CN117969973A - Electromagnetic monitoring real-time analysis system, method, device and medium - Google Patents

Abstract

The embodiment of the specification relates to the technical field of electromagnetic monitoring, and provides an electromagnetic monitoring real-time analysis system, an electromagnetic monitoring real-time analysis method, an electromagnetic monitoring real-time analysis device and a medium, wherein the electromagnetic monitoring real-time analysis system comprises: the data transmission module is configured to receive electromagnetic environment test data; the data caching module is configured to cache electromagnetic environment test data and send the electromagnetic environment test data to the processing module and/or the storage module; the processing module is configured to determine data to be analyzed and send the data to the storage module; the storage module is configured to store electromagnetic environment test data and data to be analyzed; the analysis feedback module is configured to determine a control instruction to control a data transmission flow direction of the data cache module. The method comprises the following steps: determining data to be analyzed based on the electromagnetic environment test data; determining whether a test parameter error exists based on the data to be analyzed; in response to the presence, a parameter error warning is issued. The method also operates after being read by computer instructions stored on a computer readable storage medium.

Description

Electromagnetic monitoring real-time analysis system, method, device and medium

Technical Field

The present disclosure relates to the field of electromagnetic monitoring technologies, and in particular, to an electromagnetic monitoring real-time analysis system, method, apparatus, and medium.

Background

Electromagnetic monitoring is widely applied to the fields of communication, radar, aerospace, radio frequency spectrum management and the like. Conventional electromagnetic monitoring devices are typically automatically tested according to manually pre-adjusted or preset device parameters. Before automatic testing, equipment calibration and reasonable configuration of equipment testing parameters are required to be performed in order to ensure the reliability of the test data.

After electromagnetic test data are acquired, the equipment can carry out real-time statistical analysis and pretreatment on the electromagnetic monitoring data based on a self-supporting system, but during automatic test, the problems of equipment calibration errors, equipment test parameter setting errors and the like are often hidden, and in addition, the problem of manual checking omission can exist, so that the acquired electromagnetic test data are low in quality and even cannot be used, and the problems are often found after the automatic test is completed.

Therefore, it is necessary to provide an electromagnetic monitoring real-time analysis system, method, device and medium, which can find the problem of early device calibration and/or the problem of device test parameter error setting in time in the data statistics analysis stage of the device itself, thereby ensuring the quality of the electromagnetic test data collected subsequently, improving the monitoring efficiency and accuracy, and avoiding the generation of unnecessary collection cost.

Disclosure of Invention

In order to solve the problem that the device cannot timely find the calibration problem of the prior device and/or the error setting problem of the device test parameters in the data statistics analysis stage of the device, the quality of electromagnetic test data collected later is ensured, the monitoring efficiency and accuracy are improved, and unnecessary collection cost is avoided. The specification provides and an electromagnetic monitoring real-time analysis system, method, device and medium.

The specification comprises an electromagnetic monitoring real-time analysis system, which comprises: the system 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 comprising at least one set of test item data comprising sample spectrum data, calibration data, antenna and system gain data; the data caching 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 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 flow direction of the data cache module; determining whether a test parameter error exists or not based on the data to be analyzed of the storage module; and responding to the existence of the test parameter error, and sending out parameter error early warning.

The invention comprises an electromagnetic monitoring real-time analysis method, which comprises the following steps: acquiring electromagnetic environment test data, wherein the electromagnetic environment test data comprises at least one group of test item data, and the test item data comprises sampling frequency spectrum data, calibration data, an antenna and system gain data; determining data to be analyzed based on the electromagnetic environment test data and storing the data to be analyzed; determining whether a test parameter error exists or not based on the data to be analyzed; and responding to the existence of the test parameter error, and sending out parameter error early warning.

The invention comprises an electromagnetic monitoring real-time analysis device, which comprises a processor, wherein the processor is used for executing the electromagnetic monitoring real-time analysis method.

The invention comprises a computer readable storage medium, wherein the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the electromagnetic monitoring real-time analysis method.

The beneficial effects are that: through electromagnetic monitoring real-time analysis system, can in time discover earlier stage equipment calibration problem and/or equipment test parameter mistake setting problem at the equipment itself to data statistics analysis stage, make operating personnel in time take appropriate measure and repair or adjust, and then guarantee the quality of the electromagnetic test data of follow-up gathering, improve monitoring efficiency and accuracy, avoid producing unnecessary acquisition cost.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic diagram of an electromagnetic monitoring real-time analysis system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart for determining test parameter errors and calibration, shown in accordance with some embodiments of the present description;

FIG. 3 is an exemplary block diagram of a test parameter decision model according to some embodiments of the present description;

FIG. 4 is an exemplary block diagram of an analytical feedback module configuration shown in accordance with some embodiments of the present description;

FIG. 5 is an exemplary flow chart for determining a number of test cycles according to some embodiments of the present description.

Reference numerals illustrate: the system comprises a data transmission module 110, a data caching module 120, a processing module 130, a storage module 140, an analysis feedback module 150, an interference feature 311, data to be analyzed 312, a test time 313, a test frequency band 314, initial electromagnetic environment test data 320, historical electromagnetic environment test data 330 in a historical preset time period, a test parameter judgment model 340, a frequency spectrum processing layer 341, an analysis layer 342, a frequency spectrum feature 350, a test parameter error 360, a test cycle number 410 and an automatic test time 420.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between 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.

The terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly indicates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

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

In some embodiments, the electromagnetic monitoring real-time analysis system may 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.

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

The electromagnetic environment test data are multiple groups of test item data generated after electromagnetic environment test equipment performs multiple tests.

Electromagnetic environment testing devices are used to monitor and evaluate electromagnetic environment conditions within a particular area. Electromagnetic signals and sources of interference in the environment can be collected, analyzed and recorded by electromagnetic environment testing equipment, providing for the acquisition and analysis of information such as electromagnetic radiation levels, spectral distribution, and source location.

In some embodiments, the electromagnetic environment testing device may include an input component. The operator can input calibration data through the input assembly, send calibration complete instructions, etc. For example, the electromagnetic environment testing device may include a keyboard, mouse/touch screen, voice input device, gesture input device, control buttons/switches, knobs/sliders, or programmable interface, among others.

The test item data refers to data obtained after one test is completed under a specific frequency band, antenna channel and polarization mode.

The specific frequency band refers to a frequency band of electromagnetic radiation to be tested, for example, a frequency band in which interference occurs frequently in life, a frequency band in which a certain electric appliance normally operates, and the like.

Antennas are receivers and transmitters of electromagnetic waves that capture or emit signals and communicate them to electromagnetic environment testing equipment. An antenna channel is a channel or interface of an antenna for receiving and transmitting wireless signals in an electromagnetic monitoring real-time analysis system. It is the physical interface connecting the antenna and the system, carrying the transmission and reception of wireless signals. The antenna channels may be used to transmit electromagnetic radiation of different frequencies.

The polarization is an important index of the antenna, and the S pole (south pole) and N pole (north pole) of the antenna determine that electromagnetic radiation energy cannot enter the antenna and cannot be emitted. The polarization mode includes horizontal polarization, vertical polarization, circular polarization, etc.

In some embodiments, each set of test item data may have a number of test cycles and an automatic test time corresponding thereto. For the content of the number of test cycles and the automatic test time, reference can be made to the relevant description in fig. 4.

In some embodiments, the test item data includes at least one of sampled spectrum data, calibration data, antenna and system gain data, and the like.

The sampled spectral data is information (e.g., amplitude, power, intensity, phase, etc.) of the signal at different frequencies, and may be, for example, a set of sine waves.

The calibration data is data generated by manual calibration by an operator. Calibration is an operation that is performed manually and actively by an operator prior to testing in order to avoid interference by other influencing factors (system noise, environmental noise, etc.) so as to affect the quality of the test data.

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

In some embodiments, the antenna and system gain data may be determined computationally based on noise source generating means onboard the electromagnetic environment testing device by measuring electromagnetic test signals that turn on and off the noise source.

In some embodiments, the electromagnetic environment testing device may collect electromagnetic environment test data and transmit the collected electromagnetic environment test data to the data transmission module 110.

In some embodiments, the data transmission module 110 may transmit the received electromagnetic environment test data to the data caching module 120 and/or the processing module 130, etc. For example, the data transmission module 110 may implement data transmission by using a wireless communication technology, and may specifically use a wireless sensor network (Wireless Sensor Network, WSN) to receive and transmit electromagnetic environment test data.

In some embodiments, the data caching module 120 may be configured to cache electromagnetic environment test data. For example, the data caching module 120 may cache electromagnetic environment test data from the data transmission module 110.

In some embodiments, the data caching module 120 may be configured to send the electromagnetic environment test data to the processing module 130 and/or the storage module 140 based on the control instructions of the analysis feedback module 150.

The control instructions of the analysis feedback module 150 are instructions for determining the flow direction of the data. For details of the control instruction, see the following description of fig. 1.

In some embodiments, the data caching module 120 sends the electromagnetic environment test data to the processing module 130 and/or the storage module 140, etc., according to the control instructions. For example, as shown in fig. 1, the data caching module 120 may receive a control instruction from the analysis feedback module 150 and send cached electromagnetic environment test data to the processing module 130 and/or the storage module 140 according to the control instruction.

In some embodiments, the processing module 130 may be configured to determine the data to be analyzed based on electromagnetic environment test data.

The data to be analyzed is the data to be analyzed obtained after preprocessing (counting, screening and/or sampling, etc.) the electromagnetic environment test data.

In some embodiments, the processing module 130 may pre-process the electromagnetic environment test data in a variety of ways to determine the data to be analyzed.

For example, the processing module 130 may determine, as the data to be analyzed, sample data that satisfies a preset condition by sampling the electromagnetic environment test data, filtering the sample data, and determining the sample data that satisfies the preset condition before and after the filtering.

The preset condition refers to a condition of determining the data to be analyzed preset in advance, for example, the preset condition may be that a change value of the data before and after filtering is smaller than a change threshold value, and the like. The change threshold may be preset in advance. For example, for electromagnetic environment test data generated from eight am to twenty am, the processing module 130 may divide the electromagnetic environment test data by one hour period to obtain 12 time period data. During each of these 12 time periods, data of a fixed time length (e.g., a length of 5 minutes, etc.) is randomly sampled, and the sampled data is taken as sampled sample data for that time period.

The processing module 130 may process the sampled sample data in each time period through a filter to obtain sample data before and after the filtering. If the sample filtering front-back data meets the preset condition, the processing module 130 may use the electromagnetic environment test data of the time period corresponding to the sample meeting the preset condition as the data to be analyzed.

In some embodiments, the processing module 130 may act as a partitioning criterion in terms of test period length.

If the data is not changed much after being processed by the filter, the data is indicated to have little noise, and the data is good data for analysis.

In some embodiments, the processing module 130 may send the data to be analyzed to the storage module 140 for storage.

The processing module 130 may receive the electromagnetic environment test data buffered by the data buffering module 120, or may send the processed data to be analyzed to the storage module 140.

In some embodiments, the storage module 140 may be configured to store electromagnetic environment test data, data to be analyzed, and the like.

In the electromagnetic monitoring real-time analysis system, the storage module 140 may be a hard disk drive or a cloud storage service. The storage module 140 may be configured to store electromagnetic environment test data (e.g., sampled spectrum data, calibration data, etc.), data to be analyzed, and the like.

In some embodiments, the (historical) electromagnetic environment test data, the presence or absence of test parameter errors, and the like, may be stored in the memory module 140 in the form of a database.

For example, the electromagnetic monitoring real-time analysis system is used to monitor the radio spectrum, and the storage module 140 may store the sampled spectrum data in a database. Each time the storage module 140 receives sampled spectrum data, it will be stored as a record that includes information such as time stamp, frequency range, and signal strength. The records may be organized in a time sequence for subsequent data analysis and backtracking.

In some embodiments, the storage module 140 may also store data to be analyzed. For the content of the data to be analyzed, see fig. 1 for a description of the data to be analyzed generated by the processing module 130.

In some embodiments, the storage module 140 may also store various data related to the electromagnetic monitoring real-time analysis system, such as historical test results, historical test data, initial electromagnetic environment test data, historical electromagnetic environment test data over a historical preset period of time, test frequency band, etc., as more related to the above, see the related description of fig. 2-5 below.

In some embodiments, the analytical feedback module 150 may be configured to determine control instructions to control a data transfer flow direction of the data caching module 120, the data transfer flow direction including sending 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 data to be analyzed of the storage module 140; and responding to the existence of the test parameter error, and sending out parameter error early warning.

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

In some embodiments, the control instructions are preset in the analysis feedback module 150 according to the analysis process flow of the electromagnetic environment test data.

The test parameter error refers to the normal condition of the test parameter in the data to be analyzed. The test parameter errors may be represented by at least one of text, numerical values, symbols, etc. For example, a test parameter error may be represented by a value of 0/1. If there is an error, it can be represented by 1, otherwise it is represented by 0.

The analysis feedback module 150 may perform similarity calculation on the data to be analyzed and standard test result data without test parameter errors, and if the similarity is lower than a similarity threshold, consider that the data to be analyzed has test parameter errors.

In some embodiments, the test parameter errors may include at least one of calibration errors, antenna parameter setup errors, and the like. The calibration error is an error in which a manual calibration has incorrect calibration parameters, calibration failure, calibration omission, and the like. An antenna parameter setting error means that the characteristic antenna channel is not adapted to the current situation.

In some embodiments, in response to the presence of a test parameter error, the analysis feedback module 150 will issue a parameter error warning. The means for parameter error warning includes, but is not limited to, at least one of an audible or visual alarm, an exception notification or alarm message, a remote alarm message, a use indicator light, or a log record and report, etc.

For the analysis feedback module 150, see the relevant description in fig. 2, for determining the test parameter error, interrupting the test, issuing the calibration request, etc. through the test parameter judgment model.

The electromagnetic monitoring real-time analysis system provided by the specification can timely find the calibration problem of the early-stage equipment and/or the error setting problem of the equipment test parameters in the data statistics analysis stage of the equipment, so that operators can timely take appropriate measures to repair or adjust, the quality of electromagnetic test data acquired subsequently is further ensured, the monitoring efficiency and accuracy are improved, and unnecessary acquisition cost is avoided.

In some embodiments, the electromagnetic monitoring real-time analysis system may be configured to assist the electromagnetic environment testing device in testing. The electromagnetic monitoring real-time analysis system may be configured to be executed based on a processor of the electromagnetic environment testing device. The processor includes a data transmission module 110, a data buffer module 120, a processing module 130, a storage module 140, an analysis feedback module 150, and the like.

It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways. For example, in some embodiments, the data transmission module 110 may be self-contained with a caching medium that includes the functionality of the data caching module 120. For another example, the processing module 130 and the storage module 140 may be integrated in the same module to perform the respective functions.

It should be noted that the above description of the electromagnetic monitoring real-time analysis system and the modules thereof is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the data transmission module, the data buffer module, the processing module, the storage module and the analysis feedback module disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

FIG. 2 is an exemplary flow chart for determining test parameter errors and calibration according to some embodiments of the present description. In some embodiments, the flow may be performed by the analytical feedback module 150.

S210, determining test parameter errors through a test parameter judgment model based on data to be analyzed of the storage module.

For the content of the memory module 140, the data to be analyzed and the test parameter errors, please refer to the related description of fig. 1.

The test parameter judgment model may refer to a model for determining a test parameter error. In some embodiments, the test parameter determination model may be a machine learning model. For example, the test parameter decision model may include any one or combination of a convolutional neural network (Convolutional Neural Networks, CNN) model, a neural network (Neural Networks, NN) model, or other custom model structure, etc.

In some embodiments, the input of the test parameter determination model may include data to be analyzed and the output may include test parameter errors.

The test parameter judgment model 340 can learn the normal range and abnormal mode 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 for which test parameters are known to be correct and test parameters are known to be incorrect. These sample data may be manually marked by a person or may be marked in other ways. In some embodiments, the test parameter decision model may be trained based on a plurality of first training samples with first labels. The first training sample may be sample data to be analyzed, and the first label of the first training sample may be a sample measurement parameter error. In some embodiments, the first training sample may be generated by the processing module 130 based on historical electromagnetic environment test data, and the first tag may be determined based on manual labeling. And constructing a loss function through the first label and the result of the initial test parameter judgment model, and iteratively updating parameters of the initial test parameter judgment model based on the loss function. And when the loss function of the initial test parameter judgment model meets the iteration condition, model training is completed, and a trained test parameter judgment model is obtained. The iteration condition may be that the loss function converges, the number of iterations reaches a threshold value, etc. After the test parameter judgment model is trained, the test parameter errors can be determined by inputting the data to be analyzed.

The test parameter decision model also includes other inputs, see in particular the relevant description of fig. 3.

S220, in response to the existence of the test parameter error, interrupting the test and sending out a calibration request.

The calibration request is a request from the analysis feedback module 150 to the operator, prompting the operator to adjust parameter settings (e.g., calibration parameter settings and/or antenna parameter settings, etc.) to eliminate test parameter errors after retesting.

In some embodiments, if there is a test parameter error, the test is discontinued and a calibration request is issued. For example, if a set of data to be analyzed is input to the test parameter determination model and output as a test parameter error exists, the analysis feedback module 150 may interrupt the test process and issue a calibration request to the operator.

S230, restarting the test in response to acquiring the calibration complete instruction.

After the operator receives the calibration request sent by the analysis feedback module 150, the operator performs calibration of the test parameters, and after the calibration is completed, a calibration completion instruction is sent to the analysis feedback module 150.

In some embodiments, if a calibration complete instruction is obtained, the test is restarted. For example, after completing calibration of the test parameters, the operator may enter a calibration complete command and issue the command to the analysis feedback module 150, and after receiving the command, the analysis feedback module 150 will restart the test.

After restarting the test, the electromagnetic environment test device will restart or be initialized and then perform the test operation in accordance with the test flow from scratch.

The reasons for the error of the test parameters include artificial accidental factors, test scene changes, environmental interference and the like. The cause of the test parameter error can be reduced or eliminated through calibration, but the calibration can have the problems of improper calibration parameters, calibration failure and the like, and artificial omission can occur. In some embodiments of the present disclosure, determining whether there is a test parameter error based on the data to be analyzed of the storage module 140 through the test parameter judgment model may avoid the occurrence of the artificial omission. By the configuration method of the analysis feedback module, the system can automatically detect the test parameter errors in the data to be analyzed; further analysis of error data can be avoided, and accuracy of test results is ensured; it can be ensured that the system continues to provide reliable data and results after calibration.

FIG. 3 is an exemplary block diagram of a test parameter decision model according to some embodiments of the present description.

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, the historical electromagnetic environment test data 330 within the historical preset time period, the test time 313 and the test frequency band 314 corresponding to the historical electromagnetic environment test data within the historical preset time period, and so on.

Interference characteristics refer to characteristics of relevant information that may characterize the interference. For example, the interference signature may characterize a variety of information such as the type of interference and the period of time that the interference occurred. For more details regarding interference features, reference is made to the detailed description that follows in this specification.

Initial electromagnetic environment test data refers to raw (e.g., first time) electromagnetic environment test data. The analytical feedback module 150 may obtain initial electromagnetic environment test data 320 from the storage module 140.

The historical preset time period refers to a preset time range and is used for backtracking, calling and analyzing electromagnetic environment test data in a specific time period. For example, the historical preset time period may be the last week, the last test cycle, or the like.

Historical electromagnetic environment test data 330, and corresponding test time 313 and test frequency band 314 for a historical preset period of time are obtained from the memory module 140 by the analysis feedback module 150.

As shown in fig. 3, the test parameter decision model 340 may include a spectrum processing layer 341 and an analysis layer 342.

The spectrum processing layer is a part of the test parameter judging model and performs spectrum analysis and processing on the input electromagnetic environment test data to output spectrum characteristics. The inputs to the spectrum processing layer 341 may include initial electromagnetic environment test data 320, historical electromagnetic environment test data 330 for a historical preset period of time, and so on; the output is spectral feature 350.

The spectrum characteristic refers to data or characteristics extracted from electromagnetic environment test data and used for describing the spectrum characteristic of the signal so as to reveal information such as frequency distribution, frequency band occupation condition, power intensity and the like of the signal.

The analysis layer is one of the components of the test parameter judgment model and is used for further analyzing and judging the accuracy of the test parameters.

In some embodiments, the inputs to the analysis layer 342 may include interference characteristics 311, data to be analyzed 312, spectral characteristics 350, test time 313, test frequency band 314, and the like; the output is a test parameter error 360.

For details of electromagnetic environment test data, data to be analyzed, and test parameter errors, see the relevant description in fig. 1.

In some embodiments, the output of the spectrum processing layer may be an input of the analysis layer, and the spectrum processing layer and the analysis layer may be obtained through joint training.

In some embodiments, each set of data of the jointly trained sample data includes sample interference characteristics, 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 each set of data has a test parameter error. The sample data of the combined training can be obtained through historical data, and the corresponding labels can be marked manually. Inputting the sample initial electromagnetic environment test data and the sample historical electromagnetic environment test data into a spectrum processing layer to obtain spectrum characteristics output by the spectrum processing layer; and taking the frequency spectrum characteristics as training sample data, and inputting the training sample data, the sample interference characteristics, the sample data to be analyzed, the sample test time and the sample test frequency band into an analysis layer to obtain test parameter errors output by the analysis layer. And constructing a loss function based on the sample data label and the analysis layer output, and synchronously updating parameters of the spectrum processing layer and the analysis layer. And obtaining a trained spectrum processing layer and an analysis layer through parameter updating.

The double-layer structure of the test parameter judgment model and the input various data are beneficial to more accurate and comprehensive evaluation and judgment of the electromagnetic environment, and the model is beneficial to capturing potential modes and abnormal conditions in the frequency spectrum data, so that the model can perform advanced data mining and decision making, and the accuracy and reliability of test parameter judgment are improved.

In some embodiments, the training process of the test parameter decision model (spectrum processing layer and analysis layer) may be divided into one or more stages. The number of stages may be set according to actual requirements.

In some embodiments, the training process of the test parameter decision model (spectrum processing layer and analysis layer) may include a first stage training, a second stage training, and the like. The first stage training refers to initial training, and the second stage training refers to training performed after the first stage training is completed.

In some embodiments, the first stage training may include: and training the test parameter judgment model in a first stage based on the first training set. The first training set may include a preset proportion of first class data, second class data, and third class data.

The preset proportion refers to proportion setting for constructing different types of data sets when training a test parameter judgment model. The preset proportions determine the relative proportions of the various categories of data in the training set to ensure that the model is able to learn the characteristics and patterns of the different categories of data. For example, for a dataset containing three categories, the preset ratio may be 1/3 of the data for each category to the training set. The preset proportion can be preset according to actual requirements.

The first class of data, the second class of data and the third class of data refer to three different classes of data of the first training set meeting a preset proportion.

In some embodiments, each set of data in the first type of data is a sample interference feature labeled as having test parameter errors, 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). For example, the analysis feedback module 150 may obtain the first type of data from a database storing historical electromagnetic environment test data, where the corresponding tag is that there is a test parameter error.

In some embodiments, the second class of data is sample interference features without test parameter errors, sample data to be analyzed, sample initial electromagnetic environment test data, sample historical electromagnetic environment test data, sample test time, and sample test frequency bands. For example, the analysis feedback module 150 may obtain the second type of data from a database storing historical electromagnetic environment test data, with the corresponding tag being that there is no test parameter error.

In some embodiments, each set of data in the third class of data is sample data after noise is applied. For example, the analysis feedback module 150 may obtain at least one set of sample interference characteristics, 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 with test parameter errors from a database storing historical electromagnetic environment test data, and apply one or more noises (one or more noises including, but not limited to, salt and pepper noise, gaussian noise, poisson noise, etc.) to each set of data. The analysis feedback module 150 may determine each set of data after noise is applied as the third type of data, and the corresponding tag is that there is a test parameter error.

In some embodiments, the analysis feedback module 150 may perform a first stage training of the test parameter decision model (spectrum processing layer and analysis layer) based on the first class data, the second class data, and the third class data. The training process is similar to the co-training process described above with respect to fig. 3, for more details, see the associated description above with respect to fig. 3.

By training sample data with and without test parameter errors, the test parameter judgment model can learn features and modes that distinguish and judge whether the test parameters are wrong. Meanwhile, the added noise in the third type of data can also help the model to adapt to the noise environment in practical application better.

In some embodiments, after the first stage training of the test parameter decision model (spectrum processing layer and analysis layer) is completed, the analysis feedback module 150 may perform a second stage training.

In some embodiments, the second stage training is performed on the test parameter judgment model after the first stage training based on a second training set, and the second training set may include fourth type data and fifth type data.

In some embodiments, the fourth type of data is sample interference features, 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 are detected by the test parameter judgment model after the first stage training as being in error, but marked as being in absence of test parameter errors in the database storing the historical electromagnetic environment test data.

In some embodiments, the fifth type of data is sample interference features, 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 are detected by the test parameter judgment model after the first stage training as being in error, and are marked as being in error in the test parameter, in the database storing the historical electromagnetic environment test data.

In some embodiments, the analysis feedback module 150 may perform a second-stage training of the test parameter decision model (spectrum processing layer and analysis layer) after the first-stage training based on the fourth-type data and the fifth-type data. The training process is similar to the co-training process described above with respect to fig. 3, for more details, see the associated description above with respect to fig. 3.

By performing two stages of training using the first training set and the second training set, the test parameter judgment model may include multiple classes of data training, enhance model generalization ability, consider real scenes, and improve model robustness. The characteristics enable the test parameter judgment model to be better suitable for error judgment of the test parameters under different conditions, and improve the performance and reliability of the model in practical application.

FIG. 4 is an exemplary block diagram of an analytical feedback module configuration shown in accordance with some embodiments of the present description.

In some embodiments, the analysis feedback module 150 may be further configured to determine a number of test cycles 410 for each of the at least one set of test item data. In some embodiments, the analytical feedback module 150 may be further configured to adjust the automatic test time 420 for at least one set of test item data.

For more on at least one set of test item data, see the associated description in FIG. 1.

The test cycle number refers to the number of repetitions of testing the test item data. Each group of test item data corresponds to a test cycle number. By adjusting the test cycle times, the influence of random errors in the test can be reduced, and the accuracy of the test result is improved.

In some embodiments, the analytical feedback module 150 may determine the number of test cycles per set of test item data in a variety of ways. For example, the analysis feedback module 150 may determine, by a preset algorithm, the number of test cycles corresponding to each set of test item data based on the number of test parameter errors for the set of test item data in the historical test results.

The preset algorithm refers to a predetermined algorithm for calculating the number of test cycles per set of test item data. For example, the more times and the more frequently the test parameters are wrong for a set of test item data, the greater the number of test cycles the set of test item data corresponds to. The number of test cycles is determined through a preset algorithm, so that the risk that the number of data is insufficient after manual checking and additional test is required can be avoided.

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 per set of test item data. The analysis feedback module can acquire the historical test result from the storage module. For the content of test parameter errors, see the relevant description thereof in fig. 1.

In some embodiments, the preset algorithm may be:

Test cycle number=k Number of test parameter errors present + b

Wherein k and b are preset coefficients.

In some embodiments, determining the number of test cycles per set of test item data includes further methods, see the relevant description in FIG. 5 for details.

The automatic test time refers to the time for which each set of test item data is automatically tested. The automatic test time determines the point in time at which each set of test item data is tested in the system and the test period.

In some embodiments, the length of the automatic test time for the at least one set of test item data may be determined based on the number of test cycles for the at least one set of test item data:

Length of automatic test time = Δt Number of test cycles

Wherein Δt is the length of time it takes for a single test, and can be preset.

In some embodiments, the analysis feedback module 150 may determine the automatic test time according to the test flow and the length of the automatic test time, for example, the test flow is test item 1, test item 2, and test item 3, the time spent for a single test is 10min, 5min, and 8min, respectively, and the test starts from 9:00am, and then the automatic test time for 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.

In some embodiments, the analytical feedback module 150 may determine the interference characteristics based on historical test data; an automatic test time for at least one set of test item data is determined based on the disturbance characteristics and the number of test cycles for each set of test item data. For more about historical test data, see the relevant description of FIG. 5.

In some embodiments, the analytical feedback module 150 may determine the disturbance characteristics 311 in a variety of ways based on historical test data. In some embodiments, the interference feature 311 may include the time of occurrence of sporadic jamming or industrial interference, or the like.

Based on the historical test data, the analysis feedback module 150 may count abnormal time periods of data generated due to occurrence of various kinds of interference among the data for analysis therein, with the counted results as the interference characteristics 311.

In some embodiments, the analysis feedback module 150 may determine the automatic test time 420 for at least one set of test item data in a variety of ways based on the disturbance characteristics 311 and the number of test cycles 410 for each set of test item data. For example, assuming that the test flow is test item 1, test item 2, and test item 3, interference with category 1 occurs with interference characteristics of 9:10-9:13, interference with category 1 will have a severe impact on test item 1 and test item 3, impact on test item 2 but not on test results, and assuming that 10min, 5min, and 8min of test are performed on test item 1, test item 2, and test item 3, respectively, beginning with 9:00am, then the automatic test times for test item 1, test item 2, and test item 3 are 9:00-9:10, 9:10-9:15, and 9:15-9:23, respectively, where interference with category 1 at 9:10-9:13 does not have an impact on test results for test item 2, and therefore is not to be avoided. For another example, assuming that test item 1, test item 2, and test item 3 are to be tested for 20min, 5min, and 8min, respectively, the test starts at 9:00AM, then if the test is interrupted, the automatic test time for 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 to 9:28 and 9:28 to 9:36, respectively. If the test cannot be interrupted, the automatic test times for test item 1, test item 2, and test item 3 are 9:13-9:33, 9:33-9:38, and 9:38-9:46, respectively.

In some embodiments, the automatic test time for at least one set of test item data may also be related to test requirements and test duration. For example, when the test flow is changed and test items that can be interrupted or not are included in the test flow, the automatic test time is affected to different degrees. Test requirements refer to the associated requirements of at least one set of test item data tests, e.g., whether the test can be discontinued, the requirements of the test time point, etc. Test duration refers to the duration of at least one set of test item data.

In some embodiments, determining the automatic test time for the at least one set of test item data based on the disturbance characteristics further comprises determining the automatic test time for the at least one set of test item data by a time determination model based on the disturbance characteristics, the test requirements, the test duration, and the number of test cycles for each set of test item data. Wherein the time determination model may be a machine learning model.

The disturbance characteristics 311, test requirements, test duration, and number of test cycles 410 for each set of test item data are inputs to the time determination model, and the automatic test time 420 for at least one set of test item data is an output of the time determination model.

The time determination model may be derived by model training, with each set of sample data in the sample data being a sample disturbance characteristic in the historical data, a sample test requirement, a sample test duration, and a sample test cycle number for each set of test item data. The training label is generated by counting the automatic test time of the test item data corresponding to each group of sample data in the historical data and then generating test parameter errors in the test data, and the automatic test time with the least generated test parameter errors is used as the training label corresponding to the group of sample data.

By analyzing the interference characteristics in the historical test data and considering the number of test cycles, the system is able to determine a more appropriate automatic test time and optimize the test scheme to cope with the presence of interference, which facilitates more tests in less interfering time periods. In some embodiments of the present disclosure, determining the automatic test time by the time determination model based on the interference characteristics, the test requirements, the test duration, the number of test cycles per set of test item data, and the like may further improve the accuracy of the determined automatic test time.

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 (e.g., the automatic test time is in the morning or early morning, etc.), and during the actual test, there may be some uncontrollable factors (e.g., unknown disturbances in a fixed period of time, disturbances in a building construction period, etc.). Through further configuration of the analysis feedback module, the system can automatically determine the number of test cycles required to be performed on each group of test item data according to specific conditions; the automatic test time of at least one group of test item data can be automatically adjusted according to the complexity, the test target, the actual situation and the like of each test item data, so that the efficiency and the accuracy of the test are improved.

FIG. 5 is an exemplary flow chart for determining a number of test cycles according to some embodiments of the present description. In some embodiments, the flow may be performed by the analytical feedback module 150.

S510, based on the historical test data, the test parameter frequent items and the support degrees corresponding to the test parameter frequent items are determined.

The historical test data at least comprises the historical test result of each test item, the number of test parameter errors and the like. The analysis feedback module 150 may obtain historical test data from the storage module 140.

The test parameter frequent items refer to items of different test item data corresponding to different test times. For example, the test parameter frequent items are 100 times corresponding to test item 1, 200 times corresponding to test item 1, 100 times corresponding to test item 2, and so on. The support degree corresponding to the test parameter frequent item refers to the frequency of occurrence of test parameter errors under a certain test frequency. The number of tests corresponding to a same test item is different, and the support degree corresponding to the corresponding test parameter frequent item may be different.

In some embodiments, the analytical feedback module 150 may determine test parameter frequent items and their corresponding support in a variety of ways based on historical test data. For example, the analysis feedback module 150 may count the number of test parameter errors n of each test item under the same test condition under the test number of times i in the historical test data, determine the test item with i being greater than or equal to the threshold value 1 and n being less than or equal to the threshold value 2 and the test number of times i thereof as test parameter frequent items, and determine the ratio of n to i as the support degree corresponding to the test parameter frequent items, where the magnitudes of the threshold value 1 and the threshold value 2 may be preset according to actual requirements.

In some embodiments, i+_threshold 1 indicates that the test item is tested a sufficient number of times, which may eliminate sporadic errors in testing. n.ltoreq.threshold 2 indicates that the number of test parameter errors cannot be too high, which may exclude some errors due to non-variables and abnormal testing (e.g., machine problems, theoretically testing being correct, but the results showing test errors), etc.

S520, constructing a frequent item database based on the test parameter frequent item and the support degree corresponding to the test parameter frequent item.

The frequent item database is used for representing the frequent items of the test parameters corresponding to the test items of the plurality of groups and the supporting degree corresponding to the frequent items of the test parameters.

In some embodiments, the analysis feedback module 150 may construct the frequent item database in a variety of ways based on the test parameter frequent item and the degree of support corresponding to the test parameter frequent item. For example, the analysis feedback module 150 may sort the frequent items of the test parameters according to the ascending order of the support degrees, and select a preset number of frequent items of the test parameters and the support degrees corresponding to the frequent items of the test parameters, which are ranked at the front, to construct a frequent item database. The preset number can be set as required.

S530, determining the test cycle times of each group of test item data based on the frequent item database. For content regarding the number of test cycles per set of test item data, see the relevant description thereof in FIG. 4.

In some embodiments, the analysis feedback module 150 may determine the number of test cycles per set of test item data based on the frequent item database. For example, the number of tests corresponding to the test parameter frequent item with the lowest support degree in each test item may be selected to be determined as the number of test cycles of each set of test item data.

Through executing steps in the flow, the system can select the test times corresponding to the test parameter frequent items with low support degree as the test cycle times of each group of test item data, so that the determined test cycle times are more reasonable and accurate, and the test efficiency and accuracy are improved.

It should be noted that the above description of the flow is only for the purpose of illustration and description, and does not limit the application scope of the present specification. Various modifications and changes to the above-described procedures may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.

Some embodiments of the present disclosure further provide an electromagnetic monitoring real-time analysis method, which may be implemented by an electromagnetic monitoring real-time analysis system, and the specific steps are as follows.

First, acquiring electromagnetic environment test data. This step may be implemented by the data transfer module 110.

In some embodiments, electromagnetic environment test data may be acquired, the electromagnetic environment test data including at least one set of test item data, the test item data including sample spectrum data, calibration data, antenna and system gain data. For content for acquiring electromagnetic environment test data, see fig. 1 for a description of the data transfer module 110.

And secondly, determining and storing data to be analyzed. This step may be implemented by the data caching module 120, the processing module 130, and the storage module 140.

In some embodiments, the data to be analyzed may be determined based on the electromagnetic environment test data and stored. For details regarding determining and storing the data to be analyzed, see the relevant description of the data caching module 120, the processing module 130, and the storage module 140 in fig. 1.

And thirdly, determining whether the test parameter is wrong or not and whether an error early warning is sent out or not. This step may be accomplished by the analytical feedback module 150.

In some embodiments, it may be determined whether there is a test parameter error based on the data to be analyzed; and responding to the existence of the test parameter error, and sending out parameter error early warning. For details regarding determining whether there is a test parameter error and whether an error warning is issued, see FIG. 1 for a description of the analysis feedback module 150.

In some embodiments, determining whether there is a test parameter error based on the data to be analyzed includes: and determining test parameter errors through a test parameter judgment model based on the data to be analyzed, wherein the test parameter judgment model is a machine learning model.

In some embodiments, if there is a test parameter error, the test will be discontinued and a calibration request will be issued.

In some embodiments, if a calibration complete instruction is obtained, the test will be restarted.

The above methods and steps of determining whether there is a test parameter error are performed by the analytical feedback module 150. For details regarding determining test parameter errors, see FIG. 2 for a related description of the functionality of the analytical feedback module.

In some embodiments, the electromagnetic monitoring real-time analysis method further comprises determining a number of test cycles for each of the at least one set of test item data, and adjusting an automatic test time for the at least one set of test item data.

The method and steps are performed by an analytical feedback module 150. For details of determining the number of test cycles and adjusting the automatic test time, see fig. 4 for a description of the configuration of the analytical feedback module.

In some embodiments, adjusting the automatic test time of the at least one set of test item data includes two steps. These two steps are performed by the analytical feedback module 150.

In a first step, an interference characteristic is determined.

In some embodiments, the interference characteristics may be determined based on historical test data. Interference features include the occurrence time of sporadic artifacts or industrial interference.

And secondly, determining automatic test time.

In some embodiments, an automatic test time for at least one set of test item data may be determined based on the interference characteristics and the number of test cycles per set of test item data.

For details regarding adjusting the automatic test time of the at least one set of test item data, see the relevant description of determining the automatic test time in fig. 4.

One or more embodiments of the present specification provide an electromagnetic monitoring real-time analysis apparatus including a processor for performing an electromagnetic monitoring real-time analysis method.

One or more embodiments of the present specification also provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform an electromagnetic monitoring real-time analysis method.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (10)

1. An electromagnetic monitoring real-time analysis system, comprising: the system 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 comprising at least one set of test item data comprising sample spectrum data, calibration data, antenna and system gain data;

The data caching 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 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 flow direction of the data cache module; determining whether a test parameter error exists or not based on the data to be analyzed of the storage module; and responding to the existence of the test parameter error, and sending out parameter error early warning.

2. The electromagnetic monitoring real-time analysis system of claim 1, wherein the analysis feedback module is further configured to:

determining the test parameter errors through a test parameter judgment model based on the data to be analyzed of the storage module, wherein the test parameter judgment model is a machine learning model;

in response to the test parameter error, interrupting the test and sending a calibration request;

in response to obtaining the calibration complete instruction, the test is restarted.

3. The electromagnetic monitoring real-time analysis system of claim 1, wherein the analysis feedback module is further configured to:

determining a number of test cycles for each of 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.

4. The electromagnetic monitoring real-time analysis system of claim 3, wherein the analysis feedback module is further configured to:

determining interference features based on historical test data, the interference features including times of occurrence of sporadic human or industrial interference;

an automatic test time for the at least one set of test item data is determined based on the disturbance characteristics and the number of test cycles for each set of test item data.

5. An electromagnetic monitoring real-time analysis method, which is characterized by comprising the following steps:

Acquiring electromagnetic environment test data, wherein the electromagnetic environment test data comprises at least one group of test item data, and the test item data comprises sampling frequency spectrum data, calibration data, an antenna and system gain data;

Determining data to be analyzed based on the electromagnetic environment test data and storing the data to be analyzed;

Determining whether a test parameter error exists or not based on the data to be analyzed;

and responding to the existence of the test parameter error, and sending out parameter error early warning.

6. The electromagnetic monitoring real-time analysis method according to claim 5, wherein the determining whether there is a test parameter error based on the data to be analyzed includes: determining the test parameter errors through a test parameter judgment model based on the data to be analyzed, wherein the test parameter judgment model is a machine learning model;

The responding to the test parameter error comprises the following steps: in response to the test parameter error, interrupting the test and sending a calibration request; in response to obtaining the calibration complete instruction, the test is restarted.

7. The electromagnetic monitoring real-time analysis method as set forth in claim 5, further comprising:

determining a number of test cycles for each of 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.

8. The electromagnetic monitoring real-time analysis method as set forth in claim 7, wherein said adjusting the automatic test time of the at least one set of test item data includes:

determining interference features based on historical test data, the interference features including times of occurrence of sporadic human or industrial interference;

an automatic test time for the at least one set of test item data is determined based on the disturbance characteristics and the number of test cycles for each set of test item data.

9. An electromagnetic monitoring real-time analysis device, comprising a processor for executing the electromagnetic monitoring real-time analysis method according to any one of claims 5-8.

10. A computer readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer performs the electromagnetic monitoring real-time analysis method according to any one of claims 5 to 8.