A Cloud Computing-Based Efficient Method and System for Processing Meteorological Lightning Monitoring Data

Abstract

This application provides a cloud-based method and system for efficient processing of meteorological lightning monitoring data. The method includes: assessing the warning importance of lightning monitoring data; dividing a local data cache into a first logical region and a second logical region; storing high-importance lightning monitoring data in the first logical region and setting a longer retention period; storing low-importance lightning monitoring data in the second logical region and setting a shorter retention period; initiating data eviction processing when the local data cache occupancy rate reaches a preset threshold; prioritizing data eviction from the second logical region; if clearing the second logical region still cannot meet storage requirements, then data is evicted from the first logical region, with the eviction based on the warning importance of the data. This application effectively avoids the loss of critical lightning data.

Description

Technical Field

[0001] This application relates to the field of meteorological monitoring data processing, and more specifically, to a cloud computing-based method and system for efficient processing of meteorological lightning monitoring data. Background Technology

[0002] During periods of intense lightning activity, existing meteorological lightning monitoring systems experience a rapid increase in data generation, communication channel interference, and the need for local encryption and verification mechanisms, causing local data cache storage to quickly reach its capacity limit. At this point, the system typically employs a first-in, first-out (FIFO) data eviction strategy, forcibly overwriting the oldest data blocks in the cache. This often results in the irreversible loss of data containing critical early warning information that has not yet been successfully uploaded. Consequently, the system cannot obtain a complete time series of lightning events, impacting the efficiency and accuracy of anomaly assessment and ultimately reducing the timeliness of the disaster early warning system. Summary of the Invention

[0003] This application discloses a cloud computing-based efficient method and system for processing meteorological lightning monitoring data. It aims to solve the problem that under extreme weather conditions such as intense lightning activity or limited communication, the local data cache memory of meteorological lightning monitoring sensors easily reaches its capacity limit, resulting in the loss of key lightning data, which in turn affects the timeliness and accuracy of disaster early warning systems.

[0004] In a first aspect, this application discloses a cloud computing-based method for efficient processing of meteorological lightning monitoring data, used in a meteorological lightning monitoring sensor. The method includes: acquiring lightning monitoring data; extracting features from the lightning monitoring data; assessing the warning importance of the lightning monitoring data based on the extracted features; dividing a local data cache into a first logical region and a second logical region; storing the lightning monitoring data in the first or second logical region according to the assessed warning importance, wherein lightning monitoring data of higher importance is stored in the first logical region and a longer retention period is set; lightning monitoring data of lower importance is stored in the second logical region and a shorter retention period is set; when the occupancy rate of the local data cache reaches a preset threshold, data eviction processing is initiated; in the data eviction processing, data is preferentially evicted from the second logical region; if the storage requirements cannot be met even after clearing the second logical region, data is evicted from the first logical region, the eviction based on the warning importance of the data.

[0005] Secondly, this application also discloses a cloud-based high-efficiency processing system for meteorological lightning monitoring data, used in meteorological lightning monitoring sensors. The system includes: a data acquisition module for acquiring lightning monitoring data; a feature extraction module for extracting features from the lightning monitoring data; an importance assessment module for assessing the warning importance of the lightning monitoring data based on the extracted features; a storage area partitioning module for dividing the local data cache into a first logical area and a second logical area; a data storage module for storing lightning monitoring data in the first or second logical area according to the assessed warning importance, wherein lightning monitoring data with higher importance is stored in the first logical area with a longer retention period, and lightning monitoring data with lower importance is stored in the second logical area with a shorter retention period; a data eviction process initiation module for initiating data eviction processing when the occupancy rate of the local data cache reaches a preset threshold; a data eviction module for prioritizing data eviction from the second logical area during data eviction processing; and an overflow processing module for eviction from the first logical area if the storage demand cannot be met after clearing the second logical area, the eviction being based on the warning importance of the data.

[0006] Compared with the prior art, this application has at least the following beneficial effects:

[0007] This application achieves fine-grained management of local data caching by assessing the importance of lightning monitoring data and storing the data in different logical areas according to the assessment results, setting different retention periods. When storage space is insufficient, less important data is prioritized for deletion, effectively preventing the loss of critical lightning data and solving the problem of critical data being overwritten due to the first-in-first-out strategy in existing technologies, thereby improving the accuracy and timeliness of lightning warnings. Attached Figure Description

[0008] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of this application and other related content, and should not be considered as limitations on this application.

[0009] Figure 1 A flowchart illustrating a cloud-based method for efficient processing of meteorological lightning monitoring data provided in this application.

[0010] Figure 2 This application provides a schematic diagram of the structure of a cloud computing-based high-efficiency meteorological lightning monitoring data processing system. Detailed Implementation

[0011] The technical solutions in this application will now be clearly and completely described in conjunction with the accompanying drawings.

[0012] This application proposes a cloud computing-based method for efficient processing of meteorological lightning monitoring data, applicable to meteorological lightning monitoring sensors, such as... Figure 1 As shown, it includes the following steps:

[0013] Acquire lightning monitoring data;

[0014] Feature extraction of lightning monitoring data;

[0015] Based on the extracted features, the importance of early warning based on lightning monitoring data is assessed;

[0016] The local data cache memory is divided into a first logical region and a second logical region;

[0017] Based on the assessed importance of the warning, lightning monitoring data is stored in either a first logical area or a second logical area. Lightning monitoring data of higher importance is stored in the first logical area and a longer retention period is set; lightning monitoring data of lower importance is stored in the second logical area and a shorter retention period is set.

[0018] When the local data cache storage occupancy rate reaches a preset threshold, data eviction processing is initiated.

[0019] In the data eviction process, data is prioritized for eviction from the second logical area; if the storage requirements cannot be met after the second logical area is cleared, data is then evicted from the first logical area, with the eviction process based on the data's critical importance.

[0020] This application effectively avoids the loss of critical data and ensures the timeliness and accuracy of lightning warnings under extreme weather conditions by assessing the importance of lightning monitoring data and implementing a tiered storage and elimination strategy.

[0021] To better understand the technical solution proposed in this application, some key terms and implementation environments are first explained. In this application, lightning monitoring data refers to the raw electromagnetic signal data collected by meteorological lightning monitoring sensors, which includes information such as the occurrence time of lightning events, signal strength, waveform characteristics, and approximate spatial location. This data is the foundation for lightning activity analysis and early warning. Local data cache memory refers to a storage unit integrated within the meteorological lightning monitoring sensor, used for temporary storage of lightning monitoring data collected by the sensor to address situations where data generation and upload rates are mismatched. This memory typically uses high-speed storage media such as flash memory or RAM. The method proposed in this application aims to optimize the data management strategy of this local data cache memory to improve data processing efficiency and the ability to retain critical data.

[0022] Specifically, the core of the cloud computing-based efficient processing method for meteorological lightning monitoring data proposed in this application lies in the intelligent importance assessment, hierarchical storage, and differentiated elimination of lightning monitoring data.

[0023] Firstly, in acquiring lightning monitoring data, meteorological lightning monitoring sensors can employ various methods to collect data. For example, the sensor can be equipped with a high-sensitivity electromagnetic field detection antenna array to acquire lightning monitoring data by monitoring electromagnetic pulses in the atmosphere in real time. This data can be raw analog signals or discrete sampling point sequences after preliminary digitization processing. Alternatively, the sensor can connect to an external data interface to receive collaborative monitoring data from other auxiliary monitoring devices (such as optical sensors, acoustic sensors, etc.) to enrich the dimensions of the lightning monitoring data.

[0024] Secondly, feature extraction from lightning monitoring data is fundamental to subsequent importance assessment. One approach is to use digital signal processing techniques to perform Fourier transform or wavelet transform on the raw lightning monitoring data to extract its frequency domain and time-frequency features, such as dominant frequency, bandwidth, and energy distribution. Furthermore, time domain features, such as peak signal intensity, rise time, fall time, and pulse width, can also be extracted. Another approach is to utilize pattern recognition algorithms to analyze the waveforms of the lightning monitoring data, identify different types of lightning waveform patterns, such as ground flash, cloud flash, positive flash, and negative flash, and extract these waveform patterns as features.

[0025] Furthermore, based on the extracted features, two methods are listed here for assessing the warning importance of lightning monitoring data. One approach is to set a threshold based on the original signal strength and waveform characteristics of the lightning monitoring data. When the signal strength exceeds this threshold or the waveform characteristics conform to a specific high-risk pattern, the data is directly determined to have high warning importance. For example, data with extremely high signal strength and a steep waveform is classified as high-importance data. Another approach is to establish a rule-based expert system that comprehensively judges the extracted features based on a pre-set set of rules (e.g., combining lightning occurrence frequency, historical disaster records, etc.) to assess the warning importance of lightning monitoring data. For example, if multiple high-intensity lightning events occur within a short period in a specific area, the importance level of subsequent lightning monitoring data will be increased.

[0026] Subsequently, dividing the local data cache into a first logical region and a second logical region is the foundation for implementing hierarchical storage. One implementation method is to logically divide the physical storage space of the local data cache into two independent storage regions using software-defined storage (SDS) technology. For example, a fixed size of storage space can be pre-allocated to the first and second logical regions, or their size can be dynamically adjusted according to actual needs. Another implementation method is to create different storage directories or tablespaces for data of different importance levels through a file system or database management system, thereby logically dividing the storage regions.

[0027] Next, based on the assessed importance of the warning, the lightning monitoring data is stored in either a first logical area or a second logical area. Specifically, lightning monitoring data with higher importance is stored in the first logical area with a longer retention period; lightning monitoring data with lower importance is stored in the second logical area with a shorter retention period. One implementation is that, during data writing, the data storage module directly writes the data to the corresponding logical area based on the importance assessment result. For example, if the data is assessed as high importance, it is written to the first logical area; if it is assessed as low importance, it is written to the second logical area. Simultaneously, a retention period of, for example, 24 hours is set for the data in the first logical area, and a retention period of, for example, 6 hours is set for the data in the second logical area. Another implementation is that after the data is written to a general cache, a background service process periodically scans the data in the cache, migrates it to the corresponding logical area based on its importance, and updates its retention period attribute.

[0028] Furthermore, when the local data cache storage occupancy rate reaches a preset threshold, data eviction processing is initiated. One implementation involves a storage monitoring module continuously monitoring the current occupancy rate of the local data cache storage. When the occupancy rate reaches, for example, a preset threshold of 80%, this module triggers an interrupt or sends a signal to notify the data eviction processing module to start. Another implementation involves using a scheduled task to check the storage occupancy rate at regular intervals (e.g., every minute), and if the threshold is reached, data eviction processing is initiated.

[0029] In data eviction processing, data is prioritized for eviction from the second logical area. One implementation involves the data eviction module first traversing the data in the second logical area, selecting data for eviction according to strategies such as First-In-First-Out (FIFO) or Least Frequently Used (LFU) until storage occupancy drops to a safe level or the second logical area is emptied. For example, data whose retention period in the second logical area has expired can be prioritized for eviction. Another implementation involves further refining the eviction priority within the second logical area based on the specific importance level of the data (even low-importance data may have subtle differences), for example, prioritizing the eviction of the least important data.

[0030] Finally, if the storage demand still cannot be met after clearing the second logical area, data is evicted from the first logical area based on the data's warning importance. One implementation is that if the storage occupancy rate is still higher than a preset safety threshold after the second logical area is completely cleared, the data eviction module will switch to processing the first logical area. In this case, the eviction strategy is no longer a simple first-in, first-out (FIFO) approach, but rather sorted according to the warning importance of the data, prioritizing the eviction of relatively less important data in the first logical area. For example, data can be evicted based on its importance score from low to high, or data that is important but has exceeded a certain time limit can be evicted. Another implementation is that when evicting data from the first logical area, the upload status can be considered, prioritizing the eviction of data that has already been successfully uploaded to the cloud to avoid duplicate transmission and storage.

[0031] The cloud computing-based efficient processing method for meteorological lightning monitoring data proposed in this application effectively solves the problem of loss of key lightning monitoring data under extreme weather conditions in existing technologies by introducing data importance assessment, hierarchical storage, and differentiated elimination mechanisms.

[0032] Specifically, this method first acquires lightning monitoring data and extracts features, laying the foundation for subsequent intelligent decision-making. Then, based on the extracted features, the warning importance of the lightning monitoring data is assessed. This step is one of the core innovations of this application, enabling the system to distinguish the value of different data, rather than treating them all the same. For example, by analyzing signal strength, waveform characteristics, and correlation with historical events, the system can identify key data indicating potentially high-risk lightning events.

[0033] In terms of storage management, this application divides the local data cache into a first logical region and a second logical region, and stores data separately according to the assessed importance of the warning. Data of higher importance is stored in the first logical region with a longer retention period, while data of lower importance is stored in the second logical region with a shorter retention period. This hierarchical storage strategy ensures that critical data has a longer lifespan in the local cache, maximizing its retention even if data upload is hindered.

[0034] When the local data cache memory occupancy rate reaches a preset threshold, the system initiates data eviction processing. Unlike the simple first-in, first-out (FIFO) strategy in existing technologies, this application prioritizes data eviction from the second logical area. This means that the system will first clear data with lower importance and shorter retention periods, thereby freeing up space for important data. Only when the storage demand cannot be met after clearing the second logical area will the system evict data from the first logical area, and the eviction criteria at this time are still based on the data's criticality, prioritizing the eviction of relatively less critical data in the first logical area.

[0035] Through the aforementioned mechanism, this application ensures that critical lightning monitoring data is prioritized for retention and protection when local data cache storage faces capacity pressure, avoiding the risk of accidental deletion of critical data in traditional methods. This enables anomaly detection programs at local or regional convergence points of sensors, as well as large-scale lightning activity analysis conducted in the cloud, to obtain more complete and continuous time-series information, thereby significantly improving the accuracy and timeliness of lightning event identification. Ultimately, this enhances the response capability and accuracy of the entire disaster early warning system, meeting the practical needs for rapid response and accurate early warning under extreme weather conditions.

[0036] In some embodiments, the steps for assessing the early warning importance of lightning monitoring data specifically include:

[0037] Acquire lightning monitoring data;

[0038] Feature extraction is performed on lightning monitoring data, including the original signal strength, waveform characteristics, occurrence time, and approximate spatial location;

[0039] Store summary information of recent lightning events, including the time of occurrence, approximate location, preliminary assessment of the event type, and original intensity.

[0040] Correlation analysis was performed between lightning monitoring data and summary information to generate sequence correlation scores;

[0041] Based on the extracted features and sequence correlation scores, the importance of lightning monitoring data for early warning is assessed. The assessment includes:

[0042] An initial importance assessment is made based on the raw intensity and waveform characteristics of lightning monitoring data;

[0043] If the sequence correlation score reaches the preset threshold, the importance level of the initial importance judgment of the lightning monitoring data will be increased;

[0044] If the waveform characteristics of the lightning monitoring data show a distortion pattern and the sequence correlation score is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data.

[0045] Specifically, feature extraction not only focuses on the physical properties of lightning signals, such as the original signal strength and waveform characteristics, but also incorporates the event's occurrence time and approximate spatial location. Its purpose is to provide necessary spatiotemporal context information for subsequent correlation analysis. The original signal strength can be understood as the intensity of the electromagnetic waves generated during lightning discharge, while waveform characteristics reflect the physical properties of the discharge process, such as pulse width, rise time, and fall time. The occurrence time records the specific moment of the lightning event, and the approximate spatial location indicates the geographical coordinates of the lightning event.

[0046] Furthermore, summary information of recent lightning events is stored, including the event's time of occurrence, approximate location, preliminary assessment of the event type, and initial intensity. This aims to establish a historical event database to capture the continuity and development trends of lightning activity. The summary information can be understood as a concise summary of lightning events that occurred over a past period, intended to provide historical reference for newly acquired lightning monitoring data.

[0047] In practical applications, newly acquired lightning monitoring data is correlated with summary information to generate a sequence correlation score. The sequence correlation score quantifies the degree of correlation between the current lightning event and recent historical events in terms of time, space, and intensity. For example, if the current lightning event has a close temporal and spatial connection with multiple recent high-intensity lightning events occurring in the same area, its sequence correlation score will be high, indicating that the event may belong to a developing or ongoing lightning activity sequence.

[0048] Based on this, the warning importance of lightning monitoring data is assessed using extracted features and sequence correlation scores. The assessment begins with an initial importance judgment based on the raw intensity and waveform characteristics of the lightning monitoring data; for example, data with high intensity or specific dangerous waveform patterns are assigned higher initial importance. Subsequently, if the sequence correlation score reaches a preset threshold, the importance level of the initial importance judgment for the lightning monitoring data is increased, reflecting the impact of the event's context and development trend on the warning importance. As a preferred implementation, if the waveform characteristics of the lightning monitoring data show a distortion pattern and the sequence correlation score is high, the initial importance judgment result for the lightning monitoring data is changed to high importance data. Distortion patterns may indicate abnormal discharge processes or potential hazard types, while a high sequence correlation score further confirms its key position in overall lightning activity.

[0049] This application effectively addresses the problem of insufficient accuracy in judging the importance of warnings that may result from traditional methods relying solely on a single feature for assessment by introducing spatiotemporal features such as the occurrence time and approximate spatial location of lightning events, combined with sequence correlation analysis using stored summary information of recent lightning events. Specifically, the introduction of sequence correlation scores enables the system to identify cluster effects, development trends, or anomalous patterns of lightning events, rather than simply assessing isolated individual events. For example, when a series of lightning events occur concentrated in a specific area within a short period, even if the original intensity or waveform characteristics of a single event are not extreme, its high sequence correlation score will prompt the system to increase its importance level, thereby identifying potential severe convective weather or thunderstorm development earlier. Furthermore, combining waveform distortion patterns with high sequence correlation scores as a condition for determining high-importance data can more accurately identify dangerous events with anomalous physical processes and closely related to existing lightning activity, avoiding missed or false alarms caused by misjudgments based on a single feature.

[0050] Through the above technical solution, this application can significantly improve the accuracy and robustness of the assessment of the importance of lightning monitoring data for early warning. By comprehensively considering the physical characteristics, spatiotemporal context, and correlation with historical events of lightning events, the system can more comprehensively and dynamically understand lightning activity, thereby more effectively identifying lightning events with high early warning value. This not only helps to avoid early warning delays or omissions due to insufficient information, but also optimizes the storage and eviction strategies of data in the local data cache, ensuring that important data is prioritized for retention and processing, thereby improving the response efficiency and early warning capability of the entire meteorological lightning monitoring system.

[0051] In some embodiments, the step of assessing the early warning importance of lightning monitoring data based on extracted features and sequence correlation scores includes:

[0052] Acquire information on abnormal environmental conditions, including communication channel status and basic meteorological parameters;

[0053] Based on information about abnormal environmental conditions, an environmental impact weighting factor is generated for the original signal strength.

[0054] Based on the original signal strength, waveform characteristics, and sequence correlation scores adjusted for environmental impact weighting factors, the importance of lightning monitoring data for early warning is assessed. The assessment includes:

[0055] Initial importance is determined based on the original signal strength and waveform characteristics after adjustment by the environmental impact weighting factor;

[0056] If the original signal strength and waveform characteristics after the environmental impact weighting factor adjustment indicate high importance, and the sequence correlation score is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data.

[0057] If the original signal strength and waveform characteristics after the environmental impact weighting factor adjustment indicate low importance, but the sequence correlation score is high, then the importance level of the initial importance judgment result of the lightning monitoring data will be increased.

[0058] If the original signal strength and waveform characteristics after the environmental influence weighting factor adjustment indicate high importance, but the sequence correlation score is low, then check whether there is a distortion pattern in the waveform characteristics.

[0059] If the waveform features exhibit distortion patterns, the initial importance assessment of the lightning monitoring data shall be maintained.

[0060] If the waveform features do not exhibit distortion patterns, the importance level of the initial importance assessment result for the lightning monitoring data should be reduced.

[0061] Specifically, environmental anomaly information refers to external environmental factors that may affect the quality or accuracy of lightning monitoring data interpretation. This can include communication channel conditions and simplified meteorological parameters. Communication channel conditions can be understood as the quality of the data transmission link between the sensor and the data processing system, such as signal strength, signal-to-noise ratio, and packet loss rate, reflecting the reliability of data transmission. Simplified meteorological parameters can refer to basic meteorological data for a local area, such as temperature, humidity, air pressure, and rainfall, reflecting local environmental conditions that may affect lightning signal propagation or sensor performance.

[0062] Furthermore, based on the acquired information about abnormal environmental conditions, an environmental impact weighting factor can be generated for the original signal strength. This weighting factor is used to correct the original signal strength to eliminate or reduce the interference of environmental factors on the signal strength.

[0063] Therefore, the importance of lightning monitoring data for early warning can be assessed based on the original signal strength, waveform characteristics, and sequence correlation score after adjusting for environmental impact weighting factors. This assessment process first makes an initial importance judgment based on the adjusted original signal strength and waveform characteristics. Subsequently, the importance level is further refined by combining the sequence correlation score and the distortion pattern of the waveform characteristics.

[0064] This application's solution, by acquiring information on abnormal environmental conditions, such as communication channel status and simplified meteorological parameters, can identify and quantify the potential impact of environmental factors on the raw intensity of lightning monitoring data. By generating an environmental impact weighting factor and adjusting the raw signal intensity accordingly, interference from environmental noise, signal attenuation, and other factors on the raw intensity judgment can be effectively eliminated or reduced, making the adjusted raw signal intensity more accurately reflect the intensity of the lightning event itself. Based on this, combining waveform characteristics and sequence correlation scores for early warning importance assessment can more accurately determine the potential threat of lightning events.

[0065] This application corrects the original signal intensity by introducing an environmental impact weighting factor, making the assessment of the importance of lightning monitoring data for early warning more objective and accurate, and avoiding misjudgments or omissions caused by environmental interference. This not only improves the reliability of lightning warnings but also optimizes the resource utilization efficiency of local data cache storage, ensuring that truly important lightning data is prioritized for storage and has a longer retention period, while data that is artificially high or low due to environmental influences can be handled more reasonably, thereby improving the overall performance and early warning capability of the entire meteorological lightning monitoring data processing system.

[0066] In some embodiments, the step of assessing the early warning importance of the lightning monitoring data based on the extracted features and the sequence correlation score includes:

[0067] Based on information about abnormal environmental conditions, determine the reliability of the original signal strength, waveform characteristics, and sequence correlation score;

[0068] Based on reliability, the influence of original signal strength, waveform characteristics, and sequence correlation scores on the assessment of early warning importance is adjusted;

[0069] Based on the original signal strength, waveform characteristics, and sequence correlation scores adjusted for impact level, the importance of lightning monitoring data for early warning is assessed. The assessment includes:

[0070] Initial importance is determined based on the original signal strength and waveform characteristics after adjustment according to the degree of influence;

[0071] If the original signal strength and waveform characteristics after the impact adjustment indicate high importance, and the sequence correlation score after the impact adjustment is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data.

[0072] If the original signal strength and waveform characteristics after the impact adjustment indicate low importance, but the sequence correlation score after the impact adjustment is high, then the importance level of the initial importance judgment result of the lightning monitoring data is increased.

[0073] If the original signal strength and waveform characteristics after the influence level adjustment indicate high importance, but the sequence correlation score after the influence level adjustment is low, then check whether there is a distortion pattern in the waveform characteristics.

[0074] If the waveform features exhibit distortion patterns, the initial importance assessment of the lightning monitoring data shall be maintained.

[0075] If the waveform features do not exhibit distortion patterns, the importance level of the initial importance assessment result for the lightning monitoring data should be reduced.

[0076] Specifically, assessing the reliability of the original signal strength, waveform characteristics, and sequence correlation score involves evaluating the quality of these core features used to assess the importance of early warnings based on current environmental anomaly information, such as communication channel conditions and simplified meteorological parameters. For example, when simplified meteorological parameters indicate heavy rainfall or hail, the lightning waveform characteristics may be distorted due to scattering by raindrops or ice crystals, thus reducing its reliability. The reliability of the sequence correlation score can be affected by the quality of historical data, the robustness of the correlation algorithm, and the degree of current environmental interference.

[0077] Among them, adjusting the influence of the original signal strength, waveform characteristics and sequence correlation scores on the early warning importance assessment based on reliability can be understood as dynamically adjusting the weight or contribution of each feature in the final importance assessment model according to the reliability score of each feature.

[0078] For example, the initial importance assessment uses the original signal strength and waveform characteristics after reliability adjustments. Subsequent decision-making logic, such as whether to increase the importance level, whether to classify it as high-importance data, and whether to check for waveform distortion patterns, is all based on these impact-adjusted feature values. This ensures that the entire evaluation process fully considers the impact of the environment on data quality and makes intelligent adjustments accordingly.

[0079] This application effectively solves the problem in the above-mentioned embodiments where environmental anomalies can lead to a decline in feature data quality, thus affecting the accuracy of early warning importance assessment. Specifically, when environmental anomalies indicate that a feature (e.g., original signal strength) has low reliability, its weight or influence in the importance assessment is reduced accordingly, thus avoiding misjudgments due to unreliable data. Conversely, when a feature is judged to be highly reliable, its role in the assessment is strengthened, making the assessment results more accurate. It is precisely this dynamic reliability judgment and influence adjustment mechanism that enables early warning importance assessment to more intelligently adapt to complex and changing environmental conditions, ensuring more accurate early warning judgments even with inconsistent data quality.

[0080] However, in practice, if the specific impact of local environmental disturbances on data reliability is not fully considered, reliability assessments may be inaccurate, thus affecting the accuracy of the assessment of the importance of lightning monitoring data for early warning. For example, when there are local interference sources near the sensor, relying solely on macroscopic environmental anomaly information for reliability assessment may fail to effectively distinguish between real lightning signals and local interference, leading to misjudgments of data reliability.

[0081] In this regard, this application further proposes the following steps for determining the reliability of the original signal strength, waveform characteristics, and sequence correlation score:

[0082] By combining the local gradients and abrupt changes of simplified meteorological parameters in the information on abnormal environmental conditions, local sources of environmental disturbances can be identified.

[0083] Based on the type, intensity, occurrence time, and spatial range of the identified local environmental disturbance sources, the reliability of the original signal strength, waveform characteristics, and sequence correlation score is calculated in real time. The calculation includes:

[0084] Based on the type and intensity of the local environmental disturbance source, determine the initial reliability of the original signal strength, waveform characteristics, and sequence correlation score;

[0085] Adjust the update frequency and spatial resolution of the initial reliability assessment based on the duration and scope of the local environmental disturbance source.

[0086] Based on the adjusted reliability, the influence of the original signal strength, waveform characteristics, and sequence correlation scores on the assessment of the importance of early warning is adjusted.

[0087] Specifically, identifying local environmental disturbance sources by combining local gradients and abrupt changes in simplified meteorological parameters refers to locating and identifying local interference sources that may affect the quality of lightning monitoring data by analyzing the rapid spatial or temporal trends and abnormal fluctuations of simplified meteorological parameters (such as temperature, humidity, air pressure, and wind speed). For example, a sharp drop in air pressure or a sudden increase in wind speed may indicate localized severe convective weather, and these weather phenomena may be accompanied by electromagnetic interference or sensor performance fluctuations, thus being identified as local environmental disturbance sources. The aim is to accurately capture and locate specific environmental factors that affect data reliability.

[0088] The reliability of real-time calculation of the original signal strength, waveform characteristics, and sequence correlation score, based on the type, intensity, occurrence time, and spatial range of the identified local environmental disturbance source, refers to the dynamic evaluation of the authenticity and credibility of the three key indicators—original signal strength, waveform characteristics, and sequence correlation score—in lightning monitoring data after the local environmental disturbance source has been identified, based on the specific attributes of the disturbance source (such as electromagnetic interference, rain / snow weather, equipment failure, etc., the strength of its impact, when it started, and the size of the geographical area affected). For example, strong electromagnetic interference may cause distortion of the original signal strength, while rain / snow weather may affect the clarity of waveform characteristics.

[0089] In practical applications, the calculations include: determining the initial reliability of the original signal strength, waveform characteristics, and sequence correlation score based on the type and intensity of the local environmental disturbance source. This means pre-setting or dynamically generating an initial reliability assessment value for different types of disturbance sources and their intensities. For example, for known types of strong electromagnetic interference, the initial reliability of the original signal strength can be set to be low, while for slight rain or snow, the initial reliability of the waveform characteristics may only decrease slightly. The purpose is to provide a benchmark for subsequent reliability adjustments.

[0090] Furthermore, adjusting the update frequency and spatial resolution of the initial reliability assessment based on the duration and scope of the localized environmental disturbance source means considering that the disturbance source is not static; its impact may change over time, and its scope may also vary. Therefore, for disturbance sources with short durations and small scopes, the update frequency and spatial resolution of the reliability assessment can be increased to capture their impact more promptly and precisely; while for disturbance sources with long durations and large scopes, the update frequency and spatial resolution can be appropriately reduced to save computational resources. The aim is to make the reliability assessment process more adaptable and efficient.

[0091] Therefore, adjusting the influence of the original signal strength, waveform characteristics, and sequence correlation score in the early warning importance assessment based on the real-time and refined reliability assessment results means that after obtaining real-time and refined reliability assessment results, this reliability information is fed back into the early warning importance assessment model to dynamically adjust the weight or influence of the original signal strength, waveform characteristics, and sequence correlation score in the final importance judgment.

[0092] This application, by introducing analysis of local gradients and abrupt changes in simplified meteorological parameters, enables more precise identification of local environmental disturbance sources, thus overcoming the limitations of relying solely on macroscopic environmental anomalies for reliability assessment. It is precisely because of the comprehensive consideration of the type, intensity, timing, and spatial range of the disturbance source that the reliability of the original signal strength, waveform characteristics, and sequence correlation scores can be calculated in real time and dynamically. This refined reliability calculation further ensures the timeliness and accuracy of reliability assessment under different disturbance scenarios by adjusting the update frequency and spatial resolution of the reliability assessment. Finally, based on this high-precision real-time reliability, the influence of various features in the assessment of warning importance is adaptively adjusted, effectively avoiding misjudgments or omissions caused by local interference or data distortion, significantly improving the accuracy and reliability of lightning warnings.

[0093] Through the above technical solution, this application enables refined and real-time assessment of the reliability of lightning monitoring data, especially in the presence of local environmental disturbances, accurately identifying and quantifying the impact of these disturbances on data quality. Compared to reliability judgment based solely on macroscopic environmental information, this application identifies local environmental disturbance sources by combining local gradients and abrupt changes in simplified meteorological parameters, and calculates the reliability of various characteristics in real time based on their specific attributes, significantly improving the accuracy and specificity of reliability assessment. Consequently, in subsequent assessments of the importance of early warnings, the influence of the original signal strength, waveform characteristics, and sequence correlation scores can be adjusted more precisely, effectively reducing the risk of false alarms and missed alarms caused by environmental interference or data distortion, thereby enhancing the robustness and reliability of the entire lightning early warning system.

[0094] However, in practical applications, local environmental disturbance sources may not exist singly, or their impact patterns on different data characteristics may differ. Simply judging based on the type and intensity of the disturbance source may fail to fully capture the complex interactions (e.g., superposition or cancellation) between multiple disturbance sources, and may also overlook the independent impact of the same disturbance source on different data characteristics. This can lead to insufficient accuracy in the initial reliability assessment, thereby affecting the precision of the assessment of the importance of lightning monitoring data for early warning.

[0095] In this regard, this application further proposes the following steps for determining the initial reliability of the original signal strength, waveform characteristics, and sequence correlation score based on the type and intensity of the local environmental disturbance source:

[0096] Based on the type and intensity of the local environmental disturbance source, identify the independent influence patterns of the local environmental disturbance source on the original signal intensity, waveform characteristics, and sequence correlation score;

[0097] Based on the independent impact model, determine whether there is a superposition or cancellation effect between disturbance sources;

[0098] Based on the superposition or cancellation effect, the initial reliability of the original signal strength, waveform characteristics, and sequence correlation score is adjusted.

[0099] Specifically, identifying the independent influence patterns of local environmental disturbance sources on the original signal strength, waveform characteristics, and sequence correlation score refers to analyzing the specific ways and degrees in which each identified local environmental disturbance source affects different features (such as original signal strength, waveform characteristics, and sequence correlation score) in lightning monitoring data. For example, a certain type of electromagnetic interference may primarily affect the original signal strength, while having a smaller impact on waveform characteristics; while another type of environmental noise may simultaneously cause a decrease in original signal strength and waveform distortion. This step aims to establish a mapping relationship between disturbance sources and various data features. Determining whether there is a superposition or cancellation effect between disturbance sources based on the independent influence patterns can be understood as assessing the combined impact of multiple local environmental disturbance sources on the same or different data features. For example, two independent noise sources may simultaneously cause a decrease in original signal strength, forming a superposition effect; while one noise source may cause a decrease in signal strength, and another calibration signal may partially cancel out the decrease, forming a cancellation effect. This determination can be made through a pre-set rule base, machine learning model, or expert system. In practical applications, adjusting the initial reliability of the original signal strength, waveform characteristics, and sequence correlation score based on superposition or cancellation effects involves refining the initial reliability of each data feature based on the aforementioned judgment results. If superposition exists, it may further reduce the reliability of the relevant data features; if cancellation exists, it may maintain or slightly improve the reliability of the relevant data features. For example, when two noise sources superimpose, causing severe distortion of the original signal strength, the initial reliability of the original signal strength will be significantly reduced; however, when the influence of one disturbance source is effectively canceled by another, the reliability of the relevant data features may not be significantly affected.

[0100] This application first identifies the independent influence patterns of local environmental disturbance sources on the original signal strength, waveform characteristics, and sequence correlation scores, enabling a detailed understanding of the specific impact of each disturbance source on different data dimensions. This meticulous analysis allows for accurate subsequent determination of whether there is superposition or cancellation between multiple disturbance sources, avoiding a simple linear summation or averaging of all disturbance source effects, and instead assessing them based on actual physical interactions. Furthermore, by adjusting the initial reliability of each data feature according to superposition or cancellation effects, the reliability assessment results more accurately reflect the degree of interference in complex environments, thus providing more precise and reliable foundational data for subsequent assessments of the importance of early warning systems.

[0101] Through the above technical solution, this application can more comprehensively and meticulously consider the impact of local environmental disturbance sources on various characteristics of lightning monitoring data, especially in cases where there are multiple disturbance sources or complex disturbance source influence patterns. Compared to simple judgments based solely on the type and intensity of disturbance sources, this application significantly improves the accuracy and robustness of the initial reliability assessments of the original signal strength, waveform characteristics, and sequence correlation scores by identifying independent influence patterns and determining superposition or cancellation effects. Therefore, it can effectively avoid reliability misjudgments caused by the complexity of environmental disturbances, thereby making the assessment of the importance of lightning monitoring data for early warning more accurate and improving the reliability and timeliness of meteorological lightning warnings.

[0102] However, in actual meteorological and lightning monitoring environments, multiple local environmental disturbances often occur simultaneously, and the duration, range of influence, and impact on the reliability of the original signal strength, waveform characteristics, and sequence correlation scores may vary among these disturbances. Using only a single or coarse adjustment strategy may not adequately adapt to this complex and variable environment, resulting in insufficiently refined or inefficient adjustments to the update frequency and spatial resolution, thus affecting the accuracy of the warning importance assessment and the resource utilization of data processing. To address this, this application proposes a more refined and adaptive adjustment strategy that identifies and integrates the characteristics of multiple local environmental disturbances to form a dynamic update frequency and spatial resolution adjustment strategy.

[0103] Specifically, the steps mentioned above for adjusting the update frequency and spatial resolution of the initial reliability assessment based on the duration and scope of the local environmental disturbance source include:

[0104] Identify the duration, range of influence, and impact on the reliability of the original signal strength, waveform characteristics, and sequence correlation score of multiple local environmental disturbance sources;

[0105] Based on the duration, scope of impact, and reliability impact, an independent update frequency and spatial resolution adjustment scheme is generated for each local environmental disturbance source;

[0106] Multiple adjustment schemes are integrated to form an adaptive update frequency and spatial resolution adjustment strategy. The integration includes: increasing the update frequency and spatial resolution for disturbance sources with short duration and small impact range; decreasing the update frequency and spatial resolution for disturbance sources with long duration and large impact range; and using the finest update frequency and spatial resolution in the overlapping region for disturbance sources with overlapping impact.

[0107] The identification of multiple local environmental disturbance sources, including their duration, impact range, and influence on the reliability of original signal strength, waveform characteristics, and sequence correlation scores, refers to the system's ability to accurately identify various local environmental disturbance sources in the current environment, such as localized heavy rainfall, gusts, electromagnetic interference, or localized sensor malfunctions, by analyzing the local gradients and abrupt changes of simplified meteorological parameters, combined with historical data and preset models. For each identified disturbance source, the system further analyzes its duration from start to finish, the spatial range of its influence, and the potential impact of the disturbance source on the reliability of the three key characteristics of lightning monitoring data: original signal strength, waveform characteristics, and sequence correlation scores. For example, localized heavy rainfall may significantly reduce the reliability of original signal strength and waveform characteristics, while having a relatively small impact on the sequence correlation score.

[0108] Generating an independent update frequency and spatial resolution adjustment scheme for each localized environmental disturbance source means that after identifying and evaluating the characteristics and impact of each disturbance source, the system will tailor an adjustment recommendation for update frequency and spatial resolution for each individual disturbance source based on this information. For example, for a disturbance source with a short duration, small impact range, but a significant impact on data reliability (such as a transient electromagnetic pulse), a high update frequency and high spatial resolution would be recommended; while for a disturbance source with a long duration, large impact range, but a minor impact on data reliability (such as a large-scale light fog), a lower update frequency and spatial resolution might be recommended.

[0109] The strategy of integrating multiple adjustment schemes to form an adaptive update frequency and spatial resolution adjustment means that when multiple local environmental disturbances exist, the system does not simply superimpose or average these independent adjustment schemes. Instead, it uses an intelligent fusion mechanism to form a holistic, adaptive strategy. Specifically, for disturbances with short durations and small impact areas, since their impact may change rapidly and is limited to a specific region, the update frequency and spatial resolution for that region will be increased to ensure timely capture and accurate assessment of their impact. Conversely, for disturbances with long durations and large impact areas, since their changes are relatively slow and their impact is wide, the update frequency and spatial resolution can be appropriately reduced to save computational resources. In particular, when the impact areas of different disturbances overlap, the system will prioritize the most refined update frequency and spatial resolution requirements to ensure the highest data reliability assessment accuracy even in the most complex regions.

[0110] This application's solution achieves refined identification and evaluation of multiple localized environmental disturbance sources, generating independent update frequency and spatial resolution adjustment schemes for each source, thus avoiding the limitations of single or coarse adjustment strategies. Furthermore, by intelligently fusing these independent adjustment schemes, the system can adaptively adjust the update frequency and spatial resolution of reliability judgments based on the complexity and dynamism of the actual environment. This mechanism allows the system to concentrate limited computing and storage resources on the areas and time periods most significantly affected by disturbances and requiring the most refined evaluation, while maintaining low resource consumption in relatively stable areas. This maximizes data processing efficiency and resource utilization while ensuring the accuracy of the early warning importance assessment.

[0111] Through the above technical solution, this application can significantly improve the adaptability and accuracy of reliability assessment of meteorological lightning monitoring data. This solution enables the system to dynamically adjust the granularity of data processing based on local environmental disturbance sources of different types, durations, and impact ranges, thereby avoiding assessment bias or resource waste caused by improper adjustments in complex environments. Thus, while ensuring the accuracy and timeliness of lightning warning importance assessment, it effectively optimizes system performance and resource utilization, making it particularly suitable for variable and complex actual meteorological environments.

[0112] While the system identifies the duration and impact range of local environmental disturbances and generates independent update frequency and spatial resolution adjustment schemes accordingly, ultimately fusing them to form an adaptive adjustment strategy, it fails to fully consider the potential differences in early warning importance among different local environmental disturbances when integrating multiple adjustment schemes. For example, when the impact areas of multiple disturbances overlap, using only the finest update frequency and spatial resolution may lead to uneven resource allocation or fail to prioritize the monitoring accuracy and timeliness of high-risk, high-importance disturbances. If these issues are not addressed, critical lightning warning information may not receive the most timely and accurate response under limited resources, thus affecting the overall efficiency and reliability of the early warning system.

[0113] In response, this application further proposes a method to optimize the above-mentioned fusion of multiple adjustment schemes to form an adaptive update frequency and spatial resolution adjustment strategy. By introducing an early warning priority mechanism, it ensures that the monitoring needs of highly important disturbance sources are met first.

[0114] The steps described above for integrating multiple adjustment schemes to form an adaptive update frequency and spatial resolution adjustment strategy include: obtaining the early warning priorities of multiple local environmental disturbance sources; ranking the multiple adjustment schemes according to the early warning priorities; integrating the multiple adjustment schemes sequentially according to the ranking results, wherein the adjustment schemes for disturbance sources with high early warning priorities are adopted first, and the adjustment schemes for disturbance sources with low early warning priorities are supplemented without conflicting with the adjustment schemes for disturbance sources with high early warning priorities; for disturbance sources with overlapping effects, the adjustment schemes for disturbance sources with high early warning priorities are adopted in the overlapping areas.

[0115] Specifically, prioritizing multiple local environmental disturbance sources means that the system assigns a numerical value or level to each identified local environmental disturbance source, indicating its importance or urgency, based on preset rules or real-time analysis. For example, a high priority can be set for disturbance sources that may cause severe lightning disasters (such as severe convective weather systems), while a low priority can be set for those related to general environmental noise. This warning priority can be a discrete level (such as high, medium, and low) or a continuous numerical value.

[0116] The sorting of multiple adjustment schemes based on warning priorities means that after obtaining the warning priorities of each local environmental disturbance source, the system will arrange the previously generated independent update frequency and spatial resolution adjustment schemes for each disturbance source according to these priorities. Generally, the higher the priority of the disturbance source, the higher the processing weight or the earlier the processing order of its corresponding adjustment scheme during the fusion process. For example, they can be sorted in descending order of priority.

[0117] In practical applications, multiple adjustment schemes are merged sequentially according to the ranking results. Adjustment schemes for high-priority early warning sources are adopted first, while those for low-priority sources are supplemented only if they do not conflict with those for high-priority sources. This means that during the fusion process, the adjustment scheme corresponding to the highest-priority disturbance source is considered first, serving as the foundation or dominant scheme. Subsequently, adjustment schemes for lower-priority disturbance sources are processed sequentially. When processing low-priority schemes, it is checked whether they conflict with the adopted high-priority schemes. If there is no conflict, the scheme is supplemented and merged; if a conflict exists, the high-priority scheme takes precedence, or the low-priority scheme is appropriately adjusted to avoid conflict. For example, if a high-priority scheme requires a high update frequency in a certain area, while a low-priority scheme requires a low update frequency, the high update frequency will be adopted first in that area.

[0118] Furthermore, for overlapping disturbance sources, the adjustment scheme of adopting high-priority disturbance sources within the overlapping area means that when the influence areas of two or more local environmental disturbance sources overlap, the system compares the warning priorities of these overlapping disturbance sources. Within the overlapping area, the update frequency and spatial resolution adjustment scheme corresponding to the highest priority disturbance source will be directly adopted. This ensures that, under conditions of limited resources or conflicts, the most critical warning information can receive the highest priority and most detailed monitoring and protection.

[0119] This application effectively addresses the challenge of intelligently and efficiently allocating monitoring resources when multiple disturbance sources coexist and their impacts overlap by introducing a prioritization of early warnings for localized environmental disturbance sources and using this prioritization to rank and merge adjustment schemes. By differentiating the importance of early warnings for different disturbance sources, the system can prioritize monitoring accuracy and timeliness for high-risk and high-urgency events. By prioritizing adjustment schemes for high-priority disturbance sources and allowing schemes for lower-priority disturbance sources to be supplemented without conflict, this approach ensures a reasonable allocation of monitoring resources, avoiding resource waste or omissions of critical information that might result from a one-size-fits-all, overly granular strategy. Especially in areas with overlapping impacts, directly adopting adjustment schemes for high-priority disturbance sources ensures that the monitoring system operates most efficiently and accurately in the areas requiring the most attention, thereby improving the overall reliability and response speed of early warnings.

[0120] Through the above technical solutions, this application enables refined and intelligent management of meteorological lightning monitoring data. By introducing early warning priorities, the system can dynamically adjust the allocation of monitoring resources according to the actual risk level and importance, avoiding a one-size-fits-all approach that applies the highest precision in all situations, thereby significantly improving resource utilization efficiency. Furthermore, in complex scenarios where multiple disturbance sources overlap, the priority-based fusion strategy can effectively resolve conflicts, ensuring that highly important early warning information receives priority protection, greatly improving the accuracy, timeliness, and reliability of lightning warnings, and providing more solid technical support for disaster prevention and mitigation.

[0121] However, if the method for obtaining warning priorities is static or fails to fully consider real-time dynamic factors, the resulting adaptive adjustment strategy may lack responsiveness and accuracy when facing complex and ever-changing meteorological lightning events and environmental disturbances, thus affecting the processing efficiency of lightning monitoring data and the effectiveness of warnings. For example, if the warning priorities are not updated or adjusted in a timely manner when a lightning event develops rapidly or the sensor's operating status changes, it may lead to inappropriate adjustments to the update frequency and spatial resolution of local environmental disturbance sources, thereby affecting the accuracy of subsequent data reliability judgments and warning importance assessments. If these problems are not addressed, the system may be unable to provide optimal data processing and warning support at critical moments. To address this, this application further proposes a more refined and dynamic method for obtaining warning priorities for local environmental disturbance sources, which comprehensively considers multiple aspects of information to improve the accuracy and adaptability of warning priorities.

[0122] In some embodiments of this application described above, the step of obtaining the early warning priority of multiple local environmental disturbance sources includes:

[0123] Obtain information on the development of lightning events;

[0124] Obtain the sensor's own operating status information;

[0125] Obtain priority adjustment instructions issued by the upper-level early warning system;

[0126] Based on the development trend of lightning events, adjust the priority of local environmental disturbance sources related to lightning events;

[0127] Based on the sensor's own operating status information, adjust the priority of local environmental disturbance sources that require resource consumption for processing;

[0128] Based on the priority adjustment instructions issued by the upper-level early warning system, adjust the priority of the local environmental disturbance sources involved in the instructions;

[0129] The adjusted priorities are integrated to generate the final early warning priority for multiple local environmental disturbance sources.

[0130] Specifically, acquiring information on the development trend of lightning events refers to the system's real-time monitoring and analysis of the dynamic changes in the intensity, frequency, direction of movement, and duration of lightning activity in the current area. For example, by analyzing historical lightning data, weather radar data, and other meteorological observation data, it can be determined whether a lightning event is in its nascent, developing, peak, or dissipating stage. The purpose is to enable the warning priority to be dynamically adjusted based on the urgency and potential threat of lightning events.

[0131] Obtaining the sensor's own operational status information can be understood as collecting and analyzing the working condition of the meteorological and lightning monitoring sensor, such as the sensor's battery level, communication signal strength, data transmission rate, hardware fault indications, calibration status, and ambient temperature. Specifically, this information can be obtained through the sensor's built-in diagnostic module or a remote monitoring system. The purpose is to ensure that processing resources are allocated reasonably when resources are limited or sensor performance degrades, avoiding data processing interruptions or errors due to sensor-related problems.

[0132] In practical applications, receiving priority adjustment instructions from higher-level early warning systems specifically involves receiving priority adjustment notifications from higher-level early warning or command systems (such as the National Meteorological Center or regional early warning platforms) targeting specific regions or types of disturbance sources. For example, when an upper-level system issues an emergency lightning warning or requires focused monitoring of data from a specific region, it will issue corresponding instructions. The purpose is to ensure that the local system remains synchronized with the overall early warning system and responds to broader early warning needs.

[0133] Furthermore, adjusting the priority of local environmental disturbance sources related to lightning events based on the development trend information of lightning events means that when lightning events are in a rapid development or high intensity stage, the priority of local environmental disturbance sources directly related to lightning events (e.g., local electromagnetic interference that may affect the propagation of lightning signals) will be increased to ensure that the reliability assessment and adjustment schemes of these key disturbance sources can be given priority.

[0134] In addition, adjusting the priority of local environmental disturbance sources that require resource consumption based on the sensor's own operating status information means that when the sensor experiences resource shortages (such as low power or limited communication bandwidth), the priority of local environmental disturbance sources that require a large amount of computing resources or transmission bandwidth to determine reliability or adjust the solution may be appropriately reduced, or those disturbance sources that consume less resources may be prioritized to ensure the continuous operation of the core monitoring function.

[0135] Meanwhile, according to the priority adjustment instructions issued by the upper-level early warning system, the priority of the local environmental disturbance sources involved in the adjustment instructions means that when the instructions from the upper-level system are received, the priority of the local environmental disturbance sources explicitly specified in the instructions will be directly adjusted according to the instructions, such as raising or lowering their priority, in order to meet the requirements of the upper-level early warning strategy.

[0136] Ultimately, the final early warning priority for multiple local environmental disturbance sources is generated by integrating and adjusting the priorities. This means comprehensively considering the various priority adjustments made based on the development trend of lightning events, sensor operating status, and upper-level instructions, and forming a unified, dynamic final early warning priority list for local environmental disturbance sources that reflects the current situation through methods such as weighted averaging, decision trees, or expert systems.

[0137] The solution proposed in this application addresses the aforementioned issues by introducing multi-source information and dynamically adjusting priorities. This allows the early warning priorities of local environmental disturbance sources to adapt to changing circumstances rather than remaining statically fixed. Specifically, by acquiring information on the development of lightning events, the system can perceive the urgency of lightning threats in real time, prioritizing disturbance sources related to high-threat lightning events and ensuring the accuracy of critical data. By acquiring the operational status information of the sensors themselves, the system can intelligently manage limited computing and communication resources, avoiding resource bottlenecks that could hinder the reliability assessment of important disturbance sources, thus ensuring the continuity and stability of data processing. Furthermore, by receiving priority adjustment instructions from the upper-level early warning system, this solution combines local priority assessments with a broader early warning strategy, ensuring the synergy and consistency of the entire early warning system. These dynamically adjusted priorities, after fusion, more accurately reflect the importance of each local environmental disturbance source in the current context, providing a more reliable basis for subsequent update frequency and spatial resolution adjustment strategies, making the entire data processing method more robust and adaptable.

[0138] Through the above technical solution, this application can significantly improve the accuracy and dynamism of obtaining early warning priorities for local environmental disturbance sources. Compared with methods that rely solely on a single or static factor to determine priorities, this solution comprehensively considers the development trend of lightning events, the operational status of the sensors themselves, and the instructions from the upper-level early warning system, enabling the generated early warning priorities to more accurately reflect the actual needs of the current meteorological environment and system operation. Therefore, when subsequently integrating multiple adjustment schemes to form an adaptive update frequency and spatial resolution adjustment strategy, it can prioritize and adopt more reliable and timely priorities, thereby ensuring optimal resource allocation and strategy execution for the monitoring and processing of different disturbance sources. This dynamic, multi-dimensional priority adjustment mechanism effectively solves the problem of insufficient response of traditional methods in complex and changing environments, significantly improves the efficiency of lightning monitoring data processing and the accuracy of early warnings, and provides more solid technical support for meteorological lightning early warning.

[0139] Based on the same inventive concept, this application also discloses a cloud computing-based high-efficiency data processing system for meteorological lightning monitoring sensors, such as... Figure 2 As shown, the system includes:

[0140] Data acquisition module 1 is used to acquire lightning monitoring data; feature extraction module is used to extract features from lightning monitoring data.

[0141] Importance assessment module 2 is used to assess the early warning importance of lightning monitoring data based on extracted features;

[0142] Storage area partitioning module 3 is used to divide the local data cache storage into a first logical area and a second logical area;

[0143] Data storage module 4 is used to store lightning monitoring data in a first logical area or a second logical area according to the assessed importance of the warning. Specifically, lightning monitoring data of higher importance is stored in the first logical area and a longer retention period is set; lightning monitoring data of lower importance is stored in the second logical area and a shorter retention period is set.

[0144] The data eviction process initiation module 5 is used to initiate data eviction processing when the local data cache storage occupancy rate reaches a preset threshold.

[0145] Data elimination module 6 is used to prioritize eliminating data from the second logical area during data elimination processing;

[0146] The overflow handling module 7 is used to remove data from the first logical area if the storage demand cannot be met after the second logical area is cleared, based on the warning importance of the data.

[0147] The system provided in this application achieves intelligent management of lightning monitoring data through the collaborative work of various modules, ensuring that critical early warning data can be prioritized for retention and protection when local storage resources are limited, thereby improving the timeliness and accuracy of lightning early warning.

[0148] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application.

Claims

1. A cloud computing-based method for efficient processing of meteorological lightning monitoring data, used in meteorological lightning monitoring sensors, characterized in that, include: Acquire lightning monitoring data; Feature extraction is performed on the lightning monitoring data; Based on the extracted features, the early warning importance of the lightning monitoring data is assessed; The local data cache memory is divided into a first logical region and a second logical region; Based on the assessed importance of the warning, the lightning monitoring data is stored in either the first logical area or the second logical area. Specifically, lightning monitoring data of higher importance is stored in the first logical area and a longer retention period is set; lightning monitoring data of lower importance is stored in the second logical area and a shorter retention period is set. When the occupancy rate of the local data cache memory reaches a preset threshold, data eviction processing is initiated. In the data eviction process, data is preferentially evicted from the second logical region; If the storage requirements cannot be met after the second logical area is cleared, data is removed from the first logical area, and the removal is based on the warning importance of the data.

2. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 1, characterized in that, The step of evaluating the early warning importance of the lightning monitoring data based on the extracted features includes: Feature extraction is performed on the lightning monitoring data, and the features include the original signal strength, waveform characteristics, occurrence time, and approximate spatial location; Store summary information of recent lightning events, including the time of occurrence, approximate location, preliminary assessment of the event type, and original intensity. The lightning monitoring data and the summary information are subjected to correlation analysis to generate a sequence correlation score; Based on the extracted features and the sequence correlation score, the early warning importance of the lightning monitoring data is evaluated, and the evaluation includes: An initial importance assessment is made based on the raw intensity and waveform characteristics of the lightning monitoring data; If the sequence correlation score reaches a preset threshold, the importance level of the initial importance judgment of the lightning monitoring data is increased; If the waveform characteristics of the lightning monitoring data show a distortion pattern and the sequence correlation score is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data.

3. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 2, characterized in that, The step of evaluating the early warning importance of the lightning monitoring data based on the extracted features and the sequence correlation score includes: Obtain information about abnormal environmental conditions; Based on the environmental anomaly information, an environmental impact weighting factor for the original signal strength is generated; Based on the original signal strength adjusted by the environmental impact weighting factor, the waveform characteristics, and the sequence correlation score, the early warning importance of the lightning monitoring data is assessed, and the assessment includes: An initial importance judgment is made based on the original signal strength after adjustment by the environmental impact weighting factor and the waveform characteristics; If the original signal strength after the environmental impact weighting factor adjustment and the waveform characteristics indicate high importance, and the sequence correlation score is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data. If the original signal strength after the environmental impact weighting factor adjustment and the waveform characteristics indicate low importance, but the sequence correlation score is high, then the importance level of the initial importance judgment result of the lightning monitoring data is increased. If the original signal strength after the environmental influence weighting factor adjustment and the waveform features indicate high importance, but the sequence correlation score is low, then check whether the waveform features have a distortion pattern. If the waveform features exhibit a distortion pattern, the initial importance assessment result of the lightning monitoring data shall be maintained. If the waveform features do not exhibit a distortion pattern, the importance level of the initial importance assessment result of the lightning monitoring data is reduced.

4. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 2, characterized in that, The step of evaluating the early warning importance of the lightning monitoring data based on the extracted features and the sequence correlation score includes: Based on the information about abnormal environmental conditions, determine the reliability of the original signal strength, the waveform characteristics, and the sequence correlation score; Based on the aforementioned reliability, the influence of the original signal strength, the waveform characteristics, and the sequence correlation score on the assessment of the importance of the early warning is adjusted. Based on the original signal strength adjusted for the degree of impact, the waveform characteristics, and the sequence correlation score, the warning importance of the lightning monitoring data is assessed, and the assessment includes: An initial importance judgment is made based on the original signal strength after adjustment according to the degree of influence and the waveform characteristics; If the original signal strength after the influence level adjustment and the waveform characteristics indicate high importance, and the sequence correlation score after the influence level adjustment is high, then the initial importance judgment result of the lightning monitoring data will be changed to high importance data. If the original signal strength after the influence adjustment and the waveform feature indicate low importance, but the sequence correlation score after the influence adjustment is high, then the importance level of the initial importance judgment result of the lightning monitoring data is increased. If the original signal strength after the influence level adjustment and the waveform feature indicate high importance, but the sequence correlation score after the influence level adjustment is low, then check whether the waveform feature has a distortion pattern. If the waveform features exhibit a distortion pattern, the initial importance assessment result of the lightning monitoring data shall be maintained. If the waveform features do not exhibit a distortion pattern, the importance level of the initial importance assessment result of the lightning monitoring data is reduced.

5. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 4, characterized in that, The steps for determining the reliability of the original signal strength, the waveform features, and the sequence correlation score include: By combining the local gradient and abrupt changes of the simplified meteorological parameters in the environmental anomaly information, the source of the local environmental disturbance is identified; Based on the type, intensity, occurrence time, and spatial range of the identified local environmental disturbance source, the reliability of the original signal intensity, the waveform characteristics, and the sequence correlation score is calculated in real time. The calculation includes: Based on the type and intensity of the local environmental disturbance source, determine the initial reliability of the original signal strength, the waveform characteristics, and the sequence correlation score; The update frequency and spatial resolution of the initial reliability assessment are adjusted based on the duration and impact range of the localized environmental disturbance source. Based on the adjusted reliability, the influence of the original signal strength, the waveform characteristics, and the sequence correlation score on the assessment of the importance of the early warning is adjusted.

6. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 5, characterized in that, The step of determining the initial reliability of the original signal strength, the waveform characteristics, and the sequence correlation score based on the type and intensity of the local environmental disturbance source includes: Based on the type and intensity of the local environmental disturbance source, identify the independent influence patterns of the local environmental disturbance source on the original signal intensity, the waveform characteristics, and the sequence correlation score; Based on the independent influence mode, determine whether there is a superposition or cancellation effect between the disturbance sources; Based on the superposition or cancellation effect, the initial reliability of the original signal strength, the waveform characteristics, and the sequence correlation score is adjusted.

7. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 5, characterized in that, The step of adjusting the update frequency and spatial resolution of the initial reliability assessment based on the duration and impact range of the local environmental disturbance source includes: Identify the duration, range of influence, and impact on the reliability of the original signal strength, waveform features, and sequence correlation score of multiple local environmental disturbance sources; Based on the duration, the scope of influence, and the impact on reliability, an independent update frequency and spatial resolution adjustment scheme is generated for each local environmental disturbance source; Multiple adjustment schemes are integrated to form an adaptive update frequency and spatial resolution adjustment strategy. The integration includes: increasing the update frequency and spatial resolution for disturbance sources with short duration and small impact range. For disturbance sources with long duration and wide impact range, reduce the update frequency and spatial resolution; For disturbance sources that affect overlap, the finest update frequency and spatial resolution are used within the overlap region.

8. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 7, characterized in that, The step of integrating multiple adjustment schemes to form an adaptive update frequency and spatial resolution adjustment strategy includes: Obtain the early warning priority of the multiple local environmental disturbance sources; Based on the aforementioned warning priority, multiple adjustment schemes are sorted. According to the sorting results, multiple adjustment schemes are merged in sequence. Among them, the adjustment schemes for high-early warning priority disturbance sources are adopted first, and the adjustment schemes for low-early warning priority disturbance sources are supplemented on the premise that they do not conflict with the adjustment schemes for high-early warning priority disturbance sources. For disturbance sources that cause overlap, the adjustment scheme for the high-priority disturbance source shall be adopted in the overlapping area.

9. The efficient processing method for meteorological lightning monitoring data based on cloud computing according to claim 8, characterized in that, The step of obtaining the early warning priority of the plurality of local environmental disturbance sources includes: Obtain information on the development of lightning events; Obtain the sensor's own operating status information; Obtain priority adjustment instructions issued by the upper-level early warning system; Based on the development trend information of the lightning event, the priority of local environmental disturbance sources related to the lightning event is adjusted; Based on the sensor's own operating status information, the priority of local environmental disturbance sources that require resource consumption for processing is adjusted; According to the priority adjustment instruction issued by the upper-level early warning system, the priority of the local environmental disturbance sources involved in the instruction is adjusted; By integrating the adjusted priorities, a final early warning priority is generated for the multiple local environmental disturbance sources.

10. A cloud-based high-efficiency processing system for meteorological lightning monitoring data, used for meteorological lightning monitoring sensors, characterized in that, The system includes: The data acquisition module is used to acquire lightning monitoring data; The feature extraction module is used to extract features from the lightning monitoring data; An importance assessment module is used to assess the early warning importance of the lightning monitoring data based on the extracted features; The storage area partitioning module is used to divide the local data cache storage into a first logical area and a second logical area; The data storage module is used to store the lightning monitoring data in the first logical area or the second logical area according to the assessed importance of the warning, wherein lightning monitoring data of higher importance is stored in the first logical area and a longer retention period is set; and lightning monitoring data of lower importance is stored in the second logical area and a shorter retention period is set. The data eviction process initiation module is used to initiate data eviction process when the occupancy rate of the local data cache memory reaches a preset threshold. The data elimination module is used to prioritize eliminating data from the second logical region during the data elimination process. An overflow handling module is used to remove data from the first logical area if the storage requirements cannot be met after the second logical area is cleared, wherein the removal is based on the warning importance of the data.

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