Infrared equipment calibration method and device, equipment and storage medium

Abstract

The embodiment of the invention provides an infrared equipment calibration method and device, equipment and a storage medium, and relates to the technical field of infrared thermal imaging. Comprising the following steps: acquiring an actually measured gray value obtained by measuring a plurality of standard blackbody radiation sources with different preset temperature values by an infrared device to be calibrated; determining a standard gray value corresponding to each preset temperature value through a standard curve corresponding to the to-be-calibrated infrared equipment; the standard curve is used for representing a corresponding relation between the temperature value and a standard gray value; based on a standard gray value change rate corresponding to each preset temperature value in the standard curve, determining a weight corresponding to each preset temperature value; the change rate of the standard gray value is negatively correlated with the weight; determining a gain parameter and a bias parameter based on the actually measured gray value, the standard gray value and the weight corresponding to each preset temperature value; and performing temperature measurement calibration on the to-be-calibrated infrared equipment based on the gain parameter and the bias parameter.

Description

Technical Field

[0001] The present application relates to the field of infrared thermal imaging technology, and in particular to a calibration method, device, equipment and storage medium for infrared equipment. Background Art

[0002] Infrared devices generate images or data by detecting the infrared heat radiated by objects, but due to the influence of environmental conditions (such as temperature, humidity, wind speed, etc.) and device factors (such as device drift, detector performance differences, etc.), the measurement results may have errors. Therefore, before using infrared devices, it is usually necessary to calibrate the infrared devices. Calibration is a series of calibration steps that enable infrared devices to more accurately reflect the true temperature or radiation intensity of objects, thereby reducing errors. Through calibration, it can be ensured that infrared devices can provide stable and reliable measurement results under various conditions, improving their reliability in various application scenarios.

[0003] Since the response of the detector of an infrared device usually has nonlinear characteristics, but the nonlinear model is relatively complex and requires high computing resources when applied, in the calibration process of the infrared device, a linear model with simple principles, easy to understand and implement is usually used to describe the relationship between the detector output and the radiation energy. This relationship is intuitive and easy to handle. Compared with complex nonlinear models, linear models do not require too much mathematical knowledge and computing resources, and can quickly complete the calibration work. However, linear models may not provide sufficient calibration accuracy, especially when dealing with a large dynamic measurement range, and the relationship between its output and input radiation energy is often not a strict linear relationship. Summary of the invention

[0004] The present application provides a calibration method, apparatus, device and storage medium for an infrared device, which are used to improve the calibration accuracy of a linear model calibration of an infrared device.

[0005] The first aspect of an embodiment of the present application provides a calibration method for an infrared device, including: obtaining a measured grayscale value obtained by measuring a standard blackbody radiation source at multiple different preset temperature values ​​of the infrared device to be calibrated; determining a standard grayscale value corresponding to each preset temperature value through a standard curve corresponding to the infrared device to be calibrated; the standard curve is used to represent the correspondence between temperature values ​​and standard grayscale values; determining a weight corresponding to each preset temperature value based on a standard grayscale value change rate corresponding to each preset temperature value in the standard curve; the standard grayscale value change rate is negatively correlated with the weight; determining a gain parameter and a bias parameter based on the measured grayscale value, the standard grayscale value and the weight corresponding to each preset temperature value; and performing temperature measurement calibration on the infrared device to be calibrated based on the gain parameter and the bias parameter.

[0006] The calibration method of the infrared device provided in the embodiment of the present application, when calibrating the infrared device to be calibrated, the weight corresponding to each preset temperature value is determined by the standard gray value change rate corresponding to each preset temperature value in the standard curve corresponding to the infrared device to be calibrated, and the standard gray value change rate is negatively correlated with the weight. Then based on the measured gray value, standard gray value and weight corresponding to each preset temperature value in multiple preset temperature values, the gain parameter and bias parameter for temperature measurement calibration of the infrared device to be calibrated are determined. The scheme provided in the present application sets the negative correlation between the standard gray value change rate and the weight, so that a larger standard gray value change rate corresponds to a smaller weight. It can be understood that a larger standard gray value change rate indicates that the grayscale changes faster at the preset temperature value, indicating that the nonlinear characteristics here are more obvious. Setting a lower weight at the preset temperature value where the grayscale changes faster helps to avoid overfitting the data points at the preset temperature value, thereby reducing the distortion of the data points at the preset temperature value on the linear model (including gain parameters and bias parameters), thereby improving the temperature measurement accuracy of the infrared device to be calibrated.

[0007] In combination with the first implementation method of the first aspect, the weight corresponding to each preset temperature value is determined based on the standard grayscale value change rate corresponding to each preset temperature value in the standard curve, including: deriving the standard curve to obtain the derivative function corresponding to the standard curve; determining the standard grayscale value change rate corresponding to each preset temperature value based on the derivative function; obtaining the functional relationship between the standard grayscale value change rate and the weight, and determining the weight corresponding to each preset temperature value based on the functional relationship and the standard grayscale value change rate corresponding to each preset temperature value.

[0008] In combination with the second implementation method of the first aspect, the functional relationship is an inverse proportional relationship; based on the functional relationship and the standard grayscale value change rate corresponding to each preset temperature value, the weight corresponding to each preset temperature value is determined, including: determining the least common multiple of multiple standard grayscale value change rates corresponding to multiple preset temperature values; for each preset temperature value, according to the inverse proportional relationship, the least common multiple, and the standard grayscale value change rate corresponding to the preset temperature value, determine the weight corresponding to the preset temperature value.

[0009] In combination with the third implementation method of the first aspect, the lowest common multiple of multiple standard grayscale value change rates corresponding to multiple preset temperature values ​​is determined, including: normalizing the multiple standard grayscale value change rates corresponding to the multiple preset temperature values ​​to obtain multiple processed standard grayscale value change rates; determining the lowest common multiple of the multiple processed standard grayscale value change rates.

[0010] In combination with the fourth implementation method of the first aspect, gain parameters and bias parameters are determined based on the measured grayscale value, standard grayscale value and weight corresponding to each preset temperature value, including: for each preset temperature value, weighting the measured grayscale value and standard grayscale value corresponding to the preset temperature value according to the weight corresponding to the preset temperature value to obtain the weighted measured grayscale value and weighted standard grayscale value corresponding to the preset temperature value; based on the weighted measured grayscale value and weighted standard grayscale value corresponding to each preset temperature value, gain parameters and bias parameters are determined.

[0011] In combination with the fifth implementation method of the first aspect, gain parameters and bias parameters are determined based on the measured grayscale values, standard grayscale values ​​and weights corresponding to each temperature value in multiple temperature values, including: constructing a measured grayscale matrix, a standard grayscale matrix and a weight matrix based on the measured grayscale values, standard grayscale values ​​and weights corresponding to each temperature value in multiple temperature values; constructing a parameter matrix including gain parameters and bias parameters; solving the parameter matrix based on the grayscale matrix, the standard grayscale matrix and the weight matrix to obtain gain parameters and bias parameters.

[0012] In combination with the sixth implementation method of the first aspect, before obtaining the measured grayscale value obtained by the infrared device to be calibrated measuring a standard blackbody radiation source at multiple different preset temperature values, the method also includes: when the measured grayscale difference corresponding to the infrared device to be calibrated is different from the standard grayscale difference, adjusting the integration time of the infrared device to be calibrated so that the measured grayscale difference corresponding to the infrared device to be calibrated is the same as the standard grayscale difference; wherein the measured grayscale difference is the difference between the measured grayscale values ​​of the standard blackbody radiation source measured by the infrared device to be calibrated at two temperature values; the standard grayscale difference is the difference between the standard grayscale values ​​of the two temperature values.

[0013] The second aspect of an embodiment of the present application provides a calibration device for an infrared device, including: an acquisition module, used to obtain the measured grayscale value obtained by the infrared device to be calibrated by measuring a standard blackbody radiation source at multiple different preset temperature values; a determination module, used to determine the standard grayscale value corresponding to each preset temperature value through a standard curve corresponding to the infrared device to be calibrated; the standard curve is used to represent the correspondence between the temperature value and the standard grayscale value; the determination module is also used to determine the weight corresponding to each preset temperature value based on the standard grayscale value change rate corresponding to each preset temperature value in the standard curve; the standard grayscale value change rate is negatively correlated with the weight; the determination module is also used to determine the gain parameter and the bias parameter based on the measured grayscale value, the standard grayscale value and the weight corresponding to each preset temperature value; the calibration module is used to perform temperature measurement calibration on the infrared device to be calibrated based on the gain parameter and the bias parameter.

[0014] A third aspect of an embodiment of the present application provides an electronic device, comprising: one or more processors; one or more memories; wherein the one or more memories are used to store computer program codes, the computer program codes include computer instructions, and when the one or more processors execute the computer instructions, the electronic device executes the calibration method of the infrared device provided by the first aspect and its possible implementation methods.

[0015] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores computer execution instructions. When the computer execution instructions are run on a computer, the computer executes the calibration method of the infrared device provided by the first aspect and its possible implementation methods.

[0016] A fifth aspect of an embodiment of the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the infrared device calibration method provided in the first aspect and its possible implementation methods.

[0017] Among them, the beneficial effects described in the second to fifth aspects can refer to the beneficial effect analysis of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0019] Figure 1 A schematic diagram of the structure of a calibration system for an infrared device provided in an embodiment of the present application;

[0020] Figure 2 A method flow of a calibration method for an infrared device provided in an embodiment of the present application Figure 1 ;

[0021] Figure 3 A method flow of a calibration method for an infrared device provided in an embodiment of the present application Figure 2 ;

[0022] Figure 4 A method flow of a calibration method for an infrared device provided in an embodiment of the present application Figure 3 ;

[0023] Figure 5 A schematic diagram of the structure of a calibration device for an infrared device provided in an embodiment of the present application;

[0024] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0027] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, when describing a pipeline, the "connected" and "connection" used in this application have the meaning of conduction. The specific meaning needs to be understood in conjunction with the context.

[0028] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0029] As described in the background technology, infrared devices usually need to be calibrated to adjust the measurement accuracy of the device during application. The infrared temperature measurement calibration method disclosed in the related art needs to adjust the reference temperature compensation coefficient, reference temperature, integration time, bias voltage, etc. of the infrared device to be calibrated to achieve the purpose of response rate correction, and it is necessary to adjust the response output of the device at the same time as the calibration, which is not flexible enough. If the response of the infrared device changes later, the adaptability of the parameters pre-stored in the device will decrease, and re-calibration is required, which is relatively cumbersome.

[0030] Based on this, the relevant technology also discloses the use of nonlinear models or linear models for calibration, that is, by obtaining a set of measured grayscale values ​​and standard grayscale values ​​of infrared devices, the relationship between the measured grayscale values ​​and the standard grayscale values ​​is obtained by fitting a relationship curve, and then during calibration, after obtaining the measured temperature value of the infrared device, the calibrated grayscale value can be determined according to the relationship between the measured grayscale value and the standard grayscale value.

[0031] Since the detector of the infrared device often does not respond linearly in the process of receiving radiation energy and converting it into a measurable signal (such as voltage or current), this means that there is no direct linear relationship between the output signal and the input radiation energy. Therefore, the use of nonlinear models for calibration can achieve a higher accuracy, but in the process of nonlinear model calibration, it is usually necessary to calibrate multiple parameters, and the equations formed by these parameters are often very complex. When calculating the gradient of these parameters, the calculation process is cumbersome and time-consuming, which increases the difficulty of calibration. In addition, due to the nonlinear relationship between the parameters, the equation presents a multi-peak characteristic, and it is difficult for ordinary optimization algorithms to directly find the global optimal point, which may require multiple iterations and parameter adjustments.

[0032] Since linear models have the advantages of simplicity, ease of use, and high computational efficiency, although the accuracy of linear models is limited and they cannot fully capture the nonlinear characteristics of infrared devices, linear models are often used to calibrate infrared devices in practical applications. When linear models deal with a larger dynamic measurement range, due to the different radiation intensities of objects at different temperatures and different degrees of interference from the outside world, changes in ambient temperature will affect the transmission and reception of infrared rays, thereby introducing measurement errors. For example, under the same detector window, the peak value of the blackbody radiation curve of a low-temperature target is small, resulting in a small area under the curve, that is, the integrated energy is low; the peak value of the blackbody radiation curve of a high-temperature target is large, resulting in a large area under the curve, and this area change is nonlinear. When the temperature changes by 1 degree, the change in the low-temperature target is much smaller than that of the high temperature. Therefore, the response of the infrared device shows a significant nonlinear relationship in the low temperature zone.

[0033] Based on this, the embodiment of the present application provides a calibration method for infrared equipment. When calibrating the infrared equipment to be calibrated, the weight corresponding to each preset temperature value is determined by the standard gray value change rate corresponding to each preset temperature value in the standard curve corresponding to the infrared equipment to be calibrated, and the standard gray value change rate is negatively correlated with the weight. Then, based on the measured gray value, standard gray value and weight corresponding to each preset temperature value in multiple preset temperature values, the gain parameter and bias parameter for temperature measurement calibration of the infrared equipment to be calibrated are determined. The scheme provided by the present application sets the negative correlation between the standard gray value change rate and the weight, so that a larger standard gray value change rate corresponds to a smaller weight. It can be understood that a larger standard gray value change rate indicates that the grayscale changes faster at the preset temperature value, indicating that the nonlinear characteristics here are more obvious. Setting a lower weight at the preset temperature value where the grayscale changes faster helps to avoid overfitting the data points at the preset temperature value, thereby reducing the distortion of the data points at the preset temperature value on the linear model (gain parameter and bias parameter), thereby improving the calibration accuracy of the infrared equipment.

[0034] It can be seen that the calibration method of an infrared device provided in the embodiment of the present application adopts a weighted calibration method to fit the linear model corresponding to the infrared device to be calibrated. Not only is the calibration result more accurate, but the response rate characteristics of the temperature measuring element are taken into account during the calibration process, and the influence of the response rate on the temperature measurement accuracy is reduced through the gain parameter and the bias parameter. At the same time, the computing resources required during the calibration process are relatively low, and the technical efficiency is relatively high.

[0035] It should be understood that the infrared device calibration method provided in the embodiment of the present application can be applied to the infrared device calibration system. Figure 1 The infrared device calibration system 10 may include: an infrared device to be calibrated 11 and a calibration device 12.

[0036] In some embodiments, the calibration device 12 and the infrared device to be calibrated 11 can be communicatively connected via wired or wireless means, so that the calibration device 12 can obtain the measured grayscale value obtained by the infrared device to be calibrated measuring a standard black body radiation source with multiple different preset temperature values.

[0037] In other embodiments, there may be no communication connection between the calibration device 12 and the infrared device to be calibrated 11. The staff uses the infrared device to be calibrated 11 to measure the measured grayscale values ​​of standard black body radiation sources with multiple different preset temperature values, and records the measured grayscale values ​​in the calibration device. The embodiment of the present application does not specifically limit the way in which the calibration device 12 obtains the measured grayscale values ​​of the infrared device to be calibrated 11.

[0038] In the embodiment of the present application, the calibration device 12 is used to determine the standard grayscale value corresponding to each preset temperature value through the standard curve corresponding to the infrared device to be calibrated; and based on the standard grayscale value change rate corresponding to each preset temperature value in the standard curve, determine the weight corresponding to each preset temperature value that is negatively correlated with the standard grayscale value change rate, and then determine the gain parameter and bias parameter based on the measured grayscale value, standard grayscale value and weight corresponding to each preset temperature value; and perform temperature measurement calibration on the infrared device 11 to be calibrated based on the gain parameter and the bias parameter.

[0039] In some embodiments, after determining the gain parameters and bias parameters, the calibration device 12 can send the gain parameters and bias parameters to the infrared device 11 to be calibrated, so that the infrared device 11 to be calibrated can adjust the gain and bias values ​​of the device according to the gain parameters and bias parameters to ensure that the imaging quality and accuracy reach the best state.

[0040] It should be understood that the infrared device 11 to be calibrated in the embodiment of the present application may be an infrared device such as an infrared thermal imager, an infrared thermometer, an infrared detector, etc., and the embodiment of the present application does not impose any limitation on this.

[0041] The calibration device 12 in the embodiment of the present application can be a personal computer (PC), a laptop computer, a mobile device, a tablet computer, a laptop computer and other electronic devices. The embodiment of the present application does not limit the specific form of the electronic device. Alternatively, the calibration device 12 can also be a single server, or a server cluster composed of multiple servers. In some implementations, the server cluster can be a distributed cluster server. The embodiment of the present application does not impose any restrictions on this.

[0042] It should be understood that the calibration method of the infrared device provided in the embodiment of the present application can be applied to Figure 1 The calibration device 12 in the calibration system of the infrared device shown. Figure 2 The calibration method of the infrared device provided in the embodiment of the present application includes the following steps:

[0043] S201, obtaining the measured grayscale value obtained by measuring a standard black body radiation source with multiple different preset temperature values ​​by the infrared device to be calibrated.

[0044] In the process of infrared equipment calibration, the standard blackbody radiation source is a crucial calibration tool, which is used to provide a stable and known temperature reference point. The standard blackbody radiation source is a device that can simulate the radiation characteristics of a blackbody. A blackbody is an idealized physical model that can completely absorb radiation of any wavelength at any temperature without reflection or transmission. Therefore, a blackbody radiation source can produce the same radiation characteristics as a blackbody, providing a calibration benchmark for various radiation measurement devices.

[0045] Then, according to the calibration requirements, multiple different preset temperature values ​​are set. These temperature values ​​should cover the operating temperature range of the infrared device and have a certain degree of representativeness.

[0046] As a feasible implementation method, the temperature measurement range of the infrared device to be calibrated can be divided equally with a temperature difference interval of △t℃ to obtain multiple preset temperature values ​​T1, T2, ..., Tn with an interval temperature of △t℃, and then the infrared device to be calibrated is used to measure the measured grayscale values ​​G1, G2, ..., Gn of the standard blackbody radiation source at each preset temperature value.

[0047] Optionally, the value range of △t can be 1 to 30. It can be understood that the smaller the value of △t, the denser the sampling, so that the final calibration accuracy is high, but the calibration workload will be greatly increased. The larger the value of △t, the sparser the sampling, the reduced calibration workload, but the calibration accuracy may be reduced. Therefore, as a feasible implementation method, the value range of △t is 8 to 12.

[0048] As a feasible implementation method, in order to ensure the accuracy of the measured grayscale value, when obtaining the measured grayscale value, the standard blackbody radiation source needs to be located at the center of the infrared screen of the infrared device to be calibrated, and the grayscale at the center of the blackbody target surface is obtained to obtain the measured grayscale value corresponding to each predicted temperature value.

[0049] After obtaining a plurality of measured grayscale values, the infrared device to be calibrated may send the corresponding relationship between the preset temperature value and the measured grayscale value to the calibration device, so that the calibration device can calibrate the infrared device to be calibrated.

[0050] S202: Determine the standard grayscale value corresponding to each preset temperature value through the standard curve corresponding to the infrared device to be calibrated.

[0051] The standard curve is used to represent the corresponding relationship between the temperature value and the standard gray value.

[0052] Since different models and types of infrared devices have different performance characteristics and measurement ranges, when calibrating the device to be calibrated, it is necessary to first obtain the standard curve corresponding to the infrared device to be calibrated.

[0053] In some embodiments, one or more infrared devices of the same model as the infrared device to be calibrated can be selected, and one or more infrared devices of the same model can be used to observe a group of black bodies to perform parameter surveys to obtain a standard curve corresponding to the infrared device to be calibrated. Specifically, before formal calibration, the infrared device needs to be preheated and necessary calibration operations need to be performed to ensure the measurement accuracy and stability of the infrared device. Then, the infrared device is used to observe a group of standard black body radiation sources with different temperature values ​​to obtain the standard grayscale value corresponding to each temperature value, and then the relationship between the black body temperature and the response grayscale, that is, the correspondence between the temperature value and the standard grayscale value, is obtained through polynomial fitting, to obtain the standard curve corresponding to the infrared device to be calibrated.

[0054] It is understandable that the standard curve corresponding to the infrared device to be calibrated can represent a response characteristic or measurement relationship common to the model or series of the device. For infrared devices of the same model or series, since they follow the same technical standards and specifications during the design and manufacturing process, they often show similar response characteristics when measuring the same physical quantity. This similarity allows us to summarize and describe the common behavior of this type of device by drawing a standard curve, thereby providing strong support for the calibration, calibration and application of the device.

[0055] However, it should be noted that although the standard curve can reflect the common response characteristics of the same model or series of infrared devices, the influence of individual differences and measurement conditions still needs to be considered in practical applications. Therefore, after obtaining the standard curve of the infrared device to be calibrated, the infrared device to be calibrated should be further calibrated in combination with the specific conditions of the infrared device to ensure the accuracy and reliability of the measurement results.

[0056] Therefore, after obtaining the standard curve of the infrared device to be calibrated, the solution provided in the embodiment of the present application determines the standard grayscale value corresponding to each preset temperature value through the standard curve corresponding to the infrared device to be calibrated.

[0057] S203 . Determine a weight corresponding to each preset temperature value based on a standard gray value change rate corresponding to each preset temperature value in the standard curve.

[0058] Among them, the standard gray value change rate is negatively correlated with the weight.

[0059] It can be understood that the standard curve represents the common response characteristics of the same model or series of infrared devices. The higher the rate of change of the standard grayscale value corresponding to the preset temperature value in the standard curve, that is, the higher the slope of the standard curve at the preset temperature value, indicates that the device has a stronger response ability at the preset temperature value, that is, the nonlinearity of the measurement result at the preset temperature value is higher.

[0060] Therefore, the solution provided in the embodiment of the present application makes the standard grayscale value change rate negatively correlated with the weight. The higher the standard grayscale value change rate corresponding to the preset temperature value, the lower the weight corresponding to the preset temperature value; by lowering the weight, the contribution of the data corresponding to the preset temperature value can be balanced, making the subsequent fitting results more reasonable.

[0061] As a possible implementation, see Figure 3 , S203 can be specifically implemented as follows:

[0062] S301, deriving the standard curve to obtain a derivative function corresponding to the standard curve.

[0063] It can be understood that the derivative function is used to represent the tangent slope of the function (standard curve) at any point, that is, the rate at which the standard gray value changes with the temperature value.

[0064] S302: Determine the standard grayscale value change rate corresponding to each preset temperature value based on the derivative function.

[0065] In some embodiments, each preset temperature value may be directly substituted into the derivative function to obtain the standard gray value change rate corresponding to each preset temperature value.

[0066] In other embodiments, directly deriving the experimental data may result in inaccurate results. In practical applications, the derivative can be approximately calculated using numerical differentiation methods, that is, the size of the derivative is estimated by calculating the slope difference between adjacent data points, and then the standard gray value change rate corresponding to each preset temperature value is obtained.

[0067] S303: Obtain a functional relationship between a standard gray value change rate and a weight, and determine a weight corresponding to each preset temperature value based on the functional relationship and the standard gray value change rate corresponding to each preset temperature value.

[0068] In some embodiments, the functional relationship between the standard gray value change rate and the weight is inversely proportional, that is, the product of the standard gray value change rate and the weight corresponding to each preset temperature value is a number greater than 0. At this time, when the standard gray value change rate increases, the weight tends to decrease.

[0069] Based on this, as a feasible implementation method, S303 can be specifically implemented as: determining the least common multiple of multiple standard grayscale value change rates corresponding to multiple preset temperature values; for each preset temperature value, determining the weight corresponding to the preset temperature value based on the inverse proportional relationship, the least common multiple, and the standard grayscale value change rate corresponding to the preset temperature value.

[0070] That is, the weights (w1-wn) corresponding to the n preset temperature values ​​and the standard gray value change rate (dg1-dgn) satisfy the following relationship: value=w1*dg1=w2*dg2=...=wn*dgn.

[0071] Among them, value is the least common multiple of dg1-dgn.

[0072] In this way, the weight corresponding to each preset temperature value can be determined according to the standard gray value change rate corresponding to each preset temperature value.

[0073] As a feasible implementation method, when determining the least common multiple of multiple standard grayscale value change rates corresponding to multiple preset temperature values, the multiple standard grayscale value change rates corresponding to the multiple preset temperature values ​​can be normalized first to obtain multiple processed standard grayscale value change rates, and then the least common multiple of the processed multiple standard grayscale value change rates can be determined. It can be understood that the normalized data usually has a smaller value range, which can reduce the numerical error in the calculation process, reduce the demand for computer hardware resources, and thus improve the calculation efficiency.

[0074] It can be seen that the solution provided in this embodiment determines the weight corresponding to each preset temperature value by obtaining the functional relationship between the standard gray value change rate and the weight, and the functional relationship can more accurately reflect the negative correlation between the standard gray value change rate and the weight. This precise mapping helps to reduce the error caused by subjective judgment or empirical formula, that is, to obtain a more accurate weight.

[0075] S204: Determine a gain parameter and a bias parameter based on the measured grayscale value, the standard grayscale value, and the weight corresponding to each preset temperature value.

[0076] It should be understood that the use of a linear model to calibrate an infrared device mainly involves adjusting the gain and bias parameters. These two parameters together form the basis of a linear model that describes the linear relationship between the output signal of a detector unit and the infrared irradiance it receives.

[0077] That is, the linear model can be expressed as: measured gray value = gain parameter × standard gray value + bias parameter.

[0078] Among them, the gain parameter reflects the sensitivity of the detector unit to infrared radiation. In the linear model, the gain parameter determines the slope of the output signal as the input irradiance changes. By adjusting the gain parameter, the brightness of the infrared image can be controlled so that the image can maintain an appropriate brightness level under different lighting conditions.

[0079] The bias parameter represents the output value of the detector unit when there is no infrared radiation input. By adjusting the bias parameter, the baseline offset in the image can be eliminated, making the image more accurately reflect the actual scene.

[0080] Therefore, the present application calculates the gain parameters and bias parameters corresponding to the infrared device to be calibrated, and then uses these parameters to correct the output signal of the detector unit of the infrared device to be calibrated, so as to eliminate the influence of unfavorable factors such as non-uniformity and noise, and improve the calibration accuracy of the infrared device to be calibrated.

[0081] S205: Perform temperature measurement calibration on the infrared device to be calibrated based on the gain parameter and the bias parameter.

[0082] After obtaining the gain parameter and the bias parameter, the calibration device can send the gain parameter and the bias parameter to the infrared device to be calibrated, so that the infrared device to be calibrated uses the gain parameter and the bias parameter to perform temperature measurement calibration.

[0083] It can be seen from S201-S205 that the calibration method of the infrared device provided in the embodiment of the present application, when calibrating the infrared device to be calibrated, determines the weight corresponding to each preset temperature value by the standard gray value change rate corresponding to each preset temperature value in the standard curve corresponding to the infrared device to be calibrated, and the standard gray value change rate is negatively correlated with the weight. Then, based on the measured gray value, standard gray value and weight corresponding to each preset temperature value in the multiple preset temperature values, the gain parameter and bias parameter for temperature measurement calibration of the infrared device to be calibrated are determined. The scheme provided in the present application sets the negative correlation relationship between the standard gray value change rate and the weight, so that a larger standard gray value change rate corresponds to a smaller weight. It can be understood that a larger standard gray value change rate indicates that the grayscale changes faster at the preset temperature value, indicating that the nonlinear characteristics here are more obvious. Setting a lower weight at the preset temperature value where the grayscale changes faster helps to avoid overfitting the data points at the preset temperature value, thereby reducing the distortion of the data points at the preset temperature value on the linear model (including gain parameters and bias parameters), thereby improving the temperature measurement accuracy of the infrared device to be calibrated.

[0084] For example, please refer to Table 1, the comparison of the effects of weighted calibration and non-weighted calibration is shown in Table 1:

[0085] Table 1

[0086]

[0087] It should be understood that the standard grayscale value in Table 1 refers to the grayscale value obtained using the standard curve corresponding to the infrared device, the calibrated grayscale value is the grayscale value after calibrating the measured grayscale value of the infrared device through gain parameters and bias parameters, and the measured grayscale value is the grayscale value actually measured when the infrared device is not calibrated.

[0088] It can be seen from Table 1 that when the unweighted calibration method is used, that is, the weights are not calculated, and the least squares method is directly used to solve the gain parameters and bias parameters for calibration, the calibrated grayscale value obtained is greatly different from the measured grayscale value (or standard grayscale value) under low temperature conditions, which leads to a large error in the temperature calculated according to the calibrated grayscale value.

[0089] After the temperature measurement calibration is performed using the solution provided in the embodiment of the present application, the obtained calibration grayscale value is less different from the measured grayscale value (or standard grayscale value) under low temperature conditions, and the error of the temperature calculated according to the calibration grayscale value is also small, that is, the calibration accuracy is high. Therefore, the solution provided by the present application can improve the temperature measurement accuracy of the infrared device to be calibrated.

[0090] In some embodiments, as a feasible implementation method, S204 can be specifically implemented as follows: for each preset temperature value, the measured grayscale value and the standard grayscale value corresponding to the preset temperature value are weighted according to the weight corresponding to the preset temperature value to obtain the weighted measured grayscale value and the weighted standard grayscale value corresponding to the preset temperature value; based on the weighted measured grayscale value and the weighted standard grayscale value corresponding to each preset temperature value, the gain parameter and the bias parameter are determined.

[0091] That is to say, according to the weight corresponding to the preset temperature value and the measured grayscale value, the corresponding weighted measured grayscale value is obtained; according to the weight corresponding to the preset temperature value and the standard grayscale value, the corresponding weighted standard grayscale value is obtained, and then the functional relationship between the weighted measured grayscale value and the weighted standard grayscale value corresponding to each preset temperature value is fitted to obtain the gain parameter and the bias parameter. Specifically, as a feasible implementation method, the least squares method can be used to minimize the sum of square errors between the weighted measured grayscale value and the weighted standard grayscale value, so as to obtain the gain parameter and the bias parameter that minimize the sum of square errors.

[0092] As another feasible implementation method, since matrices provide a concise and unified way to represent and operate linear equations. By converting the problem into matrix form, we can avoid lengthy algebraic expressions and use unified symbols and rules to handle various situations. This uniformity simplifies the problem statement and solution process. In other words, solving the gain parameters and bias parameters can be regarded as finding a linear transformation so that the transformed values ​​meet the given conditions. Based on this, the gain parameters and bias parameters can be obtained by constructing a parameter matrix including the gain parameters and bias parameters and solving the parameter matrix.

[0093] For details, please refer to Figure 4 , S204 can be specifically implemented as follows:

[0094] S401 . Constructing a measured grayscale matrix, a standard grayscale matrix and a weight matrix based on the measured grayscale value, the standard grayscale value and the weight corresponding to each temperature value in a plurality of temperature values.

[0095] In some embodiments, in a linear model, a constant term (or more commonly, an additional variable whose value is always 1, usually called a bias term or an intercept term) is introduced to allow the model to represent a straight line that does not pass through the origin. In practical applications, in order to include the bias term, we usually add a column of all 1s (or called a bias column) to the data set of standard grayscale values. In this way, when constructing the standard grayscale matrix, the bias term will be naturally included.

[0096] That is, as a feasible implementation method, the standard grayscale value (g1-gn) and the constant term 1 (used for the bias term) can be put into a matrix to obtain a standard grayscale matrix. The measured grayscale values ​​(G1-Gn) are put into a matrix to obtain a measured grayscale matrix. The weights (w1-wn) are put into a matrix to obtain a weight matrix.

[0097] Exemplarily, the constructed standard grayscale matrix can be: The measured grayscale matrix can be: G = [G1 G2 ... Gn]; the weight matrix can be:

[0098] S402: Construct a parameter matrix including gain parameters and bias parameters.

[0099] Exemplarily, the gain parameter K and the bias parameter B may be put into a parameter matrix coeff, and the obtained parameter matrix may be: coeff=[KB].

[0100] S403 , solving the parameter matrix based on the grayscale matrix, the calibrated grayscale matrix and the weight matrix to obtain gain parameters and bias parameters.

[0101] In some embodiments, since the goal of the gain parameter and the bias parameter is to minimize the residual sum of squares, in matrix form, the properties of the matrix (such as the normal equation) can be directly used to solve this problem, that is, to establish the relationship between the grayscale matrix, the calibration grayscale matrix, the weight matrix, and the parameter matrix.

[0102] As a feasible implementation method, the relationship between the grayscale matrix, the calibration grayscale matrix, the weight matrix and the parameter matrix can be: coeff*X T *W*G T =X T *W*X.

[0103] Among them, coeff is the parameter matrix; X is the standard grayscale matrix; W is the weight matrix; G is the measured grayscale matrix.

[0104] Then, the relationship between the above matrices is solved to obtain gain parameters and bias parameters.

[0105] It is understandable that matrix methods are usually more stable when dealing with numerical problems than algebraic methods that directly solve systems of equations. Techniques such as matrix decomposition and regularization can help reduce the impact of numerical errors and instabilities. In addition, by carefully selecting algorithms and parameters, numerical stability and the accuracy of solutions can be further improved. Matrix operations also have an intuitive geometric interpretation. In linear algebra, matrix multiplication can be viewed as a linear transformation of a vector or space. This geometric interpretation helps us better understand the properties and behavior of the model and find better solutions. In other words, using the method of constructing matrices to solve linear models has many advantages, such as simplicity, computational efficiency, numerical stability, easy extension and generalization, and software support.

[0106] In some embodiments, when the infrared detector receives infrared radiation, the response inconsistency between different pixels or array elements is caused by various factors, that is, non-uniformity. The non-uniformity of the infrared device will cause the same temperature in different areas to show different brightness, thereby affecting the accuracy of temperature measurement. Therefore, when using the infrared device to be calibrated for measurement, non-uniformity correction is required to ensure that the infrared device can provide accurate and reliable measurement results in different environments and conditions, that is, the infrared device can measure temperature more accurately.

[0107] Specifically, as a feasible implementation method, before S201, the calibration method of the infrared device provided in the embodiment of the present application also includes: when the measured grayscale difference corresponding to the infrared device to be calibrated is different from the standard grayscale difference, adjusting the integration time of the infrared device to be calibrated so that the measured grayscale difference corresponding to the infrared device to be calibrated is the same as the standard grayscale difference.

[0108] The measured grayscale difference is the difference in the measured grayscale values ​​of the standard black body radiation source measured by the infrared device to be calibrated at two temperature values; the standard grayscale difference is the difference in the standard grayscale values ​​of the two temperature values.

[0109] It should be understood that the integration time, also known as the exposure time, is the length of time that the infrared temperature measurement device accumulates signals inside the detector when receiving infrared radiation. It directly affects the sensitivity and response time of the temperature measurement. A shorter integration time can increase the response speed of the device, but may reduce the signal-to-noise ratio; a longer integration time can increase the signal-to-noise ratio, but increase the response time and may cause temperature measurement errors for moving targets.

[0110] Specifically, the infrared device to be calibrated can be used to measure the measured grayscale values ​​(G1, G2) of the standard black body radiation source at two temperature values ​​(T1, T2), and then the difference between the measured grayscale values ​​can be calculated to obtain the measured grayscale difference △G. The standard grayscale values ​​corresponding to the two temperature values ​​(T1, T2) can be obtained through the standard curve corresponding to the infrared device to be calibrated, and then the standard grayscale difference △g can be obtained.

[0111] It is understandable that if the measured grayscale difference △G is less than the standard grayscale difference △g, it means that the infrared radiation received by the infrared device at the current integration time is insufficient, resulting in a low output grayscale value. In this case, the integration time should be increased to increase the time for the detector to accumulate signals, thereby increasing the output grayscale value and making the measured grayscale difference close to the standard grayscale difference.

[0112] If the measured grayscale difference △G is greater than the standard grayscale difference △g, it means that the infrared device receives too much infrared radiation at the current integration time, resulting in a high output grayscale value. In this case, the integration time should be reduced to reduce the time the detector accumulates signals, thereby reducing the output grayscale value and making the measured grayscale difference close to the standard grayscale difference.

[0113] Exemplarily, a stepwise approximation method may be used when adjusting the integration time, that is, adjusting a smaller increment or decrement each time and re-measuring and calculating.

[0114] It can be seen that the solution provided in this embodiment adjusts the integration time of the infrared device to be calibrated before obtaining the actual measured grayscale value of the infrared device to be calibrated, that is, performs non-uniform correction on the infrared device to be calibrated, which can eliminate or weaken the influence of the non-uniformity of the infrared device on the measurement result, and can enable the infrared temperature measuring device to output consistent measurement results for targets of the same temperature under different environments and conditions, thereby ensuring the accuracy and reliability of the measurement.

[0115] The present application also provides a calibration device for infrared equipment. Figure 5 The calibration device 50 of the infrared device includes: an acquisition module 51, a determination module 52 and a calibration module 53.

[0116] Among them, the acquisition module 51 is used to obtain the measured grayscale value obtained by the infrared device to be calibrated measuring a standard blackbody radiation source at multiple different preset temperature values; the determination module 52 is used to determine the standard grayscale value corresponding to each preset temperature value through the standard curve corresponding to the infrared device to be calibrated; the standard curve is used to represent the correspondence between the temperature value and the standard grayscale value; the determination module 52 is also used to determine the weight corresponding to each preset temperature value based on the standard grayscale value change rate corresponding to each preset temperature value in the standard curve; the standard grayscale value change rate is negatively correlated with the weight; the determination module 52 is also used to determine the gain parameter and the bias parameter based on the measured grayscale value, the standard grayscale value and the weight corresponding to each preset temperature value; the calibration module 53 is used to perform temperature measurement calibration on the infrared device to be calibrated based on the gain parameter and the bias parameter.

[0117] As a feasible implementation method, the determination module 52 is specifically used to: derive the standard curve to obtain the derivative function corresponding to the standard curve; determine the standard gray value change rate corresponding to each preset temperature value based on the derivative function; obtain the functional relationship between the standard gray value change rate and the weight, and determine the weight corresponding to each preset temperature value based on the functional relationship and the standard gray value change rate corresponding to each preset temperature value.

[0118] As a feasible implementation method, the functional relationship is an inverse proportional relationship; the determination module 52 is specifically used to: determine the least common multiple of multiple standard grayscale value change rates corresponding to multiple preset temperature values; for each preset temperature value, determine the weight corresponding to the preset temperature value based on the inverse proportional relationship, the least common multiple, and the standard grayscale value change rate corresponding to the preset temperature value.

[0119] As a feasible implementation method, the determination module 52 is specifically used to: normalize multiple standard grayscale value change rates corresponding to multiple preset temperature values ​​to obtain multiple processed standard grayscale value change rates; determine the lowest common multiple of the multiple processed standard grayscale value change rates.

[0120] As a feasible implementation method, the determination module 52 is specifically used to: for each preset temperature value, weight the measured grayscale value and the standard grayscale value corresponding to the preset temperature value according to the weight corresponding to the preset temperature value, so as to obtain the weighted measured grayscale value and the weighted standard grayscale value corresponding to the preset temperature value; based on the weighted measured grayscale value and the weighted standard grayscale value corresponding to each preset temperature value, determine the gain parameter and the bias parameter.

[0121] As a feasible implementation method, the determination module 52 is specifically used to: construct a measured grayscale matrix, a standard grayscale matrix and a weight matrix based on the measured grayscale value, the standard grayscale value and the weight corresponding to each temperature value in multiple temperature values; construct a parameter matrix including gain parameters and bias parameters; solve the parameter matrix based on the grayscale matrix, the standard grayscale matrix and the weight matrix to obtain the gain parameters and the bias parameters.

[0122] As a feasible implementation method, the calibration device 50 of the infrared device also includes: an adjustment module, which is used to adjust the integration time of the infrared device to be calibrated when the measured grayscale difference corresponding to the infrared device to be calibrated is different from the standard grayscale difference, so that the measured grayscale difference corresponding to the infrared device to be calibrated is the same as the standard grayscale difference; wherein the measured grayscale difference is the difference between the measured grayscale values ​​of the standard black body radiation source measured by the infrared device to be calibrated at two temperature values; the standard grayscale difference is the difference between the standard grayscale values ​​of the two temperature values.

[0123] The present application also provides an electronic device. Figure 6 The electronic device 201 includes: one or more memories 121, one or more processors 122, a communication bus 123 and a communication interface 124. The processor 122 is connected to the memory 121 via the communication bus 123; the one or more memories 121 are used to store computer program codes, and the computer program codes include computer instructions; when the one or more processors 122 execute the computer instructions, the electronic device 201 executes the infrared device calibration method provided by the above embodiment.

[0124] Optionally, the memory 121 may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a tape, a floppy disk, and an optical data storage device, etc. The embodiments of the present application do not impose any restrictions on this.

[0125] The processor 122 can be a central processing unit (CPU), a general-purpose processor network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD) or any combination thereof, and the embodiments of the present application do not impose any restrictions on this.

[0126] The communication bus 123 may be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus. The communication bus 123 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Just one thick line is used, but it does not mean that there is only one bus or one type of communication bus.

[0127] The communication interface 124 uses any transceiver or other device for communicating with other devices or communication networks, such as control systems, radio access networks (RAN), wireless local area networks (WLAN), etc.

[0128] The embodiments of the present application also provide a computer program product including one or more instructions, where the one or more instructions are stored in a memory of a computer device and executed by a processor to complete the various processes of the above embodiments.

[0129] An embodiment of the present application also provides a computer-readable storage medium, which includes computer execution instructions. When the computer execution instructions are executed on a computer, the computer executes the calibration method of the infrared device provided in the above embodiment.

[0130] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0133] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device or processor to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.

[0135] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A calibration method for an infrared device, characterized in that: include: Obtaining the measured grayscale value obtained by measuring a standard black body radiation source with multiple different preset temperature values ​​by the infrared device to be calibrated; Determine the standard grayscale value corresponding to each preset temperature value through the standard curve corresponding to the infrared device to be calibrated; the standard curve is used to represent the corresponding relationship between the temperature value and the standard grayscale value; Determine a weight corresponding to each preset temperature value based on a standard gray value change rate corresponding to each preset temperature value in the standard curve; The standard gray value change rate is negatively correlated with the weight; Determine a gain parameter and a bias parameter based on the measured grayscale value, the standard grayscale value and the weight corresponding to each of the preset temperature values; The temperature measurement and calibration of the infrared device to be calibrated is performed based on the gain parameter and the bias parameter.

2. The method according to claim 1, characterized in that The determining the weight corresponding to each preset temperature value based on the standard gray value change rate corresponding to each preset temperature value in the standard curve includes: Deriving the standard curve to obtain a derivative function corresponding to the standard curve; Determine the standard gray value change rate corresponding to each preset temperature value based on the derivative function; A functional relationship between a standard gray value change rate and a weight is obtained, and based on the functional relationship and the standard gray value change rate corresponding to each preset temperature value, a weight corresponding to each preset temperature value is determined.

3. The method according to claim 2, characterized in that The functional relationship is an inverse proportional relationship; The determining the weight corresponding to each of the preset temperature values ​​based on the functional relationship and the standard gray value change rate corresponding to each of the preset temperature values ​​includes: Determining the least common multiple of a plurality of standard grayscale value change rates corresponding to the plurality of preset temperature values; For each of the preset temperature values, a weight corresponding to the preset temperature value is determined according to the inverse proportional relationship, the least common multiple, and a standard gray value change rate corresponding to the preset temperature value.

4. The method according to claim 3, characterized in that The determining the least common multiple of a plurality of standard grayscale value change rates corresponding to the plurality of preset temperature values ​​comprises: Normalizing a plurality of standard grayscale value change rates corresponding to the plurality of preset temperature values ​​to obtain a plurality of processed standard grayscale value change rates; The least common multiple of the processed multiple standard grayscale value change rates is determined.

5. The method according to claim 1, characterized in that The step of determining a gain parameter and a bias parameter based on the measured grayscale value, the standard grayscale value, and the weight corresponding to each of the preset temperature values ​​comprises: For each of the preset temperature values, weighting the measured grayscale value and the standard grayscale value corresponding to the preset temperature value according to the weight corresponding to the preset temperature value, to obtain the weighted measured grayscale value and the weighted standard grayscale value corresponding to the preset temperature value; The gain parameter and the bias parameter are determined based on the weighted measured grayscale value and the weighted standard grayscale value corresponding to each of the preset temperature values.

6. The method according to claim 1, characterized in that The step of determining a gain parameter and a bias parameter based on a measured grayscale value, a standard grayscale value, and a weight corresponding to each of the plurality of temperature values ​​comprises: Based on the measured grayscale value, the standard grayscale value and the weight corresponding to each of the temperature values, constructing a measured grayscale matrix, a standard grayscale matrix and a weight matrix; Constructing a parameter matrix including the gain parameter and the bias parameter; The parameter matrix is ​​solved based on the grayscale matrix, the standard grayscale matrix and the weight matrix to obtain the gain parameter and the bias parameter.

7. The method according to claim 1, characterized in that Before obtaining the measured grayscale value obtained by measuring a plurality of standard black body radiation sources with different preset temperature values ​​of the infrared device to be calibrated, the method further includes: When the measured grayscale difference corresponding to the infrared device to be calibrated is different from the standard grayscale difference, adjusting the integration time of the infrared device to be calibrated so that the measured grayscale difference corresponding to the infrared device to be calibrated is the same as the standard grayscale difference; The measured grayscale difference is the difference in the measured grayscale values ​​of the standard black body radiation source measured by the infrared device to be calibrated at two temperature values; and the standard grayscale difference is the difference in the standard grayscale values ​​of the two temperature values.

8. A calibration device for infrared equipment, characterized in that: include: An acquisition module is used to obtain the measured grayscale value obtained by measuring a standard black body radiation source with multiple different preset temperature values ​​by the infrared device to be calibrated; A determination module, used to determine the standard grayscale value corresponding to each of the preset temperature values ​​through a standard curve corresponding to the infrared device to be calibrated; the standard curve is used to represent the corresponding relationship between the temperature value and the standard grayscale value; The determination module is further used to determine the weight corresponding to each preset temperature value based on the standard gray value change rate corresponding to each preset temperature value in the standard curve; the standard gray value change rate is negatively correlated with the weight; The determination module is further used to determine the gain parameter and the bias parameter based on the measured grayscale value, the standard grayscale value and the weight corresponding to each of the preset temperature values; The calibration module is used to perform temperature measurement calibration on the infrared device to be calibrated based on the gain parameter and the bias parameter.

9. An electronic device, characterized in that: include: one or more processors; one or more memories; Wherein, the one or more memories are used to store computer program codes, and the computer program codes include computer instructions. When the one or more processors execute the computer instructions, the electronic device executes the calibration method of the infrared device as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed on a computer, the computer executes the infrared device calibration method according to any one of claims 1 to 7.

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