CN117169801B

CN117169801B - Electromagnetic environment monitoring and calibrating system, method, device and medium

Info

Publication number: CN117169801B
Application number: CN202311446615.2A
Authority: CN
Inventors: 王凡; 刘旭; 汪代均; 张光云; 刘冬; 蒋波
Original assignee: Chengdu Dechen Borui Technology Co ltd
Current assignee: Chengdu Dechen Borui Technology Co ltd
Priority date: 2023-11-02
Filing date: 2023-11-02
Publication date: 2024-03-19
Anticipated expiration: 2043-11-02
Also published as: CN117169801A; CN118191438A

Abstract

The embodiment of the specification provides an electromagnetic environment monitoring and calibrating system, a method, a device and a medium, wherein the method is executed based on the electromagnetic environment monitoring and calibrating system, the system comprises an environment monitoring module, a parameter determining module, a calibration standard source, a data acquisition module and a data processing module, and the method comprises the following steps: acquiring environmental data; determining test signal parameters based on the environment data and the switched radio frequency channel information; the test signal parameters at least comprise the frequency range, the frequency distribution and the corresponding test force of different electromagnetic frequencies of the test signal; emitting a test signal based on the test signal parameter; collecting calibration data corresponding to the test signals; and determining a monitoring compensation parameter based on the calibration data. The method is realized by an electromagnetic environment monitoring and calibrating device. The method also operates after being read by computer instructions stored on a computer readable storage medium. The invention can effectively ensure the accuracy of monitoring data by compensating and calibrating the electromagnetic signals in real time.

Description

Electromagnetic environment monitoring and calibrating system, method, device and medium

Technical Field

The present disclosure relates to the field of electromagnetic environment monitoring technologies, and in particular, to an electromagnetic environment monitoring calibration system, an electromagnetic environment monitoring calibration method, an electromagnetic environment monitoring calibration device, and a medium.

Background

Because the radio telescope has extremely high sensitivity and is extremely easy to be interfered by radio from the outside and the radio telescope, thereby influencing normal operation and scientific output, the electromagnetic environment in which the radio telescope is positioned needs to be monitored. However, during electromagnetic environment monitoring, the accuracy of the monitored data may be affected by the changes of the channel frequency caused by switching the radio frequency channel each time and the changes of the electromagnetic environment, which may cause the changes of the gain of the antenna and the loss of the system. Accordingly, there is a need for an electromagnetic environment monitoring calibration system, method, apparatus, and medium.

Disclosure of Invention

In order to automatically calibrate monitoring data when switching radio frequency channels so as to ensure the accuracy of the monitoring data, the specification provides an electromagnetic environment monitoring and calibrating system, an electromagnetic environment monitoring and calibrating method, an electromagnetic environment monitoring and calibrating device and a medium.

One or more embodiments of the present specification provide an electromagnetic environment monitoring calibration system, the system comprising: the system comprises an environment monitoring module, a parameter determining module, a calibration standard source, a data acquisition module and a data processing module; the environment monitoring module is configured to acquire environment data; the parameter determining module is configured to determine test signal parameters based on the switched radio frequency channel information, wherein the test signal parameters comprise a frequency range, frequency distribution and test forces corresponding to different electromagnetic frequencies of the test signal; the calibration standard source is configured to emit the test signal based on the test signal parameter; the data acquisition module is configured to acquire calibration data corresponding to the test signals; the data processing module is configured to determine a monitoring compensation parameter based on the calibration data.

One or more embodiments of the present specification provide an electromagnetic environment monitoring calibration method, performed based on an electromagnetic environment monitoring calibration system, the system including an environment monitoring module, a parameter determination module, a calibration standard source, a data acquisition module, and a data processing module, the method comprising: acquiring environmental data; determining test signal parameters based on the environment data and the switched radio frequency channel information; the test signal parameters at least comprise the frequency range, the frequency distribution and the corresponding test force of different electromagnetic frequencies of the test signal; emitting the test signal based on the test signal parameter; collecting calibration data corresponding to the test signals; and determining a monitoring compensation parameter based on the calibration data.

One or more embodiments of the present specification provide an electromagnetic environment monitoring calibration apparatus comprising at least one memory and at least one processor; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute some of the computer instructions to implement the foregoing method.

One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform the aforementioned method.

Benefits provided by some embodiments of the present description include, but are not limited to: (1) The electromagnetic signal can be compensated and calibrated in real time by determining the parameters of the test signal and sending out the test signal based on the parameters of the test signal and by collecting the calibration data corresponding to the test signal and determining the monitoring compensation parameters based on the calibration data, so that the accuracy of the monitoring data can be effectively ensured; (2) Based on the historic influence degree of different electromagnetic frequencies and the accuracy of the historic monitoring data, the historic reliability of the test signals of the different electromagnetic frequencies is determined, and the key reinforcement test is carried out on the electromagnetic frequencies with lower reliability, so that the reliability of the calibration result can be effectively improved; (3) The monitoring compensation parameters are determined through antenna gain data and system loss data obtained by segmenting the test signals, so that the accuracy of the monitoring compensation parameters can be effectively ensured; (4) The gain parameters are determined based on the antenna information and the environment information, the influence of the antenna information and the environment information on the gain parameters is comprehensively considered, and the obtained gain parameters can be more accurate, so that the accuracy of the antenna gain data is improved; (5) The environmental impact map is constructed based on the effective environmental data distribution, the gain parameters are determined based on the environmental impact map and the antenna information through the trained gain analysis model, and the accuracy of the gain parameters can be effectively ensured, so that a foundation can be laid for rapidly and accurately determining the antenna gain data. In addition, the gain analysis model is trained in a combined training mode, so that the number of samples required by training can be reduced, and the training efficiency can be improved.

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 block diagram of an electromagnetic environment monitoring calibration system according to some embodiments of the present description;

FIG. 2 is an exemplary flow chart of an electromagnetic environment monitoring calibration method according to some embodiments of the present description;

FIG. 3 is an exemplary flow chart of a test signal parameter determination method according to some embodiments of the present description;

FIG. 4 is an exemplary flow chart of a method of monitoring compensation parameter determination shown in accordance with some embodiments of the present description;

fig. 5 is an exemplary flow chart of a method of determining antenna gain data according to some embodiments of the present description;

fig. 6 is an exemplary schematic diagram of a gain analysis model shown in accordance with some embodiments of the present description.

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.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates 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 block diagram of an electromagnetic environment monitoring calibration system according to some embodiments of the present description.

As shown in fig. 1, the electromagnetic environment monitoring calibration system 100 may include an environment monitoring module 110, a parameter determination module 120, a calibration standard source 130, a data acquisition module 140, and a data processing module 150.

The environment monitoring module 110 is a module for monitoring the electromagnetic environment in real time to acquire environmental data.

The parameter determination module 120 refers to a module for determining electromagnetic environment monitoring related data. In some embodiments, the parameter determination module 120 may be configured to determine the test signal parameters based on the switched radio frequency channel information. The test signal parameters comprise the frequency range, the frequency distribution and the corresponding test force of different electromagnetic frequencies of the test signal.

In some embodiments, the parameter determination module 120 may be further configured to: determining basic signal parameters based on the switched radio frequency channel information; determining an enhancement signal parameter based on the environmental data; and determining a test signal parameter based on the base signal parameter and the enhancement signal parameter.

Calibration standard source 130 refers to a signal emitting device or apparatus for emitting test signals. In some embodiments, calibration standard source 130 may be used to emit test signals based on test signal parameters.

The data acquisition module 140 refers to a module for acquiring data related to electromagnetic environment monitoring. In some embodiments, the data acquisition module 140 may be configured to acquire calibration data corresponding to the test signals.

The data processing module 150 refers to a module for processing data related to electromagnetic environment monitoring. In some embodiments, the data processing module 150 may be configured to determine the monitoring compensation parameter based on the calibration data.

In some embodiments, the data processing module 150 may be further configured to: determining antenna gain data based on the calibration data and the switched radio frequency channel information; determining system loss data based on the antenna gain data, the test signal parameters, and the calibration data; and determining a monitoring compensation parameter based on the antenna gain data and the system loss data.

In some embodiments, the data processing module 150 may be further configured to: acquiring antenna information; determining a gain parameter based on the antenna information and the environmental data; and determining antenna gain data by a preset method based on the gain parameters.

For more on the environment monitoring module, the parameter determination module, the calibration standard source, the data acquisition module and the data processing module, reference may be made to the relevant description elsewhere in this specification.

It should be noted that the above description of the electromagnetic environment monitoring calibration system 100 and its modules 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 environment monitoring module 110, the parameter determination module 120, the calibration standard source 130, the data acquisition module 140, and the data processing module 150 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 described above. 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 of an electromagnetic environment monitoring calibration method according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by an electromagnetic environment monitoring calibration system.

At step 210, environmental data is obtained.

Because the radio telescope has extremely high sensitivity and is extremely easy to be interfered by radio from the outside and the radio telescope, thereby affecting normal operation and scientific output, the electromagnetic environment needs to be monitored. The environmental data refers to related data obtained during electromagnetic environment monitoring. In some embodiments, the environmental data may include environmental noise data, environmental obstacle data, environmental shock data, environmental atmosphere data, electromagnetic interference source data, and the like.

The environmental noise data may reflect noise conditions, such as noise sources, during electromagnetic environment monitoring. The environmental obstacle data may reflect an obstacle condition at the time of electromagnetic environment monitoring. An obstacle refers to an object that affects or interferes with the propagation of electromagnetic radiation, such as a building, tree, etc. The environmental vibration data can reflect vibration conditions during electromagnetic environment monitoring, such as ground vibration conditions caused by large-scale machine operation. The ambient atmosphere data may reflect the atmosphere conditions, such as the temperature, humidity, pressure, etc., of the atmosphere during electromagnetic environment monitoring. The electromagnetic interference source data can reflect the electromagnetic interference source condition during electromagnetic environment monitoring, such as the position of the electromagnetic interference source. Electromagnetic interference sources refer to devices or apparatuses capable of generating electromagnetic radiation. For example, electromagnetic interference sources may include towers, base stations, broadcasters, and the like.

In some embodiments, the environmental data may be obtained by an environmental monitoring module. In some embodiments, the environmental monitoring module may include a plurality of sensors, each for acquiring one of the environmental data. For example, the environmental monitoring module may include a sound sensor for acquiring environmental sound data. For another example, the environmental monitoring module may include a visual sensor for acquiring environmental obstacle data. For another example, the environmental monitoring module may include a vibration sensor for acquiring environmental vibration data. For another example, the environmental monitoring module may include a temperature and humidity sensor, a barometer, etc. for acquiring environmental atmosphere data, respectively.

In some embodiments, the environmental monitoring module may determine electromagnetic interference source data through a first preset table. For example, since the antennas of different radio frequency channels can receive electromagnetic signals in different directions, a first preset table can be constructed based on the antenna direction and the electromagnetic interference sources, and the corresponding relationship exists between the antenna direction and the positions and the number of the electromagnetic interference sources, so that the electromagnetic interference source data can be determined according to the antenna direction of the radio frequency channels after switching. The position of the electromagnetic interference source is understood to be the actual position of the electromagnetic interference source in the electromagnetic environment. In some embodiments, the location and number of electromagnetic interference sources may be manually entered into the electromagnetic environment monitoring system in advance.

It should be noted that, multiple sensors can be set up independently, also can integrate and set up in environmental monitoring module, can realize obtaining environmental data's function can.

Step 220, determining the test signal parameters based on the environment data and the switched radio frequency channel information.

Because electromagnetic environment monitoring is performed through the radio frequency channel, different radio frequency channels monitor electromagnetic signals with different electromagnetic frequencies, and therefore the radio frequency channel needs to be switched when monitoring the electromagnetic signals with different electromagnetic frequencies. Switching is the way in which radio frequency channels are switched, and one radio frequency channel can be switched to another.

The information of the radio frequency channel after switching refers to information related to the radio frequency channel after switching. In some embodiments, the information of the radio frequency channel after switching may include antenna information, modulation mode, power limitation, and the like corresponding to the radio frequency channel after switching.

The antenna information refers to information related to antennas corresponding to the switched radio frequency channels. For example, the antenna information may include an antenna type, an antenna reception frequency range, an antenna direction, antenna profile parameters (radius, depth, curvature, etc.), and the like.

In some embodiments, the radio frequency channel information may be preset in advance and have a corresponding relationship with the radio frequency channel, that is, different radio frequency channels correspond to different radio frequency channel information, and when the electromagnetic environment monitoring calibration system switches different radio frequency channels to implement electromagnetic environment monitoring, the parameter determining module may automatically obtain the radio frequency channel information after switching.

The test signal parameter refers to a parameter related to the test signal. In some embodiments, the test signal parameters may include a frequency range, a frequency distribution, and test strength corresponding to different electromagnetic frequencies of the test signal.

The test signal refers to a signal emitted by a calibration standard source for calibrating the monitoring data. The monitoring data may be signal data obtained by the radio frequency channel during electromagnetic environment monitoring.

The frequency range of the test signal refers to the range of electromagnetic frequency values of the test signal. For example, the frequency range of the test signal may be 30GHz to 40 GHz.

The frequency distribution of the test signal may reflect the distribution of the electromagnetic frequency of the test signal. In some embodiments, the frequency distribution of the test signal may be uniform, such as 30GHz, 30.2GHz, 30.4GHz, etc. of the electromagnetic frequency of the test signal. In some embodiments, the frequency distribution of the test signal may be non-uniform, such as 30GHz, 30.2GHz, 30.7GHz, etc. of the electromagnetic frequency of the test signal.

The test strength corresponding to different electromagnetic frequencies refers to the test times corresponding to test signals with different electromagnetic frequencies. The more the test times are, the greater the test force is, and the more accurate the calibration data corresponding to the test signals is.

In some embodiments, the test signal parameters may also include a transmit power of the test signal. When the test signal of the same electromagnetic frequency is tested a plurality of times, the transmission power of the test signal of the electromagnetic frequency may be different. In some embodiments, the transmit power of the test signal may be uniformly set according to the antenna information (e.g., antenna receive frequency range).

In some embodiments, the parameter determination module may determine the test signal parameters based on the environmental data and the switched radio frequency channel information in a variety of ways. For example, the parameter determination module may determine the test signal parameters through a second preset table. The second preset table can be constructed based on the environment data, the radio frequency channel information after switching and the test signal parameters corresponding to the calibration data with good calibration effect in the historical data.

For more details on how to determine the test signal parameters, see fig. 3 and its associated description.

Step 230, issuing a test signal based on the test signal parameters.

In some embodiments, the calibration standard source may automatically emit the test signal based on the test signal parameters. In some embodiments, based on the test signal parameters, the electromagnetic environment monitoring calibration system may generate control instructions to control the calibration standard source to emit the test signal. For example, the electromagnetic environment monitoring calibration system may include a control module that may generate control instructions (e.g., signal-issuing instructions) based on the test signal parameters, and issue corresponding test signals immediately upon receipt of the control instructions by the calibration standard source.

Step 240, collect calibration data corresponding to the test signal.

The calibration data is signal data corresponding to the test signals acquired by the data acquisition module. In some embodiments, the calibration data may include electromagnetic frequency, electromagnetic power, energy of the electromagnetic signal, wavelength of the electromagnetic signal, and the like, as acquired by the data acquisition module. It will be appreciated that different test signals correspond to different calibration data. In some embodiments, the calibration data may be collected by a data collection module. For example, the data acquisition module may acquire calibration data through the antenna of the switched radio frequency channel.

For more details on calibration data, reference may be made to the relevant description of the rest of the present specification (e.g. fig. 4 and its associated description).

Step 250, based on the calibration data, determining a monitoring compensation parameter.

The monitoring compensation parameter refers to a parameter for performing calibration compensation on a related parameter of the electromagnetic signal. For example, the monitoring compensation parameter may include at least a power compensation parameter.

In some embodiments, based on the calibration data, the data processing module may determine the monitoring compensation parameter in a variety of ways. The data processing module may compare the calibration data with the corresponding test signal parameters to obtain an electromagnetic power difference between the test signal sent by the calibration standard source and the calibration data collected by the data collecting module, and determine the electromagnetic power difference as the corresponding power compensation parameter. For example, if the emission power of the electromagnetic signal with the frequency of 30GHz is 200W and the electromagnetic power in the calibration data is 180W, the power compensation parameter of the electromagnetic signal with the frequency of 30GHz under the radio frequency channel is 20W.

For more on how to determine the monitoring compensation parameters, see fig. 4 and its associated description.

According to some embodiments of the present disclosure, through the environment data and the radio frequency channel information after switching, the test signal parameters are determined, and the test signal is sent out based on the test signal parameters, and through collecting the calibration data corresponding to the test signal, and determining the monitoring compensation parameters based on the calibration data, the electromagnetic signal can be compensated and calibrated in real time, so that the accuracy of the monitoring data can be effectively ensured.

Fig. 3 is an exemplary flow chart of a method of determining parameters of a test signal according to some embodiments of the present description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a parameter determination module.

Step 310, determining a basic signal parameter based on the switched radio frequency channel information.

The base signal parameter refers to an initial value or initial range of the test signal parameter. It will be appreciated that the basic signal parameters may include a basic frequency range, a basic frequency distribution, basic test forces corresponding to different electromagnetic frequencies, etc. of the test signal.

In some embodiments, the parameter determination module may determine the antenna receiving frequency range in the switched radio frequency channel information as the base frequency range of the test signal.

In some embodiments, the parameter determination module may divide the fundamental frequency range of the test signal according to a preset rule to determine the fundamental frequency distribution of the test signal. The preset rule refers to a preset frequency division rule. For example, the preset rule may include equally spaced (e.g., 0.2 GHz spacing between adjacent frequencies) or unequally spaced (e.g., 0.2 GHz spacing between adjacent frequencies, 0.4 GH spacing, etc.) divisions. In some embodiments, the basic test forces corresponding to different electromagnetic frequencies may be determined by manual preset, and the basic test forces corresponding to different electromagnetic frequencies may be the same.

At step 320, enhanced signal parameters are determined based on the environmental data.

The enhanced signal parameter refers to a test signal parameter for enhancing the test effect. In some embodiments, enhancing signal parameters may be understood as increasing the strength of the test signal for certain key electromagnetic frequencies based on the base signal parameters. It will be appreciated that the enhancement signal parameter may include a test strength increase value corresponding to the accent electromagnetic frequency.

In some embodiments, based on the environmental data, the parameter determination module may determine the accent electromagnetic frequency, and thus the enhanced signal parameter, through historical data. The parameter determining module may determine, based on the historical data, an electromagnetic frequency corresponding to an electromagnetic signal with a data amount monitored by the radio frequency channel exceeding a preset threshold as the key electromagnetic frequency under the historical environmental data meeting the preset condition; and correspondingly increasing the testing force corresponding to the key electromagnetic frequency, so that the final testing force corresponding to the key electromagnetic frequency is larger than the basic testing force corresponding to the key electromagnetic frequency. The preset conditions refer to preset similarity judging conditions. For example, the preset condition may be that the similarity with the current environmental data is not less than a similarity threshold or the like. It should be noted that the test force increasing value corresponding to the key electromagnetic frequency can be determined according to the actual situation.

In some embodiments, determining the enhanced signal parameters based on the environmental data may further comprise: based on the history monitoring effect, determining the history reliability of test signals with different electromagnetic frequencies under the same environmental data; and determining an enhancement signal parameter based on the historical reliability.

The history monitoring effect may reflect the accuracy of the history monitoring data. For example, the higher the accuracy of the history monitoring data, the better the history monitoring effect.

The historical monitoring data refers to electromagnetic signals of a plurality of electromagnetic frequencies detected by each radio frequency channel during electromagnetic environment monitoring. The accuracy of the historical monitoring data may reflect how well the historical monitoring data matches the actual electromagnetic interference data in the environment.

In some embodiments, the accuracy of the historical monitoring data may be determined based on historical observations of the radio astronomical telescope. For example, the parameter determination module may determine the accuracy of the historical monitoring data by analyzing the difference in signal-to-noise ratio from the expected signal-to-noise ratio in the historical observation of the radio astronomical telescope. For example, the greater the difference in signal-to-noise ratio from the expected signal-to-noise ratio of the radio astronomical telescope historical observation data, the lower the accuracy of the historical monitoring data. The expected signal-to-noise ratio refers to a preset signal-to-noise ratio value. In some embodiments, the desired signal-to-noise ratio may be an empirical, experimental, or analog value, or the like.

The historical reliability of the test signal refers to the reliability of the calibration result obtained by the test signals with different electromagnetic frequencies in the same environmental data in the historical data. In some embodiments, the historical reliability of the test signal may be determined by the accuracy of the historical monitoring data after the electromagnetic signal is compensated by the monitoring compensation parameters derived from the test signal. It will be appreciated that the higher the accuracy of the historical monitoring data, the more accurate the monitoring compensation parameters and the higher the historical reliability of the test signal.

In some embodiments, based on the historical reliability of the test signal, the parameter determination module may take as the accent electromagnetic frequency the electromagnetic frequency corresponding to the test signal for which the historical reliability is below the reliability threshold. The reliability threshold value refers to a preset reliability value. In some embodiments, the reliability threshold may be set manually.

In some embodiments, the test strength increase value (enhanced signal parameter) corresponding to the key electromagnetic frequency is related to the historical reliability of the test signal of the key electromagnetic frequency, and the lower the historical reliability is, the greater the test strength corresponding to the key electromagnetic frequency is. For example, the test strength increment value corresponding to the key electromagnetic frequency may be an integer value of a product of the historical reliability of the key electromagnetic frequency and the corresponding basic test strength.

According to some embodiments of the specification, based on environmental data and historical monitoring effects, the historical reliability of test signals of different electromagnetic frequencies is determined, and key reinforcement tests are conducted on electromagnetic frequencies with lower historical reliability, so that the reliability of calibration results is improved.

In some embodiments, determining the historical reliability of the test signal for the different electromagnetic frequencies based on the environmental data and the historical monitoring effect may further comprise: acquiring the history monitoring data of the switched radio frequency channel and the accuracy of the history monitoring data; determining the historical influence degree of different electromagnetic frequencies based on the duty ratio of the historical monitoring data amount of the different electromagnetic frequencies; and determining the historical reliability of the test signals of the different electromagnetic frequencies based on the historical influence of the different electromagnetic frequencies and the accuracy of the historical monitoring data.

In some embodiments, the historical monitoring data has a correspondence to the accuracy of the historical monitoring data, one historical monitoring data corresponding to the accuracy of one historical monitoring data. In some embodiments, the parameter determination module may obtain historical monitoring data of the switched radio frequency channel based on the historical data, and accuracy of the historical monitoring data.

The duty cycle of the historical monitoring data amounts for different electromagnetic frequencies refers to the ratio of the number of monitoring data for each electromagnetic frequency to the total number of monitoring data for the entire radio frequency channel. In some embodiments, the parameter determination module may obtain the duty cycle of the historical monitored data amount for each electromagnetic frequency by comparing the number of monitored data for each electromagnetic frequency with the total number of monitored data for the entire radio frequency channel.

The historical influence degree of different electromagnetic frequencies refers to the influence degree of monitoring data of different electromagnetic frequencies in the whole electromagnetic environment monitoring. In some embodiments, the historic impact of different electromagnetic frequencies may be represented by the duty cycle of the historic monitored data volume of different electromagnetic frequencies. The lower the duty ratio of the amount of the history monitoring data of the electromagnetic frequency, the less the electromagnetic signal of the electromagnetic frequency is in the electromagnetic environment, the less the influence on the whole electromagnetic environment monitoring is, that is, the less the history influence of the electromagnetic frequency is.

In some embodiments, the parameter determination module may determine the historical reliability of the test signal for the different electromagnetic frequencies by calculation based on the historical impact of the different electromagnetic frequencies, the accuracy of the historical monitoring data.

In some embodiments, the historical reliability of the test signal for an electromagnetic frequency may be positively correlated to the historical impact of the electromagnetic frequency and the average accuracy of the historical monitoring data for the electromagnetic frequency. For example, the historical reliability of the test signal of the electromagnetic frequency p=the historical influence of the electromagnetic frequency p×the average accuracy of the historical monitoring data of the electromagnetic frequency P. Wherein the average accuracy of the historical monitoring data of the electromagnetic frequency P may be determined based on the accuracy of the plurality of historical monitoring data of the electromagnetic frequency P.

It should be noted that, the historical reliability of the test signals with different electromagnetic frequencies may also be determined by other calculation methods or a third preset table, etc., which can reflect the relationship that the greater the historical influence of the electromagnetic frequency is, the higher the accuracy of the historical monitoring data is, and the greater the historical reliability of the corresponding electromagnetic frequency is.

According to some embodiments of the specification, based on the historic influence degree of different electromagnetic frequencies and the accuracy of the historic monitoring data, the historic reliability of the test signals of different electromagnetic frequencies is determined, and the influence degree of the monitoring data of different electromagnetic frequencies in the whole electromagnetic environment monitoring is further considered, so that the rationality of the historic reliability of the test signals of different electromagnetic frequencies can be ensured to a certain extent, and the reliability of the calibration result is further improved.

Step 330, determining test signal parameters based on the base signal parameters and the enhancement signal parameters.

In some embodiments, the parameter determining module may combine the base signal parameter and the enhancement signal parameter based on the base signal parameter and the enhancement signal parameter, and use the combined parameter as the test signal parameter. For example, the base signal parameter is (X ₁ ，Y ₁ ，Z ₁ ) The enhanced signal parameter is (X ₁ ，Y ₁ ，Z ₂ ) The test signal parameter may be (X) ₁ ，Y ₁ ，Z ₁ +Z ₂ ）。

In some embodiments of the present disclosure, based on the historic influence degree of different electromagnetic frequencies and the accuracy of the historic monitoring data, the historic reliability of the test signals of different electromagnetic frequencies is determined, and the key reinforcement test is performed on the electromagnetic frequencies with lower reliability, so that the reliability degree of the calibration result can be effectively improved.

FIG. 4 is an exemplary flow chart of a method of monitoring compensation parameter determination according to some embodiments of the present description. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, the process 400 may be performed by a data processing module.

In step 410, antenna gain data is determined based on the calibration data and the switched radio frequency channel information.

The antenna gain data refers to data related to the radiation efficiency of the antenna in a specific direction.

In some embodiments, the data processing module may determine the antenna gain data in a number of ways based on the calibration data and the switched radio frequency channel information.

In some embodiments, the data processing module may determine a corresponding antenna gain formula according to antenna information (such as an antenna type, an antenna performance, an antenna profile parameter (radius, depth, curvature, antenna length, etc.) in the switched rf channel information, and determine antenna gain data based on the corresponding antenna gain formula and the antenna information.

The antenna gain formula refers to a calculation formula for determining antenna gain data. In some embodiments, the data processing module may determine the antenna gain formula based on the antenna type. For example, for a parabolic antenna, the antenna gain formula may beWherein->Is antenna gain data; d is the diameter of the paraboloid, and is determined based on antenna information (such as antenna shape parameters); />Determining, for a wavelength of the electromagnetic signal, based on the calibration data; 4.5 is empirical data. For another example, for an upright omni-directional antenna, the antenna gain formula may be +.>Wherein->Is antenna gain data; l is the length of the antenna, and is determined based on the antenna information; / >Is the wavelength of the electromagnetic signal; 2 is empirical data.

In some embodiments, according to the corresponding antenna gain formula, the data processing module may obtain the antenna gain data by substituting corresponding data (e.g., antenna information, calibration data, etc.) in the antenna information into the corresponding antenna gain formula.

For more on how to determine the antenna gain data, see fig. 5-6 and their associated description.

Step 420, determining system loss data based on the antenna gain data, the test signal parameters, and the calibration data.

The system loss data refers to the energy loss of the electromagnetic environment monitoring calibration system. In some embodiments, the system loss data may include energy loss from all elements or modules between a signal source (e.g., a calibration standard source) to a receiver (e.g., a data acquisition module).

In some embodiments, the data processing module may calculate system loss data based on the antenna gain data, the test signal parameters, and the calibration data.

In some embodiments, the system loss data may be positively correlated with the antenna gain data and the transmit power of the test signal in the test signal parameters, and negatively correlated with the energy of the electromagnetic signal in the calibration data. Exemplary calculation formulas include: system loss data = transmit power of test signal + antenna gain data-energy of electromagnetic signal. For the manner in which the transmit power of the test signal and the energy of the electromagnetic signal are obtained, reference may be made to fig. 2 and its associated description.

In some embodiments, determining system loss data based on the antenna gain data, the test signal parameters, and the calibration data may further comprise: segmenting the test signal according to the test signal parameters to obtain segmented signals; and determining system loss data corresponding to the segmented signal based on the segmented signal parameters, calibration data corresponding to the segmented signal, and antenna gain data.

The segmented signal is a signal obtained by uniformly segmenting the test signal according to the test signal parameters. In some embodiments, the data processing module may segment the test signal uniformly according to the frequency distribution of the test signal in the test signal parameters, so that the frequency band range of the test signal in each segment is uniform in size, so as to obtain the segmented signal.

For example, the frequency range of the test signal in the test signal parameters is 30 GHz-40 GHz, the frequency distribution of the test signal is 30GHz, 32GHz, 34GHz, 36GHz, 38GHz, 40GHz, and the frequency range of the test signal in each segment is 30 GHz-32 GHz, 32 GHz-34 GHz, 34 GHz-36 GHz, 36 GHz-38 GHz, 38 GHz-40 GHz.

The segment signal parameters refer to the corresponding test signal parameters of the segment signal. In some embodiments, the method for determining the system loss data corresponding to the segmented signal based on the segmented signal parameter, the calibration data corresponding to the segmented signal, and the antenna gain data is similar to the method for determining the system loss data based on the antenna gain data, the test signal parameter, and the calibration data, and is not repeated herein. In some embodiments, the system loss data corresponding to each segmented signal may also be used to characterize the energy loss of the electromagnetic environment monitoring calibration system.

It can be understood that different segmented signals correspond to different system loss data, the narrower the segmentation is, the more accurate the obtained system loss data is, so that a foundation can be laid for the follow-up determination of more accurate monitoring compensation parameters, but meanwhile, the system operation pressure is larger and the operation efficiency is lower, and therefore, the system loss data corresponding to the segmented signals are determined by reasonably segmenting the test signals, the operation pressure of the system can be considered, and the operation efficiency is improved while the accuracy of the system loss data is improved.

In some embodiments, the segmenting the test signal may further include: the segments are made based on the historical reliabilities of the test signals of different electromagnetic frequencies, the lower the historical reliabilities the narrower the segments to which the test signals of electromagnetic frequencies belong.

Narrower segmentation means that the lower the historical reliability, the smaller the frequency segment range of the segment to which the test signal of electromagnetic frequency belongs. For example, the frequency band range of the segment to which the test signal of the conventional electromagnetic frequency belongs is 1GHz, and the frequency band range of the segment to which the test signal of the electromagnetic frequency of which the history reliability is low belongs is 0.5GHz. For a specific description of the historical reliability of test signals of different electromagnetic frequencies, reference may be made to fig. 3 and its associated description.

According to some embodiments of the present disclosure, the system loss data of the segmented signals can be more targeted by segmenting based on the historical reliabilities of the test signals with different electromagnetic frequencies, so that the accuracy of the system loss data can be effectively improved.

At step 430, monitoring compensation parameters are determined based on the antenna gain data and the system loss data.

In some embodiments, the data processing module may calculate the difference between the system loss data and the antenna gain data and take the difference between the two as the monitored compensation parameter. Illustratively, the monitored compensation parameter corresponding to the segmented signal = system loss data corresponding to the segmented signal-antenna gain data corresponding to the segmented signal.

In some embodiments of the present disclosure, accuracy of monitoring compensation parameters may be effectively ensured by determining the monitoring compensation parameters by using antenna gain data and system loss data obtained by segmenting the test signal.

Fig. 5 is an exemplary flow chart of a method of determining antenna gain data according to some embodiments of the present description. As shown in fig. 5, the process 500 includes the following steps. In some embodiments, the process 500 may be performed by a data processing module.

Step 510, obtain antenna information.

For a specific description of the antenna information, reference may be made to fig. 2 and its related description. In some embodiments, the antenna information may be determined based on the switched radio frequency channel information.

In step 520, gain parameters are determined based on the antenna information and the environmental data.

The gain parameter refers to a parameter in the antenna gain formula. For example, for a parabolic antenna, the antenna gain formula isWherein the parameters areAnd 4.5 is the gain parameter.

In some embodiments, the gain parameters corresponding to different antennas are different, and the gain parameters of the same antenna under different environmental data are different. In some embodiments, the data processing module may determine the gain parameter through a fourth preset table based on the antenna information and the environmental data. The fourth preset table may be constructed based on antenna information, environment data, and gain parameters.

In some embodiments, determining the gain parameter based on the antenna information and the environmental data may further comprise: determining effective environment data distribution corresponding to the antenna based on the antenna direction; and determining gain parameters through a gain analysis model based on the effective environment data distribution and the antenna information.

The antenna direction refers to a direction in which an antenna radiates or receives electromagnetic signals. In some embodiments, the antenna direction may be determined based on the antenna information.

The effective environmental data distribution may be used to characterize the effective impact of environmental data in various directions on the antenna received signal data. For example, for noise of the same frequency and intensity, the effect of noise on the antenna front is different from that on the antenna side.

In some embodiments, the data processing module may determine the effective environmental data distribution by using an algorithm model, calculation, or the like, based on the antenna profile parameters (radius, depth, curvature, etc.), the relative positions of each environmental data and the antenna.

For example, for a certain noise source in the environmental data, whether the environmental noise data is in the radiation surface of the antenna may be determined according to the antenna shape parameter, and if the environmental noise data is in the radiation surface of the antenna, the environmental noise data and an included angle between the environmental noise data and a positive direction of the radiation surface of the antenna (a normal vector of an antenna center) may be taken as effective environmental data. Therefore, a plurality of environmental data (including environmental noise data, environmental vibration data and the like) and the included angle of the antenna radiation surface in the positive direction can form effective environmental data distribution.

In some embodiments, the data processing module may process the effective environmental data distribution and the antenna information through a gain analysis model to determine gain parameters.

The gain analysis model refers to a model for determining gain parameters based on the effective environmental data distribution and antenna information. In some embodiments, the gain analysis model may be a machine learning model. For example, the gain analysis model may include a combination of one or more of a deep neural network (Deep Neural Networks, DNN) model, a graph neural network (Graph Neural Networks, GNN) model, or other custom model.

In some embodiments, the input of the gain analysis model may include the effective environmental data distribution and antenna information and the output of the gain analysis model may include the gain parameters.

In some embodiments, the gain analysis model may be trained based on a number of labeled training samples. The training samples can comprise sample effective environment data distribution and sample antenna information, and the labels can comprise gain parameters corresponding to the training samples. The training samples may be determined based on historical data, and the tags may be statistically determined based on gain parameters corresponding to different environmental data and antenna information in the historical data.

In some embodiments, the data processing module may input the training samples into the initial gain analysis model, update parameters of the initial gain analysis model through training iterations until the trained model meets a preset training condition, and obtain a trained gain analysis model. The preset training condition may be that the loss function is smaller than a threshold, converges, or the training period reaches the threshold. In some embodiments, the method of iteratively updating parameters of the model may include conventional model training methods such as random gradient descent.

For more details on the gain analysis model, see fig. 6 and its associated description.

According to some embodiments of the present disclosure, the gain parameters are determined by the gain analysis model based on the effective environmental data distribution and the antenna information, so that the accuracy of the gain parameters can be ensured, and the gain parameters can be more targeted, so that the accuracy of the antenna gain data can be improved.

In step 530, antenna gain data is determined by a preset method based on the gain parameters.

The preset method is a preset calculation method. In some embodiments, the preset method may include an antenna gain formula. In some embodiments, the data processing module may calculate the antenna gain data by substituting the gain parameter into a corresponding antenna gain formula.

According to the embodiments of the present disclosure, the gain parameter is determined based on the antenna information and the environment information, and the influence of the antenna information and the environment information on the gain parameter is comprehensively considered, so that the obtained gain parameter is more accurate, thereby being beneficial to improving the accuracy of the antenna gain data.

It should be noted that the above description of the flow 200, the flow 300, the flow 400, and the flow 500 is for illustration and description only, and is not intended to limit the scope of applicability of the present description. Various modifications and changes may be made to flow 200, flow 300, flow 400, and flow 500 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.

As shown in fig. 6, the gain analysis model may include an environment embedding layer 630 and a gain analysis layer 650. In some embodiments, determining the gain parameters by the gain analysis model based on the effective environmental data distribution and the antenna information may include: constructing an environmental impact map 620 based on the valid environmental data distribution 610; inputting the environmental impact map 620 into the environmental embedding layer 630, determining environmental impact characteristics 640-1; and inputting the environmental impact characteristics 640-1 and the antenna information 640-2 into the gain analysis layer 650 to obtain gain parameters 660.

The environmental impact map 620 refers to a map that may reflect the effective environmental data distribution 610. The environmental impact map 620 may include a plurality of nodes and a plurality of edges. In the environmental impact map 620, nodes may refer to nodes generated based on environmental data and antennas. The types of nodes may include environmental data nodes and antenna nodes. The environmental data nodes can be divided into environmental noise data nodes, environmental obstacle data nodes, environmental vibration data nodes and the like according to the environmental data types.

An environmental data node refers to a node that is generated from environmental data in the valid environmental data distribution 610. Illustratively, each noise source (different noise sources are distinguished according to parameters such as frequency, intensity, tone color, etc. of noise) monitored by the sound sensor, each vibration source (different vibration sources are distinguished according to parameters such as amplitude, frequency, phase, etc. of vibration) monitored by the vibration sensor, each electromagnetic interference source, etc. may be used as the environmental data node.

For example, a plurality of environmental noise data (corresponding to a plurality of noise sources) in the effective environmental data distribution 610, such as the environmental noise data A1 (corresponding to the noise source A1) and the environmental noise data A2 (corresponding to the noise source A2), may be respectively referred to as the environmental noise data node A1 and the environmental noise data node A2. For another example, a plurality of environmental shake data (corresponding to a plurality of shake sources) in the effective environmental data distribution 610, such as the environmental shake data A1 (corresponding to the shake source B1) and the environmental shake data B2 (corresponding to the shake source B2), may be respectively used as the environmental shake data node B1 and the environmental shake data node B2.

In some embodiments, the data processing module may determine the noise source by locating ambient noise data monitored by the plurality of sound sensors. For example, the sound directions of the same frequency detected by different sound sensors, and the intersection of the sound directions is the sound source position (i.e., the noise source). It should be noted that, the method for determining the vibration source is similar to the method for determining the noise source described above, and will not be repeated here.

In some embodiments, the characteristics of the environmental data node may include the type of environmental data (e.g., environmental noise data, environmental vibration data, etc.), the content of the environmental data (e.g., frequency, intensity, etc. of noise, amplitude, phase, frequency, etc. of vibration, electromagnetic interference source type, etc.).

The antenna node is a node generated by an antenna corresponding to the switched radio frequency channel. In some embodiments, the characteristics of the antenna node may include antenna information such as antenna type, antenna performance, antenna profile parameters (radius, depth, curvature, etc.), and the like.

The nodes may be connected by edges, and the characteristics of the edges may reflect the relationships between the nodes. In some embodiments, the edges of the environmental impact map 620 may include a first class of edges. The first class of edges refers to the edges that exist between each environmental data node and the antenna node. In some embodiments, the first type of edge features may reflect a distribution relationship between the corresponding environmental data and the antenna, such as an angle between the environmental data and a forward direction of a radiating surface of the antenna.

In some embodiments, the edges of the environmental impact map 620 may also include a second class of edges. The second class of edges refers to an edge that exists between any two environmental data nodes of the same environmental data type. For example, an edge exists between the ambient noise data node a1 and the ambient noise data node a 2. As another example, an edge exists between the environmental vibration data node b1 and the environmental vibration data node b 2.

In some embodiments, the second class edge may be characterized by a difference between two environmental data of the same environmental data type connected across the second class edge, and a difference in a distribution relationship between the two environmental data and the antenna.

Illustratively, the ambient noise data A1 is represented as (A ₁₁ ，A ₁₂ ，A ₁₃ ) Wherein A is ₁₁ At A1 noise frequency, A ₁₂ A is the noise intensity of A1, A ₁₃ The noise tone of A1 is the included angle between the ambient noise data A1 and the positive direction of the antenna radiation surface is beta ₁ The method comprises the steps of carrying out a first treatment on the surface of the The ambient noise data A2 is represented as (a ₂₁ ，A ₂₂ ，A ₂₃ ) Wherein A is ₂₁ At a noise frequency of A2, A ₂₂ A is the noise intensity of A2, A ₂₃ The noise tone of A2 is the included angle between the environmental noise data A2 and the positive direction of the antenna radiation surface is beta ₂ Then the difference between the ambient noise data A1 and the ambient noise data A2 may be (a ₁₁ -A ₂₁ ，A ₁₂ -A ₂₂ ，A ₁₃ -A ₂₃ ) Or (A) ₂₁ -A ₁₁ ，A ₂₂ -A ₁₂ ，A ₂₃ -A ₁₃ ) The difference between the distribution relationship between the environmental noise data A1 and the environmental noise data A2 and the antenna may be beta ₁ -β ₂ Or beta ₂ -β ₁ 。

It will be appreciated that two environmental data of the same environmental data type may be weakened or strengthened, so that an edge existing between any two environmental data nodes of the same environmental data type is taken as a second type edge, an edge existing between each environmental data node and an antenna node is taken as a first type edge, an environmental impact map is constructed, and comprehensive influence of a plurality of environmental data on gain parameters can be comprehensively considered to obtain more accurate gain parameters.

The environmental embedding layer 630 refers to a model for processing the environmental impact map 620 to determine environmental impact characteristics 640-1. In some embodiments, the environmental embedding layer 630 may be a machine learning model. For example, the environment embedding layer 630 may include, but is not limited to, a GNN model or the like.

In some embodiments, the input of the environmental embedding layer 630 may include the environmental impact map 620 and the output of the environmental embedding layer 630 may include the environmental impact feature 640-1.

The gain analysis layer 650 refers to a model for processing the environmental impact characteristics 640-1 and the antenna information 640-2 to determine gain parameters 660. In some embodiments, gain analysis layer 650 may be a machine learning model. For example, gain analysis layer 650 may include, but is not limited to, a DNN model, or the like.

In some embodiments, the input of gain analysis layer 650 may include environmental impact characteristics 640-1 and antenna information 640-2, and the output of gain analysis layer 650 may include gain parameters 660. For details of the antenna information, reference may be made to the relevant descriptions (e.g., fig. 2 and its related descriptions) in other parts of the specification.

In some embodiments, the gain analysis model may be obtained by co-training the environment embedding layer 630 and the gain analysis layer 650.

In some embodiments, the data processing module may train the initial environmental embedding layer and the initial gain analysis layer based on a number of labeled training samples. The training samples can include sample environment influence patterns and sample antenna information determined based on sample effective environment data distribution, and the obtaining mode of the training samples is the same as that of the training samples. The tag may include actual gain parameters of the sample environment influence spectrum corresponding to the sample antenna information, which are determined based on the sample effective environment data distribution, and the tag may be determined based on manual labeling or the like.

An exemplary training process includes: inputting the sample environment influence map into an initial environment embedding layer to obtain environment influence characteristics output by the initial environment embedding layer; inputting the environmental impact characteristics and the sample antenna information output by the initial environment embedding layer into an initial gain analysis layer to obtain gain parameters output by the initial gain analysis layer; and constructing a loss function based on the gain parameters output by the tag and the initial gain analysis layer, and synchronously updating the parameters of the initial environment embedded layer and the initial gain analysis layer. And obtaining a trained environment embedded layer and a trained gain analysis layer through parameter updating.

According to some embodiments of the present disclosure, an environmental impact map is constructed based on effective environmental data distribution, and gain parameters are determined through a trained gain analysis model based on the environmental impact map and antenna information, so that accuracy of the gain parameters can be effectively ensured, and a foundation can be laid for rapid and accurate determination of antenna gain data. In addition, the gain analysis model is trained in a combined training mode, so that the number of samples required by training can be reduced, and the training efficiency can be improved.

Some embodiments of the present specification also provide an electromagnetic environment monitoring calibration apparatus comprising at least one memory and at least one processor. Wherein the at least one memory is configured to store computer instructions and the at least one processor is configured to execute a portion of the computer instructions to implement the methods described in fig. 2-6.

Some embodiments of the present description also provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform the methods described in fig. 2-6.

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, however, is not intended to imply that more features than are presented in the claims are required for the present description. 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

1. An electromagnetic environment monitoring calibration system, the system comprising: the system comprises an environment monitoring module, a parameter determining module, a calibration standard source, a data acquisition module and a data processing module;

The environment monitoring module is configured to acquire environment data;

the parameter determining module is configured to determine test signal parameters based on the switched radio frequency channel information, wherein the test signal parameters comprise a frequency range, frequency distribution and test forces corresponding to different electromagnetic frequencies, and the test forces corresponding to the different electromagnetic frequencies are test times corresponding to the test signals with different electromagnetic frequencies;

the calibration standard source is configured to emit the test signal based on the test signal parameter;

the data acquisition module is configured to acquire calibration data corresponding to the test signals;

the data processing module is configured to determine a monitoring compensation parameter based on the calibration data;

the data processing module is further configured to:

determining antenna gain data based on the calibration data and the switched radio frequency channel information;

determining system loss data based on the antenna gain data, the test signal parameters, and the calibration data;

determining the monitoring compensation parameter based on the antenna gain data and the system loss data;

the data processing module is further configured to:

Acquiring antenna information;

determining a gain parameter based on the antenna information and the environmental data;

determining the antenna gain data through a preset method based on the gain parameters;

the parameter determination module is further configured to:

determining basic signal parameters based on the switched radio frequency channel information;

determining an enhancement signal parameter based on the environmental data, wherein the enhancement signal parameter is a test signal parameter for enhancing a test effect;

the test signal parameters are determined based on the base signal parameters and the enhancement signal parameters.

2. An electromagnetic environment monitoring calibration method, performed based on the electromagnetic environment monitoring calibration system of claim 1, the system comprising an environment monitoring module, a parameter determination module, a calibration standard source, a data acquisition module, and a data processing module, the method comprising:

acquiring environmental data based on the environmental monitoring module;

determining test signal parameters by the parameter determining module based on the environment data and the switched radio frequency channel information; the test signal parameters at least comprise the frequency range, the frequency distribution and the test force corresponding to different electromagnetic frequencies of the test signal, wherein the test force corresponding to different electromagnetic frequencies is the test times corresponding to the test signal with different electromagnetic frequencies;

Emitting the test signal through the calibration standard source based on the test signal parameter;

collecting calibration data corresponding to the test signals based on the data collecting module;

determining, by the data processing module, a monitoring compensation parameter based on the calibration data;

the determining, by the data processing module, a monitoring compensation parameter based on the calibration data includes:

the determining antenna gain data further includes:

acquiring antenna information;

the determining, by the parameter determining module, the test signal parameter based on the environmental data and the switched radio frequency channel information includes:

3. An electromagnetic environment monitoring and calibrating device, comprising at least one memory and at least one processor;

the at least one memory is configured to store computer instructions;

the at least one processor is configured to execute some of the computer instructions to implement the method of claim 2.

4. A computer readable storage medium storing computer instructions which, when read by a computer in the storage medium, perform the method of claim 2.