A real-time electromagnetic environment monitoring system and method

Abstract

The present invention discloses a real-time electromagnetic environment monitoring system and method, which relates to the technical field of electromagnetic environment monitoring, including using sensors to obtain real-time electromagnetic signal data and historical electromagnetic environment data and preprocessing; constructing a trend prediction model based on the electromagnetic signal data and historical electromagnetic environment data and predicting changes in the electromagnetic environment; automatically adjusting the sampling frequency according to the electromagnetic environment change trend prediction results and real-time electromagnetic signal data; and continuously optimizing the parameters in the electromagnetic environment change trend prediction model according to monitoring data and feedback. The present invention constructs a trend prediction model to analyze the electromagnetic signal data and historical environment data, which can improve the accuracy of the prediction of the electromagnetic environment change trend, and automatically adjusts the sampling frequency so that the system can adjust the frequency of data collection according to the dynamic changes of the electromagnetic environment, ensuring that the collected data can reflect the key changes in the electromagnetic environment without wasting resources, which reflects a high degree of flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic environment monitoring, and in particular to a real-time electromagnetic environment monitoring system and method. Background Art

[0002] With the rapid development of modern science and technology, the electromagnetic environment has become increasingly complex. Electromagnetic signals are widely present in many fields such as communications, electric power, industrial automation, and military. Whether it is signal transmission in communication systems, electromagnetic compatibility issues in power systems, or electronic countermeasures in the military field, accurate monitoring of the electromagnetic environment is of vital importance. Under the current technical conditions, traditional electromagnetic environment monitoring methods face many challenges. Electromagnetic signals are complex and diverse, including different parameters such as intensity, frequency, and phase, and their changes may be affected by many factors, such as changes in the natural environment, the start-up or shutdown of nearby electromagnetic equipment, and the movement of signal sources. Traditional monitoring methods are often difficult to be comprehensive, accurate, and To capture these changes in real time, the sampling frequency of most systems is fixed and cannot be automatically adjusted according to the actual changes in the electromagnetic environment. When the electromagnetic environment is relatively stable, too high a sampling frequency will cause a waste of resources, and when the electromagnetic environment changes rapidly, too low a sampling frequency may miss key information and fail to detect electromagnetic interference, abnormal signals, etc. in time, thereby affecting the accurate prediction of the changing trend of the electromagnetic environment. Although there are some attempts to use models to predict changes in the electromagnetic environment, these models often lack the ability to continuously optimize based on actual monitoring data and feedback. As time goes by and the environment changes, the prediction accuracy of the model will gradually decrease, and it cannot meet the long-term stable and accurate electromagnetic environment monitoring needs.

[0003] However, the current common solutions have many shortcomings, including: the existing monitoring system uses a fixed sampling frequency, which cannot be dynamically adjusted according to the actual changes in the electromagnetic environment. When the electromagnetic environment is stable, it will lead to unnecessary data collection and waste of resources. When the electromagnetic environment changes rapidly, it may miss important electromagnetic signal change information due to the low sampling frequency, thereby reducing resource utilization efficiency and monitoring effectiveness. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above problems existing in the existing real-time electromagnetic environment monitoring system and method, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a real-time electromagnetic environment monitoring system and method, which is suitable for solving the problem that the existing monitoring system adopts a fixed sampling frequency, which cannot be dynamically adjusted according to the actual changes in the electromagnetic environment. When the electromagnetic environment is stable, it will lead to unnecessary data collection and waste of resources. When the electromagnetic environment changes rapidly, important electromagnetic signal change information may be missed due to the low sampling frequency, thereby reducing the resource utilization efficiency and the effectiveness of monitoring.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a real-time electromagnetic environment monitoring method, which includes using sensors to obtain real-time electromagnetic signal data and historical electromagnetic environment data and perform preprocessing, then constructing a trend prediction model based on the electromagnetic signal data and historical electromagnetic environment data and predicting changes in the electromagnetic environment, then automatically adjusting the sampling frequency according to the electromagnetic environment change trend prediction results and real-time electromagnetic signal data, and finally continuously optimizing the parameters in the electromagnetic environment change trend prediction model based on actual monitoring data and feedback.

[0009] As a preferred solution of the real-time electromagnetic environment monitoring method described in the present invention, wherein: the electromagnetic signal data includes electromagnetic signal strength data, electromagnetic signal frequency data, electromagnetic signal phase data, timestamp information, sensor location information and signal complexity data; the historical electromagnetic environment data includes electromagnetic signal strength historical data, electromagnetic signal frequency historical data, electromagnetic signal complexity historical data, environmental factor-related historical data, signal source location and activity time historical data.

[0010] As a preferred scheme of the real-time electromagnetic environment monitoring method described in the present invention, the specific steps of constructing the trend prediction model are: using sensors to obtain electromagnetic signal data and historical electromagnetic environment data and perform preprocessing; constructing a trend prediction model based on a recurrent neural network and predicting the changing trend of the environment; using a machine learning algorithm to analyze the electromagnetic signal data and historical environment data; evaluating the performance of the trend prediction model; applying the evaluated and verified model to the actual electromagnetic environment monitoring system; and formulating a model update strategy based on the actual monitoring situation.

[0011] As a preferred solution of the real-time electromagnetic environment monitoring method of the present invention, the specific formula of the machine learning algorithm is as follows:

[0012]

[0013] Among them, Z t+1 It is a comprehensive evaluation value of the electromagnetic environment change trend at the next moment based on the electromagnetic signal data and historical electromagnetic environment data at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

[0014] As a preferred solution of the real-time electromagnetic environment monitoring method of the present invention, the specific formula for automatically adjusting the sampling frequency probability is as follows:

[0015]

[0016] Among them, P t is the probability of automatically adjusting the sampling frequency at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

[0017] As a preferred scheme of the real-time electromagnetic environment monitoring method described in the present invention, the specific situation of the probability of automatically adjusting the sampling frequency is as follows: when the automatically adjusted sampling frequency is less than the threshold value, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is small, and the system does not need to adjust the sampling frequency; when the automatically adjusted sampling frequency is greater than the threshold value, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is large, and the system needs to adjust the sampling frequency in time.

[0018] As a preferred solution of the real-time electromagnetic environment monitoring method described in the present invention, the specific steps of automatically adjusting the sampling frequency are as follows: using sensors to collect and preprocess a variety of electromagnetic signal data; building a trend prediction model based on a recurrent neural network; and inputting the preprocessed historical electromagnetic signal data into the trend prediction model to train the trend prediction model; inputting the latest preprocessed actual electromagnetic signal and historical electromagnetic environment data into the trend prediction model; calculating the probability of automatically adjusting the sampling frequency based on the prediction results and real-time electromagnetic signal data; setting a threshold, if the calculated probability of automatically adjusting the sampling frequency is greater than or equal to the threshold, deciding to adjust the sampling frequency; if the calculated probability of automatically adjusting the sampling frequency is less than the threshold, there is no need to adjust the sampling frequency.

[0019] In the second aspect, in order to further solve the above-mentioned technical problems, the embodiments of the present invention provide a real-time electromagnetic environment monitoring system, which includes: a data acquisition module, which is used to collect real-time electromagnetic signal data and historical electromagnetic environment data and perform preprocessing; a model building module, which is used to build a trend prediction model and predict changes in the electromagnetic environment; an automatic adjustment module, which is used to automatically adjust the sampling frequency according to the electromagnetic environment change trend prediction results and real-time electromagnetic signal data; an optimization feedback module, which is used to continuously optimize the parameters in the electromagnetic environment change trend prediction model according to actual monitoring data and feedback.

[0020] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a real-time electromagnetic environment monitoring method as described in the first aspect of the present invention is implemented.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of a real-time electromagnetic environment monitoring method as described in the first aspect of the present invention is implemented.

[0022] The beneficial effects of the present invention are as follows: the present invention constructs a trend prediction model and uses a machine learning algorithm to analyze electromagnetic signal data and historical environmental data. Compared with traditional monitoring methods based on experience or simple models, the present invention can significantly improve the accuracy of predicting the trend of electromagnetic environment changes. The function of automatically adjusting the sampling frequency enables the system to flexibly adjust the frequency of data collection according to the dynamic changes of the electromagnetic environment. Regardless of whether the electromagnetic environment suddenly changes from a stable state to an active state, or vice versa, the system can respond in a timely manner to ensure that the collected data can reflect the key changes in the electromagnetic environment without causing waste of resources, reflecting a high degree of flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1.

[0025] Figure 2 This is a dynamic adjustment diagram of the trend prediction strategy in Example 1. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0029] Example 1

[0030] Reference Figure 1 and Figure 2 , is the first embodiment of the present invention, which provides a real-time electromagnetic environment monitoring method, comprising the following steps:

[0031] S1: Use sensors to obtain real-time electromagnetic signal data and historical electromagnetic environment data and perform preprocessing.

[0032] Preferably, Figure 1 The figure shows the implementation process of the present invention. First, the real-time electromagnetic signal data and historical electromagnetic environment data are acquired by sensors and preprocessed. Then, a trend prediction model is constructed based on the electromagnetic signal data and the historical electromagnetic environment data to predict the changes in the electromagnetic environment. Subsequently, the sampling frequency is automatically adjusted according to the electromagnetic environment change trend prediction results and the real-time electromagnetic signal data. Finally, the parameters in the electromagnetic environment change trend prediction model are continuously optimized according to the actual monitoring data and feedback.

[0033] Furthermore, the electromagnetic signal data includes electromagnetic signal strength data, electromagnetic signal frequency data, electromagnetic signal phase data, timestamp information, sensor location information, and signal complexity data.

[0034] Furthermore, the historical electromagnetic environment data includes historical data of electromagnetic signal strength, historical data of electromagnetic signal frequency, historical data of electromagnetic signal complexity, historical data related to environmental factors, signal source location, and historical data of activity time.

[0035] Specifically, the preprocessing ensures the quality and consistency of the data by performing real-time preprocessing of electromagnetic signal data and historical electromagnetic environment data on the data nodes, including data cleaning, noise reduction and standardization.

[0036] S2: Build a trend prediction model based on electromagnetic signal data and historical electromagnetic environment data and predict changes in the electromagnetic environment.

[0037] Preferably, the specific steps of constructing the trend prediction model are: using sensors to acquire electromagnetic signal data and historical electromagnetic environment data and perform preprocessing.

[0038] Build a trend prediction model based on recurrent neural network and predict the changing trend of the environment.

[0039] Electromagnetic signal data and historical environmental data are analyzed using machine learning algorithms.

[0040] Evaluate the performance of trend forecasting models.

[0041] The evaluated and verified model is applied to the actual electromagnetic environment monitoring system.

[0042] Formulate model update strategies based on actual monitoring conditions.

[0043] Furthermore, the specific formula of the machine learning algorithm is as follows:

[0044]

[0045] Among them, Z t+1 It is a comprehensive evaluation value of the electromagnetic environment change trend at the next moment based on the electromagnetic signal data and historical electromagnetic environment data at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

[0046] Preferably, the constructed trend prediction model can mine the intrinsic relationship between electromagnetic signal data and historical environmental data, and predict the changing trend of the electromagnetic environment by analyzing these relationships. Compared with traditional predictions based on experience or simple statistical methods, this data-driven model prediction is more scientific and accurate.

[0047] S3: Automatically adjust the sampling frequency based on the electromagnetic environment change trend prediction results and real-time electromagnetic signal data.

[0048] Preferably, the specific formula for automatically adjusting the sampling frequency probability is as follows:

[0049]

[0050] Among them, P t is the probability of automatically adjusting the sampling frequency at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

[0051] Furthermore, the specific situation of automatically adjusting the sampling frequency probability is as follows:

[0052] When the automatically adjusted sampling frequency is less than the threshold, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is small, and the system does not need to adjust the sampling frequency.

[0053] When the automatically adjusted sampling frequency is greater than the threshold, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is large, and the system needs to adjust the sampling frequency in time.

[0054] Specifically, the specific steps of automatically adjusting the sampling frequency are as follows: using sensors to collect a variety of electromagnetic signal data and perform pre-processing.

[0055] Build a trend prediction model based on recurrent neural network.

[0056] The preprocessed historical electromagnetic signal data is input into the trend prediction model to train the trend prediction model.

[0057] The latest pre-processed factual electromagnetic signals and historical electromagnetic environment data are input into the trend prediction model.

[0058] Based on the prediction results and real-time electromagnetic signal data, the probability of automatically adjusting the sampling frequency is calculated.

[0059] A threshold is set. If the calculated probability of automatically adjusting the sampling frequency is greater than or equal to the threshold, the sampling frequency is decided to be adjusted.

[0060] If the calculated probability of automatically adjusting the sampling frequency is less than the threshold, the sampling frequency does not need to be adjusted.

[0061] Preferably, the monitoring system is more flexible in adapting to changes in the electromagnetic environment. Regardless of whether the electromagnetic environment is in a stable state or a rapidly changing state, the system can make an appropriate response by automatically adjusting the sampling frequency, ensuring that the monitoring data can reflect environmental changes without causing waste of resources.

[0062] Furthermore, the determination of the threshold needs to be based on statistical analysis of historical data, experimental simulation, and actual application scenarios and needs.

[0063] S4: Based on actual monitoring data and feedback, continuously optimize the parameters in the electromagnetic environment change trend prediction model.

[0064] Preferably, the constructed trend prediction model can mine the intrinsic relationship between electromagnetic signal data and historical environmental data, and the function of continuously optimizing model parameters enables the model to adapt to the dynamic changes of the electromagnetic environment. As time goes by and the environment changes, the model can update its own parameters in a timely manner, maintain the ability to accurately predict the changing trends of the electromagnetic environment, and avoid the problem of model failure due to environmental changes.

[0065] This embodiment also provides a real-time electromagnetic environment monitoring system, including: a data acquisition module, used to collect real-time electromagnetic signal data and historical electromagnetic environment data and perform preprocessing; a model construction module, used to build a trend prediction model and predict changes in the electromagnetic environment; an automatic adjustment module, used to automatically adjust the sampling frequency according to the electromagnetic environment change trend prediction results and real-time electromagnetic signal data; an optimization feedback module, used to continuously optimize the parameters in the electromagnetic environment change trend prediction model based on actual monitoring data and feedback.

[0066] This embodiment also provides a computer device, which is suitable for a real-time electromagnetic environment monitoring method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a real-time electromagnetic environment monitoring method proposed in the above embodiment.

[0067] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0068] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a real-time electromagnetic environment monitoring method as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0069] In summary, the present invention constructs a trend prediction model and uses a machine learning algorithm to analyze electromagnetic signal data and historical environmental data. Compared with traditional monitoring methods based on experience or simple models, it can significantly improve the accuracy of predicting the trend of electromagnetic environment changes. The function of automatically adjusting the sampling frequency enables the system to flexibly adjust the frequency of data collection according to the dynamic changes of the electromagnetic environment. Regardless of whether the electromagnetic environment suddenly changes from a stable state to an active state, or vice versa, the system can respond in a timely manner to ensure that the collected data can reflect the key changes in the electromagnetic environment without causing waste of resources, reflecting a high degree of flexibility.

[0070] Example 2

[0071] Referring to Tables 1 to 3, which are the second embodiment of the present invention, this embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0072] Table 1 shows the intensity, frequency, phase, and complexity of the electromagnetic signal data collected in this example, which provides a high-quality data basis for subsequent trend prediction and other analyses.

[0073] Table 1 Data collection results

[0074] Time (minutes) Intensity (dBm) Frequency (MHz) Phase(°) Complexity 0 -25.3 500 120 12 10 30.5 1200 200 20 20 -10.2 800 300 18 30 45.7 2000 70 25 40 -3.7 1500 180 22 50 55.2 2500 250 30

[0075] Table 2 is a comparison table of the prediction results of the trend prediction model in this example and the actual results.

[0076] Table 2 Comparison between the prediction results of the trend prediction model and the actual results

[0077]

[0078] Table 3 is a record table of sampling frequency adjustment in different time intervals in this example.

[0079] Table 3 Sampling frequency adjustment record table

[0080] Time interval (minutes) Forecast rate of change Sampling frequency (times / minute) 0-10 High rate of change (intensity, frequency) 3 10-25 Low rate of change 0.5 25-35 High rate of change (frequency, complexity) 3 35-50 Medium rate of change 1 50-60 High rate of change (intensity, complexity) 3

[0081] It can be seen from the above table that the errors between the electromagnetic signal strength, frequency, phase and complexity predicted by the present invention and the actual values ​​are all within a small range. This small error indicates that the model can accurately capture the changing trend of the electromagnetic environment, effectively predict the changes in the electromagnetic environment in advance, and can flexibly adjust the sampling frequency according to the different rates of change of the electromagnetic environment.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A real-time electromagnetic environment monitoring method, characterized in that: include: Use sensors to obtain real-time electromagnetic signal data and historical electromagnetic environment data and perform preprocessing; Build trend prediction models based on electromagnetic signal data and historical electromagnetic environment data and predict changes in the electromagnetic environment; Automatically adjust the sampling frequency according to the prediction results of electromagnetic environment change trend and real-time electromagnetic signal data; Based on actual monitoring data and feedback, the parameters in the electromagnetic environment change trend prediction model are continuously optimized.

2. The real-time electromagnetic environment monitoring method according to claim 1, characterized in that: The electromagnetic signal data includes electromagnetic signal strength data, electromagnetic signal frequency data, electromagnetic signal phase data, timestamp information, sensor location information and signal complexity data; The historical electromagnetic environment data includes electromagnetic signal strength historical data, electromagnetic signal frequency historical data, electromagnetic signal complexity historical data, environmental factor related historical data, signal source location and activity time historical data.

3. The real-time electromagnetic environment monitoring method according to claim 2, characterized in that: The specific steps of constructing the trend prediction model are: Use sensors to obtain electromagnetic signal data and historical electromagnetic environment data and perform preprocessing; Build a trend prediction model based on recurrent neural network and predict the changing trend of the environment; Analyze electromagnetic signal data and historical environmental data using machine learning algorithms; Evaluate the performance of trend forecasting models; Apply the evaluated and verified model to the actual electromagnetic environment monitoring system; Formulate model update strategies based on actual monitoring conditions.

4. The real-time electromagnetic environment monitoring method according to claim 3, characterized in that: The specific formula of the machine learning algorithm is as follows: Among them, Z t+1 It is a comprehensive evaluation value of the electromagnetic environment change trend at the next moment based on the electromagnetic signal data and historical electromagnetic environment data at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

5. The real-time electromagnetic environment monitoring method according to claim 1, characterized in that: The specific formula for automatically adjusting the sampling frequency probability is as follows: Among them, P t is the probability of automatically adjusting the sampling frequency at time t; is the i-th element in the electromagnetic signal data vector at time t; is the jth element in the historical electromagnetic environment data vector at time t; n is the number of items of electromagnetic signal data and historical electromagnetic environment data involved in the summation operation.

6. The real-time electromagnetic environment monitoring method according to claim 5, characterized in that: The specific situation of automatically adjusting the sampling frequency probability is as follows: When the automatically adjusted sampling frequency is less than the threshold, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is small, and the system does not need to adjust the sampling frequency; When the automatically adjusted sampling frequency is greater than the threshold, it means that the comprehensive difference between the real-time electromagnetic signal data and the historical electromagnetic environment data is large, and the system needs to adjust the sampling frequency in time.

7. The real-time electromagnetic environment monitoring method according to claim 6, characterized in that: The specific steps of automatically adjusting the sampling frequency are as follows: Use sensors to collect various electromagnetic signal data and perform pre-processing; Build a trend prediction model based on recurrent neural network; and inputting the pre-processed historical electromagnetic signal data into the trend prediction model to train the trend prediction model; Input the latest pre-processed factual electromagnetic signals and historical electromagnetic environment data into the trend prediction model; Calculate the probability of automatically adjusting the sampling frequency based on the prediction results and real-time electromagnetic signal data; Set a threshold. If the calculated probability of automatically adjusting the sampling frequency is greater than or equal to the threshold, then decide to adjust the sampling frequency. If the calculated probability of automatically adjusting the sampling frequency is less than the threshold, the sampling frequency does not need to be adjusted.

8. A real-time electromagnetic environment monitoring system, based on a real-time electromagnetic environment monitoring method according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, used to collect real-time electromagnetic signal data and historical electromagnetic environment data and perform pre-processing; Model building module, used to build trend prediction models and predict changes in the electromagnetic environment; An automatic adjustment module is used to automatically adjust the sampling frequency according to the prediction results of the electromagnetic environment change trend and the real-time electromagnetic signal data; The optimization feedback module is used to continuously optimize the parameters in the electromagnetic environment change trend prediction model based on actual monitoring data and feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a real-time electromagnetic environment monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a real-time electromagnetic environment monitoring method according to any one of claims 1 to 7 are implemented.

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