Electromagnetic environment abnormal data prediction and early warning system

Abstract

The invention provides an electromagnetic environment abnormal data prediction and early warning system, and relates to the technical field of electromagnetic environment monitoring. The system is configured to obtain regional electromagnetic monitoring historical data and extract abnormal electromagnetic data to form regional electromagnetic monitoring abnormal data; performing abnormal change feature analysis according to the regional electromagnetic monitoring abnormal data to form electromagnetic abnormal change feature data; and collecting regional electromagnetic monitoring real-time data, and performing electromagnetic anomaly prediction analysis in combination with the electromagnetic anomaly change feature data to form electromagnetic anomaly early warning data. According to the system, a rapid and real-time anomaly monitoring effect and adaptive anomaly response are realized through reasonable data analysis.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic environment monitoring technology, and more specifically, to an electromagnetic environment anomaly data prediction and early warning system. Background Technology

[0002] Electromagnetic environment monitoring is a technical means of assessing the electromagnetic environment by measuring and analyzing parameters such as the intensity, frequency, and distribution of electromagnetic signals within a specific area in real time or periodically. Real-time monitoring of abnormal electromagnetic conditions allows for the timely detection of such anomalies and the implementation of corresponding measures.

[0003] Currently, electromagnetic environment anomaly monitoring mostly relies on simple threshold analysis, which suffers from unreasonable judgment criteria and a significant dependence on empirical data. This makes it impossible to accurately and reliably predict electromagnetic anomalies, leading to delayed responses that could damage equipment and facilities, and an inability to quickly determine the appropriate level of response to electromagnetic anomalies.

[0004] Therefore, designing an electromagnetic environment anomaly data prediction and early warning system, and achieving rapid and real-time anomaly monitoring and adaptive anomaly response through reasonable data analysis, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide an electromagnetic environment anomaly data prediction and early warning system. This system generates electromagnetic anomaly data by deleting normally existing electromagnetic signals from historical regional electromagnetic monitoring data. It then analyzes the intensity and frequency variations of these anomalies in the spatial region to extract characteristic change data for anomaly levels. This characteristic change data can then be used to make real-time anomaly judgments on the monitored electromagnetic anomaly data, thereby achieving more efficient and real-time anomaly monitoring and adaptive matching for handling anomalies based on the monitoring results.

[0006] In a first aspect, the present invention provides an electromagnetic environment anomaly data prediction and early warning system, configured to: acquire historical data of regional electromagnetic monitoring and extract abnormal electromagnetic data to form regional electromagnetic monitoring anomaly data; perform anomaly change characteristic analysis based on the regional electromagnetic monitoring anomaly data to form electromagnetic anomaly change characteristic data; collect real-time data of regional electromagnetic monitoring and combine it with the electromagnetic anomaly change characteristic data to perform electromagnetic anomaly prediction analysis to form electromagnetic anomaly early warning data.

[0007] In this invention, the system generates electromagnetic anomaly data by deleting normally existing electromagnetic signals in a region based on historical regional electromagnetic monitoring data. It then analyzes the electromagnetic anomaly data to extract characteristic change data for the anomaly level by analyzing the changes in intensity and frequency of electromagnetic anomalies in the spatial region. This characteristic change data can then be used to make corresponding real-time anomaly judgments on the real-time monitored electromagnetic anomaly data, thereby achieving more efficient and real-time anomaly monitoring and adaptive matching for handling anomalies based on the monitoring results.

[0008] One possible approach is to acquire historical electromagnetic monitoring data for the region and extract abnormal electromagnetic data to form abnormal electromagnetic monitoring data for the region. This includes: acquiring all different normal electromagnetic signal data within the monitoring area during the historical monitoring period based on the historical electromagnetic monitoring data to form normal electromagnetic monitoring data for the region; extracting abnormal electromagnetic signal data within the historical monitoring period based on the normal electromagnetic monitoring data to form abnormal electromagnetic monitoring data for the region; and performing spatialization processing on the abnormal electromagnetic monitoring data to establish spatial data of regional historical electromagnetic anomaly changes within the monitoring area.

[0009] In this invention, electromagnetic data information of normally occurring electromagnetic signals within the monitoring area is first extracted based on historical monitoring anomaly data. Then, by excluding normal electromagnetic data, abnormal electromagnetic data within the entire historical monitoring period is identified. This abnormal electromagnetic data is then visualized to form more intuitive and easily processed graphical data. It should be noted that there are many methods for extracting normal electromagnetic data. Accurate identification can be achieved by using filtering functions or further tracing the source of the equipment and facilities generating the electromagnetic data. Typically, the acquired historical monitoring data already provides reasonable evidence for the extraction of normal electromagnetic data. Therefore, in most cases, the normal electromagnetic data identified from historical data can be directly used to extract abnormal electromagnetic data. Considering that the purpose of subsequent analysis of abnormal electromagnetic data is to accurately extract features of abnormal electromagnetic data change trends, and that the extracted features include spatial regional characteristics, visualizing the abnormal electromagnetic data for spatial regions allows for a more vivid and accurate combination of electromagnetic anomaly information and spatial features, providing more convenient and accurate data for subsequent feature extraction.

[0010] One possible approach is to spatialize historical monitoring anomaly data in a region to establish spatial data on historical electromagnetic anomaly changes within the monitoring area. This includes: mapping the monitoring area to coordinates and establishing a regional location coordinate system; using the time dimension as a reference, calibrating the anomalous electromagnetic intensity at different locations within the regional location coordinate system based on the historical monitoring anomaly data, thus forming spatial data on historical electromagnetic anomaly intensity changes; using the time dimension as a reference, calibrating the anomalous electromagnetic frequency at different locations within the regional location coordinate system based on the historical monitoring anomaly data, thus forming spatial data on historical electromagnetic frequency changes; and combining the spatial data on historical electromagnetic intensity changes and the spatial data on historical electromagnetic frequency changes to form spatial data on historical electromagnetic anomaly changes.

[0011] In this invention, the application calibrates historical abnormal electromagnetic data by taking into account the electromagnetic intensity and frequency within the monitoring area, using time as a reference, thus forming spatialized data of historical electromagnetic anomaly information. Since the impact of battery anomalies on equipment and facilities within the area is mainly considered in terms of the electromagnetic intensity and frequency of the abnormal electromagnetic fields, the spatialized data is also based on electromagnetic intensity and frequency. It is understood that historical abnormal electromagnetic data for a region does not necessarily involve continuous electromagnetic anomaly exceeding limits throughout the entire monitoring period. Therefore, the extracted anomaly change feature data is analyzed based on the number of times electromagnetic data exceeds limits. This data division is identical to the event classification method used to determine the level of data exceeding limits. Based on this, data feature extraction based on the number of anomaly exceedances in big data can obtain more accurate feature data corresponding to the level clustering through clustering, further providing an important and accurate reference standard for subsequent prediction and early warning of anomalies in real-time data using feature data.

[0012] One possible approach is to analyze the anomaly characteristics based on regional electromagnetic monitoring anomaly data to form electromagnetic anomaly change characteristic data. This includes: analyzing the spatial variation characteristics of electromagnetic intensity exceeding the standard based on historical regional anomaly electromagnetic intensity spatial data to form electromagnetic anomaly intensity spatial variation characteristic data; analyzing the spatial variation characteristics of electromagnetic frequency exceeding the standard based on historical regional anomaly electromagnetic frequency spatial data to form electromagnetic anomaly frequency spatial variation characteristic data; and extracting features for the level of anomaly exceeding the standard based on the electromagnetic anomaly intensity spatial variation characteristic data and the electromagnetic anomaly frequency spatial variation characteristic data to form electromagnetic anomaly change characteristic data.

[0013] In this invention, the spatial data formed after spatializing historical electromagnetic anomaly data includes intensity spatial data and frequency spatial data. Therefore, when extracting feature data, feature change analysis is performed on the intensity and frequency spatial data respectively to form corresponding feature change data. Then, based on the corresponding feature change data, feature information clustering is performed according to the anomaly level to form reasonable electromagnetic anomaly feature change data. It can be understood that each time an electromagnetic anomaly exceeds the standard, different levels of exceedance will be classified to make adaptive anomaly handling. The different anomaly levels are mainly determined by the degree of impact of their intensity and frequency on equipment and facilities in the area. Therefore, after obtaining feature change data based on intensity spatial data and feature change data based on frequency spatial data respectively, clustering the feature change data based on anomaly level can form comprehensive intensity and frequency change feature data corresponding to different levels.

[0014] As one possible approach, based on historical spatial data of abnormal electromagnetic intensity variations in the region, an analysis of the spatial variation characteristics of electromagnetic intensity exceeding the standard is performed to form spatial variation characteristic data of electromagnetic anomaly intensity. This includes: extracting the excess area diffusion curve of the region for each occurrence of abnormal electromagnetic intensity exceeding the standard based on the historical spatial data of abnormal electromagnetic intensity variations in the region. 'n' represents the serial number of each electromagnetic data anomaly exceeding the limit, determined sequentially by time. Based on the spatial data of historical abnormal electromagnetic intensity changes in the region, the abnormal electromagnetic intensity change curve for each instance of electromagnetic data anomaly exceeding the limit is extracted. For each instance of abnormal electromagnetic data exceeding the limit, the diffusion curve of the corresponding exceeding area is used as a reference. and abnormal electromagnetic intensity variation curve The spatial variation curve of the abnormal electromagnetic intensity exceeding the standard was determined. ,in, Collect all the spatial variation curves of abnormal electromagnetic intensities exceeding the standard. This generates spatial variation characteristic data of electromagnetic anomaly intensity.

[0015] In this invention, it is understood that when electromagnetic anomalies exceed the limits, the greater the electromagnetic intensity, the wider the range of influence. Therefore, when extracting feature data for electromagnetic anomalies exceeding the limits, the analysis of electromagnetic intensity needs to consider the change in the range of influence caused by the intensity change. This application characterizes the influence of electromagnetic intensity on exceeding the limits by using the ratio of electromagnetic intensity change to the change in the area of ​​the affected region. Of course, since the extracted feature data is needed as the basis for subsequent prediction of whether electromagnetic anomalies exceed the limits, the feature data also needs to be based on data that changes over time. This ensures that when judging the anomaly exceeding the limits with feature data as a reference, it can be identified and predicted earlier, providing more time for subsequent corresponding countermeasures.

[0016] As one possible approach, based on historical spatial data of abnormal electromagnetic frequency variations in the region, an analysis of the spatial variation characteristics of electromagnetic frequencies exceeding the limits is performed to form spatial variation characteristic data of electromagnetic abnormal frequencies. This includes: extracting the abnormal electromagnetic frequency variation curves for each instance of abnormal electromagnetic frequency exceeding the limits, based on the historical spatial data of abnormal electromagnetic frequency variations in the region. For each instance of abnormal electromagnetic data exceeding the limit, the diffusion curve of the corresponding exceeding area is used as a reference. and abnormal electromagnetic frequency variation curve The spatial variation curve of the abnormal electromagnetic frequency exceeding the standard was determined. ,in, Collect all abnormal electromagnetic frequency spatial variation curves. This generates spatial variation characteristic data of electromagnetic anomaly frequencies.

[0017] In this invention, electromagnetic frequency, like electromagnetic intensity, is also an important analytical parameter affecting the impact of electromagnetic environmental anomalies. To maintain the same foundation as the data represented by electromagnetic intensity, electromagnetic frequency data is also characterized by the ratio of electromagnetic frequency change to the area change of the affected region. This ensures a reasonable data expression and corresponding data reference when subsequently considering both electromagnetic intensity and electromagnetic frequency spatial data, improving the rationality and effectiveness of data processing. It should be noted that the frequency change curve can use the average frequency data within the affected region as a reference to ensure data representativeness. Similarly, this application also uses average intensity data as a reference for electromagnetic intensity data.

[0018] One possible approach involves extracting features based on the spatial variation characteristics of electromagnetic anomaly intensity and frequency to form electromagnetic anomaly variation feature data. This includes: labeling different events that cause electromagnetic anomalies to exceed limits based on historical regional electromagnetic monitoring data; clustering different events that cause electromagnetic anomalies to exceed limits based on their identification levels to form event sets of the same level corresponding to different identification levels; extracting features from the spatial variation characteristics of intensity and frequency for different event sets of the same level to form initial feature data of electromagnetic anomalies exceeding limits at the same level; and conducting feature comparison analysis on the initial feature data of electromagnetic anomalies exceeding limits at the same level corresponding to different event sets of electromagnetic anomalies to form electromagnetic anomaly variation feature data.

[0019] In this invention, after extracting the spatial data of electromagnetic intensity and electromagnetic frequency corresponding to the electromagnetic anomaly, it is necessary to extract reasonable feature information. Here, it is considered that the impact of exceeding electromagnetic limits on equipment and facilities varies depending on the intensity and frequency. Therefore, abnormal data exceeding limits is usually classified into reasonable levels. Level classification is common in analysis and can be quickly obtained from historical data. Of course, some historical data may not contain obvious level classifications, but these can be indirectly determined through the countermeasures taken and the degree of impact on equipment and facilities. Classifying the acquired feature information based on levels can provide more accurate level judgment criteria, thus enabling the determination of the level of abnormal exceeding limits. Of course, after obtaining feature range data based on large datasets, there may be overlap between different levels. Therefore, it is necessary to compare and eliminate such overlaps to form reasonable and unique electromagnetic anomaly change feature data.

[0020] As one possible approach, feature extraction is performed on intensity spatial variation characteristics and frequency spatial variation characteristics of different electromagnetic data anomaly exceedance event sets of the same level to form initial feature data of electromagnetic anomaly exceedance at the same level. This includes: spatial variation curves of the electromagnetic intensity of the exceedance anomaly corresponding to all events in different electromagnetic data anomaly exceedance event sets of the same level. Define the range of the change curve over time and extract the spatial variation curve of the maximum exceeding anomaly electromagnetic intensity corresponding to the level. Spatial variation curve of electromagnetic intensity exceeding the minimum level This forms the range of variation in the spatial curve of abnormal electromagnetic intensity exceeding the standard. ,in, 'k' represents the level number of the event set of different electromagnetic data anomalies exceeding the standard; the spatial variation curves of the abnormal electromagnetic frequencies corresponding to all events in the event set of different electromagnetic data anomalies exceeding the standard are shown. Define the range of the change curves over time and extract the spatial variation curves of the corresponding maximum exceeding abnormal electromagnetic frequencies. Spatial variation curve of electromagnetic frequency exceeding the minimum level. This forms the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. ,in, For different sets of electromagnetic data anomalies exceeding the standard at the same level, combine the corresponding level of anomaly electromagnetic intensity spatial curve variation range. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. This generates initial characteristic data for electromagnetic anomalies exceeding the standard at the same level.

[0021] In this invention, it is understood that anomalous data at different levels exhibit significant differences in intensity and frequency spaces. Furthermore, even within the same level of big data, intensity and frequency space data can show individual variations. Therefore, the data obtainable under big data primarily represents the range defined by the corresponding big data. Even so, the range data will still exhibit significant differences due to variations between levels. The boundary definition of the maximum and minimum curve ranges can be formed by mapping the curves corresponding to all events under big data to the same coordinate system and extracting the outer envelope boundary lines.

[0022] As one possible implementation, a comparative analysis is performed on the initial characteristic data of electromagnetic anomalies exceeding the same level for different sets of events with the same level of electromagnetic anomalies exceeding the standard, forming characteristic data of electromagnetic anomaly changes. This includes: comparing and analyzing the initial characteristic data of electromagnetic anomalies exceeding the same level for different sets of events with the same level of electromagnetic anomalies exceeding the standard in the following way: if the change range of the spatial curve of the electromagnetic intensity of the anomalies exceeding the standard for different sets of events with the same level of electromagnetic anomalies exceeding the standard... and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If none of them correspond to each other, then the spatial variation range of the electromagnetic intensity variation curves corresponding to the same level of event set with different electromagnetic data anomalies will be determined. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified; if different electromagnetic data anomalies exceeded the standard, the corresponding level of event set with abnormal electromagnetic intensity spatial curve variation range was considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If only one range corresponds to an intersection, then the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set with different electromagnetic data anomalies exceeding the standard will be considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified; if different electromagnetic data anomalies exceeded the standard, the corresponding level of event set with abnormal electromagnetic intensity spatial curve variation range was considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If two corresponding ranges intersect, then after deleting the intersecting regions, the spatial curve variation range of the electromagnetic intensity of the newly formed event set corresponding to the different electromagnetic data anomalies exceeding the standard at the same level will be determined. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified.

[0023] In this invention, the initial feature data may have overlapping feature ranges. Therefore, it is necessary to make reasonable comparisons and adjustments to the feature ranges of different levels. It is understood that since electromagnetic intensity and electromagnetic frequency have a synergistic effect in causing electromagnetic anomalies, they cannot be compared separately for the same type of parameters. A comprehensive comparative analysis is also required. For example, if the feature ranges corresponding to intensity or frequency have an intersection while the other does not, the intersection area can be retained. After all, the analysis and comparison need to ensure that the ranges of intensity and frequency are within the corresponding feature ranges in order to confirm the corresponding anomaly level.

[0024] One possible approach is to collect real-time electromagnetic monitoring data for the region and combine it with electromagnetic anomaly change characteristic data to perform electromagnetic anomaly prediction and analysis, thereby generating electromagnetic anomaly early warning data. This includes: extracting the corresponding real-time abnormal electromagnetic intensity spatial variation curves for each monitoring sub-region with different abnormal electromagnetic intensities within the monitoring area based on the real-time electromagnetic monitoring data for the region. and the corresponding real-time abnormal electromagnetic frequency spatial variation curve Based on the electromagnetic anomaly change characteristic data, the spatial variation curves of real-time abnormal electromagnetic intensity corresponding to different monitoring sub-regions are generated. Real-time abnormal electromagnetic frequency spatial variation curve Perform the following anomaly prediction analysis: If there exists a real-time abnormal electromagnetic intensity spatial variation curve corresponding to any monitoring sub-region... Real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set of defined electromagnetic data anomalies exceeding the standard. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If a monitoring sub-region is identified, the corresponding sub-region will be designated as the corresponding identification level; otherwise, if no real-time abnormal electromagnetic intensity spatial variation curve exists for any monitoring sub-region... Real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set of defined electromagnetic data anomalies exceeding the standard. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. Then, we continue to acquire real-time data from all monitored sub-regions for comparative analysis.

[0025] In this invention, after acquiring the characteristic data of electromagnetic anomalies, real-time electromagnetic data can be reasonably monitored and analyzed. It should be noted that, for real-time data, different abnormal electromagnetic signals within the monitoring area may not necessarily affect the entire area due to factors such as signal source location and electromagnetic strength. Therefore, intensity and frequency space data are extracted based on continuous intervals of comprehensive abnormal electromagnetic intensity within a certain allowable threshold, forming real-time data for corresponding sub-regions. This allows for the determination of whether any sub-regions exceed the limits, greatly improving the accuracy and rationality of the analysis. Of course, only when both the intensity and frequency spaces fall within the characteristic range of the corresponding level can the corresponding level be determined.

[0026] The beneficial effects of the electromagnetic environment anomaly data prediction and early warning system provided by this invention are as follows:

[0027] This system generates electromagnetic anomaly data by deleting normally existing electromagnetic signals in the region based on historical regional electromagnetic monitoring data. It then analyzes the changes in intensity and frequency of electromagnetic anomalies in the spatial region to extract characteristic change data for anomaly levels. This characteristic change data can then be used to make corresponding real-time anomaly judgments on the real-time monitored electromagnetic anomaly data, thereby achieving more efficient and real-time anomaly monitoring and adaptive matching of anomaly handling based on monitoring results. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a diagram illustrating the operational steps of the electromagnetic environment anomaly data prediction and early warning system provided in this embodiment of the invention.

[0030] Figure 2This is a schematic diagram of the electromagnetic environment anomaly data prediction and early warning system provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Electromagnetic environment monitoring is a technical means of assessing the electromagnetic environment by measuring and analyzing parameters such as the intensity, frequency, and distribution of electromagnetic signals within a specific area in real time or periodically. Real-time monitoring of abnormal electromagnetic conditions allows for the timely detection of such anomalies and the implementation of corresponding measures.

[0033] Currently, electromagnetic environment anomaly monitoring mostly relies on simple threshold analysis, which suffers from unreasonable judgment criteria and a significant dependence on empirical data. This makes it impossible to accurately and reliably predict electromagnetic anomalies, leading to delayed responses that could damage equipment and facilities, and an inability to quickly determine the appropriate level of response to electromagnetic anomalies.

[0034] refer to Figures 1-2 This invention provides an electromagnetic environment anomaly data prediction and early warning system. The system generates electromagnetic anomaly data by deleting normally existing electromagnetic signals in a region based on historical regional electromagnetic monitoring data. It then analyzes the electromagnetic anomaly data for changes in intensity and frequency in the spatial region to extract characteristic change data for anomaly levels. This characteristic change data can then be used to make corresponding real-time anomaly judgments on the real-time monitored electromagnetic anomaly data, thereby achieving more efficient and real-time anomaly monitoring and adaptive matching for handling anomalies based on monitoring results.

[0035] The electromagnetic environment anomaly data prediction and early warning system is configured as follows:

[0036] S1: Obtain historical data of regional electromagnetic monitoring and extract abnormal electromagnetic data to form regional electromagnetic monitoring abnormal data.

[0037] The process involves acquiring historical electromagnetic monitoring data for the region and extracting abnormal electromagnetic data to form abnormal electromagnetic monitoring data for the region. This includes: acquiring all different normal electromagnetic signal data within the monitoring area during the historical monitoring period based on the historical electromagnetic monitoring data to form normal electromagnetic monitoring data for the region; extracting abnormal electromagnetic signal data within the historical monitoring period based on the normal electromagnetic monitoring data to form abnormal electromagnetic monitoring data for the region; and performing spatialization processing on the abnormal electromagnetic monitoring data to establish spatial data of regional historical electromagnetic anomaly changes within the monitoring area.

[0038] First, electromagnetic data information of normally occurring electromagnetic signals within the monitoring area is extracted based on historical monitoring anomaly data. Then, by excluding normal electromagnetic data, anomalous electromagnetic data within the entire historical monitoring period is identified. This anomalous electromagnetic data is then visualized to create more intuitive and easily processed graphical data. It should be noted that there are many methods for extracting normal electromagnetic data. Accurate identification can be achieved by using filtering functions or further tracing the source of the electromagnetic data through the equipment and facilities generating it. Typically, the historical monitoring data already provides reasonable evidence for the extraction of normal electromagnetic data; therefore, in most cases, the normal electromagnetic data identified in historical data can be directly used to extract anomalous electromagnetic data. Considering that the purpose of subsequent analysis of anomalous electromagnetic data is to accurately extract features of anomalous electromagnetic data trends, and that the extracted features include spatial regional characteristics, visualizing the anomalous electromagnetic data for specific spatial regions allows for a more vivid and accurate combination of electromagnetic anomaly information and spatial features, providing more convenient and accurate data for subsequent feature extraction.

[0039] Spatial processing is performed on historical monitoring anomaly data in the region to establish spatial data of historical electromagnetic anomaly changes within the monitoring area. This includes: mapping the monitoring area to coordinates and establishing a regional location coordinate system; using the time dimension as a reference, calibrating the anomalous electromagnetic intensity at different locations within the regional location coordinate system based on the historical monitoring anomaly data, forming spatial data of historical anomalous electromagnetic intensity changes; using the time dimension as a reference, calibrating the anomalous electromagnetic frequency at different locations within the regional location coordinate system based on the historical monitoring anomaly data, forming spatial data of historical anomalous electromagnetic frequency changes; and combining the spatial data of historical anomalous electromagnetic intensity changes and the spatial data of historical anomalous electromagnetic frequency changes to form spatial data of historical electromagnetic anomaly changes.

[0040] This application creates spatialized data of historical electromagnetic anomalies by calibrating the electromagnetic intensity and frequency within the monitoring area, taking into account the time dimension. Since the impact of battery anomalies on equipment and facilities within the area is primarily determined by the electromagnetic intensity and frequency of the abnormal electromagnetic fields, the spatialized data is also based on these parameters. It is understood that historical electromagnetic anomalies in the region do not consistently exceed limits throughout the entire monitoring period; therefore, the extracted anomaly change feature data is analyzed based on the number of times electromagnetic data exceeds limits. This data segmentation is identical to the event segmentation method used to determine the level of data exceeding limits. Based on this, data feature extraction using the number of anomaly exceedances as the unit in big data analysis can obtain more accurate feature data corresponding to the corresponding level clusters through clustering, further providing an important and accurate reference standard for subsequent anomaly prediction and early warning using feature data.

[0041] S2: Analyze the abnormal change characteristics based on the abnormal data of regional electromagnetic monitoring to form electromagnetic abnormal change characteristic data.

[0042] Anomaly change characteristic analysis is performed on regional electromagnetic monitoring anomaly data to form electromagnetic anomaly change characteristic data, including: based on the regional historical spatial data of abnormal electromagnetic intensity changes, anomaly intensity spatial change characteristic analysis is performed on electromagnetic intensity exceeding the standard, forming electromagnetic anomaly intensity spatial change characteristic data; based on the regional historical spatial data of abnormal electromagnetic frequency changes, anomaly frequency spatial change characteristic analysis is performed on electromagnetic frequency exceeding the standard, forming electromagnetic anomaly frequency spatial change characteristic data; based on the electromagnetic anomaly intensity spatial change characteristic data and electromagnetic anomaly frequency spatial change characteristic data, anomaly exceeding level characteristic features are extracted to form electromagnetic anomaly change characteristic data.

[0043] Spatial data generated after spatializing historical electromagnetic anomaly data includes both intensity and frequency spatial data. Therefore, feature extraction involves analyzing the feature changes of both intensity and frequency spatial data separately to generate corresponding feature change data. Then, based on these feature change data, feature information is clustered according to the anomaly level to form reasonable electromagnetic anomaly feature change data. It's understandable that each time an electromagnetic anomaly exceeds the limit, different levels of exceedance are classified to implement adaptive anomaly handling. These different anomaly levels are primarily determined by the degree of impact of their intensity and frequency on equipment and facilities within the area. Therefore, after acquiring feature change data based on both intensity and frequency spatial data, clustering these feature change data based on anomaly level generates comprehensive intensity and frequency change feature data corresponding to different levels.

[0044] Based on historical spatial data of abnormal electromagnetic intensity variations in the region, an analysis of the spatial variation characteristics of electromagnetic intensity exceeding the standard is conducted to form spatial variation characteristic data of electromagnetic anomaly intensity. This includes: extracting the excess area diffusion curve of the region for each occurrence of abnormal electromagnetic intensity exceeding the standard, based on the historical spatial data of abnormal electromagnetic intensity variations in the region. 'n' represents the serial number of each electromagnetic data anomaly exceeding the limit, determined sequentially by time. Based on the spatial data of historical abnormal electromagnetic intensity changes in the region, the abnormal electromagnetic intensity change curve for each instance of electromagnetic data anomaly exceeding the limit is extracted. For each instance of abnormal electromagnetic data exceeding the limit, the diffusion curve of the corresponding exceeding area is used as a reference. and abnormal electromagnetic intensity variation curve The spatial variation curve of the abnormal electromagnetic intensity exceeding the standard was determined. ,in, Collect all the spatial variation curves of abnormal electromagnetic intensities exceeding the standard. This generates spatial variation characteristic data of electromagnetic anomaly intensity.

[0045] It is understandable that when electromagnetic anomalies exceed the limits, the greater the electromagnetic intensity, the wider the range of influence. Therefore, when extracting feature data for electromagnetic anomalies exceeding the limits, the analysis of electromagnetic intensity needs to consider the changes in the range of influence caused by intensity changes. This application characterizes the impact of electromagnetic intensity on exceeding the limits by using the ratio of electromagnetic intensity change to the change in the area of ​​the affected region. Of course, since the extracted feature data is needed as the basis for subsequent prediction of whether electromagnetic anomalies exceed the limits, the feature data also needs to be based on data that changes over time. This ensures that when judging anomalies by using feature data as a reference, it is possible to identify and predict them earlier, providing more time for taking corresponding countermeasures.

[0046] Based on historical spatial data of abnormal electromagnetic frequency variations in the region, an analysis of the spatial variation characteristics of electromagnetic frequencies exceeding the limits is conducted to form spatial variation characteristic data of abnormal electromagnetic frequencies. This includes: extracting the abnormal electromagnetic frequency variation curves for each instance of abnormal electromagnetic frequency exceeding the limits, based on the historical spatial data of abnormal electromagnetic frequency variations in the region. For each instance of abnormal electromagnetic data exceeding the limit, the diffusion curve of the corresponding exceeding area is used as a reference. and abnormal electromagnetic frequency variation curve The spatial variation curve of the abnormal electromagnetic frequency exceeding the standard was determined. ,in, Collect all abnormal electromagnetic frequency spatial variation curves. This generates spatial variation characteristic data of electromagnetic anomaly frequencies.

[0047] Similar to electromagnetic intensity, electromagnetic frequency is also an important analytical parameter for the impact of electromagnetic environmental anomalies. To maintain the same data foundation as electromagnetic intensity, electromagnetic frequency data is also characterized by the ratio of electromagnetic frequency changes to changes in the area of ​​the affected region. This ensures a reasonable data representation and corresponding data reference when comprehensively considering electromagnetic intensity and electromagnetic frequency spatial data, improving the rationality and effectiveness of data processing. It should be noted that the frequency variation curve can use the average frequency data of the affected region as a reference to ensure data representativeness. Similarly, this application also uses average intensity data as a reference for electromagnetic intensity data.

[0048] Based on the spatial variation characteristics of electromagnetic anomaly intensity and frequency, features for different levels of anomaly exceedance are extracted to form electromagnetic anomaly change characteristic data. This includes: labeling the identification level of different events that cause electromagnetic data anomalies to exceed limits based on historical regional electromagnetic monitoring data; clustering different events that cause electromagnetic data anomalies to exceed limits based on their identification levels to form event sets of the same level corresponding to different identification levels; extracting features from the spatial variation characteristics of intensity and frequency for different event sets of the same level of electromagnetic data anomalies to form initial characteristic data of electromagnetic anomalies exceeding limits at the same level; and conducting feature comparison analysis on the initial characteristic data of electromagnetic anomalies exceeding limits at the same level corresponding to different event sets of electromagnetic data anomalies exceeding limits to form electromagnetic anomaly change characteristic data.

[0049] After extracting the spatial data of electromagnetic intensity and frequency corresponding to the electromagnetic anomaly, it is necessary to extract appropriate feature information. Here, it's important to consider that the impact of exceeding electromagnetic limits on equipment and facilities varies depending on the intensity and frequency. Therefore, abnormal data exceeding limits is usually classified into appropriate levels. Level classification is common in analysis and can be quickly obtained from historical data. Of course, some historical data may not contain obvious level classifications, but these can be indirectly determined by the countermeasures taken and the degree of impact on equipment and facilities. Classifying the acquired feature information based on levels can provide more accurate level judgment criteria, thus enabling the determination of the level of abnormal exceedance. Of course, after obtaining feature range data from large datasets, there may be overlap between different levels. Therefore, it is necessary to compare and eliminate such overlaps to form reasonable and unique electromagnetic anomaly change feature data.

[0050] For different sets of electromagnetic data anomalies exceeding the same level, feature extraction is performed on the intensity spatial variation characteristics and frequency spatial variation characteristics to form initial feature data for electromagnetic anomalies exceeding the same level. This includes: the spatial variation curves of the electromagnetic intensity of the anomalies exceeding the limit for all events in different sets of electromagnetic data anomalies exceeding the limit. Define the range of the change curve over time and extract the spatial variation curve of the maximum exceeding anomaly electromagnetic intensity corresponding to the level. Spatial variation curve of electromagnetic intensity exceeding the minimum level This forms the range of variation in the spatial curve of abnormal electromagnetic intensity exceeding the standard. ,in, 'k' represents the level number of the event set of different electromagnetic data anomalies exceeding the standard; the spatial variation curves of the abnormal electromagnetic frequencies corresponding to all events in the event set of different electromagnetic data anomalies exceeding the standard are shown. Define the range of the change curves over time and extract the spatial variation curves of the corresponding maximum exceeding abnormal electromagnetic frequencies. Spatial variation curve of electromagnetic frequency exceeding the minimum level. This forms the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. ,in, For different sets of electromagnetic data anomalies exceeding the standard at the same level, combine the corresponding level of anomaly electromagnetic intensity spatial curve variation range. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. This generates initial characteristic data for electromagnetic anomalies exceeding the standard at the same level.

[0051] It is understandable that outlier data at different levels exhibit significant differences in intensity and frequency spaces. Furthermore, even within the same level of big data, there will be individual variations in intensity and frequency space data. Therefore, the data obtainable from big data primarily represents the range defined by the specific big data dataset. Even so, the range data will still show clear differences due to variations between levels. The boundary definition of the maximum and minimum curve ranges can be formed by mapping the curves corresponding to all events under the big data dataset to the same coordinate system and extracting the outer envelope boundary lines.

[0052] Feature comparison analysis is performed on the initial characteristic data of electromagnetic anomalies of the same level corresponding to different sets of events with electromagnetic anomalies exceeding the standard, to form electromagnetic anomaly change characteristic data. This includes comparing and analyzing the initial characteristic data of electromagnetic anomalies of the same level corresponding to different sets of events with electromagnetic anomalies exceeding the standard in the following way: if the change range of the spatial curve of the electromagnetic intensity of the anomalies exceeding the standard corresponding to different sets of events with electromagnetic anomalies exceeding the standard is within the range of the anomalies exceeding the standard. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If none of them correspond to each other, then the spatial variation range of the electromagnetic intensity variation curves corresponding to the same level of event set with different electromagnetic data anomalies will be determined. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified; if different electromagnetic data anomalies exceeded the standard, the corresponding level of event set with abnormal electromagnetic intensity spatial curve variation range was considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If only one range corresponds to an intersection, then the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set with different electromagnetic data anomalies exceeding the standard will be considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified; if different electromagnetic data anomalies exceeded the standard, the corresponding level of event set with abnormal electromagnetic intensity spatial curve variation range was considered. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If two corresponding ranges intersect, then after deleting the intersecting regions, the spatial curve variation range of the electromagnetic intensity of the newly formed event set corresponding to the different electromagnetic data anomalies exceeding the standard at the same level will be determined. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified.

[0053] Initial feature data may have overlapping feature ranges, so it is necessary to make reasonable comparisons and adjustments to feature ranges of different levels. It is understandable that electromagnetic intensity and electromagnetic frequency are synergistic in causing electromagnetic anomalies, so they cannot be compared separately for the same type of parameters. Comprehensive comparative analysis is also required. For example, if the feature ranges corresponding to intensity or frequency have an overlap while the other does not, the overlapping area can be preserved. After all, the analysis and comparison need to ensure that the ranges of intensity and frequency are within the corresponding feature ranges to confirm the corresponding anomaly level.

[0054] S3: Collect real-time electromagnetic monitoring data of the area, and combine it with the electromagnetic anomaly change characteristic data to perform electromagnetic anomaly prediction and analysis, and form electromagnetic anomaly early warning data.

[0055] Real-time electromagnetic monitoring data of the region is collected, and electromagnetic anomaly prediction analysis is performed in conjunction with the characteristic data of electromagnetic anomaly changes to form electromagnetic anomaly early warning data. This includes: extracting the spatial variation curves of real-time abnormal electromagnetic intensity for each monitoring sub-region with different abnormal electromagnetic intensities within the monitoring area based on the real-time electromagnetic monitoring data of the region. and the corresponding real-time abnormal electromagnetic frequency spatial variation curve Based on the electromagnetic anomaly change characteristic data, the spatial variation curves of real-time abnormal electromagnetic intensity corresponding to different monitoring sub-regions are generated. Real-time abnormal electromagnetic frequency spatial variation curve Perform the following anomaly prediction analysis: If there exists a real-time abnormal electromagnetic intensity spatial variation curve corresponding to any monitoring sub-region... Real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set of defined electromagnetic data anomalies exceeding the standard. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. If a monitoring sub-region is identified, the corresponding sub-region will be designated as the corresponding identification level; otherwise, if no real-time abnormal electromagnetic intensity spatial variation curve exists for any monitoring sub-region... Real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the same level of event set of defined electromagnetic data anomalies exceeding the standard. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. Then, we continue to acquire real-time data from all monitored sub-regions for comparative analysis.

[0056] After acquiring the characteristic data of electromagnetic anomalies, real-time electromagnetic data can be monitored and analyzed appropriately. It's important to note that for real-time data, different anomalous electromagnetic signals within the monitoring area may not necessarily affect the entire area due to factors such as signal source location and electromagnetic strength. Therefore, intensity and frequency space data are extracted based on the continuous intervals of the comprehensive anomalous electromagnetic intensity within a certain allowable threshold, forming real-time data for corresponding sub-regions. This allows for the determination of whether any sub-regions exceed the limits, significantly improving the accuracy and rationality of the analysis. Of course, only when both the intensity and frequency spaces fall within the characteristic range of the corresponding level can the corresponding level be determined.

[0057] This application also provides the specific components of the system. It includes a data acquisition unit for acquiring historical and real-time regional electromagnetic monitoring data; a feature extraction unit for extracting abnormal electromagnetic data from the historical regional electromagnetic monitoring data acquired by the data acquisition unit, forming abnormal regional electromagnetic monitoring data, and performing abnormal change feature analysis to form electromagnetic abnormal change feature data; and a real-time monitoring and analysis unit for performing electromagnetic anomaly prediction analysis based on the real-time regional electromagnetic monitoring data acquired by the data acquisition unit and the electromagnetic abnormal change feature data formed by the feature extraction unit, forming electromagnetic anomaly early warning data.

[0058] In summary, the beneficial effects of the electromagnetic environment anomaly data prediction and early warning system provided by the embodiments of the present invention are as follows:

[0059] This system generates electromagnetic anomaly data by deleting normally existing electromagnetic signals in the region based on historical regional electromagnetic monitoring data. It then analyzes the changes in intensity and frequency of electromagnetic anomalies in the spatial region to extract characteristic change data for anomaly levels. This characteristic change data can then be used to make corresponding real-time anomaly judgments on the real-time monitored electromagnetic anomaly data, thereby achieving more efficient and real-time anomaly monitoring and adaptive matching of anomaly handling based on monitoring results.

[0060] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0061] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0062] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0063] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0064] The “protocol” mentioned in the embodiments of this application may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. The embodiments of this application do not specifically limit this.

[0065] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0066] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0067] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0068] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0069] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0070] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0071] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0072] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0078] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An electromagnetic environment anomaly data prediction and early warning system, characterized in that, Configured as: Historical data of regional electromagnetic monitoring is acquired, and abnormal electromagnetic data is extracted to form regional electromagnetic monitoring abnormal data. Based on the abnormal electromagnetic monitoring data of the area, an abnormal change characteristic analysis is performed to form electromagnetic abnormal change characteristic data; Real-time electromagnetic monitoring data of the area is collected, and electromagnetic anomaly prediction analysis is performed by combining the electromagnetic anomaly change characteristic data to form electromagnetic anomaly early warning data.

2. The electromagnetic environment anomaly data prediction and early warning system according to claim 1, characterized in that, The process of acquiring historical electromagnetic monitoring data for the region and extracting abnormal electromagnetic data to form abnormal electromagnetic monitoring data for the region includes: Based on the historical electromagnetic monitoring data of the region, all different normal electromagnetic signal data in the monitoring area during the historical monitoring period are obtained to form the regional historical monitoring normal electromagnetic data. Based on the historical normal electromagnetic data of the region, abnormal electromagnetic signal data within the historical monitoring period of the region's electromagnetic historical data are extracted to form abnormal electromagnetic data of the region's historical monitoring. The historical monitoring anomaly data of the area are spatialized to establish spatial data of regional historical electromagnetic anomaly changes within the monitoring area.

3. The electromagnetic environment anomaly data prediction and early warning system according to claim 2, characterized in that, The step of spatializing the historical monitoring anomaly data of the area to establish spatial data of historical electromagnetic anomaly changes within the monitoring area includes: The monitoring area is mapped to coordinates to establish a regional location coordinate system for the monitoring area; Taking the time dimension as a reference, based on the historical monitoring anomaly data of the region, the abnormal electromagnetic intensity at different locations in the region's location coordinate system is calibrated according to the time dimension, forming spatial data of the changes in the region's historical abnormal electromagnetic intensity. Taking the time dimension as a reference, based on the historical monitoring anomaly data of the region, the abnormal electromagnetic frequencies at different locations in the region's location coordinate system are calibrated according to the time dimension, forming spatial data of the region's historical abnormal electromagnetic frequency changes. The spatial data of historical electromagnetic anomaly changes in the region are combined with the spatial data of historical electromagnetic anomaly changes in the region to form the spatial data of historical electromagnetic anomaly changes in the region.

4. The electromagnetic environment anomaly data prediction and early warning system according to claim 3, characterized in that, The step of analyzing the abnormal changes based on the abnormal electromagnetic monitoring data of the region to form electromagnetic abnormal change characteristic data includes: Based on the historical spatial data of abnormal electromagnetic intensity changes in the region, an analysis of the spatial variation characteristics of electromagnetic intensity exceeding the standard is performed to form spatial variation characteristic data of electromagnetic anomaly intensity. Based on the historical spatial data of abnormal electromagnetic frequency changes in the region, an analysis of the spatial variation characteristics of electromagnetic frequencies exceeding the standard is performed to form spatial variation characteristic data of abnormal electromagnetic frequencies. Based on the spatial variation feature data of electromagnetic anomaly intensity and the spatial variation feature data of electromagnetic anomaly frequency, features for the anomaly exceeding the standard level are extracted to form the electromagnetic anomaly variation feature data.

5. The electromagnetic environment anomaly data prediction and early warning system according to claim 4, characterized in that, The step involves analyzing the spatial variation characteristics of electromagnetic intensity exceeding the standard based on historical spatial data of abnormal electromagnetic intensity variations in the region, forming spatial variation characteristic data of electromagnetic anomaly intensity, including: Based on the historical spatial data of abnormal electromagnetic intensity changes in the region, the excess area diffusion curves for each instance of abnormal electromagnetic data exceeding the standard are extracted. , where n represents the number of the different events that occurred in the electromagnetic data anomaly according to the time dimension; Based on the historical spatial data of abnormal electromagnetic intensity changes in the region, extract the abnormal electromagnetic intensity change curves for each instance of abnormal electromagnetic data exceeding the standard. ; For each instance of abnormal electromagnetic data exceeding the limit, the corresponding excess area diffusion change curve is used. and the curve of abnormal electromagnetic intensity exceeding the standard The spatial variation curve of the abnormal electromagnetic intensity exceeding the standard was determined. ,in, ; A collection of all the aforementioned abnormal electromagnetic intensity spatial variation curves This forms the spatial variation characteristic data of the electromagnetic anomaly intensity.

6. The electromagnetic environment anomaly data prediction and early warning system according to claim 5, characterized in that, The step involves analyzing the spatial variation characteristics of electromagnetic frequencies exceeding the standard based on historical spatial data of abnormal electromagnetic frequencies in the region, forming spatial variation characteristic data of electromagnetic abnormal frequencies, including: Based on the historical spatial data of abnormal electromagnetic frequency changes in the region, extract the abnormal electromagnetic frequency change curves for each instance of abnormal electromagnetic data exceeding the limit. ; For each instance of abnormal electromagnetic data exceeding the limit, the corresponding excess area diffusion change curve is used. and the curve of the abnormal electromagnetic frequency exceeding the standard The spatial variation curve of the abnormal electromagnetic frequency exceeding the standard was determined. ,in, ; A collection of all the aforementioned abnormal electromagnetic frequency spatial variation curves This forms the spatial variation characteristic data of the electromagnetic anomaly frequency.

7. The electromagnetic environment anomaly data prediction and early warning system according to claim 6, characterized in that, The step of extracting features targeting the level of electromagnetic anomaly exceedance based on the spatial variation feature data of electromagnetic anomaly intensity and the spatial variation feature data of electromagnetic anomaly frequency to form the electromagnetic anomaly variation feature data includes: Based on the historical electromagnetic monitoring data of the area, the different events that cause abnormal exceedances of electromagnetic data are identified and their levels are determined. Clustering is performed on different events that cause electromagnetic data anomalies to exceed the standard based on the recognition level, forming a set of events of the same level for electromagnetic data anomalies exceeding the standard corresponding to different recognition levels; For different sets of electromagnetic data anomalies exceeding the standard at the same level, feature extraction is performed on the intensity spatial variation feature data and the frequency spatial variation feature data to form the initial feature data of electromagnetic anomalies exceeding the standard at the same level. Feature comparison analysis is performed on the initial feature data of electromagnetic anomalies exceeding the same level corresponding to different sets of events with electromagnetic anomalies exceeding the same level, to form the electromagnetic anomaly change feature data.

8. The electromagnetic environment anomaly data prediction and early warning system according to claim 7, characterized in that, The process involves extracting features from different sets of electromagnetic data anomalies of the same level, targeting both intensity spatial variation features and frequency spatial variation features, to form initial feature data for electromagnetic anomalies of the same level, including: Spatial variation curves of the abnormal electromagnetic intensity corresponding to all events in different sets of events of the same level for electromagnetic data anomalies. Define the range of the change curve over time and extract the spatial variation curve of the maximum exceeding anomaly electromagnetic intensity corresponding to the level. Spatial variation curve of electromagnetic intensity exceeding the minimum level This forms the range of variation in the spatial curve of abnormal electromagnetic intensity exceeding the standard. ,in, k represents the level number of the same level event set of different electromagnetic data anomalies exceeding the standard; Spatial variation curves of the abnormal electromagnetic frequencies corresponding to all events in the same level event set of the aforementioned electromagnetic data anomalies. Define the range of the change curves over time and extract the spatial variation curves of the corresponding maximum exceeding abnormal electromagnetic frequencies. Spatial variation curve of electromagnetic frequency exceeding the minimum level. This forms the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. ,in, ; For different sets of electromagnetic data anomalies exceeding the standard at the same level, the corresponding range of variation in the spatial curve of the electromagnetic intensity of the anomalies exceeding the standard at that level is considered. and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. This generates the corresponding initial characteristic data of the electromagnetic anomaly exceeding the standard at the same level.

9. The electromagnetic environment anomaly data prediction and early warning system according to claim 8, characterized in that, The step of performing feature comparison analysis on the initial feature data of electromagnetic anomalies exceeding the same level corresponding to different sets of events of the same level of electromagnetic anomaly exceedance to form the electromagnetic anomaly change feature data includes: The initial characteristic data of electromagnetic anomalies exceeding the same level corresponding to different sets of events of the same level are compared and analyzed in the following way: If the electromagnetic data anomaly exceeds the standard at the same level as the event set corresponding to the level of abnormal electromagnetic intensity spatial curve variation range, then... and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. If none of them correspond to an intersection, then the range of variation of the spatial curve of the abnormal electromagnetic intensity corresponding to the event set of the same level of abnormal electromagnetic data exceeding the standard will be determined. and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. The electromagnetic anomaly change characteristic data were identified as corresponding to these data. If the electromagnetic data anomaly exceeds the standard at the same level as the event set corresponding to the level of abnormal electromagnetic intensity spatial curve variation range, then... and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. If only one range corresponds to an intersection, then the range of variation of the spatial curve of the abnormal electromagnetic intensity corresponding to the event set of different electromagnetic data anomalies of the same level will be determined. and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. The electromagnetic anomaly change characteristic data were identified as corresponding to these data. If the electromagnetic data anomaly exceeds the standard at the same level as the event set corresponding to the level of abnormal electromagnetic intensity spatial curve variation range, then... and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. If two ranges intersect, then after deleting the intersecting regions, the newly formed spatial curve variation range of electromagnetic intensity anomalies corresponding to different sets of events of the same level of electromagnetic data anomaly exceeding the standard will be determined. and the range of variation in the spatial curve of abnormal electromagnetic frequencies exceeding the standard. The corresponding electromagnetic anomaly change characteristic data were identified.

10. The electromagnetic environment anomaly data prediction and early warning system according to claim 9, characterized in that, The collected real-time electromagnetic monitoring data of the area is combined with the electromagnetic anomaly change characteristic data to perform electromagnetic anomaly prediction analysis, forming electromagnetic anomaly early warning data, including: Based on the real-time electromagnetic monitoring data of the region, extract the corresponding real-time abnormal electromagnetic intensity spatial variation curves for each monitoring sub-region with different abnormal electromagnetic intensities within the monitoring region. and the corresponding real-time abnormal electromagnetic frequency spatial variation curve ; Based on the electromagnetic anomaly change characteristic data, the spatial variation curves of the real-time abnormal electromagnetic intensity corresponding to different monitoring sub-regions are analyzed. and the real-time abnormal electromagnetic frequency spatial variation curve Perform the following anomaly prediction analysis: If there exists a spatial variation curve of the real-time abnormal electromagnetic intensity corresponding to any of the monitoring sub-regions. and the real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the defined set of events of the same level of electromagnetic data anomaly exceeding the standard. and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. If so, the corresponding monitoring sub-area will be marked as the corresponding identification level; If there is no corresponding real-time abnormal electromagnetic intensity spatial variation curve for any of the monitored sub-regions and the real-time abnormal electromagnetic frequency spatial variation curve All of these belong to the range of electromagnetic intensity spatial curve changes corresponding to the defined set of events of the same level of electromagnetic data anomaly exceeding the standard. and the range of variation of the electromagnetic frequency spatial curve exceeding the specified level. Then, continue to acquire real-time data from all the monitored sub-regions for comparative analysis.

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