A lightning protection device intelligent state evaluation method and system based on multi-source data fusion

Abstract

This invention discloses a method and system for intelligent status assessment of lightning protection devices based on multi-source data fusion. The method includes the following steps: acquiring inherent risk indicators of the scenario, historical manual inspection data, and lightning strike parameter data; calculating the annual growth rate and lightning strike risk status of each lightning protection performance parameter; determining the risk level of the lightning protection scenario based on the inherent risk indicators of the scenario; configuring online monitoring and manual inspection plans according to the risk level; generating parameter trend status based on the annual growth rate; acquiring real-time online monitoring data and current manual inspection data and introducing a comprehensive correction coefficient for correction to obtain the fused value of the lightning protection performance parameters; dynamically selecting a qualified threshold based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance, and the lightning strike risk status; comparing the fused value with the qualified threshold to generate the status assessment result of the lightning protection device. This invention realizes intelligent classification of lightning protection scenarios, dynamic determination of the status of lightning protection devices, and proactive early warning.

Description

Technical Field

[0001] This invention belongs to the field of lightning protection technology, specifically, it relates to an intelligent status assessment method and system for lightning protection devices based on multi-source data fusion. Background Technology

[0002] Lightning disasters, as one of the most destructive natural disasters, can directly damage core assets such as buildings, power facilities, and communication equipment, and trigger secondary disasters such as fires and explosions. As a fundamental means of mitigating lightning disasters, the accurate and timely assessment of the operational status of lightning protection devices is a crucial link in improving lightning safety capabilities.

[0003] Currently, determining whether a lightning protection device is operating normally mainly relies on two methods: manual on-site inspection and intelligent online monitoring. Traditional manual on-site inspection depends on personnel carrying specialized equipment to the site to directly measure key parameters such as grounding resistance, surge protector leakage current, and temperature. Its advantage lies in direct contact with the lightning protection device, obtaining accurate physical condition information and measured data. However, it suffers from drawbacks such as insufficient real-time performance, low inspection efficiency, personnel safety risks, and inefficient resource allocation. Intelligent online monitoring deploys intelligent monitoring terminals integrating sensors and communication modules at key parts of the lightning protection device to collect parameters such as grounding resistance, surge protector leakage current, and temperature in real time. Its advantage lies in strong real-time monitoring, but it has shortcomings such as data reliability and lack of calibration, lack of physical condition verification, and insufficient trend analysis and risk correlation.

[0004] Existing technologies attempt to combine manual inspection with online monitoring, but these are mostly simple superpositions rather than deep integrations. They fail to address core technical issues such as the isolation between online monitoring data and manual inspection data, the difficulty in identifying progressive hazards in advance, and the mismatch between resource allocation and actual hazards. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings and deficiencies of the existing technology and provide a method for intelligent status assessment of lightning protection devices based on multi-source data fusion. This method solves the technical problems of inaccurate judgment, inability to identify progressive hidden dangers in advance, and imbalance in the allocation of detection resources caused by the reliance on a single data source in the status assessment of existing lightning protection devices.

[0006] The second objective of this invention is to provide an intelligent status assessment system for lightning protection devices based on multi-source data fusion.

[0007] The objective of this invention is achieved through the following technical solution: a method for intelligent status assessment of lightning protection devices based on multi-source data fusion, comprising the following steps:

[0008] S1. Obtain historical manual testing data and historical lightning strike parameter data of the inherent risk index of the lightning protection device to be evaluated, the lightning protection performance parameters, and the lightning strike risk status. Calculate the annual growth rate of each lightning protection performance parameter and generate a lightning strike risk status based on the historical lightning strike parameter data. Determine the risk level of the lightning protection scenario according to preset rules based on the inherent risk index of the scenario, the annual growth rate, and the lightning strike risk status.

[0009] S2. Configure the online monitoring data acquisition frequency and manual inspection execution cycle of the lightning protection device according to the risk level;

[0010] S3. Generate parameter trend status by comparing the annual growth rate with the trend threshold corresponding to the risk level, obtain real-time online monitoring data and current manual detection data of lightning protection performance parameters, and introduce a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to correct and obtain the fused value of lightning protection performance parameters.

[0011] S4. Dynamically select a qualified threshold based on the trend of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status. Compare the fused value with the selected qualified threshold to generate a status assessment result of the lightning protection device.

[0012] Preferably, in step S1, the calculation process of the annual growth rate includes:

[0013] S11. For each lightning protection performance parameter, fit a linear regression equation:

[0014] Y = kt + b

[0015] Where Y represents historical manual detection data, t represents the detection year number, t=1,2,…,n, n represents the number of detection years, k represents the slope, and b represents the intercept;

[0016] S12. Calculate the annual growth rate η of each lightning protection performance parameter. i :

[0017] ,

[0018] Where i represents the type of lightning protection performance parameter, and Y0 is the average value of the corresponding lightning protection performance parameter measured manually over the past n years. The lightning protection performance parameters include grounding resistance, surge protector leakage current, and surge protector temperature.

[0019] Preferably, in step S1, the historical lightning strike parameter data includes lightning strike counts and peak lightning current; the method for generating the lightning strike risk status includes:

[0020] Calculate the annual growth rate of lightning strike frequency based on lightning strike counts, and obtain the peak value of the maximum lightning current in the most recent year:

[0021] If the number of lightning strikes in the most recent year is ≥2, or the peak value of the maximum lightning current is ≥100kA, or the annual growth rate of the frequency of lightning strikes is ≥50%, it is judged as high risk.

[0022] Otherwise, if the lightning strike count in the most recent year is ≥2, or the maximum lightning current peak value is ≥50kA, or the annual growth rate of lightning strike frequency is ≥30%, it is judged as medium risk.

[0023] Otherwise, if the number of lightning strikes in the most recent year is ≤1 and the peak value of the maximum lightning current is <50kA, or the annual growth rate of lightning strike frequency is ≥20%, it is judged as low risk.

[0024] Preferably, the preset rules include:

[0025] For each risk level, a risk level is determined when at least one of the inherent risk indicators of the scenario corresponding to that risk level is satisfied and at least one of the trend risk indicators corresponding to that risk level is satisfied, or when at least two of the inherent risk indicators of the scenario corresponding to that risk level are satisfied; and when the conditions of multiple risk levels are met at the same time, the highest risk level is taken.

[0026] The trend risk indicators include the annual growth rate reaching the deterioration threshold corresponding to the risk level and the lightning strike risk status reaching the risk level corresponding to the risk level.

[0027] Preferably, in step S3, the method for generating the parameter trend status includes: comparing the annual growth rate of each lightning protection performance parameter with the trend threshold corresponding to each risk level.

[0028] If the annual growth rate of at least two lightning protection performance parameters reaches the corresponding multi-parameter deterioration threshold, it is determined to be multi-parameter deterioration.

[0029] If the annual growth rate of only one lightning protection performance parameter reaches the corresponding single parameter deterioration threshold, it is judged as single parameter deterioration.

[0030] If the annual growth rate of all lightning protection performance parameters is negative and the absolute value is not less than the corresponding improvement threshold, then it is judged to be improving.

[0031] If the annual growth rate of all lightning protection performance parameters does not reach the corresponding deterioration threshold and does not reach the corresponding improvement threshold, it is judged as stable.

[0032] Preferably, the formula for calculating the fused value of the lightning protection performance parameters is as follows:

[0033] V 融合 =α×(ω1V 在线 +ω2V 人工 ),

[0034] Among them, V在线 V is the online correction value obtained by performing a moving average process on the real-time online monitoring data. 人工 The mean value is the result of taking the average of the current manual detection data. ω1 and ω2 are the weights of the online monitoring data and the manual detection data, respectively, and ω1+ω2=1. α is the comprehensive correction coefficient.

[0035] Preferably, the values ​​of the online monitoring data weight ω1 and the manual detection data weight ω2 are related to the risk level as follows:

[0036] Under high-risk conditions, ω1 < ω2; under medium-risk conditions, ω1 = ω2; under low-risk conditions, ω1 < ω2, and the value of ω1 corresponding to the low-risk conditions is less than the value of ω1 corresponding to the high-risk conditions.

[0037] Preferably, the rule for determining the value of the comprehensive correction coefficient is as follows:

[0038] When the parameter trend is multiple parameters deteriorating, or the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold, or the lightning strike risk status is high risk, the comprehensive correction coefficient is the first preset coefficient, and the first preset coefficient is greater than 1.

[0039] When the parameter trend is a single parameter deterioration, or the fluctuation amplitude of the real-time online monitoring data of the grounding resistance exceeds the second preset fluctuation threshold, or the lightning strike risk status is medium risk, the comprehensive correction coefficient is the second preset coefficient, and the second preset coefficient is greater than 1 and less than the first preset coefficient.

[0040] When the parameter trend is stable, the fluctuation range of the real-time online monitoring data of grounding resistance is between the third preset fluctuation threshold and the second preset fluctuation threshold, and the lightning strike risk status is low, the comprehensive correction coefficient is the third preset coefficient, and the third preset coefficient is equal to 1.

[0041] When the parameter trend is positive, the fluctuation range of the real-time online monitoring data of the grounding resistance is less than the third preset fluctuation threshold, and the lightning strike risk status is low, the comprehensive correction coefficient is taken as the fourth preset coefficient, and the fourth preset coefficient is less than 1.

[0042] Preferably, the dynamic selection of the qualified threshold includes: selecting a strict threshold or a standard threshold based on the parameter trend, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status; wherein,

[0043] When the parameter trend is a single parameter deterioration or multiple parameter deterioration, or the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold, or the lightning strike risk status is high risk, select the strict threshold.

[0044] When the parameter trend is stable or improving, and the fluctuation range of the real-time online monitoring data of grounding resistance is between the third preset fluctuation threshold and the second preset fluctuation threshold, and the lightning strike risk status is low, and the deviation between the online monitoring data and the manual detection data is less than the preset deviation threshold, the standard threshold is selected.

[0045] The strict threshold is a preset percentage of the standard threshold.

[0046] A multi-source data fusion intelligent status assessment system for lightning protection devices includes:

[0047] The risk classification module is used to acquire historical manual detection data and historical lightning strike parameter data of the inherent risk indicators of the scene and lightning protection performance parameters, calculate the annual growth rate of each lightning protection performance parameter, generate the lightning strike risk status based on the historical lightning strike parameter data, and determine the risk level of the lightning protection scene according to preset rules.

[0048] The resource configuration module is used to configure the frequency of online monitoring data collection and the manual inspection execution cycle according to the risk level.

[0049] The data fusion module is used to generate parameter trend status by comparing the annual growth rate with the trend threshold corresponding to the risk level, obtain real-time online monitoring data and current manual detection data, and introduce a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to obtain the fused value of the lightning protection performance parameters.

[0050] The status assessment module is used to dynamically select a qualified threshold based on the trend status of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status. The module compares the fused value with the selected qualified threshold to generate the status assessment result of the lightning protection device.

[0051] The present invention has the following advantages and effects compared with the prior art:

[0052] (1) This invention integrates four dimensions: inherent risks of the scenario, parameter trend status (including historical parameter trend status of grounding resistance and historical parameter trend status of surge protector SPD), and lightning strike risk status, to construct a multi-level risk classification index system, and automatically determines the risk level based on preset rules; then, it configures the online monitoring frequency and manual detection cycle according to the risk level; it realizes the forward-looking prediction of progressive hidden dangers and overcomes the technical problems of isolated online and manual detection data, one-sided risk classification, and unbalanced detection resource allocation in the prior art.

[0053] (2) The present invention introduces a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to correct the weighted fusion result. That is, a four-dimensional fusion model of "online correction value + manual mean + historical trend + lightning strike correlation" is established, so that the fusion value simultaneously reflects the historical aging trend of the lightning protection device, the current real-time anomaly and the lightning strike risk, and can identify progressive hidden dangers in advance. This overcomes the technical problems of isolated dual detection data, insufficient foresight and inability to identify aging trends in the existing technology.

[0054] (3) Based on the parameter trend status, the fluctuation range of the real-time online monitoring data of grounding resistance and the lightning risk status, the present invention dynamically selects a strict threshold or a standard threshold, and combines the three-level evaluation results of qualified, pending review and unqualified, which overcomes the defects of the traditional fixed threshold judgment method that cannot adapt to parameter changes and lightning risk, and improves the sensitivity and accuracy of the judgment. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the intelligent status assessment method for lightning protection devices based on multi-source data fusion according to the present invention. Detailed Implementation

[0056] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0057] Example 1

[0058] like Figure 1 The diagram illustrates a flowchart of an intelligent status assessment method for lightning protection devices based on multi-source data fusion, according to the present invention. The method follows four core steps sequentially: risk classification, resource allocation, data fusion, and status assessment. Furthermore, as a preferred embodiment, the method also includes an anomaly verification step, forming a complete closed-loop assessment system.

[0059] Step 1: Risk Classification

[0060] This step comprehensively assesses the risk level of a lightning protection scenario from three dimensions: inherent scenario risk, trend risk of lightning protection performance parameters, and lightning strike risk. The lightning protection scenario includes lightning protection infrastructure and lightning protection devices. The specific risk level classification is determined based on inherent scenario risk indicators and trend risk indicators. Among these, lightning protection performance parameters include grounding resistance, surge protection device (SPD) leakage current, and SPD temperature.

[0061] 1.1 Data Acquisition

[0062] First, obtain the following four types of data:

[0063] Inherent risk indicators of the scenario: including annual lightning density (unit: times / km) 2The data includes the location of the lightning protection device, the type of the scene (such as explosion-hazardous locations, critical infrastructure, low-rise civil buildings, locations without precision electronic equipment, etc.), and the expected number of lightning strikes (according to GB50057 Classification of Lightning Protection for Buildings). These data reflect the inherent hazard level of the location where the lightning protection device is located and can be obtained from geographic information systems, design drawings, or national standards.

[0064] Historical manual inspection data: Annual average values ​​of grounding resistance, SPD leakage current, and SPD peak temperature over the past 3-5 years (n≥3 years of inspection). This data records the actual performance changes of the lightning protection device over the past few years and is the core basis for assessing aging trends.

[0065] Historical online monitoring data: Monthly averages of grounding resistance, SPD leakage current, and SPD temperature over the past 1-3 years are collected. This data is used to calculate the deviation between the monthly averages of online monitoring and the averages of manual inspection, serving as an auxiliary basis for determining the risk level.

[0066] Historical lightning strike parameter data: annual lightning strike count (N) and annual maximum lightning current peak value (I) for the past 1-3 years. 雷max These data reflect the intensity and frequency of external lightning threats.

[0067] 1.2 Calculation of Annual Growth Rate

[0068] This invention transforms the qualitative risk of aging trends into a quantitative annual growth rate of lightning protection performance parameters. The specific calculation steps are as follows:

[0069] (1) For each lightning protection performance parameter (grounding resistance, SPD leakage current, SPD temperature), a linear regression equation is fitted based on its historical manual testing data over the past n years:

[0070] Y = kt + b

[0071] Where Y represents historical manual detection data, t represents the detection year number, t=1,2,…,n, n represents the number of detection years, k represents the slope, and b represents the intercept.

[0072] (2) Calculate the annual growth rate η of each lightning protection performance parameter according to the following formula. i :

[0073] ,

[0074] Where Y0 is the average value of the corresponding lightning protection performance parameter measured manually over the past n years, the subscript i indicates the type of lightning protection performance parameter, i=R,I,T, and the annual growth rate η of the grounding resistance is obtained respectively. R Annual growth rate η of SPD leakage current I and the annual growth rate η of SPD temperature T .

[0075] 1.3 Deterioration status of parameters at each risk level

[0076] Based on the calculated annual growth rate, determine whether the parameter deterioration status is valid for the high-risk, medium-risk, and low-risk levels respectively:

[0077] High-risk parameter deterioration state: If the annual growth rate of at least two lightning protection performance parameters reaches the multi-parameter deterioration threshold (η) of the high-risk level. R ≥6%, η I ≥8%, η T (at least two of the parameters ≥5%), or only one parameter reaching the single parameter deterioration threshold (η) of the high-risk level. R ≥8% or η I ≥10% or η T If the percentage is ≥6%, then the high-risk parameter deterioration state is established.

[0078] Medium-risk parameter deterioration state: If the annual growth rate of at least two parameters reaches the multi-parameter deterioration threshold (η) of the medium-risk level. R =4%~6%, η I =6%~8%, η T =At least two of the parameters in the range of 3% to 4.99%), or only one parameter reaches the single parameter deterioration threshold (η) of the medium-risk level. R =6%~8% or η I =8%~10% or η T If the percentage is 5%~6%, then the medium-risk parameter deterioration state is established.

[0079] Low-risk parameter deterioration state: If the annual growth rate of at least two parameters reaches the multi-parameter deterioration threshold (η) of the low-risk level. R =3%~4%, η I =4%~6%, η T =At least two of the parameters in the range of 2% to 3%, or only one parameter reaches the single parameter deterioration threshold (η) of the low-risk level. R =4%~6% or η I =6%~8% or η T If the percentage is 4%~5%, then the low-risk parameter deterioration state is valid.

[0080] It should be noted that the deterioration thresholds for each of the above risk levels refer to the limits for determining whether the annual growth rate has reached the degree of deterioration, as detailed in Tables 1-1 to 1-3.

[0081] 1.4 Lightning Strike Risk Status Generation

[0082] Calculate the annual growth rate η of lightning strike frequency based on historical lightning strike parameter data. L :

[0083] Where t is the year of detection, and N t Count the lightning strikes in year t; and obtain the peak value of the maximum lightning current I in the most recent year. 雷max .

[0084] The lightning strike risk level (high risk, medium risk, low risk) is determined according to the following rules:

[0085] If the lightning strike count N ≥ 2 or I in the most recent year 雷max ≥100kA or η L If the risk level is ≥50%, it is considered high risk.

[0086] If the lightning strike count N ≥ 2 or I in the most recent year 雷max ≥50kA or η L If the risk level is ≥30%, it is classified as medium risk.

[0087] If the number of lightning strikes in the most recent year N≤1 and I 雷max <50kA, or η L If the risk level is ≥20%, it is considered low risk.

[0088] When the conditions for multiple risk states are met simultaneously, the highest risk level is selected.

[0089] 1.5 Comprehensive Risk Level Assessment

[0090] Based on the inherent risk indicators of the scenario, the deterioration status of the parameters of each risk level determined above, and the lightning strike risk status, the final risk level (high risk, medium risk, low risk) of the lightning protection scenario is determined according to the following preset rules:

[0091] For any given risk level, a risk level is determined when at least one of the inherent risk indicators of the scenario corresponding to that risk level is met, and at least one of the trend risk indicators corresponding to that risk level is met, or at least two of the inherent risk indicators of the scenario corresponding to that risk level are met. When the conditions for multiple risk levels are met simultaneously, the highest risk level is selected (the principle of choosing the highest level).

[0092] For any risk level, the trend risk indicator must satisfy at least one of the following conditions:

[0093] (1) The annual growth rate corresponding to the risk level reaches its deterioration threshold (i.e., the parameter deterioration state of the risk level is established, including multi-parameter deterioration or single-parameter deterioration, and the specific values ​​are shown in Table 1-1, Table 1-2 and Table 1-3 for deterioration thresholds).

[0094] (2) The lightning strike risk status reaches the risk level corresponding to the risk level (for example, for a high risk level, the lightning strike risk status is required to be high risk).

[0095] Specifically, the specific conditions for the inherent risk indicators of each risk level are shown in Tables 1-1, 1-2, and 1-3.

[0096] Table 1-1 Indicators and Deterioration Thresholds for High-Risk Lightning Protection Scenarios

[0097]

[0098] Table 1-2 Indicators and Deterioration Thresholds for Lightning Protection Scenarios with a Risk Level of Medium Risk

[0099]

[0100] Table 1-3 Indicators and Deterioration Thresholds for Lightning Protection Scenarios with Low Risk Level

[0101]

[0102] As a preferred implementation, the following three types of auxiliary factors may be further considered when determining the risk level. The following qualified thresholds refer to the standard thresholds corresponding to the risk level.

[0103] (a) Comparison of manually measured values ​​with acceptable thresholds (applicable to high, medium, and low risks)

[0104] When a manually detected value reaches more than 90% of the high-risk level standard threshold (i.e., the detected value ≥ standard threshold × 90%), it is considered as a single instance approaching the qualified threshold. This condition can be used as one of the conditions for determining the trend risk indicator to determine whether the risk level meets the high-risk level.

[0105] When two consecutive manual test values ​​both reach more than 90% of the threshold of the risk level standard, it is considered that the two most recent manual test values ​​are continuously approaching the qualified threshold. This condition can be used as one of the conditions for judging the trend risk indicator to determine whether the risk level meets the medium risk level.

[0106] When the manually measured values ​​of each lightning protection performance parameter are all below 50% of the standard threshold for the risk level (i.e., measured value < standard threshold × 50%) for 12 consecutive months, and the fluctuation range of the online monitoring value of grounding resistance is consistently less than 5%, it is considered to be in a long-term stable state. This condition can be used as one of the conditions for judging trend risk indicators to determine whether the risk level meets the low-risk level.

[0107] (ii) Comparison of fluctuation range of online grounding resistance value with qualified threshold (applicable to high and medium risk)

[0108] When the online monitoring value of grounding resistance reaches more than 90% of the high-risk standard threshold for 7 consecutive days (for example, if the standard threshold of grounding resistance is 4Ω in a high-risk scenario, then it is ≥3.6Ω for 7 consecutive days), this condition can be used as one of the conditions for determining the trend risk indicator to determine whether the risk level meets the high-risk level.

[0109] When the online monitoring value of grounding resistance reaches more than 90% of the medium-risk standard threshold for 15 consecutive days (for example, if the standard threshold of grounding resistance is 10Ω in a medium-risk scenario, then it is ≥9Ω for 15 consecutive days), this condition can be used as one of the conditions for determining the trend risk indicator to determine whether the risk level meets the medium-risk level.

[0110] (iii) Historical recurrence factors (applicable only to high-risk individuals)

[0111] If a lightning protection device has previously exhibited issues such as excessive grounding resistance, SPD parameters, or lightning damage that, after rectification, shows signs of recurrence or relapse, this condition can be used as one of the criteria for determining trend risk indicators to ascertain whether the risk level meets the high-risk level.

[0112] The above-mentioned auxiliary conditions can be used to enhance the confidence level of the corresponding risk level when determining the risk level according to the rules in Tables 1-1, 1-2 and 1-3, or to assist decision-making in boundary situations.

[0113] This invention uses linear regression to fit historical manually monitored data, transforming qualitative aging trends into quantitative annual growth rates, thus overcoming the shortcomings of traditional methods that rely solely on experience. Simultaneously, it incorporates the growth rate of lightning strike frequency and peak lightning current into the risk level classification system, enabling the risk level to dynamically reflect changes in external threats.

[0114] Step 2: Differentiated Detection Resource Allocation

[0115] Based on the risk level determined in Step 1, configure the frequency of online monitoring data collection and the manual inspection execution cycle to achieve on-demand resource allocation. Specific configurations are as follows:

[0116] (1) High-risk scenarios:

[0117] Online monitoring: Grounding resistance is collected every 60 minutes and uploaded in real time; SPD leakage current and temperature are collected in real time and alarms are triggered immediately if abnormalities occur; lightning strike parameter data are statistically analyzed in real time, and alarms are triggered immediately if a single lightning current ≥100kA; manual sampling inspection of SPD parameters is carried out once every six months.

[0118] Manual inspection: 1 inspection every 6 months for full coverage, including grounding resistance retest, SPD leakage current and SPD temperature full inspection, physical condition verification, grounding body corrosion investigation, and lightning strike damage inspection.

[0119] (2) Medium-risk scenarios:

[0120] Online monitoring: Grounding resistance is collected and summarized in real time every 7 days; SPD leakage current and SPD temperature are collected every 60 minutes and alarms are triggered when the threshold is exceeded; Lightning parameters are summarized and uploaded every 12 hours, and alarms are triggered when a single lightning current is ≥50kA; Manual spot checks of SPD parameters are conducted once a year.

[0121] Manual inspection: 1 key parameter inspection every 12 months, including grounding resistance retest, SPD leakage current and SPD temperature full inspection, down conductor connection inspection, and inspection of key parts after lightning strike.

[0122] (3) Low-risk scenarios:

[0123] Online monitoring: Optional simple equipment is available. Grounding resistance and SPD parameters are collected once a month; lightning strike parameters are summarized and uploaded daily, and an alarm is triggered when a single lightning current is ≥50kA; a manual full inspection of SPD parameters is carried out once every 12 months.

[0124] Manual inspection: Routine inspection once every 12 months, including grounding resistance retest, SPD leakage current and SPD temperature full inspection, appearance inspection, grounding body status investigation, and lightning strike trace inspection; when parameters are stable, the frequency can be extended to once every 18-24 months.

[0125] (4) Enhanced configuration for special scenarios: When the parameter trend is in a deteriorating state (multiple parameters deteriorating or a single parameter deteriorating), the lightning strike frequency growth rate exceeds the corresponding risk level threshold, or the online monitoring value of grounding resistance exceeds the standard continuously, manual on-site detection will be triggered immediately, and the online monitoring frequency will be increased.

[0126] Specifically, the aforementioned dynamic configuration mechanism effectively avoids the under-detection of potential hazards in high-risk scenarios due to insufficient detection frequency, and also avoids the waste of resources caused by excessive detection in low-risk scenarios.

[0127] Step 3: Data Fusion

[0128] 3.1 Generating parameter trend status

[0129] In this invention, the trend threshold refers to the boundary value used to judge the changing trend of the annual growth rate of lightning protection performance parameters, including the deterioration threshold (used to determine whether the parameter is deteriorating, divided into multi-parameter deterioration threshold and single-parameter deterioration threshold) and the improvement threshold (used to determine whether the parameter is improving).

[0130] After determining the risk level of the lightning protection scenario, the annual growth rate of each lightning protection performance parameter is compared with the corresponding trend threshold based on the trend threshold corresponding to that risk level, generating parameter trend status (including multi-parameter deterioration, single-parameter deterioration, stability, and improvement):

[0131] Multi-parameter deterioration state: If the annual growth rate of at least two lightning protection performance parameters reaches the multi-parameter deterioration threshold corresponding to this risk level.

[0132] Single parameter deterioration state: If the annual growth rate of only one lightning protection performance parameter reaches the single parameter deterioration threshold corresponding to this risk level.

[0133] Positive trend: If the annual growth rate of all parameters is negative and the absolute value is not less than the corresponding positive trend threshold (e.g., the annual growth rate η of each parameter). i ≤−3%.

[0134] Stable state: If the annual growth rate of all parameters does not reach the corresponding deterioration threshold and does not reach the corresponding improvement threshold.

[0135] 3.2 Data Preprocessing

[0136] Online monitoring data correction: Instantaneous extreme values ​​caused by sudden changes in soil moisture and electromagnetic interference are removed. The grounding resistance and SPD leakage current are calculated using a moving average of the most recent 10 valid data acquisitions to obtain the online correction value V. 在线 The SPD temperature is taken as the average of 5 minutes during the abnormal period as the online correction value.

[0137] Standardization of manual testing data: The grounding resistance, SPD leakage current, and SPD temperature at the same testing point are measured three times each, and the arithmetic mean is taken as the manual mean V. 人工 .

[0138] As a preferred embodiment, this method further includes data deviation verification: calculating the deviation between the monthly average of online monitoring and the annual average of manual detection: Deviation = |Online Monthly Average - Manual Annual Average| / Manual Annual Average × 100%. If the deviation is ≥ 15%, the detection conditions (environment, equipment accuracy, measurement location) are traced back and the trend data is corrected; if the deviation is ≥ 15% for two consecutive years, the online monitoring equipment is replaced or the lightning protection device is rectified. This verification step ensures the consistency of historical dual detection data.

[0139] 3.3 Assigning Data Weights

[0140] The weights ω1 of online monitoring data and ω2 of manual detection data are assigned according to risk level. 1+ ω2=1), in this embodiment,

[0141] High-risk scenario: ω1=0.4, ω2=0.6;

[0142] Medium-risk scenario: ω1=0.5, ω2=0.5;

[0143] Low-risk scenario: ω1=0.3, ω2=0.7.

[0144] This allocation principle reflects the greater reliance on the accuracy of manual detection in high-risk situations, while appropriately increasing the weight of online monitoring in low-risk situations to reduce labor costs.

[0145] 3.4 Determine the comprehensive correction coefficient

[0146] In this embodiment, the first preset fluctuation threshold corresponds to a fluctuation amplitude of 20%, which is used to determine continuous anomalies; the second preset fluctuation threshold corresponds to a fluctuation amplitude of 8%; and the third preset fluctuation threshold corresponds to a fluctuation amplitude of 3%. "Continuous" means that the fluctuation amplitude exceeds the threshold within multiple monitoring periods (e.g., multiple consecutive sampling points or multiple consecutive days). The specific number of periods can be set according to the actual monitoring frequency and engineering experience (e.g., for real-time online monitoring, 3 consecutive sampling points or 7 consecutive days, etc.).

[0147] Based on the parameter trend status (multiple parameters deteriorating, single parameter deteriorating, stable, improving), the fluctuation range of real-time online monitoring data of grounding resistance, and the lightning risk status (high risk, medium risk, low risk), the comprehensive correction coefficient is determined according to the following rules. Specifically, the specific values ​​used in this embodiment are as follows, and can be adjusted according to engineering experience in actual applications:

[0148] When the parameter trend is that multiple parameters are deteriorating, or the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold (i.e., the fluctuation amplitude is greater than 20%), or the lightning strike risk status is high risk, the comprehensive correction coefficient is taken as 1.1.

[0149] When the parameter trend is a single parameter deterioration, or the fluctuation of the real-time online monitoring data of grounding resistance is greater than the second preset fluctuation threshold (i.e., the fluctuation is greater than 8%, usually in the range of 8% to 15%), or the lightning strike risk status is medium risk, the comprehensive correction coefficient is taken as 1.05.

[0150] When the parameter trend is stable, and the fluctuation range of the real-time online monitoring data of the grounding resistance is between the third preset fluctuation threshold and the second preset fluctuation threshold (i.e., the fluctuation range is 3% to 8%), and the lightning strike risk status is low, the comprehensive correction coefficient is taken as 1.0.

[0151] When the parameter trend is positive, the fluctuation range of the real-time online monitoring data of grounding resistance is less than the third preset fluctuation threshold (i.e., less than 3%), and the lightning strike risk status is low, the comprehensive correction coefficient is 0.95.

[0152] 3.5 Calculation of Fusion Value

[0153] Calculate the combined value of each lightning protection performance parameter separately:

[0154] V 融合 =α×(ω1V 在线 +ω2V 人工),

[0155] Among them, V 在线 V is the online correction value obtained by performing a moving average process on the real-time online monitoring data. 人工 The mean value is the result of averaging the current manually detected data, and α is the comprehensive correction coefficient.

[0156] 3.6 Lightning Strike Related Auxiliary

[0157] In a preferred embodiment, the method further includes a lightning strike correlation auxiliary step. The measured values ​​of the lightning protection performance parameters after the lightning strike are recorded, and their fluctuation range relative to the average parameter values ​​over a period prior to the lightning strike (e.g., the 7 calendar days before the lightning strike) is calculated.

[0158] ,

[0159] When the fluctuation range is ≥20%, it is considered that the parameter fluctuation after the lightning strike is significant. The lightning strike-related data will be included in the auxiliary basis for the fusion judgment to adjust the comprehensive correction coefficient or enhance the verification triggering conditions. For example, if the average grounding resistance was 4.2Ω 7 days before the lightning strike and the grounding resistance was measured to be 5.1Ω after the lightning strike, the fluctuation range is approximately 21.4%, which meets the condition.

[0160] The aforementioned auxiliary conditions can effectively identify the instantaneous impact of lightning strikes on the performance of lightning protection devices, enhancing the sensitivity and reliability of the assessment.

[0161] Step 4: Status Assessment

[0162] 4.1 Dynamically select the qualified threshold

[0163] Select a strict threshold or a standard threshold based on the parameter trend, the fluctuation range of real-time online monitoring data of grounding resistance, and the lightning strike risk status:

[0164] Strict threshold: When the parameter trend shows deterioration of multiple parameters or a single parameter, or when the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold (i.e., the fluctuation amplitude is greater than 20%), or when the lightning strike risk status is high risk, a strict threshold is selected. In this embodiment, the strict threshold is 90% of the standard threshold.

[0165] Standard threshold: When the parameter trend is stable or improving, and the fluctuation range of the real-time online monitoring data of grounding resistance is between the second preset fluctuation threshold and the third preset fluctuation threshold (i.e., the fluctuation range is 3% to 8%), the lightning strike risk status is low, and the deviation between the online monitoring data and the manual detection data is less than the preset deviation threshold (e.g., 10%), the standard threshold is selected.

[0166] 4.2 Threshold Standard Example

[0167] (1) Grounding resistance:

[0168] For high-risk situations: the standard threshold is 4Ω, and the strict threshold is 3.6Ω;

[0169] For medium risk: the standard threshold is 10Ω, and the strict threshold is 9Ω;

[0170] For low-risk situations: the standard threshold is 30Ω, and the strict threshold is 27Ω.

[0171] When making a judgment, a fusion value ≤ standard threshold (or strict threshold) is considered qualified.

[0172] (2) SPD leakage current: the standard threshold is 20μA, the strict threshold is 18μA; the unqualified threshold is 27μA (i.e., the fusion value ≥27μA is unqualified); the range to be reviewed is 18μA~27μA.

[0173] (3) SPD temperature: The standard threshold is 60℃, the strict threshold is 54℃; the unqualified threshold is 70℃; the range to be reviewed is 54℃~70℃. In addition, in scenarios where the parameter trend is deteriorating or the lightning strike risk is high, when the SPD temperature fusion value is ≥60℃, a secondary review is triggered.

[0174] 4.3 Generation of Status Assessment Results

[0175] If the fusion value exceeds the unqualified threshold of the corresponding lightning protection performance parameter (e.g., SPD leakage current ≥ 27μA or SPD temperature ≥ 70℃), it is judged as unqualified.

[0176] If the fusion value is within the range to be verified (e.g., SPD leakage current is 18-27 μA or SPD temperature is 54-70℃), it is determined to be to be verified.

[0177] If the fusion value is lower than the qualified threshold (e.g., grounding resistance fusion value ≤ standard threshold or strict threshold, SPD leakage current <18μA or <20μA, SPD temperature <54℃ or <60℃), it is judged as qualified.

[0178] When the status assessment result is pending review or unqualified, the subsequent abnormal review process can be triggered.

[0179] Step 5: Anomaly Review

[0180] This step establishes a closed-loop mechanism of "first-level review - second-level review - trend update" to ensure that any abnormalities or potential risks are thoroughly investigated and addressed.

[0181] 5.1 Level 1 Review (to be conducted by the unit's safety personnel)

[0182] Triggering conditions: SPD degradation indicator is normal and temperature is within the range to be verified, online grounding resistance value approaches the threshold for 3 consecutive days, deviation between online monitoring data and manual detection data is ≥10%, single lightning current reaches the corresponding risk level threshold, etc.

[0183] Execution process: Arrive at the site within 24 hours, check the appearance of the SPD, the tightness of the wiring, lightning strike marks and the status of the grounding device, and conduct one additional manual inspection to calibrate the online monitoring data.

[0184] Anomaly detection:

[0185] If environmental data is abnormal (such as heavy rain or strong electromagnetic interference), there is no record of lightning strikes, and the parameter trend is stable, it is determined to be a transient interference, the alarm is eliminated, and continuous monitoring is performed.

[0186] If the environment is normal, there is no record of lightning strikes, and the parameter trend is deteriorating, it is determined to be a progressive hidden danger and upgraded to a level two review.

[0187] If a lightning strike record exists, it is determined to be lightning impact damage, and a secondary review is triggered simultaneously.

[0188] If any visible hidden dangers are found (such as loose down conductors or damaged SPD), the process will be upgraded directly to Level 2 review.

[0189] 5.2 Secondary verification (performed by inspectors from a professional testing company)

[0190] Triggering conditions: fusion value exceeds the unqualified threshold, manual detection indicates deterioration, first-level review cannot rule out abnormalities, parameter fluctuation ≥20% after lightning strike, etc.

[0191] Execution timeframe: 12 hours for high-risk scenarios, 24 hours for medium-risk scenarios, and 72 hours for low-risk scenarios.

[0192] Testing content: Retesting of SPD deterioration indicators, measurement of power frequency reference voltage, detection of grounding electrode corrosion, detection of lightning strike damage depth, and calibration of online monitoring equipment.

[0193] Final judgment: Issue a professional testing report, clearly stating the conclusion that the product is qualified, requires rectification within a specified period, or must be discontinued immediately.

[0194] 5.3 Trend Update

[0195] All manually measured values, online calibration data, lightning strike statistics (lightning strike count, peak lightning current) and hazard rectification records generated during this review were added to the historical database. The linear regression equation was refitted, and the annual growth rate of each parameter and the lightning strike risk status were updated.

[0196] The following uses the lightning protection device of a chemical warehouse in a petrochemical enterprise as an example, combined with specific data, to demonstrate in detail the complete implementation process of the present invention.

[0197] Step 1: Risk Classification of Lightning Protection Scenarios

[0198] S1-1: Data Collection

[0199] Prerequisite information for the scene: Annual lightning density 7.2 times / km 2 (≥6.77 times / km²), classifying it as an explosion hazard location, with an estimated lightning strike frequency greater than 0.05 times / year. The inherent risk indicators of the scenario meet two high-risk conditions.

[0200] Manual testing data: The annual average values ​​of grounding resistance, SPD leakage current, and SPD temperature peak values ​​were collected from 2020 to 2024, as shown in Table 1.

[0201] Online monitoring data: Monthly average values ​​of grounding resistance, SPD leakage current, and SPD temperature were collected from 2022 to 2024, as shown in Table 2.

[0202] Lightning strike parameter data: Annual lightning strike counts and annual maximum lightning current peak values ​​were collected from 2022 to 2024, as shown in Table 3.

[0203] Table 1. Manual testing data from 2020 to 2024

[0204]

[0205] Table 2. Monthly Average Data from Online Monitoring, 2022-2024

[0206]

[0207] Table 3 Lightning Parameter Data, 2022-2024

[0208]

[0209] S1-2: Calculation of Annual Growth Rate

[0210] (1) Linear regression model fitting:

[0211] By fitting the linear regression equation Y=kt+b to the manual inspection data from the past 5 years, the slope k of the grounding resistance is obtained. R =0.275, SPD leakage current slope k I =2.75, SPD temperature peak slope k T =3;

[0212] (2) Calculation of annual growth rate:

[0213] Through formula calculate,

[0214] Among them, the average grounding resistance Y 0R=3.72Ω, annual growth rate of grounding resistance η R ≈7.4%;

[0215] SPD leakage current average Y 0I =16.8μA, annual growth rate of SPD leakage current η I ≈16.4%;

[0216] SPD temperature peak average Y 0T =53.4℃, annual growth rate η of SPD temperature T ≈5.6%;

[0217] S1-3: Determination of parameter deterioration status for each risk level

[0218] Based on the annual growth rate η R ≈7.4%, η I ≈16.4%, η T ≈5.6%

[0219] High-risk parameter deterioration state: η R ≥6%, η I ≥8%, η T If all three conditions are met (≥5%), the high-risk parameter deterioration state is established.

[0220] Medium-risk parameter deterioration state: η R =7.4%, which is within the range of 6% to 8%, meeting the condition for deterioration of a single parameter in the medium-risk range. Therefore, the state of deterioration of the medium-risk parameter is established.

[0221] Low-risk parameter deterioration state: Not satisfied.

[0222] S1-4: Lightning Strike Risk Status Generation

[0223] The lightning strike count for the most recent year (2024) was 5 ≥ 2, the maximum peak lightning current was 120kA ≥ 100kA, and the annual growth rate of lightning strike frequency η was [missing information]. L ≈66.7% ≥50%. Meets the high-risk criteria and is therefore classified as a high-risk state.

[0224] S1-5: Comprehensive Risk Level Assessment

[0225] The inherent risk indicators satisfy two conditions (explosion hazard location, high lightning density), and the parameter deterioration state is established, indicating a high-risk risk level. Therefore, the high-risk condition is met, and according to the principle of choosing the higher risk level, the final risk level is high-risk.

[0226] Step 2: Resource Allocation (High-Risk Allocation)

[0227] (1) Online monitoring configuration: Grounding resistance is uploaded in real time every 60 minutes; SPD leakage current and temperature are collected in real time and alarmed immediately; lightning strike parameters are statistically analyzed in real time, and alarms are triggered immediately when the single lightning current is ≥100kA; manual sampling inspection of surge protector parameters is carried out every six months.

[0228] (2) Manual inspection configuration: 1 inspection every 6 months, including grounding resistance retest, surge protector leakage current and temperature full inspection, physical condition verification, grounding body corrosion investigation, and lightning damage inspection.

[0229] Step 3: Data Fusion

[0230] S3-1: Generating parameter trend status

[0231] The risk level is high, and the trend threshold corresponding to high risk is used:

[0232] Multi-parameter deterioration condition: η R ≥6%, η I ≥8%, η T If ≥5%, all three conditions are met, and the condition is classified as multi-parameter deterioration. Single-parameter deterioration condition: η I ≥10% also meets the requirement.

[0233] S3-2: Data Preprocessing

[0234] Online correction values: grounding resistance 4.2Ω, SPD leakage current 22μA, SPD temperature 59℃.

[0235] Artificial average: average of 3 measurements, grounding resistance 4.3Ω, SPD leakage current 23μA, SPD temperature 60℃.

[0236] Deviation verification: Grounding resistance deviation ≈ 2.3%, SPD leakage current deviation ≈ 4.3%, SPD temperature deviation ≈ 1.7%, all < 15%, no correction required.

[0237] S3-3: Assigning Data Weights

[0238] High risk: ω1=0.4, ω2=0.6.

[0239] S3-4: Determine the overall correction coefficient

[0240] The parameter trend indicates a deterioration of multiple parameters, and the lightning strike risk is high. According to the rules, the comprehensive correction coefficient α = 1.1 is taken.

[0241] S3-5: Fusion Value Calculation

[0242] The combined grounding resistance value = 1.1 × (0.4 × 4.2 + 0.6 × 4.3) = 4.686 Ω.

[0243] SPD leakage current fusion value = 1.1 × (0.4 × 2² + 0.6 × 2³) = 24.86 μA,

[0244] SPD temperature fusion value = 1.1 × (0.4 × 59 + 0.6 × 60) = 65.56℃

[0245] S3-6: Lightning Strike Related Assistance

[0246] A strong lightning current of 120kA exists, and the leakage current and temperature fluctuation of the SPD after the lightning strike are ≥15%, which are included in the auxiliary judgment.

[0247] Step 4: Status Assessment

[0248] S4-1: Dynamically select the qualified threshold

[0249] The parameter trend indicates deterioration in multiple parameters, and the lightning strike risk is high. A strict threshold is selected. The strict threshold is 90% of the standard threshold.

[0250] S4-2: Threshold Standard (High Risk)

[0251] Grounding resistance: strict threshold is 3.6Ω, standard threshold is 4Ω.

[0252] SPD leakage current: The strict threshold is 18μA, the range to be reviewed is 18~27μA, and the unacceptable threshold is 27μA.

[0253] SPD temperature: The strict threshold is 54℃, the range to be reviewed is 54~70℃, and the unqualified threshold is 70℃; when the parameter trend is deteriorating (multiple parameters or single parameter deterioration) or the lightning strike risk is high risk, if the SPD temperature fusion value reaches 60℃, the secondary review will be triggered immediately.

[0254] S4-3: Evaluation Results

[0255] The combined grounding resistance value is 4.686Ω, which is greater than 3.6Ω and therefore unqualified.

[0256] The combined leakage current of the SPD is 24.86 μA, which is within the range to be verified.

[0257] The SPD temperature fusion value is 65.56℃ ≥ 60℃, triggering a secondary review, and is in the pending review range.

[0258] Overall conclusion: The overall status assessment result is unqualified, and an anomaly review needs to be triggered immediately.

[0259] Step 5: Anomaly Review

[0260] S5-1: Level 1 Review

[0261] The unit's safety personnel conducted an on-site inspection within 24 hours: no environmental abnormalities were found, the grounding electrode showed slight corrosion, and the SPD wiring was secure. Based on the lightning strike record, the incident was determined to be a combination of lightning impact and a progressive hazard, escalating the inspection to a level two review.

[0262] S5-2: Second-level review

[0263] Professional inspectors will arrive within 12 hours for in-depth testing.

[0264] Grounding device: The down conductor is reliable, and the local corrosion depth of the grounding electrode is 0.8mm.

[0265] SPD equipment: Deterioration indicator deteriorates, power frequency reference voltage deviates by ≥20%.

[0266] The online equipment has been calibrated and is functioning normally.

[0267] Final judgment: Unqualified, must be immediately taken out of service and replaced with SPD, and anti-corrosion treatment must be applied to the grounding electrode.

[0268] S5-3: Trend Update

[0269] The manual test values, online calibration data, and lightning strike statistics from this review will be added to the historical database, the linear regression equation will be refitted, and the annual growth rate and lightning strike risk status will be updated for the next round of assessment.

[0270] Example 2

[0271] A multi-source data fusion intelligent status assessment system for lightning protection devices includes:

[0272] The risk classification module is used to acquire historical manual detection data and historical lightning strike parameter data of the inherent risk indicators of the scene and lightning protection performance parameters, calculate the annual growth rate of each lightning protection performance parameter, generate the lightning strike risk status based on the historical lightning strike parameter data, and determine the risk level of the lightning protection scene according to preset rules.

[0273] The resource configuration module is used to configure the frequency of online monitoring data collection and the manual inspection execution cycle according to the risk level.

[0274] The data fusion module is used to generate parameter trend status by comparing the annual growth rate with the trend threshold corresponding to the risk level, obtain real-time online monitoring data and current manual detection data, and introduce a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to obtain the fused value of the lightning protection performance parameters.

[0275] The status assessment module is used to dynamically select a qualified threshold based on the trend status of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status. The module compares the fused value with the selected qualified threshold to generate the status assessment result of the lightning protection device.

[0276] The above embodiments are preferred embodiments of the present invention and are not intended to limit the present invention. Any changes or other equivalent substitutions made without departing from the technical solution of the present invention are included within the protection scope of the present invention.

Claims

1. A method for intelligent status assessment of lightning protection devices using multi-source data fusion, characterized in that, Including the following steps: S1. Obtain historical manual testing data and historical lightning strike parameter data of the inherent risk indicators and lightning protection performance parameters of the lightning protection device to be evaluated, calculate the annual growth rate of each lightning protection performance parameter, and generate a lightning strike risk status based on the historical lightning strike parameter data; Based on the inherent risk indicators, annual growth rate and lightning strike risk status of the scenario, the risk level of the lightning protection scenario is determined according to preset rules. S2. Configure the online monitoring data acquisition frequency and manual inspection execution cycle of the lightning protection device according to the risk level; S3. Generate parameter trend status by comparing the annual growth rate with the trend threshold corresponding to the risk level, obtain real-time online monitoring data and current manual detection data of lightning protection performance parameters, and introduce a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to correct and obtain the fused value of lightning protection performance parameters. S4. Dynamically select a qualified threshold based on the trend of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status. Compare the fused value with the selected qualified threshold to generate a status assessment result of the lightning protection device.

2. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, In step S1, the calculation process of the annual growth rate includes: S11. For each lightning protection performance parameter, fit a linear regression equation: Y = kt + b Where Y represents historical manual detection data, t represents the detection year number, t=1,2,…,n, n represents the number of detection years, k represents the slope, and b represents the intercept; S12. Calculate the annual growth rate η of each lightning protection performance parameter. i : ， Where i represents the type of lightning protection performance parameter, and Y0 is the average value of the corresponding lightning protection performance parameter measured manually over the past n years. The lightning protection performance parameters include grounding resistance, surge protector leakage current, and surge protector temperature.

3. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, In step S1, the historical lightning strike parameter data includes lightning strike counts and peak lightning current; the method for generating the lightning strike risk status includes: Calculate the annual growth rate of lightning strike frequency based on lightning strike counts, and obtain the peak value of the maximum lightning current in the most recent year: If the number of lightning strikes in the most recent year is ≥2, or the peak value of the maximum lightning current is ≥100kA, or the annual growth rate of the frequency of lightning strikes is ≥50%, it is judged as high risk. Otherwise, if the lightning strike count in the most recent year is ≥2, or the maximum lightning current peak value is ≥50kA, or the annual growth rate of lightning strike frequency is ≥30%, it is judged as medium risk. Otherwise, if the number of lightning strikes in the most recent year is ≤1 and the peak value of the maximum lightning current is <50kA, or the annual growth rate of lightning strike frequency is ≥20%, it is judged as low risk.

4. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, The preset rules include: For each risk level, a risk level is determined when at least one of the inherent risk indicators of the scenario corresponding to that risk level is satisfied and at least one of the trend risk indicators corresponding to that risk level is satisfied, or when at least two of the inherent risk indicators of the scenario corresponding to that risk level are satisfied; and when the conditions of multiple risk levels are met at the same time, the highest risk level is taken. The trend risk indicators include the annual growth rate reaching the deterioration threshold corresponding to the risk level and the lightning strike risk status reaching the risk level corresponding to the risk level.

5. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, In step S3, the method for generating the parameter trend status includes: comparing the annual growth rate of each lightning protection performance parameter with the trend threshold corresponding to each risk level. If the annual growth rate of at least two lightning protection performance parameters reaches the corresponding multi-parameter deterioration threshold, it is determined to be multi-parameter deterioration. If the annual growth rate of only one lightning protection performance parameter reaches the corresponding single parameter deterioration threshold, it is judged as single parameter deterioration. If the annual growth rate of all lightning protection performance parameters is negative and the absolute value is not less than the corresponding improvement threshold, then it is judged to be improving. If the annual growth rate of all lightning protection performance parameters does not reach the corresponding deterioration threshold and does not reach the corresponding improvement threshold, it is judged as stable.

6. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, The formula for calculating the fused value of the lightning protection performance parameters is as follows: V 融合 =α×(ω1V 在线 +ω2V 人工 ), Among them, V 在线 V is the online correction value obtained by performing a moving average process on the real-time online monitoring data. 人工 The mean value is the result of taking the average of the current manual detection data. ω1 and ω2 are the weights of the online monitoring data and the manual detection data, respectively, and ω1+ω2=1. α is the comprehensive correction coefficient.

7. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 6, characterized in that, The relationship between the values ​​of the online monitoring data weight ω1 and the manual detection data weight ω2 and the risk level is as follows: Under high-risk conditions, ω1 < ω2; under medium-risk conditions, ω1 = ω2; under low-risk conditions, ω1 < ω2, and the value of ω1 corresponding to the low-risk conditions is less than the value of ω1 corresponding to the high-risk conditions.

8. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 5, characterized in that, The rules for determining the value of the comprehensive correction coefficient are as follows: When the parameter trend is multiple parameters deteriorating, or the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold, or the lightning strike risk status is high risk, the comprehensive correction coefficient is the first preset coefficient, and the first preset coefficient is greater than 1. When the parameter trend is a single parameter deterioration, or the fluctuation amplitude of the real-time online monitoring data of the grounding resistance exceeds the second preset fluctuation threshold, or the lightning strike risk status is medium risk, the comprehensive correction coefficient is the second preset coefficient, and the second preset coefficient is greater than 1 and less than the first preset coefficient. When the parameter trend is stable, the fluctuation range of the real-time online monitoring data of the grounding resistance is between the third preset fluctuation threshold and the second preset fluctuation threshold, and the lightning strike risk status is low, the comprehensive correction coefficient is the third preset coefficient, and the third preset coefficient is equal to 1. When the parameter trend is positive, the fluctuation range of the real-time online monitoring data of the grounding resistance is less than the third preset fluctuation threshold, and the lightning strike risk status is low, the comprehensive correction coefficient is taken as the fourth preset coefficient, and the fourth preset coefficient is less than 1.

9. The intelligent status assessment method for lightning protection devices based on multi-source data fusion according to claim 1, characterized in that, The dynamic selection of the qualified threshold includes: selecting a strict threshold or a standard threshold based on the trend of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status; wherein... When the parameter trend is a single parameter deterioration or multiple parameter deterioration, or the fluctuation amplitude of the real-time online monitoring data of grounding resistance continuously exceeds the first preset fluctuation threshold, or the lightning strike risk status is high risk, select the strict threshold. When the parameter trend is stable or improving, and the fluctuation range of the real-time online monitoring data of grounding resistance is between the third preset fluctuation threshold and the second preset fluctuation threshold, and the lightning strike risk status is low, and the deviation between the online monitoring data and the manual detection data is less than the preset deviation threshold, the standard threshold is selected. The strict threshold is a preset percentage of the standard threshold.

10. A multi-source data fusion-based intelligent status assessment system for lightning protection devices, using the multi-source data fusion-based intelligent status assessment method for lightning protection devices as described in any one of claims 1-9, characterized in that, Includes the following modules: The risk classification module is used to acquire historical manual detection data and historical lightning strike parameter data of the inherent risk indicators of the scene and lightning protection performance parameters, calculate the annual growth rate of each lightning protection performance parameter, generate the lightning strike risk status based on the historical lightning strike parameter data, and determine the risk level of the lightning protection scene according to preset rules. The resource configuration module is used to configure the frequency of online monitoring data collection and the manual inspection execution cycle according to the risk level. The data fusion module is used to generate parameter trend status by comparing the annual growth rate with the trend threshold corresponding to the risk level, obtain real-time online monitoring data and current manual detection data, and introduce a comprehensive correction coefficient determined based on the parameter trend status, the fluctuation range of real-time online monitoring data of grounding resistance and the lightning strike risk status to obtain the fused value of the lightning protection performance parameters. The status assessment module is used to dynamically select a qualified threshold based on the trend status of the parameters, the fluctuation range of the real-time online monitoring data of the grounding resistance, and the lightning strike risk status. The module compares the fused value with the selected qualified threshold to generate the status assessment result of the lightning protection device.

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