An electromagnetic monitoring system, method, apparatus, and medium

Abstract

The embodiment of the specification provides a kind of electromagnetic monitoring management system, method, device and medium, comprising: data transmission module, data cache module, processing module, storage module and analysis feedback module;The data transmission module is configured to receive electromagnetic environment test data, the electromagnetic environment test data includes at least a group of test item data, the test item data includes sampling spectrum data, calibration data, antenna and system gain data;The data cache module is configured to cache the electromagnetic environment test data;And based on the control instruction of the analysis feedback module, the electromagnetic environment test data is sent to the processing module and / or the storage module;The processing module is configured to determine to be analyzed data based on the electromagnetic environment test data, and the to-be-analyzed data is sent to the storage module;The storage module is configured to store the electromagnetic environment test data and the to-be-analyzed data.

Description

[0001] Divisional Statement

[0002] This application is a divisional application of the Chinese application with the application number 202410389399.0 and the application date of 2024-04-02, and the invention name of "An electromagnetic monitoring real-time analysis system, method, device and medium". TECHNICAL FIELD

[0003] The present specification relates to the technical field of electromagnetic monitoring, and particularly relates to an electromagnetic monitoring management and control system, method, device and medium. BACKGROUND

[0004] Electromagnetic monitoring is widely used in the fields of communication, radar, aerospace, and radio spectrum management. Traditional electromagnetic monitoring equipment generally performs automatic testing according to manual pre-adjustment or pre-set equipment parameters. Before automatic testing, in order to ensure the reliability of the test data, equipment calibration and reasonable configuration of equipment test parameters need to be done well.

[0005] After obtaining electromagnetic test data, the equipment can perform real-time statistical analysis and preprocessing on the electromagnetic monitoring data based on its own matching system. However, during automatic testing, the problems of equipment calibration error and incorrect equipment test parameter setting are often hidden, and there may be human verification omissions. As a result, the quality of the obtained electromagnetic test data is low, and even cannot be used, and such problems are often discovered after automatic testing is completed.

[0006] Therefore, it is necessary to provide an electromagnetic monitoring management and control system, method, device and medium, which can discover the problems of equipment calibration and incorrect equipment test parameter setting in time during the stage of statistical analysis of data by the equipment itself, so as to ensure the quality of the subsequently collected electromagnetic test data, improve the monitoring efficiency and accuracy, and avoid unnecessary collection costs. SUMMARY

[0007] In order to solve the above problems that the problems of equipment calibration and incorrect equipment test parameter setting cannot be discovered in time during the stage of statistical analysis of data by the equipment itself, so as to ensure the quality of the subsequently collected electromagnetic test data, improve the monitoring efficiency and accuracy, and avoid unnecessary collection costs. The present specification provides an electromagnetic monitoring management and control system, method, device and medium.

[0008] One or more embodiments of the present specification provide a management and control system for electromagnetic monitoring, comprising: a data transmission module, a data caching module, a processing module, a storage module, and an analysis feedback module; the data transmission module is configured to receive electromagnetic environment test data, the electromagnetic environment test data comprising at least one set of test item data, the test item data comprising sampling spectrum data, calibration data, antenna and system gain data; the data caching module is configured to cache the electromagnetic environment test data; and based on the control instruction of the analysis feedback module, the electromagnetic environment test data is sent to the processing module and / or the storage module; the processing module is configured to determine to-be-analyzed data based on the electromagnetic environment test data, and send the to-be-analyzed data to the storage module; the storage module is configured to store the electromagnetic environment test data and the to-be-analyzed data; the analysis feedback module is configured to determine the control instruction to control the data transmission direction of the data caching module; based on the to-be-analyzed data of the storage module, the test parameter error is determined through a test parameter judgment model, wherein the test parameter judgment model is a machine learning model; the training process of the test parameter judgment model includes first stage training and second stage training, the first stage training refers to initial training, and the second stage training refers to training after the first stage training is completed, the first stage training includes: performing first stage training on the test parameter judgment model based on a first training set; the first training set can include a preset proportion of first type data, second type data and third type data; the first type data, the second type data and the third type data refer to three different types of data that meet the preset proportion of the first training set; in response to the existence of the test parameter error, a parameter error warning is issued.

[0009] One or more embodiments of the present specification provide a management and control method for electromagnetic monitoring, comprising: receiving electromagnetic environment test data, the electromagnetic environment test data comprising at least one set of test item data, the test item data comprising sampling spectrum data, calibration data, antenna and system gain data; determining to-be-analyzed data based on the electromagnetic environment test data, and storing the electromagnetic environment test data and the to-be-analyzed data; based on the to-be-analyzed data, determining whether there is a test parameter error through a test parameter judgment model, wherein the test parameter judgment model is a machine learning model; the training process of the test parameter judgment model includes first stage training and second stage training, the first stage training refers to initial training, and the second stage training refers to training after the first stage training is completed, the first stage training includes: performing first stage training on the test parameter judgment model based on a first training set; the first training set can include a preset proportion of first type data, second type data and third type data; the first type data, the second type data and the third type data refer to three different types of data that meet the preset proportion of the first training set;

[0010] In response to the existence of the test parameter error, a parameter error warning is issued. BRIEF DESCRIPTION OF DRAWINGS

[0011] The present specification will be further described in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers refer to the same structures, wherein:

[0012] Figure 1 is a structural schematic diagram of an electromagnetic monitoring real-time analysis system according to some embodiments of the present specification;

[0013] Figure 2 is an exemplary flowchart of determining a test parameter error and calibration according to some embodiments of the present specification;

[0014] Figure 3 is an exemplary structural diagram of a test parameter judgment model according to some embodiments of the present specification;

[0015] Figure 4 is an exemplary structural diagram of an analysis feedback module configuration according to some embodiments of the present specification;

[0016] Figure 5 is an exemplary flowchart of determining a test cycle number according to some embodiments of the present specification. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, without creative labor, the present specification can also be applied to other similar scenarios according to these drawings. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0018] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0019] Unless otherwise indicated, the use of "or" includes "and / or," such that, for example, a condition is true when A or B is true, or both A and B are true. The phrases "one or more" or "at least one" can be construed to mean that "one," "two," "three," or any whole number of items or components can be present. The phrases "at least one of' or "comprises at least one of' followed by a list of two or more items can have different interpretations. At least one of the items can be one or more than one of the items. Each line following the initial "comprise at least one of" or "comprises at least one of" can have one or more of the items from the list. For example, if a composition is said to comprise at least one of A, B, and C, then the composition can comprise A, B, C, A and B, A and C, B and C, or A and B and C. The phrases "at least one of' or "comprises at least one of' followed by a list of two or more items can have different interpretations. At least one of the items can be one or more than one of the items. Each line following the initial "comprise at least one of" or "comprises at least one of" can have one or more of the items from the list. For example, if a composition is said to comprise at least one of A, B, and C, then the composition can comprise A, B, C, A and B, A and C, B and C, or A and B and C. The terms "comprising," "including," and "having" are intended to be open-ended terms. The composition, method, or process can include other items in addition to the list of items recited. The terms "first," "second," and other such numerical terms referring to elements do not imply a sequence or order unless clearly indicated by the context. Thus, a first element billed as following a second element can be modified so as to be positioned before the second element or vice versa.

[0020] Flow diagrams are used in the description of embodiments consistent with the description to illustrate the operation of the systems in accordance with such embodiments. It will be understood that the operations of the preceding or following operations need not be performed in the exact order shown. Rather, various steps can be handled in reverse order or simultaneously. Additionally, other operations can be inserted, or steps rearranged, removed or combined, without departing from the scope of the processes.

[0021] Figure 1 is a structural schematic diagram of an electromagnetic monitoring real-time analysis system according to some embodiments of the present description.

[0022] In some embodiments, the electromagnetic monitoring real-time analysis system 100 can include a data transmission module 110, a data caching module 120, a processing module 130, a storage module 140, and an analysis feedback module 150.

[0023] In some embodiments, the data transmission module 110 can be configured to receive electromagnetic environment test data, the electromagnetic environment test data including at least one set of test item data.

[0024] The electromagnetic environment test data is a plurality of sets of test item data generated after a plurality of tests performed by an electromagnetic environment test device.

[0025] The electromagnetic environment test device is used to monitor and evaluate the electromagnetic environment conditions in a specific area. Electromagnetic signals and interference sources in the environment can be collected, analyzed, and recorded by the electromagnetic environment test device, providing acquisition and analysis of information such as electromagnetic radiation levels, spectral distribution, and interference source positioning.

[0026] In some embodiments, the electromagnetic environment test device can include an input component. An operator can input calibration data, send a calibration completion instruction, etc. through the input component. For example, the electromagnetic environment test device can include a keyboard, a mouse / touch screen, a voice input device, a gesture input device, a control button / switch, a knob / slider, or a programmable interface, etc.

[0027] Test item data refers to data obtained after completing a test under a specific frequency band, antenna channel, and polarization mode.

[0028] A specific frequency band refers to a frequency band of electromagnetic radiation that needs to be tested, for example, a frequency band that often appears in life with interference, a frequency band that a certain electrical appliance usually works at, etc.

[0029] An antenna is a receiver and transmitter of electromagnetic waves, which captures or emits signals and transmits them to the electromagnetic environment test equipment. An antenna channel is a channel or interface of an antenna used for receiving and transmitting wireless signals in the electromagnetic monitoring real-time analysis system. It is a physical interface connecting the antenna and the system, carrying the transmission and reception of wireless signals. The antenna channel can be used to transmit electromagnetic radiation of different frequencies.

[0030] Polarization mode is an important indicator of an antenna. The S pole (south pole) and N pole (north pole) of the antenna determine whether electromagnetic radiation can enter the antenna and whether it can be emitted. Polarization modes include horizontal polarization, vertical polarization, or circular polarization, etc.

[0031] In some embodiments, each set of test item data can have corresponding test cycle times and automatic test times. For details of the test cycle times and automatic test times, please refer to the related description in Figure 4 .

[0032] In some embodiments, the test item data includes at least one of sampling spectrum data, calibration data, antenna and system gain data, etc.

[0033] The sampling spectrum data is the information of the signal at different frequencies (such as amplitude, power, intensity or phase, etc.), which can be a set of sinusoidal waves, for example.

[0034] The calibration data is the data generated by manual calibration by the operator. Calibration is a manual operation initiated by the operator before testing, aiming to avoid other influencing factors (system noise, environmental noise, etc.) to affect the quality of test data.

[0035] The antenna and system gain data refers to the ratio of the radiated power flux density of the antenna in a specified direction to the maximum radiated power flux density of the reference antenna at the same input power.

[0036] In some embodiments, the antenna and system gain data can be determined based on the noise source generating device built-in the electromagnetic environment test equipment, by measuring the electromagnetic test signals when the noise source is turned on and turned off.

[0037] In some embodiments, the electromagnetic environment test equipment can collect electromagnetic environment test data and transmit the collected electromagnetic environment test data to the data transmission module 110.

[0038] In some embodiments, the data transmission module 110 can transmit the received electromagnetic environment test data to the data buffering module 120 and / or the processing module 130, etc. For example, the data transmission module 110 can realize data transmission through wireless communication technology, etc., and specifically can adopt the mode of a wireless sensor network (WSN) to receive and transmit electromagnetic environment test data.

[0039] In some embodiments, the data buffering module 120 can be configured to buffer electromagnetic environment test data. For example, the data buffering module 120 can buffer electromagnetic environment test data from the data transmission module 110.

[0040] In some embodiments, the data buffering module 120 can be configured to send electromagnetic environment test data to the processing module 130 and / or the storage module 140 based on the control instruction of the analysis feedback module 150.

[0041] The control instruction of the analysis feedback module 150 is an instruction for determining the data flow direction. For specific content of the control instruction, see Figure 1 the related description below.

[0042] In some embodiments, the data buffering module 120 sends electromagnetic environment test data to the processing module 130 and / or the storage module 140, etc. according to the control instruction. For example, as Figure 1 shown, the data buffering module 120 can receive the control instruction from the analysis feedback module 150, and send the buffered electromagnetic environment test data to the processing module 130 and / or the storage module 140 according to the control instruction.

[0043] In some embodiments, the processing module 130 can be configured to determine to-be-analyzed data based on electromagnetic environment test data.

[0044] To-be-analyzed data is data to be analyzed obtained after pre-processing (statistics, screening, and / or sampling, etc.) of electromagnetic environment test data.

[0045] In some embodiments, the processing module 130 can pre-process electromagnetic environment test data in various ways to determine to-be-analyzed data.

[0046] For example, the processing module 130 can pre-process electromagnetic environment test data by sampling, filtering sample data, and determining sample data satisfying a preset condition before and after filtering as to-be-analyzed data.

[0047] The preset condition refers to a condition for determining the data to be analyzed in advance. For example, the preset condition can be that the change value of the data before and after filtering is less than a change threshold. The change threshold can be preset in advance. For example, for electromagnetic environment test data generated from 8 am to 20 pm of the day, the processing module 130 can divide the data according to an hour length, and obtain data of 12 time periods. In each of the 12 time periods, data of a fixed time length (such as 5 minutes) is randomly sampled, and the sampled data is taken as sample data of the time period.

[0048] The processing module 130 can process the sample data in each time period through a filter to obtain sample data before and after filtering. If the sample data before and after filtering satisfies the preset condition, the processing module 130 can take the electromagnetic environment test data of the time period corresponding to the sample satisfying the preset condition as the data to be analyzed.

[0049] In some embodiments, the processing module 130 can take the test period length as a division standard.

[0050] If the data changes little after being processed through the filter, it indicates that there is little noise in the data, and this part of data is good data for analysis.

[0051] In some embodiments, the processing module 130 can send the data to be analyzed to the storage module 140 for storage.

[0052] The processing module 130 can receive electromagnetic environment test data cached from the data cache module 120, and can also send the data to be analyzed obtained by processing to the storage module 140.

[0053] In some embodiments, the storage module 140 can be configured to store electromagnetic environment test data and data to be analyzed.

[0054] In the electromagnetic monitoring real-time analysis system 100, the storage module 140 can be a hard disk drive or a cloud storage service. The storage module 140 can be used to store electromagnetic environment test data (such as sampling spectrum data, calibration data, etc.) and data to be analyzed.

[0055] In some embodiments, (historical) electromagnetic environment test data, whether there is a test parameter error, and data to be analyzed can be stored in the storage module 140 in the form of a database.

[0056] For example, the electromagnetic monitoring real-time analysis system is used for monitoring radio frequency spectrum, the storage module 140 can save the sampled spectrum data in a database. The storage module 140 stores each received sampled spectrum data as a record, which includes information such as time stamp, frequency range and signal strength. The above record can be organized in chronological order for subsequent data analysis and backtracking.

[0057] In some embodiments, the storage module 140 can also store the to-be-analyzed data. For details of the to-be-analyzed data, see the related description of the to-be-analyzed data generated by the processing module 130 in Figure 1

[0058] In some embodiments, the storage module 140 can also store various data related to the electromagnetic monitoring real-time analysis system 100, such as historical test results, historical test data, initial electromagnetic environment test data, historical electromagnetic environment test data in a preset time period, test time, test frequency band, etc. For more details, see the related description of Figures 2 to 5

[0059] In some embodiments, the analysis feedback module 150 can be configured to determine a control instruction to control the data transmission direction of the data caching module 120, the data transmission direction including sending the electromagnetic environment test data to the processing module 130 or the storage module 140; and determining whether there is a test parameter error based on the to-be-analyzed data of the storage module 140; and issuing a parameter error warning in response to the existence of the test parameter error.

[0060] The control instruction is an instruction for determining the control of the data transmission direction according to whether the electromagnetic environment test data is sent to the processing module or the storage module for processing.

[0061] In some embodiments, the control instruction is preset in the analysis feedback module 150 according to the analysis and processing flow of the electromagnetic environment test data.

[0062] The test parameter error refers to the normal condition of the test parameter in the to-be-analyzed data. The test parameter error can be represented by at least one of text, numerical value, symbol, etc. For example, the test parameter error can be represented by numerical value 0 / 1. If there is an error, it can be represented by 1, otherwise by 0.

[0063] The analysis feedback module 150 can perform similarity calculation on the to-be-analyzed data and the standard test result data without test parameter error, and if the similarity is lower than the similarity threshold, it is considered that the to-be-analyzed data has a test parameter error.

[0064] ​​In some embodiments, the test parameter error can include at least one of a calibration error and an antenna parameter setting error. The calibration error is an error that the manual calibration has improper calibration parameters, calibration failure, calibration omission, etc. The antenna parameter setting error refers to the fact that the antenna channel is not adapted to the current situation.

[0065] In some embodiments, in response to the existence of the test parameter error, the analysis feedback module 150 will issue a parameter error warning. The parameter error warning includes at least one of a sound or visual alarm, an abnormal notification or alarm message, a remote alarm information, a use of an indicator light, or a log record and report, etc. For the content of the analysis feedback module 150 determining the test parameter error, interrupting the test, and issuing a calibration request, etc., please refer to the related description in Figure 2 .

[0066] The electromagnetic monitoring real-time analysis system provided in the specification can timely find the pre-stage equipment calibration problem and / or the equipment test parameter error setting problem in the data statistical analysis stage of the device itself, so that the operator can timely take appropriate measures for repair or adjustment, thereby ensuring the quality of the subsequent collected electromagnetic test data, improving the monitoring efficiency and accuracy, and avoiding unnecessary collection costs.

[0067] In some embodiments, the electromagnetic monitoring real-time analysis system 100 can be configured to assist the electromagnetic environment test equipment in testing. The electromagnetic monitoring real-time analysis system can be configured to be executed based on the processor of the electromagnetic environment test equipment. The processor includes a data transmission module 110, a data cache module 120, a processing module 130, a storage module 140, and an analysis feedback module 150, etc.

[0068] It should be understood that Figure 1 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the data transmission module 110 can have a cache medium containing the functions of the data cache module 120. For another example, the processing module 130 and the storage module 140 can be integrated in the same module to perform their respective functions.

[0069] It should be noted that the above description of the electromagnetic monitoring real-time analysis system and its modules is for the convenience of description, and cannot limit the specification to the scope of the embodiments. It can be understood that, for those skilled in the art, after understanding the principle of the system, the modules can be combined arbitrarily or connected with other modules to form a subsystem without departing from the principle. In some embodiments, Figure 1The data transmission module, the data cache module, the processing module, the storage module and the analysis feedback module disclosed in the present specification can be different modules in a system, or one module can implement the functions of two or more modules described above. For example, the modules can share one storage module, and each module can also have its own storage module. Variations such as this are within the scope of protection of the present specification.

[0070] Figure 2 is an exemplary flowchart of determining test parameter errors and calibration according to some embodiments of the present specification. In some embodiments, the flow 200 can be performed by the analysis feedback module 150.

[0071] At step 210, based on the to-be-analyzed data of the storage module, a test parameter error is determined by a test parameter judgment model.

[0072] For the content of the storage module 140, the to-be-analyzed data and the test parameter error, please refer to the related description of Figure 1 .

[0073] The test parameter judgment model can refer to a model for determining test parameter errors. In some embodiments, the test parameter judgment model can be a machine learning model. For example, the test parameter judgment model can include any one or combination of a convolutional neural network (CNN) model, a neural network (NN) model or other custom model structure, etc.

[0074] In some embodiments, the input of the test parameter judgment model can include to-be-analyzed data, and the output can include test parameter errors.

[0075] The test parameter judgment model 340 can learn the normal range and abnormal patterns of the test parameters through a training process, and can make predictions and judgments according to the input data. The training data set includes sample data of known correct test parameters and incorrect test parameters. These sample data can be manually marked by humans, or marked by other means. In some embodiments, the test parameter judgment model can be trained based on a large number of first training samples with first labels. The first training sample can be sample data to be analyzed, and the first label of the first training sample can be that the sample measurement parameter is incorrect. In some embodiments, the first training sample can be generated based on historical electromagnetic environment test data by the processing module 130, and the first label can be determined based on manual annotation. A loss function is constructed based on the first label and the result of the initial test parameter judgment model, and the parameters of the initial test parameter judgment model are iteratively updated based on the loss function. When the loss function of the initial test parameter judgment model meets the iteration condition, the model training is completed, and a trained test parameter judgment model is obtained. The iteration condition can be that the loss function converges, the number of iterations reaches a threshold, etc. After the test parameter judgment model is trained, the test parameter error can be determined by inputting the data to be analyzed.

[0076] The test parameter judgment model also includes other inputs, which are described in detail in the related description of Figure 3 .

[0077] Step 220, in response to the existence of test parameter error, interrupting the current test, and issuing a calibration request.

[0078] The calibration request is a request issued by the analysis feedback module 150 to the operator, prompting the operator to adjust the parameter setting (such as calibration parameter setting and / or antenna parameter setting, etc.) to eliminate the test parameter error after retesting.

[0079] In some embodiments, if there is a test parameter error, the current test is interrupted, and a calibration request is issued. For example, if a certain group of data to be analyzed is input into the test parameter judgment model, and the output is that there is a test parameter error, the analysis feedback module 150 can interrupt the test process and issue a calibration request to the operator.

[0080] Step 230, in response to obtaining the calibration completion instruction, restarting the test.

[0081] After the operator receives the calibration request issued by the analysis feedback module 150, the test parameter calibration will be performed, and after the calibration is completed, the calibration completion instruction is sent to the analysis feedback module 150.

[0082] In some embodiments, if the calibration completion instruction is obtained, the test is restarted. For example, after the calibration of the test parameters is completed, the operator can enter the calibration completion instruction and issue the instruction to the analysis feedback module 150, and the analysis feedback module 150 will restart the test after receiving the instruction.

[0083] After the test is restarted, the electromagnetic environment test equipment will be restarted or initialized, and then the test operation will be performed according to the test procedure from the beginning.

[0084] The causes of the test parameter error include human accidental factors, changes in test scenarios, and environmental disturbances. The causes of the test parameter error can be reduced or eliminated by calibration, but calibration may have problems such as inappropriate calibration parameters, calibration failure, and human oversight. In some embodiments of the present specification, whether there is a test parameter error is determined based on the to-be-analyzed data of the storage module 140 by the test parameter determination model, which can avoid the above-mentioned human oversight. Through the above configuration method of the analysis feedback module, the system can automatically detect the test parameter error in the to-be-analyzed data; further analysis of the error data can be avoided, ensuring the accuracy of the test results; and it can be ensured that the system continues to provide reliable data and results after calibration.

[0085] Figure 3 is an exemplary structural diagram of the test parameter determination model according to some embodiments of the present specification.

[0086] In some embodiments, the input of the test parameter determination model 340 further includes the interference feature 311, the initial electromagnetic environment test data 320, and the historical electromagnetic environment test data in the historical preset time period 330, the test time 313 and the test frequency band 314 corresponding to the historical electromagnetic environment test data in the historical preset time period, etc.

[0087] The interference feature refers to a feature that can represent relevant information of the interference. For example, the interference feature can represent the type of interference and the time period of the occurrence of the interference, and various information. More details about the interference feature can be found in the detailed description later in the present specification.

[0088] The initial electromagnetic environment test data refers to the original (e.g., the first) electromagnetic environment test data. The analysis feedback module 150 can obtain the initial electromagnetic environment test data 320 from the storage module 140.

[0089] The historical preset time period refers to a pre-set time range, which is used to trace back, retrieve, and analyze the electromagnetic environment test data in a specific time period. For example, the historical preset time period can be the past week, or the last test cycle, etc.

[0090] The historical electromagnetic environment test data 330 in the historical preset time period, and the corresponding test time 313 and test frequency band 314 are obtained by the analysis feedback module 150 from the storage module 140.

[0091] As shown in Figure 3 The test parameter judgment model 340 can include a spectrum processing layer 341 and an analysis layer 342.

[0092] The spectrum processing layer is a part of the test parameter judgment model, which performs spectrum analysis and processing on the input electromagnetic environment test data to output spectrum features. The input of the spectrum processing layer 341 can include initial electromagnetic environment test data 320 and historical electromagnetic environment test data 330 in a historical preset time period, etc.; and the output is spectrum features 350.

[0093] The spectrum feature refers to data or features extracted from electromagnetic environment test data to describe the characteristics of signal spectrum, to reveal the information of frequency distribution, frequency band occupation, power intensity, etc.

[0094] The analysis layer is one of the components of the test parameter judgment model, which is used to further analyze and judge the accuracy of the test parameters.

[0095] In some embodiments, the input of the analysis layer 342 can include interference features 311, data to be analyzed 312, spectrum features 350, test time 313 and test frequency band 314, etc.; and the output is test parameter error 360.

[0096] For the content of electromagnetic environment test data, data to be analyzed and test parameter error, please refer to the related description in Figure 1 .

[0097] In some embodiments, the output of the spectrum processing layer can be the input of the analysis layer, and the spectrum processing layer and the analysis layer can be jointly trained.

[0098] In some embodiments, each set of sample data for joint training includes sample interference features, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band, and the label of the sample data is whether there is a test parameter error in each set of data. The sample data for joint training can be obtained from historical data, and the corresponding label can be manually annotated. The sample initial electromagnetic environment test data and the sample historical electromagnetic environment test data are input into the spectrum processing layer to obtain the spectrum features output by the spectrum processing layer; the spectrum features are input into the analysis layer as training sample data, together with the sample interference features, the sample data to be analyzed, the sample test time, and the sample test frequency band, to obtain the test parameter error output by the analysis layer. A loss function is constructed based on the sample data label and the analysis layer output, and the parameters of the spectrum processing layer and the analysis layer are updated synchronously. Through parameter updating, the trained spectrum processing layer and analysis layer are obtained.

[0099] The above double-layer structure of the test parameter judgment model and the input of multiple data help the system to more accurately and comprehensively evaluate and judge the electromagnetic environment, help the model to capture potential patterns and abnormal situations in the spectrum data, and enable the model to perform advanced data mining and decision-making, thereby improving the accuracy and reliability of test parameter judgment.

[0100] In some embodiments, the training process of the test parameter judgment model (spectrum processing layer and analysis layer) can be divided into one or more stages. The number of stages can be set according to actual needs.

[0101] In some embodiments, the training process of the test parameter judgment model (spectrum processing layer and analysis layer) can include first stage training and second stage training, etc. The first stage training refers to the initial training, and the second stage training refers to the training after the first stage training is completed.

[0102] In some embodiments, the first stage training can include: performing first stage training on the test parameter judgment model based on a first training set. The first training set can include a preset proportion of first type data, second type data, and third type data.

[0103] The preset proportion refers to the proportion set for constructing different category data sets when training the test parameter judgment model. The preset proportion determines the relative proportion of each category data in the training set to ensure that the model can learn the features and patterns of different category data. For example, for a data set containing three categories, the preset proportion can be 1 / 3 of the data of each category in the training set. The preset proportion can be preset according to actual needs.

[0104] The first type data, the second type data, and the third type data refer to three different types of data of the first training set that meet the preset proportion.

[0105] In some embodiments, each group of data in the first type of data is a sample interference feature, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band that has a test parameter error. For example, the analysis feedback module 150 can obtain the first type of data from a database storing historical electromagnetic environment test data, and the corresponding label is that there is a test parameter error.

[0106] In some embodiments, the second type of data is a sample interference feature, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band that does not have a test parameter error. For example, the analysis feedback module 150 can obtain the second type of data from a database storing historical electromagnetic environment test data, and the corresponding label is that there is no test parameter error.

[0107] In some embodiments, each group of data in the third type of data is sample data to which noise is applied. For example, the analysis feedback module 150 can obtain at least one group of sample interference features, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band that has a test parameter error from a database storing historical electromagnetic environment test data, and apply one or more noises (including but not limited to salt and pepper noise, Gaussian noise, and / or Poisson noise, etc.) to each group of data. The analysis feedback module 150 can determine each group of data to which noise is applied as the third type of data, and the corresponding label is that there is a test parameter error.

[0108] In some embodiments, the analysis feedback module 150 can perform joint training of the first stage training of the test parameter judgment model (the spectrum processing layer and the analysis layer) based on the first type of data, the second type of data, and the third type of data. The training process is similar to the above-mentioned joint training process, and more details are described in the above-mentioned related description. Figure 3 The above-mentioned joint training process is similar, and more details are described in the above-mentioned related description. Figure 3 The above-mentioned related description.

[0109] By training sample data with and without test parameter errors, the test parameter judgment model can learn features and patterns that distinguish and judge whether the test parameter is correct. At the same time, adding noise to the third type of data can also help the model better adapt to the noise environment in actual application.

[0110] In some embodiments, after the first stage training of the test parameter judgment model (the spectrum processing layer and the analysis layer) is completed, the analysis feedback module 150 can perform the second stage training.

[0111] In some embodiments, the second stage training is performed on the first stage trained test parameter judgment model based on a second training set, which can include fourth type data and fifth type data.

[0112] In some embodiments, the fourth type data is a sample interference feature, sample to-be-analyzed data, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band, which is incorrectly detected as having test parameter errors by the first stage trained test parameter judgment model, but is labeled as not having test parameter errors in the database storing historical electromagnetic environment test data.

[0113] In some embodiments, the fifth type data is a sample interference feature, sample to-be-analyzed data, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band, which is incorrectly detected as not having test parameter errors by the first stage trained test parameter judgment model, but is labeled as having test parameter errors in the database storing historical electromagnetic environment test data.

[0114] In some embodiments, the analysis feedback module 150 can perform joint training of the second stage training on the first stage trained test parameter judgment model (spectrum processing layer and analysis layer) based on the fourth type data and the fifth type data. The training process is similar to Figure 3 the joint training process described above, and more details can be found in Figure 3 the related description above.

[0115] By using the first training set and the second training set for two-stage training, the test parameter judgment model can include multi-type data training, enhance model generalization ability, consider real scenarios, and improve model robustness. These features enable the test parameter judgment model to better adapt to test parameter error judgment in different situations and improve the performance and reliability of the model in actual applications.

[0116] Figure 4 is an exemplary structural diagram of the analysis feedback module configuration according to some embodiments of the present specification.

[0117] In some embodiments, the analysis feedback module 150 can be further configured to determine the test cycle number 410 of each set of test item data in the at least one set of test item data. In some embodiments, the analysis feedback module 150 can be further configured to adjust the automatic test time 420 of the at least one set of test item data.

[0118] For more information about the at least one set of test item data, please refer to Figure 1 the related description in

[0119] The test cycle number refers to the number of repetitions of the test item data for testing. Each set of test item data corresponds to a test cycle number. By adjusting the test cycle number, the influence of random errors in testing can be reduced, and the accuracy of the test results can be improved.

[0120] In some embodiments, the analysis feedback module 150 can determine the test cycle number of each set of test item data in various ways. For example, the analysis feedback module 150 can determine the test cycle number corresponding to each set of test item data based on the number of test parameter errors of each set of test item data in the historical test results through a preset algorithm.

[0121] The preset algorithm refers to an algorithm that is determined in advance and used to calculate the test cycle number of each set of test item data. For example, the more the number of test parameter errors and the more frequent the test parameter errors of a set of test item data, the greater the test cycle number corresponding to the set of test item data. By determining the test cycle number through a preset algorithm, the risk of discovering that the number of data is insufficient after manual checking and requiring additional testing can be avoided.

[0122] The historical test results are the results of tests that have been completed at a historical time. The historical test results include the number of test parameter errors of each set of test item data. The analysis feedback module can obtain the historical test results from the storage module. For the content of the test parameter errors, see the related description thereof in Figure 1 .

[0123] In some embodiments, the preset algorithm can be:

[0124] Test cycle number = k * number of test parameter errors + b

[0125] Wherein, k and b are preset coefficients.

[0126] In some embodiments, determining the test cycle number of each set of test item data further includes a further method, and the specific content is described in the related description in Figure 5 .

[0127] The automatic test time refers to the time for automatically testing each set of test item data. The automatic test time determines the time point and test time period of each set of test item data in the system for testing.

[0128] In some embodiments, the length of the automatic test time of at least one set of test item data can be determined according to the test cycle number of the at least one set of test item data:

[0129] Length of automatic test time = Δt * test cycle number

[0130] Wherein, Δt is the length of time required for a single test, which can be preset.

[0131] In some embodiments, the analysis feedback module 150 can determine the automatic test time according to the test procedure and the length of the automatic test time, for example, the test procedure is test item 1, test item 2 and test item 3, the length of time spent on a single test is 10 min, 5 min and 8 min respectively, and the test starts at 9:00 AM, then the automatic test time of test item 1, test item 2 and test item 3 is 9:00-9:10, 9:10-9:15 and 9:15-9:23 respectively.

[0132] In some embodiments, the analysis feedback module 150 can determine the interference feature based on the historical test data, and determine the automatic test time of at least one group of test item data based on the interference feature and the test cycle number of each group of test item data. For more information about the historical test data, please refer to the description of Figure 5 .

[0133] In some embodiments, the analysis feedback module 150 can determine the interference feature 311 based on the historical test data in various ways. In some embodiments, the interference feature 311 can include the occurrence time of occasional human interference or industrial interference, etc.

[0134] Based on the historical test data, the analysis feedback module 150 can count the data abnormal time period due to the occurrence of various interferences in the data used for analysis, and take the statistical result as the interference feature 311.

[0135] In some embodiments, the analysis feedback module 150 can determine the automatic test time 420 of at least one set of test item data based on the interference feature 311 and the test cycle number 410 of each set of test item data in a plurality of ways. For example, assuming that the test procedure is test item 1, test item 2 and test item 3, the interference feature is 9:10-9:13, the interference of category 1 occurs, the interference of category 1 has a serious impact on test item 1 and test item 3, and has an impact on test item 2 but does not affect the test result, and assuming that test item 1, test item 2 and test item 3 are tested for 10 minutes, 5 minutes and 8 minutes respectively, and the test starts at 9:00 AM, then the automatic test time of test item 1, test item 2 and test item 3 is 9:00-9:10, 9:10-9:15 and 9:15-9:23 respectively, wherein, because the interference of category 1 at 9:10-9:13 does not affect the test result of test item 2, it is not necessary to avoid it. For another example, assuming that test item 1, test item 2 and test item 3 are tested for 20 minutes, 5 minutes and 8 minutes respectively, and the test starts at 9:00 AM, then if the test is interrupted, the automatic test time of test item 1 is 9:00-9:10, 9:13-9:23; the automatic test time of test item 2 and test item 3 is 9:23-9:28 and 9:28-9:36 respectively. If the test cannot be interrupted, the automatic test time of test item 1, test item 2 and test item 3 is 9:13-9:33, 9:33-9:38 and 9:38-9:46 respectively.

[0136] In some embodiments, the automatic test time of at least one set of test item data can also be related to test requirements and test duration. For example, when the test procedure changes, or the test procedure includes test items that can be interrupted and test items that cannot be interrupted, the automatic test time will be affected to different degrees. The test requirements refer to the relevant requirements of the test of at least one set of test item data, for example, whether the test can be interrupted, the requirement of the test time point, etc. The test duration refers to the duration of at least one set of test item data.

[0137] In some embodiments, determining the automatic test time of at least one set of test item data based on the interference feature further includes determining the automatic test time of at least one set of test item data based on the interference feature, the test requirements, the test duration and the test cycle number of each set of test item data by a time determination model. The time determination model can be a machine learning model.

[0138] The interference feature 311, the test requirements, the test duration and the test cycle number 410 of each set of test item data are inputs of the time determination model, and the automatic test time 420 of at least one set of test item data is the output of the time determination model.

[0139] The time determination model can be obtained through model training. Each set of sample data in the sample data is a sample interference feature, a sample test requirement, a sample test duration, and a sample test cycle number of each set of test item data in the historical data. The training label is the automatic test time of the test item data corresponding to each set of sample data in the historical data after the test. The test data generated by the test after the test parameter error occurs in the test data, and the automatic test time with the least test parameter error is taken as the training label corresponding to the sample data.

[0140] By analyzing the interference features in the historical test data and considering the test cycle number, the system can determine a more suitable automatic test time and optimize the test scheme to cope with the existence of interference, which helps to perform more tests in a period with less interference. In some embodiments of the present specification, based on the interference features, test requirements, test duration, and test cycle number of each set of test item data, the automatic test time is determined through the time determination model, which can further improve the accuracy of the determined automatic test time.

[0141] Each set of test item data may need to be cycled multiple times, and the automatic test time corresponding to each set of test item data may be different (for example, the automatic test time is in the morning or in the early morning, etc.). In the actual test process, there may be some uncontrollable factors (such as unknown interference in a fixed period, interference in a building construction period, etc.). Through further configuration of the analysis feedback module, the system can automatically determine the test cycle number required for each set of test item data according to the specific circumstances; can automatically adjust the automatic test time of at least one set of test item data according to the complexity of each test item data, the test target, and the actual situation, etc., thereby improving the efficiency and accuracy of the test.

[0142] Figure 5 is an exemplary flowchart for determining the test cycle number according to some embodiments of the present specification. In some embodiments, the flowchart 500 can be executed by the analysis feedback module 150.

[0143] Step 510, based on the historical test data, determining the test parameter frequent item and the support degree corresponding to the test parameter frequent item.

[0144] The historical test data at least includes the historical test results and the test parameter error number of each test item. The analysis feedback module 150 can obtain the historical test data from the storage module 140.

[0145] The test parameter frequent item refers to an item in which different test item data corresponds to different test times. For example, the test parameter frequent item is that test item 1 corresponds to 100 test times, test item 1 corresponds to 200 test times, test item 2 corresponds to 100 test times, and the like. The support degree corresponding to the test parameter frequent item refers to the frequency of test parameter error occurrence under a certain test time. The same test item corresponds to different test times, and the support degree corresponding to the test parameter frequent item may be different.

[0146] In some embodiments, the analysis feedback module 150 can determine the test parameter frequent item and the support degree corresponding thereto based on the historical test data in multiple ways. For example, the analysis feedback module 150 can count the number of test parameter errors of each test item under the same test condition in the historical test data, and determine the test item and the test time i of the test item as the test parameter frequent item when i≥threshold 1 and n≤threshold 2, and determine the ratio of n to i as the support degree corresponding to the test parameter frequent item, wherein the sizes of the threshold 1 and the threshold 2 can be preset according to actual needs.

[0147] In some embodiments, i≥threshold 1 indicates that the test time of the test item is sufficient, which can exclude the occasional error of the test. n≤threshold 2 indicates that the number of test parameter errors cannot be too high, which can exclude some errors due to non-variables and non-normal tests (for example, machine problems, theoretically correct test, but the result shows that the test is wrong), and the like.

[0148] Step 520, based on the test parameter frequent item and the support degree corresponding to the test parameter frequent item, a frequent item database is constructed.

[0149] The frequent item database refers to a database that can represent a plurality of groups of test item data corresponding to the test parameter frequent item and the support degree corresponding to the test parameter frequent item.

[0150] In some embodiments, the analysis feedback module 150 can construct the frequent item database based on the test parameter frequent item and the support degree corresponding to the test parameter frequent item in multiple ways. For example, the feedback module 150 can sort the test parameter frequent item in ascending order of the support degree, and select a preset number of test parameter frequent items and the support degrees corresponding thereto to construct the frequent item database. The preset number can be set according to needs.

[0151] Step 530, based on the frequent item database, the test cycle number of each group of test item data is determined. For the content of the test cycle number of each group of test item data, see the related description thereof in Figure 4 .

[0152] In some embodiments, the analysis feedback module 150 can determine the test cycle number of each group of test item data based on the frequent item database. For example, the test number corresponding to the test parameter frequent item with the lowest support degree in each test item can be selected as the test cycle number of each group of test item data.

[0153] By performing the steps in flow 500, the system can select the test number corresponding to the test parameter frequent item with the lowest support degree as the test cycle number of each group of test item data, so that the determined test cycle number is more reasonable and accurate, thereby improving the test efficiency and accuracy.

[0154] It should be noted that the above description of the flow (such as flow 200, flow 500) is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the flow 200, flow 500 under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0155] Some embodiments of the present specification also provide an electromagnetic monitoring real-time analysis method, which can be implemented by the electromagnetic monitoring real-time analysis system 100, and the specific steps are as follows.

[0156] First, obtain electromagnetic environment test data. This step can be implemented by the data transmission module 110.

[0157] In some embodiments, electromagnetic environment test data can be obtained, which includes at least one group of test item data, and the test item data includes sampling spectrum data, calibration data, antenna and system gain data. For details of obtaining electromagnetic environment test data, see the related description of the data transmission module 110 in Figure 1 .

[0158] Second, determine and store the data to be analyzed. This step can be implemented by the data buffering module 120, the processing module 130 and the storage module 140.

[0159] In some embodiments, the data to be analyzed can be determined and stored based on the electromagnetic environment test data. For details of determining and storing the data to be analyzed, see the related description of the data buffering module 120, the processing module 130 and the storage module 140 in Figure 1 .

[0160] Third, determine whether there is a test parameter error and whether to issue an error warning. This step can be implemented by the analysis feedback module 150.

[0161] In some embodiments, it can be determined whether there is a test parameter error based on the data to be analyzed; in response to the existence of a test parameter error, a parameter error warning is issued. For specific content about determining whether there is a test parameter error and whether to issue an error warning, see Figure 1 the related description of the analysis feedback module 150 in

[0162] In some embodiments, determining whether there is a test parameter error based on the data to be analyzed includes determining a test parameter error based on the data to be analyzed by a test parameter judgment model, wherein the test parameter judgment model is a machine learning model.

[0163] In some embodiments, if there is a test parameter error, the current test will be interrupted, and a calibration request will be issued.

[0164] In some embodiments, if a calibration completion instruction is obtained, the test will be restarted.

[0165] The above method and steps of determining whether there is a test parameter error are executed by the analysis feedback module 150. For specific content about determining a test parameter error, see Figure 2 the related description of the analysis feedback module function.

[0166] In some embodiments, the electromagnetic monitoring real-time analysis method further includes determining the test cycle number of each set of test item data in at least one set of test item data, and adjusting the automatic test time of at least one set of test item data.

[0167] The method and steps are executed by the analysis feedback module 150. For specific content about determining the test cycle number and adjusting the automatic test time, see Figure 4 the related description of the analysis feedback module configuration method.

[0168] In some embodiments, adjusting the automatic test time of the at least one set of test item data includes two steps. The two steps are executed by the analysis feedback module 150.

[0169] First, determine the interference feature.

[0170] In some embodiments, the interference feature can be determined based on historical test data. The interference feature includes the occurrence time of occasional human interference or industrial interference.

[0171] Second, determine the automatic test time.

[0172] In some embodiments, the automatic test time of at least one set of test item data can be determined based on the interference feature and the test cycle number of each set of test item data.

[0173] For details of adjusting the automatic test time of the at least one group of test item data, refer to the related description of determining the automatic test time in Figure 4

[0174] One or more embodiments of the present specification provide an electromagnetic monitoring real-time analysis device, comprising a processor configured to execute an electromagnetic monitoring real-time analysis method.

[0175] One or more embodiments of the present specification also provide a computer readable storage medium storing computer instructions, wherein the computer executes an electromagnetic monitoring real-time analysis method after reading the computer instructions in the computer readable storage medium.

[0176] The above has described the basic concept, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification for those skilled in the art. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0177] Meanwhile, the present specification uses specific words to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different positions in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0178] In addition, unless the claim explicitly states, the order of the processing elements and sequences described in the present specification, the use of numerals and letters, or the use of other names, is not intended to limit the order of the processes and methods of the present specification. Although some currently considered useful embodiments of the invention are discussed in the above disclosure through various examples, it should be understood that such details are only for the purpose of illustration, and the additional claims are not limited to the disclosed embodiments, on the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the spirit and scope of the embodiments of the present specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on existing servers or mobile devices.

[0179] ​For simplicity and to facilitate understanding of one or more embodiments, a description of an embodiment sometimes refers to a plurality of features in a single embodiment, drawing, or description of an embodiment. However, this method of disclosure is not to be interpreted as meaning that the claimed embodiment requires more features than are explicitly recited in the claims. In fact, claims that do not specifically claim a combination of features are intended to cover the various

[0180] Some embodiments use numerical values to describe components, quantities of attributes. It should be understood that such numerical values used in the description of embodiments are in some examples modified by the adjectives "about," "approximately," or "substantially." Unless otherwise stated, "about," "approximately," or "substantially" indicate that the described value allows for a ±20% variation. Accordingly, numerical parameters in the description and claims are approximations, and can vary depending upon the desired characteristics set forth in each instance. In some embodiments, numerical parameters are determined by the use of standard techniques for making such determinations, such as the standard techniques of rounding off log values to the nearest whole number, and the like. Although numerical ranges and parameters setting forth the broadest scope of embodiments herein are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, can contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0181] Each patent, patent application, patent publication, and other material, such as articles, books, specifications, publications, documents, and the like, referenced herein are hereby incorporated by reference in their entirety for the teachings relevant to the sentence and / or paragraph in which the reference is made. Discrepancies between applications history documents and the present specification, other than limitations on the scope of the claims, are excepted. It is specifically intended that the description, definitions, and / or terminology used in the incorporated material be governed by the disclosures in the present specification, which are only meant to be methods of illustration. In the event of a discrepancy between the incorporated material and the present disclosure, the present disclosure will control.

[0182] Finally, it should be understood that the embodiments described herein are merely exemplary of the principles of the embodiments described herein. Other variations having essentially the same structure and function but different values for components, and / or different materials and arrangements are intended to be covered by the claims. Thus, although the present embodiments have been described in detail with reference to the examples outlined above, alternative embodiments can be implemented and are within the scope of the present embodiments. Accordingly, the present embodiments are not limited to the above examples, but are intended to cover any and all alternatives resulting from combining features of the above examples in any manner.

Claims

1. An electromagnetic monitored tube and ball control system characterized by, The application comprises a data transmission module, a data cache module, a processing module, a storage module, and an analysis feedback module. The data transmission module is configured to receive electromagnetic environment test data, which includes at least one set of test item data. The test item data includes sampling spectrum data, calibration data, antenna and system gain data. The data cache module is configured to cache the electromagnetic environment test data. Based on the control instruction of the analysis feedback module, the electromagnetic environment test data is sent to the processing module and / or the storage module. The processing module is configured to determine the data to be analyzed based on the electromagnetic environment test data, and send the data to be analyzed to the storage module. The storage module is configured to store the electromagnetic environment test data and the data to be analyzed. The analysis feedback module is configured to determine the control instruction to control the data transmission direction of the data cache module. Based on the data to be analyzed of the storage module, the test parameter error is determined through a test parameter judgment model. The test parameter judgment model is a machine learning model. The training process of the test parameter judgment model includes first stage training and second stage training. The first stage training refers to initial training, and the second stage training refers to training after the first stage training is completed. The first stage training includes: based on a first training set, the test parameter judgment model is trained in the first stage. The first training set can include a preset proportion of first type data, second type data and third type data. The first type data, the second type data and the third type data refer to three different types of data that meet the preset proportion of the first training set. In response to the existence of the test parameter error, a parameter error warning is issued. The second stage training is based on a second training set to train the test parameter judgment model after the first stage training. The second training set includes fourth type data and fifth type data. The fourth type data is a sample interference feature, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time and sample test frequency band that are incorrectly detected as existing test parameter errors by the test parameter judgment model after the first stage training, but are labeled as non-existing test parameter errors in the database storing historical electromagnetic environment test data. The fifth type data is a sample interference feature, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time and sample test frequency band that are incorrectly detected as non-existing test parameter errors by the test parameter judgment model after the first stage training, but are labeled as existing test parameter errors in the database storing historical electromagnetic environment test data. The analysis feedback module is further configured to determine the test cycle number of each set of test item data in the at least one set of test item data, and adjust the automatic test time of the at least one set of test item data.

2. The system of claim 1, wherein, ​ 3. The system of claim 2, wherein, The analysis feedback module is further configured to determine an interference feature based on historical test data, the interference feature including a time of occurrence of occasional human interference or industrial interference; and determine an automatic test time of the at least one set of test item data based on the interference feature and the test cycle number of each set of test item data.

4. The system of claim 3, wherein, The analysis feedback module is further configured to determine an automatic test time of at least one set of test item data by a time determination model based on the interference feature, test requirements, test duration, and test cycle number of each set of test item data; and wherein the time determination model is a machine learning model.

5. A method of managing electromagnetic monitoring, comprising: Receiving electromagnetic environment test data, the electromagnetic environment test data including at least one set of test item data, the test item data including sampled spectrum data, calibration data, antenna and system gain data; determining to-be-analyzed data based on the electromagnetic environment test data, and storing the electromagnetic environment test data and the to-be-analyzed data; Determining whether there is a test parameter error by a test parameter judgment model based on the to-be-analyzed data, wherein the test parameter judgment model is a machine learning model; a training process of the test parameter judgment model includes first stage training and second stage training, the first stage training refers to initial training, and the second stage training refers to training after the first stage training is completed, the first stage training includes: performing first stage training on the test parameter judgment model based on a first training set; the first training set can include a preset proportion of first type data, second type data, and third type data; the first type data, the second type data, and the third type data refer to three different types of data that meet the preset proportion of the first training set; and in response to the test parameter error, issuing a parameter error warning; The second stage training is performed on the test parameter judgment model after the first stage training based on a second training set, the second training set includes fourth type data and fifth type data; the fourth type data is sample interference feature, sample to-be-analyzed data, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band that are incorrectly detected as having a test parameter error by the test parameter judgment model after the first stage training, but are labeled as not having a test parameter error in a database storing historical electromagnetic environment test data; and the fifth type data is sample interference feature, sample to-be-analyzed data, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency band that are incorrectly detected as not having a test parameter error by the test parameter judgment model after the first stage training, but are labeled as having a test parameter error in the database storing historical electromagnetic environment test data.

6. The method of claim 5, wherein, The method further includes determining a test cycle number of each set of test item data in the at least one set of test item data; and adjusting an automatic test time of the at least one set of test item data.

7. An electromagnetic monitoring device for managing and controlling, characterized in that, The device comprises at least one memory for storing computer instructions and at least one processor for executing the computer instructions or part of the instructions to implement the electromagnetic monitoring management method according to any one of claims 5-6.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when a computer reads the computer instructions, the computer executes the electromagnetic monitoring management method according to any one of claims 5-6.

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