A method for debugging the gap of waveguide for W-band antenna

Abstract

The present invention provides a method for debugging a waveguide slot for a W-band antenna, and relates to the technical field of data processing. The method includes collecting historical waveguide slot design data under the W-band, and extracting the maximum overlap of design parameters according to target design parameters to form historical waveguide slot similar design data; performing design parameter correlation change relationship analysis based on slot factor changes on historical waveguide slot similar design data to form slot factor correlation design parameter change data; performing slot debugging optimization analysis based on slot factor correlation design parameter change data and combining target design parameters to form slot debugging optimization analysis result data. The method forms reasonable and effective debugging design guidance information by utilizing historical debugging design big data, so as to greatly improve the efficiency of debugging and reduce the difficulty of debugging.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for debugging a waveguide slot of a W-band antenna. Background Art

[0002] The W band refers to an electromagnetic band with a frequency range of 75~110GHz, which belongs to the 3mm band. It has broad application prospects in high-frequency communications, high-resolution radar and other fields. The performance of the waveguide antenna designed for the W band is mainly determined by the antenna slot. The design of the antenna slot is affected by many design factors, so it presents variability in slot design and optimization. For designers with little design experience, it is difficult to grasp the design elements of the slot to accurately optimize the slot.

[0003] With the development of design, product design has gradually formed a trend of intelligence and data, which greatly reduces the difficulty of design and improves the efficiency of design. The design data for gap debugging has also increased day by day. How to use these design data to achieve guided optimization of gap debugging design to improve the efficiency of debugging design and reduce the difficulty of debugging design has become an important research direction.

[0004] Therefore, it is an urgent problem to design a waveguide slot debugging method for W-band antennas, and to form reasonable and effective debugging design guidance information by utilizing historical debugging design big data to greatly improve the debugging efficiency and reduce the difficulty of debugging. Summary of the invention

[0005] The purpose of the present invention is to provide a method for debugging the waveguide slot of a W-band antenna, by obtaining the historical waveguide slot design data of the W-band to obtain the slot design big data in which the design parameter information in the historical data is as close as possible to the current target design parameter information, and then the difference between the historical data and the target design parameters is analyzed, and the change law of these different design parameters relative to the slot design data is established, and then the regular optimization and adjustment of the design parameters of the existing slot design can be performed based on the historical big data to complete the debugging of the slot design. In this way, the debugging of the slot has a reasonable debugging direction, and the current design data can be quickly adjusted with the help of big data to achieve the target design requirements, which greatly improves the efficiency of the slot debugging, and also reduces the difficulty of the slot debugging, making the slot debugging optimization design more reasonable.

[0006] In a first aspect, the present invention provides a method for debugging a waveguide slot for a W-band antenna, comprising collecting historical waveguide slot design data under the W-band, and extracting the maximum overlap of design parameters according to target design parameters to form historical waveguide slot similar design data; performing design parameter correlation change relationship analysis based on slot factor changes on the historical waveguide slot similar design data to form slot factor correlated design parameter change data; performing slot debugging optimization analysis based on the slot factor correlated design parameter change data and in combination with the target design parameters to form slot debugging optimization analysis result data.

[0007] In the present invention, the method obtains the historical waveguide slot design data of the W band to obtain the slot design big data whose design parameter information in the historical data is as close as possible to the current target design parameter information, and then performs a difference analysis on the difference between the historical data and the target design parameters, establishes the change law of these different design parameters relative to the slot design data, and then can optimize and adjust the regularity of the design parameters of the existing slot design based on the historical big data to complete the debugging of the slot design. In this way, the debugging of the slot has a reasonable debugging direction, and the current design data can be quickly adjusted with the help of big data to achieve the target design requirements, which greatly improves the efficiency of the slot debugging, and also reduces the difficulty of the slot debugging, making the slot debugging optimization design more reasonable.

[0008] As a possible implementation method, historical waveguide slot design data under the W band is collected, and the maximum overlap of design parameters is extracted according to the target design parameters to form historical waveguide slot similar design data, including: determining the parameters of the result design parameters corresponding to the target design parameters in different historical waveguide slot designs in the historical waveguide slot design data; setting a data volume threshold, determining the historical waveguide slot design in which the parameters of the result design parameters are partially equal to the parameters of the target design parameters, determining them as the object historical waveguide slot design, and determining the design parameters with equal parameters as the object design parameters; for all the object historical waveguide slot designs, extracting the object historical waveguide slot designs in which the number of the parameters of the object design parameters is the same and reaches the data volume threshold, to form historical waveguide slot similar design data.

[0009] In the present invention, it is considered that gap debugging is affected by many factors. Therefore, when collecting basic big data, it is necessary to reasonably consider the conditions of big data collection to ensure that the collected big data can form accurate gap debugging guiding feature data. Here, for the big data collection of gap debugging, the type of debugging is mainly considered, that is, the analysis and design data under the W band. After all, the gap design gap between different bands is still relatively large. At the same time, the comparability of the design data is also considered. If the design information considered by the gap design data obtained by the big data and the target design parameters have no intersection at all, such big data analysis is invalid. Therefore, when collecting big data, the focus is on whether the current design parameters of the historical big data overlap with the target design parameters. The higher the overlap, the stronger the comparability and reference of the data. Of course, the consideration based on the overlap will cause a reduction in the amount of data due to the more overlapping data, which may affect the extraction of subsequent feature information. Therefore, when performing the overlap analysis based on the design parameters, it should also be considered whether the amount of historical data that can be collected under the determined overlap meets the needs of the analysis. Here, the data volume threshold is set to control it so as to reasonably ensure that the analysis has enough data volume. In addition, it should be noted that the extraction based on the maximum overlap can be understood as different historical waveguide slot design data. If there are overlapping design parameters between the two, that is, the parameters corresponding to the target design parameters are the same, and the parameter values ​​of the same parameters are also the same, then it is determined that the two historical waveguide slot design data meet the requirements and can be extracted, that is, only when the parameter values ​​of the overlapping parameters are equal and the number of overlapping parameters is the same can it be determined that they are big data that can be collected.

[0010] As a possible implementation method, the historical waveguide gap similarity design data is analyzed based on the design parameter correlation change relationship of the gap factor change to form the gap factor correlation design parameter change data, including: extracting the parameters of the result design parameters of the historical waveguide gap similarity design data that are different from the parameters of the target design parameters, and determining them as differential design parameters; for the historical waveguide gap similarity design data, determining the historical gap factor value corresponding to each object historical waveguide gap design, and clustering the object historical waveguide gap design based on the single factor change of the gap factor to form different historical similar single factor gap design data sets; and combining all historical similar single factor gap design data sets to form the gap factor correlation design parameter change data.

[0011] In the present invention, after obtaining the historical waveguide slot design data with high correlation, it is necessary to extract the characteristic information. Here, the slot debugging of the current target slot is mainly to optimize the design parameters that do not meet the target design requirements so that the design parameters meet the requirements of the target design. Therefore, how to guide these design parameters that do not meet the target design requirements to be reasonably debugged becomes the key. And the changes in these design parameters will eventually form slot design parameters that are different from the current ones. Therefore, the present invention starts from the difference design parameters, and the slot design data formed by the different values ​​of these difference design parameters in the big data is used to establish the influence relationship of the difference design parameters on the change of the slot factor, so that it can provide guidance for the subsequent adjustment of the slot factor according to the target design parameters that need to be achieved, and then obtain the parameter values ​​that meet the target design requirements.

[0012] As a possible implementation method, for historical waveguide gap similarity design data, the historical gap factor value corresponding to each object historical waveguide gap design is determined, and the object historical waveguide gap design is clustered based on the single factor change of the gap factor to form different historical similar single factor gap design data sets, including: for all object historical waveguide gap designs, the corresponding historical gap factor value is determined, and clustering is performed in the following manner: if there is only one historical gap factor value different among all historical gap factor values ​​corresponding to different object historical waveguide gap designs, the corresponding object historical waveguide gap designs are determined to be of the same type, and the historical gap factor values ​​with different factor values ​​are calibrated as difference gap factor values; the object historical waveguide gap designs of the same type are aggregated to form different historical similar single factor design data; for each historical similar single factor design data, all object historical waveguide gap designs are arranged in order from small to large according to the corresponding difference gap factor values ​​to form ordered historical similar single factor design data; for each ordered historical similar single factor design data, a difference change relationship function of each difference design parameter is established according to the sequential change law of the difference gap factor value. , where M represents the number of different ordered historical similar single factor design data, n represents the number of different object historical waveguide slot designs in the ordered historical similar single factor design data numbered M, and k represents the number of different difference design parameters. Represents the value of the difference design parameter numbered k in the difference change relationship function; for each ordered historically similar single-factor design data, the difference change relationship function of all the difference design parameters corresponding to the set , forming the corresponding historically similar single-factor gap design data set.

[0013] In the present invention, it can be understood that the change of any one of the gap factors, i.e., the position, size, and shape of the gap, is the result of the combined influence of multiple design parameters including weight, size, power, strength, etc. Therefore, when analyzing the correlation change relationship between the design parameters and the gap factors, the collected big data is clustered and divided based on the single change of the gap factor to form a data set of design parameters that change with different single gap factor changes. In this way, for each data set, it is possible to establish how each corresponding difference design parameter changes under a single gap factor change. In order to more intuitively determine what kind of regular change effect the gap factor change will have on each difference design parameter, when clustering, the single gap factor changes are arranged in sequence, and then the change relationship data of these difference design parameters when the single gap factor changes monotonically is obtained.

[0014] As a possible implementation method, gap debugging optimization analysis is performed according to the gap factor-associated design parameter change data and in combination with the target design parameters to form gap debugging optimization analysis result data, including: for all historical similar single-factor gap design data sets, the historical similar single-factor gap design data set whose difference design parameters are closest to the target design parameters is determined and calibrated as the debugging comparison similar single-factor gap design data set; based on the debugging comparison similar single-factor gap design data set and in combination with the target design parameters, gap debugging optimization analysis is performed to form gap debugging optimization analysis result data.

[0015] In the present invention, after obtaining the influence relationship data of the gap factor change relative to the difference design parameters, a reasonable gap debugging design can be carried out in combination with the gap between the current design parameters and the target design parameters, so that the gap debugging is more efficient and reasonable. It should be noted that for gap debugging, in theory, multiple gap factors can be considered to change at the same time to meet the requirements of the target design parameters, but in actual situations, the changes of multiple gap factors at the same time will lead to the complexity of the process, such as adding new processing procedures, re-adjusting the generation process, etc., which will lead to an increase in the difficulty of the production process and an increase in the cost of production. Therefore, compared with the adjustment of a single gap factor to meet the requirements of the target design parameters, although some design parameters may achieve higher design goals when all the difference design parameters meet the requirements, resulting in increased design and production costs due to redundancy of the design, this increase is still relatively small compared to the change of multiple factors. Therefore, the debugging and optimization design of a single gap factor should be carried out first, and then the debugging and optimization design of the gap factors of multiple factors should be carried out to form a reasonable debugging method step.

[0016] As a possible implementation method, for all historical similar single-factor gap design data sets, determine the historical similar single-factor gap design data set whose difference design parameters are closest to the target design parameters, and calibrate them as debugging and comparison similar single-factor gap design data sets, including: for different historical similar single-factor gap design data sets, determine the same difference design parameters and target design parameters The effective distance between ,in: , Indicates the process difficulty factor corresponding to different design parameters; determines the minimum effective distance value , and the corresponding historical similar single-factor gap design data set is determined as the debugging and comparison similar single-factor gap design data set.

[0017] In the present invention, the single factor gap factor adjustment considers big data analysis to establish a design data set for different gap factor changes, and the actual debugging needs to take into account the cost, focusing on the selection of the design data set from the process difficulty. The process difficulty factor can be determined according to the actual situation, or it can be determined based on big data analysis.

[0018] As a possible implementation method, gap debugging optimization analysis is performed based on debugging and comparing similar single-factor gap design data sets, and combined with target design parameters, to form gap debugging optimization analysis result data, including: performing optimization analysis based on single-factor debugging based on debugging and comparing similar single-factor gap design data sets, combined with target design parameters, to form single-factor optimization analysis result data; performing multi-factor debugging optimization analysis based on single-factor optimization analysis result data, combined with effective distance values ​​corresponding to remaining historical similar single-factor gap design data sets, to form multi-factor optimization analysis result data; performing processability comparison analysis based on single-factor optimization analysis result data and multi-factor optimization analysis result data, to form gap debugging optimization analysis result data.

[0019] In the present invention, according to the determined debugging and comparison of similar single-factor gap design data sets, when performing single-factor analysis, it is first determined whether a single change in the gap factor can make the differential design parameters all reach the corresponding target design requirements. If so, the additional process cost incurred when all differential design parameters are raised to the current value compared to the target basic value when all differential design parameters just reach the design target is determined, providing data reference for actual process considerations. If not, only multi-factor debugging design can be performed.

[0020] As a possible implementation method, based on the debugging and comparison of similar single-factor gap design data, combined with the target design parameters, an optimization analysis based on single-factor debugging is performed to form single-factor optimization analysis result data, including: based on the debugging and comparison of similar single-factor gap design data, each difference change relationship function , the following debugging judgment is performed on the difference gap factor value: If the debugging of the difference gap factor value satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, then the minimum single factor debugging amount of the difference gap factor is determined , and determine the difference gap factor value to obtain the minimum debugging amount of single factor After that, the single factor total excess equivalent value of all difference design parameters relative to the target design parameters ,in: , is the reached value of the corresponding difference design parameter numbered k; if the debugging of the difference gap factor value does not satisfy that all difference design parameters reach the parameter value corresponding to the target design parameter, a single factor debugging failure result is formed.

[0021] In the present invention, the single factor analysis needs to determine the values ​​of each design parameter when the target design requirements are met, and then determine the exceeding conditions based on these values ​​to provide a reference for the analysis of process costs.

[0022] As a possible implementation method, a multi-factor debugging optimization analysis is performed based on the single-factor optimization analysis result data and in combination with the effective distance values ​​corresponding to the remaining historical similar single-factor gap design data sets to form multi-factor optimization analysis result data, including: gradually selecting the historical similar single-factor gap design data set with the smallest effective distance value from the remaining historical similar single-factor gap design data sets each time, and performing the following steps of multi-factor optimization analysis: combining the debugging and comparison of similar single-factor gap design data sets and the extracted historical similar single-factor gap design data sets to determine whether the combined debugging of the difference gap factor values ​​satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters: if so, then determine the multi-factor debugging amounts for all different difference design parameters, and determine the multi-factor over-standard equivalent values ​​when the difference design parameters obtain the multi-factor debugging amounts. If not, continue to extract new historical similar single-factor gap design data sets for judgment until the combination debugging of the difference gap factor values ​​satisfies that all difference design parameters reach the parameter values ​​corresponding to the target design parameters, and determine the corresponding multi-factor excess equivalent values. .

[0023] In the present invention, it can be understood that if the multi-factor debugging design includes all the gap factors involved at the same time, although it can definitely form a solution that meets the target design requirements, too many factor adjustments will cause a large increase in process costs. Therefore, when performing multi-factor debugging, the number of gap factors considered is gradually increased to determine the minimum number of gap factors to obtain a debugging solution that meets the design goals. Of course, an equivalent value of the excess is also provided as a reference for process cost analysis.

[0024] As a possible implementation method, a process comparison analysis is performed based on the single-factor optimization analysis result data and the multi-factor optimization analysis result data to form the gap debugging optimization analysis result data, including: Based on the single-factor optimization analysis result data and the multi-factor optimization analysis result data, the following process comparison analysis is performed: If the single-factor optimization debugging and the multi-factor optimization debugging both meet the parameter values ​​corresponding to the target design parameters of all the different design parameters, then compare the single-factor total excess equivalent value and multi-factor excess equivalent value , the optimization debugging mode with a smaller value is determined as the gap debugging optimization mode; if the single-factor optimization debugging does not satisfy that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, and the multi-factor optimization debugging satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, then the multi-factor optimization debugging mode is determined as the gap debugging optimization mode.

[0025] In the present invention, after completing the debugging analysis of single factors and multiple factors, it is necessary to determine the best debugging solution. It should be noted that for some gap factors, the cost of the process solution formed by a combination of multiple factors may be less than the cost of debugging a single factor using the gap factors corresponding to the process with high cost difficulty. Therefore, when both single factors and multiple factors can meet the design requirements, the equivalent value of the excess is used to make a choice to ensure the rationality of the debugging.

[0026] The beneficial effects of the W-band antenna waveguide slot debugging method provided by the present invention are as follows:

[0027] This method obtains the historical waveguide slot design data of the W band to obtain the slot design big data with the design parameter information in the historical data as close as possible to the current target design parameter information, and then conducts a difference analysis on the difference between the historical data and the target design parameters, establishes the change law of these different design parameters relative to the slot design data, and then can optimize and adjust the design parameters of the existing slot design based on the historical big data to complete the debugging of the slot design. In this way, the debugging of the slot has a reasonable debugging direction, and the current design data can be quickly adjusted with the help of big data to meet the target design requirements, which greatly improves the efficiency of the slot debugging, and also reduces the difficulty of the slot debugging, making the slot debugging optimization design more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A step diagram of a method for debugging a waveguide slot of a W-band antenna provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] The W band refers to an electromagnetic band with a frequency range of 75~110GHz, which belongs to the 3mm band. It has broad application prospects in high-frequency communications, high-resolution radar and other fields. The performance of the waveguide antenna designed for the W band is mainly determined by the antenna slot. The design of the antenna slot is affected by many design factors, so it presents variability in slot design and optimization. For designers with little design experience, it is difficult to grasp the design elements of the slot to accurately optimize the slot.

[0032] With the development of design, product design has gradually formed a trend of intelligence and data, which greatly reduces the difficulty of design and improves the efficiency of design. The design data for gap debugging has also increased day by day. How to use these design data to achieve guided optimization of gap debugging design to improve the efficiency of debugging design and reduce the difficulty of debugging design has become an important research direction.

[0033] refer to Figure 1The embodiment of the present invention provides a method for debugging the waveguide slot of a W-band antenna. The method obtains the historical waveguide slot design data of the W-band to obtain the slot design big data in which the design parameter information in the historical data is as close as possible to the current target design parameter information, and then performs a difference analysis on the difference between the historical data and the target design parameters, establishes the change law of these different design parameters relative to the slot design data, and then can optimize and adjust the regularity of the design parameters of the existing slot design based on the historical big data to complete the debugging of the slot design. In this way, the debugging of the slot has a reasonable debugging direction, and the current design data can be quickly adjusted with the help of big data to meet the target design requirements, which greatly improves the efficiency of the slot debugging, and also reduces the difficulty of the slot debugging, making the slot debugging optimization design more reasonable.

[0034] The method for debugging the waveguide slot of a W-band antenna specifically includes the following steps:

[0035] S1: Collect historical waveguide slot design data under the W band, and extract the maximum coincidence of design parameters according to the target design parameters to form historical waveguide slot similarity design data.

[0036] The historical waveguide slot design data under the W band are collected, and the maximum overlap of the design parameters is extracted according to the target design parameters to form the historical waveguide slot similar design data, including: determining the parameters of the result design parameters corresponding to the target design parameters in different historical waveguide slot designs in the historical waveguide slot design data; setting a data volume threshold, determining the historical waveguide slot design in which the parameters of the result design parameters are partially equal to the parameters of the target design parameters, determining them as the object historical waveguide slot design, and determining the design parameters with equal parameters as the object design parameters; for all the object historical waveguide slot designs, extracting the object historical waveguide slot designs in which the number of the parameters of the object design parameters is the same and reaches the data volume threshold, to form the historical waveguide slot similar design data.

[0037] Considering that gap debugging is affected by many factors, when collecting basic big data, it is necessary to reasonably consider the conditions for big data collection to ensure that the collected big data can form accurate gap debugging guiding feature data. Here, for the big data collection of gap debugging, the type of debugging is mainly considered, that is, the analysis and design data under the W band. After all, the gap design gaps in different bands are relatively large. At the same time, the comparability of the design data is also considered. If the design information considered by the gap design data obtained by big data and the target design parameters have no intersection at all, such big data analysis is invalid. Therefore, when collecting big data, the focus is on whether the current design parameters of the historical big data overlap with the target design parameters. The higher the overlap, the stronger the comparability and reference of the data. Of course, the consideration based on the overlap will cause a reduction in the amount of data due to the more overlapping data, which may affect the subsequent extraction of feature information. Therefore, when conducting the overlap analysis based on design parameters, it should also be considered whether the amount of historical data that can be collected under the determined overlap meets the needs of analysis. Here, the data volume threshold is set to control it to reasonably ensure that the analysis has enough data volume. In addition, it should be noted that the extraction based on the maximum overlap can be understood as different historical waveguide slot design data. If there are overlapping design parameters between the two, that is, the parameters corresponding to the target design parameters are the same, and the parameter values ​​of the same parameters are also the same, then it is determined that the two historical waveguide slot design data meet the requirements and can be extracted, that is, only when the parameter values ​​of the overlapping parameters are equal and the number of overlapping parameters is the same can it be determined that they are big data that can be collected.

[0038] S2: Analyze the relationship between the design parameter correlation changes based on the gap factor changes for the historical waveguide slot similar design data to form the slot factor correlation design parameter change data.

[0039] The historical waveguide gap similarity design data are analyzed for the design parameter correlation change relationship based on the gap factor change to form the gap factor correlation design parameter change data, including: extracting the parameters that are different from the parameters of the target design parameters in the result design parameters of the historical waveguide gap similarity design data, and determining them as the difference design parameters; for the historical waveguide gap similarity design data, determining the historical gap factor value corresponding to each object historical waveguide gap design, and clustering the object historical waveguide gap design based on the single factor change of the gap factor to form different historical similar single factor gap design data sets; and combining all historical similar single factor gap design data sets to form the gap factor correlation design parameter change data.

[0040] After obtaining the historical waveguide slot design data with high correlation, it is necessary to extract the characteristic information. The slot debugging of the current target slot considered here is mainly to optimize the design parameters that do not meet the target design requirements so that the design parameters meet the requirements of the target design. Therefore, how to guide these design parameters that do not meet the target design requirements to be reasonably debugged becomes the key. And the changes in these design parameters will eventually form slot design parameters that are different from the current ones. Therefore, the present invention starts from the difference design parameters, and the slot design data formed by the different values ​​of these difference design parameters in the big data is used to establish the influence relationship of the difference design parameters on the change of the slot factor, so that it can provide guidance for the subsequent adjustment of the slot factor according to the target design parameters that need to be achieved, and then obtain the parameter values ​​that meet the target design requirements.

[0041] For the historical waveguide gap similarity design data, the historical gap factor value corresponding to each object historical waveguide gap design is determined, and the object historical waveguide gap design is clustered based on the single factor change of the gap factor to form different historical similar single factor gap design data sets, including: for all object historical waveguide gap designs, the corresponding historical gap factor values ​​are determined, and clustering is performed in the following manner: if all historical gap factor values ​​corresponding to different object historical waveguide gap designs have only one historical gap factor value that is different, then the corresponding object historical waveguide gap designs are determined to be of the same type, and the historical gap factor values ​​with different factor values ​​are calibrated as difference gap factor values;

[0042] Collect the same type of object historical waveguide gap designs to form different historical similar single factor design data; for each historical similar single factor design data, arrange all the object historical waveguide gap designs in order from small to large according to the corresponding difference gap factor values ​​to form ordered historical similar single factor design data; for each ordered historical similar single factor design data, establish the difference change relationship function of each difference design parameter according to the order change law of the difference gap factor value , where M represents the number of different ordered historical similar single factor design data, n represents the number of different object historical waveguide slot designs in the ordered historical similar single factor design data numbered M, and k represents the number of different difference design parameters. Represents the value of the difference design parameter numbered k in the difference change relationship function; for each ordered historically similar single-factor design data, the difference change relationship function of all the difference design parameters corresponding to the set , forming the corresponding historically similar single-factor gap design data set.

[0043] It is understandable that the change of any one of the gap factors, i.e., the position, size, and shape of the gap, is the result of the combined influence of multiple design parameters, including weight, size, power, strength, etc. Therefore, when analyzing the correlation change relationship between the design parameters and the gap factors, the collected big data is clustered and divided based on the single change of the gap factor to form a data set of design parameters that change with different single gap factor changes. In this way, for each data set, it is possible to establish how each corresponding difference design parameter changes under a single gap factor change. In order to more intuitively determine what kind of regular change effect the gap factor change will have on each difference design parameter, when clustering, the single gap factor changes are arranged in sequence, and then the change relationship data of these difference design parameters when the single gap factor changes monotonically is obtained.

[0044] S3: Correlate the design parameter change data according to the gap factor, and perform gap debugging optimization analysis in combination with the target design parameters to form gap debugging optimization analysis result data.

[0045] According to the gap factor associated design parameter change data, gap debugging optimization analysis is performed in combination with the target design parameters to form gap debugging optimization analysis result data, including: for all historical similar single-factor gap design data sets, the historical similar single-factor gap design data set whose difference design parameters are closest to the target design parameters is determined, and calibrated as the debugging comparison similar single-factor gap design data set; according to the debugging comparison similar single-factor gap design data set, in combination with the target design parameters, gap debugging optimization analysis is performed to form gap debugging optimization analysis result data.

[0046] After obtaining the influence relationship data of the gap factor change relative to the difference design parameters, a reasonable gap debugging design can be carried out in combination with the gap between the current design parameters and the target design parameters to make the gap debugging more efficient and reasonable. It should be noted that for gap debugging, in theory, multiple gap factors can be considered to change at the same time to meet the requirements of the target design parameters, but in actual situations, changing multiple gap factors at the same time will lead to the complexity of the process, such as adding new processing procedures, readjusting the generation process, etc., which will increase the difficulty of the production process and the cost of production. Therefore, compared with the adjustment of a single gap factor to meet the requirements of the target design parameters, although some design parameters may achieve higher design goals when all the difference design parameters meet the requirements, resulting in redundancy in the design and increase in design and production costs, this increase is still relatively small compared to the change of multiple factors. Therefore, the first thing to do is to debug and optimize the design of a single gap factor, and then debug and optimize the design of multiple gap factors to form a reasonable debugging method step.

[0047] For all historical similar single factor gap design data sets, determine the historical similar single factor gap design data set whose difference design parameters are closest to the target design parameters, and calibrate them as the debugging and comparison similar single factor gap design data set, including: for different historical similar single factor gap design data sets, determine the same difference design parameters and target design parameters The effective distance between ,in: , Indicates the process difficulty factor corresponding to different design parameters; determines the minimum effective distance value , and the corresponding historical similar single-factor gap design data set is determined as the debugging and comparison similar single-factor gap design data set.

[0048] For single-factor gap factor adjustment, we consider big data analysis to establish a design data set for different gap factor changes, and the actual cost of debugging needs to be taken into account. We focus on the selection of design data sets from the perspective of process difficulty. The process difficulty factor can be determined based on actual conditions or based on big data analysis.

[0049] According to the debugging and comparison of similar single-factor gap design data sets, and in combination with the target design parameters, gap debugging optimization analysis is performed to form gap debugging optimization analysis result data, including: according to the debugging and comparison of similar single-factor gap design data, in combination with the target design parameters, optimization analysis based on single-factor debugging is performed to form single-factor optimization analysis result data; according to the single-factor optimization analysis result data, in combination with the effective distance value corresponding to the remaining historical similar single-factor gap design data sets, multi-factor debugging optimization analysis is performed to form multi-factor optimization analysis result data; according to the single-factor optimization analysis result data and the multi-factor optimization analysis result data, processability comparison analysis is performed to form gap debugging optimization analysis result data.

[0050] According to the determined debugging and comparison of similar single-factor gap design data sets, when conducting single-factor analysis, first determine whether a single change in the gap factor can make the differential design parameters all reach the corresponding target design requirements. If so, determine the additional process cost incurred when all differential design parameters are raised to the current value when all differential design parameters reach the design target, compared with the target base value, to provide data reference for actual process considerations. If not, then only multi-factor debugging design can be performed.

[0051] According to the debugging and comparison of similar single-factor gap design data, combined with the target design parameters, the optimization analysis based on single-factor debugging is performed to form the single-factor optimization analysis result data, including: according to the debugging and comparison of similar single-factor gap design data, each difference change relationship function , the following debugging judgment is performed on the difference gap factor value: If the debugging of the difference gap factor value satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, then the minimum single factor debugging amount of the difference gap factor is determined , and determine the difference gap factor value to obtain the minimum debugging amount of single factor After that, the single factor total excess equivalent value of all difference design parameters relative to the target design parameters ,in: , is the reached value of the corresponding difference design parameter numbered k; if the debugging of the difference gap factor value does not satisfy that all difference design parameters reach the parameter value corresponding to the target design parameter, a single factor debugging failure result is formed.

[0052] Single-factor analysis requires determining the values ​​of each design parameter when the target design requirements are met, and then determining the exceeding of the standards based on these values ​​to provide a reference for process cost analysis.

[0053] According to the single factor optimization analysis result data, and in combination with the effective distance values ​​corresponding to the remaining historical similar single factor gap design data sets, a multi-factor debugging optimization analysis is performed to form the multi-factor optimization analysis result data, including: gradually selecting the historical similar single factor gap design data set with the smallest effective distance value from the remaining historical similar single factor gap design data sets each time, and performing the following steps of multi-factor optimization analysis: combining the debugging and comparison of similar single factor gap design data sets and the extracted historical similar single factor gap design data sets, to determine whether the combined debugging of the difference gap factor values ​​satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters: if so, then determine the multi-factor debugging amounts for all the different design parameters, and determine the multi-factor over-standard equivalent values ​​when the difference design parameters obtain the multi-factor debugging amounts. If not, continue to extract new historical similar single-factor gap design data sets for judgment until the combination debugging of the difference gap factor values ​​satisfies that all difference design parameters reach the parameter values ​​corresponding to the target design parameters, and determine the corresponding multi-factor excess equivalent values. .

[0054] It is understandable that if the multi-factor debugging design includes all the gap factors involved at the same time, although it will definitely be able to form a solution that meets the target design requirements, too many factor adjustments will cause a large increase in process costs. Therefore, when conducting multi-factor debugging, the number of gap factors considered is gradually increased to determine the minimum number of gap factors to obtain a debugging solution that meets the design goals. Of course, equivalent values ​​that exceed the standard are also provided as a reference for process cost analysis.

[0055] According to the single factor optimization analysis result data and the multi-factor optimization analysis result data, a process comparison analysis is performed to form the gap debugging optimization analysis result data, including: According to the single factor optimization analysis result data and the multi-factor optimization analysis result data, the following process comparison analysis is performed: If the single factor optimization debugging and the multi-factor optimization debugging both meet the parameter values ​​corresponding to the target design parameters of all the different design parameters, then the single factor total excess equivalent value is compared and multi-factor excess equivalent value , the optimization debugging mode with a smaller value is determined as the gap debugging optimization mode; if the single-factor optimization debugging does not satisfy that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, and the multi-factor optimization debugging satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, then the multi-factor optimization debugging mode is determined as the gap debugging optimization mode.

[0056] After completing the debugging analysis of single factors and multiple factors, it is necessary to determine the best debugging solution. It should be noted that for some gap factors, the cost of the process solution formed by multiple combinations may be less than the cost of single factor debugging using the gap factors corresponding to some processes with high cost difficulty. Therefore, when both single factors and multiple factors can meet the design requirements, the equivalent value of exceeding the standard is used to make trade-offs to ensure the rationality of the debugging.

[0057] In summary, the beneficial effects of the W-band antenna waveguide slot debugging method provided by the embodiment of the present invention are as follows:

[0058] This method obtains the historical waveguide slot design data of the W band to obtain the slot design big data with the design parameter information in the historical data as close as possible to the current target design parameter information, and then conducts a difference analysis on the difference between the historical data and the target design parameters, establishes the change law of these different design parameters relative to the slot design data, and then can optimize and adjust the design parameters of the existing slot design based on the historical big data to complete the debugging of the slot design. In this way, the debugging of the slot has a reasonable debugging direction, and the current design data can be quickly adjusted with the help of big data to meet the target design requirements, which greatly improves the efficiency of the slot debugging, and also reduces the difficulty of the slot debugging, making the slot debugging optimization design more reasonable.

[0059] In the embodiment of the present invention, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association relationship between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0060] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present invention does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present invention should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0061] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present invention. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0062] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present invention do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present invention.

[0063] The "protocol" involved in the embodiments of the present invention may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol applied in future communication systems, and the embodiments of the present invention do not specifically limit this.

[0064] In the embodiments of the present invention, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances, but do not limit the time, nor do they require the device to perform judgment actions when implementing, nor do they mean the existence of other limitations.

[0065] In the description of the embodiments of the present invention, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present invention is only a kind of association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present invention, in the embodiments of the present invention, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0066] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0069] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0070] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0071] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0072] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0078] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for debugging a waveguide slot of a W-band antenna, characterized in that: include: Collect historical waveguide slot design data under the W band, and extract the maximum coincidence of design parameters according to the target design parameters to form historical waveguide slot similarity design data; Performing a design parameter correlation change relationship analysis based on a slot factor change on the historical waveguide slot similar design data to form slot factor correlation design parameter change data; According to the gap factor correlation design parameter change data, and in combination with the target design parameters, gap debugging optimization analysis is performed to form gap debugging optimization analysis result data; The historical waveguide slot design data under the W band is collected, and the maximum coincidence of the design parameters is extracted according to the target design parameters to form the historical waveguide slot similarity design data, including: Determining the parameters of resultant design parameters corresponding to the target design parameters on different historical waveguide slot designs in the historical waveguide slot design data; Setting a data volume threshold, determining a historical waveguide slot design in which the parameters of the result design parameters are partially equal to the parameters of the target design parameters, determining the historical waveguide slot design as the target historical waveguide slot design, and determining the design parameters with equal parameters as the target design parameters; For all the object historical waveguide slot designs, extract the object historical waveguide slot designs whose number of the same object design parameter parameters reaches the data amount threshold, to form the historical waveguide slot similarity design data; The historical waveguide slot similar design data is analyzed for design parameter correlation change relationship based on slot factor change to form slot factor correlation design parameter change data, including: Extracting parameters that are different from the parameters of the target design parameters from the parameters of the result design parameters of the historical waveguide slot similarity design data, and determining them as differential design parameters; For the historical waveguide slot similarity design data, determine the historical slot factor value corresponding to each of the object historical waveguide slot designs, and perform single factor change clustering on the object historical waveguide slot designs based on the slot factor to form different historical similar single factor slot design data sets; Collect all the historical similar single-factor gap design data sets to form the gap factor associated design parameter change data; For the historical waveguide gap similarity design data, determine the historical gap factor value corresponding to each of the object historical waveguide gap designs, and perform single factor change clustering based on the gap factor on the object historical waveguide gap design to form different historical similar single factor gap design data sets, including: For all the object historical waveguide slot designs, the corresponding historical slot factor values ​​are determined and clustered in the following manner: If only one historical gap factor value is different among all the historical gap factor values ​​corresponding to the different object historical waveguide gap designs, the corresponding object historical waveguide gap designs are determined to be of the same type, and the historical gap factor values ​​with different factor values ​​are calibrated as difference gap factor values; Collecting the same type of historical waveguide slot designs of the objects to form different historical similar single factor design data; For each of the historical similar single factor design data, all the object historical waveguide slot designs are arranged in order from small to large according to the corresponding difference slot factor values ​​to form ordered historical similar single factor design data; For each of the ordered historical similar single factor design data, a difference change relationship function of each of the difference design parameters is established according to the sequential change law of the difference gap factor value. , wherein M represents the serial numbers of different ordered historical similar single factor design data, n represents the serial numbers of different object historical waveguide slot designs in the ordered historical similar single factor design data numbered M, and k represents the serial numbers of different difference design parameters, represents the value of the difference design parameter numbered k in the difference variation relationship function; For each of the ordered historically similar single-factor design data, the difference change relationship function of all the corresponding difference design parameters is collected. , forming the corresponding historical similarity single factor gap design data set; According to the gap factor associated design parameter change data, and in combination with the target design parameters, gap debugging optimization analysis is performed to form gap debugging optimization analysis result data, including: For all the historical similar single-factor gap design data sets, determine the historical similar single-factor gap design data set whose difference design parameter is closest to the target design parameter, and mark it as the debugging and comparison similar single-factor gap design data set; According to the debugging and comparing similar single-factor gap design data sets, and in combination with the target design parameters, gap debugging optimization analysis is performed to form the gap debugging optimization analysis result data.

2. The method for debugging a waveguide gap of a W-band antenna according to claim 1, characterized in that: The step of determining the historical similar single factor gap design data set whose difference design parameter is closest to the target design parameter for all the historical similar single factor gap design data sets, and marking the historical similar single factor gap design data set as the debugging and comparison similar single factor gap design data set, comprises: For different historically similar single factor gap design data sets, the same difference design parameters are determined. With the target design parameters The effective distance between ,in: , Indicates the process difficulty factors corresponding to the different design parameters; Determine the minimum effective distance value and determining the corresponding historical similar single-factor gap design data set as the debugging and comparison similar single-factor gap design data set.

3. The method for debugging the waveguide gap of a W-band antenna according to claim 2, characterized in that: The gap debugging optimization analysis is performed based on the debugging and comparison of similar single-factor gap design data sets, and combined with the target design parameters to form the gap debugging optimization analysis result data, including: According to the debugging and comparison of similar single-factor gap design data, an optimization analysis based on single-factor debugging is performed in combination with the target design parameters to form single-factor optimization analysis result data; According to the single factor optimization analysis result data, and in combination with the effective distance values ​​corresponding to the remaining historical similar single factor gap design data sets, a multi-factor debugging optimization analysis is performed to form a multi-factor optimization analysis result data; A processability comparison analysis is performed based on the single-factor optimization analysis result data and the multi-factor optimization analysis result data to form the gap debugging optimization analysis result data.

4. The method for debugging a waveguide gap of a W-band antenna according to claim 3, characterized in that: The method of comparing similar single-factor gap design data according to the debugging and combining the target design parameters to perform optimization analysis based on single-factor debugging to form single-factor optimization analysis result data includes: According to the debugging and comparison of each difference change relationship function in similar single factor gap design data , the following debugging judgment is performed on the difference gap factor value: If the adjustment of the difference gap factor value satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, the minimum adjustment amount of the single factor of the difference gap factor is determined. , and determine the difference gap factor value to obtain the minimum debugging amount of the single factor After that, the single factor total excess equivalent value of all the difference design parameters relative to the parameter value of the target design parameter ,in: , is the achieved value of the difference design parameter corresponding to number k; If the debugging of the difference gap factor value does not satisfy that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, a single factor debugging failure result is formed.

5. The method for debugging the waveguide gap of a W-band antenna according to claim 4, characterized in that: The multi-factor debugging optimization analysis is performed based on the single-factor optimization analysis result data and in combination with the effective distance values ​​corresponding to the remaining historical similar single-factor gap design data sets to form multi-factor optimization analysis result data, including: The historically similar single-factor gap design data set with the smallest effective distance value is gradually selected from the remaining historically similar single-factor gap design data sets each time, and the multi-factor optimization analysis of the following steps is performed: Combined with the debugging and comparing similar single-factor gap design data set and the extracted historical similar single-factor gap design data set, it is determined that the combined debugging of the difference gap factor values ​​satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters: If possible, determine the multi-factor adjustment amount of all the different design parameters, and determine the multi-factor over-standard equivalent value when the different design parameters obtain the multi-factor adjustment amount. ; If not, continue to extract new historical similar single-factor gap design data sets for judgment until the combination debugging of the difference gap factor values ​​satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, and determine the corresponding multi-factor excess equivalent values. .

6. The method for debugging the waveguide gap of a W-band antenna according to claim 5, characterized in that: The process of performing processability comparison analysis based on the single factor optimization analysis result data and the multi-factor optimization analysis result data to form the gap debugging optimization analysis result data includes: According to the single factor optimization analysis result data and the multi-factor optimization analysis result data, the following processability comparison analysis is performed: If both the single factor optimization and multi-factor optimization meet the requirement that all the differential design parameters reach the parameter values ​​corresponding to the target design parameters, then the total excess equivalent values ​​of the single factors are compared. and the equivalent value of the multi-factor excess , the optimization debugging mode with a smaller value is determined as the gap debugging optimization mode; If the single-factor optimization debugging does not satisfy that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, and the multi-factor optimization debugging satisfies that all the difference design parameters reach the parameter values ​​corresponding to the target design parameters, the multi-factor optimization debugging mode is determined as the gap debugging optimization mode.

Images