Lightning disaster early warning system and method based on multi-source data

Abstract

The invention relates to the technical field of meteorological disaster early warning, in particular to a lightning disaster early warning system and method based on multi-source data, and the system comprises an atmospheric electric field monitoring module, an early warning strategy customization module, a risk area recognition module and a real-time early warning release module. According to the invention, through multi-source data integration and analysis and in combination with the regional electric field intensity and lightning frequency, the lightning activity intensity is accurately evaluated, the monitoring sensitivity and accuracy are significantly improved, and detailed data support is provided for intensity change, so that a more targeted early warning strategy is formulated; the safety level and the response requirement are optimized through frequency and intensity analysis, resource allocation is more reasonable, response time is more efficient, potential risk areas are further identified through path analysis, thunder and lightning conduction paths are refined, the risk areas are accurately calibrated, the early warning level is dynamically adjusted, it is ensured that early warning information has timeliness and space adaptability, and the early warning efficiency is improved. Powerful support is provided for power grid safety and lightning protection management, and the intelligent disaster prevention level is comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological disaster early warning, and in particular to a lightning disaster early warning system and method based on multi-source data. Background Art

[0002] The field of meteorological disaster warning technology focuses on monitoring, analyzing and forecasting weather-related disasters in nature (such as lightning, storms, wildfires, floods, etc.) in order to provide warning information before the disaster occurs and reduce its negative impact on society, economy and environment. This field relies on a variety of data sources and technologies, including satellite remote sensing, radar monitoring, atmospheric electric field observation, ground meteorological stations, etc., and integrates and analyzes data to assess the changing trends of weather and environment and establish a refined disaster warning model. With the development of artificial intelligence and big data processing technology, meteorological disaster warning systems have become more accurate and efficient, providing scientific support for disaster prevention and emergency decision-making.

[0003] Among them, the theme of the lightning disaster warning system focuses on the real-time monitoring and early warning of lightning disasters using multi-source data and algorithm integration technology. The system generates high-precision lightning warning information through comprehensive analysis of multiple parameters such as three-dimensional lightning data, atmospheric electric field information, radar data, etc. The system is mainly used for the safety monitoring of key infrastructure such as power grids, preventing power outages or equipment damage caused by lightning disasters, and providing important guarantees for the stable operation of power grids and lightning protection risk management.

[0004] Existing technologies have limitations in real-time monitoring and data integration. Data integration is not comprehensive enough, which makes it difficult to conduct high-precision analysis of the activity trends of disasters such as lightning. This technical framework relies on a single or a small number of data sources and cannot fully capture the frequency and intensity differences of lightning activities over a large area, affecting the refined early warning of lightning disasters, resulting in insufficient coverage and timeliness of early warning information. The division of risk areas in existing technologies lacks detailed path analysis and cannot effectively identify the specific transmission paths of lightning events, making it difficult for early warning coverage to match actual disaster transmission trends, resulting in an overly broad or insufficient warning range, affecting the efficiency of disaster response. Existing technologies have limited ability to adjust early warning strategies and are difficult to dynamically update in combination with real-time meteorological data, which can easily lead to lagging early warning strategies and make it difficult to adapt to rapid changes in disasters, affecting risk management and emergency response of key infrastructure such as power grids. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a lightning disaster early warning system and method based on multi-source data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A lightning disaster early warning system based on multi-source data includes:

[0007] The atmospheric electric field monitoring module monitors the atmospheric electric field information of the relevant area based on radar monitoring, analyzes the real-time monitoring results of lightning activities, evaluates the density of lightning activities in the monitoring area through the fusion of the electric field intensity value of the subarea and the lightning frequency data, and obtains the regional lightning density assessment results;

[0008] The early warning strategy customization module receives the lightning density assessment result of the area, analyzes the frequency and intensity of lightning activities in the area, formulates a preliminary early warning strategy, evaluates the safety level and response requirements of the area according to the preliminary early warning strategy, adjusts the early warning level and operation range, optimizes resource allocation and response time, and outputs a detailed early warning strategy;

[0009] The risk area identification module uses the refined warning strategy to analyze the lightning event conduction path between regions, identify potential risk areas, generate conduction path analysis results, and based on the conduction path analysis results, evaluate and calibrate the lightning disaster risk areas to obtain risk area calibration results;

[0010] The real-time warning release module determines the lightning activity risk level in the risk area according to the risk area calibration results, sets the corresponding warning level, adjusts the warning strategy in combination with the real-time meteorological data in the area, optimizes the safe operation of the power grid, and outputs the adjusted real-time warning results.

[0011] As a further solution of the present invention, the steps for obtaining the regional lightning density assessment result are specifically as follows:

[0012] Based on the data of atmospheric electric field in relevant areas monitored by radar, the atmospheric electric field information of the area is monitored, the ratio parameter of the mean electric field intensity and the mean lightning frequency is calculated, the ratio parameter is analyzed with the set threshold, the regional data combination with the ratio exceeding the threshold is screened, and the preliminary parameter set of the dangerous lightning activity area is generated;

[0013] Based on the preliminary parameter set of the dangerous lightning activity area, the electric field strength and lightning frequency in the selected area are weighted combined using the formula:

[0014]

[0015] Generate regional composite impact factor value, where I represents the regional composite impact factor value, E is the mean value of the electric field strength in the subarea, F is the mean value of the lightning frequency, and w e and w f is the weight factor of electric field intensity and lightning frequency, α is the basic adjustment parameter, β is the adjustment coefficient of the variation between frequency and intensity, and δ is the composite offset coefficient;

[0016] Based on the regional composite impact factor value, the impact factor value of each dangerous lightning activity area is extracted and associated with the corresponding regional area data, the regional area ratio is calculated, the regional lightning density is evaluated and analyzed, and the regional lightning density evaluation result is obtained.

[0017] As a further solution of the present invention, the steps of obtaining the preliminary early warning strategy are specifically as follows:

[0018] According to the lightning density assessment results of the region, through the lightning activity information of differentiated zones within the region, the lightning frequency parameters and lightning intensity parameters of each zone are extracted, the frequency parameters and intensity parameters of the zones are standardized, and a preliminary monitoring parameter set of the lightning activity of the zones is established;

[0019] Based on the preliminary monitoring parameter set of the lightning activity in the sub-area, combined with the frequency and intensity parameters of lightning activity, the formula is adopted:

[0020]

[0021] Calculate and generate the preliminary warning factor value, where p represents the preliminary warning factor value, e represents the lightning intensity parameter, f represents the lightning frequency parameter, and k represents the lightning intensity parameter. f and k e are the weight parameters of frequency and intensity, c is the baseline adjustment coefficient, and d is the adjustment parameter of frequency and intensity changes;

[0022] The preliminary warning factor value is combined with the regional characteristic data, and the warning level of the district is marked according to the distribution characteristics of the warning factor and the characteristics of the geographical division to obtain a preliminary warning strategy.

[0023] As a further solution of the present invention, the step of obtaining the detailed early warning strategy is specifically:

[0024] Based on the warning factor value and original safety level of the partition in the preliminary warning strategy, extract the original risk weight coefficient and responsiveness coefficient of each partition, compare the original warning factor of the partition with the real-time safety level, and generate the initial risk combination result of the partition;

[0025] According to the initial risk combination results of the partition, the risk factors are matched with the partition characteristic data one by one, using the formula:

[0026] L=p·v+q·r+g·(v·q)

[0027] Get the refined warning level value of the sub-zone, where L represents the refined warning level value of the sub-zone, p is the preliminary warning factor value, v is the risk weight coefficient, q is the responsiveness coefficient, r is the environmental characteristic adjustment parameter, and g is the regional adjustment coefficient;

[0028] Based on the refined warning level values ​​of the partitions, the warning level distribution of each partition is analyzed, the warning level values ​​of the partitions are compared with regional resource data, and a refined warning strategy is obtained by adjusting the warning level distribution and resource requirements.

[0029] As a further solution of the present invention, the steps of obtaining the conduction path analysis results are specifically as follows:

[0030] Based on the detailed warning strategy, the lightning event frequency of the region is identified, the relationship between adjacent regions of each region is extracted, and the regional conduction impact value matrix is ​​established by combining the path conduction weight and the regional event frequency;

[0031] Based on the regional conduction impact value matrix, the conduction path length and path strength of each region are analyzed. According to the path strength, path length and event frequency in the region, the path length and conduction impact value are combined, and the formula is used:

[0032]

[0033] Generate a path effect analysis matrix, where R is the risk weight of the transmission path, P i is the frequency of events in the region, L i is the path length, O i is the path strength, W is the path strength normalization constant, and D is the path distribution difference coefficient;

[0034] The path effect analysis matrix is ​​used to compare the risk weight values ​​of each region, and the regions are normalized to generate the conduction path analysis results.

[0035] As a further solution of the present invention, the steps for obtaining the risk area calibration result are specifically as follows:

[0036] Based on the results of the conduction path analysis, the lightning conduction risk parameters of the sub-area are extracted, the geographical location, lightning activity intensity and frequency data of the sub-area are analyzed, the density and ductility of the conduction path of the sub-area are determined, and the preliminary assessment results of the conduction risk characteristics are generated;

[0037] The preliminary assessment results of the conduction risk characteristics are called, and according to the density, conduction strength and ductility of the conduction paths of the sub-districts, combined with the original lightning disaster records, the lightning disaster risk level of the sub-districts is quantified to generate the lightning disaster risk level results of the sub-districts;

[0038] According to the lightning disaster risk level results of the sub-district, the risk area of ​​the sub-district is calibrated, the risk level and geographical coverage of the sub-district are analyzed, the risk level value is calibrated as a benchmark, the scope of the risk area is determined, and the risk area calibration result is generated.

[0039] As a further solution of the present invention, the steps for obtaining the adjusted real-time warning result are specifically as follows:

[0040] In combination with the risk area calibration result and the lightning activity data, the meteorological parameter set and the risk area value set are called to extract the initial lightning activity risk level, and by analyzing the element value and the risk threshold in the initial lightning activity risk level, the risk area elements greater than the threshold in the lightning activity risk level are screened to obtain the risk area set;

[0041] Based on the risk area set, the humidity, temperature, wind speed, and air pressure in the real-time meteorological parameters are called to calculate the meteorological impact coefficient and perform weighted processing using the formula:

[0042]

[0043] Generate a weighted meteorological influence coefficient set, where h represents humidity, t represents temperature, U represents wind speed, Q represents air pressure, λ represents the weight coefficient of meteorological factors, and A represents the meteorological influence coefficient;

[0044] Utilizing the weighted meteorological impact coefficient set, combined with the real-time warning threshold, the risk level value in the region is called, the correlation strength between the risk level and the meteorological impact coefficient is analyzed, the trend of risk level value changes is determined, the correspondence between the risk level value of the region and the warning threshold is analyzed, and the adjusted real-time warning result is generated.

[0045] A lightning disaster early warning method based on multi-source data, the lightning disaster early warning method based on multi-source data is executed based on the above-mentioned lightning disaster early warning system based on multi-source data, comprising the following steps:

[0046] S1: Based on the data of atmospheric electric field in the relevant area monitored by radar, monitor the atmospheric electric field information in the area, extract the electric field strength and lightning frequency data in the sub-area, sort out the electric field strength and lightning frequency in the sub-area, compare the difference of electric field strength between sub-areas, calculate the weighted fusion value of strength and frequency, analyze the numerical value and the regional lightning activity standard, and obtain the lightning activity density assessment result;

[0047] S2: calling the lightning activity density assessment result, analyzing the lightning frequency and intensity distribution, identifying and classifying the regional lightning frequency and intensity data, setting the corresponding warning level, combining the warning level with the safety level of the partition, and constructing a detailed warning strategy;

[0048] S3: Based on the detailed warning strategy, extract regional lightning activity and conduction path data, analyze risk areas item by item, and mark secondary lightning disaster information in risk areas, compare lightning conduction paths with partition locations, summarize and analyze risk areas with path conduction characteristics, integrate path analysis and conduction characteristics, and generate lightning event conduction path analysis results;

[0049] S4: Based on the analysis results of the lightning event conduction path, the risk levels of the risk areas are compared in turn, and the lightning disaster risk levels of the zones are marked and classified through the electric field strength, lightning frequency and path conduction characteristic data to generate a lightning disaster risk area calibration result;

[0050] S5: According to the lightning disaster risk zone calibration results, identify the lightning activity risk level in the risk zone, set the risk level and warning level in turn, integrate real-time meteorological data to adjust the warning level of the risk zone, determine resource allocation and response time, and generate adjusted real-time warning results.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, through the integration and analysis of multi-source data, not only the real-time status of lightning activity is dynamically monitored, but also the regional electric field strength and lightning frequency data are combined to accurately evaluate the density of lightning activity in the area, effectively improve the sensitivity and accuracy of lightning monitoring, and provide more detailed data support for timely acquisition of density changes. Adaptive early warning strategies are further formulated. By analyzing frequency and intensity parameters, safety level assessment and response requirements are optimized, and the pertinence and practicality of early warning strategies are effectively improved, so that resource allocation is more reasonable and response time is more efficient. Potential lightning risk areas are identified through path analysis, and the conduction paths of lightning events are refined and quantified, providing accurate identification capabilities for potential risk areas, providing clear risk area calibration for the prevention and control of lightning disasters, achieving accurate disaster prevention and efficient resource scheduling, dynamically adjusting early warning levels and strategies, ensuring that early warning information has high timeliness and spatial adaptability, effectively supporting safe operation of power grids and lightning protection risk management, not only optimizing the accuracy of lightning monitoring, but also improving the response efficiency of early warning strategies, providing efficient support for the security of key infrastructure, and comprehensively enhancing the level of intelligence in responding to lightning disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a system flow chart of the present invention;

[0054] Figure 2 It is a flow chart of the regional lightning density assessment results in the present invention;

[0055] Figure 3 is a flow chart of the preliminary early warning strategy in the present invention;

[0056] Figure 4 A flowchart for refining the early warning strategy in the present invention;

[0057] Figure 5 A flow chart showing the results of the conduction path analysis in the present invention;

[0058] Figure 6 It is a flow chart of the risk area calibration results in the present invention;

[0059] Figure 7 It is a flow chart of the adjusted real-time warning result in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0062] See also Figure 1 The present invention provides a technical solution: a lightning disaster early warning system based on multi-source data includes:

[0063] The atmospheric electric field monitoring module monitors the atmospheric electric field information of the relevant area based on radar monitoring, analyzes the real-time monitoring results of lightning activities, evaluates the density of lightning activities in the monitoring area through the fusion of the electric field intensity value of the subarea and the lightning frequency data, and obtains the regional lightning density assessment results;

[0064] The warning strategy customization module receives the regional lightning density assessment results, analyzes the frequency and intensity of lightning activities in the region, formulates a preliminary warning strategy, evaluates the regional safety level and response requirements based on the preliminary warning strategy, adjusts the warning level and operation range, optimizes resource allocation and response time, and outputs a detailed warning strategy;

[0065] The risk area identification module uses a refined warning strategy to analyze the lightning event conduction path between regions, identify potential risk areas, generate conduction path analysis results, and based on the conduction path analysis results, evaluate and calibrate the lightning disaster risk areas to obtain risk area calibration results;

[0066] The real-time warning release module determines the risk level of lightning activity in the risk area according to the risk area calibration results, sets the corresponding warning level, adjusts the warning strategy based on the real-time meteorological data in the area, optimizes the safe operation of the power grid, and outputs the adjusted real-time warning results.

[0067] The results of regional lightning density assessment include electric field strength, lightning frequency, and density level. The preliminary warning strategy includes risk prediction model, warning signal setting, and safety response guidelines. The detailed warning strategy includes safety level, response requirements, and resource allocation. The conduction path analysis results include conduction path details, risk area identification, and risk level assessment. The risk area calibration results include disaster risk assessment, risk area division, and calibration indicators. The adjusted real-time warning results include warning level adjustment, strategy optimization details, and safe operation status.

[0068] See also Figure 2 , the specific steps for obtaining the regional lightning density assessment results are as follows:

[0069] Based on the data of atmospheric electric field in relevant areas monitored by radar, the atmospheric electric field information of the area is monitored, the ratio parameter of the mean electric field intensity and the mean lightning frequency is calculated, the ratio parameter is analyzed with the set threshold, the regional data combination with the ratio exceeding the threshold is screened, and the preliminary parameter set of the dangerous lightning activity area is generated;

[0070] Based on the electric field strength value and lightning frequency data in the monitoring area, the electric field strength of each partition is recorded hourly through the data acquisition equipment deployed at the monitoring site. The recorded electric field strength data are divided by region and integrated to form a preliminary data set. The time series mean of the electric field strength value of each partition in the data set is calculated to obtain the mean electric field strength of the partition. The corresponding lightning frequency is recorded hourly at the same time, and the time series mean of the lightning frequency value of each partition is calculated. The two means are combined and the ratio parameter is obtained through ratio operation. This ratio is used to express the relative strength of the electric field strength and the lightning frequency. The obtained ratio parameter is compared with a specific threshold. The regional data whose ratio exceeds the set threshold is screened and marked as a potential area of ​​dangerous lightning activity. The areas above the threshold are further marked. Based on the data of each partition, a preliminary parameter set of dangerous lightning activity areas can be generated to provide preliminary positioning of the target area for further lightning density assessment in subsequent steps.

[0071] Based on the preliminary parameter set of dangerous lightning activity areas, the electric field strength and lightning frequency in the selected area are weighted combined using the formula:

[0072]

[0073] Generate regional composite impact factor value, where I represents the regional composite impact factor value, E is the mean value of the electric field strength in the subarea, F is the mean value of the lightning frequency, and w e and w f is the weight factor of electric field intensity and lightning frequency, α is the basic adjustment parameter, β is the adjustment coefficient of the variation between frequency and intensity, and δ is the composite offset coefficient;

[0074] The formula is useful in that it uses a weighted combination of electric field intensity and lightning frequency, and introduces weight, offset and amplitude adjustment factors to comprehensively calculate the composite impact factor of regional lightning activity, so that it reflects the relative impact of electric field and frequency changes on the intensity of lightning activity.

[0075] Where E = 200 (mean value of electric field intensity, obtained by calculating the time series mean of the regional electric field using the monitoring equipment), F = 15 (mean value of lightning frequency, obtained by calculating the hourly mean frequency of lightning occurrence in the region using the monitoring equipment), w e =0.6 (electric field strength weight factor, obtained based on data analysis of the actual impact weight of regional lightning activity), w f =0.4 (lightning frequency weight factor, obtained by analyzing the weight of lightning frequency on the composite impact), δ=5 (offset coefficient, using the range parameter of the prior analysis), α=1.2 (basic adjustment parameter, based on the mean correction of the original data), β=0.8 (frequency and intensity variation adjustment coefficient, obtained through fitting experiments);

[0076] Substitute into the calculation:

[0077]

[0078] The result shows that the composite impact factor value is 0.893, indicating that the density of lightning activity in the selected area is relatively high, which can be used for further calculation and analysis of regional lightning density.

[0079] Based on the regional composite impact factor value, the impact factor value of each dangerous lightning activity area is extracted and associated with the corresponding regional area data, the regional area ratio is calculated, the regional lightning density is evaluated and analyzed, and the regional lightning density evaluation result is obtained;

[0080] Based on the calculation results of the composite impact factor value, in each selected dangerous lightning activity area, the area ratio of each area is established according to the area data of each area, and the impact factor value and the area ratio are combined to generate the regional density index. In this calculation process, the regional impact factor is integrated by the area-weighted ratio method. The density index obtained by area weighting reflects the relative lightning density level of each area in the overall monitoring range. The density index is calculated by the sum of the products of the partition area and the composite impact factor value to calculate the index of each area. The density index of each area is dynamically weighted and adjusted respectively, and finally the regional lightning density assessment result is obtained. The density index can further provide a reference for the dynamic monitoring and trend analysis of the regional lightning density.

[0081] See also Figure 3 , the specific steps for obtaining the preliminary early warning strategy are:

[0082] According to the regional lightning density assessment results, through the lightning activity information of differentiated zones within the region, the lightning frequency parameters and lightning intensity parameters of each zone are extracted, the frequency parameters and intensity parameters of the zones are standardized, and the preliminary monitoring parameter set of the zone lightning activity is established;

[0083] Receive the results of the regional lightning density assessment, and extract the lightning frequency parameters and lightning intensity parameters of each partition based on the real-time lightning activity data of each partition. First, use the data acquisition system to record the lightning frequency data in a continuous time period, and count the lightning frequency every hour hourly to obtain the preliminary frequency data of each partition. Perform time series analysis according to the daily or monthly average to obtain the final frequency mean. Then, perform similar operations on the lightning intensity data, record the lightning intensity value of each partition every hour, eliminate obviously abnormal values, and average the intensity values ​​by time period. Perform standardization processing on the frequency and intensity parameters, eliminate abnormal or abnormally fluctuating data points, ensure that the frequency and intensity parameters are within a reasonable range, and obtain a preliminary monitoring parameter set for the lightning activity in the partition.

[0084] Based on the preliminary monitoring parameter set of the lightning activity in the sub-area, combined with the frequency and intensity parameters of the lightning activity, the formula is adopted:

[0085]

[0086] Calculate and generate the preliminary warning factor value, where p represents the preliminary warning factor value, e represents the lightning intensity parameter, f represents the lightning frequency parameter, and k represents the lightning intensity parameter. f and k e are the weight parameters of frequency and intensity, c is the baseline adjustment coefficient, and d is the adjustment parameter of frequency and intensity changes;

[0087] The benefit of the formula is that by introducing multiple adjustment parameters, the relative weights of lightning intensity and frequency can be adjusted under differentiated zoning conditions, which enhances the flexibility and adaptability of the warning factor;

[0088] Where, e = 250 (the average lightning intensity collected by the real-time monitoring system), f = 30 (the average frequency obtained by the lightning frequency recording system), k f =0.7 (weight parameter, based on the weight analysis of the impact of frequency on lightning activity), k e =0.5 (weight parameter, based on the importance analysis of the impact of intensity on lightning activity), c = 1.1 (benchmark adjustment factor, derived from the statistical data of original lightning frequency and intensity), d = 0.6 (adjustment parameter, used to balance the dynamic changes of frequency and intensity in different zones);

[0089] Substitute into the calculation:

[0090]

[0091] The result shows that the preliminary warning factor value of the sub-zone is 0.946, which means that there is a certain warning demand for lightning activities in the current sub-zone, and this factor value will be used to generate further warning strategies.

[0092] Combine the preliminary warning factor values ​​with the regional characteristic data, mark the warning level of the district according to the distribution characteristics of the warning factors and the characteristics of the geographical divisions, and obtain the preliminary warning strategy;

[0093] Using the preliminary warning factor value of the zone and combining it with the geographic information data in the region, the warning factor distribution of the zone is calculated for each zone's actual regional characteristics such as terrain features, population density, and key infrastructure. The regional factor is combined with the regional warning level, and the dynamic distribution of the zone warning factor is used to mark the warning level. For zones with high warning factor values, the factor value of the zone is matched with the level identifier. All zones will establish corresponding warning measures and response strategies within the region based on their specific warning factor values ​​to obtain a preliminary warning strategy.

[0094] See also Figure 4 , the steps to obtain the early warning strategy are as follows:

[0095] Based on the warning factor value and original safety level of the partition in the preliminary warning strategy, extract the original risk weight coefficient and responsiveness coefficient of each partition, compare the original warning factor of the partition with the real-time safety level, and generate the initial risk combination result of the partition;

[0096] Based on the warning factor values ​​of each partition in the preliminary warning strategy and the original safety level data of the partition, the original risk weight coefficient and responsiveness coefficient of the partition are extracted, and the original risk weight data of the continuous time period is obtained through the data monitoring system. The data is processed according to the daily or monthly average value to obtain the mean risk weight of each partition, and the responsiveness data associated with it is obtained. The actual relationship between the warning factor and the safety level is analyzed item by item to calculate the mean responsiveness of each partition, and the basic data of the risk combination is constructed based on the original safety level and the warning factor value. Parameters such as risk weight and responsiveness are associated to generate the initial risk combination results of the partition.

[0097] According to the initial risk combination results of the partition, the risk factors are matched with the partition characteristic data item by item, using the formula:

[0098] L=p·v+q·r+g·(v·q)

[0099] Get the refined warning level value of the sub-zone, where L represents the refined warning level value of the sub-zone, p is the preliminary warning factor value, v is the risk weight coefficient, q is the responsiveness coefficient, r is the environmental characteristic adjustment parameter, and g is the regional adjustment coefficient;

[0100] The benefit of the formula is that it forms a dynamically adjustable warning level through the product relationship between the warning factor, risk weight and environmental responsiveness of each zone, so as to match the personalized risk assessment needs of different zones;

[0101] Among them, p = 0.85 (district warning factor value, obtained from the real-time monitoring data of the district), v = 1.2 (original risk weight coefficient, calculated by the mean of the original data), q = 1.1 (response coefficient, obtained based on the comparison and analysis of the warning factor and the original safety level), r = 0.9 (environmental characteristic adjustment parameter, obtained by the numerical mapping of the district environmental characteristics), g = 1.3 (regional adjustment coefficient, set by the relative risk influence between comprehensive districts);

[0102] Substitute into the calculation:

[0103] L=0.85·1.2+1.1·0.9+1.3·(1.2·1.1)

[0104] L = 1.02 + 0.99 + 1.716 = 3.726

[0105] The results show that the refined warning level value of the zone is 3.726, which reflects the risk level of the zone after adjustment of comprehensive warning factors, risk weights and environmental characteristics, and provides a basis for subsequent adjustments to the zone's resource allocation.

[0106] Based on the detailed warning level values ​​of the sub-areas, analyze the warning level distribution of each sub-area, compare the warning level values ​​of the sub-areas with the regional resource data, and obtain a detailed warning strategy by adjusting the warning level distribution and resource requirements;

[0107] Based on the refined warning level values ​​of the zones, using the geographical environment data and risk levels of each zone, the warning level values ​​of the zones are matched with regional resources by comparing the warning level values ​​within the zones with the data of adjustable resources, and the required resource allocation is calculated. Resources are dynamically allocated according to the risk level distribution of different zones, and finally the warning levels and resource allocation requirements of each zone are integrated to obtain a refined warning strategy.

[0108] See also Figure 5 , the specific steps for obtaining the results of the conduction path analysis are:

[0109] Based on the detailed warning strategy, the frequency of lightning events in the region is identified, the relationship between adjacent regions of each region is extracted, and the regional conduction impact value matrix is ​​established by combining the path conduction weight and the frequency of regional events.

[0110] Based on the basic data of the conduction path analysis results, the frequency of lightning events is first extracted from the data set of each region, and its distribution in different time periods is verified through the time series characteristics in the data. The adjacent relationship of the region is identified in combination with the regional boundary data. Above the regional boundary, the conduction path data of each region is gradually analyzed according to the frequency characteristics of lightning events, and the randomness of lightning event propagation with adjacent regions is calculated. The conduction strength of each path is compared according to the numerical value of the regional boundary. The frequency data and the conduction weights of the adjacent paths of each region are partitioned and compared and multiplied step by step to obtain the conduction impact value matrix. The different impact levels of the conduction relationship are determined according to the matching values ​​of the path and weight data.

[0111] Based on the regional conduction impact value matrix, the conduction path length and path strength of each region are analyzed. According to the path strength, path length and event frequency in the region, the path length and conduction impact value are combined using the formula:

[0112]

[0113] Generate a path effect analysis matrix, where R is the risk weight of the transmission path, P i is the frequency of events in the region, L i is the path length, O i is the path strength, W is the path strength normalization constant, and D is the path distribution difference coefficient;

[0114] The benefit of the formula is that it forms the risk weight of the transmission path between regions by comprehensively calculating the regional event frequency, path length and path strength parameters, accurately reflecting the dynamic differences in path transmission risks;

[0115] P i Indicates the event frequency of a certain area. The number of lightning events per unit time is obtained through monitoring equipment. Assume that the event frequency in the area is 15 times;

[0116] L i represents the path length, which is obtained from the geographical information measurement data of the regional conduction path, for example, the path length is 10 kilometers; i Indicates the path strength, which is calculated based on the lightning intensity classification standard. For example, the strength of a path is 8.

[0117] W is the path strength normalization constant. The normalization factor is determined according to the statistical distribution of the path strength values ​​and is set to 0.9;

[0118] D is the coefficient of path distribution difference, referring to the complexity of the path and the influence of the regional structure setting, for example, this coefficient is set to 1.2;

[0119] Calculate the product of path length and path strength and take the square root of the absolute value:

[0120] |L i ·O i | 0.5 =|10·8| 0.5 =|80| 0.5 =8.94

[0121] Multiply the square root of the path length intensity by the event frequency and sum:

[0122]

[0123] Using the normalization factor and the path distribution difference coefficient adjustment, the conduction path risk weight is finally calculated:

[0124]

[0125] The results show that the risk weight of the conduction path is 124.54, which represents the combined risk intensity of the region and adjacent paths. This weight value is incorporated into the path effect analysis matrix for regional risk assessment.

[0126] Through the path effect analysis matrix, the risk weight value of each region is compared, and the regions are normalized to generate the conduction path analysis results;

[0127] Through the path effect analysis matrix, the risk weight value of each region is compared, and the corresponding high-risk area is selected according to the regional relationship determined in the conduction path matrix and the size of each weight value. After integrating the risk weight value of each region with the conduction impact value matrix, the numerical data of each region is normalized to reduce the deviation between data. Through matrix merging, the risk conduction impact data of differentiated regions in the conduction path analysis results are included in a unified risk assessment level table. The normalized weight results in the path effect analysis matrix are used to establish a regional risk assessment level form, and high-risk areas are marked.

[0128] See also Figure 6 , the specific steps for obtaining the risk area calibration results are:

[0129] Based on the results of the conduction path analysis, the lightning conduction risk parameters of the sub-area are extracted, the geographical location, lightning activity intensity and frequency data of the sub-area are analyzed, the density and ductility of the conduction path of the sub-area are determined, and the preliminary assessment results of the conduction risk characteristics are generated;

[0130] Based on the results of the conduction path analysis, the lightning conduction risk parameters of each zone are extracted, and the original data of the geographical location, terrain characteristics and lightning activity intensity of each zone are obtained. By analyzing the density and ductility of the lightning conduction paths in each zone item by item, it is determined which paths cause higher risk levels. According to the dense distribution of the conduction paths and their extension to the affected areas, the zones with high-risk conduction paths are screened. Based on the conduction risk parameters of each zone, the preliminary assessment results of the conduction risk characteristics are generated for further processing and evaluation.

[0131] The preliminary assessment results of the conduction risk characteristics are called up, and according to the density, conduction strength and ductility of the conduction paths in the sub-districts, combined with the original lightning disaster records, the lightning disaster risk level of the sub-districts is quantified to generate the lightning disaster risk level results of the sub-districts;

[0132] Based on the preliminary assessment results of the zoning conduction risk characteristics, the conduction path density, path ductility and conduction strength parameters of the zoning are used, combined with the original lightning disaster record data of each zoning, to compare the lightning activity frequency and intensity of different zonings. In this risk assessment, the lightning disaster risk level of each zoning is gradually quantified, and the risk conduction characteristics of the zoning are matched with its original records. According to data mapping and risk distribution diagrams, a list containing the risk level of each zoning is constructed. All risk data are summarized to generate the zoning lightning disaster risk level results, which provides a basis for subsequent zoning risk calibration.

[0133] According to the results of the lightning disaster risk level of the sub-district, the risk area of ​​the sub-district is calibrated, the risk level and geographical coverage of the sub-district are analyzed, the risk level value is calibrated as the benchmark, the scope of the risk area is determined, and the risk area calibration results are generated;

[0134] Based on the results of the zoning lightning disaster risk level, the risk areas of each zone are calibrated, and the risk level results of the zone are matched with their geographical coverage. According to the risk level of each zone, it is calibrated according to the corresponding level to confirm the specific location and range of the dangerous area. By comparing the lightning disaster risk level value of each zone and the actual randomness of the disaster, the calibration range of the zone is determined, and the calibration range of each zone is integrated with the overall lightning disaster distribution in the region to generate the risk area calibration result.

[0135] See also Figure 7 , the specific steps for obtaining the adjusted real-time warning results are:

[0136] Combined with the risk area calibration results and lightning activity data, the meteorological parameter set and the risk area value set are called to extract the initial lightning activity risk level. By analyzing the element values ​​and risk thresholds in the initial lightning activity risk level, the risk area elements greater than the threshold in the lightning activity risk level are screened to obtain the risk area set.

[0137] Combined with the risk area calibration results and lightning activity data, the meteorological parameter set is called and integrated. The temperature, humidity and wind speed parameters of the meteorological parameter set are called one by one, and the regional calibration values ​​of the risk area value set are corresponding one by one. Through the comparison operation, each risk value in the initial lightning activity risk level is compared and screened with the risk threshold one by one. The areas with an initial lightning activity risk level greater than the risk threshold are recorded and identified as high-risk areas. The areas are marked separately, and a high-risk area calibration index is established. The area information with a calibration value greater than the threshold is converted into a regional set index value, corresponding to the preliminary high-risk area set. The high-risk area set is used as the preliminary screened risk area. The obtained risk area set can be used as the basic data set for further calibration of subsequent meteorological influencing factors.

[0138] Based on the risk area set, the real-time meteorological parameters of humidity, temperature, wind speed, and air pressure are called to calculate the meteorological impact coefficient and perform weighted processing using the formula:

[0139]

[0140] Generate a weighted meteorological influence coefficient set, where h represents humidity, t represents temperature, U represents wind speed, Q represents air pressure, λ represents the weight coefficient of meteorological factors, and A represents the meteorological influence coefficient;

[0141] The benefit of the formula is that by introducing the air pressure parameter Q, the calculation of the meteorological influence coefficient A can more comprehensively reflect the regional meteorological conditions, and the air pressure as the denominator product term makes the influence weights of humidity and temperature under different wind speed conditions more balanced and stable;

[0142] In the formula, the calculation of the meteorological influence coefficient A depends on the combination of humidity h, temperature t, wind speed U and air pressure Q. By calling the monitoring values ​​in the real-time meteorological data, the humidity h = 60, temperature t = 28, wind speed U = 12, and air pressure Q = 1013 are obtained, and the weight coefficient λ is set to 0.85 under the current conditions. The specific values ​​are substituted into the formula and calculated step by step;

[0143] Find the sum of the squares of humidity and temperature: h 2 +t 2 =60 2 +28 2 =3600+784=4384;

[0144] Calculate the ratio of the numerator to the product of wind speed and air pressure:

[0145] Taking the square root of this ratio we get:

[0146] Finally, multiply by the weight coefficient λ to get the A value: A = 0.6 × 0.85 = 0.51;

[0147] The results show that the meteorological impact coefficient P is 0.51 under current meteorological conditions, and the coefficient will be used as a key parameter in the subsequent update of risk area weights.

[0148] Using the weighted meteorological impact coefficient set, combined with the real-time warning threshold, call the risk level value in the region, analyze the correlation strength between the risk level and the meteorological impact coefficient, determine the trend of risk level value changes, analyze the corresponding relationship between the risk level value of the region and the warning threshold, and generate adjusted real-time warning results;

[0149] Using the meteorological impact coefficient set and further combining it with the real-time warning threshold set, the regional risk level values ​​are compared, and the weighted meteorological parameters in the meteorological impact coefficient set are called one by one. Through multiple comparisons, the risk level of each region is analyzed and matched with the real-time warning threshold T one by one. All high-risk areas are screened in turn, and the warning level of the high-risk area is updated to a higher level according to the threshold in the real-time warning level set to form an updated warning level set. The matched high-risk area is updated into the new warning level to obtain the adjusted real-time warning result, which provides a real-time analysis basis for subsequent risk warning and response.

[0150] The lightning disaster early warning method based on multi-source data is executed based on the above-mentioned lightning disaster early warning system based on multi-source data, and includes the following steps:

[0151] S1: Based on the data of atmospheric electric field in the relevant area monitored by radar, monitor the atmospheric electric field information in the area, extract the electric field strength and lightning frequency data in the sub-area, sort out the electric field strength and lightning frequency in the sub-area, compare the difference of electric field strength between sub-areas, calculate the weighted fusion value of strength and frequency, analyze the numerical value and the regional lightning activity standard, and obtain the lightning activity density assessment result;

[0152] S2: Call the lightning activity density assessment results, analyze the lightning frequency and intensity distribution, identify and classify the regional lightning frequency and intensity data, set the corresponding warning level, combine the warning level with the safety level of the zone, and build a detailed warning strategy;

[0153] S3: Based on the detailed warning strategy, extract the regional lightning activity and conduction path data, analyze the risk area item by item, and mark the secondary lightning disaster information in the risk area. Compare the lightning conduction path and the partition location, summarize and analyze the risk area with path conduction characteristics, integrate the path analysis and conduction characteristics, and generate the lightning event conduction path analysis results;

[0154] S4: Based on the analysis results of the lightning event conduction path, the risk levels of the risk areas are compared in turn. Through the electric field strength, lightning frequency and path conduction characteristic data, the lightning disaster risk levels of the zones are marked and classified to generate the lightning disaster risk area calibration results;

[0155] S5: Based on the calibration results of the lightning disaster risk area, identify the risk level of lightning activity in the risk area, set the risk level and warning level in turn, integrate real-time meteorological data to adjust the warning level of the risk area, determine resource allocation and response time, and generate adjusted real-time warning results.

[0156] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A lightning disaster early warning system based on multi-source data, characterized in that: The system comprises: The atmospheric electric field monitoring module monitors the atmospheric electric field information of the relevant area based on radar monitoring, analyzes the real-time monitoring results of lightning activities, evaluates the density of lightning activities in the monitoring area through the fusion of the electric field intensity value of the subarea and the lightning frequency data, and obtains the regional lightning density assessment results; The early warning strategy customization module receives the lightning density assessment result of the area, analyzes the frequency and intensity of lightning activities in the area, formulates a preliminary early warning strategy, evaluates the safety level and response requirements of the area according to the preliminary early warning strategy, adjusts the early warning level and operation range, optimizes resource allocation and response time, and outputs a detailed early warning strategy; The risk area identification module uses the refined warning strategy to analyze the lightning event conduction path between regions, identify potential risk areas, generate conduction path analysis results, and based on the conduction path analysis results, evaluate and calibrate the lightning disaster risk areas to obtain risk area calibration results; The real-time warning release module determines the lightning activity risk level in the risk area according to the risk area calibration results, sets the corresponding warning level, adjusts the warning strategy in combination with the real-time meteorological data in the area, optimizes the safe operation of the power grid, and outputs the adjusted real-time warning results.

2. The lightning disaster early warning system based on multi-source data according to claim 1 is characterized in that: The steps for obtaining the regional lightning density assessment result are specifically as follows: Based on the data of atmospheric electric field in relevant areas monitored by radar, the atmospheric electric field information of the area is monitored, the ratio parameter of the mean electric field intensity and the mean lightning frequency is calculated, the ratio parameter is analyzed with the set threshold, the regional data combination with the ratio exceeding the threshold is screened, and the preliminary parameter set of the dangerous lightning activity area is generated; Based on the preliminary parameter set of the dangerous lightning activity area, the electric field strength and lightning frequency in the selected area are weighted combined using the formula: Generate regional composite impact factor value, where I represents the regional composite impact factor value, E is the mean value of the electric field strength in the subarea, F is the mean value of the lightning frequency, and w e and w f is the weight factor of electric field intensity and lightning frequency, α is the basic adjustment parameter, β is the adjustment coefficient of the variation between frequency and intensity, and δ is the composite offset coefficient; Based on the regional composite impact factor value, the impact factor value of each dangerous lightning activity area is extracted and associated with the corresponding regional area data, the regional area ratio is calculated, the regional lightning density is evaluated and analyzed, and the regional lightning density evaluation result is obtained.

3. The lightning disaster early warning system based on multi-source data according to claim 2 is characterized in that: The steps for obtaining the preliminary early warning strategy are specifically as follows: According to the lightning density assessment results of the region, through the lightning activity information of differentiated zones within the region, the lightning frequency parameters and lightning intensity parameters of each zone are extracted, the frequency parameters and intensity parameters of the zones are standardized, and a preliminary monitoring parameter set of the lightning activity of the zones is established; Based on the preliminary monitoring parameter set of the lightning activity in the sub-area, combined with the frequency and intensity parameters of lightning activity, the formula is adopted: Calculate and generate the preliminary warning factor value, where p represents the preliminary warning factor value, e represents the lightning intensity parameter, f represents the lightning frequency parameter, and k represents the lightning intensity parameter. f and k e are the weight parameters of frequency and intensity, c is the baseline adjustment coefficient, and d is the adjustment parameter of frequency and intensity changes; The preliminary warning factor value is combined with the regional characteristic data, and the warning level of the district is marked according to the distribution characteristics of the warning factor and the characteristics of the geographical division to obtain a preliminary warning strategy.

4. The lightning disaster early warning system based on multi-source data according to claim 3 is characterized in that: The steps for obtaining the detailed early warning strategy are specifically as follows: Based on the warning factor value and original safety level of the partition in the preliminary warning strategy, extract the original risk weight coefficient and responsiveness coefficient of each partition, compare the original warning factor of the partition with the real-time safety level, and generate the initial risk combination result of the partition; According to the initial risk combination results of the partition, the risk factors are matched with the partition characteristic data one by one, using the formula: L=p·v+q·r+g·(v·q) Get the refined warning level value of the sub-zone, where L represents the refined warning level value of the sub-zone, p is the preliminary warning factor value, v is the risk weight coefficient, q is the responsiveness coefficient, r is the environmental characteristic adjustment parameter, and g is the regional adjustment coefficient; Based on the refined warning level values ​​of the partitions, the warning level distribution of each partition is analyzed, the warning level values ​​of the partitions are compared with regional resource data, and a refined warning strategy is obtained by adjusting the warning level distribution and resource requirements.

5. The lightning disaster early warning system based on multi-source data according to claim 4 is characterized in that: The steps for obtaining the conduction path analysis results are specifically as follows: Based on the detailed warning strategy, the lightning event frequency of the region is identified, the relationship between adjacent regions of each region is extracted, and the regional conduction impact value matrix is ​​established by combining the path conduction weight and the regional event frequency; Based on the regional conduction impact value matrix, the conduction path length and path strength of each region are analyzed. According to the path strength, path length and event frequency in the region, the path length and conduction impact value are combined, and the formula is used: Generate a path effect analysis matrix, where R is the risk weight of the transmission path, P i is the frequency of events in the region, L i is the path length, O i is the path strength, W is the path strength normalization constant, and D is the path distribution difference coefficient; The path effect analysis matrix is ​​used to compare the risk weight values ​​of each region, and the regions are normalized to generate the conduction path analysis results.

6. The lightning disaster early warning system based on multi-source data according to claim 5 is characterized in that: The steps for obtaining the risk area calibration result are specifically as follows: Based on the results of the conduction path analysis, the lightning conduction risk parameters of the sub-area are extracted, the geographical location, lightning activity intensity and frequency data of the sub-area are analyzed, the density and ductility of the conduction path of the sub-area are determined, and the preliminary assessment results of the conduction risk characteristics are generated; The preliminary assessment results of the conduction risk characteristics are called, and according to the density, conduction strength and ductility of the conduction paths of the sub-districts, combined with the original lightning disaster records, the lightning disaster risk level of the sub-districts is quantified to generate the lightning disaster risk level results of the sub-districts; According to the lightning disaster risk level results of the sub-district, the risk area of ​​the sub-district is calibrated, the risk level and geographical coverage of the sub-district are analyzed, the risk level value is calibrated as a benchmark, the scope of the risk area is determined, and the risk area calibration result is generated.

7. The lightning disaster early warning system based on multi-source data according to claim 6 is characterized in that: The steps for obtaining the adjusted real-time warning result are specifically as follows: In combination with the risk area calibration result and the lightning activity data, the meteorological parameter set and the risk area value set are called to extract the initial lightning activity risk level, and by analyzing the element value and the risk threshold in the initial lightning activity risk level, the risk area elements greater than the threshold in the lightning activity risk level are screened to obtain the risk area set; Based on the risk area set, the humidity, temperature, wind speed, and air pressure in the real-time meteorological parameters are called to calculate the meteorological impact coefficient and perform weighted processing using the formula: Generate a weighted meteorological influence coefficient set, where h represents humidity, t represents temperature, U represents wind speed, Q represents air pressure, λ represents the weight coefficient of meteorological factors, and A represents the meteorological influence coefficient; Utilizing the weighted meteorological impact coefficient set, combined with the real-time warning threshold, the risk level value in the region is called, the correlation strength between the risk level and the meteorological impact coefficient is analyzed, the trend of risk level value changes is determined, the correspondence between the risk level value of the region and the warning threshold is analyzed, and the adjusted real-time warning result is generated.

8. A lightning disaster early warning method based on multi-source data, characterized in that: The lightning disaster early warning system based on multi-source data according to any one of claims 1 to 7 comprises the following steps: Based on radar monitoring of the atmospheric electric field in the relevant area, monitor the regional atmospheric electric field information, extract the electric field strength and lightning frequency data within the sub-area, sort out the electric field strength and lightning frequency of the sub-area, compare the difference in electric field strength between sub-areas, calculate the weighted fusion value of intensity and frequency, analyze the numerical value and the regional lightning activity standard, and obtain the lightning activity density assessment result; Calling the lightning activity density assessment result, analyzing the lightning frequency and intensity distribution, identifying and classifying regional lightning frequency and intensity data, setting the corresponding warning level, combining the warning level with the safety level of the zone, and constructing a detailed warning strategy; Based on the detailed warning strategy, the regional lightning activity and conduction path data are extracted, the risk areas are analyzed item by item, and the secondary lightning disaster information of the risk areas is marked. The lightning conduction path and the partition location are compared, and the risk areas with path conduction characteristics are summarized and analyzed. The path analysis and conduction characteristics are integrated to generate the lightning event conduction path analysis results; Based on the analysis results of the lightning event conduction path, the risk levels of the risk areas are compared in turn, and the lightning disaster risk levels of the zones are marked and classified through the electric field strength, lightning frequency and path conduction characteristic data to generate the lightning disaster risk area calibration results; According to the lightning disaster risk zone calibration results, the lightning activity risk level in the risk zone is identified, the risk level and warning level are set in turn, the real-time meteorological data is integrated to adjust the warning level of the risk zone, the resource allocation and response time are determined, and the adjusted real-time warning results are generated.

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