Parameter analysis system and method for electromagnetic monitoring

Abstract

The embodiment of the invention provides a parameter analysis system and method for electromagnetic monitoring. The parameter analysis system comprises 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, and the data caching module is configured to cache the electromagnetic environment test data; and sending the electromagnetic environment test data to the processing module and / or the storage module based on the control instruction of the analysis feedback 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 electromagnetic environment test data and to-be-analyzed data; the analysis feedback module is configured to determine a control instruction to control the data transmission flow direction of the data caching module; based on the to-be-analyzed data of the storage module, whether a test parameter error exists or not is determined through a test parameter judgment model, and the test parameter judgment model is a machine learning model.

Description

Description of the case

[0001] This application is a divisional application filed for the Chinese application with the application date of April 2, 2024, application number 202410389399.0, and invention name “A real-time analysis system, method, device and medium for electromagnetic monitoring”. Technical Field

[0002] The present specification relates to the field of electromagnetic monitoring technology, and in particular to a parameter analysis system and method for electromagnetic monitoring. Background Art

[0003] Electromagnetic monitoring is widely used in communications, radar, aerospace, and radio spectrum management. Traditional electromagnetic monitoring equipment is generally tested automatically according to manual pre-debugging or preset equipment parameters. Before automatic testing, in order to ensure the reliability of test data, it is necessary to do a good job of equipment calibration and reasonable configuration of equipment test parameters.

[0004] After acquiring the electromagnetic test data, the equipment can perform real-time statistical analysis and preprocessing of the electromagnetic monitoring data based on its own supporting system. However, during automatic testing, problems such as equipment calibration errors and incorrect equipment test parameter settings are often hidden, and there may be omissions in manual verification. As a result, the electromagnetic test data obtained is of low quality or even unusable, and such problems are often discovered only after the automatic test is completed.

[0005] Therefore, it is necessary to provide a parameter analysis system and method for electromagnetic monitoring, which can promptly discover early equipment calibration problems and / or incorrect equipment test parameter settings during the data statistical analysis stage of the equipment itself, thereby ensuring the quality of subsequently collected electromagnetic test data, improving monitoring efficiency and accuracy, and avoiding unnecessary collection costs. Summary of the invention

[0006] In order to solve the problem that the equipment calibration problem and / or the incorrect setting of equipment test parameters cannot be found in time during the data statistical analysis stage of the equipment itself, so as to ensure the quality of the electromagnetic test data collected subsequently, improve the monitoring efficiency and accuracy, and avoid unnecessary collection costs, this specification provides a parameter analysis system and method for electromagnetic monitoring. One or more embodiments of the present specification provide a parameter analysis system for electromagnetic monitoring, characterized in that it includes: 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, the electromagnetic environment test data includes at least one set of test item data, and 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 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 in the storage module, determine 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 test parameter judgment model includes a spectrum processing layer and an analysis layer.

[0007] One or more embodiments of the present specification provide a parameter analysis method for 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 data to be analyzed based on the electromagnetic environment test data, and storing the electromagnetic environment test data and the data to be analyzed; based on the data to be analyzed, 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 test parameter judgment model includes a spectrum processing layer and an analysis layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 is a structural schematic diagram of an electromagnetic monitoring real-time analysis system according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of determining test parameter errors and calibration according to some embodiments of the present specification;

[0011] Figure 3 is an exemplary structural diagram of a test parameter determination model according to some embodiments of this specification;

[0012] Figure 4 is an exemplary structural diagram of the configuration of the analysis feedback module according to some embodiments of this specification;

[0013] Figure 5 is an exemplary flow chart for determining the number of test cycles according to some embodiments of the present specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein are 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.

[0016] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0018] Figure 1 It is a structural schematic diagram of an electromagnetic monitoring real-time analysis system shown in some embodiments of this specification.

[0019] In some embodiments, the electromagnetic monitoring real-time analysis system 100 may include a data transmission module 110 , a data cache module 120 , a processing module 130 , a storage module 140 and an analysis feedback module 150 .

[0020] In some embodiments, the data transmission module 110 may be configured to receive electromagnetic environment test data, where the electromagnetic environment test data includes at least one set of test item data.

[0021] Electromagnetic environment test data refers to multiple sets of test item data generated after multiple tests by electromagnetic environment test equipment.

[0022] Electromagnetic environment test equipment is used to monitor and evaluate the electromagnetic environment conditions in a specific area. Electromagnetic environment test equipment can collect, analyze and record electromagnetic signals and interference sources in the environment, and provide information such as electromagnetic radiation levels, spectrum distribution, and interference source location.

[0023] In some embodiments, the electromagnetic environment test device may include an input component. The operator may input calibration data through the input component, send a calibration completion instruction, etc. For example, the electromagnetic environment test device may 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.

[0024] Test item data refers to the data obtained after completing a test in a specific frequency band, antenna channel and polarization mode.

[0025] The specific frequency band refers to the frequency band of electromagnetic radiation that needs to be tested, for example, the frequency band where interference often occurs in life, the frequency band where a certain electrical appliance usually works, etc.

[0026] Antennas are receivers and transmitters of electromagnetic waves. They capture or send signals and pass them to electromagnetic environment test equipment. Antenna channels are channels or interfaces of antennas used to receive and send wireless signals in electromagnetic monitoring real-time analysis systems. It is the physical interface connecting the antenna and the system, carrying the transmission and reception of wireless signals. Antenna channels can be used to transmit electromagnetic radiation of different frequencies.

[0027] Polarization 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.

[0028] In some embodiments, each set of test item data may have a corresponding test cycle number and automatic test time. For more information about the test cycle number and automatic test time, see Figure 4 Related description in .

[0029] 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.

[0030] The sampled spectrum data is information about a signal at different frequencies (such as amplitude, power, intensity or phase, etc.), and may be, for example, a set of sinusoidal waves.

[0031] Calibration data is the data generated by manual calibration by the operator. Calibration is an operation performed manually by the operator before testing to avoid interference from other influencing factors (system noise, environmental noise, etc.) that may affect the quality of test data.

[0032] 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.

[0033] In some embodiments, based on the noise source generating device provided in the electromagnetic environment testing equipment, the antenna and system gain data can be calculated and determined by measuring the electromagnetic test signals with the noise source turned on and off.

[0034] In some embodiments, the electromagnetic environment testing device may collect electromagnetic environment testing data, and transmit the collected electromagnetic environment testing data to the data transmission module 110 .

[0035] In some embodiments, the data transmission module 110 may transmit the received electromagnetic environment test data to the data cache module 120 and / or the processing module 130, etc. For example, the data transmission module 110 may implement data transmission through wireless communication technology, etc., and specifically may use a wireless sensor network (Wireless Sensor Network, WSN) to receive and transmit the electromagnetic environment test data.

[0036] In some embodiments, the data cache module 120 may be configured to cache electromagnetic environment test data. For example, the data cache module 120 may cache electromagnetic environment test data from the data transmission module 110 .

[0037] In some embodiments, the data cache 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 instruction of the analysis and feedback module 150 .

[0038] The control instruction of the analysis feedback module 150 is an instruction used to determine the direction of data flow. For details of the control instruction, see Figure 1 The following are related instructions.

[0039] In some embodiments, the data cache module 120 sends the electromagnetic environment test data to the processing module 130 and / or the storage module 140 according to the control instruction. Figure 1As shown, the data cache module 120 can receive a control instruction from the analysis and feedback module 150 , and send the cached electromagnetic environment test data to the processing module 130 and / or the storage module 140 according to the control instruction.

[0040] In some embodiments, the processing module 130 may be configured to determine the data to be analyzed based on the electromagnetic environment test data.

[0041] The data to be analyzed is the data to be analyzed obtained after preprocessing (statistics, screening and / or sampling, etc.) the electromagnetic environment test data.

[0042] 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.

[0043] For example, the processing module 130 may sample the electromagnetic environment test data, filter the sample data, and determine the sample data that meets preset conditions before and after filtering as the data to be analyzed.

[0044] The preset condition refers to a condition preset in advance to determine the data to be analyzed. For example, the preset condition may be that the change value of the data before and after filtering is less than the change threshold, etc. The change threshold may be preset in advance. For example, for the electromagnetic environment test data generated from 8:00 a.m. to 20:00 p.m. on the same day, the processing module 130 may divide the data into 12 time periods according to the length of one hour. In each of the 12 time periods, data of a fixed time length (such as 5 minutes in length, etc.) is randomly sampled, and the sampled data is used as the sampled sample data of the time period.

[0045] 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 meet the preset conditions, the processing module 130 can use the electromagnetic environment test data of the time period corresponding to the sample meeting the preset conditions as the data to be analyzed.

[0046] In some embodiments, the processing module 130 may use the length of the test cycle as a division criterion.

[0047] If the data does not change much after being processed by the filter, it means that there is little noise in the data and this part of the data is good data for analysis.

[0048] In some embodiments, the processing module 130 may send the data to be analyzed to the storage module 140 for storage.

[0049] The processing module 130 can not only receive the electromagnetic environment test data cached by the data cache module 120 , but also send the processed data to be analyzed to the storage module 140 .

[0050] In some embodiments, the storage module 140 may be configured to store electromagnetic environment test data, data to be analyzed, and the like.

[0051] 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 sampled spectrum data, calibration data, etc.) and data to be analyzed.

[0052] In some embodiments, (historical) electromagnetic environment test data, whether there are test parameter errors, and data to be analyzed may be stored in the storage module 140 in the form of a database.

[0053] Exemplarily, the electromagnetic monitoring real-time analysis system is used to monitor the radio spectrum, and the storage module 140 can store the sampled spectrum data in a database. Each time the sampled spectrum data received by the storage module 140 is stored as a record, the record includes information such as timestamp, frequency range and signal strength. The above records can be organized in chronological order to facilitate subsequent data analysis and backtracking.

[0054] In some embodiments, the storage module 140 can also store data to be analyzed. For details about the data to be analyzed, see Figure 1 Detailed description of the data to be analyzed generated by the processing module 130.

[0055] 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 within a historical preset time period, test time, test frequency band, etc. For more information about the above, please refer to the following Figures 2 to 5 Related description.

[0056] In some embodiments, the analysis feedback module 150 can be configured to determine control instructions to control the data transmission direction of the data cache module 120, and the data transmission direction includes 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 data to be analyzed in the storage module 140; and issuing a parameter error warning in response to the existence of a test parameter error.

[0057] The control instructions are sent to the processing module or the storage module for processing according to the electromagnetic environment test data to determine the instructions for controlling the direction of data transmission.

[0058] In some embodiments, the control instructions are pre-set in the analysis feedback module 150 according to the analysis and processing flow of the electromagnetic environment test data.

[0059] The test parameter error refers to the normal condition of the test parameter in the data to be analyzed. 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 a numerical value 0 / 1. If there is an error, it can be represented by 1, otherwise it can be represented by 0.

[0060] The analysis feedback module 150 may calculate the similarity between the data to be analyzed and the standard test result data without test parameter errors. If the similarity is lower than a similarity threshold, it is considered that the data to be analyzed has test parameter errors.

[0061] In some embodiments, the test parameter error may include at least one of a calibration error and an antenna parameter setting error, etc. A calibration error is an error in manual calibration such as improper calibration parameters, calibration failure, calibration omission, etc. An antenna parameter setting error refers to a characterization that the antenna channel is not adapted to the current situation.

[0062] In some embodiments, in response to the presence of a test parameter error, the analysis and feedback module 150 will issue a parameter error warning. The parameter error warning method includes but is not limited to at least one of an audible or visual alarm, an abnormal notification or alarm message, a remote alarm message, a use indicator light, or a log record and report. For details about the analysis and feedback module 150 determining a test parameter error through a test parameter judgment model, interrupting the test, and issuing a calibration request, see Figure 2 Related description in .

[0063] The electromagnetic monitoring real-time analysis system provided in this manual can promptly discover early equipment calibration problems and / or incorrect equipment test parameter settings during the data statistical analysis stage of the equipment itself, allowing operators to take appropriate measures to repair or adjust them in a timely manner, thereby ensuring the quality of subsequently collected electromagnetic test data, improving monitoring efficiency and accuracy, and avoiding unnecessary collection costs.

[0064] 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.

[0065] 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 its own cache medium, including the functions of the data cache module 120. For another example, the processing module 130 and the storage module 140 can be integrated into the same module to perform their respective functions.

[0066] It should be noted that the above description of the electromagnetic monitoring real-time analysis system and its modules is only for the convenience of description and cannot limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The data transmission module, data cache module, processing module, storage module and analysis feedback module disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0067] Figure 2 is an exemplary flow chart of determining test parameter errors and calibration according to some embodiments of the present specification. In some embodiments, process 200 may be performed by analysis feedback module 150 .

[0068] Step 210: Based on the data to be analyzed in the storage module, determine the test parameter error through the test parameter judgment model.

[0069] For information on storage module 140, data to be analyzed, and test parameter errors, see Figure 1 Related description.

[0070] The test parameter judgment model may refer to a model for determining test parameter errors. In some embodiments, the test parameter judgment model may be a machine learning model. For example, the test parameter judgment model may include any one or combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other custom model structures.

[0071] 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.

[0072] The test parameter judgment model 340 can learn the normal range and abnormal mode of the test parameters through the training process, and can make predictions and judgments based on the input data. The training data set includes sample data with known correct test parameters and wrong test parameters. These sample data can be manually marked or marked in other ways. 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 a sample measurement parameter error. In some embodiments, the first training sample can be generated by the processing module 130 based on historical electromagnetic environment test data, and the first label can be determined based on manual annotation. The loss function is constructed by 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 the trained test parameter judgment model is obtained. Among them, 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.

[0073] The test parameter judgment model also includes other inputs, see Figure 3 Related description.

[0074] Step 220: In response to the existence of a test parameter error, the test is interrupted and a calibration request is issued.

[0075] The calibration request is a request sent by the analysis feedback module 150 to the operator, prompting the operator to adjust parameter settings (such as calibration parameter settings and / or antenna parameter settings, etc.) to eliminate test parameter errors after retesting.

[0076] In some embodiments, if there is a test parameter error, the test is interrupted and a calibration request is issued. For example, if a set 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.

[0077] Step 230: In response to obtaining the calibration completion instruction, restart the test.

[0078] After receiving the calibration request from the analysis feedback module 150 , the operator will perform test parameter calibration, and after the calibration is completed, send a calibration completion instruction to the analysis feedback module 150 .

[0079] In some embodiments, if a calibration completion instruction is obtained, the test is restarted. For example, after completing the test parameter calibration, the operator can enter a calibration completion instruction and send the instruction to the analysis feedback module 150. After receiving the instruction, the analysis feedback module 150 will restart the test.

[0080] After the restart test, the electromagnetic environment test equipment will restart or be initialized, and then perform the test operation according to the test process from the beginning.

[0081] The causes of test parameter errors include accidental human factors, changes in test scenarios, and environmental interference. The causes of test parameter errors can be reduced or eliminated through calibration, but calibration may have problems such as inappropriate calibration parameters and calibration failure, and human omissions may occur. In some embodiments of the present specification, the test parameter judgment model is used to determine whether there is a test parameter error based on the data to be analyzed in the storage module 140, which can avoid the above-mentioned human omissions. By analyzing the above-mentioned configuration method of the feedback module, the system can automatically detect test parameter errors in the data to be analyzed; further analysis of erroneous data can be avoided to ensure the accuracy of the test results; and the system can continue to provide reliable data and results after calibration.

[0082] Figure 3 It is an exemplary structural diagram of the test parameter judgment model shown in some embodiments of this specification.

[0083] In some embodiments, the input of the test parameter judgment model 340 also includes interference characteristics 311, initial electromagnetic environment test data 320, historical electromagnetic environment test data 330 within a historical preset time period, test time 313 and test frequency band 314 corresponding to the historical electromagnetic environment test data within a historical preset time period, etc.

[0084] Interference characteristics refer to characteristics that can characterize relevant information about interference. For example, interference characteristics can characterize various information such as the type of interference and the time period in which the interference occurs. For more information about interference characteristics, please refer to the detailed description later in this specification.

[0085] The initial electromagnetic environment test data refers to the original (eg, the first) electromagnetic environment test data. The analysis and feedback module 150 can obtain the initial electromagnetic environment test data 320 from the storage module 140 .

[0086] The historical preset time period refers to a pre-set time range used to trace back, retrieve and analyze electromagnetic environment test data within a specific time period. For example, the historical preset time period can be the past week or the previous test cycle.

[0087] The historical electromagnetic environment test data 330 within the historical preset time period, and the corresponding test time 313 and test frequency band 314 are acquired by the analysis and feedback module 150 from the storage module 140 .

[0088] like Figure 3 As shown, the test parameter determination model 340 may include a spectrum processing layer 341 and an analysis layer 342 .

[0089] 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 characteristics. The input of the spectrum processing layer 341 may include initial electromagnetic environment test data 320 and historical electromagnetic environment test data 330 within a historical preset time period, etc.; the output is spectrum characteristics 350.

[0090] Spectral characteristics refer to data or features extracted from electromagnetic environment test data to describe the spectral characteristics of signals, so as to reveal information such as the signal's frequency distribution, frequency band occupancy, and power intensity.

[0091] 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.

[0092] In some embodiments, the input of the analysis layer 342 may include interference characteristics 311 , data to be analyzed 312 , spectrum characteristics 350 , test time 313 , and test frequency band 314 , etc.; and the output is test parameter error 360 .

[0093] For information about electromagnetic environment test data, data to be analyzed, and test parameter errors, see Figure 1 Related description in .

[0094] In some embodiments, the output of the spectrum processing layer may be the input of the analysis layer, and the spectrum processing layer and the analysis layer may be obtained by joint training.

[0095] In some embodiments, each group 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 group of data. The sample data for joint training can be obtained through historical data, and the corresponding labels can be manually marked. 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 used as training sample data, and the sample interference features, sample data to be analyzed, sample test time, and sample test frequency band are input into the analysis layer to obtain the test parameter error output by the analysis layer. A loss function is constructed based on the sample data label and the output of the analysis layer, and the parameters of the spectrum processing layer and the analysis layer are updated synchronously. Through parameter updating, a trained spectrum processing layer and analysis layer are obtained.

[0096] The above-mentioned double-layer structure of the test parameter judgment model and the input of various data can help the system to make a more accurate and comprehensive assessment and judgment of the electromagnetic environment, help the model capture potential patterns and abnormal conditions in the spectrum data, enable the model to perform advanced data mining and decision-making, and improve the accuracy and reliability of test parameter judgment.

[0097] In some embodiments, the training process of the test parameter determination 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.

[0098] In some embodiments, the training process of the test parameter judgment model (spectrum processing layer and analysis layer) may include first-stage training and second-stage training, etc. The first-stage training refers to initial training, and the second-stage training refers to training performed after the first-stage training is completed.

[0099] In some embodiments, the first stage training may include: performing the first stage training on the test parameter judgment model based on the first training set. The first training set may include a preset proportion of the first type of data, the second type of data, and the third type of data.

[0100] The preset ratio refers to the ratio setting used to build different categories of data sets when training the test parameter judgment model. The preset ratio determines the relative proportion of each category of data in the training set to ensure that the model can learn the characteristics and patterns of different categories of data. For example, for a data set containing three categories, the preset ratio can be that the data of each category accounts for 1 / 3 of the training set. The preset ratio can be preset according to actual needs.

[0101] The first type of data, the second type of data, and the third type of data refer to three different types of data in the first training set that meet a preset ratio.

[0102] In some embodiments, each group of data in the first category of data is labeled as sample interference characteristics with 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 can obtain the first category of data from a database storing historical electromagnetic environment test data, and the corresponding label is that there is a test parameter error.

[0103] In some embodiments, the second type of data is sample interference characteristics 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 band. 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.

[0104] In some embodiments, each group of data in the third category of data is sample data after noise is applied. For example, the analysis feedback module 150 can obtain at least one group of sample interference characteristics with 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 from a database storing historical electromagnetic environment test data, and apply one or more noises to each group of data (one or more noises include but are not limited to salt and pepper noise, Gaussian noise, and / or Poisson noise, etc.). The analysis feedback module 150 can determine each group of data after the noise is applied as the third category of data, and the corresponding label is that there is a test parameter error.

[0105] In some embodiments, the analysis feedback module 150 can perform joint training of the test parameter judgment model (spectrum processing layer and analysis layer) in the first stage of training based on the first type of data, the second type of data, and the third type of data. Figure 3 The joint training process is similar to the above. For more information, see Figure 3 The above description.

[0106] By training sample data with and without test parameter errors, the test parameter judgment model can learn the features and patterns to distinguish and judge whether the test parameters are wrong. At the same time, adding noise to the third type of data can also help the model better adapt to the noisy environment in practical applications.

[0107] In some embodiments, after the first stage training of the test parameter judgment model (spectrum processing layer and analysis layer) is completed, the analysis feedback module 150 may perform a second stage training.

[0108] In some embodiments, the second stage of training is performed on the test parameter judgment model after the first stage of training based on the second training set, and the second training set may include the fourth category of data and the fifth category of data.

[0109] In some embodiments, the fourth category of 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, which are incorrectly detected as having test parameter errors by the test parameter judgment model after the first stage of training, but are marked as not having test parameter errors in the database storing historical electromagnetic environment test data.

[0110] In some embodiments, the fifth category of data is 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, which are incorrectly detected by the test parameter judgment model after the first stage of training as having no test parameter errors, but are marked as having test parameter errors in the database storing historical electromagnetic environment test data.

[0111] In some embodiments, the analysis feedback module 150 can perform joint training of the test parameter judgment model (spectrum processing layer and analysis layer) after the first stage training based on the fourth type of data and the fifth type of data. Figure 3 The joint training process is similar to the above. For more information, see Figure 3 The above description.

[0112] By using the first training set and the second training set for two stages of training, the test parameter judgment model can include multi-class data training, enhance the model's generalization ability, consider real scenarios, and improve the model's robustness. These features enable the test parameter judgment model to better adapt to test parameter misjudgments in different situations and improve the performance and reliability of the model in practical applications.

[0113] Figure 4 It is an exemplary structural diagram of the configuration of the analysis feedback module shown in some embodiments of this specification.

[0114] In some embodiments, the analysis feedback module 150 may be further configured to determine the number of test cycles 410 for each of the at least one set of test item data. In some embodiments, the analysis feedback module 150 may be further configured to adjust the automatic test time 420 for the at least one set of test item data.

[0115] For more information about at least one set of test item data, see Figure 1 Related description in .

[0116] The number of test cycles refers to the number of times the test item data is repeated. Each group of test item data corresponds to a test cycle number. By adjusting the number of test cycles, the impact of random errors in the test can be reduced and the accuracy of the test results can be improved.

[0117] In some embodiments, the analysis and feedback module 150 may determine the number of test cycles for each set of test item data in a variety of ways. For example, the analysis and feedback module 150 may determine the number of test cycles corresponding to each set of test item data based on the number of test parameter errors for each set of test item data in the historical test results using a preset algorithm.

[0118] The preset algorithm refers to a predetermined algorithm used to calculate the number of test cycles for each set of test item data. For example, the more test parameter errors a set of test item data has and the more frequent the test parameter errors are, the greater the number of test cycles corresponding to the set of test item data. Determining the number of test cycles by using a preset algorithm can avoid the risk of finding that the amount of data is insufficient after manual verification and requiring additional testing.

[0119] The historical test results are the results of the tests that have been completed in the historical time. The historical test results include the number of test parameter errors for each set of test item data. The analysis feedback module can obtain the historical test results from the storage module. For more information about test parameter errors, see Figure 1 The relevant description of it in .

[0120] In some embodiments, the preset algorithm may be: Number of test cycles = k*number of test parameter errors + b

[0121] Among them, k and b are preset coefficients.

[0122] In some embodiments, determining the number of test cycles for each set of test item data also includes a further method, for details, see Figure 5 Related description in .

[0123] The automatic test time refers to the time for automatic testing of each set of test item data. The automatic test time determines the time point and test period for each set of test item data to be tested in the system.

[0124] In some embodiments, the length of the automatic test time of at least one set of test item data may be determined according to the number of test cycles of at least one set of test item data: The length of automatic test time = Δt*number of test cycles

[0125] Among them, Δt is the length of time required for a single test and can be preset.

[0126] In some embodiments, the analysis feedback module 150 can determine the automatic test time according to the test process and the length of the automatic test time. For example, the test process is test item 1, test item 2 and test item 3, and the time required for a single test is 10 minutes, 5 minutes and 8 minutes respectively. The test starts at 9:00 AM, 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.

[0127] In some embodiments, the analysis and feedback module 150 can determine interference features based on historical test data; based on the interference features and the number of test cycles for each set of test item data, determine the automatic test time for at least one set of test item data. For more information about historical test data, see Figure 5 Related description.

[0128] In some embodiments, the analysis and feedback module 150 may determine the interference feature 311 in a variety of ways based on historical test data. In some embodiments, the interference feature 311 may include the occurrence time of occasional human interference or industrial interference.

[0129] Based on the historical test data, the analysis feedback module 150 can count the abnormal data time periods caused by various interferences in the data used for analysis, and use the statistical results as interference features 311.

[0130] In some embodiments, the analysis and feedback module 150 can determine the automatic test time 420 of at least one group of test item data in a variety of ways based on the interference feature 311 and the number of test cycles 410 for each group of test item data. For example, assuming that the test process is test item 1, test item 2 and test item 3, the interference feature is that the interference of category 1 will occur from 9:10 to 9:13, and the interference of category 1 will have a serious impact on test item 1 and test item 3, and will have an impact on test item 2 but will not affect the test result. Assuming that test item 1, test item 2 and test item 3 are to be 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 to 9:10, 9:10 to 9:15 and 9:15 to 9:23 respectively, wherein, because the interference of category 1 from 9:10 to 9:13 does not affect the test result of test item 2, it does not need to be avoided deliberately. For another example, suppose that the test items 1, 2 and 3 are to be 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 and 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, 2 and 3 is 9:13-9:33, 9:33-9:38 and 9:38-9:46 respectively.

[0131] In some embodiments, the automatic test time of at least one set of test item data may also be related to the test requirements and the test duration. For example, when the test process changes, or the test process includes test items that can be interrupted or cannot be interrupted, the automatic test time will be affected to varying degrees. Test requirements refer to the requirements related to the test of at least one set of test item data, such as whether the test can be interrupted, the requirements for the test time point, etc. Test duration refers to the duration of at least one set of test item data.

[0132] 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 through a time determination model based on the interference feature, test requirements, test duration, and the number of test cycles for each set of test item data. The time determination model may be a machine learning model.

[0133] The interference feature 311, the test requirement, the test duration and the number of test cycles 410 for 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.

[0134] The time determination model can be obtained through model training. Each group of sample data in the sample data is the sample interference characteristics, sample test requirements, sample test duration, and the number of sample test cycles for each group of test item data in the historical data. The training label is generated by testing the test data generated by the automatic test time of the test item data corresponding to each group of sample data in the historical data. The automatic test time that generates the least test parameter error is used as the training label corresponding to the group of sample data.

[0135] By analyzing the interference characteristics in the historical test data and considering the number of test cycles, the system can determine a more appropriate automatic test time and optimize the test plan to cope with the presence of interference, which helps to perform more tests in a time period with less interference. In some embodiments of the present specification, based on the interference characteristics, test requirements, test duration, and the number of test cycles for each group of test item data, the automatic test time is determined by a time determination model, which can further improve the accuracy of the determined automatic test time.

[0136] 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 early morning, etc.). In the actual test process, there may be some uncontrollable factors (such as unknown interference in fixed time periods, interference during construction time periods, etc.). Through further configuration of the analysis and feedback module, the system can automatically determine the number of test cycles required for each set of test item data according to specific circumstances; it 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 objectives, and the actual situation, thereby improving the efficiency and accuracy of the test.

[0137] Figure 5 is an exemplary flow chart of determining the number of test cycles according to some embodiments of the present specification. In some embodiments, process 500 may be executed by the analysis feedback module 150 .

[0138] Step 510: Determine test parameter frequent items and corresponding supports of the test parameter frequent items based on historical test data.

[0139] The historical test data at least includes the historical test results of each test item and the number of test parameter errors, etc. The analysis and feedback module 150 can obtain the historical test data from the storage module 140 .

[0140] Test parameter frequent items refer to items with different test item data corresponding to different test times. For example, test parameter frequent items are test item 1 corresponding to 100 test times, test item 1 corresponding to 200 test times, test item 2 corresponding to 100 test times, etc. The support corresponding to the test parameter frequent items refers to the frequency of test parameter errors under a certain number of tests. If the number of tests corresponding to the same test item is different, the support corresponding to the corresponding test parameter frequent items may be different.

[0141] In some embodiments, the analysis and feedback module 150 can determine the test parameter frequent items and their corresponding support in a variety of ways based on the historical test data. For example, the analysis and feedback module 150 can count the number of test parameter errors n for each test item under the same test conditions in the historical test data under the test times i, determine the test items and their test times i with i≥threshold 1 and n≤threshold 2 as the test parameter frequent items, and determine the ratio of n to i as the support corresponding to the test parameter frequent items, wherein the values ​​of threshold 1 and threshold 2 can be preset according to actual needs.

[0142] In some embodiments, i≥threshold 1 indicates that the number of tests for the test item is sufficient, which can exclude occasional errors in 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 abnormal tests (for example, there is a problem with the machine, the test is theoretically correct, but the result shows that the test is wrong), etc.

[0143] Step 520: construct a frequent item database based on the test parameter frequent items and the support corresponding to the test parameter frequent items.

[0144] The frequent item database refers to a database that can represent the test parameter frequent items corresponding to multiple groups of test items and the support corresponding to the test parameter frequent items.

[0145] In some embodiments, the analysis feedback module 150 can construct a frequent item database in a variety of ways based on the test parameter frequent items and the support corresponding to the test parameter frequent items. For example, the feedback module 150 can sort the test parameter frequent items in ascending order of support, and select a preset number of test parameter frequent items with a high ranking and their corresponding support to construct a frequent item database. The preset number can be set as needed.

[0146] Step 530: Determine the number of test cycles for each set of test item data based on the frequent item database. Figure 4 The relevant description of it in .

[0147] In some embodiments, the analysis feedback module 150 may determine the number of test cycles for each 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 in each test item may be selected as the number of test cycles for each set of test item data.

[0148] By executing the steps in process 500, the system can select the test number corresponding to the frequent items of the test parameters with low support as the test cycle number for 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.

[0149] It should be noted that the description of the above-mentioned processes (such as process 200 and process 500) is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 and process 500 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0150] Some embodiments of the present specification also provide a real-time analysis method for electromagnetic monitoring, which can be implemented by a real-time analysis system 100 for electromagnetic monitoring. The specific steps are as follows.

[0151] The first step is to obtain electromagnetic environment test data, which can be implemented by the data transmission module 110 .

[0152] In some embodiments, electromagnetic environment test data may be obtained, 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. For more information on obtaining electromagnetic environment test data, see Figure 1 Detailed description of the data transmission module 110 is provided in FIG.

[0153] The second step is to determine and store the data to be analyzed. This step can be implemented by the data cache module 120 , the processing module 130 and the storage module 140 .

[0154] In some embodiments, the data to be analyzed can be determined and stored based on the electromagnetic environment test data. For details on determining and storing the data to be analyzed, see Figure 1 Detailed description of the data cache module 120, the processing module 130 and the storage module 140.

[0155] The third step is to 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.

[0156] 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 presence of a test parameter error, a parameter error warning is issued. For details on determining whether there is a test parameter error and whether to issue an error warning, see Figure 1 For relevant description about the analysis feedback module 150.

[0157] In some embodiments, determining whether there is a test parameter error based on the data to be analyzed includes: determining the test parameter error through a test parameter judgment model based on the data to be analyzed, wherein the test parameter judgment model is a machine learning model.

[0158] In some embodiments, if there is a test parameter error, the test will be interrupted and a calibration request will be issued.

[0159] In some embodiments, if a calibration complete instruction is obtained, the test will be restarted.

[0160] The above method and steps for determining whether there is a test parameter error are performed by the analysis feedback module 150. For details on determining the test parameter error, see Figure 2 A description of the functionality of the analysis feedback module.

[0161] In some embodiments, the electromagnetic monitoring real-time analysis method further includes determining the number of test cycles for 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.

[0162] The method and steps are performed by the analysis feedback module 150. For details on determining the number of test cycles and adjusting the automatic test time, see Figure 4 Description of how to configure the analytics feedback module.

[0163] In some embodiments, adjusting the automatic test time of the at least one set of test item data includes two steps, which are performed by the analysis feedback module 150 .

[0164] The first step is to determine the interference characteristics.

[0165] In some embodiments, interference characteristics may be determined based on historical test data, wherein the interference characteristics include the occurrence time of occasional human interference or industrial interference.

[0166] The second step is to determine the automatic test time.

[0167] In some embodiments, the automatic test time of at least one set of test item data may be determined based on the interference feature and the number of test cycles of each set of test item data.

[0168] For details on adjusting the automatic test time of at least one set of test item data, see Figure 4 Determining the timing of automatic testing is described in .

[0169] One or more embodiments of the present specification provide an electromagnetic monitoring real-time analysis device, including a processor, wherein the processor is used to execute an electromagnetic monitoring real-time analysis method.

[0170] One or more embodiments of the present specification also provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the real-time analysis method for electromagnetic monitoring.

[0171] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0172] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0173] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this 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 an existing server or mobile device.

[0174] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0175] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0176] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0177] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A parameter analysis system for electromagnetic monitoring, characterized in that: include: 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, wherein the electromagnetic environment test data includes at least one set of test item data, and the test item data includes sampled 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 and feedback module, send the electromagnetic environment test data 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 in the storage module, 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 test parameter judgment model includes a spectrum processing layer and an analysis layer.

2. The system according to claim 1, characterized in that The analysis feedback module is further configured: In response to the test parameter error, a parameter error warning is issued, the test is interrupted, and a calibration request is issued; In response to obtaining the calibration completion instruction, the test is restarted.

3. The system according to claim 1, characterized in that The analysis feedback module is further configured to: Based on historical test data, determine the test parameter frequent items and the corresponding support of the test parameter frequent items; Based on the test parameter frequent items and the corresponding support of the test parameter frequent items, a frequent item database is constructed; Determining the number of test cycles for each group of test item data in the at least one group of test item data based on the frequent item database; Adjust the automatic test time of the at least one set of test item data.

4. The system according to claim 3, characterized in that The analysis feedback module is further configured to: Determining interference characteristics based on historical test data, wherein the interference characteristics include the occurrence time of occasional human interference or industrial interference; Based on the interference feature and the number of test cycles of each group of test item data, an automatic test time of the at least one group of test item data is determined.

5. The system according to claim 4, characterized in that The analysis feedback module is further configured to: Based on the interference characteristics, test requirements, test duration and the number of test cycles for each group of test item data, the automatic test time of at least one group of test item data is determined through a time determination model; wherein the time determination model is a machine learning model.

6. A parameter analysis method for electromagnetic monitoring, comprising: Receive electromagnetic environment test data, the electromagnetic environment test data including at least one set of test item data, the test item data including sampling spectrum data, calibration data, antenna and system gain data; Determining data to be analyzed based on the electromagnetic environment test data, and storing the electromagnetic environment test data and the data to be analyzed; Based on the data to be analyzed, 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 test parameter judgment model includes a spectrum processing layer and an analysis layer.

7. The method according to claim 6, characterized in that The method further comprises: In response to the test parameter error, a parameter error warning is issued, the test is interrupted, and a calibration request is issued; In response to obtaining the calibration completion instruction, the test is restarted.

8. The method according to claim 6, characterized in that The method further comprises: Based on historical test data, determine the test parameter frequent items and the corresponding support of the test parameter frequent items; Based on the test parameter frequent items and the corresponding support of the test parameter frequent items, a frequent item database is constructed; Determining the number of test cycles for each group of test item data in the at least one group of test item data based on the frequent item database; Adjust the automatic test time of the at least one set of test item data.

9. The method according to claim 8, characterized in that The method further comprises: Determining interference characteristics based on historical test data, wherein the interference characteristics include the occurrence time of occasional human interference or industrial interference; Based on the interference feature and the number of test cycles of each group of test item data, an automatic test time of the at least one group of test item data is determined.

10. The method according to claim 9, characterized in that The determining the automatic test time of the at least one group of test item data based on the interference feature and the number of test cycles of each group of test item data comprises: Based on the interference characteristics, test requirements, test duration and the number of test cycles for each group of test item data, the automatic test time of at least one group of test item data is determined through a time determination model; wherein the time determination model is a machine learning model.

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