infrared data provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the structure of the dot matrix temperature measurement unit in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the infrared array imaging unit in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0022] Figure 4 A schematic diagram of the light spot structure in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0023] Figure 5 The flowchart illustrates the process of controlling timing synchronization error within a preset error in the temperature calibration method for area array infrared data provided in this application embodiment.

[0024] Figure 6 An example diagram illustrating how the timing synchronization error is controlled within a preset error in the temperature calibration method for area array infrared data provided in this application embodiment.

[0025] Figure 7 This is a flowchart illustrating the process of determining the central region in the temperature calibration method for area array infrared data provided in this application embodiment.

[0026] Figure 8 This is a flowchart of the segmentation calibration unit in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0027] Figure 9 This is a flowchart illustrating the smoothing process of temperature values ​​in the temperature calibration method for area array infrared data provided in this application embodiment.

[0028] Figure 10 This is a flowchart illustrating the generation of a temperature gradient distribution map in the temperature calibration method for area array infrared data provided in this application embodiment.

[0029] Figure 11 This is a flowchart illustrating the generation of alarm information in the temperature calibration method for area array infrared data provided in this application embodiment.

[0030] Figure 12 This is a flowchart illustrating the transmission of area array infrared data to an external terminal in the temperature calibration method provided in this application embodiment.

[0031] Figure 13 An example diagram of normalization correction in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0032] Figure 14 An example diagram of the temperature gradient distribution in the temperature calibration method for area array infrared data provided in the embodiments of this application.

[0033] Figure 15 A functional block diagram of a temperature calibration system for area array infrared data provided in an embodiment of this application. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0035] To make the description of this disclosure more detailed and complete, illustrative descriptions of the implementation methods and specific embodiments of this application are provided below; however, this is not the only form of implementing or utilizing the specific embodiments of this application. The implementation methods cover the features of multiple specific embodiments and the method steps and their order for constructing and operating these specific embodiments. However, other specific embodiments can also be used to achieve the same or equivalent functions and step sequences. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0037] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The word "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more. Other quantifiers should be understood similarly. The preferred embodiments described herein are only for illustration and explanation of this application and are not intended to limit this application. Furthermore, the embodiments of this application and the features in the embodiments can be combined with each other without conflict.

[0038] Area array infrared thermometry technology has been widely used in industrial high-temperature monitoring and equipment thermal fault diagnosis because it can reveal the spatial temperature distribution characteristics of the measured target. However, the accuracy of traditional area array infrared thermometry has always been hampered by insurmountable bottlenecks, falling far short of the precision level of unit array thermometry.

[0039] Currently, the calibration method for area array infrared temperature measurement is mainly a pure algorithm correction scheme. The pure algorithm correction scheme only relies on the data collected by the area array detector itself and makes corrections through environmental temperature compensation, blackbody single-point calibration, etc. This type of method lacks the closed-loop participation of an external high-precision temperature measurement reference, making it difficult to meet the high-precision temperature measurement requirements in high-temperature environments.

[0040] To address this need, related technologies have proposed a unit-area array fusion temperature measurement scheme. However, such schemes can usually only achieve simple single-point value replacement, making it difficult to guarantee an effective correspondence between the unit array reference and the area array data. The calibration effect is unstable in high-temperature dynamic scenarios. At the same time, the correction of the entire area temperature measurement data is relatively simplified, and the correction of the entire area data is completed by simply scaling the scale. This results in poor temperature calibration consistency in different areas of the area array, and cannot ensure the accuracy and reliability of the area array temperature measurement data.

[0041] In view of this, please refer to Figures 1-15 This application proposes a temperature calibration method and system for area array infrared data, which can ensure the calibration consistency of temperature in each area of ​​the area array and maintain stable calibration accuracy in high-temperature dynamic scenarios.

[0042] Please refer to Figure 1 This is a flowchart of a temperature calibration method for area array infrared data provided in an embodiment of this application. The calibration method includes the following steps: Step S1: Synchronously acquire the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit; Understandably, a dot matrix temperature measurement unit is a temperature measurement component with high-precision temperature measurement characteristics. It is usually used to measure the temperature of targets within a specific field of view and can output high-precision reference temperature data of the target corresponding to the temperature measurement field of view, which can be used as a reference for subsequent temperature calibration.

[0043] The infrared external array imaging unit is used to acquire the infrared radiation distribution information of the target under test. Its output is raw infrared grayscale data, which can intuitively reflect the infrared radiation intensity distribution at different locations of the target under test.

[0044] In actual data acquisition, the dot matrix temperature measurement unit and the infrared external array imaging unit work independently. This application needs to simultaneously acquire the reference temperature data output by the dot matrix temperature measurement unit and the infrared raw grayscale data output by the infrared external array imaging unit. For example, by using hardware triggering or software timestamp matching, the acquired reference temperature data and infrared raw grayscale data can accurately correspond to the temperature measurement status of the target at the same moment, avoiding calibration deviation caused by inconsistent acquisition time.

[0045] Step S2: Align the acquisition timing of the reference temperature data with that of the infrared raw grayscale data to control the timing synchronization error within a preset error. In this embodiment of the application, although the synchronous acquisition of the reference temperature data and the original infrared grayscale data has been completed in step S1, the dot matrix temperature measurement unit and the infrared array imaging unit will still produce a small timing synchronization error during the actual acquisition process due to differences in hardware response speed and data transmission link.

[0046] Furthermore, if the timing synchronization error is not effectively controlled, in high-temperature dynamic temperature measurement scenarios, the temperature of the target being measured may change within the deviation time, which will cause the reference temperature data and the original infrared grayscale data to not accurately correspond to the same state of the target being measured, thereby affecting the accuracy of subsequent calibration. Therefore, it is necessary to align the acquisition timing of the reference temperature data and the original infrared grayscale data and control their timing synchronization error within the preset error.

[0047] As an optional implementation method, please refer to Figure 5 This is a flowchart illustrating the method for controlling timing synchronization errors within a preset error in the temperature calibration of area array infrared data provided in this application embodiment. The method involves aligning the acquisition timing of the reference temperature data with that of the original infrared grayscale data to control the timing synchronization error within the preset error, including: Step S20: Obtain the first timestamp when the dot matrix temperature measurement unit collects the reference temperature data, and the second timestamp when the infrared array imaging unit collects the infrared raw grayscale data; Step S21: Match the first timestamp with the second timestamp so that the timing synchronization error between the matched reference temperature data and the original infrared grayscale data is ≤10ms.

[0048] Understandably, a timestamp is a time identifier used to accurately record the actual moment when data is collected. It is necessary to first obtain the first timestamp when the dot matrix temperature measurement unit collects the reference temperature data, and the second timestamp when the infrared array imaging unit collects the infrared raw grayscale data.

[0049] Specifically, the first timestamp is the record of the moment when the dot matrix temperature measurement unit completes the acquisition of the reference temperature data and generates the data, and the second timestamp is the record of the moment when the infrared array imaging unit completes the acquisition of the infrared raw grayscale data and generates the data.

[0050] In the embodiments of this application, please refer to Figure 6 This is an example diagram illustrating how the timing synchronization error is controlled within a preset error in the temperature calibration method for area array infrared data provided in this application embodiment. After obtaining the first timestamp and the second timestamp, it is necessary to compare and match the first timestamp and the second timestamp. For example, by using software filtering and data timing adjustment, reference temperature data and infrared raw grayscale data with timestamp differences that meet the requirements can be selected, or reference temperature data with a small time difference and infrared raw grayscale data can be time-calibrated to ensure that the timing synchronization error between the matched reference temperature data and infrared raw grayscale data is ≤10ms.

[0051] It should be noted that the aforementioned timing synchronization error of ≤10ms is determined in conjunction with the temperature measurement requirements of high-temperature scenarios. This can effectively avoid the problem of deviation in the corresponding temperature state of the measured target due to excessive timing deviation, and ensure the accurate correspondence between the reference temperature data and the original infrared grayscale data in the time dimension. This application does not impose any restrictions on the specific value of the timing synchronization error.

[0052] Step S3: Based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit, determine the central region in the infrared raw grayscale data that corresponds to the temperature measurement field of view of the dot matrix temperature measurement unit; In this embodiment of the application, after the timing alignment of the reference temperature data and the original infrared grayscale data is completed, it is necessary to further establish the spatial correspondence between the reference temperature data and the original infrared grayscale data.

[0053] A dot matrix temperature measurement unit typically has a specific temperature measurement field of view, enabling high-precision temperature measurement of the target within that specific field of view. In contrast, an infrared external array imaging unit acquires the raw infrared grayscale data of the entire target surface and outputs a pixel array covering the entire target surface. Therefore, it is necessary to determine the central region in the raw infrared grayscale data that corresponds to the temperature measurement field of view of the dot matrix temperature measurement unit.

[0054] As an optional implementation method, please refer to Figure 7 This is a flowchart illustrating the process of determining the central region in the temperature calibration method for area array infrared data provided in this application embodiment. The determination of the central region in the original infrared grayscale data corresponding to the temperature measurement field of view of the area array temperature measurement unit, based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared area array imaging unit, includes: Step S30: Based on the temperature measurement field of view of the dot matrix temperature measurement unit and the pixel resolution of the infrared external array imaging unit, establish a spatial coordinate mapping relationship between the temperature measurement field of view and the pixel positions in the infrared raw grayscale data. Step S31: Using the spatial coordinate mapping relationship, determine the pixel region corresponding to the light spot of the dot matrix temperature measuring unit in the original infrared grayscale data as the central region.

[0055] In an optional embodiment, please refer to Figure 2 This is a schematic diagram of the structure of the dot matrix temperature measurement unit in the temperature calibration method for area array infrared data provided in this application embodiment. It can be observed that the temperature measurement field of view of the dot matrix temperature measurement unit is typically a spot area with a specific angular range. Please refer to... Figure 3 This is a schematic diagram of the infrared array imaging unit in the temperature calibration method for area infrared data provided in this application embodiment. The infrared array imaging unit images the entire area of ​​the target under test through a pixel array.

[0056] Please refer to Figure 4 This is a schematic diagram of the light spot structure in the temperature calibration method for area array infrared data provided in the embodiments of this application. In practical applications, the dot matrix temperature measurement unit and the infrared external array imaging unit are usually installed in a coaxial or fixed relative position, so that the spatial positional relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit is determined. Therefore, the temperature measurement field boundary of the dot matrix temperature measurement unit can be mapped to the pixel coordinate system of the area array image through the principle of geometric optics.

[0057] As an optional implementation, the pixel region corresponding to the temperature measurement field of the dot matrix temperature measurement unit in the area array image can be calculated based on the field angle parameters of the dot matrix temperature measurement unit and the pixel resolution of the infrared area array imaging unit, thereby establishing a spatial coordinate mapping relationship between the temperature measurement field of the dot matrix temperature measurement unit and the pixel positions in the original infrared grayscale data.

[0058] Furthermore, the pixel region corresponding to the light spot of the dot matrix temperature measuring unit in the original infrared grayscale data can be determined through the above spatial coordinate mapping relationship.

[0059] Specifically, the light spot of the dot matrix temperature measuring unit is a light spot area with a certain area size. Through spatial coordinate mapping, the boundary contour of the light spot is converted into pixel coordinates in the area array image. The pixels surrounded by the light spot boundary constitute a continuous pixel area, which is the pixel area corresponding to the light spot of the dot matrix temperature measuring unit in the infrared raw grayscale data, that is, the central area.

[0060] It is understandable that the reference temperature data output by the dot matrix temperature measurement unit reflects the overall temperature information within the coverage area of ​​its spot, rather than the temperature of a single pixel. Therefore, the corresponding central area should be the pixel area composed of all the pixels covered by the spot.

[0061] Step S4: Extract the grayscale values ​​of each pixel in the central region from the raw infrared grayscale data, and construct a calibration reference pair by combining the average grayscale value of the central region with the reference temperature data; It should be noted that the reference temperature data output by the dot matrix temperature measurement unit is a specific temperature value, reflecting the overall temperature level within the area covered by the light spot; while the infrared raw grayscale data output by the infrared array imaging unit is the grayscale value of each pixel, which has not yet been converted into a temperature value.

[0062] The central region is the pixel area corresponding to the spot of the dot matrix temperature measurement unit in the area array image. This pixel area contains multiple pixels, each of which corresponds to a gray value. Therefore, it is necessary to extract the gray values ​​of each pixel in the central region from the original infrared gray data, calculate the arithmetic mean of these gray values, and obtain the average gray value of the central region.

[0063] Furthermore, the average gray value of the central area and the reference temperature data are used to construct a calibration reference pair, forming a one-to-one calibration reference pair. This allows the gray values ​​of other areas in the array to be converted to temperature based on the calibration reference pair, thereby achieving calibration of the entire array temperature measurement data.

[0064] Step S5: Using the pixel area of ​​the central region as the standard unit, divide the imaging area of ​​the infrared raw grayscale data into several calibration units; In this embodiment, the reference temperature data output by the dot matrix temperature measurement unit reflects the average temperature within the coverage area of ​​its spot. The central region corresponding to the spot in the area array image contains multiple pixels. The gray values ​​of these pixels are averaged and then used to establish a calibration reference pair with the reference temperature data.

[0065] For pixels in other areas of the area array image, if temperature conversion is performed directly on a single pixel basis, the calibration results will be unstable due to large fluctuations in the grayscale value of a single pixel. Therefore, it is necessary to divide the entire imaging area into several calibration units with an area equal to that of the central area.

[0066] As an optional implementation method, please refer to Figure 8 This is a flowchart of the segmentation calibration unit in the temperature calibration method for area array infrared data provided in this application embodiment. The above-mentioned method uses the pixel area of ​​the central region as the standard unit to divide the imaging area of ​​the original infrared grayscale data into several calibration units, including: Step S50: Obtain the pixel area of ​​the central region; Step S51: Using the pixel area as the standard unit, the imaging area is divided into equal grids so that the pixel area of ​​each calibration unit is equal to the pixel area of ​​the central region.

[0067] In an optional embodiment, the pixel area of ​​the central region needs to be obtained first, and the imaging area of ​​the entire infrared raw grayscale data is divided into equal grids using the pixel area of ​​the central region as the standard unit.

[0068] Specifically, the imaging region is divided into several equal-sized calibration units according to rows and columns, ensuring that the pixel area of ​​each calibration unit is equal to the pixel area of ​​the central region. If the boundary of the imaging region cannot be completely divided by the standard unit, the boundary blocks can be appropriately processed, such as by partial overlap or zero padding, to ensure that the pixel area of ​​each calibration unit is as close as possible to the pixel area of ​​the central region.

[0069] It is understandable that since the average gray value of the central area and the reference temperature data have already established a calibration reference pair, and the other calibration units are the same in area as the central area, the average gray value of each calibration unit can be converted to temperature based on the same calibration reference pair, thereby ensuring that the temperature data of the entire surface has a unified reference during the calibration process.

[0070] Step S6: Calculate the average gray value of each calibration unit, and convert the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair to generate calibrated area array infrared data.

[0071] In this embodiment of the application, after the imaging area is divided into grids, it is necessary to perform temperature conversion on each calibration unit. According to the calibration reference, the average gray value of each calibration unit is converted into the corresponding temperature value to generate calibrated area array infrared data.

[0072] It should be noted that the correlation between the raw infrared grayscale data of the infrared external array imaging unit and the temperature of the target being measured is common knowledge in the field of infrared thermometry. That is, the infrared external array imaging unit receives the infrared radiation signal of the target being measured and converts it into grayscale data. The higher the temperature of the target being measured, the stronger the intensity of the infrared radiation emitted outward, and the stronger the radiation signal received by the infrared external array imaging unit, and the larger the corresponding output grayscale value. In other words, the grayscale value and temperature have a clear positive correlation.

[0073] Based on this common knowledge, theoretically, the corresponding temperature value can be calculated from the known gray value. However, when directly using the original gray value for temperature conversion, the conversion result often has significant deviations due to the characteristics of the infrared array imaging unit itself and environmental factors, which cannot meet the requirements of high-precision temperature measurement.

[0074] Based on this, since the reference temperature data has high temperature measurement accuracy and reflects the actual temperature of the target in the spot area, and the calibration reference pair is composed of the average gray value of the central area and the reference temperature data output by the dot matrix temperature measurement unit, the calibration of the entire infrared raw gray value data can be achieved when the calibration reference pair is known.

[0075] Specifically, for each calibration unit, it is necessary to calculate the average gray value of all pixels within that calibration unit. The gray value is positively correlated with temperature, and the calibration reference pair provides a precise correspondence between the average gray value of the central region and the actual temperature. Therefore, for other calibration units, the temperature value corresponding to that calibration unit can be determined based on the relative magnitude of its average gray value and the average gray value of the central region, combined with the positive correlation.

[0076] Furthermore, after completing the temperature value calculation and conversion of all calibration units, calibrated area array infrared data is generated. This calibrated area array infrared data not only retains the original temperature spatial distribution information of the area array imaging, but also matches the benchmark level of the dot matrix temperature measurement unit in terms of temperature measurement accuracy, providing an accurate data foundation for subsequent engineering applications such as temperature gradient map generation and intelligent alarm.

[0077] As an optional implementation method, please refer to Figure 9 This is a flowchart illustrating the temperature value smoothing process in the temperature calibration method for area array infrared data provided in this application embodiment. After converting the average grayscale value of each calibration unit into the corresponding temperature value based on the calibration reference pair, the method further includes: Step S70: The temperature values ​​converted by each calibration unit are smoothed by interpolation algorithm so that the temperature value of the central region in the normalized and corrected area array infrared data deviates from the reference temperature data output by the dot matrix temperature measurement unit by ≤±0.5℃.

[0078] In the embodiments of this application, after the temperature conversion of each calibration unit is completed, since each calibration unit performs temperature conversion independently, there may be discontinuities in the temperature values ​​between adjacent calibration units. This manifests as obvious blocky boundaries or step-like jumps in the temperature field, which do not match the actual temperature distribution characteristics of the measured target and will affect the accuracy of the subsequent temperature gradient map generation. Therefore, this application preferably performs smoothing processing on the converted temperature values ​​of each calibration unit to eliminate the boundary discontinuity problem caused by block calibration.

[0079] In an optional embodiment, the converted temperature values ​​of each calibration unit can be smoothed using an interpolation algorithm. This interpolation algorithm is based on the temperature difference between adjacent calibration units and uses interpolation calculations to transition the temperature values ​​in the boundary region.

[0080] Specifically, for two adjacent calibration units, their converted temperature values ​​are obtained respectively. Linear interpolation, bilinear interpolation, or other interpolation algorithms are used to calculate the temperature values ​​of each pixel at the junction of the two calibration units, so that the temperature change in the junction area presents a smooth transition rather than a step jump. This effectively eliminates the discontinuity of the temperature field originally caused by block processing, and the spatial distribution of the temperature data of the whole surface is more in line with the actual temperature distribution of the target being measured.

[0081] After smoothing, it is also necessary to ensure that the deviation between the temperature value of the central area and the reference temperature data output by the dot matrix temperature measurement unit is controlled within ±0.5℃.

[0082] This application ensures that the normalized and corrected area array infrared data not only has good continuity in spatial distribution, but also achieves effective alignment with the reference level of the dot matrix temperature measurement unit in terms of temperature measurement accuracy through the smoothing processing of the interpolation algorithm and the limitation of the deviation threshold.

[0083] As an optional implementation method, please refer to Figure 10 This is a flowchart illustrating the generation of a temperature gradient distribution map in the temperature calibration method for area array infrared data provided in this application embodiment. After generating the calibrated area array infrared data, the method further includes: Step S71: Based on the calibrated area array infrared data, generate the corresponding temperature gradient distribution map using a bilinear interpolation algorithm; and, calibrate the isotherms in the temperature gradient distribution map using an isotherm fitting algorithm. The temperature gradient distribution map includes the spatial distribution of the temperature field of the target being measured, the location of hot spots, and the temperature difference range.

[0084] In the embodiments of this application, after the normalization correction is completed and the calibrated area infrared data is obtained, the area infrared data will exist in the form of discrete pixels, each pixel corresponding to a temperature value. It is impossible to intuitively judge the overall temperature distribution trend, nor can it quickly identify hot spots and areas with drastic temperature changes.

[0085] Preferably, this application converts discrete area array infrared data into a visualized temperature gradient distribution map to intuitively present the temperature field information of the target being measured.

[0086] For example, a corresponding temperature gradient distribution map can be generated using a bilinear interpolation algorithm, a commonly used image processing algorithm used to convert discrete pixel data into a continuous color level image.

[0087] Specifically, for calibrated area infrared data, each pixel has a temperature value, which can be mapped to a corresponding color. For example, high-temperature regions are mapped to red or light colors, and low-temperature regions are mapped to blue or dark colors. Using bilinear interpolation, for any two adjacent pixels, the temperature value at the midpoint is calculated linearly based on the temperature values ​​of these two pixels and their distance relationship. This fills the discrete pixels into a continuous temperature-gradient image. After bilinear interpolation, a temperature gradient distribution map is generated. In this map, temperature changes are presented as smooth color gradient transitions, rather than discrete color block boundaries, making the spatial distribution of the temperature field of the measured target readily apparent.

[0088] Based on the generated temperature gradient distribution map, isotherms can be marked on the map using isotherm fitting algorithms. For example, a specific temperature threshold can be selected, and the isotherm fitting algorithm can be used to find all points with temperature values ​​equal to the threshold and connect these points into a smooth curve, thus intuitively reflecting the temperature distribution.

[0089] In an optional embodiment, the temperature gradient distribution map includes information such as the spatial distribution of the temperature field of the target being measured, hotspot locations, and temperature difference ranges. The spatial distribution of the temperature field reflects the overall temperature distribution of the target's surface; hotspot locations refer to local areas where the temperature is significantly higher than the surrounding area, which can be quickly located by the darkest areas or the areas with the densest isotherms in the image; the temperature difference range refers to the difference between the highest and lowest temperatures on the target's surface, which can be intuitively determined by comparing the color levels of the highest and lowest temperature areas in the image, allowing for a quick and accurate understanding of the target's temperature state.

[0090] As an optional implementation method, please refer to Figure 11 This is a flowchart illustrating the generation of alarm information in the temperature calibration method for area array infrared data provided in this application embodiment. After generating the calibrated area array infrared data, the method further includes: Step S80: Extract the temperature characteristic parameters of the target from the calibrated area array infrared data; Step S81: When the extracted temperature feature parameters exceed the corresponding preset threshold, generate an alarm message; The temperature characteristic parameters include the highest temperature, lowest temperature, maximum temperature difference, and location of hotspot areas of the target being measured.

[0091] In an optional embodiment, after generating calibrated area array infrared data, temperature feature parameters reflecting temperature distribution characteristics can be extracted from the area array infrared data.

[0092] For example, the temperature characteristic parameters here may include the highest temperature, lowest temperature, maximum temperature difference, and location of hotspot areas of the target being measured.

[0093] Among them, the highest temperature refers to the maximum value of the temperature of all pixels in the calibrated area infrared data, reflecting the point with the highest temperature on the target being measured; the lowest temperature refers to the minimum value of the temperature of all pixels, reflecting the point with the lowest temperature; the maximum temperature difference is the difference between the highest temperature and the lowest temperature, reflecting the uniformity of the overall temperature distribution of the target being measured; the hot spot area refers to the local area with a temperature significantly higher than the surrounding area, which can be determined by identifying the set of pixels whose temperature values ​​exceed a certain range. All of the above temperature characteristic parameters can be directly calculated from the calibrated area infrared data.

[0094] Based on this, this application sets a corresponding preset threshold for each temperature characteristic parameter.

[0095] For example, for the highest temperature, an upper limit threshold can be set. When the highest temperature of the target being measured exceeds the upper limit threshold, it indicates that there is a risk of overheating. For the lowest temperature, a lower limit threshold can be set. When the lowest temperature is below the lower limit threshold, it indicates that there may be an abnormal cold zone. For the maximum temperature difference, an allowable temperature difference range can be set. When the actual temperature difference exceeds the temperature difference range, it indicates that the temperature distribution is uneven and there may be local defects. For the location of hot spots, an area or temperature intensity threshold for the hot spot can be set. When the hot spot exceeds the allowable range, an alarm is triggered.

[0096] Of course, the type of temperature characteristic parameters and the size of the preset threshold corresponding to each temperature characteristic parameter can be adjusted according to the specific needs of the actual application scenario. This application does not impose any restrictions on this.

[0097] When the extracted temperature feature parameters exceed the corresponding preset threshold, an alarm message will be automatically generated so that operators can promptly grasp the abnormal temperature status of the measured target and take corresponding measures to achieve rapid response to thermal failures, process abnormalities, and other situations in industrial field equipment.

[0098] As an optional implementation method, please refer to Figure 12 This is a flowchart illustrating the transmission of area array infrared data to an external terminal in the temperature calibration method for area array infrared data provided in this application embodiment. After generating the calibrated area array infrared data, the method further includes: Step S82: Transmit the calibrated area array infrared data, the temperature gradient distribution map, and / or the alarm information to an external terminal; wherein, the external terminal includes one or more of an industrial MES system, a remote monitoring platform, or a smart terminal.

[0099] In this embodiment of the application, after completing the calibration of the area array infrared data, the generation of the temperature gradient map, and the determination of the intelligent alarm, the area array infrared data, temperature gradient map, or alarm information can be output to the corresponding external terminal so that the operator can provide timely feedback.

[0100] The specific type of external terminal can be selected according to the needs of the application scenario, such as an industrial MES (Manufacturing Execution System), a remote monitoring platform, or a smart terminal. Flexible data output configuration can meet the needs of different industrial scenarios for temperature measurement data applications.

[0101] As an optional implementation, the dot matrix temperature measurement unit is a unit dot matrix temperature measuring instrument, and the infrared array imaging unit is an organic infrared array camera, a quantum dot infrared array camera, or an indium gallium arsenide infrared array camera.

[0102] In this embodiment, the dot matrix temperature measurement unit preferably adopts a unit dot matrix thermometer. A unit dot matrix thermometer is a high-precision temperature measurement device that can perform single-point temperature measurement on targets within a specific field of view. By introducing a unit dot matrix thermometer as an external temperature measurement reference, the problem of insufficient calibration accuracy of the infrared array imaging unit itself can be effectively compensated.

[0103] Furthermore, the infrared array imaging unit preferably adopts an organic infrared array camera or a quantum dot infrared array camera. The organic infrared array camera is based on organic infrared detection technology, and the quantum dot infrared array camera is based on quantum dot infrared detection technology. These two types of detectors have the advantage of lower cost compared with traditional indium gallium arsenide infrared array detectors, and are key components for realizing a low-cost, high-precision area array temperature measurement scheme.

[0104] However, organic infrared detectors and quantum dot infrared detectors suffer from problems such as uneven responsivity between pixels and dark current drift in applications. This leads to deviations in the correspondence between the grayscale values ​​of each pixel and the actual temperature of the target when directly outputting raw infrared grayscale data. Furthermore, these deviations are inconsistent across different pixels, making it difficult for traditional calibration algorithms to effectively adapt. This application combines a unit array thermometer with an organic infrared array camera or a quantum dot infrared array camera, significantly reducing equipment costs while ensuring overall surface temperature measurement accuracy.

[0105] In an optional embodiment, the infrared array imaging unit can also be configured as an indium gallium arsenide infrared array camera.

[0106] It is understandable that indium gallium arsenide detectors have higher sensitivity and accuracy. The specific calibration benchmarks for construction, region division and temperature conversion can still use the same processing logic as in the above embodiments to be suitable for high-end industrial scenarios with more stringent requirements for temperature measurement accuracy. This application will not elaborate further here.

[0107] In one specific embodiment, this application uses a sample heated to 1000°C as the target to be tested. A unit-array precision thermometer and an infrared array imaging unit are used, combined with an AI (Artificial Intelligence) computing power board equipped with the temperature calibration method of the area array infrared data of this application. The AI ​​computing power board should integrate computing power resources of more than 6 TOPS (TeraOperations Per Second) and have a pre-set algorithm model optimized for infrared fusion temperature measurement tasks, as well as an industrial display terminal for receiving data. The following calibration and testing were performed.

[0108] Specifically, this application coaxially fixes the unit-array precision temperature measuring instrument and the infrared array imaging unit, establishes a spatial coordinate mapping relationship between the array temperature measuring unit and the infrared array imaging unit, and determines the central region in the original infrared grayscale data corresponding to the temperature measuring field of view of the array temperature measuring unit. The basic response characteristics of the infrared array imaging unit are pre-calibrated according to the equipment parameters and written into the algorithm configuration file. Simultaneously, alarm rules are preset according to testing requirements, such as the sample's highest temperature not exceeding 1000℃ and the maximum temperature difference not exceeding 50℃; an alarm is immediately triggered when over-temperature or over-range conditions occur.

[0109] Furthermore, the high-temperature sample was heated to 1000℃ and kept at a stable temperature. Simultaneously, the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit were acquired. The timing synchronization error between the reference temperature data and the raw infrared grayscale data was controlled within 5ms to ensure the time consistency of the acquired data.

[0110] Based on this, please refer to Figure 13 This is an example diagram of normalization correction in the temperature calibration method for area array infrared data provided in this application embodiment. It can be observed that in this embodiment, the gray value of each pixel in the central area is extracted, the average gray value of the central area is calculated, and the reference temperature value (1000℃) output by the dot matrix temperature measurement unit is obtained simultaneously. The average gray value of the central area and the reference temperature value are used to construct a calibration reference pair.

[0111] Furthermore, using the pixel area of ​​the central region as the standard unit, the imaging area of ​​the raw infrared grayscale data is divided into several calibration units, with each calibration unit having the same pixel area as the central region. Under the same exposure conditions, temperature calibration is performed on all calibration units across the entire surface based on the calibration reference pair and pre-calibration parameters.

[0112] After temperature conversion of each calibration unit is completed, the converted temperature values ​​of each calibration unit are smoothed using an interpolation algorithm to achieve normalization correction. After correction, the deviation between the temperature value of the central region and the reference temperature value output by the dot matrix temperature measurement unit does not exceed ±0.5℃.

[0113] Based on the calibrated area array infrared data, please refer to Figure 14 This is an example of a temperature gradient distribution map in the temperature calibration method for area array infrared data provided in this application embodiment. The temperature gradient distribution map is generated by bilinear interpolation and isotherms are fitted to identify the hot spots, highest temperature, lowest temperature, and overall temperature difference on the sample surface. Intelligent judgment is performed on the temperature data, extracting the temperature characteristic parameters of the target object from the calibrated area array infrared data. In this test, the highest temperature was 1000℃, the maximum temperature difference was 22℃, and the preset alarm rules were not triggered.

[0114] Meanwhile, the calibrated area array infrared data and temperature gradient distribution map are output to the industrial display terminal in real time, and the data is uploaded to the factory MES system through the communication module to complete the accurate temperature measurement and monitoring of high temperature samples.

[0115] Overall, this embodiment verifies the feasibility and effectiveness of the area array infrared data temperature calibration method of this application through a complete practical process. The entire process, from system initialization to data output, achieves high-precision area array temperature measurement of high-temperature samples.

[0116] It should be noted that the above embodiments are merely one specific implementation of this application, intended to help understand the technical solution of this application, and do not constitute a limitation on the scope of protection of this application. Those skilled in the art, based on reading this application, can make equivalent substitutions, combinations, or modifications to the technical features described in the embodiments, or adaptively adjust the specific parameters, hardware selection, and step sequence according to the actual application scenario.

[0117] The temperature calibration method based on the above area array infrared data is described in the following text. Figure 15 This is a functional block diagram of a temperature calibration system for area array infrared data provided in an embodiment of this application. The elimination system includes: The acquisition module is used to acquire the raw time-domain signal; The processing module is used to perform first-order difference calculation on each sampling point of the original time-domain signal to obtain a difference signal sequence; The identification module is used to determine a mutation threshold based on the differential signal sequence, and to identify signal mutation points in the original time-domain signal based on the mutation threshold; The marking module is used to mark the signal abrupt change point and the subsequent preset number of sampling points as signal abrupt change segments; The correction module is used to take the amplitude of the sampling point before the signal mutation point as the reference baseline value, and perform flattening processing on the signal mutation segment based on the reference baseline value to obtain the corrected time domain signal.

[0118] For further details regarding the implementation of the above-mentioned technical solution in the temperature calibration system for area array infrared data, please refer to the description of the temperature calibration method for area array infrared data provided in the above-mentioned application embodiments, which will not be repeated here.

[0119] This application provides a temperature calibration method and system for area array infrared data. It simultaneously acquires the reference temperature data of the dot matrix temperature measurement unit and the raw infrared grayscale data of the infrared area array imaging unit. The timing synchronization error between the reference temperature data and the raw infrared grayscale data is controlled within a preset error range, achieving precise matching between the reference temperature data and the raw infrared grayscale data in the time dimension. Based on the spatial coordinate mapping relationship, a central region corresponding to the dot matrix temperature measurement field of view is determined, ensuring that the reference temperature data and the raw infrared grayscale data accurately correspond to the same physical location of the target being measured. Subsequently, the grayscale values ​​of each pixel within the central region are extracted and the average grayscale value is calculated. A calibration reference pair is constructed with the reference temperature data, mitigating the deviation caused by single-pixel anomalies.

[0120] Furthermore, the imaging area is divided into several calibration units using the pixel area of ​​the central region as the standard unit, avoiding the problem of inconsistent calibration standards caused by differences in regional area. Finally, the average gray value of each calibration unit is calculated, converted into the corresponding temperature value based on the calibration reference, and calibrated area array infrared data is generated. This ensures that the temperature value of each calibration unit is corrected based on the high-precision temperature value of the central region, avoiding the problem of poor overall calibration consistency caused by only single-point replacement or linear scaling in traditional fusion schemes. This ensures the temperature calibration consistency of each region of the area array and maintains stable calibration accuracy even in high-temperature dynamic scenarios.

[0121] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other can be through some interfaces, or indirect coupling or communication connection between devices or units, and can be electrical, mechanical, or other forms.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for temperature calibration of area array infrared data, characterized in that, Includes the following steps: Simultaneously acquire the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit; The acquisition timing of the reference temperature data and the raw infrared grayscale data is aligned to control the timing synchronization error within a preset error. Based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit, the central region in the infrared raw grayscale data corresponding to the temperature measurement field of view of the dot matrix temperature measurement unit is determined. The gray values ​​of each pixel in the central region are extracted from the raw infrared gray data, and the average gray value of the central region is used to construct a calibration reference pair with the reference temperature data. Using the pixel area of ​​the central region as the standard unit, the imaging area of ​​the infrared raw grayscale data is divided into several calibration units; Calculate the average gray value of each calibration unit, and convert the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair to generate calibrated area array infrared data.

2. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, Aligning the acquisition timing of the reference temperature data with that of the raw infrared grayscale data to control the timing synchronization error within a preset error includes: The first timestamp when the dot matrix temperature measurement unit collects the reference temperature data and the second timestamp when the infrared external array imaging unit collects the infrared raw grayscale data are obtained. The first timestamp is matched with the second timestamp so that the timing synchronization error between the matched reference temperature data and the original infrared grayscale data is ≤10ms.

3. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, The step of determining the central region in the raw infrared grayscale data corresponding to the temperature measurement field of view of the dot matrix temperature measurement unit based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit includes: Based on the temperature measurement field of view of the dot matrix temperature measurement unit and the pixel resolution of the infrared external array imaging unit, a spatial coordinate mapping relationship is established between the temperature measurement field of view and the position of each pixel in the infrared raw grayscale data. By using the spatial coordinate mapping relationship, the pixel region corresponding to the light spot of the dot matrix temperature measuring unit in the original infrared grayscale data is determined as the central region.

4. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, The process of dividing the imaging area of ​​the raw infrared grayscale data into several calibration units, using the pixel area of ​​the central region as the standard unit, includes: Obtain the pixel area of ​​the central region; Using the pixel area as a standard unit, the imaging region is divided into equal grids, so that the pixel area of ​​each calibration unit is equal to the pixel area of ​​the central region.

5. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, After converting the average grayscale value of each calibration unit into the corresponding temperature value based on the calibration reference pair, the process further includes: The temperature values ​​converted by each calibration unit are smoothed by an interpolation algorithm, so that the temperature value of the central region in the normalized and corrected area array infrared data deviates from the reference temperature data output by the dot matrix temperature measurement unit by ≤±0.5℃.

6. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, After generating the calibrated area array infrared data, the process also includes: Based on the calibrated area array infrared data, a corresponding temperature gradient distribution map is generated using a bilinear interpolation algorithm; and isotherms are calibrated in the temperature gradient distribution map using an isotherm fitting algorithm. The temperature gradient distribution map includes the spatial distribution of the temperature field of the target being measured, the location of hot spots, and the temperature difference range.

7. The temperature calibration method for area array infrared data as described in claim 6, characterized in that, After generating the calibrated area array infrared data, the process also includes: The temperature characteristic parameters of the target under test are extracted from the calibrated area array infrared data; When the extracted temperature feature parameters exceed the corresponding preset threshold, an alarm message is generated; The temperature characteristic parameters include the highest temperature, lowest temperature, maximum temperature difference, and location of hotspot areas of the target being measured.

8. The temperature calibration method for area array infrared data as described in claim 7, characterized in that, After generating the calibrated area array infrared data, the process also includes: The calibrated area array infrared data, the temperature gradient distribution map, and / or the alarm information are transmitted to an external terminal; wherein the external terminal includes one or more of an industrial MES system, a remote monitoring platform, or a smart terminal.

9. The temperature calibration method for area array infrared data as described in claim 1, characterized in that, The dot matrix temperature measurement unit is a unit dot matrix temperature measuring instrument, and the infrared array imaging unit is an organic infrared array camera, a quantum dot infrared array camera, or an indium gallium arsenide infrared array camera. The dot matrix temperature measurement unit and the infrared array imaging unit are coaxially fixed.

10. A temperature calibration system for area array infrared data, characterized in that, include: The acquisition module is used to synchronously acquire the reference temperature data output by the dot matrix temperature measurement unit and the raw infrared grayscale data output by the infrared array imaging unit. The alignment module is used to align the acquisition timing of the reference temperature data with that of the infrared raw grayscale data, so as to control the timing synchronization error within a preset error. The mapping module is used to determine the central region in the infrared raw grayscale data that corresponds to the temperature measurement field of view of the dot matrix temperature measurement unit based on the spatial coordinate mapping relationship between the dot matrix temperature measurement unit and the infrared external array imaging unit. An extraction module is used to extract the gray values ​​of each pixel in the central region from the raw infrared grayscale data, and construct a calibration reference pair by combining the average gray value of the central region with the reference temperature data. The partitioning module is used to divide the imaging area of ​​the infrared raw grayscale data into several calibration units, using the pixel area of ​​the central region as the standard unit. The calibration module is used to calculate the average gray value of each calibration unit, convert the average gray value of each calibration unit into the corresponding temperature value based on the calibration reference pair, and generate calibrated area array infrared data.

Images