A method, apparatus, equipment and medium for testing the performance of a quartz crystal oscillator

Abstract

This invention relates to a method, apparatus, device, and medium for testing the performance of a quartz crystal oscillator. The method includes: acquiring a sequence of thermal images of the quartz crystal oscillator under test during operation; preprocessing the thermal image sequence to obtain a histogram sequence and adjacent frame sequences; using a temperature prediction model to obtain the operating temperature of the quartz crystal oscillator at the next moment; using a frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next moment; obtaining the output frequency of a standard quartz crystal oscillator based on the operating temperature of the quartz crystal oscillator at the next moment using a frequency-temperature curve of a standard quartz crystal oscillator; comparing the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determining whether the output of the quartz crystal oscillator under test is stable based on the comparison result. This invention can effectively test the stability of quartz crystal oscillators.

Description

Technical Field

[0001] This invention relates to the field of quartz crystal oscillator testing technology, and in particular to a method, apparatus, equipment and medium for testing the performance of quartz crystal oscillators. Background Technology

[0002] With the continuous development of electronic technology, quartz crystal oscillators, as key components in electronic devices, face increasingly higher requirements for performance stability and accuracy. In quartz crystal oscillator design, two methods are commonly used to achieve high frequency stability: temperature compensation and precision temperature control. Temperature-compensated crystal oscillators generate a temperature-varying voltage through a compensation voltage generator circuit to compensate for frequency drift at high and low temperatures, thereby achieving high stability.

[0003] Currently, the frequency accuracy of quartz crystal oscillators is typically tested using a resolution better than 1×10⁻⁻⁻⁻⁴. 7 A frequency counter is used to measure the output frequency of a quartz crystal oscillator under rated operating voltage. This method is expensive due to the use of a high-resolution frequency counter and is not suitable for long-term operation. In addition, this method does not take into account the impact of temperature changes on the quartz crystal oscillator, making it difficult to determine the stability of the quartz crystal oscillator under actual working conditions. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, apparatus, equipment and medium for testing the performance of a quartz crystal oscillator, which can effectively test the stability of the quartz crystal oscillator.

[0005] The technical solution adopted by this invention to solve its technical problem is: to provide a performance testing method for a quartz crystal oscillator, comprising the following steps:

[0006] Acquire a sequence of thermal images of the quartz crystal oscillator under test during operation;

[0007] The thermal imaging sequence is preprocessed to obtain a histogram sequence and adjacent frame sequences;

[0008] The thermal imaging sequence, adjacent frame sequence, and histogram sequence are input into the temperature prediction model to obtain the operating temperature of the quartz crystal oscillator at the next moment.

[0009] Calculate the average operating temperature of the quartz crystal oscillator within a preset time window, and input the operating temperature of the quartz crystal oscillator at the next moment, the average operating temperature of the quartz crystal oscillator, the current air pressure, and the current humidity into the frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next moment.

[0010] The output frequency of the standard quartz crystal oscillator is obtained from the frequency-temperature curve of the standard quartz crystal oscillator based on the operating temperature of the quartz crystal oscillator at the next moment.

[0011] The output frequency of the quartz crystal oscillator at the next moment is compared with the output frequency of the standard quartz crystal oscillator, and the stability of the output of the quartz crystal oscillator under test is determined based on the comparison result.

[0012] The preprocessing of the thermal imaging sequence to obtain a histogram sequence and adjacent frame sequences specifically includes:

[0013] The thermal imaging sequence is normalized to obtain a normalized thermal imaging sequence.

[0014] Gaussian filtering is applied to the normalized thermal imaging sequence;

[0015] Convert the Gaussian-filtered thermal imaging sequence into a histogram sequence;

[0016] Calculate the difference between each two adjacent frames in the thermal imaging sequence to obtain the adjacent frame sequence.

[0017] The temperature prediction model includes:

[0018] The first feature extraction part is used to extract features from the thermal imaging sequence using a temporal convolutional network to obtain temporal and spatial features;

[0019] The second feature extraction part is used to extract features from the histogram sequence using a convolutional network to obtain global features of the temperature distribution;

[0020] The third feature extraction part is used to extract features from the adjacent frame sequences using a long short-term memory network with an attention mechanism to obtain short-term trend features of temperature changes;

[0021] The feature concatenation part is used to reduce the dimensionality of the time and space features, the global features of temperature distribution and the short-term trend features of temperature change to the same dimension using attention-based weighted pooling, and then concatenate the dimensionality-reduced features to obtain the concatenated features.

[0022] The prediction section is used to output the operating temperature of the quartz crystal oscillator at the next moment through the fully connected layer, based on the splicing features.

[0023] The frequency prediction model is based on logistic regression and is expressed as follows: ,in, The output frequency of the quartz crystal oscillator at the next moment. This is the operating temperature of the quartz crystal oscillator at the next moment. This refers to the average operating temperature of the quartz crystal oscillator. The current humidity. The current air pressure. These are the weighting coefficients.

[0024] The step of comparing the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determining whether the output of the quartz crystal oscillator under test is stable based on the comparison result, specifically includes:

[0025] Calculate the absolute value of the difference between the output frequency of the quartz crystal oscillator at the next moment and the output frequency of the standard quartz crystal oscillator;

[0026] Determine whether the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds a threshold percentage;

[0027] If the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds a threshold percentage, it is determined that the output of the quartz crystal oscillator under test is unstable under the test environment.

[0028] If the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator does not exceed a threshold percentage, then the output of the quartz crystal oscillator under test is determined to be stable under the test environment.

[0029] The technical solution adopted by this invention to solve its technical problem is: to provide a performance testing device for a quartz crystal oscillator, comprising:

[0030] The acquisition module is used to acquire a sequence of thermal images of the quartz crystal oscillator under test during operation.

[0031] The preprocessing module is used to preprocess the thermal imaging sequence to obtain a histogram sequence and adjacent frame sequences;

[0032] The temperature prediction module is used to input the thermal imaging sequence, adjacent frame sequence and histogram sequence into the temperature prediction model to obtain the operating temperature of the quartz crystal oscillator at the next moment.

[0033] The frequency prediction module is used to calculate the average operating temperature of the quartz crystal oscillator within a preset time window. The operating temperature of the quartz crystal oscillator at the next moment, the average operating temperature of the quartz crystal oscillator, the current air pressure, and the current humidity are input into the frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next moment.

[0034] The standard frequency acquisition module is used to obtain the output frequency of the standard quartz crystal oscillator based on the operating temperature of the quartz crystal oscillator at the next moment through the frequency-temperature curve of the standard quartz crystal oscillator.

[0035] The comparison module is used to compare the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determine whether the output of the quartz crystal oscillator under test is stable based on the comparison result.

[0036] The temperature prediction model includes:

[0037] The first feature extraction part is used to extract features from the thermal imaging sequence using a temporal convolutional network to obtain temporal and spatial features;

[0038] The second feature extraction part is used to extract features from the histogram sequence using a convolutional network to obtain global features of the temperature distribution;

[0039] The third feature extraction part is used to extract features from the adjacent frame sequences using a long short-term memory network with an attention mechanism to obtain short-term trend features of temperature changes;

[0040] The feature concatenation part is used to reduce the dimensionality of the time and space features, the global features of temperature distribution and the short-term trend features of temperature change to the same dimension using attention-based weighted pooling, and then concatenate the dimensionality-reduced features to obtain the concatenated features.

[0041] The prediction section is used to output the operating temperature of the quartz crystal oscillator at the next moment through the fully connected layer, based on the splicing features.

[0042] The frequency prediction model is based on logistic regression and is expressed as follows: ,in, The output frequency of the quartz crystal oscillator at the next moment. This is the operating temperature of the quartz crystal oscillator at the next moment. This refers to the average operating temperature of the quartz crystal oscillator. The current humidity. The current air pressure. These are the weighting coefficients.

[0043] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned performance testing method for a quartz crystal oscillator.

[0044] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned performance testing method for a quartz crystal oscillator.

[0045] Beneficial effects

[0046] By adopting the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: The present invention captures a series of thermal images of the quartz crystal oscillator under test during operation, converts the thermal image series into a histogram series, and combines a temperature prediction model and a frequency prediction model to achieve accurate prediction of the output frequency of the quartz crystal oscillator under any working environment without using a frequency counter, thereby enabling effective testing of the stability of the quartz crystal oscillator. Attached Figure Description

[0047] Figure 1 This is a flowchart of the performance testing method for the quartz crystal oscillator according to the first embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the temperature prediction model in the first embodiment of the present invention. Detailed Implementation

[0049] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0050] The first embodiment of the present invention relates to a performance testing method for a quartz crystal oscillator, such as... Figure 1 As shown, it includes the following steps:

[0051] Step 1: Obtain a sequence of thermal images of the quartz crystal oscillator under test during operation.

[0052] In this step, the test environment is set at 25℃, 50% humidity, and 5L / min airflow to ensure the quartz crystal oscillator under test operates in a stable environment. The oscillator is then started and allowed to stabilize. Once stable, a series of thermal images of the oscillator during operation are captured using a thermal imaging device. The thermal imaging device used in this step can be a FLIR E75 thermal imager with a resolution of 640×480, a frame rate of 30Hz, and a temperature range of 0~120℃.

[0053] Step 2: Preprocess the thermal imaging sequence to obtain a histogram sequence and adjacent frame sequences. The preprocessing method in this step is as follows:

[0054] First, the thermal imaging sequence is normalized to obtain a normalized thermal imaging sequence. The normalization method can be expressed as follows:

[0055] ;

[0056] in, This is the k-th frame image in the normalized thermal imaging sequence. This is the k-th frame image in the thermal imaging sequence. The image frame with the lowest overall grayscale value in the thermal imaging sequence. This is the image frame with the highest overall grayscale value in the thermal imaging sequence.

[0057] Next, Gaussian filtering is applied to the normalized thermal image sequence. In this embodiment, the Gaussian filter kernel size is 5×5 and the standard deviation is 1.0.

[0058] Then, the Gaussian-filtered thermal imaging sequence is converted into a histogram sequence, where the histogram bin number is 64;

[0059] Finally, the difference between each group of adjacent frame sequences in the Gaussian-filtered thermal imaging sequence is calculated to obtain the adjacent frame sequence.

[0060] Step 3: Input the thermal imaging sequence, adjacent frame sequence and histogram sequence into the temperature prediction model to obtain the operating temperature of the quartz crystal oscillator at the next moment.

[0061] like Figure 2 As shown, the temperature prediction model in this step includes:

[0062] The first feature extraction part is used to extract features from the thermal imaging sequence using a temporal convolutional network to obtain temporal and spatial features. Specifically, a pre-trained 2D convolutional network can be used to extract features from each frame of the thermal imaging sequence. These features are then arranged chronologically and input into the temporal convolutional network for temporal feature fusion to capture both spatial and temporal features. The 2D convolutional network has a 3×3 kernel size, a stride of 1, and contains 64 filters.

[0063] The second feature extraction part is used to extract features from the histogram sequence using a convolutional network to obtain global features of the temperature distribution. Specifically, a 1D convolutional network can be used to process the histogram sequence. The kernel size of the 1D convolutional network is 3, the stride is 1, and it contains 16 filters.

[0064] The third feature extraction part is used to extract the features of the adjacent frame sequence using a long short-term memory network with an attention mechanism to obtain the short-term trend features of temperature change. In specific implementation, a long short-term memory network with an attention mechanism and 128 hidden layer units can be used to process the adjacent frame sequence.

[0065] The feature concatenation part is used to reduce the dimensionality of the time and space features, the global features of temperature distribution and the short-term trend features of temperature change to the same dimension using attention-based weighted pooling, and then concatenate the dimensionality-reduced features to obtain the concatenated features.

[0066] The prediction section is used to output the operating temperature of the quartz crystal oscillator at the next moment through the fully connected layer, based on the splicing features.

[0067] This temperature prediction model fully utilizes multimodal information, employing decomposition and specialization to extract features from input images from different perspectives. It possesses strong temporal dynamic capture capabilities, particularly through feature extraction from adjacent frame sequences, enabling it to sensitively respond to temperature changes and handle situations involving rapid heating or cooling. Furthermore, it leverages global features extracted from histogram sequences, combined with temporal and spatial features extracted from thermal imaging sequences, to capture details of local temperature changes, making the model more robust in complex and changing scenarios. This temperature prediction model can accurately predict the operating temperature of a quartz crystal oscillator at the next moment.

[0068] Step 4: Calculate the average operating temperature of the quartz crystal oscillator within a preset time window (e.g., 5 sampling times). Input the operating temperature of the quartz crystal oscillator at the next time moment, the average operating temperature of the quartz crystal oscillator, the current air pressure, and the current humidity into the frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next time moment.

[0069] The frequency prediction model in this step is based on logistic regression and can be expressed as:

[0070] ;

[0071] in, The output frequency of the quartz crystal oscillator at the next moment. This is the operating temperature of the quartz crystal oscillator at the next moment. This refers to the average operating temperature of the quartz crystal oscillator. The current humidity. The current air pressure. These are the weighting coefficients.

[0072] The frequency prediction model in this step combines multiple physical variables and interaction terms to capture the complexity of quartz crystal oscillator frequency changes. This logistic regression model describes the coupling of temperature and pressure by introducing an interaction term for temperature and pressure, and captures the thermal inertia of temperature changes by introducing an average temperature, thereby accurately predicting the output frequency of the quartz crystal oscillator at the next moment and improving prediction accuracy.

[0073] Step 5: Based on the operating temperature of the quartz crystal oscillator at the next moment, obtain the output frequency of the standard quartz crystal oscillator using its frequency-temperature curve. The frequency-temperature curve of the standard quartz crystal oscillator in this step can be obtained by testing the temperature characteristics of the standard quartz crystal oscillator. After obtaining the frequency-temperature curve, the output frequency can be obtained by looking up a table based on the operating temperature of the quartz crystal oscillator.

[0074] Step 6: Compare the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determine whether the output of the quartz crystal oscillator under test is stable based on the comparison result. This step is detailed below:

[0075] First, calculate the absolute value of the difference between the output frequency of the quartz crystal oscillator at the next moment and the output frequency of the standard quartz crystal oscillator, i.e. ,in, The absolute value of the difference. The output frequency of the quartz crystal oscillator at the next moment. The output frequency of the standard quartz crystal oscillator obtained in step 5;

[0076] Then, determine the absolute value of the difference. With the output frequency of the standard quartz crystal oscillator Whether the ratio exceeds the threshold percentage, i.e., to determine ,in, The threshold percentage can be set to 2%;

[0077] If the absolute value of the difference With the output frequency of the standard quartz crystal oscillator The ratio exceeds the threshold percentage If so, it is determined that the output of the quartz crystal oscillator under test is unstable under the test environment;

[0078] If the absolute value of the difference With the output frequency of the standard quartz crystal oscillator The ratio did not exceed the threshold percentage. If the output of the quartz crystal oscillator under test is stable under the test environment, it is determined that the output of the quartz crystal oscillator under test is stable.

[0079] It is easy to see that this invention, by capturing a series of thermal images of the quartz crystal oscillator under test during operation and converting the thermal image series into a histogram series, combined with a temperature prediction model and a frequency prediction model, enables accurate prediction of the output frequency of the quartz crystal oscillator under any operating environment without using a frequency counter, thereby enabling effective testing of the stability of the quartz crystal oscillator.

[0080] The second embodiment of the present invention relates to a performance testing device for a quartz crystal oscillator, comprising:

[0081] The acquisition module is used to acquire a sequence of thermal images of the quartz crystal oscillator under test during operation.

[0082] The preprocessing module is used to preprocess the thermal imaging sequence to obtain a histogram sequence and adjacent frame sequences;

[0083] The temperature prediction module is used to input the thermal imaging sequence, adjacent frame sequence and histogram sequence into the temperature prediction model to obtain the operating temperature of the quartz crystal oscillator at the next moment.

[0084] The frequency prediction module is used to calculate the average operating temperature of the quartz crystal oscillator within a preset time window. The operating temperature of the quartz crystal oscillator at the next moment, the average operating temperature of the quartz crystal oscillator, the current air pressure, and the current humidity are input into the frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next moment.

[0085] The standard frequency acquisition module is used to obtain the output frequency of the standard quartz crystal oscillator based on the operating temperature of the quartz crystal oscillator at the next moment through the frequency-temperature curve of the standard quartz crystal oscillator.

[0086] The comparison module is used to compare the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determine whether the output of the quartz crystal oscillator under test is stable based on the comparison result.

[0087] The preprocessing module includes:

[0088] The normalization unit is used to normalize the thermal imaging sequence to obtain a normalized thermal imaging sequence.

[0089] The filtering unit is used to perform Gaussian filtering on the normalized thermal imaging sequence.

[0090] The conversion unit is used to convert the Gaussian-filtered thermal imaging sequence into a histogram sequence.

[0091] The calculation unit is used to calculate the difference between each two adjacent frames in the thermal imaging sequence to obtain the adjacent frame sequence.

[0092] The temperature prediction model includes:

[0093] The first feature extraction part is used to extract features from the thermal imaging sequence using a temporal convolutional network to obtain temporal and spatial features;

[0094] The second feature extraction part is used to extract features from the histogram sequence using a convolutional network to obtain global features of the temperature distribution;

[0095] The third feature extraction part is used to extract features from the adjacent frame sequences using a long short-term memory network with an attention mechanism to obtain short-term trend features of temperature changes;

[0096] The feature concatenation part is used to reduce the dimensionality of the time and space features, the global features of temperature distribution and the short-term trend features of temperature change to the same dimension using attention-based weighted pooling, and then concatenate the dimensionality-reduced features to obtain the concatenated features.

[0097] The prediction section is used to output the operating temperature of the quartz crystal oscillator at the next moment through the fully connected layer, based on the splicing features.

[0098] The frequency prediction model is based on logistic regression and is expressed as follows: ,in, The output frequency of the quartz crystal oscillator at the next moment. This is the operating temperature of the quartz crystal oscillator at the next moment. This refers to the average operating temperature of the quartz crystal oscillator. The current humidity. The current air pressure. These are the weighting coefficients.

[0099] The comparison module includes:

[0100] The difference calculation unit is used to calculate the absolute value of the difference between the output frequency of the quartz crystal oscillator and the output frequency of the standard quartz crystal oscillator at the next moment.

[0101] The judgment unit is used to determine whether the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds a threshold percentage.

[0102] The first determination unit is used to determine that the output of the quartz crystal oscillator under test is unstable under the test environment when the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds a threshold percentage.

[0103] The second determination unit is used to determine that the output of the quartz crystal oscillator under test is stable under the test environment when the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator does not exceed a threshold percentage.

[0104] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the performance testing method for a quartz crystal oscillator of the first embodiment.

[0105] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the performance testing method for a quartz crystal oscillator of the first embodiment.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of testing the performance of a quartz crystal oscillator, characterized by, The method comprises the following steps: obtaining a thermal image sequence of a quartz crystal oscillator in operation; preprocessing the thermal image sequence to obtain a histogram sequence and a neighboring frame sequence; inputting the thermal image sequence, the neighboring frame sequence and the histogram sequence into a temperature prediction model to obtain the working temperature of the quartz crystal oscillator at the next moment; the temperature prediction model comprises: a first feature extraction part for extracting the features of the thermal image sequence by using a time convolution network to obtain time and space features; a second feature extraction part for extracting the features of the histogram sequence by using a convolution network to obtain global features of temperature distribution; a third feature extraction part for extracting the features of the neighboring frame sequence by using a long short-term memory network with an attention mechanism to obtain short-term trend features of temperature change; a feature splicing part for using attention-based weighted pooling to reduce the time and space features, the global features of temperature distribution and the short-term trend features of temperature change to the same dimension, and splicing the reduced features to obtain spliced features; a prediction part for outputting the working temperature of the quartz crystal oscillator at the next moment by a full connection layer from the spliced features; calculating the average working temperature of the quartz crystal oscillator in a preset time window, inputting the working temperature of the quartz crystal oscillator at the next moment, the average working temperature of the quartz crystal oscillator, the current air pressure and the current humidity into a frequency prediction model to obtain the output frequency of the quartz crystal oscillator at the next moment; obtaining the output frequency of the standard quartz crystal oscillator from the frequency-temperature curve of the standard quartz crystal oscillator according to the working temperature of the quartz crystal oscillator at the next moment; comparing the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and determining whether the output of the quartz crystal oscillator under test is stable according to the comparison result.

2. The method of claim 1, wherein the quartz crystal oscillator is a crystal oscillator. The preprocessing of the thermal image sequence to obtain the histogram sequence and the neighboring frame sequence comprises: normalizing the thermal image sequence to obtain a normalized thermal image sequence; performing Gaussian filtering on the normalized thermal image sequence; converting the Gaussian filtered thermal image sequence into a histogram sequence; calculating the difference between every two adjacent frames in the thermal image sequence to obtain a neighboring frame sequence.

3. The method of claim 1, wherein the quartz crystal oscillator is a crystal oscillator. The frequency prediction model is constructed based on a logistic regression, and is expressed as: wherein, is the output frequency of the quartz crystal oscillator at the next moment, is the working temperature of the quartz crystal oscillator at the next moment, is the average working temperature of the quartz crystal oscillator, is the current humidity, is the current air pressure, is a weight coefficient.

4. The method of claim 1, wherein the quartz crystal oscillator is a crystal oscillator. The comparison of the output frequency of the quartz crystal oscillator at the next moment with the output frequency of the standard quartz crystal oscillator, and the determination of whether the output of the quartz crystal oscillator under test is stable according to the comparison result, comprises: calculating the absolute value of the difference between the output frequency of the quartz crystal oscillator at the next moment and the output frequency of the standard quartz crystal oscillator; determining whether the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds a threshold percentage; if the ratio of the absolute value of the difference to the output frequency of the standard quartz crystal oscillator exceeds the threshold percentage, determining that the output of the quartz crystal oscillator under test is unstable in the test environment. If a ratio of an absolute value of the difference to an output frequency of the standard quartz crystal oscillator does not exceed a threshold percentage, it is determined that the output of the quartz crystal oscillator under test is stable in the test environment.

5. A device for testing the performance of a quartz crystal oscillator, characterized by The method comprises: an acquisition module configured to acquire a sequence of thermal images of the quartz crystal oscillator under test when the quartz crystal oscillator under test is operating; a preprocessing module configured to preprocess the sequence of thermal images to obtain a sequence of histograms and a sequence of adjacent frames; a temperature prediction module configured to input the sequence of thermal images, the sequence of adjacent frames, and the sequence of histograms into a temperature prediction model to obtain an operating temperature of the quartz crystal oscillator at a next time point; the temperature prediction model comprises: a first feature extraction part configured to extract features of the sequence of thermal images using a time convolution network to obtain time and space features; a second feature extraction part configured to extract features of the sequence of histograms using a convolution network to obtain global features of temperature distribution; a third feature extraction part configured to extract features of the sequence of adjacent frames using a long short-term memory network with an attention mechanism to obtain short-term trend features of temperature change; a feature concatenation part configured to use attention-based weighted pooling to reduce the time and space features, the global features of temperature distribution, and the short-term trend features of temperature change to the same dimension, and concatenate the reduced features to obtain concatenated features; a prediction part configured to output the operating temperature of the quartz crystal oscillator at the next time point through a fully connected layer using the concatenated features; a frequency prediction module configured to calculate an average operating temperature of the quartz crystal oscillator in a preset time window, input the operating temperature of the quartz crystal oscillator at the next time point, the average operating temperature of the quartz crystal oscillator, a current air pressure, and a current humidity into a frequency prediction model to obtain an output frequency of the quartz crystal oscillator at the next time point; a standard frequency acquisition module configured to obtain the output frequency of the standard quartz crystal oscillator according to the operating temperature of the quartz crystal oscillator at the next time point through a frequency-temperature curve of the standard quartz crystal oscillator; a comparison module configured to compare the output frequency of the quartz crystal oscillator at the next time point with the output frequency of the standard quartz crystal oscillator, and determine whether the output of the quartz crystal oscillator under test is stable according to a comparison result.

6. The device for testing the performance of a quartz crystal oscillator according to claim 5, wherein, The frequency prediction model is constructed based on a logistic regression, and is expressed as: wherein, is an output frequency of the quartz crystal oscillator at a next moment, is an operating temperature of the quartz crystal oscillator at the next moment, is an average operating temperature of the quartz crystal oscillator, is a current humidity, is a current air pressure, is a weight coefficient.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the performance test method of the quartz crystal oscillator according to any one of claims 1-4.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the performance test method of the quartz crystal oscillator according to any one of claims 1-4.

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