Intelligent thunder and lightning early warning system for large oil and gas storage base

Abstract

The invention relates to the field of thunder and lightning early warning, and discloses an intelligent thunder and lightning early warning system for a large oil and gas storage base, which consists of a multi-source monitoring module, a data processing and analyzing module, an early warning module and a linkage protection module, wherein the multi-source monitoring module is responsible for collecting data from multiple dimensions, and the data processing and analyzing module is used for preprocessing the data and calculating the probability of lightning occurrence through a specific model; the early warning module sends out a corresponding early warning signal according to the danger level, and the linkage protection module automatically triggers a corresponding protection measure according to the early warning signal; by adopting multi-source data fusion, probability model analysis and a hierarchical linkage mechanism, the problems of false alarm and missing alarm existing in a traditional single monitoring method are effectively solved, accurate early warning and intelligent protection of the oil and gas storage base are achieved, and the thunder and lightning safety protection capacity of the large oil and gas storage base is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of lightning early warning, and in particular to an intelligent lightning early warning system for a large oil and gas storage base. Background Art

[0002] In the energy industry, large-scale oil and gas storage bases serve as crucial hubs for national energy security, storing vast quantities of flammable and explosive hazardous chemicals such as oil and natural gas. However, lightning disasters pose a serious threat to the safe operation of such bases. Direct lightning strikes, induced lightning strikes, and electromagnetic pulses (EMPs) can cause fires and explosions in oil and gas storage tanks, damage electrical equipment, and paralyze communications systems, resulting in significant economic losses and casualties, as well as severe environmental pollution and social impacts.

[0003] At present, the existing lightning warning systems on the market are mainly based on a single monitoring method, such as using ground electric field meters to monitor changes in the atmospheric electric field, or making macro-forecasts of lightning weather through satellite cloud images.

[0004] Although lightning early warning can be achieved, there are still some defects, such as: On the one hand, a single monitoring method is easily affected by environmental factors, and the accuracy and reliability of the monitoring data are insufficient, which can easily lead to false alarms or missed reports. On the other hand, the early warning system lacks targeted design for the complex scenarios of large oil and gas storage bases, and cannot achieve accurate early warning and coordinated protection of key areas within the base. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent lightning early warning system for large oil and gas storage bases to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent lightning early warning system for large oil and gas storage bases, comprising: The multi-source monitoring module includes an atmospheric electric field monitoring unit, a satellite cloud image receiving unit, a lightning location monitoring unit, and an in-base environment monitoring unit. It is used to collect multi-dimensional data on atmospheric electric field changes, meteorological macro-situations, lightning locations, and the environment of key areas within the base. The data processing and analysis module is used to pre-process the data collected by the multi-source monitoring module, including data cleaning, spatiotemporal alignment, and data standardization, and output the predicted overall lightning occurrence probability through the preset lightning occurrence probability analysis model; The early warning module is used to issue early warning information to base management personnel according to different risk levels based on the prediction results of the data processing and analysis module; The linkage protection module is used to automatically activate the corresponding lightning protection equipment and emergency protection measures in the base after receiving early warning information.

[0007] As a further technical solution, the expression of the overall lightning occurrence probability analysis model is: ; is the probability of lightning occurrence; in, is the rate of change of atmospheric electric field intensity, which is expressed as: , is the unit time, is the change in atmospheric electric field intensity, is the reference electric field change rate, Used to reflect the impact of changing electric field intensity on lightning. is the lightning location density, Used to enhance the impact of high-density areas, is the reference lightning density, is the cumulonimbus cloud top temperature, is the reference temperature, is the temperature sensitivity coefficient, Used to simulate the characteristics of low temperature promoting lightning formation, is the curve of the change of the environmental temperature and humidity mutation index over time, Used to accumulate the impact of environmental changes over a period of time, is the starting time, For the current moment; 、 、 、 is the weight coefficient, which is determined based on historical data analysis.

[0008] As a further technical solution, the process of determining different hazard levels is as follows: The overall probability of lightning occurrence will be obtained and the preset lightning warning interval Make a comparison; when When When , it is judged as high risk.

[0009] As a further technical solution, the early warning module further includes: The early warning information verification unit can divide the storage base into several key sub-areas based on the base geographic information system GIS, analyze the probability of lightning occurrence in each key sub-area, and analyze whether the overall lightning occurrence probability risk level is true based on the lightning occurrence probability of each key sub-area. If true, the early warning information will be issued normally; otherwise, the current risk level will be adjusted before issuing the early warning information.

[0010] As a further technical solution, the calculation formula for obtaining the lightning occurrence probability of each key sub-area is: ; in, is the lightning location density in the current key sub-area, is the rate of change of the atmospheric electric field intensity in the current key sub-region, is the risk weight coefficient of the current key sub-area.

[0011] As a further technical solution, the probability of lightning occurrence in each key sub-area After sorting in descending order, the key sub-area ranked first is determined to be the key sub-area with the highest probability of lightning occurrence; The corresponding and the preset lightning warning interval After comparison, determine the lightning occurrence probability risk level of the current key sub-area; If the lightning occurrence probability risk level of the current key sub-area is consistent with the overall lightning occurrence probability risk level, the judgment is true; If the lightning occurrence probability risk level of the current key sub-area is inconsistent with the overall lightning occurrence probability risk level, the absolute difference between the lightning occurrence probability of the current key sub-area and the overall lightning occurrence probability is calculated. If the absolute difference is greater than the risk level difference threshold, the lightning occurrence probability risk level of the current key sub-area is used as the overall lightning occurrence probability risk level; otherwise, the overall lightning occurrence probability risk level remains unchanged.

[0012] As a further technical solution, the calculation formula of the environmental temperature and humidity mutation index is: ,in, is the temperature change rate, is the humidity change rate.

[0013] As a further technical solution, the emergency protection measures of the linkage protection module include: In the event of a low-risk warning, the equipment inspection robot is activated to perform infrared thermal imaging inspections on the lightning protection equipment; In the event of a medium-risk warning, the exposed interfaces of the oil and gas storage tank area will be automatically closed and purged with nitrogen; When a high-risk warning is issued, the one-button power-off program is triggered to cut off the power supply to all non-emergency equipment, and the uninterruptible power supply UPS is activated to ensure the operation of the fire protection and monitoring systems.

[0014] Beneficial effects of the present invention: (1) Through the fusion monitoring of multi-source data such as atmospheric electric field, satellite cloud images, lightning positioning and base environment, combined with the probability model analysis of multiple parameters such as comprehensive electric field change rate, lightning density and cumulonimbus cloud temperature, the limitations of traditional single monitoring are overcome, the accuracy of lightning prediction is significantly improved, and false alarms and missed alarms are effectively reduced, thus achieving multi-dimensional precise monitoring and reducing the false alarm rate; (2) Traditional early warning systems use a unified warning level for the entire base and are unable to identify local high-risk areas. The present invention divides the base into gridded sub-areas, analyzes the probability of lightning occurrence in each area in real time, and automatically calibrates the global warning level with the highest-risk sub-area. When abnormal data appears in a local area, the warning level can be dynamically adjusted, and protective measures for the corresponding area can be accurately triggered to avoid the waste of resources caused by a one-size-fits-all response. The warning range is refined from the entire base to key equipment units, minimizing interference with normal operations while ensuring safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention is an intelligent lightning early warning system for large oil and gas storage bases, comprising: The multi-source monitoring module includes an atmospheric electric field monitoring unit, a satellite cloud image receiving unit, a lightning location monitoring unit, and an in-base environmental monitoring unit. These units collect multi-dimensional data on atmospheric electric field changes, meteorological macro-situations, lightning locations, and the environment of key areas within the base. For example, the atmospheric electric field monitoring unit deploys electric field sensors in a 1km×1km grid within the base to collect the rate of change of atmospheric electric field intensity at each grid point in real time. The satellite cloud image receiving unit acquires meteorological satellite data covering the base and a 20km radius around it to identify the temperature and movement trajectory of cumulonimbus cloud tops. The lightning location monitoring unit connects to a wide-area lightning detection network and outputs the coordinates of cloud-to-ground lightning location points with a precision of 500m in real time. The in-base environmental monitoring unit deploys temperature and humidity sensors to collect the environmental temperature and humidity mutation index. The data processing and analysis module is used to pre-process the data collected by the multi-source monitoring module, including data cleaning, spatiotemporal alignment, and data standardization. It outputs the predicted overall lightning occurrence probability through the preset lightning occurrence probability analysis model. Data cleaning includes: Outlier detection: using the 3σ criterion, i.e., data points exceeding ±3 times the standard deviation of the mean, or an outlier detection algorithm based on the isolation forest; Missing value processing: Fill missing values ​​through linear interpolation, Kalman filtering, or time series prediction models based on historical data; Noise filtering: Apply sliding average filtering to atmospheric electric field data and use wavelet transform to denoise satellite cloud images; Space-time alignment includes: Time synchronization: Unify data with different sampling frequencies into the same time window, such as 1-minute intervals, using nearest neighbor interpolation or spline interpolation; Spatial registration: Match lightning location data with the base geographic information system (GIS) based on latitude and longitude coordinates to establish a unified spatial grid, such as 1 km × 1 km; Data normalization includes: Z-score normalization, threshold normalization; The early warning module is used to issue early warning information to base management personnel according to different risk levels based on the prediction results of the data processing and analysis module; The linkage protection module is used to automatically activate the corresponding lightning protection equipment and emergency protection measures in the base after receiving early warning information.

[0019] In this embodiment, by constructing a multi-source heterogeneous data fusion monitoring system, atmospheric electric fields, satellite cloud images, lightning positioning, and base environment, the limitations of traditional single monitoring methods are broken through, and full-dimensional perception of the lightning incubation process is achieved; combined with data cleaning, spatiotemporal alignment and standardization processing, multi-source data noise and inconsistency are eliminated, and the quality of model input is improved; based on the probability analysis model, a graded early warning mechanism can dynamically quantify the lightning risk level into low, medium, and high. The linkage protection module triggers differentiated emergency measures through preset thresholds, forming a perception-analysis-disposal closed loop, thereby reducing the false alarm rate, while avoiding high-risk missed reports, and ensuring the safety of key facilities such as oil and gas storage tanks and electrical equipment.

[0020] The expression of the overall lightning occurrence probability analysis model is: ; is the probability of lightning occurrence; in, is the rate of change of atmospheric electric field intensity, which is expressed as: , is the unit time, is the change in atmospheric electric field intensity, is the reference electric field change rate, Used to reflect the impact of changing electric field intensity on lightning. is the lightning location density, Used to enhance the impact of high-density areas, is the reference lightning density, is the cumulonimbus cloud top temperature, is the reference temperature, is the temperature sensitivity coefficient, Used to simulate the characteristics of low temperature promoting lightning formation, is the curve of the change of the environmental temperature and humidity mutation index over time, Used to accumulate the impact of environmental changes over a period of time, is the starting time, For the current moment; 、 、 、 is the weight coefficient, which is determined based on historical data analysis.

[0021] In this embodiment, the atmospheric electric field change rate, lightning location density, cumulonimbus cloud top temperature, and ambient temperature and humidity mutation index are incorporated into the unified probability model: , through the weight coefficient 、 、 、 Dynamically characterize the coupling effects of different parameters on lightning formation; Since the essence of lightning formation is the discharge phenomenon after the atmospheric charge accumulates to a critical value, the rate of change of the electric field directly reflects the charge accumulation speed, so the Characterizes the relative change intensity, when Exceeding the reference value When Follow Increases and increases, resulting in the overall probability of lightning Approaching 1, it is consistent with the rule that the stronger the electric field, the higher the probability of lightning; the weight coefficient By fitting historical data, the contribution of electric field changes to lightning can be quantified, such as in plain areas. Take 1.2, and take 1.5 in mountainous areas due to the terrain uplift effect; Since the number of lightning locations per unit area reflects the regional discharge activity, high-density areas usually indicate the center of thunderstorms; therefore, the square term is used. Enhance the impact of high-density areas, such as when When the contribution is 4 times of the original value, it is consistent with the cluster effect of lightning activity; according to the regional climate characteristics, such as high thunderstorm area Take 0.8, and take 0.5 in low thunderstorm areas; As the temperature at the top of the cumulonimbus cloud is lower, the collision between ice crystals and water droplets in the cloud is more intense, and the charge separation is more likely to occur; Simulating the characteristics of low temperature promoting lightning formation, when Below base temperature When the temperature drops, the value is positive and increases, which drives the overall probability of lightning occurrence. Rising; refer to meteorological research, take To balance the coupling effects of temperature and other parameters; Since sudden changes in temperature and humidity, such as a sudden drop in temperature and a sudden rise in humidity caused by strong convection, are precursor conditions for thunderstorms, we use the integral It is used to accumulate the impact of environmental changes over a period of time, reflecting the rule that persistent unstable conditions are more likely to induce lightning; it is adjusted according to the season, such as the frequent occurrence of severe convection in summer. Take 0.3, and 0.1 in winter; In the above formula, the parameters in the denominator are coupled through product, reflecting the mechanism of multiple factors synergistically triggering lightning. For example, when the electric field is strong but there are no cumulonimbus clouds and the temperature is low, the probability of lightning is still low; the overall formula is make There is a nonlinear jump near the parameter threshold, which is consistent with the critical threshold characteristics of lightning occurrence.

[0022] The process for determining different hazard levels is: The overall probability of lightning occurrence will be obtained and the preset lightning warning interval Make a comparison; is the critical value of false alarm rate ≤ 10% in historical data, is the critical value of false alarm rate ≤ 5% in historical data; when When When , it is judged as high risk.

[0023] In this embodiment, by presetting the probability interval Risk levels are categorized to enable precise, tiered management of early warning signals. Low-risk scenarios (P<P1) trigger equipment inspections and status monitoring to avoid wasted resources. Medium-risk scenarios (P1≤P≤P2) initiate sealing of exposed interfaces and nitrogen purging to block the transmission path of flammable media. High-risk scenarios (P>P2) implement one-touch power outages and UPS power guarantees, prioritizing lifeline equipment like fire protection and monitoring systems. This tiered strategy improves the alignment of emergency response with risk levels while reducing unnecessary protective actions, such as avoiding nitrogen consumption in low-risk scenarios, ensuring a balanced balance between safety and cost-effectiveness.

[0024] The early warning module also includes: The early warning information verification unit can divide the storage base into several key sub-areas based on the base geographic information system GIS, analyze the probability of lightning occurrence in each key sub-area, and analyze whether the overall lightning occurrence probability risk level is true based on the lightning occurrence probability of each key sub-area. If true, the early warning information will be issued normally; otherwise, the current risk level will be adjusted before issuing the early warning information.

[0025] In this implementation, the spatial partitioning verification mechanism of the base's geographic information system (GIS) is used to divide the base into gridded sub-areas, such as storage tank areas, oil pipeline areas, and power distribution rooms. A weighted calculation of regional lightning location density and the rate of change of the atmospheric electric field is used to identify local high-risk hotspots. When the risk level of a sub-area differs from the overall warning level, the global warning level is adjusted based on the local high risk, addressing the issue of traditional overall warning models being insensitive to spatial differences. For example, if a storage tank area triggers a high risk due to a strong local electric field, even if the overall probability does not reach the threshold, a targeted warning will still be issued for that area to prevent over-protection of critical facilities.

[0026] The calculation formula for obtaining the lightning occurrence probability of each key sub-area is: ; in, is the lightning location density in the current key sub-area, is the rate of change of the atmospheric electric field intensity in the current key sub-region, is the risk weight coefficient of the current key sub-area.

[0027] In this embodiment, the regional lightning occurrence probability formula is: The lightning location density and the change rate of the atmospheric electric field intensity in the current key sub-region reflect the local lightning activity intensity and charge accumulation characteristics of the sub-region. Compared with the global parameters 、 , more accurately depicting spatial differences; through and Calculate the deviation of sub-region parameters relative to the global; risk weight coefficient of the current key sub-region Used to quantify the safety importance of a sub-area, determined by factors such as the flammability and explosion level of the equipment, the density of personnel, and the consequences of historical accidents. For example, for a storage tank area: =1.2, high risk, lightning strike may cause explosion; oil pipeline area: =1.0, medium risk, need to pay attention to leakage risk; office area: =0.8, low risk, personnel safety is the main concern; Indicates that regional probability is based on global probability, and local parameter deviation and risk weight are corrected to avoid local anomalies being masked by global average; when a sub-region or When significantly higher than the global value, such as >2 or >2 , It may far exceed the global probability, triggering an upgrade of the warning level.

[0028] The probability of lightning occurrence in each key sub-area After sorting in descending order, the key sub-area ranked first is determined to be the key sub-area with the highest probability of lightning occurrence; The corresponding and the preset lightning warning interval After comparison, determine the lightning occurrence probability risk level of the current key sub-area; If the lightning occurrence probability risk level of the current key sub-area is consistent with the overall lightning occurrence probability risk level, the judgment is true; If the lightning occurrence probability risk level of the current key sub-area is inconsistent with the overall lightning occurrence probability risk level, the absolute difference between the lightning occurrence probability of the current key sub-area and the overall lightning occurrence probability is calculated. If the absolute difference is greater than the risk level difference threshold, the lightning occurrence probability risk level of the current key sub-area is used as the overall lightning occurrence probability risk level; otherwise, the overall lightning occurrence probability risk level remains unchanged.

[0029] In this embodiment, the cross-validation mechanism of sub-region risk level and overall warning is used to solve the misjudgment problem caused by the averaging of traditional models; slight fluctuations such as absolute difference are filtered by thresholds. ≤Risk level difference threshold It does not adjust the warning level in real time to prevent frequent changes in the global warning level due to local sensor noise or short-term abnormalities; it sets a safety lock for high-risk levels, that is, it does not allow active downgrade, which is in line with the safety principle of oil and gas bases that it is better to overestimate risks than to underestimate threats; and it retains the calibration capability of local risks. When the local risk is significantly higher than the global risk, that is, When the overall risk is misjudged as medium but the actual local risk is low, the level will be upgraded compulsorily to ensure that the protection measures in key areas are activated in time; then, when the overall risk is misjudged as medium but the actual local risk is low, , allowing local low risks to modify the global level and reduce unnecessary emergency response costs.

[0030] The calculation formula of the environmental temperature and humidity mutation index is: ,in, is the temperature change rate, is the humidity change rate.

[0031] In this embodiment, since the formation of thunderstorms requires the violent convergence of warm and moist air near the ground and dry and cold air at high altitudes, sudden changes in temperature and humidity, such as a sudden drop in temperature and a sudden rise in humidity, are precursors to the development of severe convection. Reflects the vertical movement intensity of air masses. Rapid temperature drop may indicate cold air invasion or downdraft. Humidity abrupt change Reflects the condensation efficiency of water vapor. Cumulonimbus clouds are more likely to form in high humidity environments. , it can identify the unstable environmental state during the thunderstorm incubation period in advance. Compared with single monitoring of temperature or humidity, the comprehensive index can more sensitively capture critical conditions and advance the warning time.

[0032] The emergency protection measures of the linkage protection module include: In the event of a low-risk warning, the equipment inspection robot is activated to perform infrared thermal imaging inspections on the lightning protection equipment; In the event of a medium-risk warning, the exposed interfaces of the oil and gas storage tank area will be automatically closed and purged with nitrogen; When a high-risk warning is issued, the one-button power-off program is triggered to cut off the power supply to all non-emergency equipment, and the uninterruptible power supply UPS is activated to ensure the operation of the fire protection and monitoring systems.

[0033] In this embodiment, the hierarchical linkage protection strategy drives differentiated treatment through risk levels: when the risk is low, the infrared thermal imaging inspection robot is started, and AI image recognition technology is used to detect potential hidden dangers such as abnormal grounding resistance and insulator cracks to achieve non-sensitive prevention; when the risk is medium, the exposed interfaces such as the tank breathing valve and emergency shut-off valve are automatically closed, and the flammable gas is replaced by nitrogen purge to suppress spark-induced explosions; when the risk is high, the one-button power-off program is triggered to cut off non-emergency loads such as lighting and pump groups based on the priority list, and at the same time, the UPS is enabled to ensure the continuous operation of key equipment such as fire pumps, surveillance cameras, and emergency lighting.

[0034] This strategy dynamically matches protective measures to risk levels, reducing downtime losses by approximately 30% compared to the traditional "one-size-fits-all" power outage approach, while ensuring 100% availability of core safety systems.

[0035] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0036] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent lightning warning system for large oil and gas storage bases, characterized by: include: The multi-source monitoring module includes an atmospheric electric field monitoring unit, a satellite cloud image receiving unit, a lightning location monitoring unit, and an in-base environment monitoring unit. It is used to collect multi-dimensional data on atmospheric electric field changes, meteorological macro-situations, lightning locations, and the environment of key areas within the base. The data processing and analysis module is used to pre-process the data collected by the multi-source monitoring module, including data cleaning, spatiotemporal alignment, and data standardization, and output the predicted overall lightning occurrence probability through the preset lightning occurrence probability analysis model; The early warning module is used to issue early warning information to base management personnel according to different risk levels based on the prediction results of the data processing and analysis module; The linkage protection module is used to automatically activate the corresponding lightning protection equipment and emergency protection measures in the base after receiving early warning information.

2. The intelligent lightning early warning system for a large oil and gas storage base according to claim 1 is characterized in that: The expression of the overall lightning occurrence probability analysis model is: ; is the probability of lightning occurrence; in, is the rate of change of atmospheric electric field intensity, which is expressed as: , is the unit time, is the change in atmospheric electric field intensity, is the reference electric field change rate, Used to reflect the impact of changing electric field intensity on lightning. is the lightning location density, Used to enhance the impact of high-density areas, is the reference lightning density, is the cumulonimbus cloud top temperature, is the reference temperature, is the temperature sensitivity coefficient, Used to simulate the characteristics of low temperature promoting lightning formation, is the curve of the change of the environmental temperature and humidity mutation index over time, Used to accumulate the impact of environmental changes over a period of time, is the starting time, For the current moment; 、 、 、 is the weight coefficient, which is determined based on historical data analysis.

3. The intelligent lightning warning system for a large oil and gas storage base according to claim 2 is characterized in that: The process for determining different hazard levels is: The overall probability of lightning occurrence will be obtained and the preset lightning warning interval Make a comparison; when When When , it is judged as high risk.

4. The intelligent lightning early warning system for a large oil and gas storage base according to claim 3 is characterized in that: The early warning module also includes: The early warning information verification unit can divide the storage base into several key sub-areas based on the base geographic information system GIS, analyze the probability of lightning occurrence in each key sub-area, and analyze whether the overall lightning occurrence probability risk level is true based on the lightning occurrence probability of each key sub-area. If true, the early warning information will be issued normally; otherwise, the current risk level will be adjusted before issuing the early warning information.

5. The intelligent lightning warning system for a large oil and gas storage base according to claim 4 is characterized in that: The calculation formula for obtaining the lightning occurrence probability of each key sub-area is: ; in, is the lightning location density in the current key sub-area, is the rate of change of the atmospheric electric field intensity in the current key sub-region, is the risk weight coefficient of the current key sub-area.

6. The intelligent lightning early warning system for a large oil and gas storage base according to claim 5 is characterized in that: The probability of lightning occurrence in each key sub-area After sorting in descending order, the key sub-area ranked first is determined to be the key sub-area with the highest probability of lightning occurrence; The corresponding and the preset lightning warning interval After comparison, determine the lightning occurrence probability risk level of the current key sub-area; If the lightning occurrence probability risk level of the current key sub-area is consistent with the overall lightning occurrence probability risk level, the judgment is true; If the lightning occurrence probability risk level of the current key sub-area is inconsistent with the overall lightning occurrence probability risk level, the absolute difference between the lightning occurrence probability of the current key sub-area and the overall lightning occurrence probability is calculated. If the absolute difference is greater than the risk level difference threshold, the lightning occurrence probability risk level of the current key sub-area is used as the overall lightning occurrence probability risk level; otherwise, the overall lightning occurrence probability risk level remains unchanged.

7. The intelligent lightning warning system for a large oil and gas storage base according to claim 2 is characterized in that: The calculation formula of the environmental temperature and humidity mutation index is: ,in, is the temperature change rate, is the humidity change rate.

8. The intelligent lightning early warning system for a large oil and gas storage base according to claim 1 is characterized in that: The emergency protection measures of the linkage protection module include: In the event of a low-risk warning, the equipment inspection robot is activated to perform infrared thermal imaging inspections on the lightning protection equipment; In the event of a medium-risk warning, the exposed interfaces of the oil and gas storage tank area will be automatically closed and purged with nitrogen; When a high-risk warning is issued, the one-button power-off program is triggered to cut off the power supply to all non-emergency equipment, and the uninterruptible power supply UPS is activated to ensure the operation of the fire protection and monitoring systems.

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