A method and system for fitting element errors of millimeter wave array antenna

Abstract

The present invention discloses a method and system for fitting the element error of a millimeter wave array antenna, which relates to the field of wireless communication. The method comprises: establishing an antenna data set; conducting an environmental impact assessment and constructing key environmental factors; establishing an extreme environmental interval of key environmental factors based on the frequency of the use environment, performing level segmentation, and establishing a stepped environmental level of key environmental factors; constructing a test environment and preparing array samples; performing a test, performing mapped element monitoring, and constructing a monitoring data set; inputting into a fitting model, constructing an error loss network, and performing model optimization of the fitting model; and completing the element error fitting of the millimeter wave array antenna through the fitting model. The method solves the technical problems of the limited correction effect and poor adaptability to environmental changes of the existing millimeter wave array antenna element error fitting, and achieves the technical effect of optimizing the error correction effect and improving the environmental adaptability of the antenna.

Description

Technical Field

[0001] The present application relates to wireless communication related fields, and in particular to an array element error fitting method and system for a millimeter wave array antenna. Background Art

[0002] With the rapid development of wireless communication technology, millimeter wave array antennas, as key components for achieving high-speed data transmission and precise positioning, have been widely used in radar, communication and other fields. However, due to the characteristics of short wavelength and large transmission loss of millimeter waves, millimeter wave array antennas face many challenges in actual use, especially the problem of array element error. Array element error not only affects the performance of the antenna, but also causes the overall performance of the system to decline. In the existing millimeter wave array antenna design, theoretical analysis and simulation experiments are usually used to predict and optimize the performance of the antenna. Although this error correction method can reduce the error to a certain extent, it ignores the influence of various complex factors (such as temperature, humidity, electromagnetic interference, etc.) in the actual use environment on the antenna performance, resulting in limited error correction effect and poor environmental adaptability.

[0003] In the current related technologies, the element error fitting of millimeter wave array antennas has technical problems such as limited correction effect and poor adaptability to environmental changes. Summary of the invention

[0004] The present application provides an array element error fitting method and system for a millimeter wave array antenna, and adopts technical means such as constructing an antenna data set, evaluating environmental impacts, establishing a stepped environmental level, constructing a test environment, executing tests, constructing a monitoring data set, inputting into a fitting model, and optimizing the model, thereby achieving accurate fitting of the array element errors of the millimeter wave array antenna, achieving the technical effect of optimizing the error correction effect and improving the environmental adaptability of the antenna.

[0005] The present application provides an array element error fitting method for a millimeter wave array antenna, comprising:

[0006] An antenna data set of a millimeter wave array antenna is established, wherein the antenna data set includes an array element attribute data set and an array element structure data set; the environmental impact assessment of the millimeter wave array antenna is performed using the antenna data set to construct key environmental factors; the use environment of the millimeter wave array antenna is obtained based on the antenna data set, an extreme environmental range of key environmental factors is established based on the frequency of the use environment, and the level segmentation of the extreme environmental range is performed to establish a stepped environmental level of the key environmental factors; a test environment is constructed through the stepped environmental level of the key environmental factors, and an array sample is prepared, and a vector network analyzer and a millimeter wave frequency converter mapped with the array elements in the millimeter wave array antenna are configured; the array sample is placed in the test environment, and a test is performed, and the mapped array element monitoring is performed through the vector network analyzer and the millimeter wave frequency converter to construct a monitoring data set, and the monitoring data set is mapped with the test environment identification; after the monitoring data set is sorted based on the mapping identification, it is input into a fitting model, and an error loss network is constructed to perform model optimization of the fitting model, and the error loss network is used to supervise the fitting model to complete the model optimization; when the fitting model converges, the array element error fitting of the millimeter wave array antenna is completed through the fitting model.

[0007] In a possible implementation, the environmental impact assessment of the millimeter wave array antenna is performed using the antenna data set, key environmental factors are constructed, and the following processing is performed:

[0008] Call the use environment of the millimeter wave array antenna and configure the environmental factor variables; perform control variable data collection under the use environment based on the environmental factor variables and construct a pre-data set; perform unit change impact analysis under the environmental factor variables through the pre-data set to generate a first key evaluation coefficient; perform extreme change impact analysis under the environmental factor variables through the pre-data set to generate a second key evaluation coefficient; complete the environmental impact evaluation based on the first key evaluation coefficient and the second key evaluation coefficient, and construct the key environmental factors.

[0009] In a possible implementation, the monitoring data set is sorted based on the mapping identifier and the following processing is performed:

[0010] The monitoring data set is sequentially sorted according to the mapping identifier to establish a sequential sorting result; a sensitivity factor for abnormality identification is established, and a first local identification window and a second local identification window are established through the sensitivity factor and the data interval of the sequential sorting result, wherein the length of the first local identification window is smaller than the second local identification window; the sequential sorting result is sequentially traversed through the first local identification window and the second local identification window to perform abnormality identification; and the monitoring data set is sorted according to the abnormality identification result.

[0011] In a possible implementation, the sequential traversal of the sequential sorting results through the first local recognition window and the second local recognition window is performed to perform abnormality recognition and perform the following processing:

[0012] The first local recognition window and the second local recognition window are sampled at the edge interval points respectively, the edge interval point sampling results are selected and analyzed, and the selected values ​​are used as window limit values; the corresponding first local recognition window and the second local recognition window are fitted and identified with the window limit values ​​to establish an abnormal identification; and the abnormal identification is completed according to the frequency of the monomer data being abnormally identified.

[0013] In a possible implementation, the error loss network is constructed, and model optimization of the fitting model is performed, and the following processing is performed:

[0014] The error loss network is established through the formula as follows: ;in, Characterize the error loss network, For the measurements, is the total amount of measurement data in the organized monitoring data set, Characterization Measurement data, For the fitted model The fitted values, For the The estimated variance of the measurement data; the model level of the fitting model is configured according to the step environment level. When the sorted monitoring data set is input into the fitting model, the corresponding model level is called through the mapping identifier for data processing, and the parameters of the fitting model are optimized through the error loss network.

[0015] In a possible implementation, the following processing is performed:

[0016] A pre-compensation network is established based on the converged fitting model; when the millimeter wave array antenna sends and / or receives signals, environmental data detection is performed, and the pre-compensation network is called to perform signal processing based on the environmental data detection result to complete error compensation.

[0017] In a possible implementation, the following processing is performed:

[0018] Obtain the duration of the millimeter wave array antenna in the environmental data detection result; configure the continuously inserted attenuation compensation according to the duration; after optimizing the pre-compensation network through the attenuation compensation, perform signal processing.

[0019] The present application also provides an array element error fitting system for a millimeter wave array antenna, comprising:

[0020] An antenna data set establishment module, the antenna data set establishment module is used to establish an antenna data set of a millimeter wave array antenna, the antenna data set includes an array element attribute data set and an array element structure data set; a key environmental factor construction module, the key environmental factor construction module is used to perform an environmental impact assessment of the millimeter wave array antenna with the antenna data set, and construct key environmental factors; a step environmental level establishment module for key environmental factors, the step environmental level establishment module for key environmental factors is used to obtain the use environment of the millimeter wave array antenna based on the antenna data set, establish an extreme environmental interval of the key environmental factor based on the frequency of the use environment, and perform level segmentation of the extreme environmental interval to establish a step environmental level of the key environmental factor; a test environment construction module, the test environment construction module is used to construct a test environment through the step environmental level of the key environmental factor , and prepare array samples, configure a vector network analyzer and a millimeter wave frequency converter mapped with the array elements in the millimeter wave array antenna; a monitoring data set construction module, the monitoring data set construction module is used to place the array sample into the test environment, and perform the test, and monitor the array elements mapped by the vector network analyzer and the millimeter wave frequency converter, and construct a monitoring data set, and the monitoring data set is mapped with the test environment identification; a fitting model optimization module, the fitting model optimization module is used to sort the monitoring data set based on the mapping identification, input it into the fitting model, and construct an error loss network, and perform model optimization of the fitting model, and the error loss network is used to supervise the fitting model to complete the model optimization; an array element error fitting module, the array element error fitting module is used to complete the array element error fitting of the millimeter wave array antenna through the fitting model after the fitting model converges.

[0021] The present application proposes an array element error fitting method and system for a millimeter wave array antenna. First, an antenna data set of the millimeter wave array antenna is established. The antenna data set includes an array element attribute data set and an array element structure data set. Then, the antenna data set is used to perform an environmental impact assessment of the millimeter wave array antenna, and key environmental factors are constructed. Then, the use environment of the millimeter wave array antenna is obtained based on the antenna data set. The extreme environmental range of the key environmental factors is established based on the frequency of the use environment, and the level segmentation of the extreme environmental range is performed to establish a stepped environmental level of the key environmental factors. Then, a test environment is constructed through the stepped environmental level of the key environmental factors, and an array sample is prepared, and the configuration with the millimeter wave array A vector network analyzer and a millimeter wave frequency converter are used to map the array elements in the antenna, and then the array sample is placed in the test environment and tested. The mapped array elements are monitored through the vector network analyzer and the millimeter wave frequency converter, and a monitoring data set is constructed. The monitoring data set is mapped with the test environment identification, and the monitoring data set is sorted based on the mapping identification, input into the fitting model, and an error loss network is constructed to perform model optimization of the fitting model. The error loss network is used to supervise the fitting model to complete the model optimization. Finally, when the fitting model converges, the array element error fitting of the millimeter wave array antenna is completed through the fitting model, thereby achieving the technical effect of optimizing the error correction effect and improving the environmental adaptability of the antenna. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic flow chart of an array element error fitting method for a millimeter wave array antenna provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of an array element error fitting system for a millimeter wave array antenna provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: antenna data set establishing module 10, key environmental factor constructing module 20, step environment level establishing module 30 of key environmental factors, test environment constructing module 40, monitoring data set constructing module 50, fitting model optimization module 60, array element error fitting module 70. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides a method for fitting element errors of a millimeter wave array antenna, such as Figure 1 As shown, the method includes:

[0030] Step S100, establish an antenna data set of a millimeter wave array antenna, the antenna data set including an array element attribute data set and an array element structure data set. Specifically, in a millimeter wave array antenna, the array element refers to a basic unit constituting an array, which is responsible for receiving or transmitting a millimeter wave signal, the array element attribute refers to the physical and electrical characteristics of the array element itself, such as size, shape, material, operating frequency, polarization mode, etc., and the array element structure refers to the layout and arrangement of the array element in the array, including the position of the array element, the spacing between each other, the connection relationship, etc. Specifically, through experimental measurement, equipment parameter query or related literature and other means, obtain detailed attribute data of each array element, organize these attribute data according to a unified standard and format, and form an array element attribute data set; according to the design drawing or actual layout of the millimeter wave array antenna, determine the position, arrangement, spacing between each other and other information of the array element in the array, use CAD (computer-aided design) software or other drawing tools to draw a structural diagram of the array antenna, and mark the specific position of each array element, convert the structural diagram into a digital data format, including coordinate information, connection relationship, etc., to form an array element structure data set. The array element attribute dataset and the array element structure dataset together constitute the antenna dataset.

[0031] Step S200, using the antenna data set to perform an environmental impact assessment of the millimeter wave array antenna, and construct key environmental factors. Specifically, collect environmental data related to the millimeter wave array antenna, such as the expected deployment location, climate data (temperature, humidity, wind speed, etc.), electromagnetic environment data, etc., analyze the factors that may affect the performance of the millimeter wave array antenna based on the collected environmental data, such as the impact of temperature on materials, the impact of humidity on electrical characteristics, the impact of electromagnetic environment on signal transmission, etc., preliminarily evaluate the possible impact of each environmental factor on antenna performance, and use statistics or machine learning methods to determine which factors are key environmental factors that need to be focused on. The key environmental factors refer to environmental factors that have a significant impact on the performance of the millimeter wave array antenna, and changes in these environmental factors will cause the antenna performance to degrade or become unstable. Through quantitative analysis, determine the specific numerical range or conditions of the key environmental factors, such as determining the temperature range and humidity range that affect the antenna performance, organize the determined key environmental factors into a list, and provide a detailed description and quantitative indicators for each factor.

[0032] In a possible implementation, the environmental impact assessment of the millimeter wave array antenna is performed with the antenna data set to construct key environmental factors. Step S200 further includes step S210, calling the use environment of the millimeter wave array antenna and configuring environmental factor variables. Specifically, the actual environment in which the millimeter wave array antenna is deployed and operated is determined, such as outdoor, indoor, high temperature, low temperature, high humidity, low humidity, etc., and a series of environmental factor variables that may affect the antenna performance are defined according to the characteristics of the use environment, such as temperature, humidity, air pressure, wind speed, wind direction, electromagnetic interference, etc., and a reasonable range of variation is set for each environmental factor variable based on the possible conditions of the actual use environment. Step S220, based on the environmental factor variables, control variable data collection is performed under the use environment to construct a pre-data set. Specifically, the conditions of the actual use environment are simulated in a laboratory or test environment, including setting specific values ​​of environmental factor variables, and under each experimental condition, the performance of the millimeter wave array antenna is tested and data collected in detail, such as gain, directivity, bandwidth, etc., and the collected data is sorted according to the experimental conditions and test items to construct a pre-data set. Step S230, perform unit change impact analysis under environmental factor variables through the pre-data set to generate a first key evaluation coefficient. Specifically, select any environmental factor variable from the pre-data set for analysis, analyze the change of antenna performance parameters when the environmental factor variable changes in unit (such as the temperature increases by 1°C), and generate a coefficient for quantitatively evaluating the impact of the environmental factor variable on antenna performance based on the analysis results, that is, the first key evaluation coefficient. Step S240, perform extreme change impact analysis under environmental factor variables through the pre-data set to generate a second key evaluation coefficient. Specifically, also select any environmental factor variable from the pre-data set for analysis, analyze the change of antenna performance parameters when the environmental factor variable changes in extremes (such as the temperature reaches the highest or lowest value), and generate a coefficient for quantitatively evaluating the impact of the environmental factor variable on antenna performance when the environmental factor variable changes in extremes, that is, the second key evaluation coefficient. Step S250, complete the environmental impact evaluation based on the first key evaluation coefficient and the second key evaluation coefficient, and construct the key environmental factors. Specifically, the first key evaluation coefficient and the second key evaluation coefficient are comprehensively analyzed to evaluate the comprehensive impact of each environmental factor variable on antenna performance. According to the evaluation results, the environmental factor variables are sorted and screened to determine which factors are key environmental factors. Based on the sorting and screening results, a list or model of key environmental factors is constructed. This implementation method quantitatively evaluates the degree of influence of each environmental factor on antenna performance through unit change impact analysis and extreme change impact analysis, comprehensively and systematically evaluates the performance of millimeter wave array antennas under different environmental factors, and achieves the technical effect of improving the accuracy of key environmental factor construction.

[0033] Step S300, based on the antenna data set, obtain the use environment of the millimeter wave array antenna, establish the extreme environment interval of the key environmental factors based on the frequency of the use environment, and perform level segmentation of the extreme environment interval to establish the ladder environment level of the key environmental factors. Specifically, through market research, customer demand analysis or field survey, etc., determine the actual use environment of the millimeter wave array antenna, including geographical location, climatic conditions, electromagnetic environment, etc. For each key environmental factor, analyze its change frequency and range at different times, locations or use conditions, and determine the limit value of each key environmental factor based on historical data, industry standards or expert advice, that is, the maximum or minimum value that the key environmental factor may reach, and use the limit value of each key environmental factor as the endpoint of the interval to form the extreme environment interval of the key environmental factor.

[0034] According to the actual situation of the use environment, test requirements or engineering experience, determine the basis for dividing the extreme environment interval, such as equal interval division, equal proportion division, etc. According to the determined division basis, divide the extreme environment interval into several sub-intervals, each sub-interval represents an environmental level (a specific range of environmental conditions). Name each environmental level and assign it a unique identifier for data management and analysis, organize the established environmental level list, and form a ladder environmental level system for millimeter wave array antennas.

[0035] Step S400, construct a test environment through the step environmental level of the key environmental factors, prepare array samples, and configure a vector network analyzer and a millimeter wave frequency converter mapped with the array elements in the millimeter wave array antenna. Specifically, according to the size and test requirements of the millimeter wave array antenna, select a suitable test site to ensure that the electromagnetic environment in the site is clean and interference-free. According to the step environmental level of the key environmental factors, use corresponding environmental control equipment (such as a temperature control box, a humidity controller, etc.) to set the environmental parameters of the test site so that it meets the requirements of a certain step environmental level. During the test, use sensors and monitoring equipment to continuously monitor the changes in environmental parameters and adjust them as needed to ensure the stability and consistency of the test environment. According to the array element properties in the antenna data set, select materials that meet the requirements to prepare array samples. According to the array element structure data set and the antenna design drawings, use appropriate processing equipment and technology to make array samples, so that the size, shape and connection relationship of the array samples are consistent with the millimeter wave array antenna design. Perform quality inspection on the prepared array samples, including size measurement, electrical performance testing, etc., to ensure that the quality of the array samples meets the requirements, wherein the array samples have the same structure and performance characteristics as the real millimeter wave array antenna. According to the test requirements, select an appropriate vector network analyzer to measure the electrical performance of the array elements in the array sample. According to the operating frequency and test requirements of the array sample, configure the corresponding millimeter wave frequency converter to convert the test signal into millimeter wave frequency so that the array sample can be tested within the millimeter wave frequency band.

[0036] Step S500, placing the array sample in the test environment, and performing the test, mapping the array element monitoring through the vector network analyzer and the millimeter wave frequency converter, and constructing a monitoring data set, and the monitoring data set is mapped with the test environment identification. Specifically, the prepared array sample is placed in the test environment, and the relevant parameters of the vector network analyzer and the millimeter wave frequency converter, such as frequency range, power level, scanning mode, etc., are set according to the test requirements and the working characteristics of the array sample, so that the setting of the test parameters can fully cover the working frequency band and performance requirements of the array sample. Start the vector network analyzer and the millimeter wave frequency converter, test the array sample, and gradually change the parameters of the test environment (such as temperature, humidity, etc.) according to the test plan to simulate the working conditions under different step environment levels. Under each step environment level, each array element in the array sample is monitored by the vector network analyzer and the millimeter wave frequency converter, and the electrical performance parameters of each array element, such as transmission coefficient, reflection coefficient, phase difference, etc., are monitored and recorded in real time. The monitored data is organized into a data set, including the electrical performance parameters of each array element, parameter settings of the test environment, test time and other information. Identification information mapped to the test environment is added to each monitoring data set to accurately trace and identify the environmental conditions corresponding to each monitoring data set. The identification information may include the stepped environmental level of the test environment, specific environmental parameter settings, etc.

[0037] Step S600, after the monitoring data set is sorted based on the mapping identifier, it is input into the fitting model, and an error loss network is constructed to perform model optimization of the fitting model, and the error loss network is used to supervise the fitting model to complete the model optimization. Specifically, according to the mapping identifier, the monitoring data set is sorted and classified, such as removing outliers, so that each monitoring data set matches its corresponding test environment ladder environment level and specific environmental parameter settings. According to the characteristics and test requirements of the millimeter wave array antenna, a fitting model is constructed based on statistical methods, machine learning or deep learning algorithms, and the fitting model is used to predict the performance of the millimeter wave array antenna based on input data (such as environmental parameters, array element monitoring data, etc.). An error loss network is constructed, and the error loss network takes the prediction results of the fitting model and the actual monitoring data as input, calculates the loss value through the loss function, and passes it to the fitting model as a feedback signal. Use an optimization algorithm (such as gradient descent, Adam, etc.) to minimize the loss value in the error loss network. In each iteration, the parameters of the fitting model are updated according to the gradient of the loss value to improve its prediction performance, and the optimization process is repeated until the preset stop condition is met (such as reaching the maximum number of iterations, the loss value is less than the threshold, etc.).

[0038] In a possible implementation, the monitoring data set is sorted based on the mapping identifier, and step S600 further includes step S610, sorting the monitoring data set in sequence according to the mapping identifier to establish a sequential sorting result. Specifically, the mapping identifier of each data point is extracted from the monitoring data set, and the monitoring data set is sorted according to the mapping identifier using a sorting algorithm (such as quick sort, merge sort, etc.), and the sorted monitoring data set is used as the sequential sorting result. Step S620, establish a sensitivity factor for abnormal identification, and establish a first local identification window and a second local identification window by using the sensitivity factor and the data interval of the sequential sorting result, wherein the length of the first local identification window is less than the second local identification window. Specifically, one or more sensitivity factors are set according to application requirements and data set characteristics, and the sensitivity factor is a parameter used to adjust the sensitivity of the abnormal identification algorithm, which affects the selection of the window size and the threshold for abnormal discrimination. The interval between adjacent data points is calculated in the sequential sorting result, and the size of the first local identification window (smaller) and the second local identification window (larger) is set based on the sensitivity factor and the data interval, wherein the local identification window is a data subset used to perform abnormal identification within a specific data range. Step S630, perform a sequential traversal of the sequential sorting results through the first local identification window and the second local identification window, and perform anomaly identification. Specifically, place the first local identification window and the second local identification window at the starting position of the sequential sorting results, traverse the sequential sorting results in sequence, move the window each time and check the data points in the window, and apply anomaly identification algorithms (such as statistical-based methods, machine learning models, etc.) in the window to detect anomalies. Step S640, complete the arrangement of the monitoring data set according to the anomaly identification results. Specifically, mark the identified anomalies in the monitoring data set, and selectively delete, replace or retain the anomalies according to business needs to complete the arrangement of the monitoring data set. This implementation method improves the quality and availability of data by performing anomaly identification, and achieves the technical effect of providing accurate data input for the fitting model.

[0039] In a possible implementation, the first local recognition window and the second local recognition window are sequentially traversed to perform abnormal identification. Step S630 further includes step S631, sampling edge interval points of the first local recognition window and the second local recognition window respectively, selecting and analyzing the edge interval point sampling results, and using the selected value as the window limit value. Specifically, a number of points are selected at the edge of each local recognition window (the first local recognition window and the second local recognition window) for sampling. These points are located near the start and end positions of the window. The edge interval points obtained by sampling are statistically analyzed or processed by a specific algorithm (such as the median, mean, maximum value, minimum value, etc.) to determine the window limit value. These limit values ​​represent the boundary range of the data in the window. Step S632, the corresponding first local recognition window and the second local recognition window are used to perform window data fitting identification through the window limit value to establish an abnormal identification. Specifically, in each local recognition window, an appropriate data fitting method (such as linear fitting, polynomial fitting, etc.) is used to fit the data points to obtain the overall trend or model of the data, and each data point in the window is compared with the fitted data model. If a data point deviates significantly from the model (such as exceeding the window limit value), it is marked as an abnormal point and a corresponding abnormal identification is established. Step S633, complete the abnormal identification according to the frequency of the monomer data being marked as abnormal. Specifically, the number or frequency of each data point being marked as abnormal in all local recognition windows is counted, and according to the set threshold or condition, it is determined whether a data point is an abnormal value. For example, if a data point is marked as abnormal in multiple windows, it is likely to be a real abnormal value. This implementation method comprehensively considers the performance of the data at different scales by counting the frequency of each data point being marked as abnormal, and achieves the technical effect of accurately identifying abnormal values.

[0040] In a possible implementation, the error loss network is constructed and the model optimization of the fitting model is performed. Step S600 further includes step S650, in which the error loss network is established by a formula as follows:

[0041] ;

[0042] in, Characterize the error loss network, For the measurements, is the total amount of measurement data in the organized monitoring data set, Characterization Measurement data, For the fitted model The fitted values, For the The estimated variance of the measured data. Specifically, the formula of the error loss network is used to quantify the difference between the model prediction value and the true measurement value, and is used to measure the accuracy of the model prediction. In the model training process, the model parameters are optimized by minimizing the value of the error loss network. Step S660, the model level of the fitting model is configured according to the step environment level. When the sorted monitoring data set is input into the fitting model, the corresponding model level is called by the mapping identifier for data processing, and the parameters of the fitting model are optimized by the error loss network. Specifically, the corresponding model level is configured for each step environment level, that is, models of different complexity are used to accurately fit the data of different step environment levels. When the sorted monitoring data set is input into the fitting model, the corresponding model level is called according to the mapping identifier for data processing, the error loss network is used to calculate the error loss between the model prediction and the true measurement value, and the parameters of the model are updated by optimization algorithms such as back propagation, thereby optimizing the prediction performance of the model. This implementation method configures different model levels according to the step environment level, so that the model can adapt to different data characteristics and processing requirements more flexibly, and achieves the technical effect of improving the adaptability and generalization ability of the model.

[0043] Step S700, when the fitting model converges, the element error fitting of the millimeter wave array antenna is completed by the fitting model. Specifically, after executing the model optimization, by observing the change trend of the loss function, the performance on the validation set, etc., it is judged whether the fitting model has converged. The signs of convergence include that the loss value tends to be stable, no longer significantly reduced, and the performance on the validation set reaches the preset requirements. The monitoring data sets of the array antenna elements collected in the actual test environment are sorted out. These data sets contain the performance parameters of the elements under different environmental conditions. The prepared input data are input into the converged fitting model. The fitting model predicts the error value of each element under different environmental conditions based on the input data and the parameters and rules learned during its training process. The fitting results can be presented in a table, chart or other visual form. Compare the error value predicted by the fitting model with the error value obtained by the actual test to evaluate the accuracy and reliability of the fitting result. If the fitting result is inaccurate or abnormal, return to step S600 to re-optimize the model or adjust the preprocessing method of the input data. Once the fitting results are verified and confirmed to be accurate, the fitting results can be applied to the error correction of the millimeter wave array antenna. According to the fitting results, the array elements of the array antenna are adjusted or compensated accordingly to reduce the error and improve the performance stability and accuracy of the array antenna. The embodiment of the present application adopts technical means such as constructing antenna data sets, evaluating environmental impacts, establishing step environmental levels, constructing test environments, performing tests, constructing monitoring data sets, inputting into fitting models, and optimizing models to achieve accurate fitting of millimeter wave array antenna element errors, achieve the technical effect of optimizing error correction effects and improving the environmental adaptability of antennas.

[0044] In one possible implementation, step S700 further includes step S710, establishing a pre-compensation network based on the converged fitting model. Specifically, based on the fitting model, a set of pre-compensation algorithms is designed. The goal of the pre-compensation algorithm is to pre-process or post-process the signal before the signal is sent or after it is received to offset or reduce the signal distortion introduced by the array element error. The pre-compensation algorithm is implemented as a pre-compensation network in the form of hardware or software. The pre-compensation network can be a digital signal processing module or an analog circuit. Step S720, when the millimeter wave array antenna sends and / or receives signals, performs environmental data detection, and calls the pre-compensation network to perform signal processing based on the environmental data detection results to complete error compensation. Specifically, when the millimeter wave array antenna starts to send or receive signals, it performs environmental data detection, including detecting the temperature, humidity, electromagnetic interference and other factors that may affect signal transmission in the surrounding environment. Based on the environmental data detection results, the pre-compensation network is called to pre-process or post-process the signal according to the environmental data and the array element error information provided by the fitting model. After the signal is processed by the pre-compensation network, the distortion introduced by the array element error will be greatly reduced, and the signal can continue to be sent or received. This implementation method can perform pre-processing or post-processing before or after the signal is sent or received by establishing a pre-compensation network, thereby effectively reducing the signal distortion introduced by the array element error and achieving the technical effect of significantly improving the system performance.

[0045] In a possible implementation, step S720 further includes step S721, obtaining the duration of the millimeter wave array antenna in the environmental data detection result. Specifically, perform environmental data detection, collect data on environmental factors such as temperature, humidity, electromagnetic interference, etc. that may affect signal transmission, record the results of environmental data detection, and pay attention to the duration of changes in these results. For example, if the temperature changes significantly in a short period of time, then the duration of such changes is an important consideration. Step S722, configure the continuously inserted attenuation compensation according to the duration. Specifically, analyze the duration of the environmental data detection result, judge the speed and trend of environmental changes, and configure the continuously inserted attenuation compensation based on the results of the analysis. In the communication system, due to the influence of environmental factors (such as hardware performance changes caused by temperature and humidity changes), the strength or quality of the signal will gradually decay. Attenuation compensation is a technology or algorithm for offsetting or reducing such attenuation. This compensation can be a time-varying attenuation function or a set of predefined compensation values ​​for different durations. According to the configured attenuation compensation, adjust the parameters or algorithms of the pre-compensation network to adapt to the current environmental conditions. Step S723, after the pre-compensation network is optimized by the attenuation compensation, signal processing is performed. Specifically, after the attenuation compensation is configured, the pre-compensation network is optimized to ensure that it can effectively offset the signal distortion introduced by array element errors and environmental factors, and the optimized pre-compensation network is used to process the signal, including pre-processing before sending or post-processing after receiving. This implementation method detects the duration of environmental factors and configures the continuously inserted attenuation compensation accordingly, so as to more accurately predict and compensate for the signal distortion introduced by environmental changes, thereby achieving the technical effect of optimizing the overall performance of the system.

[0046] In the above, refer to Figure 1 A method for fitting element errors of a millimeter wave array antenna according to an embodiment of the present invention is described in detail. Figure 2 An array element error fitting system of a millimeter wave array antenna according to an embodiment of the present invention is described.

[0047] According to an embodiment of the present invention, an array element error fitting system for a millimeter wave array antenna is used to solve the technical problems of limited correction effect and poor adaptability to environmental changes in the array element error fitting of existing millimeter wave array antennas, so as to achieve the technical effect of optimizing the error correction effect and improving the environmental adaptability of the antenna. An array element error fitting system for a millimeter wave array antenna includes: an antenna data set establishment module 10, a key environmental factor construction module 20, a step environment level establishment module 30 for key environmental factors, a test environment construction module 40, a monitoring data set construction module 50, a fitting model optimization module 60, and an array element error fitting module 70.

[0048] The antenna data set establishment module 10 is used to establish an antenna data set of a millimeter wave array antenna, and the antenna data set includes an array element attribute data set and an array element structure data set; the key environmental factor construction module 20 is used to perform an environmental impact assessment of the millimeter wave array antenna with the antenna data set, and to construct key environmental factors; the step environmental level establishment module 30 of the key environmental factors is used to obtain the use environment of the millimeter wave array antenna based on the antenna data set, establish the extreme environmental interval of the key environmental factors based on the frequency of the use environment, and perform level segmentation of the extreme environmental interval to establish the step environmental level of the key environmental factors; the test environment construction module 40 is used to construct a test environment through the step environmental level of the key environmental factors, and to prepare array samples, and to match A vector network analyzer and a millimeter wave frequency converter are placed in the millimeter wave array antenna for array element mapping; a monitoring data set construction module 50 is used to place the array sample into the test environment and perform the test, and monitor the array elements mapped by the vector network analyzer and the millimeter wave frequency converter to construct a monitoring data set, and the monitoring data set is mapped with the test environment identification; a fitting model optimization module 60 is used to sort the monitoring data set based on the mapping identification, input it into the fitting model, and construct an error loss network to perform model optimization of the fitting model, and the error loss network is used to supervise the fitting model to complete the model optimization; an array element error fitting module 70 is used to complete the array element error fitting of the millimeter wave array antenna through the fitting model after the fitting model converges.

[0049] The specific configuration of the key environmental factor construction module 20 will be described in detail below. As described above, the antenna data set is used to perform environmental impact assessment of the millimeter wave array antenna and construct key environmental factors. The key environmental factor construction module 20 may further include: an environmental factor variable configuration unit for calling the use environment of the millimeter wave array antenna and configuring environmental factor variables; a pre-data set construction unit for performing control variable data collection under the use environment based on the environmental factor variables to construct a pre-data set; a first key evaluation coefficient generation unit for performing unit change impact analysis under environmental factor variables through the pre-data set to generate a first key evaluation coefficient; a second key evaluation coefficient generation unit for performing extreme change impact analysis under environmental factor variables through the pre-data set to generate a second key evaluation coefficient; an environmental impact assessment unit for completing environmental impact assessment based on the first key evaluation coefficient and the second key evaluation coefficient to construct key environmental factors.

[0050] The specific configuration of the fitting model optimization module 60 will be described in detail below. As described above, based on the mapping identifier, the monitoring data set is sorted, and the fitting model optimization module 60 may further include: a sequential sorting unit is used to sort the monitoring data set according to the mapping identifier and establish a sequential sorting result; a local recognition window establishment unit is used to establish a sensitivity factor for abnormal recognition, and establish a first local recognition window and a second local recognition window through the data interval of the sensitivity factor and the sequential sorting result, wherein the length of the first local recognition window is less than the second local recognition window; an abnormal recognition unit is used to sequentially traverse the sequential sorting result through the first local recognition window and the second local recognition window to perform abnormal recognition; a monitoring data set sorting unit is used to complete the monitoring data set sorting according to the abnormal recognition result.

[0051] Among them, the sequential traversal of the sequential sorting results through the first local recognition window and the second local recognition window is performed to perform exception recognition, and the exception recognition unit may further include: a window limit value generating subunit is used to sample the edge interval points of the first local recognition window and the second local recognition window respectively, select and analyze the edge interval point sampling results, and use the selected value as the window limit value; an exception identification establishing subunit is used to perform window data fitting and identification on the corresponding first local recognition window and the second local recognition window through the window limit value, and establish an exception identification; a frequency analysis subunit is used to complete exception recognition according to the frequency of monomer data being abnormally identified.

[0052] Wherein, the error loss network is constructed and the model optimization of the fitting model is performed. The fitting model optimization module 60 may further include: an error loss network establishment unit is used to establish the error loss network through a formula as follows: ;in, Characterize the error loss network, For the measurements, is the total amount of measurement data in the organized monitoring data set, Characterization Measurement data, For the fitted model The fitted values, For the The estimated variance of the measurement data; the parameter optimization unit is used to configure the model level of the fitting model according to the step environment level. When the sorted monitoring data set is input into the fitting model, the corresponding model level is called through the mapping identifier for data processing, and the parameters of the fitting model are optimized through the error loss network.

[0053] The specific configuration of the array element error fitting module 70 will be described in detail below. As described above, the array element error fitting module 70 may further include: a pre-compensation network establishment unit for establishing a pre-compensation network based on a converged fitting model; a signal processing unit for performing environmental data detection when the millimeter wave array antenna performs signal transmission and / or reception, and calling the pre-compensation network for signal processing based on the environmental data detection result to complete error compensation.

[0054] Among them, the signal processing unit may further include: a duration acquisition subunit for acquiring the duration of the millimeter wave array antenna in the environmental data detection result; an attenuation compensation configuration subunit for configuring continuously inserted attenuation compensation according to the duration; and a pre-compensation network optimization subunit for performing signal processing after optimizing the pre-compensation network through the attenuation compensation.

[0055] An array element error fitting system for a millimeter wave array antenna provided by an embodiment of the present invention can execute an array element error fitting method for a millimeter wave array antenna provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0056] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0057] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for fitting element errors of a millimeter wave array antenna, characterized in that: The method comprises: Establishing an antenna data set of a millimeter wave array antenna, wherein the antenna data set includes an array element attribute data set and an array element structure data set; Conduct environmental impact assessment of millimeter wave array antennas using the antenna data set to construct key environmental factors; Acquire the use environment of the millimeter wave array antenna based on the antenna data set, establish the extreme environment interval of the key environmental factor based on the frequency of the use environment, perform level segmentation of the extreme environment interval, and establish a stepped environment level of the key environmental factor; Constructing a test environment through the stepped environmental levels of the key environmental factors, preparing array samples, and configuring a vector network analyzer and a millimeter wave frequency converter mapped to the array elements in the millimeter wave array antenna; Placing the array sample into the test environment and performing the test, performing the mapped array element monitoring through the vector network analyzer and the millimeter wave frequency converter, and constructing a monitoring data set, wherein the monitoring data set is mapped to the test environment identification; After arranging the monitoring data set based on the mapping identifier, the data set is input into the fitting model, and an error loss network is constructed to perform model optimization of the fitting model, wherein the error loss network is used to supervise the fitting model to complete the model optimization; When the fitting model converges, the element error fitting of the millimeter wave array antenna is completed through the fitting model.

2. The method for fitting element errors of a millimeter wave array antenna according to claim 1, characterized in that: The environmental impact assessment of the millimeter wave array antenna using the antenna data set to construct key environmental factors also includes: Calling the use environment of the millimeter wave array antenna and configuring environmental factor variables; Based on the environmental factor variables, control variable data collection is performed under the use environment to construct a pre-data set; Performing unit change impact analysis under environmental factor variables through the pre-data set to generate a first key evaluation coefficient; Performing an extreme change impact analysis under environmental factor variables through the pre-data set to generate a second key evaluation coefficient; Based on the first key evaluation coefficient and the second key evaluation coefficient, an environmental impact assessment is completed and key environmental factors are constructed.

3. The method for fitting element errors of a millimeter wave array antenna according to claim 1, characterized in that: The collating of the monitoring data set based on the environmental mapping identifier further includes: Sorting the monitoring data set in sequence according to the mapping identifier and establishing a sequence sorting result; Establishing a sensitivity factor for abnormal identification, and establishing a first local identification window and a second local identification window through the sensitivity factor and the data interval of the sequential sorting result, wherein the length of the first local identification window is smaller than the length of the second local identification window; Performing a sequential traversal of the sequential sorting results through the first local recognition window and the second local recognition window to perform anomaly recognition; Complete the monitoring data set organization based on the anomaly identification results.

4. The method for fitting element errors of a millimeter wave array antenna according to claim 3, characterized in that: The sequential traversal of the sequential sorting results through the first local recognition window and the second local recognition window to perform abnormality recognition also includes: Performing edge interval point sampling on the first local recognition window and the second local recognition window respectively, performing selection analysis on the edge interval point sampling results, and using the selected value as the window limit value; The first local identification window and the second local identification window corresponding to the window limit value are used to perform fitting identification of data within the window to establish an abnormal identification; Anomaly identification is completed based on the frequency with which individual data is marked as abnormal.

5. The method for fitting element errors of a millimeter wave array antenna according to claim 4, characterized in that: The constructing the error loss network and performing model optimization of the fitting model also includes: The error loss network is established through the formula as follows: ； in, Characterize the error loss network, For the measurements, is the total amount of measurement data in the organized monitoring data set, Characterization Measurement data, For the fitted model The fitted values, For the The estimated variance of the measurements; The model level of the fitting model is configured according to the step environment level. When the sorted monitoring data set is input into the fitting model, the corresponding model level is called through the mapping identifier to perform data processing, and the parameters of the fitting model are optimized through the error loss network.

6. The method for fitting element errors of a millimeter wave array antenna according to claim 1, characterized in that: The method further comprises: Establish a pre-compensation network based on the converged fitting model; When the millimeter wave array antenna sends and / or receives signals, environmental data detection is performed, and a pre-compensation network is called to perform signal processing based on the environmental data detection result to complete error compensation.

7. The method for fitting element errors of a millimeter wave array antenna according to claim 6, characterized in that: The method further comprises: Obtaining the duration of the millimeter wave array antenna in the environmental data detection result; Configuring a continuously inserted attenuation compensation according to the duration; After the pre-compensation network is optimized through the attenuation compensation, signal processing is performed.

8. An element error fitting system for a millimeter wave array antenna, characterized in that: The system is used to implement the array element error fitting method of a millimeter wave array antenna according to any one of claims 1 to 7, and the system comprises: An antenna data set establishment module, the antenna data set establishment module is used to establish an antenna data set of a millimeter wave array antenna, the antenna data set includes an array element attribute data set and an array element structure data set; A key environmental factor construction module, wherein the key environmental factor construction module is used to perform an environmental impact assessment of a millimeter wave array antenna using the antenna data set to construct key environmental factors; A ladder environment level establishment module for key environmental factors, the ladder environment level establishment module for key environmental factors is used to obtain the use environment of the millimeter wave array antenna based on the antenna data set, establish the extreme environment interval of the key environmental factors based on the frequency of the use environment, and perform level segmentation of the extreme environment interval to establish the ladder environment level of the key environmental factors; A test environment construction module, the test environment construction module is used to construct a test environment through the stepped environmental levels of the key environmental factors, prepare array samples, and configure a vector network analyzer and a millimeter wave frequency converter mapped with array elements in the millimeter wave array antenna; A monitoring data set construction module, the monitoring data set construction module is used to place the array sample into the test environment and perform the test, and to monitor the array elements mapped by the vector network analyzer and the millimeter wave frequency converter to construct a monitoring data set, and the monitoring data set is mapped to the test environment identification; A fitting model optimization module, wherein the fitting model optimization module is used to sort the monitoring data set based on the mapping identifier, input the data set into the fitting model, and construct an error loss network to perform model optimization of the fitting model, wherein the error loss network is used to supervise the fitting model to complete model optimization; The array element error fitting module is used to complete the array element error fitting of the millimeter wave array antenna through the fitting model after the fitting model converges.

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