Automobile air conditioner evaporator temperature field monitoring method and system

Abstract

The present application belongs to the technical field of image processing, and particularly relates to a kind of automobile air conditioner evaporator temperature field monitoring method and system, method includes: the gray image of acquisition evaporator, infrared image and thermocouple temperature data, obtain the initial infrared radiation intensity of each pixel point;Based on Planck's law, the initial emissivity of each thermocouple is calculated, and the target pixel point is determined;After double threshold segmentation, three types of structure regions of evaporator are obtained;The initial emissivity of all target pixel points is obtained by inverse distance weighted interpolation;According to the average temperature of target pixel point and the activation energy, oxidation rate coefficient and initial emissivity of the structure region it is in, its dynamic emissivity is obtained, combined with its real-time infrared radiation intensity, the corrected temperature data is obtained;According to the corrected temperature data and temperature threshold, the defect probability and total defect degree of target pixel point are obtained, and hierarchical early warning is implemented.The present application improves the accuracy of evaporator temperature field monitoring.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method and system for monitoring the temperature field of an automotive air conditioning evaporator. Background Technology

[0002] The automotive air conditioning evaporator is a core component of the automotive thermal management system, and its performance and lifespan directly affect the reliability of the entire vehicle and the safety of passengers. The core components of the evaporator include heat transfer tubes and fins, which are tightly connected by brazing to form a core structure for heat absorption during refrigerant phase change. During the quality verification phase, the evaporator needs to undergo a continuous high-temperature resistance test for up to 72 hours in a constant-temperature sealed chamber at approximately 80°C to simulate extreme high-temperature conditions such as the engine compartment and to test the thermal stability of its structure and materials. In the above tests, infrared thermal radiation imaging technology has become a commonly used core technology for monitoring the temperature field. This technology captures the infrared radiation energy emitted by the surface of an object and obtains the temperature distribution map by inverting it according to Boltzmann's law and Planck's law.

[0003] However, due to the low infrared emissivity of the material on the evaporator surface and the fact that the heat transfer tubes and fins of the evaporator are mostly connected by brazing, the brazing points on the surface of the evaporator core will inevitably undergo slight oxidation in a high-temperature environment lasting up to 72 hours, causing the connection to loosen. Once the brazing area fails, it will directly lead to a decrease in the heat transfer efficiency of the heat transfer tubes and fins, thereby causing local overheating of the evaporator. Therefore, the local emissivity of the evaporator surface will undergo uneven dynamic changes. Traditional infrared thermal imaging methods usually preset a global and constant emissivity value. When applied to the monitoring of the evaporator temperature field, it will lead to a large deviation in the monitoring results of the evaporator's high-temperature resistance, and will not be able to accurately capture the real local thermal anomalies caused by structural defects. Summary of the Invention

[0004] To address the issue of uneven dynamic changes in local emissivity on the surface of existing evaporators, and the fact that traditional infrared thermal imaging methods typically preset a global and constant emissivity value, which leads to significant deviations in the monitoring results of the evaporator's high-temperature resistance when applied to evaporator temperature field monitoring, and fails to accurately capture the true local thermal anomalies caused by structural defects, this invention provides a method and system for monitoring the temperature field of automotive air conditioning evaporators.

[0005] In a first aspect, the present invention provides a method for monitoring the temperature field of an automotive air conditioning evaporator, comprising:

[0006] This invention acquires grayscale images of the evaporator, temperature data at thermocouples on the evaporator surface, and the initial infrared radiation intensity of each pixel. Based on Planck's law, it calculates the initial emissivity of each thermocouple using the temperature data and corresponding initial infrared radiation intensity. All pixels except the thermocouples are designated as target pixels. The grayscale image is segmented using a double-threshold algorithm to obtain the heat transfer tube region, fin region, and brazing region of the evaporator. An inverse distance weighted interpolation algorithm is used to interpolate the initial emissivity of all target pixels based on the initial emissivity of the thermocouples in each region. The emissivity of each target pixel from the start of the experiment to the current time is calculated. Average temperature; based on the activation energy and oxidation rate coefficient of the corresponding material in each region, as well as the average temperature and initial emissivity of the target pixel, the dynamic emissivity of each target pixel is calculated; combined with the real-time infrared radiation intensity and dynamic emissivity of the target pixel at the current moment, the corrected temperature data is obtained by inversion based on Planck's law; based on the preset temperature threshold and the corrected temperature data, the defect probability of each target pixel is calculated for screening defective pixels; the ratio of the total defect area of ​​the evaporator to the total area of ​​its image is taken as the total defect degree of the evaporator, and a graded early warning is implemented based on the total defect degree, thereby realizing the monitoring of the temperature field of the evaporator.

[0007] This invention calculates the initial emissivity at the thermocouple by combining Planck's law with the measured temperature of the thermocouple and the initial infrared radiation intensity. A grayscale dual-threshold segmentation algorithm distinguishes three types of regions: heat transfer tubes, fins, and brazing. An inverse distance weighted interpolation algorithm is then used to obtain the initial emissivity of the target pixel, providing realistic initial parameters for temperature inversion. The dynamic emissivity of each target pixel is calculated using its average temperature, material activation energy, and oxidation rate coefficient, improving the accuracy of temperature inversion at each working stage and adapting to long-term monitoring under complex conditions, thus overcoming the limitation of non-dynamic emissivity adjustment. Defect probability is calculated using a preset temperature threshold and corrected temperature data. The ratio of the total defect area to the total image area quantifies the overall defect severity and provides graded early warnings. Furthermore, parameters can be adjusted to adapt to different evaporator structures and materials, meeting the requirements for accurate temperature field monitoring.

[0008] Preferably, the calculation of the initial emissivity of each thermocouple based on Planck's law, using the temperature data of the thermocouples and the corresponding initial infrared radiation intensity, includes: ;in, It is the first The current position is thermocouple The initial emissivity; It is the first The current position is thermocouple The initial infrared radiation intensity; It is the first The current position is thermocouple The theoretical infrared radiation intensity corresponding to a blackbody; Indicates the sequence number of the time.

[0009] This method, based on Planck's law and combining thermocouple temperature data with the corresponding initial infrared radiation intensity, accurately obtains the first... Time and location thermocouple The initial emissivity effectively breaks through the limitations of traditional infrared thermography which presets a global constant emissivity, and avoids the problem of uneven emissivity on the evaporator surface caused by material characteristics and brazing oxidation. It provides accurate parameter support for the dynamic changes of each monitoring point over time for infrared thermal imaging temperature field inversion, significantly improves the accuracy of temperature field monitoring in high-temperature evaporator testing, helps to accurately capture structural and thermal performance anomalies such as brazing failure and local overheating, and provides reliable data for evaporator quality verification.

[0010] Preferably, the method for obtaining the initial emissivity of the target pixel includes: ;in, It is the first At this moment, the position is The initial emissivity of the target pixel; It is located in The set of all thermocouples within the region where the target pixel is located; It is located in Target pixels compared to thermocouples Weighting coefficients; It is the first The current position is thermocouple The initial emissivity; Indicates the sequence number of the time.

[0011] This method uses the first... Based on the set of all thermocouples within the region of the target pixel at time t, and combined with the weighting coefficients of the target pixel relative to each thermocouple, the t is... The initial emissivity of each thermocouple at its current location is weighted and summed to calculate the initial emissivity of the target pixel. This effectively solves the problem of obtaining the emissivity when there is no direct temperature measurement data for the target pixel. The introduction of weighting coefficients can highlight the differences in the influence of thermocouples at different locations on the emissivity of the target pixel, making the calculation results more consistent with the spatial distribution characteristics of the emissivity on the evaporator surface. This avoids the deviation caused by simply assigning uniform values ​​to the emissivity in the region, further improving the accuracy of the emissivity data of each pixel. This provides more reliable basic parameters for the subsequent inversion of the evaporator temperature field based on infrared thermal imaging, helps to capture local thermal anomalies on the evaporator surface more meticulously, and provides strong support for its high-temperature performance verification and the reliability assessment of the vehicle thermal management system.

[0012] Preferably, the method for obtaining the weighting coefficients includes: ;in, It is located in The target pixel compared to the thermocouple Weighting coefficients; It is located in Target pixels and thermocouples The Euclidean distance between them; It is located in Target pixels and sets medium thermocouple The Euclidean distance between them; It is located in The set of all thermocouples within the region where the target pixel is located; Indicates the thermocouple serial number. Represents a set The serial number of the thermocouple; It is an exponential parameter.

[0013] This method uses the Euclidean distance between the target pixel and each thermocouple in the thermocouple set within its region as the core basis, introduces a power parameter to obtain weight coefficients, and highlights the influence weight of near-distance thermocouples on the target pixel through the power operation of the reciprocal of the distance. At the same time, normalization processing is used to ensure that the sum of the weight coefficients is 1, ensuring the rigor of the calculation logic. The introduction of the power parameter can flexibly adjust the sensitivity of the distance to the weight, adapting to the complex structure of the heat transfer tubes and fins of the evaporator core. This allows the weight allocation to not only conform to the spatial gradient characteristics of emissivity, but also avoid the limitations of a single distance weight mode. It further optimizes the weighted calculation accuracy of the initial emissivity of the target pixel, providing a more realistic basis parameter for infrared thermal imaging temperature field inversion, helping to more accurately identify local thermal anomalies on the evaporator surface caused by brazing oxidation and structural defects, and providing stronger technical support for its high-temperature performance verification and the reliability control of the vehicle thermal management system.

[0014] Preferably, the step of interpolating the initial emissivity of all target pixels based on the initial emissivity of thermocouples in each region further includes: randomly selecting two verification regions without thermocouples in the heat transfer pipe region, fin region, and brazing region respectively, obtaining a total of 6 verification regions; directly measuring the true initial emissivity of each verification region using a high-precision infrared spectrometer; and comparing it with the interpolated initial emissivity; if the average relative deviation of a certain verification region is greater than a preset threshold, then adjusting the power parameter of the inverse distance weighted interpolation method for that region. The interpolation calculation is repeated until the average relative deviation of all structural regions is less than or equal to the preset threshold, thereby obtaining the initial emissivity of all target pixels at each time point.

[0015] Preferably, the calculation of the dynamic emissivity of each target pixel includes: ;in, It is the first At this moment, the position is The dynamic emissivity of the target pixel; It is the first At this moment, the position is The initial emissivity of the target pixel; They are located at The oxidation rate coefficient and activation energy of the region where the target pixel is located; It is the molar gas constant; Indicates from the start time of the experiment to the [number]th [time]. The actual cumulative time of each moment; It is located in The average temperature of the target pixel; It is a time accumulation coefficient; Indicates the sequence number of the time; Represented by natural constant An exponential function with base 1.

[0016] This method uses the first... Based on the initial emissivity of the target pixel at any given time, the oxidation rate coefficient and activation energy of the region where the pixel is located, as well as the average temperature and time accumulation parameters, are incorporated. Combined with the relationship between the molar gas constant and the natural exponent, a dynamic emissivity is obtained, achieving precise quantification of the evaporator emissivity changing with operating conditions. The introduction of the oxidation rate coefficient and activation energy can match the material oxidation characteristics of different regions. The combination of average temperature and the exponent reflects the regulatory effect of temperature on the oxidation reaction rate. The actual accumulation time and time accumulation parameters accurately capture the cumulative law of oxidation effect during long-term high-temperature testing. A dynamic correlation model between emissivity and operating parameters is constructed from multiple dimensions, completely breaking through the limitations of traditional methods. This method overcomes the limitations of traditional methods that use fixed values ​​instead of dynamic changes. It relies on the exponential relationship between activation energy and temperature to reflect the essence of the oxidation reaction, ensuring theoretical rigor. Furthermore, by adapting regional parameters to the structural heterogeneity of the evaporator core, it enables the dynamic emissivity to match the actual scenario of the oxidation degree dynamically evolving with time and temperature under high-temperature conditions in real time, significantly improving the fit between emissivity data and real-world operating conditions. Based on this dynamic emissivity, infrared thermal imaging temperature field inversion can effectively avoid temperature measurement deviations caused by dynamic changes in emissivity, helping to more accurately identify potential defects such as brazing oxidation failure and localized overheating, providing more reliable data support for evaporator high-temperature performance verification.

[0017] Preferably, the step of calculating the defect probability of each target pixel based on a preset temperature threshold and corrected temperature data includes: ;in, It is the first At this moment, the position is The defect probability of the target pixel; It is located in The target pixel in the first Temperature data after time correction; It is the temperature threshold; This indicates normalization processing; It is the absolute value symbol; Indicates the sequence number of the time.

[0018] Preferably, the method for screening defective pixels includes: setting a probability threshold, and determining all target pixels with a defect probability greater than or equal to the probability threshold as defective pixels.

[0019] Preferably, the implementation of graded early warning to monitor the evaporator temperature field includes: when the total defect level is less than or equal to 0.4, the evaporator temperature field at the current moment is determined to be in a normal state, and no early warning is issued; when the total defect level is greater than 0.4 and less than 0.7, the evaporator temperature field at the current moment is determined to be in a slightly abnormal state, and it is necessary to prompt for enhanced monitoring of the evaporator temperature field; when the total defect level is greater than or equal to 0.7, the evaporator temperature field at the current moment is determined to be in a severely abnormal state, and it is necessary to suspend the test and return the evaporator to the factory for repair.

[0020] Secondly, the present invention provides an automotive air conditioning evaporator temperature field monitoring system, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned automotive air conditioning evaporator temperature field monitoring method is implemented.

[0021] By adopting the above technical solution, a computer program for monitoring the temperature field of an automotive air conditioner evaporator is generated and stored in a memory, so that it can be loaded and executed by a processor. A terminal device can then be made based on the memory and the processor for convenient use.

[0022] The beneficial effects of this invention are as follows: It solves the error problem caused by the fixed emissivity in traditional technologies. By combining Planck's law with measured temperature data from thermocouples and initial infrared radiation intensity, the initial emissivity at the thermocouple is directly calculated. A grayscale dual-threshold segmentation algorithm is used to accurately distinguish three types of regions: heat transfer tubes, fins, and brazing. Then, an inverse distance weighted interpolation algorithm is used to obtain the initial emissivity of the target pixel, providing initial parameter support that fits the actual structural characteristics for temperature inversion. It overcomes the limitation of traditional monitoring ignoring the dynamic adjustment of emissivity with temperature changes and material oxidation processes. The dynamic emissivity is calculated by the average temperature of the target pixel, the material activation energy, and the oxidation rate coefficient, which greatly improves the accuracy of temperature inversion at different working stages and is suitable for long-term monitoring under complex working conditions. Furthermore, the probability of defects is calculated by using a preset temperature threshold and corrected temperature data. The ratio of the total defect area to the total image area is used to quantify the total defect degree and implement graded early warning, significantly improving the accuracy of monitoring. The complete process can be adapted to different evaporator structures and materials by adjusting relevant parameters, making it widely applicable and meeting the needs of accurate temperature field monitoring of automotive air conditioning evaporators. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for monitoring the temperature field of an automotive air conditioning evaporator according to the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses a method for monitoring the temperature field of an automotive air conditioning evaporator, referring to... Figure 1 This includes steps S1 to S4:

[0027] S1. Acquire grayscale and infrared images of the evaporator; obtain the initial infrared radiation intensity of each pixel based on the infrared image; acquire temperature data at the thermocouple on the evaporator surface.

[0028] It should be noted that color images contain redundant color information, and converting them to grayscale images can significantly reduce computation while preserving key features in the brightness dimension. Images in industrial environments are susceptible to electronic noise and light fluctuations; Gaussian filtering can effectively smooth noise and prevent it from interfering with subsequent feature judgments. Details such as evaporator fin gaps and brazed seams have low contrast in the original image; histogram equalization can stretch the grayscale range and enhance the visual recognition of these details. Temperature calculations in infrared thermal imaging rely on emissivity parameters, but the emissivity of the evaporator surface changes dynamically due to oxidation and structural differences, and traditional methods with fixed pre-set emissivity have large errors. Thermocouples, on the other hand, directly contact the evaporator surface, and the temperature they collect is the true temperature, providing a true reference for the dynamic emissivity correction model and ultimately ensuring the accuracy of temperature field monitoring results.

[0029] Specifically, the evaporator is fixed on a fixture, and a high-precision industrial camera is selected and its installation position is fixed to ensure that the optical axis of the camera lens is parallel and points in the same direction as the optical axis of the subsequent infrared thermal imager, laying the foundation for subsequent spatial registration. Under uniform lighting conditions, color images of the evaporator are acquired at a frequency of 1 frame per second to avoid the impact of lighting fluctuations on image quality. The color images are converted into grayscale images, and redundant color information is removed to reduce the amount of computation while retaining key features of the brightness dimension. Gaussian filtering is used to denoise the grayscale images to smooth electronic noise interference and avoid affecting subsequent feature judgment. The grayscale range is stretched by a histogram equalization algorithm to enhance the visual recognition of low-contrast details such as fin gaps and brazed seams, ultimately forming a set of preprocessed grayscale images.

[0030] Furthermore, an infrared thermal imager was selected, and its pixel mapping relationship with the industrial camera was established using the checkerboard calibration method to complete spatial registration and ensure that the images acquired by the two devices correspond precisely in spatial position. The evaporator and the infrared thermal imager were placed in the same indoor environment with small temperature fluctuations and stabilized for 30 minutes to eliminate the influence of transient fluctuations in ambient temperature. The infrared thermal imager was set to acquire images at a frequency of 1 frame per second to generate infrared images with a high signal-to-noise ratio.

[0031] Specifically, taking the top left pixel of the infrared image as the origin, and horizontally to the right as... The axis, vertically downwards is Using axes, a Cartesian coordinate system is constructed to obtain the position information of all pixels in the infrared image; radiometric calibration is performed on the infrared image by converting the grayscale value of each pixel obtained from the detector's electrical signal output into the initial infrared radiation intensity of each pixel. For example, the first... Current position The initial infrared radiation intensity of the pixel at point is denoted as , Indicates the sequence number of the time.

[0032] Furthermore, multiple high-precision thermocouple probes were tightly attached to key locations on the evaporator surface using high-temperature thermally conductive adhesive, and the leads were connected to a data acquisition instrument with a sampling frequency of once per second. The evaporator was placed in an indoor environment with minimal temperature fluctuations, which was consistent with the environment for subsequent infrared initial radiation intensity acquisition. Temperature data at each thermocouple was collected at each moment.

[0033] S2. Based on the initial infrared radiation intensity of the pixel, obtain the initial infrared radiation intensity of each thermocouple. Combined with the temperature data of the thermocouple, obtain the initial emissivity of each thermocouple. Combined with the structural region constraints of the visible light image, interpolation is performed to obtain the initial emissivity of all target pixels.

[0034] It should be noted that the initial emissivity is the benchmark for the dynamic correction model, but it is not a globally constant value. Due to differences in materials and surface roughness, the heat transfer tube area, fin area, and brazing area on the evaporator surface have inherent differences in emissivity. Since thermocouples can only collect the temperature of discrete points and cannot cover all pixels, this invention calculates the precise emissivity of discrete points and interpolates it using structural region constraints based on visible light images to obtain the initial emissivity of each pixel. This method can both preserve the physical characteristics of different regions and achieve full pixel coverage, providing a precise starting point for the dynamic correction of emissivity in subsequent high-temperature experiments.

[0035] Specifically, in a Cartesian coordinate system, using calibrated camera parameters, the physical installation position of each thermocouple is precisely mapped to a two-dimensional pixel coordinate system of the infrared radiation intensity map. Each thermocouple is considered a reference point, composed of several pixels. The center point of these pixels in the Cartesian coordinate system is taken as the position of the thermocouple, denoted as […]. ; will the first At time t, the average initial infrared radiation intensity of these pixels is used as the first... The initial infrared radiation intensity of the thermocouple at time t; for example, the first... Current thermocouple The initial infrared radiation intensity is denoted as , Indicates the sequence number of the time; obtain the number according to step S1. Current thermocouple Temperature data, denoted as Unit: degrees Celsius Indicates the sequence number of the time. This indicates the serial number of the thermocouple.

[0036] It should be noted that, according to Planck's law and the definition of emissivity, which are well-known in the field of infrared thermometry, there is a definite relationship between the spectral radiation intensity of an actual object and the spectral radiation intensity of a blackbody at a specific wavelength and temperature. That is, the spectral radiation intensity of an actual object is equal to the product of the spectral radiation intensity of the blackbody at the same wavelength and temperature and the emissivity of the actual object. Considering that infrared thermal imagers detect the total radiation intensity within the working band, the equivalent emissivity and equivalent wavelength are introduced to simplify the integral relationship, so that the total radiation intensity still satisfies the above proportional logic. This equivalent treatment is a conventional simplification method in the field of infrared thermometry.

[0037] Furthermore, based on the above relationship, for each thermocouple, given its temperature data and initial infrared radiation intensity at each moment, the internal processor of the infrared thermal imager can automatically calculate the theoretical infrared radiation intensity of the corresponding blackbody at the same temperature. For example, according to the... Current thermocouple Measured temperature data Calculations using Planck's law yielded that at the same temperature, the first... Current thermocouple The theoretical infrared radiation intensity corresponding to a blackbody is denoted as . Then the first At this moment, the position is thermocouple The formula for calculating the initial emissivity is:

[0038] ;

[0039] in, It is the first The current position is thermocouple The initial emissivity; It is the first The current position is thermocouple The initial infrared radiation intensity; It is the first The current position is thermocouple The theoretical infrared radiation intensity corresponding to a blackbody; The sequence number indicates the time. When the temperature data of the thermocouple is known, the initial emissivity of the thermocouple can be accurately deduced by measuring the initial infrared radiation intensity. The greater the initial infrared radiation intensity of the thermocouple and the smaller the theoretical infrared radiation intensity, the greater the initial emissivity of the thermocouple, and vice versa. All pixels except the location of the thermocouple are recorded as target pixels.

[0040] It should be noted that since the number of thermocouples is much smaller than the total number of pixels in the infrared image, an interpolation algorithm is needed to extend the discrete initial emissivity to all pixels in the image. To improve the extension accuracy, structural region constraints are performed in conjunction with the preprocessed grayscale image.

[0041] Specifically, for the preprocessed grayscale image set in step S1, the image is automatically segmented into three types of structural regions using a dual-threshold segmentation algorithm: heat transfer pipe region, fin region, and brazing region; for example, the region located in... The set of all thermocouples within the region containing the target pixel is denoted as . The inverse distance weighted interpolation algorithm is employed. For each target pixel, interpolation is performed using only the position information of thermocouples within its local area. For example, when a target pixel belongs to a brazing area, interpolation is performed using only the position information of thermocouples within the brazing area. Target pixels closer to thermocouples within the brazing area are assigned higher weight coefficients, based on their position within the brazing area. Taking the target pixel as an example for analysis, this target pixel is compared to the thermocouple. The formula for calculating the weighting coefficient is as follows:

[0042] ;

[0043] in, It is located in The target pixel compared to the thermocouple The weighting coefficient reflects the thermocouple Location information at the target pixel The proportion of the interpolation results; It is located in Target pixels and thermocouples The Euclidean distance between them; It is located in Target pixels and sets medium thermocouple The Euclidean distance between them; It is located in The set of all thermocouples within the region where the target pixel is located; Indicates the thermocouple serial number. Represents a set The serial number of the thermocouple; It is an exponential parameter.

[0044] Specifically, when thermocouple With position The closer the target pixels are, the better. The smaller the size, the greater its distance. Reciprocal of the power The larger the value, the higher the position. The target pixel compared to the thermocouple Weighting coefficients Also bigger.

[0045] Furthermore, the location is obtained The target pixel is compared to the weight coefficients of all thermocouples in its region, where the sum of the weight coefficients is 1; combined with thermocouples The initial emission rate, the position is located The initial emissivity of the target pixel is calculated as follows:

[0046] ;

[0047] in, It is the first At this moment, the position is The initial emissivity of the target pixel; It is located in The set of all thermocouples within the region where the target pixel is located; Target pixel Compared to thermocouples Weighting coefficients; It is the first At this moment, the position is thermocouple The initial emissivity; Indicates the sequence number of the time; when the thermocouple The higher the initial emission rate, the more likely the location is to be affected. The larger the weight coefficient of the target pixel, the greater the initial emissivity of the target pixel, and vice versa.

[0048] Specifically, to verify the interpolation accuracy, two verification areas without thermocouples were randomly selected from the heat transfer tube area, fin area, and brazing area, respectively, resulting in a total of six verification areas. A high-precision infrared spectrometer was used to directly measure the true initial emissivity of each verification area, and this was compared with the interpolated initial emissivity. If the average relative deviation of a certain verification area exceeded a preset threshold, the power parameter of the inverse distance weighted interpolation method was adjusted for that area. The higher the power, the greater the weight of nearby thermocouples, which can enhance the fitting of local characteristics within the region; the lower the power, the more uniform the weight distribution, which can balance the influence of thermocouples at different locations within the region and avoid amplifying local deviations; interpolation calculations are performed again until the average relative deviation of all structural regions is less than or equal to the preset threshold, thereby obtaining the initial emissivity of all target pixels at each time point; the preset threshold is set based on the measurement accuracy of the infrared spectrometer and the tolerance of subsequent temperature inversion to emissivity errors, and is preferably 6%.

[0049] S3. Based on the initial emissivity of the target pixel at each time point and the activation energy, oxidation rate coefficient, and molar gas constant of the corresponding material, obtain the dynamic emissivity of the target pixel at each time point.

[0050] It should be noted that the dynamic change in emissivity during high-temperature testing mainly stems from surface oxidation, a process that exhibits regional variability and time-cumulative characteristics: the activation energy and oxidation rate coefficient of heat transfer tubes, fins, and brazing areas differ due to their different materials; and the thickness of the oxide layer increases nonlinearly with test time and temperature, resulting in a nonlinear change in emissivity.

[0051] Specifically, by consulting high-temperature oxidation data of relevant materials, the activation energies of the corresponding materials in the heat transfer tube region, fin region, and brazing region were obtained, with units of [unit missing]. For example, placing the location in The activation energy of the material corresponding to the region where the target pixel is located is denoted as Activation energy reflects the energy barrier for oxidation damage reactions in materials; the lower the activation energy, the lower the energy barrier that the oxidation reaction needs to overcome, and the easier the material is to be oxidized; the molar gas constant is obtained, denoted as . ,Pick .

[0052] Furthermore, under the same operating conditions as this high-temperature test, oxidation tests were conducted on the materials in each region. The oxidation rate coefficient of each region was obtained by fitting the emissivity growth curve. For example, the region located at... The oxidation rate coefficient corresponding to the region where the target pixel is located is denoted as The oxidation rate coefficient is a dimensionless parameter used to reflect the relative rate of emissivity growth in different regions under the same temperature rise conditions. The heat transfer tube region is mainly composed of copper. Due to the high humidity in the actual engine compartment environment and the ease with which copper oxidizes at high temperatures, its oxidation rate coefficient is set to be relatively high, at 0.006 per hour. The fin region is mainly composed of aluminum. Since a dense aluminum oxide film rapidly forms on the aluminum surface during the experiment, inhibiting further oxidation, its oxidation rate coefficient is set to be the lowest, at 0.0008 per hour. The brazing region is mainly composed of tin-lead solder. Because tin-lead solder oxidizes most easily at high temperatures, and micro-cracks easily form at the welding interface, accelerating oxidation, its oxidation rate coefficient is set to be the highest, at 0.009 per hour. The time accumulation coefficient is obtained based on fitting of high-temperature oxidation test data. The fitted value range is 0.3~0.8, preferably. .

[0053] Based on the position Taking the target pixel as an example for analysis: Real-time data is collected using an infrared thermal imager at the current moment. The previous temperature data was averaged, and the average value was used as the location. Average temperature of target pixels The unit is Kelvin; the obtained position is in Oxidation rate coefficient of the region where the target pixel is located and activation energy Then the first At this moment, the position is The formula for calculating the dynamic emissivity of the target pixel is as follows:

[0054] ;

[0055] in, It is the first At this moment, the position is The dynamic emissivity of the target pixel; It is the first At this moment, the position is The initial emissivity of the target pixel; They are located at The oxidation rate coefficient and activation energy of the region where the target pixel is located; It is the molar gas constant, taken as ; Indicates from the start time of the experiment to the [number]th [time]. The actual cumulative time of each moment, in hours; It is located in The average temperature of the target pixel; It is a time accumulation coefficient used to reflect the nonlinear growth law of oxide layer thickness, which conforms to the actual oxidation law; Indicates the sequence number of the time; Represented by natural constant It is an exponential function with base 0; the dynamic emissivity of the target pixel increases with the increase of the initial emissivity, oxidation rate coefficient, average temperature and actual cumulative time of the target pixel, and increases with the decrease of activation energy.

[0056] Similarly, the dynamic emissivity of all target pixels at each time step can be obtained.

[0057] S4. Based on the dynamic emissivity of each target pixel, obtain the defect probability of each target pixel for screening defective pixels; based on the screened pixels, obtain the total defect area of ​​the evaporator, and further obtain the total defect degree of the evaporator; based on the total defect degree, give corresponding early warning measures.

[0058] It should be noted that traditional infrared thermal imaging methods, which use a global constant emissivity, cannot reflect the dynamic differences in emissivity caused by oxidation in the heat transfer tube area, fin area, and brazing area on the evaporator surface. This can easily lead to temperature inversion deviations and make it difficult to accurately identify local overheating defects. This step uses the obtained dynamic emissivity correction model to invert the accurate temperature and combines probability quantization and cluster analysis to achieve objective identification of defect areas, thus overcoming the limitations of traditional methods.

[0059] Specifically, using the infrared thermal imager calibrated in step S1, the data collected during the experiment is... Infrared radiation intensity map at a given time; based on location Taking the target pixel as an example, its first... The dynamic emission rate at any given time The initial infrared radiation intensity of the target pixel was measured to be According to the location The target pixel in the first The dynamic emissivity and initial infrared radiation intensity at time t, based on Planck's law, are used to inversely deduce the position at which the radiation source is located. The target pixel in the first The temperature data after time correction is denoted as In actual operation, this process is automatically completed by the infrared thermal imager system. That is, the system directly calculates and outputs the corrected temperature data based on the input dynamic emissivity and the measured infrared radiation intensity.

[0060] Furthermore, obtain the first of all target pixels. The corrected temperature data at that moment; then, in the initial stage of the experiment, i.e. At that time, the highest normal operating temperature of the defect-free area of ​​the evaporator is measured using thermocouples, and a 5% safety margin is added as the temperature threshold. Based on the corrected temperature data and temperature threshold, calculate the temperature of each target pixel at the [missing information]. The probability of a defect at a given time, with the position being... Taking the target pixel as an example for analysis, its first... The formula for calculating the defect probability at time t is as follows:

[0061]

[0062] in, It is the first At this moment, the position is The defect probability of the target pixel is within the range of Inside; It is based on the position The target pixel in the first The dynamic emissivity and initial infrared radiation intensity at time t, based on Planck's law, are used to inversely deduce the position at which the radiation source is located. The target pixel in the first Temperature data after time correction; It is the temperature threshold; This indicates normalization processing, which can be done using methods such as maximum and minimum value normalization; It is the absolute value symbol; Indicates the sequence number of the time; when the target pixel is at the [time value missing]... The greater the difference between the time-corrected temperature data and the temperature threshold, the more likely the target pixel will be at the 1st time. The greater the probability of a defect at a given moment, the higher the probability of a defect, and vice versa.

[0063] Furthermore, a probability threshold is set, for example, 0.7, which can be adjusted according to the experimental accuracy; all target pixels with a defect probability greater than or equal to the probability threshold of 0.7 are judged as defect pixels; the total number of all defect pixels is used to represent the total defect area of ​​the evaporator, and the total number of pixels in the evaporator image is used to represent the total image area of ​​the evaporator; the ratio of the two is used as the total defect degree of the evaporator at the current moment.

[0064] Specifically, a graded early warning system is implemented based on the total defect severity: for example, when the total defect severity is less than or equal to 0.4, the temperature field of the evaporator at the current moment is determined to be in a normal state, and no warning is issued; when the total defect severity is greater than 0.4 but less than 0.7, the temperature field of the evaporator at the current moment is determined to be in a slightly abnormal state, and it is necessary to prompt the strengthening of monitoring of the evaporator temperature field; when the total defect severity is greater than or equal to 0.7, the temperature field of the evaporator at the current moment is determined to be in a severely abnormal state, and it is necessary to suspend the test and return the evaporator to the factory for repair.

[0065] This invention also discloses an automotive air conditioning evaporator temperature field monitoring system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an automotive air conditioning evaporator temperature field monitoring method according to the present invention.

[0066] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method of monitoring the temperature field of an evaporator of an automotive air conditioner, characterized by, The method comprises the following steps: acquiring a grayscale image and an infrared image of the evaporator; obtaining initial infrared radiation intensity of each pixel point according to the infrared image; acquiring temperature data of the thermocouples on the surface of the evaporator; calculating initial emissivity of each thermocouple based on the temperature data of the thermocouples and corresponding initial infrared radiation intensity according to Planck's law; all pixel points except the thermocouples are regarded as target pixel points; the heat transfer tube region, the fin region and the brazing region of the evaporator are obtained by using a double-threshold segmentation algorithm on the grayscale image; interpolating initial emissivity of all target pixel points based on initial emissivity of the thermocouples in each region by using an inverse distance weighted interpolation algorithm; calculating average temperature of the target pixel points from the starting time of the test to the current time; calculating dynamic emissivity of each target pixel point according to activation energy and oxidation rate coefficient of the corresponding material, average temperature and initial emissivity of the target pixel point; combining real-time infrared radiation intensity and dynamic emissivity of the target pixel points at the current time, and inversely calculating corrected temperature data based on Planck's law; calculating defect probability of each target pixel point according to the preset temperature threshold and the corrected temperature data, which is used for screening defect pixel points; the ratio of the number of defect pixel points to the total number of all pixel points in the grayscale image is taken as the total defect degree of the evaporator, and hierarchical early warning is implemented, thereby realizing monitoring of the temperature field of the evaporator.

2. The method of claim 1, wherein, The method for calculating initial emissivity of each thermocouple based on the temperature data of the thermocouples and corresponding initial infrared radiation intensity according to Planck's law comprises the following steps: ； in, It is the first The current position is thermocouple The initial emissivity; It is the first The current position is thermocouple The initial infrared radiation intensity; It is the first The current position is thermocouple The theoretical infrared radiation intensity corresponding to a blackbody; Indicates the sequence number of the time.

3. The method of claim 1, wherein the temperature field of the evaporator is monitored by using a temperature sensor. The method for obtaining initial emissivity of the target pixel points comprises the following steps: ​ ； in, It is the first At this moment, the position is The initial emissivity of the target pixel; It is located in The set of all thermocouples within the region where the target pixel is located; It is located in Target pixels compared to thermocouples Weighting coefficients; It is the first The current position is thermocouple The initial emissivity; Indicates the sequence number of the time.

4. The method of claim 3, wherein the temperature field of the evaporator is monitored by using a temperature sensor. The method for obtaining the weight coefficient comprises the following steps: ； wherein, is a weight coefficient of a target pixel point located at compared with a thermocouple ; is a Euclidean distance between a target pixel point located at and a thermocouple ; is a Euclidean distance between a target pixel point located at and a thermocouple in the set ; is a set of all thermocouples in a region in which a target pixel point located at is located; denotes a serial number of a thermocouple, denotes a serial number of a thermocouple in the set ; is a power parameter.

5. The method of claim 4, wherein the temperature field of the evaporator is monitored by using a temperature sensor. The method for interpolating initial emissivity of all target pixel points based on initial emissivity of the thermocouples in each region by using an inverse distance weighted interpolation algorithm further comprises the following steps: In the heat pipe area, fin area, brazing area, 2 verification areas are randomly selected respectively, and 6 verification areas are obtained. The real initial emissivity of each verification area is directly measured by high-precision infrared spectrometer, and the initial emissivity obtained by interpolation is compared: if the average relative deviation of a certain verification area is greater than the preset threshold, the power parameter of the inverse distance weighted interpolation method is adjusted for the area ; re-interpolation calculation is carried out until the average relative deviation of all structure areas is less than or equal to the preset threshold, and then the initial emissivity of all target pixel points at each time is obtained.

6. The method of claim 1, wherein, The method for calculating dynamic emissivity of each target pixel point comprises the following steps: ； wherein, is the dynamic emissivity of the target pixel point at the time instant when the position of the target pixel point is at ; is the initial emissivity of the target pixel point at the time instant when the position of the target pixel point is at ; are the oxidation rate coefficient and the activation energy corresponding to the region where the target pixel point is located at ; is the molar gas constant; represents the actual cumulative time from the starting time instant of the test to the time instant ; is the average temperature of the target pixel point at the time instant when the position of the target pixel point is at ; is the time cumulative coefficient; represents the serial number of the time instant; represents an exponential function with the natural constant e 7. The method of claim 1, wherein the temperature field of the evaporator is monitored by using a temperature sensor. The method for calculating defect probability of each target pixel point according to the preset temperature threshold and the corrected temperature data comprises the following steps: ​ in, It is the first At this moment, the position is The defect probability of the target pixel; It is located in The target pixel in the first Temperature data after time correction; It is the temperature threshold; This indicates normalization processing; It is the absolute value symbol; Indicates the sequence number of the time.

8. The method of claim 1, wherein, The method for screening defect pixel points comprises the following steps: A probability threshold is set, and all target pixel points with defect probability greater than or equal to the probability threshold are determined as defect pixel points.

9. The method of claim 1, wherein, The method for implementing hierarchical early warning and realizing monitoring of the temperature field of the evaporator comprises the following steps: When the total defect degree is less than or equal to 0.4, it is determined that the temperature field of the evaporator at the current time is in a normal state, and no early warning is performed; when the total defect degree is greater than 0.4 and less than 0.7, it is determined that the temperature field of the evaporator at the current time is in a slightly abnormal state, and at this time, it is necessary to prompt to strengthen monitoring of the temperature field of the evaporator; when the total defect degree is greater than or equal to 0.7, it is determined that the temperature field of the evaporator at the current time is in a serious abnormal state, and at this time, it is necessary to suspend the test and return to the factory for repair.

10. An automotive air conditioning evaporator temperature field monitoring system characterized by, The device comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the method for monitoring the temperature field of the evaporator of the automobile air conditioner according to any one of claims 1-9. ​

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