Performance test method for quasi-optical mode converter

Abstract

The invention relates to a performance test method for a quasi-optical mode converter, and relates to the technical field of vacuum electronics, and the method comprises the steps: obtaining the structural parameters of the quasi-optical mode converter, carrying out the adjacency simulation test of the quasi-optical mode converter based on the structural parameters, and obtaining a virtual test result; when the virtual test result is not passed, optimizing the structure parameters to obtain recommended structure parameters; when the virtual test result is passed, high-order electromagnetic waves are generated through the quasi-optical mode exciter to perform entity test on the quasi-optical mode converter, and an entity test result is obtained; and when the entity test result is passed, performing performance qualification identification on the optical mode converter. The technical problems that in the prior art, due to the fact that entity testing is directly adopted, prior analysis is lacked, the testing process is complex, the debugging period is long, and the testing efficiency is affected are solved, and the technical effect of improving the testing efficiency of the quasi-optical mode converter is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of vacuum electronics, and in particular to a performance testing method for a quasi-optical mode converter. Background Art

[0002] Quasi-optical mode converter is an important component for achieving efficient output of high-power gyrotrons and is widely used in fields such as high-resolution radar and wireless communications.

[0003] In the existing performance test methods of quasi-optical mode converters, physical tests are usually used to directly measure the converter to verify its mode conversion capability, efficiency and loss characteristics. Since there is no preliminary performance evaluation reference at the beginning of the test, signal excitation and result measurement need to be repeated during the test, which makes the test process complicated. In addition, each parameter adjustment may require a series of test operations to be repeated, so the debugging cycle becomes longer. For example, in some studies, in order to achieve higher power transmission efficiency, the radiator and mirror system need to be designed and experimentally verified multiple times. These deficiencies have seriously affected the test efficiency of quasi-optical mode converters and cannot meet the needs of modern rapidly developing technologies for efficient testing. Summary of the invention

[0004] The present invention aims to solve the technical problems in the prior art that direct physical testing and lack of prior analysis lead to complex testing procedures and long debugging cycles, which affect testing efficiency, and provides a performance testing method for a quasi-optical mode converter.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a performance testing method for a quasi-optical mode converter, the method comprising: obtaining structural parameters of the quasi-optical mode converter, and based on the structural parameters, performing an adjacency simulation test on the quasi-optical mode converter to obtain a virtual test result; when the virtual test result is a failure, optimizing the structural parameters to obtain recommended structural parameters; when the virtual test result is a pass, generating a high-order electromagnetic wave through a quasi-optical mode exciter to perform a physical test on the quasi-optical mode converter to obtain a physical test result; when the physical test result is a pass, marking the quasi-optical mode converter as having a qualified performance.

[0007] The beneficial effects of the present invention are as follows: first, by obtaining the structural parameters of the quasi-optical mode converter, an adjacent simulation test is performed to obtain a virtual test result. Through virtual testing, potential problems can be discovered and solved in advance in the design stage, reducing the number and time of physical testing, thereby improving the overall test efficiency. When the virtual test result fails, the structural parameters are optimized to obtain recommended structural parameters, thereby improving the performance and conversion efficiency of the quasi-optical mode converter. When the virtual test result passes, a high-order electromagnetic wave exciter is used to perform a physical test, which can more realistically simulate the actual working conditions, thereby more accurately reflecting the performance of the quasi-optical mode converter in practical applications. This method of combining virtual testing with physical testing not only simplifies the test process and shortens the debugging cycle, but also improves the accuracy and reliability of the test results. In addition, a quality tracking system is established through performance qualification identification, which enhances the controllability of product quality and provides a guarantee for the stability and reliability of the quasi-optical mode converter in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a performance testing method for a quasi-optical mode converter provided by the present invention;

[0009] Figure 2 A schematic diagram of a flow chart of obtaining a physical test result in a performance test method of a quasi-optical mode converter provided by the present invention;

[0010] Figure 3 A schematic diagram of a flow chart of obtaining recommended structural parameters in a performance testing method for a quasi-optical mode converter provided by the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0014] like Figure 1 As shown, an embodiment of the present invention provides a performance test method for a quasi-optical mode converter, the method comprising:

[0015] Step S1: obtaining structural parameters of a quasi-optical mode converter, and performing an adjacency simulation test on the quasi-optical mode converter based on the structural parameters to obtain a virtual test result.

[0016] Specifically, the design file of the interactive quasi-optical mode converter is used to obtain the structural parameters of the quasi-optical mode converter, including dimensions (such as length, width), material properties (such as dielectric constant, conductivity) and geometric structure. The quasi-optical mode converter is subjected to an adjacent simulation test, that is, the historical test data of a quasi-optical mode converter with similar structural parameters to the quasi-optical mode converter is used to determine whether the performance of the quasi-optical mode converter meets the requirements, thereby obtaining a virtual test result. This virtual test result includes two situations: "pass" and "fail". When the virtual test result is "pass", it means that the performance of the quasi-optical mode converter meets the expected requirements under the simulated environment; when the virtual test result is "fail", it means that the quasi-optical mode converter has performance problems under the simulated environment, such as low mode conversion efficiency, incorrect output mode, etc.

[0017] Through step S1, the previous test data can be used to quickly evaluate the performance of the quasi-optical mode converter and discover potential design problems in advance.

[0018] Step S2: When the virtual test result is a failure, optimizing the structural parameters to obtain recommended structural parameters.

[0019] Specifically, when the virtual test result in step S1 is not passed, the structural parameters are used as input variables, the performance target is set (such as conversion efficiency>90%), and the optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) is used to optimize the structural parameters to obtain new structural parameters, and then the adjacency simulation test is performed again according to the new structural parameters to obtain new virtual test results. Check whether the new virtual test result meets the design requirements. If it still does not pass, continue to optimize the structural parameters, and after multiple iterative calculations, until the virtual test result passes. The structural parameters when the virtual test result passes are output as recommended structural parameters. This recommended structural parameter can significantly improve the performance of the quasi-optical mode converter and can be used as a reference for further optimization of the design.

[0020] By optimizing the structural parameters, the structural parameters of the quasi-optical mode converter can be automatically and efficiently adjusted, thereby improving the efficiency of finding suitable structural parameters and avoiding the blindness of manual adjustment.

[0021] Step S3: When the virtual test result is passed, a quasi-optical mode exciter generates a high-order electromagnetic wave to perform a physical test on the quasi-optical mode converter to obtain a physical test result.

[0022] Specifically, the quasi-optical mode exciter is a device that can generate specific high-order electromagnetic waves. When the virtual test result is passed, the output of the quasi-optical mode exciter is connected to the input of the quasi-optical mode converter, the quasi-optical mode exciter is used to generate high-order electromagnetic waves, and the output electromagnetic wave parameters are measured at the output of the quasi-optical mode converter using instruments such as a power meter and a spectrum analyzer, thereby obtaining the physical test result. Similar to the virtual test results, the physical test results are also divided into two cases: "pass" and "fail". When the physical test result is "pass", it means that the performance of the quasi-optical mode converter meets the requirements in the actual environment; when the physical test result is "fail", it means that the quasi-optical mode converter has performance problems in the actual environment.

[0023] Through physical testing, the performance of the quasi-optical mode converter can be verified in a real environment to ensure that it can meet the expected performance requirements under real working conditions.

[0024] Step S4: When the physical test result is passed, the quasi-optical mode converter is marked as having qualified performance.

[0025] Specifically, when the physical test result in step S3 is passed, the quasi-optical mode converter is marked as having passed performance by marking, labeling (such as a QR code or RFID tag) on ​​the quasi-optical mode converter, or noting in the relevant test records, etc., and its test data and tracking information are recorded. The performance qualification mark facilitates product quality control during production and research and development, and facilitates identification of quasi-optical mode converters that have passed the test and meet the performance standards, which helps to improve production efficiency and product quality management.

[0026] Further, such as Figure 2 As shown, step S3 includes:

[0027] When the virtual test result is passed, the quasi-optical mode converter is physically tested by generating high-order electromagnetic waves through the quasi-optical mode exciter:

[0028] Step S31: Using a three-dimensional mobile platform equipped with a vector network analyzer, detect the horizontal component electric field of the output field at a distance of 60 mm, 100 mm, 140 mm, 160 mm, and 180 mm from the axis of the quasi-optical mode converter.

[0029] Step S32: collecting output field normalized temperature distribution equipotential maps through a thermal imager.

[0030] Step S33: Perform beam energy stability verification based on the horizontal component electric field of the output field to obtain a first verification result.

[0031] Step S34: Perform a Gaussian distribution check based on the output field normalized temperature distribution equipotential diagram to obtain a second check result.

[0032] Step S35: When both the first verification result and the second verification result are passed, the entity test result is passed.

[0033] Step S36: Otherwise, the entity test result is failure.

[0034] Specifically, when the virtual test result is passed, a high-order electromagnetic wave is generated by a quasi-optical mode exciter to perform a physical test on the quasi-optical mode converter. The specific test steps include: first, a vector network analyzer is mounted on a three-dimensional mobile platform. Then, the three-dimensional mobile platform is controlled to move to a specific distance from the axis of the quasi-optical mode converter, that is, 60mm, 100mm, 140mm, 160mm, and 180mm. At each position, the vector network analyzer measures the horizontal component electric field of the output field of the quasi-optical mode converter. Among them, the three-dimensional mobile platform is a device that can accurately control the mobile position and supports fine-tuning along the X, Y, and Z axes in space to achieve measurement or detection of different positions. The horizontal component electric field is the component of the electric field in the horizontal plane and is one of the key parameters for electromagnetic wave mode conversion. Next, a thermal imager is used to detect the output field of the quasi-optical mode converter. The thermal imager will collect the temperature information of the output field, obtain the temperature distribution data caused by electromagnetic wave heating, and convert it into a normalized temperature distribution equipotential map. This normalized temperature distribution equipotential map standardizes the temperature distribution data and then generates an equipotential line map, which can intuitively reflect the distribution of heating effects caused by the electromagnetic field.

[0035] The beam energy stability check is performed based on the measured output field horizontal component electric field, and the energy distribution of the electromagnetic wave is analyzed to determine whether it remains stable within a specified range, thereby obtaining a first check result. The first check result is divided into two cases: pass and fail. The beam energy stability check can check the energy stability of the output beam of the quasi-optical mode converter.

[0036] A quasi-Gaussian distribution check is performed based on the collected output field normalized temperature distribution equipotential diagram to check whether the temperature distribution of the output field conforms to the Gaussian distribution morphology, verify the beamforming capability of the converter, and obtain the second check result. The second check result is also divided into two cases: pass and fail. The quasi-Gaussian distribution check can check whether the temperature distribution of the output field of the quasi-optical mode converter conforms to the expected physical model, which helps to determine whether its internal physical process is normal. If the temperature distribution does not conform to the quasi-Gaussian distribution, it may indicate that there is a problem with the energy conversion or propagation mechanism inside the quasi-optical mode converter.

[0037] When both the first verification result and the second verification result are passed, the entity test result is determined to be passed. If one of the first verification result and the second verification result is not passed or both are not passed, the entity test result is determined to be not passed.

[0038] Through the above steps, the performance of the quasi-optical mode converter in terms of beam energy stability and temperature distribution characteristics is comprehensively considered to determine the physical test results, so that the performance of the quasi-optical mode converter can be evaluated more comprehensively and accurately.

[0039] Further, step S32 includes: obtaining an output field horizontal component electric field verification model, processing the output field horizontal component electric field, and obtaining the first verification result; wherein the step of constructing the output field horizontal component electric field verification model includes:

[0040] Step S321: Obtain the horizontal component of the output field detected at 60mm, 100mm, 140mm, 160mm, and 180mm from the axis of the quasi-optical mode converter and record the electric field.

[0041] Step S322: When the field spot area variance at multiple cross sections of the output field horizontal component recording the electric field is less than or equal to the variance threshold, the alignment check result is marked as passed; otherwise, the alignment check result is marked as failed.

[0042] Step S323: When the phase fluctuation trends of the output window ranges at multiple cross sections of the output field horizontal component recording the electric field are the same, the phase consistency check result is marked as passed; otherwise, the phase consistency check result is marked as failed.

[0043] Step S324: When both the collimation check result and the phase consistency check result are passed, the check result is marked as passed; otherwise, the check result is marked as failed.

[0044] Step S325: using the verification result identification information as supervision and the output field horizontal component recorded electric field as input, training the convolution classifier to obtain the output field horizontal component electric field verification model.

[0045] Specifically, when the beam energy stability is verified according to the horizontal component electric field of the output field, a verification model of the horizontal component electric field of the output field is first constructed, and the horizontal component electric field of the output field is input into the verification model for processing, so as to obtain a first verification result.

[0046] The output field horizontal component electric field verification model is constructed based on the convolution classifier. By collecting the beam energy stability verification data of multiple quasi-optical mode converters as sample data, the convolution classifier is trained to obtain the output field horizontal component electric field verification model. The specific training process is as follows:

[0047] First, for multiple quasi-optical mode converters, a vector network analyzer is used to detect the horizontal component electric field of the output field at 60mm, 100mm, 140mm, 160mm, and 180mm from the axis of the quasi-optical mode converter, and the electric field data at these positions are recorded to form the output field horizontal component recorded electric field. These output field horizontal component recorded electric fields will be used as input data for subsequent training of the convolution classifier. After obtaining the output field horizontal component recorded electric fields of multiple quasi-optical mode converters, these data need to be identified and set pass or fail labels for these data.

[0048] The electric field is recorded according to the measured horizontal component of the output field, and the field spots on different cross sections (60mm, 100mm, 140mm, 160mm, 180mm from the axis of the quasi-optical mode converter) are identified using image processing algorithms (such as edge detection and threshold segmentation). These field spots are areas with high electric field intensity and can be extracted by setting a threshold. On each cross section, the area of ​​each field spot is calculated by segmenting the electric field intensity image or using a region recognition algorithm. For all field spots on the same cross section, the variance of their areas is calculated to obtain the variance of the field spot areas at multiple cross sections. These field spot areas reflect the degree of difference in the electric field distribution at different cross sections. The smaller the variance, the more uniform the electric field distribution. The calculated variance is compared with the pre-set variance threshold. If the variance is less than or equal to the variance threshold, the collimation check result is marked as passed; otherwise, it is marked as failed.

[0049] For each cross section, record the phase data in the output window (obtained by a vector network analyzer) and observe the change in the phase in the output window. The phase change with position is obtained by calculating the phase difference between adjacent measurement points. According to the change of phase with position, calculate the change trend of the phase data of multiple cross sections, and determine whether the phase fluctuation trend of the output window range at multiple cross sections is the same. The phase fluctuation trend can be determined by comparing the mean, variance and other statistics of the phase difference of different cross sections. If the phase fluctuation trends of multiple cross sections are similar, for example, they are gradually increasing or decreasing, or remain relatively stable, it means that the beam is relatively stable, and the phase consistency check result is marked as passed; otherwise, it is marked as failed.

[0050] Check the collimation test results and phase consistency test results. If both are passed, the test result is marked as passed; otherwise, it is marked as failed. By comprehensively considering the test results of collimation and phase consistency, the characteristics of the horizontal component electric field of the output field can be more comprehensively evaluated.

[0051] The verification result identification information (pass or fail) is used as the supervision information, and the electric field recorded by the horizontal component of the output field is used as the input data. The convolution classifier is trained using a deep learning framework (such as TensorFlow, Keras or PyTorch). Select a suitable convolutional neural network structure to build the convolution classifier. For example, a classic convolutional neural network structure such as LeNet-5 can be selected. The input data (the electric field data recorded by the horizontal component of the output field) and the supervision data (the verification result identification information) are divided into a training set, a validation set and a test set. Generally, it is divided according to a certain ratio, such as 70% of the data as a training set, 15% as a validation set, and 15% as a test set. In the training stage, the input data of the training set is input into the convolution classifier, and after calculations by the convolution layer, the pooling layer and the fully connected layer, a prediction result (predicted to be passed or failed) is obtained. The loss function between the prediction result and the supervision data (the real verification result identification information) is calculated, such as the cross entropy loss function. According to the value of the loss function, an optimization algorithm (such as the stochastic gradient descent algorithm and its variants Adagrad, Adadelta, etc.) is used to update the weight parameters in the convolution classifier so that the value of the loss function gradually decreases. After each training cycle, the trained model is verified using the validation set data to observe the performance indicators of the model such as accuracy and recall on the validation set. If the performance indicators no longer improve or overfitting occurs (such as the accuracy on the validation set begins to decrease), stop training. Use the test set data to evaluate the trained convolution classifier, and calculate the performance indicators such as accuracy, recall, F1 score on the test set to judge the generalization ability of the model. If the performance indicators of the model are not ideal, the model can be optimized, such as adjusting the structure of the convolutional neural network (increasing or decreasing the number of convolutional layers and pooling layers, etc.), adjusting the training parameters (such as learning rate, training cycle, etc.) or increasing the amount of data, and then retraining until a satisfactory output field horizontal component electric field calibration model is obtained.

[0052] The output field horizontal component electric field verification model can realize the automatic verification of the output field horizontal component electric field. When there is new output field horizontal component electric field data, the model can be used directly for verification, which improves the efficiency and accuracy of the verification and reduces the error of manual analysis.

[0053] Further, step S33 includes: obtaining a Gaussian distribution calibration model, processing the output field normalized temperature distribution equipotential diagram, and obtaining the second calibration result; wherein the Gaussian distribution calibration model construction step includes:

[0054] Step S331: Obtain output field normalized temperature distribution equipotential map recording data.

[0055] Step S332: constructing a histogram of the output field normalized temperature distribution equipotential map recording data.

[0056] Step S333: When the histogram is high in the middle and low on both sides, and at least 80 percent of the data is symmetrically distributed, the verification result of the output field normalized temperature distribution equipotential map recorded data is marked as passed; otherwise, the verification result of the output field normalized temperature distribution equipotential map recorded data is marked as failed.

[0057] Step S334: using the verification result identification as supervision and the output field normalized temperature distribution equipotential map record data as input, training the convolution classifier to obtain the Gaussian-like distribution verification model.

[0058] Specifically, when performing quasi-Gaussian distribution verification based on the output field normalized temperature distribution equipotential map, firstly, a quasi-Gaussian distribution verification model is constructed, and the output field normalized temperature distribution equipotential map is input into the quasi-Gaussian distribution verification model for processing, so as to obtain a second verification result.

[0059] Similar to the output field horizontal component electric field calibration model, the Gaussian distribution calibration model is also built based on the convolution classifier. By collecting the Gaussian distribution calibration data of multiple quasi-optical mode converters as sample data, the convolution classifier is trained to obtain the Gaussian distribution calibration model. The specific training process is as follows:

[0060] First, for multiple quasi-optical mode converters, the normalized temperature distribution equipotential diagrams of the output field of the quasi-optical mode converter are collected by a thermal imager, and then the recorded data corresponding to these equipotential diagrams are obtained. Using statistical analysis tools or software, with the temperature interval as the horizontal axis and the frequency of data occurrence in the interval as the vertical axis, the obtained output field normalized temperature distribution equipotential diagram recorded data is constructed into a histogram. Constructing a histogram can more intuitively observe the distribution characteristics of the output field normalized temperature distribution equipotential diagram recorded data. For example, whether the temperature data is concentrated in a certain interval or is relatively dispersed.

[0061] Observe the shape characteristics of the constructed histogram to determine whether it satisfies a quasi-Gaussian distribution, that is, whether it is high in the middle and low on both sides, and whether at least 80% of the data is symmetrically distributed. If these conditions are met, the verification result of the output field normalized temperature distribution equipotential map recording data is marked as passed; otherwise, it is marked as failed.

[0062] The verification result identification (pass or fail) is used as the supervision information, and the output field normalized temperature distribution equipotential map record data is used as the input data to train the convolution classifier to obtain a Gaussian distribution verification model. The training process of the Gaussian distribution verification model is similar to the training process of the output field horizontal component electric field verification model. Those skilled in the art can refer to the above description of the training process of the output field horizontal component electric field verification model to clearly understand the training process of the Gaussian distribution verification model.

[0063] The Gaussian distribution calibration model can automatically calibrate the new output field normalized temperature distribution and other potential map recording data. In the subsequent test process, there is no need to manually construct histograms and judge features again. The calibration results can be quickly obtained by directly using the model, which improves the efficiency and accuracy of the calibration.

[0064] Further, step S1 includes:

[0065] Step S11: configuring a structural parameter threshold, wherein the structural parameter threshold represents a maximum fault tolerance deviation of the structural parameter.

[0066] Step S12: Obtain sample structural parameters and sample performance test record data of a sample quasi-optical mode converter.

[0067] Step S13: When the structural parameter deviation between the sample structural parameter and the structural parameter meets the structural parameter threshold, the sample performance test record data is added into the first data set to be analyzed.

[0068] Step S14: when the first data set to be analyzed meets the first data volume, traverse the sample structure parameters of the first data set to be analyzed, and based on the structure parameter threshold, collect the second data set to be analyzed.

[0069] Step S15: When the second data set to be analyzed meets the second data volume, the passing ratio of the first data set to be analyzed is counted and set as a first passing probability, and the passing ratio of the second data set to be analyzed is counted and set as a second passing probability, wherein the first passing probability has a first weight, the second passing probability has a second weight, and the first weight is greater than the second weight.

[0070] Step S16: performing a weighted mean analysis on the first pass probability and the second pass probability according to the first weight and the second weight to obtain a fusion pass probability.

[0071] Step S17: When the fusion pass probability is greater than or equal to the pass probability threshold, the virtual test result is passed; otherwise, the virtual test result is failed.

[0072] Specifically, the structural parameter threshold refers to the maximum tolerance deviation range set for the structural parameters of the quasi-optical mode converter, that is, the upper limit of the allowable error. This threshold is used to determine whether the deviation of the structural parameters is within an acceptable range during the converter test. According to the design requirements of the quasi-optical mode converter, the tolerance range of each structural parameter (such as size, material, etc.) is set to ensure that it is within a reasonable error range.

[0073] The sample quasi-optical mode converter is a sample of the quasi-optical mode converter used for testing and analysis, with specific structural parameters and corresponding sample performance test record data. These samples are selected from the overall quasi-optical mode converters and are used to infer the performance of the entire quasi-optical mode converter group. The sample structural parameters (such as size, angle, material, etc.) and sample performance test results (such as test pass or fail, power loss, output stability, etc.) of the sample quasi-optical mode converter are collected.

[0074] Traverse the sample structure parameters, calculate the structure parameter deviation between the sample structure parameters and the structure parameters (the structure parameters of the quasi-optical mode converter to be detected), and then compare it with the structure parameter threshold. If the deviation meets the structure parameter threshold (within the threshold range), add the sample structure parameters and the corresponding sample performance test record data to the first data set to be analyzed.

[0075] When the number of samples in the first data set to be analyzed reaches the first data volume, the sample structure parameters in the first data set to be analyzed are traversed, and the sample structure parameters whose deviations from the sample structure parameters in the first data set to be analyzed meet the structure parameter threshold are screened out from the remaining sample structure parameters and sample performance test results, and these sample structure parameters and the corresponding sample performance test record data are added to the second data set to be analyzed. The first data volume refers to the number of samples in the first data set to be analyzed, which is a preset quantity threshold.

[0076] When the second data set to be analyzed meets the second data volume, the pass ratios of the first data set to be analyzed and the second data set to be analyzed are calculated respectively. Among them, the second data volume is similar to the first data volume, and is a preset quantity threshold, which represents the number of samples of the second data set to be analyzed. For the first data set to be analyzed, the number of samples that pass the performance test is counted, and the first pass probability is obtained by dividing it by the total number of samples; similarly, the second data set to be analyzed is calculated to obtain the second pass probability. Corresponding weights are set for the first pass probability and the second pass probability, the first pass probability has a first weight, and the second pass probability has a second weight. Since the sample structure parameters in the first data set to be analyzed are closer to the structure parameters of the quasi-optical mode converter to be detected, a higher weight is assigned to the first pass probability, that is, the first weight is greater than the second weight.

[0077] According to the first weight and the second weight, a weighted mean analysis is performed on the first pass probability and the second pass probability to obtain a fusion pass probability. Through the weighted mean analysis, the pass probabilities of the two data sets can be comprehensively considered to obtain a more comprehensive and reasonable fusion pass probability.

[0078] Compare the fused pass probability with the pass probability threshold. The pass probability threshold is a set minimum pass probability used to determine whether the test result passes. If the fused pass probability is greater than or equal to the pass probability threshold, the virtual test result is determined to pass; otherwise, it is determined to fail. In this way, the virtual test result of the quasi-optical mode converter can be accurately determined according to the pre-set standards, providing a basis for subsequent physical tests, etc.

[0079] The above steps provide an accurate determination method for the virtual test of the quasi-optical mode converter, which not only compares the structural parameters with the sample performance data, but also analyzes the passing probability of multiple data sets through weighted mean, thereby improving the accuracy and stability of the virtual test. In the virtual test stage, the samples that pass can be effectively screened, reducing the workload of the physical test, thereby improving the overall test efficiency and reducing the development cycle and cost.

[0080] Further, such as Figure 3 As shown, step S2 includes:

[0081] Step S21: Obtaining the structural parameter constraint interval.

[0082] Step S22: constructing a high-dimensional distribution space according to the structural parameter constraint interval, wherein any point in the high-dimensional distribution space represents a group of structural parameters.

[0083] Step S23: uniformly distribute in the high-dimensional distribution space according to a preset distance threshold to obtain an initial structure particle set.

[0084] Step S24: taking the fusion passing probability as the fitness function, optimizing based on the initial structure particle set to obtain the recommended structure parameters.

[0085] Specifically, the structural parameter constraint interval is determined based on the design requirements, manufacturing process limitations, physical principles and other factors of the quasi-optical mode converter. This structural parameter constraint interval is the value range of the structural parameters of the quasi-optical mode converter, which is used to limit the optimization interval of the structural parameter optimization. For example, from the perspective of design requirements, a certain structural parameter must meet certain optical principles, and its value range may be restricted; from the perspective of manufacturing process limitations, a reasonable value range will also be determined taking into account the processing accuracy. The determination of the structural parameter constraint interval provides boundary conditions for constructing a high-dimensional distribution space, ensuring that the structural parameter combination considered is feasible.

[0086] Use mathematical modeling tools (such as MATLAB, Python) to construct a high-dimensional distribution space based on the structural parameter constraint interval. If there are n structural parameters and each structural parameter has its corresponding constraint interval, then this high-dimensional distribution space is an n-dimensional space, and the points in the space (x1, x2, ..., x n ) represents a set of structural parameters, where x i Satisfy the constraint interval of the i-th structural parameter. Constructing a high-dimensional distribution space can comprehensively represent all possible structural parameter combinations of the quasi-optical mode converter, providing a search space for the subsequent optimization process.

[0087] In the high-dimensional distribution space, some points are uniformly selected according to the preset distance threshold, and these points constitute the initial structure particle set. Among them, the preset distance threshold is the minimum distance between adjacent particles in the set space, which is used to ensure that the particles are evenly distributed in the space to avoid excessive concentration or duplication. The initial structure particle set provides a starting search point for the optimization process. The uniform distribution can ensure a wider coverage in the entire high-dimensional distribution space and avoid missing possible optimal solutions.

[0088] The fusion pass probability is used as the fitness function. For each particle in the initial structure particle set (i.e., the point representing a set of structural parameters), its fitness value (i.e., fusion pass probability) is calculated. Then, the optimization algorithm is used to search for the best on the basis of the initial structure particle set. Taking the particle swarm optimization algorithm as an example, each particle updates its position according to its own speed and position, as well as the position of the best particle in the group. In this process, the fitness value (fusion pass probability) of the new position is continuously calculated. After multiple iterations, the particle with the highest fitness value (i.e., the highest fusion pass probability) is found, and the structural parameters represented by it are the recommended structural parameters. By searching for the best with the fusion pass probability as the fitness function, a better combination of structural parameters can be found while meeting the performance requirements (higher fusion pass probability), providing a reference for the design of quasi-optical mode converters.

[0089] The optimal structural parameters are obtained by optimizing the structural parameters of the quasi-optical mode converter by establishing a high-dimensional distribution space and applying algorithms such as particle swarm optimization. By gradually optimizing the parameters with a uniformly distributed initial particle set and using the fusion pass probability as the fitness function, the accuracy of structural parameter selection can be effectively improved, and the selected parameters can be guaranteed to achieve a higher pass probability in performance testing. This process not only improves the accuracy of the test, but also reduces the workload of manual debugging and trial and error through intelligent optimization, thereby improving the research and development efficiency of the quasi-optical mode converter.

[0090] The above steps can effectively improve the selection accuracy of structural parameters and ensure that the selected parameters achieve a higher pass probability in performance testing by gradually optimizing the parameters through a uniformly distributed initial particle set and using the fusion pass probability as the fitness function. This process not only improves the accuracy of the test, but also reduces the workload of manual debugging and trial and error through intelligent optimization, thereby improving the performance test efficiency of the quasi-optical mode converter.

[0091] Further, step S24 includes:

[0092] Step S241: obtaining a set of initial structure particle fusion passing probabilities of the initial structure particle set.

[0093] Step S242: When the number of initial structure particles that meet the passing probability threshold in the fusion passing probability set is 0, obtain i initial structure particle target distribution positions with larger fusion passing probability and j initial structure particle starting distribution positions with smaller fusion passing probability, where i and j are integers and i is less than j.

[0094] Step S243: extracting a first starting distribution position according to the j starting distribution positions of the initial structure particles.

[0095] Step S244: extracting the first target distribution position according to the i initial structure particle target distribution positions.

[0096] Step S245: connecting the first starting distribution position and the first target distribution position in the high-dimensional distribution space to obtain the line-adjacent particle fusion passing probability of the line-adjacent initial structure particles whose distribution distance to the connecting line is less than or equal to the distribution distance threshold.

[0097] Step S246: When the line-adjacent particle fusion passing probability is less than the starting distribution position fusion passing probability of the first starting distribution position, the first target distribution position is updated based on the i initial structure particle target distribution positions.

[0098] Step S247: Otherwise, the first target distribution position is used as the target and the first initial distribution position is used as the starting point to search and obtain newly added structure particles.

[0099] Step S248: performing iterative optimization based on the newly added structural particles to obtain the recommended structural parameters.

[0100] Specifically, for each particle in the initial structure particle set, the fusion pass probability is calculated according to the above method for calculating the fusion pass probability, thereby obtaining the initial structure particle fusion pass probability set. For example, if there are n particles in the initial structure particle set, the fusion pass probabilities P1, P2, ..., P n, then the probability set of initial structure particles merging through is {P1, P2, ..., P n The fusion of initial structure particles provides a basis for subsequent judgment of the performance of initial structure particles through probability set, and can understand the initial state of each particle in the initial structure particle set.

[0101] Check the elements in the initial structure particle fusion probability set, and count the number of particles that meet the passing probability threshold. If this number is 0, find the distribution positions of i particles with a larger fusion passing probability (recorded as the initial structure particle target distribution position) and the distribution positions of j particles with a smaller fusion passing probability (recorded as the initial structure particle starting distribution position) from the initial structure particle set. For example, the initial structure particle fusion passing probability set can be sorted, and then the particle positions corresponding to the first i larger values ​​and the particle positions corresponding to the last j smaller values ​​can be selected. Among them, i and j are integers, and i is less than j. When no particle meets the passing probability threshold, by distinguishing the target distribution position and the starting distribution position, a direction is provided for subsequent optimization, that is, exploring from the position with a small fusion passing probability to the position with a large fusion passing probability.

[0102] A position is randomly or according to a certain rule (such as according to the index order, etc.) extracted from the initial distribution positions of the j initial structure particles, and is defined as the first initial distribution position.

[0103] A position is randomly or according to a certain rule (such as according to the index order, etc.) extracted from the i initial structure particle target distribution positions and defined as the first target distribution position.

[0104] In the high-dimensional distribution space, the distance from each initial structure particle to the connecting line connecting the first starting distribution position and the first target distribution position is calculated. The distance calculation may use a high-dimensional space distance formula, such as the generalized form of the Euclidean distance formula in high-dimensional space. Select the initial structure particles whose distance is less than or equal to the distribution distance threshold as the line-adjacent initial structure particles, and then obtain the fusion passing probability of these particles, which is recorded as the line-adjacent particle fusion passing probability. By analyzing the line-adjacent particle fusion passing probability, the performance of the particles near the path connecting the first starting distribution position and the first target distribution position can be understood, providing a basis for subsequent decision-making.

[0105] Compare the line neighboring particle fusion passing probability and the initial distribution position fusion passing probability of the first initial distribution position. If the line neighboring particle fusion passing probability is less than the initial distribution position fusion passing probability, it means that it may not be optimal in the current target direction and the target distribution position needs to be reselected. At this time, the first target distribution position is updated according to the i initial structure particle target distribution positions. For example, the next initial structure particle target distribution position that has not been selected can be selected. This update mechanism can avoid falling into a local optimal solution during the optimization process and explore a better combination of structural parameters by adjusting the target distribution position.

[0106] If the probability of the fusion of the adjacent particles is greater than or equal to the probability of the fusion of the starting distribution position of the first starting distribution position, it means that there is value in exploring the current direction. Taking the first target distribution position as the target and the first starting distribution position as the starting point, search in the high-dimensional distribution space according to a certain search strategy (such as gradually moving along the connecting line and making fine adjustments, etc.) to obtain new structural particles. The introduction of new structural particles provides a new combination of structural parameters for the optimization process, which helps to find better recommended structural parameters.

[0107] The newly added structural particles are added to the optimization process, and the fusion pass probability is used as the fitness function. The above optimization steps (comparison, update, search, etc.) are repeated, and after multiple iterations, the stop condition is met (such as reaching a certain number of iterations, the fusion pass probability reaches a satisfactory value, etc.), and finally the recommended structural parameters are obtained. Through iterative optimization, the combination of structural parameters can be continuously optimized, and finally the recommended structural parameters with better performance in terms of fusion pass probability are obtained.

[0108] The above steps construct a high-dimensional distribution space and perform iterative optimization based on fusion probability, and continuously adjust and improve the combination of structural parameters based on the initial particle set. This process makes full use of efficient search and probability evaluation mechanisms to ensure that the recommended structural parameters have optimal performance, thereby greatly improving the design efficiency and test reliability of quasi-optical mode converters.

[0109] Furthermore, the method further includes: when the entity test result is a failure, marking the quasi-optical mode converter as having a performance abnormality.

[0110] Specifically, when the physical test result is a failure, the performance of the quasi-optical mode converter needs to be marked as abnormal in order to subsequently troubleshoot the problem and optimize the design. Performance abnormality identification can guide subsequent analysis operations. For example, technicians can quickly locate the quasi-optical mode converter based on the identification, and then find out the cause of the performance abnormality based on the information attached to the performance abnormality identification, combined with previous virtual tests, structural parameter analysis, etc., which may be structural parameter deviations, manufacturing process defects, or other external factors. In terms of quality control, performance abnormality identification helps to count and analyze the overall performance of the quasi-optical mode converter. By classifying and counting quasi-optical mode converters with performance abnormality identification, it can be understood which batches or types of quasi-optical mode converters are prone to performance problems, thereby improving production processes, designs, etc.

[0111] The performance testing method of a quasi-optical mode converter provided by an embodiment of the present invention has at least the following technical effects:

[0112] First, by obtaining the structural parameters of the quasi-optical mode converter, an adjacent simulation test is performed to obtain virtual test results. Through virtual testing, potential problems can be discovered and solved in advance in the design stage, reducing the number and time of physical testing, thereby improving the overall test efficiency. When the virtual test results fail, the optimization algorithm is used to optimize the structural parameters to obtain the recommended structural parameters, thereby improving the performance and conversion efficiency of the quasi-optical mode converter. When the virtual test results pass, a high-order electromagnetic wave exciter is used to perform physical testing, which can more realistically simulate the actual working conditions, thereby more accurately reflecting the performance of the quasi-optical mode converter in practical applications. This method of combining virtual testing with physical testing not only simplifies the test process and shortens the debugging cycle, but also improves the accuracy and reliability of the test results. In addition, a quality tracking system is established through performance qualification identification and abnormal identification, which enhances the controllability of product quality and provides a guarantee for the stability and reliability of the quasi-optical mode converter in practical applications.

[0113] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A performance test method for a quasi-optical mode converter, characterized in that: include: Obtaining structural parameters of the quasi-optical mode converter, and performing an adjacency simulation test on the quasi-optical mode converter based on the structural parameters to obtain a virtual test result; When the virtual test result is a failure, optimizing the structural parameters to obtain recommended structural parameters; When the virtual test result is passed, a quasi-optical mode exciter generates a high-order electromagnetic wave to perform a physical test on the quasi-optical mode converter to obtain a physical test result; When the physical test result is passed, the quasi-optical mode converter is marked as having qualified performance.

2. The method according to claim 1, characterized in that When the virtual test result is passed, a quasi-optical mode exciter generates a high-order electromagnetic wave to perform a physical test on the quasi-optical mode converter to obtain a physical test result, including: When the virtual test result is passed, the quasi-optical mode converter is physically tested by generating high-order electromagnetic waves through the quasi-optical mode exciter: The vector network analyzer is mounted on a three-dimensional mobile platform to detect the horizontal component electric field of the output field at 60mm, 100mm, 140mm, 160mm, and 180mm from the axis of the quasi-optical mode converter; The normalized temperature distribution equipotential map of the output field is collected by a thermal imager; Performing a beam energy stability check based on the horizontal component electric field of the output field to obtain a first check result; Performing a Gaussian distribution check based on the output field normalized temperature distribution equipotential diagram to obtain a second check result; When both the first verification result and the second verification result are passed, the entity test result is passed; Otherwise, the entity test result is failed.

3. The method according to claim 2, characterized in that Performing a beam energy stability check based on the horizontal component electric field of the output field to obtain a first check result includes: Obtaining a verification model for the horizontal component electric field of the output field, processing the horizontal component electric field of the output field, and obtaining the first verification result; The steps of constructing the output field horizontal component electric field verification model include: Obtain the recorded electric field of the horizontal component of the output field detected at 60 mm, 100 mm, 140 mm, 160 mm, and 180 mm from the axis of the quasi-optical mode converter; When the variance of the field spot areas at the multiple cross sections of the electric field recorded by the horizontal component of the output field is less than or equal to the variance threshold, the alignment check result is marked as passed, otherwise, the alignment check result is marked as failed; When the phase fluctuation trends of the output window ranges at multiple cross sections of the output field horizontal component recording the electric field are the same, the phase consistency check result is marked as passed; otherwise, the phase consistency check result is marked as failed; When both the collimation check result and the phase consistency check result are passed, the check result is marked as passed; otherwise, the check result is marked as failed; The verification result identification information is used as supervision, and the electric field recorded by the horizontal component of the output field is used as input to train the convolution classifier to obtain the output field horizontal component electric field verification model.

4. The method according to claim 2, characterized in that A Gaussian-like distribution check is performed based on the output field normalized temperature distribution equipotential diagram to obtain a second check result, including: Obtaining a Gaussian distribution calibration model, processing the output field normalized temperature distribution equipotential diagram, and obtaining the second calibration result; The Gaussian distribution calibration model construction step includes: Obtain output field normalized temperature distribution equipotential map recording data; Constructing a histogram of the output field normalized temperature distribution equipotential map recording data; When the histogram is high in the middle and low on both sides, and at least 80 percent of the data is symmetrically distributed, the verification result of the output field normalized temperature distribution equipotential map recorded data is marked as passed, otherwise, the verification result of the output field normalized temperature distribution equipotential map recorded data is marked as failed; The verification result identification is used as supervision, and the output field normalized temperature distribution equipotential map record data is used as input to train the convolution classifier to obtain the Gaussian-like distribution verification model.

5. The method according to claim 1, characterized in that Based on the structural parameters, performing an adjacency simulation test on the quasi-optical mode converter to obtain a virtual test result includes: Configuring a structural parameter threshold, wherein the structural parameter threshold represents a maximum fault tolerance deviation of the structural parameter; Obtaining sample structural parameters and sample performance test record data of a sample quasi-optical mode converter; When the structural parameter deviation between the sample structural parameter and the structural parameter meets the structural parameter threshold, adding the sample performance test record data into the first data set to be analyzed; When the first data set to be analyzed meets the first data volume, traversing the sample structure parameters of the first data set to be analyzed, and collecting the second data set to be analyzed based on the structure parameter threshold; When the second data set to be analyzed meets the second data volume, the passing ratio of the first data set to be analyzed is counted and set as a first passing probability, and the passing ratio of the second data set to be analyzed is counted and set as a second passing probability, wherein the first passing probability has a first weight, the second passing probability has a second weight, and the first weight is greater than the second weight; Performing a weighted mean analysis on the first pass probability and the second pass probability according to the first weight and the second weight to obtain a fusion pass probability; When the fusion pass probability is greater than or equal to the pass probability threshold, the virtual test result is passed; otherwise, the virtual test result is failed.

6. The method according to claim 5, characterized in that When the virtual test result is not passed, optimizing the structural parameters to obtain recommended structural parameters includes: Obtain structural parameter constraint intervals; According to the structural parameter constraint interval, a high-dimensional distribution space is constructed, wherein any point in the high-dimensional distribution space represents a set of structural parameters; Uniformly distributing in the high-dimensional distribution space according to a preset distance threshold to obtain an initial structure particle set; The fusion passing probability is used as the fitness function, and optimization is performed based on the initial structure particle set to obtain the recommended structure parameters.

7. The method according to claim 6, characterized in that Taking the fusion passing probability as the fitness function, optimizing based on the initial structure particle set to obtain the recommended structure parameters includes: Obtaining an initial structure particle fusion passing probability set of the initial structure particle set; When the number of initial structure particles that meet the passing probability threshold when the fusion passing probability set is 0, i initial structure particles with larger fusion passing probability and j initial structure particles with smaller fusion passing probability are obtained, where i and j are integers and i is less than j. Extracting a first starting distribution position according to the j starting distribution positions of the initial structure particles; Extracting a first target distribution position according to the i initial structure particle target distribution positions; Connecting the first starting distribution position and the first target distribution position in the high-dimensional distribution space to obtain a line-adjacent particle fusion passing probability of a line-adjacent initial structure particle whose distribution distance to the connecting line is less than or equal to a distribution distance threshold; When the line adjacent particle fusion passing probability is less than the starting distribution position fusion passing probability of the first starting distribution position, updating the first target distribution position based on the i initial structure particle target distribution positions; Otherwise, taking the first target distribution position as the target and the first initial distribution position as the starting point, searching to obtain newly added structure particles; An iterative optimization is performed based on the newly added structural particles to obtain the recommended structural parameters.

8. The method according to claim 1, characterized in that Also includes: When the physical test result is failure, the quasi-optical mode converter is marked as having abnormal performance.

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