Performance test method and system for infrared detector

Abstract

The invention relates to the field of performance testing, in particular to a performance testing method and system for an infrared detector, and the method comprises the steps: obtaining a plurality of change curves of the infrared detector about response voltage and testing temperature; the infrared detector performance testing method comprises the following steps: acquiring a change curve, decomposing the change curve by using an emd algorithm to obtain a plurality of imf components, calculating the optimal superposition weight of each imf component, superposing each imf component by using the optimal superposition weight to obtain a denoised change curve, and performing abnormal data point identification on the denoised change curve to test the performance of an infrared detector. According to the method, denoising is carried out in the process of superposing all imf components, and meanwhile effective information can be reserved as much as possible.

Description

Technical Field

[0001] The present invention relates to the field of performance testing, and particularly to a method and system for testing the performance of an infrared detector. Background Art

[0002] An infrared detector is a sensor that obtains temperature information by detecting infrared radiation emitted by an object. An object can emit infrared radiation above absolute zero, and the intensity and wavelength of the infrared radiation are closely related to the temperature of the object. The infrared detector identifies temperature changes of an object by receiving and analyzing radiation information, and is applied to flame detection in seismic and shock-resistant environments, as well as flame detection inside a moving vehicle or ship. During the production process of an infrared detector, its performance needs to be tested. In the performance test, the performance of the infrared detector is detected by analyzing data of a curve of the response voltage of the infrared detector changing with temperature.

[0003] A Chinese patent document with the publication number CN117889965B discloses a method for testing the performance of a medium-wave and short-wave dual-color infrared detector. The method includes: obtaining medium-wave electrical data and short-wave electrical data of the medium-wave and short-wave dual-color infrared detector at different time periods; determining the correlation coefficient of the medium-wave electrical data and the short-wave electrical data corresponding to the time period; further determining the abnormal degree of the correlation coefficient corresponding to the time period; according to the numerical distribution difference of the abnormal degrees of the correlation coefficients of all time periods in each clustering cluster obtained by clustering, determining the characteristic index of the clustering cluster, and according to the characteristic indexes of all clustering clusters and the number of correlation coefficients in each clustering cluster, determining the performance index of the medium-wave and short-wave dual-color infrared detector; performing a performance test on the medium-wave and short-wave dual-color infrared detector according to the value of the performance index to obtain a test result.

[0004] During the actual test process of an infrared detector, the output signal of the detector often has noise interference. The emd (Empirical Mode Decomposition) algorithm can be used to decompose the curve of the response voltage of the infrared detector changing with temperature to obtain imf components (Intrinsic Mode Function), and then corresponding weights are assigned to the imf components and superimposed to achieve the effect of noise reduction. How to assign the optimal weights to each imf component is the key to achieving the optimal noise reduction effect. Summary of the Invention

[0005] In order to calculate the optimal weights of each imf component and improve the noise reduction effect of the change curve, the present invention provides a method and system for testing the performance of an infrared detector.

[0006] In a first aspect, the present invention provides a method for testing the performance of an infrared detector, adopting the following technical solution: Obtain multiple variation curves of the infrared detector regarding the response voltage and the test temperature; Use the EMD algorithm to decompose the variation curves to obtain multiple IMF components, calculate the optimal superposition weights of each IMF component, use the optimal superposition weights to superpose each IMF component to obtain the denoised variation curves, and identify abnormal data points in the denoised variation curves to realize the performance test of the infrared detector; The calculation method of the optimal superposition weight of the IMF component is as follows: calculate the expected superposition weight of each IMF component, and the expected superposition weight is positively correlated with the mean value of the temperature change rates of all data points in the corresponding variation curve; set the optimal superposition weight of each IMF component, and construct the objective function H, and the expression is: ; In the formula, represents the i-th variation curve, represents the b-th IMF component of the i-th variation curve, n represents the total number of IMF components, represents the optimal superposition weight of the b-th IMF component of the i-th variation curve, represents the maximum value of the expected superposition weights among the n IMF components corresponding to the i-th variation curve; use the swarm intelligence algorithm to solve the objective function, and take the value of the optimal superposition weight when the objective function is the minimum as the optimal superposition weight of the corresponding IMF component.

[0007] By assigning the optimal superposition weights to the IMF components, and then, through the superposition with the optimal superposition weights, the purpose of denoising the variation curves is achieved, and at the same time, the effective information of each component can be retained to the greatest extent, that is, while meeting the denoising requirements, the useful information is retained to the greatest extent, improving the accuracy of the performance detection results of the infrared detector.

[0008] Preferably, the calculation method of the initial superposition weight of the IMF component is: Sort the multiple IMF components, and superpose the sorted IMF components in the order of increasing quantity to obtain multiple reconstructed curves; calculate the Pearson correlation coefficient between the reconstructed curves and the variation curves, and take the Pearson correlation coefficient as the initial superposition weight of the last IMF component in the reconstructed curves.

[0009] Preferably, the expression of the expected superposition weight is:

[0010] In the formula, represents the expected superposition weight of the b-th IMF component of the i-th variation curve, represents the mean value of the temperature change rates of all data points in the i-th variation curve, It represents the information entropy of the b-th IMF component of the i-th change curve. It represents the initial superposition weight of the b-th IMF component of the i-th change curve, and exp represents the exponential function with base e. It represents the activation function.

[0011] By calculating the expected superposition weight using multiple dimensions, the accuracy of the calculation result of the expected superposition weight is improved.

[0012] Preferably, the expression of the expected superposition weight is:

[0013] In the formula, It represents the expected superposition weight of the b-th IMF component of the i-th change curve. It represents the information entropy of the b-th IMF component of the i-th change curve. It represents the initial superposition weight of the b-th IMF component of the i-th change curve, and exp represents the exponential function with base e.

[0014] Preferably, the method for obtaining the change curve of the infrared detector with respect to the response voltage and the test temperature is as follows: Construct a three-dimensional coordinate system, where the x-axis is time, the y-axis is the test temperature, and the z-axis is the response voltage; when testing the infrared detector, map the obtained data points into the three-dimensional coordinate system; for each data point, calculate the rate of change of temperature with respect to time, and use the rate of change of temperature with respect to time as the temperature change rate; perform clustering on the temperature change rate to obtain multiple clustering clusters; project the data points corresponding to the temperature change rate within the same clustering cluster onto the plane formed by the y-axis and the z-axis, and connect the data points within the same clustering cluster to obtain the change curve.

[0015] By classifying the data points according to the rate of change to obtain multiple change curves, the performance of the infrared detector can be conveniently analyzed through the classified change curves, and the working efficiency of the analysis process is improved.

[0016] Preferably, use the DBSCAN algorithm to perform clustering on the temperature change rate to obtain multiple clustering clusters.

[0017] Using the DBSCAN algorithm for clustering makes the clustering result have high accuracy.

[0018] Preferably, the calculation method of the temperature change rate is: calculate the difference in temperature between the current data point and the previous data point, calculate the difference in time between the current time point and the previous time point, and use the ratio of the difference in temperature to the difference in time as the temperature change rate at the current time point.

[0019] Preferably, the expression for superimposing each IMF component using the optimal superposition weight is:

[0020] Wherein, represents the i-th change curve after denoising, represents the b-th IMF component of the i-th change curve, represents the optimal superposition weight of the b-th IMF component of the i-th change curve, b represents the index of the IMF component, and n represents the total number of IMF components.

[0021] Preferably, the box plot method is used to identify abnormal data points of the change curve after denoising.

[0022] By detecting abnormal data points of the change curve, the performance test of the infrared detector is realized, and the work efficiency is improved.

[0023] In a second aspect, the present invention provides a performance test system for an infrared detector, adopting the following technical solution: A performance test system for an infrared detector, a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a performance test method for an infrared detector according to the above is realized.

[0024] Generate a computer program for the above-mentioned performance test method for an infrared detector, and store it in the memory to be loaded and executed by the processor. Thus, a system is made according to the memory and the processor, which is convenient to use.

[0025] The present invention has the following technical effects: By assigning an optimal superposition weight to the IMF component, and then performing superposition through the optimal superposition weight to achieve the purpose of denoising the change curve, and at the same time of denoising, the effective information of each component can be retained to the greatest extent, that is, while meeting the denoising requirements, the useful information is retained to the greatest extent, and the accuracy of the performance detection result of the infrared detector is improved. Description of the Drawings

[0026] Figure 1 is a flowchart of a performance test method for an infrared detector according to an embodiment of the present invention. Detailed Embodiments

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0028] An embodiment of the present invention discloses a method for testing the performance of an infrared detector, referring to Figure 1 , including the following steps, specifically as follows: S1: Obtain multiple change curves of the infrared detector regarding the response voltage and the test temperature.

[0029] During the performance test of the infrared detector, in order to accurately evaluate its response characteristics under different temperature conditions, a blackbody radiation source is used to control the test temperature during the test process. The infrared detector to be tested is placed in front of the blackbody radiation source, and the infrared detector and the blackbody radiation source are fixed to ensure that the radiation reception angle and distance are fixed to ensure the consistency of the test. During the process of the change of the temperature of the blackbody radiation source, the infrared detector will respond to the radiation intensity generated at different temperatures and generate a corresponding output voltage, and the output voltage is used as the response voltage.

[0030] During the process of the change of the temperature of the blackbody radiation source, a temperature sensor is used to monitor the temperature of the blackbody radiation source in real time, and at the same time, a voltage measuring instrument is used to collect the response voltage of the infrared detector. Multiple data points are generated during the process of the change of the temperature of the blackbody radiation source. Each data point corresponds to the temperature of the blackbody radiation source, the response voltage of the detector, and the acquisition time of this data point.

[0031] Construct a three-dimensional coordinate system, where the x-axis is time, the y-axis is the test temperature, and the z-axis is the response voltage; when testing the infrared detector, map the obtained data points into the three-dimensional coordinate system; for each data point, calculate the rate of change of temperature with time, and use the rate of change of temperature with time as the temperature change rate, and perform clustering on the temperature change rate to obtain multiple clustering clusters; the calculation method of the temperature change rate is: calculate the difference between the temperature of the current data point and the previous data point, calculate the difference between the current time point and the previous time point, and use the ratio of the temperature difference to the time difference as the temperature change rate at the current time point; perform clustering on the temperature change rate to obtain multiple clustering clusters; project the data points corresponding to the temperature change rate within the same clustering cluster onto the plane formed by the y-axis and the z-axis, and connect the data points within the same clustering cluster to obtain a change curve. Each clustering cluster corresponds to a change curve, and thus multiple change curves are obtained.

[0032] S2: Use the EMD algorithm to decompose the change curve to obtain multiple IMF components, and calculate the optimal superposition weights of each IMF component.

[0033] Use the EMD algorithm to process the i-th change curve Perform EMD decomposition to obtain multiple IMF components arranged from high frequency to low frequency. Each IMF component represents a local oscillation component in the variation curve, and its frequency and amplitude reflect different signal variation characteristics. Usually, the main trend or useful components of the variation curve are contained in the low-frequency IMF components, while the high-frequency IMF components often contain more noise or details. Therefore, when assigning weights, the weights of the low-frequency components should be made larger and the weights of the high-frequency components should be made smaller to achieve the denoising effect.

[0034] The optimal superposition weight calculation method for IMF components includes the following steps: S21: Calculate the initial superposition weights of the IMF components.

[0035] Sort the multiple IMF components, and sequentially superimpose the sorted IMF components in an increasing order of quantity to obtain multiple reconstructed curves; calculate the Pearson correlation coefficient between the reconstructed curve and the variation curve, and use the Pearson correlation coefficient as the initial superposition weight of the last IMF component in the reconstructed curve.

[0036] Exemplarily, for the i-th variation curve After decomposition, we get , , , …, , where , , , …, are sorted in descending order of frequency. The superposition method is as follows: , , …, , where , , …, are the reconstructed curves after superposition. Calculate the Pearson correlation coefficient between the reconstructed curve and the variation curve , and use the Pearson correlation coefficient as the component's initial superposition weight; similarly, use the Pearson correlation coefficient between the reconstructed curve and the variation curve as the component's initial superposition weight, …, use the Pearson correlation coefficient between the reconstructed curve and the variation curve as the component's initial superposition weight. Calculate the initial weights according to the similarity between the components after superposition and the variation curve. Since the low-frequency components contain more useful information and the high-frequency components contain more noise, the initial weights reflect the importance of the corresponding components in the original curve.

[0037] S22: Calculate the expected superposition weight of each IMF component, where the expected superposition weight is positively correlated with the mean value of the temperature change rates of all data points in the corresponding change curve.

[0038] In one implementation, the expression for the expected superposition weight is:

[0039]

[0040]

[0041] In the formula, represents the expected superposition weight of the b-th IMF component of the i-th change curve, represents the mean value of the temperature change rates of all data points in the i-th change curve, represents the information entropy of the b-th IMF component of the i-th change curve, represents the initial superposition weight of the b-th IMF component of the i-th change curve, exp represents the exponential function with base e, represents the activation function, which is used to map the value to (0, 1), represents the frequency at which the j-th response voltage appears, log is the logarithmic function with base 2.

[0042] During the actual application process of the infrared detector, such as when a fire occurs, the change in the ambient temperature is relatively drastic, with a high temperature and a fast change. At this time, the tolerance for noise is higher, so as to obtain more useful information. Therefore, the expected superposition weight is positively correlated with ; The larger the value of, the greater the chaos of the b-th IMF component of the i-th change curve, and the more noise it contains. Therefore, the corresponding expected superposition weight is larger. Conversely, the smaller the value of, the smaller the chaos of the b-th IMF component of the i-th change curve, and the less noise it contains. Therefore, the corresponding expected superposition weight is smaller.

[0043] The larger the value of the expected superposition weight, the higher the tolerance for the error of the corresponding IMF component during the superposition of IMF components, that is, the higher the tolerance for noise, and the worse the denoising effect. During the superposition of IMF components, the corresponding IMF component is closer to the original data, and the worse the denoising effect; conversely, the smaller the value of the expected superposition weight, the lower the tolerance for the error of the corresponding IMF component during the superposition of IMF components, that is, the lower the tolerance for noise, and the better the denoising effect.

[0044] In one implementation, the expression for the expected superposition weight is:

[0045]

[0046] In the formula, represents the expected superposition weight of the b-th IMF component of the i-th change curve, represents the information entropy of the b-th IMF component of the i-th change curve, represents the initial superposition weight of the b-th IMF component of the i-th change curve, and exp represents the exponential function with base e, represents the frequency at which the j-th response voltage appears, and the base of the log function is 2.

[0047] S23: Calculate the optimal superposition weight of each IMF component.

[0048] In one embodiment, a target function H is constructed, The expression of the target function H is: ; In the formula, represents the i-th change curve, represents the b-th IMF component of the i-th change curve, n represents the total number of IMF components, represents the optimal superposition weight of the b-th IMF component of the i-th change curve, represents the maximum value of the expected superposition weights among the n IMF components corresponding to the i-th change curve; The group intelligence algorithm is used to solve the target function, and the value of the optimal superposition weight when the target function is the minimum value is used as the optimal superposition weight of the corresponding IMF component. The group intelligence algorithm adopts the particle swarm optimization algorithm, the number of particles is 50, the maximum number of iterations is set to 100 times, the learning factor is 1.5, and the inertia weight linearly decreases from 0.9 to 0.4.

[0049] represents the difference between the change curve obtained by superposing the weights of each IMF component and the corresponding i-th change curve. The smaller its value, the smaller the difference between the change curve obtained by superposition and the i-th change curve. The target function represents the expected difference between the change curve obtained by superposing the weights of components and the corresponding i-th change curve. Solving the minimum value of the target function means that under the condition of satisfying the expected superposition weight (in order to retain more useful information), components remove more noise, and finally the change curve obtained by superposition contains more useful information and less noise.

[0050] In one embodiment, the expression of the target function H is:

[0051] In the formula, denotes the i-th change curve, denotes the b-th IMF component of the i-th change curve, denotes the optimal superposition weight of the b-th IMF component of the i-th change curve, denotes the maximum value of the expected superposition weights among the n IMF components corresponding to the i-th change curve; the group intelligence algorithm is used to solve the objective function, and the value of the initial superposition weight when the objective function is at its minimum is used as the optimal superposition weight of the corresponding IMF component.

[0052] By denotes the integral between the change curve after superposition by assigning weights to the component and the corresponding i-th change curve. The smaller the value, the smaller the difference between the change curve after superposition by assigning weights and the i-th change curve; conversely, the larger the value, the greater the difference.

[0053] S3: The optimal superposition weights are used to superpose each IMF component to obtain the denoised change curve, and the denoised change curve is used to identify abnormal data points to realize the performance test of the infrared detector.

[0054] The expression for the superposition process is:

[0055] In the formula, denotes the i-th denoised change curve, denotes the b-th IMF component of the i-th change curve, denotes the optimal superposition weight of the b-th IMF component of the i-th change curve, b represents the index of the IMF component, and n represents the total number of IMF components. In the process of superposing each IMF component, the greater the weight corresponding to the IMF component, the more effective information is retained, but the greater the corresponding noise; conversely, the smaller the weight corresponding to the IMF component, the less effective information is retained, but the smaller the corresponding noise. The optimal weight obtained by calculation finds a balance between the effective information and the noise, so that while denoising in the process of superposing each IMF component, as much effective information as possible can be retained.

[0056] The box plot method is used to identify the abnormal data points of the denoised change curve. When the number of abnormal data points is greater than the preset number threshold, it indicates that there is an abnormality in the performance of the infrared detector, and then the performance test result of the infrared detector is obtained. The number threshold is set artificially according to the actual situation. Exemplarily, the number threshold is 10.

[0057] An embodiment of the present invention also discloses a performance testing system for an infrared detector, which includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a performance testing method for an infrared detector according to the present invention is implemented.

[0058] The above system further includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0059] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A performance test method for an infrared detector, characterized in that: Includes steps: Obtain multiple change curves of the infrared detector regarding the response voltage and the test temperature; The EMD algorithm is used to decompose the change curve to obtain multiple IMF components, and the optimal superposition weight of each IMF component is calculated. The IMF components are superimposed using the optimal superposition weight to obtain the denoised change curve, and the abnormal data points of the denoised change curve are identified to test the performance of the infrared detector. The optimal superposition weight calculation method of the IMF component is: calculate the expected superposition weight of each IMF component, and the expected superposition weight is positively correlated with the mean of the temperature change rate of all data points in the corresponding change curve; set the optimal superposition weight of each IMF component, and construct the objective function H, which is expressed as: ; In the formula, represents the i-th change curve, represents the bth imf component of the ith variation curve, n represents the total number of imf components, represents the optimal superposition weight of the bth IMF component of the ith variation curve, It represents the maximum value of the expected superposition weight among the n IMF components corresponding to the ith change curve; the swarm intelligence algorithm is used to solve the objective function, and the value of the optimal superposition weight when the objective function is the minimum is taken as the optimal superposition weight of the corresponding IMF component.

2. The performance testing method of an infrared detector according to claim 1, characterized in that: The initial stacking weight calculation method of the IMF component is: The multiple IMF components are sorted, and the sorted IMF components are superimposed in increasing order to obtain multiple reconstructed curves; the Pearson correlation coefficient between the reconstructed curve and the change curve is calculated, and the Pearson correlation coefficient is used as the initial superposition weight of the last IMF component in the reconstructed curve.

3. The performance testing method of an infrared detector according to claim 2, characterized in that: The expression for the expected stacking weight is: In the formula, represents the expected superposition weight of the bth IMF component of the ith variation curve, represents the mean value of the temperature change rate of all data points in the i-th change curve, represents the information entropy of the bth IMF component of the ith change curve, represents the initial superposition weight of the bth IMF component of the ith variation curve, exp represents the exponential function with e as the base, Represents the activation function.

4. The performance testing method of an infrared detector according to claim 2, characterized in that: The expression for the expected stacking weight is: In the formula, represents the expected superposition weight of the bth IMF component of the ith variation curve, represents the information entropy of the bth IMF component of the ith change curve, represents the initial superposition weight of the bth IMF component of the ith variation curve, and exp represents the exponential function with e as the base.

5. The performance testing method of an infrared detector according to claim 1, characterized in that: The method for obtaining the change curve of the infrared detector regarding the response voltage and the test temperature is: A three-dimensional coordinate system is constructed, in which the x-axis is time, the y-axis is test temperature, and the z-axis is response voltage. When testing the infrared detector, the acquired data points are mapped into the three-dimensional coordinate system. For each data point, the rate of change of temperature over time is calculated, and the rate of change of temperature over time is used as the temperature change rate. The temperature change rate is clustered to obtain multiple clusters. The data points corresponding to the temperature change rate in the same cluster are projected into the plane formed by the y-axis and the z-axis, and the data points in the same cluster are connected to obtain the change curve.

6. The performance testing method of an infrared detector according to claim 5, characterized in that: The DBSCAN algorithm is used to cluster the temperature change rate to obtain multiple clusters.

7. The performance testing method of an infrared detector according to claim 5, characterized in that: The temperature change rate is calculated by calculating the temperature difference between the current data point and the previous data point, calculating the time difference between the current time point and the previous time point, and taking the ratio of the temperature difference to the time difference as the temperature change rate at the current time point.

8. The performance testing method of an infrared detector according to claim 1, characterized in that: The expression for superimposing each IMF component using the optimal superposition weight is: In the formula, represents the i-th change curve after denoising, represents the bth imf component of the ith variation curve, represents the optimal superposition weight of the bth IMF component of the ith variation curve, b represents the index of the IMF component, and n represents the total number of IMF components.

9. The performance testing method of an infrared detector according to claim 1, characterized in that: The box plot method is used to identify abnormal data points of the denoised change curve.

10. A performance test system for an infrared detector, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a performance testing method for an infrared detector according to any one of claims 1 to 9 is implemented.

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