A performance testing method and system for infrared detectors

Abstract

The present invention relates to the field of performance testing, and more particularly to a performance testing method and system for infrared detectors. The method comprises: obtaining multiple change curves of the infrared detector relating to response voltage and test temperature; decomposing the change curves using an EMD algorithm to obtain multiple IMF components; calculating the optimal superposition weight for each IMF component; superimposing the IMF components using the optimal superposition weight to obtain a denoised change curve; and identifying abnormal data points in the denoised change curve to test the performance of the infrared detector. The present invention denoises the IMF components during superposition while retaining as much valid information as possible.

Description

Technical Field

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

[0002] An infrared detector is a sensor that acquires temperature information by detecting infrared radiation emitted by an object. Objects emit infrared radiation above absolute zero, and the intensity and wavelength of this radiation are closely related to the object's temperature. Infrared detectors detect temperature changes by receiving and analyzing this radiation. They are used for flame detection in seismic and impact-resistant environments, as well as in moving vehicles and ship interiors. Infrared detector performance testing is required during the production process. This testing involves analyzing the curve of the detector's response voltage versus temperature.

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

[0004] During the actual testing of infrared detectors, noise often interferes with the detector's output signal. This can be achieved by applying the EMD (Empirical Mode Decomposition) algorithm to the infrared detector's response voltage versus temperature curve to obtain IMF components (Intrinsic Mode Function). De-noising can then be achieved by assigning corresponding weights to the IMF components and superimposing them. Assigning the optimal weight to each IMF component is key to achieving optimal denoising. Summary of the Invention

[0005] In order to calculate the optimal weight of each IMF component and improve the denoising effect of the variation curve, the present invention provides a performance testing method and system for an infrared detector.

[0006] In a first aspect, the present invention provides a performance testing method for an infrared detector, which adopts the following technical solution:

[0007] Obtain multiple change curves of the infrared detector regarding the response voltage and the test temperature;

[0008] The EMD algorithm is used to decompose the change curve into multiple IMF components. 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. Abnormal data points are identified on the denoised change curve to test the performance of the infrared detector.

[0009] The optimal superposition weight calculation method of the IMF component is as follows: calculate the expected superposition weight of each IMF component, which 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 follows:

[0010] Where, represents the i-th change curve, represents the bth imf component of the i-th change curve, n represents the total number of imf components, represents the optimal superposition weight of the bth IMF component of the i-th change curve, It represents the maximum value of the expected superposition weight among the n IMF components corresponding to the i-th 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.

[0011] By assigning optimal superposition weights to the IMF components and then superimposing them with the optimal superposition weights, the purpose of denoising the change curve is achieved. At the same time, the effective information of each component can be retained to the greatest extent. That is, while meeting the denoising needs, the useful information is retained to the greatest extent, thereby improving the accuracy of the infrared detector performance test results.

[0012] Preferably, the initial superposition weight calculation method of the imf component is:

[0013] 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.

[0014] Preferably, the expression of expected superposition weight is:

[0015]

[0016] Where, represents the expected superposition weight of the bth IMF component of the ith change 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 i-th change curve, represents the initial superposition weight of the bth IMF component of the i-th change curve, exp represents the exponential function with e as the base, Represents the activation function.

[0017] By using multiple dimensions to calculate the expected superposition weight, the accuracy of the expected superposition weight calculation results is improved.

[0018] Preferably, the expression of expected superposition weight is:

[0019]

[0020] Where, represents the expected superposition weight of the bth IMF component of the ith change curve, represents the information entropy of the bth IMF component of the i-th change curve, represents the initial superposition weight of the bth IMF component of the ith change curve, and exp represents the exponential function with e as the base.

[0021] Preferably, the method for obtaining the curve of the infrared detector regarding the response voltage and the test temperature is:

[0022] A three-dimensional coordinate system is constructed, in which 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, the acquired data points are mapped into the three-dimensional coordinate system. For each data point, the rate of change of temperature with time is calculated and used as the temperature change rate. The temperature change rates are clustered to obtain multiple clusters. The data points corresponding to the temperature change rate in the same cluster are projected onto the plane formed by the y-axis and the z-axis, and the data points in the same cluster are connected to obtain a change curve.

[0023] By classifying the data points according to the rate of change, a plurality of change curves are obtained. The classified change curves facilitate the analysis of the performance of the infrared detector, thereby improving the work efficiency of the analysis process.

[0024] Preferably, the temperature change rate is clustered using the DBSCAN algorithm to obtain multiple clusters.

[0025] The DBSCAN algorithm is used for clustering, which makes the clustering results more accurate.

[0026] Preferably, 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.

[0027] Preferably, the expression for superimposing each IMF component using the optimal superposition weight is:

[0028]

[0029] Where, represents the i-th change curve after denoising, represents the bth imf component of the i-th change curve, represents the optimal superposition weight of the bth 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.

[0030] Preferably, a box plot method is used to identify abnormal data points of the denoised change curve.

[0031] The performance of the infrared detector can be tested by detecting abnormal data points of the change curve, thereby improving work efficiency.

[0032] In a second aspect, the present invention provides a performance testing system for infrared detectors, which adopts the following technical solutions:

[0033] A performance test system for an infrared detector comprises a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the performance test method for an infrared detector described above is implemented.

[0034] The above-mentioned performance test method of an infrared detector is generated into a computer program and stored in a memory so as to be loaded and executed by a processor. Thus, a system is made based on the memory and the processor for easy use.

[0035] The present invention has the following technical effects:

[0036] By assigning optimal superposition weights to the IMF components and then superimposing them with the optimal superposition weights, the purpose of denoising the change curve is achieved. At the same time, the effective information of each component can be retained to the greatest extent, that is, the useful information is retained to the greatest extent while meeting the denoising needs, thereby improving the accuracy of the infrared detector performance test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention provides a flow chart of a method for testing the performance of an infrared detector. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0039] The embodiment of the present invention discloses a performance test method of an infrared detector, referring to Figure 1 , including the following steps, as follows:

[0040] S1: Obtain multiple change curves of the infrared detector regarding the response voltage and the test temperature.

[0041] During the performance test of infrared detectors, a blackbody radiation source is used to control the test temperature in order to accurately evaluate their response characteristics under different temperature conditions. The infrared detector to be tested is placed in front of the blackbody radiation source and fixed to the blackbody radiation source to ensure consistent radiation reception angle and distance. As the temperature of the blackbody radiation source changes, the infrared detector will respond to the radiation intensity generated at different temperatures and generate a corresponding output voltage, which is used as the response voltage.

[0042] As the temperature of the blackbody radiation source changes, a temperature sensor monitors the source's temperature in real time, while a voltage measuring instrument collects the infrared detector's response voltage. Multiple data points are generated during this temperature change, each corresponding to the source's temperature, the detector's response voltage, and the time at which the data point was collected.

[0043] A three-dimensional coordinate system is constructed, with time on the x-axis, test temperature on the y-axis, and response voltage on the z-axis. When testing an 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 used as the temperature change rate. The temperature change rates are then clustered to obtain multiple clusters. The temperature change rate is calculated by calculating the temperature difference between the current data point and the previous data point, the time difference between the current time point and the previous time point, and the ratio of the temperature difference to the time difference as the temperature change rate at the current time point. The temperature change rates are then clustered to obtain multiple clusters. Data points corresponding to the temperature change rate within the same cluster are projected onto the plane defined by the y- and z-axes. The data points within the cluster are then connected to obtain a change curve. Each cluster corresponds to a change curve, resulting in multiple change curves.

[0044] S2: Use the EMD algorithm to decompose the change curve to obtain multiple IMF components, and calculate the optimal superposition weight of each IMF component.

[0045] Use the emd algorithm to change the i-th segment change curve Performing EMD decomposition yields multiple IMF components arranged from high to low frequency. Each IMF component represents a local oscillation in the variation curve, and its frequency and amplitude reflect different signal variation characteristics. Low-frequency IMF components typically contain the main trend or useful components of the variation curve, while high-frequency IMF components tend to contain more noise or details. Therefore, when assigning weights, low-frequency components should be given greater weight and high-frequency components should be given less weight to achieve the best denoising effect.

[0046] The optimal superposition weight calculation method of the IMF component includes the following steps:

[0047] S21: Calculate the initial superposition weights of the IMF components.

[0048] 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.

[0049] For example, for the i-th change curve Decompose to get 、 、 、…、 ,in, 、 、 、…、 The sorting method is to arrange them from high to low according to frequency. The superposition method is:

[0050] , ,…, ,in, 、 ,…, is the reconstructed curve after superposition, calculate the reconstructed curve With the change curve The Pearson correlation coefficient is used as The initial superposition weight of the component; similarly, the reconstructed curve With the change curve The Pearson correlation coefficient is The initial superposition weights of the components, ..., will reconstruct the curve With the change curve The Pearson correlation coefficient is The initial superposition weight of the components. After superimposing each component, the initial weight is calculated based on the similarity with the change curve. Since low-frequency components contain more useful information and high-frequency components contain more noise, the initial weight reflects the importance of the corresponding component in the original curve.

[0051] S22: Calculate the expected superposition weight of each imf component. The expected superposition weight is positively correlated with the mean of the temperature change rate of all data points in the corresponding change curve.

[0052] In one implementation, the expression for the expected overlay weight is:

[0053]

[0054]

[0055]

[0056] Where, represents the expected superposition weight of the bth IMF component of the ith change 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 i-th change curve, represents the initial superposition weight of the bth IMF component of the i-th change curve, exp represents the exponential function with e as the base, Represents the activation function, which is used to The value of is mapped to (0, 1), It represents the frequency of occurrence of the j-th response voltage. Log is a logarithmic function with a base of 2.

[0057] In the actual application of infrared detectors, such as when a fire occurs, the ambient temperature changes more dramatically. The higher the temperature and the faster it changes, the higher the tolerance to noise will be, thus obtaining more useful information. Therefore, the expected superposition weight is related to There is a positive correlation; The larger the value of , the greater the chaos of the bth IMF component of the ith change curve, and the more noise it contains. Therefore, the corresponding expected superposition weight is larger. Conversely, The smaller the value of , the less chaotic the bth IMF component of the ith variation curve is, and the less noise it contains. Therefore, the corresponding expected superposition weight is smaller.

[0058] The larger the value of the expected stacking weight, the higher the tolerance for the corresponding IMF component errors when the IMF components are superimposed, that is, the higher the tolerance for noise, the worse the denoising effect. When the IMF components are superimposed, the closer the corresponding IMF components are to the original data, the worse the denoising effect. Conversely, the smaller the value of the expected stacking weight, the lower the tolerance for the corresponding IMF component errors when the IMF components are superimposed, that is, the lower the tolerance for noise, and the better the denoising effect.

[0059] In one implementation, the expression for the expected overlay weight is:

[0060]

[0061]

[0062] Where, represents the expected superposition weight of the bth IMF component of the ith change curve, represents the information entropy of the bth IMF component of the i-th change curve, represents the initial superposition weight of the bth IMF component of the i-th change curve, exp represents the exponential function with e as the base, It represents the frequency of occurrence of the j-th response voltage, and the base of the log function is 2.

[0063] S23: Calculate the optimal superposition weight of each IMF component.

[0064] In one embodiment, the objective function H is constructed,

[0065] The objective function H is expressed as: Where, represents the i-th change curve, represents the bth imf component of the i-th change curve, n represents the total number of imf components, represents the optimal superposition weight of the bth IMF component of the i-th change curve, The optimal superposition weight for the corresponding IMF component is determined by the particle swarm optimization algorithm (PSO) with 50 particles, a maximum number of iterations of 100, a learning factor of 1.5, and a linear decrease in the inertia weight from 0.9 to 0.4.

[0066] It indicates the difference between the change curve obtained by adding weights to each IMF component and the corresponding i-th change curve. The smaller its value is, the smaller the difference between the change curve obtained by adding weights and the i-th change curve is. The expected difference between the change curve after the components are weighted and superimposed and the corresponding i-th change curve, solving the minimum value of the objective function means that under the condition of satisfying the expected superposition weight (in order to retain more useful information), The components remove more noise, so that the superimposed change curve contains more useful information and less noise.

[0067] In one embodiment, the objective function H is expressed as:

[0068]

[0069] Where, represents the i-th change curve, represents the bth imf component of the i-th change curve, represents the optimal superposition weight of the bth IMF component of the i-th change curve, It represents the maximum value of the expected superposition weight among the n IMF components corresponding to the i-th change curve; the swarm intelligence algorithm is used to solve the objective function, and the value of the initial superposition weight when the objective function is the minimum is taken as the optimal superposition weight of the corresponding IMF component.

[0070] pass Express The integral between the change curve after the components are superimposed with weights and the corresponding i-th change curve is smaller, indicating that the difference between the change curve after the components are superimposed with weights is smaller, and conversely, the larger the value, the greater the difference.

[0071] S3: Use the optimal superposition weight to superimpose each IMF component to obtain the denoised change curve, and identify abnormal data points on the denoised change curve to test the performance of the infrared detector.

[0072] The expression of the superposition process is:

[0073]

[0074] Where, represents the i-th change curve after denoising, represents the bth imf component of the i-th change curve, represents the optimal stacking weight for the bth IMF component of the i-th variation curve, where b is the index of the IMF component and n is the total number of IMF components. When stacking the IMF components, larger weights retain more valid information but also increase the corresponding noise. Conversely, smaller weights retain less valid information but also decrease the corresponding noise. The calculated optimal weight strikes a balance between valid information and noise, allowing for denoising while retaining as much valid information as possible during the stacking of the IMF components.

[0075] A boxplot is used to identify abnormal data points in the denoised curve. When the number of abnormal data points exceeds a preset threshold, it indicates an abnormality in infrared detection performance, thereby obtaining the infrared detector's performance test results. The threshold is set manually based on actual conditions; for example, it is 10.

[0076] An embodiment of the present invention further discloses a performance testing system for an infrared detector, comprising a processor and a memory, wherein the memory stores computer program instructions. 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.

[0077] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0078] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A performance testing method for an infrared detector, characterized in that: Including 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 into multiple IMF components. 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. Abnormal data points are identified on the denoised change curve to test the performance of the infrared detector. The optimal superposition weight calculation method of the IMF component is as follows: calculate the expected superposition weight of each IMF component, which 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 to , construct the objective function H, the expression is: Where, represents the i-th change curve, represents the bth imf component of the i-th change curve, n represents the total number of imf components, represents the optimal superposition weight of the bth IMF component of the i-th change curve, It represents the maximum value of the expected superposition weight among the n IMF components corresponding to the i-th 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 test method also includes: calculating the initial superposition weight of the IMF component, the calculation method is: 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 overlay weight is: Where, represents the expected superposition weight of the bth IMF component of the ith change 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 i-th change curve, represents the initial superposition weight of the bth IMF component of the i-th change 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 overlay weight is: Where, represents the expected superposition weight of the bth IMF component of the ith change curve, represents the information entropy of the bth IMF component of the i-th change curve, represents the initial superposition weight of the bth IMF component of the ith change 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 curve of the infrared detector's response voltage and test temperature is as follows: A three-dimensional coordinate system is constructed, in which 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, the acquired data points are mapped into the three-dimensional coordinate system. For each data point, the rate of change of temperature with time is calculated and used as the temperature change rate. The temperature change rates are clustered to obtain multiple clusters. The data points corresponding to the temperature change rate in the same cluster are projected onto the plane formed by the y-axis and the z-axis, and the data points in the same cluster are connected to obtain a 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: Where, represents the i-th change curve after denoising, represents the bth imf component of the i-th change curve, represents the optimal superposition weight of the bth 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.

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 in 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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