Electromagnetic Environment Monitoring Compensation Parameter Determination Method and System

Abstract

This specification provides an embodiment of a method and system for determining electromagnetic environment monitoring compensation parameters. The system, based on the electromagnetic environment monitoring compensation parameter determination, includes the following steps: acquiring environmental data; determining test signal parameters based on the environmental data and switched radio frequency channel information; the test signal parameters include at least the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies; emitting a test signal based on the test signal parameters; collecting calibration data corresponding to the test signal; determining antenna gain data based on the calibration data and switched radio frequency channel information; segmenting the test signal according to the test signal parameters to obtain segmented signals; determining system loss data corresponding to the segmented signals based on the segmented signal parameters, the corresponding calibration data, and the antenna gain data; and determining monitoring compensation parameters based on the antenna gain data and the system loss data.

Description

[0001] Case Analysis

[0002] This application is a divisional application of Chinese application filed on November 2, 2023, with application number 202311446615.2 and invention title "An Electromagnetic Environment Monitoring and Calibration System, Method, Apparatus and Medium". Technical Field

[0003] This specification relates to the field of electromagnetic environment monitoring technology, and in particular to a method and system for determining electromagnetic environment monitoring compensation parameters. Background Technology

[0004] Because radio telescopes are extremely sensitive, they are highly susceptible to radio interference from both external sources and their own internal sources, which can affect their normal operation and scientific output. Therefore, it is necessary to monitor the electromagnetic environment in which the radio telescope operates. However, during electromagnetic environment monitoring, changes in channel frequency due to each switching of the radio frequency channel, as well as changes in the electromagnetic field in the environment, can cause variations in antenna gain and system losses, all of which can affect the accuracy of the monitoring data. Therefore, it is necessary to provide a method and system for determining electromagnetic environment monitoring compensation parameters. Summary of the Invention

[0005] This manual provides a method and system for determining electromagnetic environment monitoring compensation parameters.

[0006] This specification provides one or more embodiments of a method for determining electromagnetic environment monitoring compensation parameters, which is executed by a system based on the determination of electromagnetic environment monitoring compensation parameters. The method includes: acquiring environmental data; determining test signal parameters based on the environmental data and the switched radio frequency channel information; the test signal parameters include at least the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies of the test signal; emitting a test signal based on the test signal parameters; collecting calibration data corresponding to the test signal; determining antenna gain data based on the calibration data and the switched radio frequency channel information; segmenting the test signal according to the test signal parameters to obtain segmented signals; determining system loss data corresponding to the segmented signals based on the segmented signal parameters, the calibration data corresponding to the segmented signals, and the antenna gain data; and determining monitoring compensation parameters based on the antenna gain data and the system loss data.

[0007] In some embodiments, segmenting the test signal according to test signal parameters includes: segmenting the test signal based on the historical reliability of the test signals at different electromagnetic frequencies, wherein the test signal of an electromagnetic frequency with lower historical reliability belongs to a narrower segment.

[0008] In some embodiments, determining the antenna gain data further includes: acquiring antenna information; determining gain parameters based on the antenna information and the environmental data; and determining the antenna gain data based on the gain parameters using a preset method.

[0009] In some embodiments, determining the gain parameter includes: determining the effective environmental data distribution corresponding to the antenna based on the antenna direction; and determining the gain parameter through a gain analysis model based on the effective environmental data distribution and the antenna information, wherein the gain analysis model is a machine learning model.

[0010] In some embodiments, determining the test signal parameters based on the environmental data and the switched RF channel information includes: determining basic signal parameters based on the switched RF channel information; determining enhanced signal parameters based on the environmental data; and determining the test signal parameters based on the basic signal parameters and the enhanced signal parameters.

[0011] This specification provides one or more embodiments of an electromagnetic environment monitoring and compensation parameter determination system. The system includes an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module, and a data processing module. The environmental monitoring module is configured to acquire environmental data. The parameter determination module is configured to determine test signal parameters based on the environmental data and switched radio frequency channel information. The test signal parameters include at least the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies. The calibration standard source is configured to emit the test signal based on the test signal parameters. The data acquisition module is configured to acquire calibration data corresponding to the test signal. The data processing module is configured to: determine antenna gain data based on the calibration data and the switched radio frequency channel information; segment the test signal according to the test signal parameters to obtain segmented signals; determine system loss data corresponding to the segmented signals based on the segmented signal parameters, the calibration data corresponding to the segmented signals, and the antenna gain data; and determine monitoring and compensation parameters based on the antenna gain data and the system loss data.

[0012] This specification provides one or more embodiments of an electromagnetic environment monitoring and calibration device, the device including at least one memory and at least one processor; the at least one memory is used to store computer instructions; the at least one processor is used to execute a portion of the computer instructions to implement the aforementioned method.

[0013] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the aforementioned method.

[0014] The beneficial effects of some embodiments in this specification include, but are not limited to: (1) by determining the test signal parameters through environmental data and the switched radio frequency channel information, and issuing test signals based on the test signal parameters, and by collecting the calibration data corresponding to the test signals and determining the monitoring compensation parameters based on the calibration data, the electromagnetic signals can be compensated and calibrated in real time, thereby effectively ensuring the accuracy of the monitoring data; (2) by determining the historical reliability of test signals of different electromagnetic frequencies based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, and by focusing on strengthening the testing of electromagnetic frequencies with lower reliability, the reliability of calibration results can be effectively improved; (3) (3) By segmenting the test signal to obtain antenna gain data and system loss data, the accuracy of the monitoring compensation parameters can be effectively guaranteed; (4) The gain parameters are determined based on antenna information and environmental information. The influence of antenna information and environmental information on the gain parameters is comprehensively considered, which can make the obtained gain parameters more accurate, thereby improving the accuracy of antenna gain data; (5) An environmental impact map is constructed based on the distribution of effective environmental data. Based on the environmental impact map and antenna information, the gain parameters are determined through a trained gain analysis model, which can effectively guarantee the accuracy of the gain parameters, thus laying the foundation for quickly and accurately determining antenna gain data. In addition, the joint training method is used to train the gain analysis model, which can not only reduce the number of samples required for training, but also improve training efficiency. Attached Figure Description

[0015] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0016] Figure 1 This is a block diagram of an electromagnetic environment monitoring and calibration system according to some embodiments of this specification;

[0017] Figure 2 This is an exemplary flowchart of an electromagnetic environment monitoring and calibration method according to some embodiments of this specification;

[0018] Figure 3 This is an exemplary flowchart of a method for determining test signal parameters according to some embodiments of this specification;

[0019] Figure 4 This is an exemplary flowchart of a method for determining monitoring compensation parameters according to some embodiments of this specification;

[0020] Figure 5 This is an exemplary flowchart of an antenna gain data determination method according to some embodiments of this specification;

[0021] Figure 6 This is an exemplary schematic diagram of a gain analysis model shown according to some embodiments of this specification. Detailed Implementation

[0022] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0023] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0024] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0025] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0026] Figure 1 This is a block diagram of an electromagnetic environment monitoring and calibration system according to some embodiments of this specification.

[0027] like Figure 1 As shown, the electromagnetic environment monitoring and calibration system 100 may include an environmental monitoring module 110, a parameter determination module 120, a calibration standard source 130, a data acquisition module 140, and a data processing module 150.

[0028] The environmental monitoring module 110 is a module used to monitor the electromagnetic environment in real time in order to obtain environmental data.

[0029] The parameter determination module 120 is a module used to determine relevant data for electromagnetic environment monitoring. In some embodiments, the parameter determination module 120 can be used to determine test signal parameters based on the switched radio frequency channel information. The test signal parameters include the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies.

[0030] In some embodiments, the parameter determination module 120 may further be used to: determine basic signal parameters based on the switched radio frequency channel information; determine enhanced signal parameters based on environmental data; and determine test signal parameters based on the basic signal parameters and the enhanced signal parameters.

[0031] The calibration standard source 130 refers to a signal transmitting device or equipment used to emit test signals. In some embodiments, the calibration standard source 130 can be used to emit test signals based on test signal parameters.

[0032] The data acquisition module 140 is a module used to acquire data related to electromagnetic environment monitoring. In some embodiments, the data acquisition module 140 can be used to acquire calibration data corresponding to test signals.

[0033] The data processing module 150 refers to a module used to process data related to electromagnetic environment monitoring. In some embodiments, the data processing module 150 can be used to determine monitoring compensation parameters based on calibration data.

[0034] In some embodiments, the data processing module 150 may further be used to: determine antenna gain data based on calibration data and switched RF channel information; determine system loss data based on antenna gain data, test signal parameters and calibration data; and determine monitoring compensation parameters based on antenna gain data and system loss data.

[0035] In some embodiments, the data processing module 150 may further be used to: acquire antenna information; determine gain parameters based on the antenna information and environmental data; and determine antenna gain data based on the gain parameters using a preset method.

[0036] For more information on the environmental monitoring module, parameter determination module, calibration standard source, data acquisition module, and data processing module, please refer to the relevant descriptions in other parts of this manual.

[0037] It should be noted that the above description of the electromagnetic environment monitoring and calibration system 100 and its modules is for convenience only and should not be construed as limiting this specification to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The environmental monitoring module 110, parameter determination module 120, calibration standard source 130, data acquisition module 140, and data processing module 150 disclosed herein can be different modules within a single system, or a single module can perform the functions of two or more of the aforementioned modules. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0038] Figure 2 This is an exemplary flowchart of an electromagnetic environment monitoring and calibration method according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be performed by an electromagnetic environment monitoring and calibration system.

[0039] Step 210: Obtain environmental data.

[0040] Because radio telescopes have extremely high sensitivity, they are highly susceptible to radio interference from both external sources and their own internal sources, which can affect their normal operation and scientific output. Therefore, monitoring the electromagnetic environment is necessary. Environmental data refers to the relevant data acquired during electromagnetic environment monitoring. In some embodiments, environmental data may include environmental noise data, environmental obstacle data, environmental vibration data, ambient atmospheric data, and electromagnetic interference source data, etc.

[0041] Among these, environmental noise data reflects the noise situation during electromagnetic environment monitoring, such as noise sources. Environmental obstacle data reflects the obstacle situation during electromagnetic environment monitoring. Obstacles refer to objects that affect or interfere with the propagation of electromagnetic radiation, such as buildings and trees. Environmental vibration data reflects the vibration situation during electromagnetic environment monitoring, such as ground vibrations caused by the operation of large machinery. Ambient atmospheric data reflects the atmospheric conditions during electromagnetic environment monitoring, such as atmospheric temperature, humidity, and air pressure. Electromagnetic interference source data reflects the electromagnetic interference source situation during electromagnetic environment monitoring, such as the location of the electromagnetic interference source. Electromagnetic interference sources refer to equipment or devices that can generate electromagnetic radiation. For example, electromagnetic interference sources can include power towers, base stations, and radio stations.

[0042] In some embodiments, environmental data can be acquired through an environmental monitoring module. In some embodiments, the environmental monitoring module may include multiple sensors, each used to acquire one type of environmental data. For example, the environmental monitoring module may include a sound sensor for acquiring environmental sound data. Another example is that the environmental monitoring module may include a vision sensor for acquiring environmental obstacle data. Yet another example is that the environmental monitoring module may include a vibration sensor for acquiring environmental vibration data. Still another example is that the environmental monitoring module may include temperature and humidity sensors, a barometer, etc., for acquiring ambient atmospheric data.

[0043] In some embodiments, the environmental monitoring module can determine electromagnetic interference source data using a first preset table. For example, since antennas on different radio frequency channels can receive electromagnetic signals from different directions, a first preset table can be constructed based on antenna direction and electromagnetic interference sources. There is a correspondence between antenna direction and the location and number of electromagnetic interference sources, and the electromagnetic interference source data can be determined based on the antenna direction of the switched radio frequency channel. Here, the location of the electromagnetic interference source can be understood as its actual location in the electromagnetic environment. In some embodiments, the location and number of electromagnetic interference sources can be manually input into the electromagnetic environment monitoring system in advance.

[0044] It should be noted that multiple sensors can be set up independently or integrated into the environmental monitoring module, as long as they can acquire environmental data.

[0045] Step 220: Determine the test signal parameters based on environmental data and the switched RF channel information.

[0046] Since electromagnetic environment monitoring is conducted through radio frequency (RF) channels, and different RF channels monitor electromagnetic signals of different frequencies, it is necessary to switch RF channels when monitoring electromagnetic signals of different frequencies. Switching is the method of changing RF channels, which can convert one RF channel to another.

[0047] The switched RF channel information refers to information related to the switched RF channel. In some embodiments, the switched RF channel information may include antenna information, modulation scheme, power limit, etc., corresponding to the switched RF channel.

[0048] Antenna information refers to information related to the antenna corresponding to the switched radio frequency channel. For example, antenna information may include antenna type, antenna receiving frequency range, antenna direction, antenna shape parameters (radius, depth, curvature, etc.).

[0049] In some embodiments, the radio frequency channel information can be preset and correspond to the radio frequency channel, that is, different radio frequency channels correspond to different radio frequency channel information. When the electromagnetic environment monitoring and calibration system switches different radio frequency channels to achieve electromagnetic environment monitoring, the parameter determination module can automatically obtain the switched radio frequency channel information.

[0050] Test signal parameters refer to parameters related to the test signal. In some embodiments, test signal parameters may include the frequency range, frequency distribution, and test force corresponding to different electromagnetic frequencies.

[0051] The test signal refers to the signal emitted by the calibration standard source, used to calibrate the monitoring data. The monitoring data can be signal data acquired by the radio frequency channel during electromagnetic environment monitoring.

[0052] The frequency range of a test signal refers to the range of its electromagnetic frequency values. For example, the frequency range of a test signal could be 30 GHz to 40 GHz.

[0053] The frequency distribution of a test signal reflects the distribution of its electromagnetic frequencies. In some embodiments, the frequency distribution of the test signal can be uniform, such as when the electromagnetic frequencies of the test signal are 30 GHz, 30.2 GHz, or 30.4 GHz. In some embodiments, the frequency distribution of the test signal can be non-uniform, such as when the electromagnetic frequencies of the test signal are 30 GHz, 30.2 GHz, or 30.7 GHz.

[0054] The testing intensity corresponding to different electromagnetic frequencies refers to the number of tests performed for different electromagnetic frequency test signals. A higher number of tests indicates a greater testing intensity, resulting in more accurate calibration data for the test signals.

[0055] In some embodiments, the test signal parameters may further include the transmit power of the test signal. When a test signal of the same electromagnetic frequency is tested multiple times, the transmit power of the test signal at that electromagnetic frequency may be different. In some embodiments, the transmit power of the test signal may be uniformly set according to antenna information (such as the antenna receiving frequency range).

[0056] In some embodiments, based on environmental data and the switched RF channel information, the parameter determination module can determine the test signal parameters in various ways. For example, the parameter determination module can determine the test signal parameters using a second preset table. This second preset table can be constructed based on environmental data, the switched RF channel information, and the test signal parameters corresponding to calibration data with good calibration results from historical data.

[0057] For more information on how to determine test signal parameters, please refer to [link / reference]. Figure 3 And its related descriptions.

[0058] Step 230: Send a test signal based on the test signal parameters.

[0059] In some embodiments, the calibration standard source can automatically emit a test signal based on the test signal parameters. In some embodiments, the electromagnetic environment monitoring calibration system can generate control commands based on the test signal parameters to control the calibration standard source to emit the test signal. Exemplarily, the electromagnetic environment monitoring calibration system may include a control module that can generate control commands (such as signal emission commands) based on the test signal parameters, and the calibration standard source will immediately emit a corresponding test signal upon receiving the control command.

[0060] Step 240: Collect calibration data corresponding to the test signal.

[0061] Calibration data refers to the signal data corresponding to the test signal acquired by the data acquisition module. In some embodiments, calibration data may include electromagnetic frequency, electromagnetic power, electromagnetic signal energy, and electromagnetic signal wavelength acquired by the data acquisition module. Understandably, different test signals correspond to different calibration data. In some embodiments, calibration data can be acquired by the data acquisition module. For example, the data acquisition module can acquire calibration data through the antenna of a switched radio frequency channel.

[0062] For more information on calibration data, please refer to the relevant instructions in other parts of this manual (such as...). Figure 4 (and related descriptions).

[0063] Step 250: Determine the monitoring compensation parameters based on the calibration data.

[0064] Monitoring compensation parameters refer to parameters used to calibrate and compensate relevant parameters of electromagnetic signals. For example, monitoring compensation parameters may include at least power compensation parameters.

[0065] In some embodiments, based on calibration data, the data processing module can determine monitoring compensation parameters in various ways. For example, the data processing module can compare the calibration data with its corresponding test signal parameters to obtain the electromagnetic power difference between the test signal emitted by the calibration standard source and the calibration data acquired by the data acquisition module, and determine this electromagnetic power difference as the corresponding power compensation parameter. For instance, if the transmit power of an electromagnetic signal at a frequency of 30 GHz is 200 W, and the electromagnetic power in the calibration data is 180 W, then the power compensation parameter for the electromagnetic signal at a frequency of 30 GHz under this radio frequency channel is 20 W.

[0066] For more information on how to determine monitoring compensation parameters, please refer to [link / reference needed]. Figure 4 And its related descriptions.

[0067] In some embodiments of this specification, test signal parameters are determined by environmental data and the switched radio frequency channel information, and test signals are emitted based on the test signal parameters. Furthermore, by collecting calibration data corresponding to the test signals and determining monitoring compensation parameters based on the calibration data, electromagnetic signals can be compensated and calibrated in real time, thereby effectively ensuring the accuracy of monitoring data.

[0068] Figure 3 This is an exemplary flowchart illustrating a method for determining test signal parameters according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by a parameter determination module.

[0069] Step 310: Determine the basic signal parameters based on the switched RF channel information.

[0070] Fundamental signal parameters refer to the initial values ​​or ranges of the test signal parameters. Understandably, fundamental signal parameters can include the fundamental frequency range, fundamental frequency distribution, and the fundamental test intensity corresponding to different electromagnetic frequencies.

[0071] In some embodiments, the parameter determination module can determine the antenna receiving frequency range in the switched RF channel information as the fundamental frequency range of the test signal.

[0072] In some embodiments, the parameter determination module can 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 pre-defined frequency division rule. For example, the preset rule may include equal intervals (e.g., a 0.2 GHz interval between two adjacent frequencies) or non-equal intervals (e.g., intervals of 0.2 GHz, 0.4 GHz, etc., between two adjacent frequencies). In some embodiments, the fundamental test intensity corresponding to different electromagnetic frequencies can be manually preset, and the fundamental test intensity corresponding to different electromagnetic frequencies can be the same.

[0073] Step 320: Determine the parameters for enhancing the signal based on environmental data.

[0074] Enhanced signal parameters refer to test signal parameters used to strengthen the test effect. In some embodiments, enhanced signal parameters can be understood as increasing the test intensity of test signals at certain key electromagnetic frequencies based on the basic signal parameters. It can be understood that enhanced signal parameters may include the increase in test intensity corresponding to the key electromagnetic frequencies.

[0075] In some embodiments, based on environmental data, the parameter determination module can determine key electromagnetic frequencies using historical data, and then determine the enhanced signal parameters. For example, the parameter determination module can, based on historical data, determine the electromagnetic frequencies corresponding to electromagnetic signals whose data volume monitored by the RF channel exceeds a preset threshold under historical environmental data that meets preset conditions, as key electromagnetic frequencies; then, it can correspondingly increase the testing intensity corresponding to the key electromagnetic frequencies, making the final testing intensity corresponding to the key electromagnetic frequencies greater than the basic testing intensity corresponding to the key electromagnetic frequencies. Here, preset conditions refer to pre-set similarity judgment conditions. For example, preset conditions could be that the similarity to the current environmental data is not less than a similarity threshold. It should be noted that the increase in testing intensity corresponding to the key electromagnetic frequencies can be determined according to actual circumstances.

[0076] In some embodiments, determining the enhanced signal parameters based on environmental data may further include: determining the historical reliability of test signals of different electromagnetic frequencies under the same environmental data based on historical monitoring results; and determining the enhanced signal parameters based on the historical reliability.

[0077] Historical monitoring results can reflect the accuracy of historical monitoring data. For example, the higher the accuracy of historical monitoring data, the better the historical monitoring results.

[0078] Historical monitoring data refers to the electromagnetic signals at multiple electromagnetic frequencies detected by each radio frequency channel during electromagnetic environment monitoring. The accuracy of historical monitoring data reflects the degree of consistency between historical monitoring data and actual electromagnetic interference data in the environment.

[0079] In some embodiments, the accuracy of historical monitoring data can be determined based on historical observation data from radio telescopes. For example, the parameter determination module can determine the accuracy of historical monitoring data by analyzing the difference between the signal-to-noise ratio (SNR) in historical radio telescope observation data and the expected SNR. For instance, the greater the difference between the SNR of historical radio telescope observation data and the expected SNR, the lower the accuracy of the historical monitoring data. Here, the expected SNR refers to a pre-set SNR value. In some embodiments, the expected SNR can be an empirical value, an experimental value, or a simulated value, etc.

[0080] The historical reliability of a test signal refers to the reliability of calibration results obtained from test signals of different electromagnetic frequencies under the same environmental data in historical data. In some embodiments, the historical reliability of a test signal can be determined by compensating the electromagnetic signal with monitoring compensation parameters obtained from the test signal, and then determining the accuracy of the historical monitoring data. Understandably, the higher the accuracy of the historical monitoring data and the more accurate the monitoring compensation parameters, the higher the historical reliability of the test signal.

[0081] In some embodiments, based on the historical reliability of the test signal, the parameter determination module can select the electromagnetic frequencies corresponding to test signals with historical reliability below a reliability threshold as key electromagnetic frequencies. The reliability threshold refers to a pre-set reliability value. In some embodiments, the reliability threshold can be set manually.

[0082] In some embodiments, the increase in test intensity (enhanced signal parameter) corresponding to a key electromagnetic frequency is related to the historical reliability of the test signal at that key electromagnetic frequency; the lower the historical reliability, the greater the test intensity corresponding to that key electromagnetic frequency. For example, the increase in test intensity corresponding to a key electromagnetic frequency can be an integer value that is the product of the historical reliability of the key electromagnetic frequency and its corresponding base test intensity.

[0083] Some embodiments in this specification determine the historical reliability of test signals at different electromagnetic frequencies based on environmental data and historical monitoring results, and focus on strengthening the testing of electromagnetic frequencies with low historical reliability, which helps to improve the reliability of calibration results.

[0084] In some embodiments, determining the historical reliability of test signals at different electromagnetic frequencies based on environmental data and historical monitoring results may further include: acquiring historical monitoring data of the switched radio frequency channel and the accuracy of the historical monitoring data; determining the historical influence of different electromagnetic frequencies based on the proportion of historical monitoring data at different electromagnetic frequencies; and determining the historical reliability of test signals at different electromagnetic frequencies based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data.

[0085] In some embodiments, there is a correspondence between historical monitoring data and the accuracy of historical monitoring data, with one historical monitoring data point corresponding to one accuracy level. In some embodiments, the parameter determination module can obtain the historical monitoring data of the switched radio frequency channel and the accuracy level of the historical monitoring data based on the historical data.

[0086] The proportion of historical monitoring data 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 can obtain the proportion of historical monitoring data for each electromagnetic frequency by comparing the number of monitoring data for each electromagnetic frequency with the total number of monitoring data for the entire radio frequency channel.

[0087] The historical impact of different electromagnetic frequencies refers to the degree of influence of monitoring data from different electromagnetic frequencies on the overall electromagnetic environment monitoring. In some embodiments, the historical impact of different electromagnetic frequencies can be represented by the proportion of historical monitoring data for each electromagnetic frequency. The lower the proportion of historical monitoring data for an electromagnetic frequency, the less electromagnetic signal of that frequency exists in the electromagnetic environment, and the smaller its impact on the overall electromagnetic environment monitoring; in other words, the smaller the historical impact of that electromagnetic frequency.

[0088] In some embodiments, based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, the parameter determination module can calculate and determine the historical reliability of test signals at different electromagnetic frequencies.

[0089] In some embodiments, the historical reliability of the electromagnetic frequency test signal can be positively correlated with the historical influence of the electromagnetic frequency and the average accuracy of the historical monitoring data of the electromagnetic frequency. For example, the historical reliability of the electromagnetic frequency P test signal = historical influence of electromagnetic frequency P × average accuracy of historical monitoring data of electromagnetic frequency P. The average accuracy of the historical monitoring data of electromagnetic frequency P can be determined based on the accuracy of multiple historical monitoring data of electromagnetic frequency P.

[0090] It should be noted that the historical reliability of test signals of different electromagnetic frequencies can also be determined by other calculation methods or third preset tables, etc. The relationship that the greater the historical influence of the electromagnetic frequency and the higher the accuracy of the historical monitoring data, the greater the historical reliability of the corresponding electromagnetic frequency can be demonstrated.

[0091] Some embodiments in this specification determine the historical reliability of test signals at different electromagnetic frequencies based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data. Furthermore, they consider the influence of monitoring data at different electromagnetic frequencies on the overall electromagnetic environment monitoring, thereby ensuring the rationality of the historical reliability of test signals at different electromagnetic frequencies to a certain extent, and further improving the reliability of calibration results.

[0092] Step 330: Determine the test signal parameters based on the basic signal parameters and the enhanced signal parameters.

[0093] In some embodiments, based on the basic signal parameters and the enhanced signal parameters, the parameter determination module can merge the basic signal parameters and the enhanced signal parameters, and use the merged parameters as the test signal parameters. For example, if the basic signal parameters are (X1, Y1, Z1) and the enhanced signal parameters are (X1, Y1, Z2), then the test signal parameters can be (X1, Y1, Z1+Z2).

[0094] In some embodiments of this specification, the historical reliability of test signals of different electromagnetic frequencies is determined based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data. By focusing on strengthening the testing of electromagnetic frequencies with lower reliability, the reliability of calibration results can be effectively improved.

[0095] Figure 4 This is an exemplary flowchart illustrating a method for determining monitoring compensation parameters according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by a data processing module.

[0096] Step 410: Determine antenna gain data based on calibration data and the switched RF channel information.

[0097] Antenna gain data refers to data related to the radiation efficiency of an antenna in a specific direction.

[0098] In some embodiments, the data processing module can determine antenna gain data in a variety of ways based on calibration data and switched RF channel information.

[0099] In some embodiments, the data processing module can determine the corresponding antenna gain formula based on the antenna information (such as antenna type, antenna performance, antenna shape parameters (radius, depth, curvature, antenna length, etc.)) in the switched RF channel information, and determine the antenna gain data based on the corresponding antenna gain formula and antenna information.

[0100] The antenna gain formula is a calculation formula used to determine antenna gain data. In some embodiments, the data processing module can determine the antenna gain formula based on the antenna type. For example, for a parabolic antenna, the antenna gain formula can be G = 10lg{4.5×(D / λ0)} 2 In this formula, G represents the antenna gain; D is the diameter of the parabolic surface, determined based on antenna information (such as antenna shape parameters); λ0 is the wavelength of the electromagnetic signal, determined based on calibration data; and 4.5 is empirical data. For example, for a vertical omnidirectional antenna, the antenna gain formula can be G = 10lg(2L / λ0), where G is the antenna gain; L is the antenna length, determined based on antenna information; λ0 is the wavelength of the electromagnetic signal; and 2 is empirical data.

[0101] In some embodiments, according to the corresponding antenna gain formula, the data processing module can obtain antenna gain data by substituting the corresponding data (such as antenna information, calibration data, etc.) in the antenna information into the corresponding antenna gain formula.

[0102] For more information on how to determine antenna gain data, please refer to [link / reference]. Figures 5-6 And its related descriptions.

[0103] Step 420: Determine system loss data based on antenna gain data, test signal parameters, and calibration data.

[0104] System loss data refers to the energy loss of an electromagnetic environment monitoring and calibration system. In some embodiments, system loss data may include the energy loss of all components or modules from the signal source (such as a calibration standard source) to the receiver (such as a data acquisition module).

[0105] In some embodiments, the data processing module can calculate system loss data based on antenna gain data, test signal parameters, and calibration data.

[0106] In some embodiments, system loss data may be positively correlated with 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. An exemplary calculation formula includes: System loss data = Transmit power of test signal + Antenna gain data - Energy of electromagnetic signal. For information on how to obtain the transmit power of the test signal and the energy of the electromagnetic signal, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0107] In some embodiments, determining system loss data based on antenna gain data, test signal parameters, and calibration data may further include: segmenting the test signal according to the test signal parameters to obtain segmented signals; and determining the system loss data corresponding to the segmented signals based on the segmented signal parameters, the calibration data corresponding to the segmented signals, and the antenna gain data.

[0108] A segmented signal refers to a signal obtained by uniformly segmenting a test signal according to test signal parameters. In some embodiments, the data processing module can uniformly segment the test signal according to the frequency distribution of the test signal in the test signal parameters, so that the frequency range of the test signal in each segment is consistent in size, thus obtaining a segmented signal.

[0109] For example, if the frequency range of the test signal in the test signal parameters is 30GHz to 40GHz, and the frequency distribution of the test signal is 30GHz, 32GHz, 34GHz, 36GHz, 38GHz, and 40GHz, then the frequency range of the test signal in each segment is 30GHz to 32GHz, 32GHz to 34GHz, 34GHz to 36GHz, 36GHz to 38GHz, and 38GHz to 40GHz, respectively.

[0110] Segmented signal parameters refer to the corresponding test signal parameters of the segmented signal. In some embodiments, the method for determining the system loss data corresponding to the segmented signal based on the segmented signal parameters, the corresponding calibration data, and the antenna gain data is similar to the method for determining the system loss data based on the antenna gain data, test signal parameters, and calibration data described above, and will not be repeated here. In some embodiments, the system loss data corresponding to each segmented signal can also be used to characterize the energy loss of the electromagnetic environment monitoring and calibration system.

[0111] Understandably, different segmented signals correspond to different system loss data. The narrower the segment, the more accurate the system loss data, which can lay the foundation for determining more accurate monitoring and compensation parameters. However, this will lead to greater system computational pressure and lower computational efficiency. Therefore, by reasonably segmenting the test signal and determining the system loss data corresponding to the segmented signal, the accuracy of the system loss data can be improved while also taking into account the system's computational pressure and improving computational efficiency.

[0112] In some embodiments, the above-mentioned segmentation of the test signal may further include: segmenting based on the historical reliability of the test signals of different electromagnetic frequencies, wherein the segment to which the test signal of the electromagnetic frequency with the lower historical reliability belongs is narrower.

[0113] A narrower segmentation means that the frequency range of the test signal belonging to an electromagnetic frequency with lower historical reliability is smaller. For example, the frequency range of a segment belonging to a test signal of a conventional electromagnetic frequency is 1 GHz, while the frequency range of a segment belonging to a test signal of an electromagnetic frequency with lower historical reliability is 0.5 GHz. For a detailed explanation of the historical reliability of test signals of different electromagnetic frequencies, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0114] Some embodiments in this specification segment test signals based on the historical reliability of different electromagnetic frequencies, which can make the system loss data of the segmented signals more targeted, thereby effectively improving the accuracy of the system loss data.

[0115] Step 430: Determine the monitoring compensation parameters based on the antenna gain data and system loss data.

[0116] In some embodiments, the data processing module can calculate the difference between system loss data and antenna gain data, and use this difference as a monitoring compensation parameter. For example, the monitoring compensation parameter corresponding to the segmented signal = system loss data corresponding to the segmented signal - antenna gain data corresponding to the segmented signal.

[0117] In some embodiments of this specification, the accuracy of the monitoring compensation parameters can be effectively guaranteed by determining the antenna gain data and system loss data obtained by segmenting the test signal.

[0118] Figure 5 This is an exemplary flowchart illustrating an antenna gain data determination method according to some embodiments of this specification. Figure 5 As shown, process 500 includes the following steps. In some embodiments, process 500 may be executed by a data processing module.

[0119] Step 510: Obtain antenna information.

[0120] For detailed information on antenna details, please refer to [link / reference]. Figure 2 And related descriptions. In some embodiments, antenna information may be determined based on the switched radio frequency channel information.

[0121] Step 520: Determine the gain parameters based on antenna information and environmental data.

[0122] Gain parameter refers to the parameter in the antenna gain formula. For example, for a parabolic antenna, the antenna gain formula is G = 10lg{4.5×(D / λ0)} 2}, where parameter 4.5 is the gain parameter.

[0123] In some embodiments, different antennas have different gain parameters, and the same antenna may also have different gain parameters under different environmental data. In some embodiments, based on antenna information and environmental data, the data processing module can determine the gain parameters through a fourth preset table. This fourth preset table can be constructed based on antenna information, environmental data, and gain parameters.

[0124] In some embodiments, determining the gain parameter based on antenna information and environmental data may further include: determining the effective environmental data distribution corresponding to the antenna based on the antenna direction; and determining the gain parameter through a gain analysis model based on the effective environmental data distribution and antenna information.

[0125] Antenna orientation refers to the direction in which an antenna radiates or receives electromagnetic signals. In some embodiments, antenna orientation can be determined based on antenna information.

[0126] Effective environmental data distribution can be used to characterize the effective impact of environmental data from different directions on the antenna's received signal data. For example, for noise of the same frequency and intensity, the impact of noise on the front of the antenna is different from that on the sides of the antenna.

[0127] In some embodiments, the data processing module can determine the distribution of effective environmental data based on antenna shape parameters (radius, depth, curvature, etc.), the relative position of various environmental data and the antenna, and by using algorithm models, calculations, and other methods.

[0128] For example, for a noise source in the environmental data, it can be first determined whether the environmental noise data is within the antenna's radiating surface based on the antenna's shape parameters. If it is within the antenna's radiating surface, the environmental noise data and the angle between it and the positive direction of the antenna's radiating surface (the normal vector of the antenna center) can be considered as valid environmental data. Thus, multiple environmental data (including environmental noise data, environmental vibration data, etc.) and their angles with the positive direction of the antenna's radiating surface can constitute a valid environmental data distribution.

[0129] In some embodiments, the data processing module can process the effective environmental data distribution and antenna information through a gain analysis model to determine the gain parameters.

[0130] A gain analysis model is a model used to determine gain parameters based on the effective environmental data distribution and antenna information. In some embodiments, the gain analysis model can be a machine learning model. For example, the gain analysis model may include one or more combinations of Deep Neural Networks (DNN) models, Graph Neural Networks (GNN) models, or other custom models.

[0131] In some embodiments, the input to the gain analysis model may include effective environmental data distribution and antenna information, and the output of the gain analysis model may include gain parameters.

[0132] In some embodiments, the gain analysis model can be trained based on a large number of labeled training samples. The training samples may include the distribution of effective environmental data and antenna information, and the labels may include the gain parameters corresponding to the training samples. The training samples can be determined based on historical data, and the labels can be determined statistically based on the gain parameters corresponding to different environmental data and antenna information in the historical data.

[0133] In some embodiments, the data processing module can input training samples into an initial gain analysis model, and iteratively update the parameters of the initial gain analysis model through training until the trained model meets preset training conditions, thus obtaining a trained gain analysis model. The preset training conditions can be a loss function less than a threshold, convergence, or the training period reaching a threshold. In some embodiments, the method for iteratively updating the model parameters can include conventional model training methods such as stochastic gradient descent.

[0134] For more information on gain analysis models, please refer to [link / reference]. Figure 6 And its related descriptions.

[0135] Some embodiments in this specification determine gain parameters through gain analysis models based on effective environmental data distribution and antenna information. This ensures the accuracy of the gain parameters and makes them more targeted, thereby improving the accuracy of antenna gain data.

[0136] Step 530: Determine antenna gain data based on gain parameters using a preset method.

[0137] A preset method refers to a pre-defined calculation method. In some embodiments, the preset method may include an antenna gain formula. In some embodiments, the data processing module can obtain antenna gain data by substituting the gain parameter into the corresponding antenna gain formula.

[0138] Some embodiments in this specification determine gain parameters based on antenna information and environmental information. By comprehensively considering the influence of antenna information and environmental information on gain parameters, the obtained gain parameters can be more accurate, thereby improving the accuracy of antenna gain data.

[0139] It should be noted that the above descriptions of processes 200, 300, 400, and 500 are for illustrative purposes only and do not limit the scope of this specification. Those skilled in the art can make various modifications and changes to processes 200, 300, 400, and 500 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0140] Figure 6 This is an exemplary schematic diagram of a gain analysis model shown according to some embodiments of this specification.

[0141] like Figure 6 As shown, the gain analysis model may include an environment embedding layer 630 and a gain analysis layer 650. In some embodiments, determining the gain parameter through the gain analysis model based on the effective environmental data distribution and antenna information may include: constructing an environmental impact map 620 based on the effective environmental data distribution 610; inputting the environmental impact map 620 into the environment embedding layer 630 to determine environmental impact features 640-1; and inputting the environmental impact features 640-1 and antenna information 640-2 into the gain analysis layer 650 to obtain the gain parameter 660.

[0142] An environmental impact map 620 refers to a map that reflects the distribution 610 of effective environmental data. The environmental impact map 620 may include multiple nodes and multiple edges. In the environmental impact map 620, nodes can be nodes generated based on environmental data and antennas. Node types can include environmental data nodes and antenna nodes. Environmental data nodes can be classified according to the type of environmental data, such as environmental noise data nodes, environmental obstacle data nodes, and environmental vibration data nodes.

[0143] An environmental data node refers to a node generated from environmental data in the effective environmental data distribution 610. For example, each noise source detected by the sound sensor (different noise sources are distinguished according to parameters such as frequency, intensity, and timbre), each vibration source detected by the vibration sensor (different vibration sources are distinguished according to parameters such as amplitude, frequency, and phase of vibration), and each electromagnetic interference source can all serve as an environmental data node.

[0144] For example, multiple environmental noise data points (corresponding to multiple noise sources) in the effective environmental data distribution 610, such as environmental noise data A1 (corresponding to noise source A1) and environmental noise data A2 (corresponding to noise source A2), can be used as environmental noise data nodes a1 and a2, respectively. Similarly, multiple environmental vibration data points (corresponding to multiple vibration sources) in the effective environmental data distribution 610, such as environmental vibration data A1 (corresponding to vibration source B1) and environmental vibration data B2 (corresponding to vibration source B2), can be used as environmental vibration data nodes b1 and b2, respectively.

[0145] In some embodiments, the data processing module can locate the noise source by analyzing environmental noise data monitored by multiple sound sensors. For example, the intersection of the directions of the same frequency detected by different sound sensors is the location of the sound source (i.e., the noise source). It should be noted that the method for determining vibration sources is similar to the method for determining noise sources described above, and will not be repeated here.

[0146] In some embodiments, the characteristics of an environmental data node may include environmental data type (such as environmental noise data, environmental vibration data, etc.) and environmental data content (such as noise frequency, intensity, vibration amplitude, phase, frequency, etc., electromagnetic interference source type, etc.).

[0147] An antenna node refers to a node generated using the antenna corresponding to the switched radio frequency channel. In some embodiments, the characteristics of an antenna node may include antenna information, such as antenna type, antenna performance, and antenna shape parameters (radius, depth, curvature, etc.).

[0148] Multiple nodes can be connected by edges, and the characteristics of the edges can reflect the relationships between the nodes. In some embodiments, the edges of the environmental impact map 620 may include first-type edges. First-type edges refer to the edges that exist between each environmental data node and the antenna node. In some embodiments, the characteristics of the first-type edges can reflect the distribution relationship between the corresponding environmental data and the antenna, such as the angle between the environmental data and the positive direction of the antenna's radiating surface.

[0149] In some embodiments, the edges of the environmental impact map 620 may also include a second type of edge. A second type of edge refers to an edge between any two environmental data nodes of the same environmental data type. For example, an edge between environmental noise data node a1 and environmental noise data node a2. Another example is an edge between environmental vibration data node b1 and environmental vibration data node b2.

[0150] In some embodiments, the features of the second type of edge may be the difference between two environmental data of the same environmental data type connected at both ends of the second type of edge, and the difference in the distribution relationship between the two environmental data and the antenna.

[0151] For example, the environmental noise data A1 is represented as (A 11 A 12 A 13 ), where A 11 Let A1 be the noise frequency, and A be the noise frequency. 12 Let A1 be the noise intensity, and A be the noise intensity. 13 The noise timbre of A1 is given by the angle β1 between the ambient noise data A1 and the positive direction of the antenna radiating surface; the ambient noise data A2 is represented as (A 21 A 22 A 23 ), where A 21 Let A2 be the noise frequency, and A be the noise frequency. 22 Let A2 be the noise intensity, and A be the noise intensity. 23 Let A2 be the noise timbre, and let β2 be the angle between the ambient noise data A2 and the positive direction of the antenna radiating surface. Then, the difference between the ambient noise data A1 and the ambient noise data A2 can 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 environmental noise data A1 and environmental noise data A2 and the antenna can be β1-β2 or β2-β1.

[0152] Understandably, two environmental data points of the same type may weaken or enhance each other. Therefore, by taking an edge between any two environmental data nodes of the same type as a second type edge and an edge between each environmental data node and an antenna node as a first type edge, an environmental impact map can be constructed. This allows for a comprehensive consideration of the combined impact of multiple environmental data points on the gain parameter, resulting in a more accurate gain parameter.

[0153] The environment embedding layer 630 refers to the model used to process the environmental impact map 620 and determine the environmental impact features 640-1. In some embodiments, the environment embedding layer 630 can be a machine learning model. For example, the environment embedding layer 630 can include, but is not limited to, a GNN model.

[0154] In some embodiments, the input to the environment embedding layer 630 may include an environmental impact map 620, and the output of the environment embedding layer 630 may include environmental impact features 640-1.

[0155] The gain analysis layer 650 refers to the model used to process environmental influence features 640-1 and antenna information 640-2 to determine the gain parameter 660. In some embodiments, the gain analysis layer 650 can be a machine learning model. For example, the gain analysis layer 650 can include, but is not limited to, a DNN model.

[0156] In some embodiments, the input to the gain analysis layer 650 may include environmental influence characteristics 640-1 and antenna information 640-2, and the output of the gain analysis layer 650 may include gain parameters 660. For details regarding the antenna information, please refer to the relevant descriptions in other parts of this specification (e.g., Figure 2 (and related descriptions).

[0157] In some embodiments, the gain analysis model can be obtained by jointly training the environment embedding layer 630 and the gain analysis layer 650.

[0158] In some embodiments, the data processing module can train an initial environment embedding layer and an initial gain analysis layer based on a large number of labeled training samples. The training samples may include a sample environment impact map determined based on the distribution of effective sample environment data and sample antenna information; the acquisition method for these training samples is the same as that for the training samples described above. The labels may include the actual gain parameters corresponding to the sample environment impact map and sample antenna information determined based on the distribution of effective sample environment data; the labels may be determined based on methods such as manual annotation.

[0159] An exemplary training process includes: inputting the sample environmental impact map into the initial environment embedding layer to obtain the environmental impact features output by the initial environment embedding layer; inputting the environmental impact features output by the initial environment embedding layer and the sample antenna information into the initial gain analysis layer to obtain the gain parameters output by the initial gain analysis layer; constructing a loss function based on the labels and the gain parameters output by the initial gain analysis layer, and synchronously updating the parameters of the initial environment embedding layer and the initial gain analysis layer. Through parameter updates, the trained environment embedding layer and gain analysis layer are obtained.

[0160] In some embodiments of this specification, an environmental impact map is constructed based on the distribution of effective environmental data. Based on this environmental impact map and antenna information, a trained gain analysis model is used to determine the gain parameters. This effectively ensures the accuracy of the gain parameters, thus laying the foundation for quickly and accurately determining antenna gain data. Furthermore, using a joint training method to train the gain analysis model not only reduces the number of samples required for training but also improves training efficiency.

[0161] Some embodiments of this specification also provide an electromagnetic environment monitoring and calibration device, which includes at least one memory and at least one processor. The at least one memory is used to store computer instructions, and the at least one processor is used to execute a portion of the computer instructions to achieve... Figures 2-6 The method described in [the document / document].

[0162] Some embodiments of this specification also provide a computer-readable storage medium that stores computer instructions, which, when read by a computer, are executed by the computer. Figures 2-6 The method described in [the document / document].

[0163] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, 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 and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0164] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0165] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0166] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0167] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates 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 be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0168] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0169] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for determining electromagnetic environment monitoring compensation parameters, characterized in that, The method for determining system execution based on electromagnetic environment monitoring and compensation parameters includes: Acquire environmental data; Based on the environmental data and the switched RF channel information, the test signal parameters are determined; the test signal parameters include at least the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies of the test signal. The test signal is emitted based on the test signal parameters; Collect the calibration data corresponding to the test signal; Based on the calibration data and the switched RF channel information, determine the antenna gain data; The test signal is segmented according to the test signal parameters to obtain segmented signals; Based on the segmented signal parameters, the calibration data corresponding to the segmented signal, and the antenna gain data, the system loss data corresponding to the segmented signal is determined; Based on the antenna gain data and the system loss data, the monitoring compensation parameters are determined; The step of segmenting the test signal according to the test signal parameters includes: Based on the historical reliability of the test signals at different electromagnetic frequencies, the test signals are segmented, wherein the segment to which the test signal of the electromagnetic frequency belongs is narrower. The historical reliability refers to the reliability of the calibration results obtained from test signals of different electromagnetic frequencies under the same environmental data in historical data. The historical reliability of the test signals is determined by compensating the electromagnetic signals with the monitoring compensation parameters obtained from the test signals, and the accuracy of the historical monitoring data is then determined.

2. The method according to claim 1, characterized in that, The determination of antenna gain data also includes: Obtain antenna information; Based on the antenna information and the environmental data, the gain parameters are determined; Based on the gain parameters, the antenna gain data is determined using a preset method.

3. The method according to claim 2, characterized in that, The determination of the gain parameters includes: Based on the antenna orientation, determine the distribution of effective environmental data corresponding to the antenna; Based on the effective environmental data distribution and the antenna information, the gain parameters are determined through a gain analysis model, which is a machine learning model.

4. The method according to claim 1, characterized in that, The determination of test signal parameters based on the environmental data and the switched RF channel information includes: Based on the switched radio frequency channel information, the basic signal parameters are determined; Based on the environmental data, enhancement signal parameters are determined; the enhancement signal parameters refer to the test signal parameters used to enhance the test effect; the enhancement signal parameters include the increase in test intensity corresponding to the key electromagnetic frequency; The test signal parameters are determined based on the basic signal parameters and the enhanced signal parameters.

5. A system for determining electromagnetic environment monitoring and compensation parameters, characterized in that, The system includes an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module, and a data processing module. The environmental monitoring module is configured to acquire environmental data; The parameter determination module is configured to determine test signal parameters based on the environmental data and the switched RF channel information; the test signal parameters include at least the frequency range, frequency distribution, and test intensity 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 parameters; The data acquisition module is configured to acquire calibration data corresponding to the test signal; The data processing module is configured as follows: Based on the calibration data and the switched RF channel information, determine the antenna gain data; The test signal is segmented according to the test signal parameters to obtain segmented signals, including: segmenting the test signal based on the historical reliability of the test signal at different electromagnetic frequencies, wherein the segment to which the test signal of the electromagnetic frequency with lower historical reliability belongs is narrower; the historical reliability refers to the reliability of the calibration results obtained from test signals of different electromagnetic frequencies under the same environmental data in historical data; the historical reliability of the test signal is used to compensate the electromagnetic signal using monitoring compensation parameters obtained from the test signal, and the accuracy of the historical monitoring data is determined; Based on the segmented signal parameters, the calibration data corresponding to the segmented signal, and the antenna gain data, the system loss data corresponding to the segmented signal is determined; Based on the antenna gain data and the system loss data, the monitoring compensation parameters are determined.

6. The system according to claim 5, characterized in that, The data processing module is further configured to: Obtain antenna information; Based on the antenna information and the environmental data, the gain parameters are determined; Based on the gain parameters, the antenna gain data is determined using a preset method.

7. The system according to claim 6, characterized in that, The data processing module is further configured to: Based on the antenna orientation, determine the distribution of effective environmental data corresponding to the antenna; Based on the effective environmental data distribution and the antenna information, the gain parameters are determined through a gain analysis model, which is a machine learning model.

8. The system according to claim 5, characterized in that, The parameter determination module is further configured to: Based on the switched radio frequency channel information, the basic signal parameters are determined; Based on the environmental data, enhancement signal parameters are determined; the enhancement signal parameters refer to the test signal parameters used to enhance the test effect; the enhancement signal parameters include the increase in test intensity corresponding to the key electromagnetic frequency; The test signal parameters are determined based on the basic signal parameters and the enhanced signal parameters.

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