Vehicle active lightning strike early warning and protection system and method based on electric field induction

Abstract

This invention discloses a vehicle active lightning strike warning and protection system and method based on electric field induction, relating to the field of vehicle safety technology. The invention acquires and synchronizes multi-source data related to vehicle lightning risk through a multi-source sensing and acquisition module, outputting a standardized data stream; a precursor identification module performs separation and pattern recognition, outputting precursor event information and credibility indicators; an air breakdown risk assessment module quantifies the breakdown risk near the vehicle; a climate prior fusion module corrects drift in prior climate data indexed by month and hour, outputting a corrected risk index; and a fusion decision and execution module uses multiple indicators as input to generate a comprehensive risk index, performs hierarchical judgment, outputs corresponding protection commands, and controls the actuators to implement corresponding hierarchical protection actions. This invention achieves active warning and hierarchical protection for vehicles in lightning environments, improving the safety of the entire vehicle and its occupants, as well as the reliability of electronic systems.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety technology, specifically to a vehicle active lightning strike warning and protection system and method based on electric field induction. Background Technology

[0002] Lightning is a typical strong pulse electromagnetic phenomenon. Its transient high current and high voltage can cause serious damage to vehicle bodies and onboard electronic equipment, and may also endanger the personal safety of occupants. Existing vehicle lightning protection technologies mainly rely on the "Faraday cage effect" of the vehicle's metal shell for passive protection, but they generally lack real-time electric field monitoring and corona discharge signal identification methods, making it impossible to actively detect lightning precursors and obtain early warning information before a lightning strike, resulting in delayed protective measures. Due to the insulating properties of tires, vehicles are prone to static charge accumulation in strong electric fields, increasing the probability of forming an upward leader. Existing solutions lack real-time monitoring and active release mechanisms for the vehicle body's charge state, and the risk of charge accumulation on the vehicle body is not effectively controlled. Furthermore, they fail to effectively integrate climate and environmental data, resulting in a lack of correction and verification of external environmental conditions in risk assessment. Traditional solutions are mostly based on an "all-or-nothing" protection logic, relying only on the vehicle body's shielding effect when a lightning strike occurs, lacking graded response and gradual intervention mechanisms, and unable to take targeted protective measures at different risk levels.

[0003] Therefore, there is still an urgent need for a comprehensive technical solution that can achieve active detection of lightning precursors, risk classification assessment, and multi-level protection for vehicles / occupants, in order to improve vehicle safety in lightning environments. Summary of the Invention

[0004] To address the problems of existing vehicle lightning protection systems, such as lack of early warning recognition, insufficient vehicle body charge control, low risk assessment accuracy, and limited protection response, this invention proposes a vehicle active lightning strike early warning and protection system and method based on electric field induction.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] A vehicle active lightning strike warning and protection system based on electric field induction, the system comprising:

[0007] The multi-source sensing and acquisition module is used to acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, which includes at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data.

[0008] The precursor identification module is used to perform signal separation and pattern recognition based on the electric field measurement data and corona pulse data, so as to distinguish the corona discharge pulse from the vehicle itself or environmental background noise, identify potential lightning strike precursor events, and output precursor event information with time stamps and precursor confidence index.

[0009] The air breakdown risk assessment module is used to assess the possibility of a lightning strike conduction channel forming near the vehicle based on the electric field measurement data and millimeter-wave radar data, combined with the precursor event information and precursor confidence index, and output the breakdown risk index.

[0010] The climate prior fusion module is used to perform drift correction based on the climate prior data and the global statistical results of the electric field measurement data, and output the climate prior risk index.

[0011] The integrated decision-making and execution module is used to generate a comprehensive risk index by taking the precursor confidence index, breakdown risk index, and climate prior risk index as the main inputs and incorporating the vehicle body charge data. The comprehensive risk index is compared with the classification threshold, and protection instructions are output. The module also controls the execution mechanism to implement the corresponding classification protection actions.

[0012] As a preferred embodiment of the present invention, the multi-source sensing and acquisition module includes:

[0013] An electric field sensor array is deployed on the roof, rearview mirror, and bumper areas to collect multi-channel electric field measurement data and output it in slices with a coverage time window.

[0014] A high-frequency pulse sensor for acquiring corona pulse data in the 0.5–10MHz frequency band;

[0015] The vehicle body charge monitoring unit is used to collect the static charge of the vehicle body and indicate in the output whether the static charge has reached a danger threshold, thus generating vehicle body charge data. The danger threshold is set to be no less than 500. ;

[0016] Vehicle-mounted millimeter-wave radar is used to acquire the motion vector and reflectivity parameters of cumulonimbus clouds to form millimeter-wave radar data.

[0017] The data interface unit is used to receive meteorological observations and lightning location information, and to generate climate prior data based on the "month × hour" index;

[0018] The time synchronization and standardization processing unit is used to perform unified clock synchronization and standardized formatting processing on the multi-source data collected above, and output a standardized data stream.

[0019] As a preferred embodiment of the present invention, the precursor identification module includes:

[0020] The signal separation unit receives electric field measurement data and corona pulse data, and encodes the input time series into corona discharge pulse component factors. and environmental background noise component factors ;

[0021] Dual decoder structure, including corona discharge pulse decoder and ambient background noise decoder Based on the corona discharge pulse component factors respectively and environmental background noise component factors Reconstruct the corona discharge pulse signal and the environmental background noise signal;

[0022] This represents a decoupling verification mechanism used to exchange the environmental background noise component factors between input data from different time segments. And calculate the exchange verification error;

[0023] The precursor event discrimination unit is used to extract amplitude, energy density and temporal continuity features based on the reconstructed corona discharge pulse signal, and in combination with the exchange verification error, identify potential lightning precursor events, and output time-stamped precursor event information and precursor confidence index.

[0024] As a preferred embodiment of the present invention, the formula for calculating the exchange verification error is as follows:

[0025] ;

[0026] In the formula, Indicates the first The and the first Verification error between input data of different time segments; , The first The first time segment, the first Corona discharge pulse component factors for a time segment; , The first The first time segment, the first Environmental background noise component factors for each time segment; For the first The calibration reference for each time segment is the corona discharge pulse signal. For the first The calibration reference for each time segment is the ambient background noise signal.

[0027] As a preferred embodiment of the present invention, the air breakdown risk assessment module includes:

[0028] An electric field feature extraction unit is used to calculate the electric field intensity gradient and the rate of change of the electric field intensity gradient over time based on the electric field measurement data.

[0029] The cumulonimbus cloud parameter extraction unit is used to extract the motion vector and reflectivity parameters of cumulonimbus clouds based on millimeter-wave radar data.

[0030] The risk triggering association unit is used to trigger an air breakdown risk assessment when the precursor confidence index exceeds a preset threshold. After triggering, the unit locates the hazard source through the precursor event information and establishes a spatiotemporal correspondence between the hazard source and the electric field intensity distribution and the cumulonimbus cloud motion vector. The unit combines the electric field intensity gradient and the cumulonimbus cloud motion vector to form a breakdown risk judgment input.

[0031] The breakdown probability calculation unit is used to calculate the breakdown risk index of a lightning strike conduction channel forming near the vehicle. The calculation formula is as follows:

[0032] ;

[0033] In the formula, for The risk indicators are constantly being breached; For Sigmoid mapping functions; for The electric field intensity gradient at time t. The electric field intensity gradient is the rate of change over time. Let be the metric function for the motion intensity of cumulonimbus clouds, where These represent the vector components of cumulonimbus cloud motion, corresponding to the velocities in the X, Y, and Z directions, respectively. This represents the maximum value of the reflectivity parameter. As an indicator of the reliability of early warning signs; These are the weight parameters.

[0034] As a preferred embodiment of the present invention, the climate prior fusion module includes:

[0035] The global statistical analysis unit is used to obtain the drift reference value of the electric field intensity distribution based on the time series statistical results of electric field measurement data;

[0036] The drift correction unit is used to compare the climate prior data with the drift benchmark value, and dynamically adjust the climate prior data using a correction function to obtain the climate prior risk index.

[0037] As a preferred embodiment of the present invention, the correction function is expressed as:

[0038]

[0039] In the formula, For timestamps under a unified clock; express A priori climate risk indicators at any given moment; Indicates month with hours The corresponding prior climate data, Indicates in The drift reference value is obtained from statistical analysis of electric field measurement data at any given moment; The fusion weight parameter has a value range of [0,1].

[0040] As a preferred embodiment of the present invention, the fusion decision and execution module includes:

[0041] The risk index generation unit is used to receive the early warning credibility index. Breaking through risk indicators Climate a priori risk indicators and vehicle body charge data And combined with time smoothing factor Calculate the comprehensive risk index:

[0042] ;

[0043] In the formula, , They represent time, The overall risk index at any given moment; These are weight parameters; The dangerous threshold for the static charge on the vehicle body;

[0044] The classification and determination unit is used to classify the comprehensive risk index. With the set grading threshold Perform interval comparisons to generate corresponding risk levels;

[0045] The instruction generation unit is used to generate corresponding protection instructions under different risk levels;

[0046] The execution control unit is used to send the protection command to the corresponding vehicle-mounted actuator and receive feedback information on the execution status in real time.

[0047] As a preferred embodiment of the present invention, the generation of the corresponding risk level specifically includes:

[0048] when At that time, the risk level was determined to be level zero;

[0049] when At that time, the risk level was determined to be Level 1;

[0050] when At that time, the risk level was determined to be Level 2;

[0051] when At that time, the risk level was determined to be Level 3;

[0052] The generation of corresponding protection instructions under different risk levels is specifically as follows:

[0053] When the risk level is Level 1, an audible and visual warning command is generated, and a prompt message is displayed on the central control screen;

[0054] When the risk level is level 2, commands are generated to close the skylight, retract the antenna, and activate the surge protector.

[0055] When the risk level is level three, instructions are generated to release the charge from the conductive strip under the vehicle, activate the electromagnetic shielding device, and trigger the occupant's hazard avoidance posture guidance.

[0056] A method for active lightning strike warning and protection of vehicles based on electric field induction, the method comprising:

[0057] Acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, which includes at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data;

[0058] Based on the electric field measurement data and corona pulse data, signal separation and pattern recognition are performed to distinguish corona discharge pulses from vehicle or environmental background noise, identify potential lightning strike precursor events, and output time-stamped precursor event information and precursor confidence index.

[0059] Based on the electric field measurement data and millimeter-wave radar data, combined with the precursor event information and precursor confidence index, the possibility of a lightning strike conduction channel forming near the vehicle is assessed, and a breakdown risk index is output.

[0060] Based on the aforementioned climate prior data, drift correction is performed using the global statistical results of electric field measurement data, and a climate prior risk index is output.

[0061] Using the aforementioned precursor confidence index, breakdown risk index, and climate prior risk index as the main inputs, and incorporating the vehicle body charge data, a comprehensive risk index is generated. The comprehensive risk index is compared with the classification threshold, a protection command is output, and the actuator is controlled to implement the corresponding classification protection action.

[0062] The beneficial effects of this invention are as follows: By collaboratively collecting data from an electric field sensor and a high-frequency pulse sensor, and combining signal separation and pattern recognition methods, it can effectively distinguish between corona discharge pulses and background noise, thereby identifying lightning precursor events in advance and outputting reliable quantitative indicators to provide triggering conditions for subsequent risk assessment; by jointly analyzing the electric field intensity gradient, electric field intensity change rate, cumulonimbus cloud motion vector, and reflectivity parameters, and introducing the spatiotemporal correspondence of precursor event information, the breakdown probability is calculated to achieve a quantitative assessment of the risk of the formation of a conductive channel near the vehicle; by performing drift correction on meteorological statistical data based on the "month × hour" index, the risk assessment results can simultaneously reflect external climate trends and local electric field changes, thereby improving prediction accuracy; by comprehensively calculating the precursor reliability index, breakdown risk index, climate prior risk index, and vehicle charge data, a comprehensive risk index is generated and compared with multi-level thresholds to achieve risk level classification of level zero, level one, level two, and level three. Different risk levels correspond to audible and visual warnings, vehicle protection measures, and occupant safety interventions, forming a progressive active protection system. At high risk levels, the system not only controls the sunroof, antenna, conductive strip, and electromagnetic shielding device, but also prompts occupants to adopt a safe posture through the human-machine interface and seat vibration, thereby reducing the risk of damage to occupants and onboard electronic equipment from side flashes and electromagnetic pulses. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0064] Figure 1 This is a schematic diagram of the modular structure of the system of the present invention;

[0065] Figure 2 This is a schematic diagram of the multi-source sensing and acquisition module according to an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of the early warning sign recognition module according to an embodiment of the present invention;

[0067] Figure 4 This is a schematic diagram of the air breakdown risk assessment module according to an embodiment of the present invention;

[0068] Figure 5 This is a schematic diagram of the climate prior fusion module according to an embodiment of the present invention;

[0069] Figure 6 This is a schematic diagram of the fusion decision-making and execution module according to an embodiment of the present invention;

[0070] Figure 7This is a flowchart of the method of the present invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0072] like Figure 1 As shown, this is an embodiment of the present invention, which provides a vehicle active lightning strike warning and protection system based on electric field induction, including a multi-source sensing and acquisition module, a precursor identification module, an air breakdown risk assessment module, a climate prior fusion module, and a fusion decision and execution module.

[0073] The multi-source sensing and acquisition module is used to acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, including at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data.

[0074] like Figure 2 As shown, the multi-source sensing and acquisition module in this embodiment includes:

[0075] Electric field sensor array: Fixedly deployed on the vehicle roof, left and right rearview mirror housings, and front and rear bumper areas, it is used to collect electric field intensity at multiple spatial locations. The electric field sensor array generates multi-channel input signals, with each channel signal maintaining time synchronization during acquisition. The signals are segmented using a coverage time window, enabling the acquisition of temporally continuous electric field measurement data during continuous driving. This electric field measurement data is then uniformly organized into an electric field measurement data stream, serving as input for subsequent precursor identification and air breakdown risk assessment.

[0076] High-frequency pulse sensor: Located on the roof of the vehicle, it can acquire transient high-frequency electromagnetic signals in the 0.5–10MHz frequency band. The sampling bandwidth of the high-frequency pulse sensor covers the pulse frequency range commonly seen in corona discharge and lightning leader discharge, and can distinguish pulse signals from external lightning precursors. The acquired signals are output as corona pulse data.

[0077] Vehicle body charge monitoring unit: Located between the vehicle's metal body and chassis structure, it employs a capacitive or electrostatic charge sensor structure to collect the amount of static charge on the vehicle body. The vehicle body charge monitoring unit compares the real-time measured static charge value with a set danger threshold (not less than 500). The system compares the results with the vehicle body charge monitoring unit's output. When the monitoring result reaches or exceeds the danger threshold, the output data includes a danger state indication signal. The output of the vehicle body charge monitoring unit constitutes the vehicle body charge data, which is used by the fusion decision and execution module.

[0078] Vehicle-mounted millimeter-wave radar: Installed at the front of the vehicle, it acquires the motion vector and reflectivity parameters of target cumulonimbus clouds, reflecting the cloud's direction of movement, speed, and internal water vapor and ice crystal content. The processed information forms millimeter-wave radar data, providing dynamic meteorological input for the air breakdown risk assessment module.

[0079] Data Interface Unit: Establishes data connections with external meteorological observation stations and lightning location systems via a wireless communication module to receive meteorological observation information and lightning location information. The received data is indexed and processed to form climate prior data corresponding to "month × hour". This data includes lightning occurrence probability level information obtained based on long-term observation statistics, which is used for subsequent risk fusion.

[0080] The time synchronization and standardization processing unit, connected to the above-mentioned acquisition submodules, performs unified clock synchronization on electric field measurement data, corona pulse data, vehicle charge data, millimeter-wave radar data, and climate prior data, ensuring consistency of multi-source data in the time dimension. Simultaneously, this processing unit standardizes data of different formats and dimensions, unifying them into a standardized data stream for real-time processing and retrieval in subsequent precursor identification, air breakdown risk assessment, and risk fusion modules.

[0081] The precursor identification module is used to perform signal separation and pattern recognition based on electric field measurement data and corona pulse data, so as to distinguish corona discharge pulses from vehicle or environmental background noise, identify potential lightning strike precursor events, and output precursor event information with time stamps and precursor confidence index.

[0082] like Figure 3 As shown, the precursory symptom recognition module in this embodiment includes:

[0083] The signal separation unit receives electric field measurement data and corona pulse data, performs preprocessing on the input time series data such as bandpass filtering, amplitude normalization, and time alignment, and uses a deep representation encoder to map the input sequence into two types of latent representation factors:

[0084] Corona discharge pulse component factor , used to characterize potential lightning precursor signals;

[0085] Environmental background noise component factors , used to characterize non-precursor noise in the vehicle itself or in the environment;

[0086] This separation method ensures that the corona pulse and background noise are decoupled in the feature space.

[0087] The dual-decoder architecture includes:

[0088] Corona discharge pulse decoder Input is the corona discharge pulse component factor. and environmental background noise component factors The output is a reconstructed corona discharge pulse signal;

[0089] Ambient background noise decoder The input consists of the same factor pairs, and the output is the reconstructed environmental background noise signal;

[0090] The dual-decoder structure described above can reconstruct the corona pulse signal and the environmental background noise signal respectively, forming a signal pair corresponding to the original input, which can be used for subsequent verification and discrimination.

[0091] This represents a decoupled verification mechanism used to exchange environmental background noise component factors between input data from different time segments. This is to test the decoupling effect. Specifically, for the first... The and the first The input for each time segment is swapped with its background noise component factor, and the swap verification error is calculated. The formula is:

[0092] ;

[0093] In the formula, Number the two different time segments; , The first The first time segment, the first Corona discharge pulse component factors for a time segment; , The first The first time segment, the first Environmental background noise component factors for each time segment; For the first The calibration reference for each time segment is the corona discharge pulse signal. For the first The calibration reference for each time segment is the ambient background noise signal;

[0094] By exchanging environmental background noise factors at different time segments and calculating cross-validation errors, the independence and decoupling effect of factor separation can be verified, thereby improving the credibility of the identification.

[0095] The precursor event discrimination unit is used to extract the amplitude (maximum instantaneous amplitude of the corona discharge pulse signal within the detection frequency band), energy density (integrated energy value within the 0.5–10MHz frequency band), and temporal continuity characteristics (whether the pulse events occur continuously, determined based on statistical pulse intervals) of the reconstructed corona discharge pulse signal from the dual decoder output. This is combined with the exchange verification error calculated by the decoupling verification mechanism. It identifies potential precursor events of lightning strikes and outputs time-stamped precursor event information and precursor confidence index;

[0096] For example, the information about a precursor events is represented as follows: ;

[0097] The confidence index for early warning signs is expressed as follows: ;

[0098] in, Indicates information about precursory events; For timestamps, it represents the absolute or relative time (e.g., millisecond-level UTC time, or vehicle system time) when the precursor event is identified. The sensor channel number corresponds to the specific data collection location in the electric field sensor array (such as the roof, left rearview mirror, right rearview mirror, front bumper, and rear bumper). The peak amplitude of the corona pulse, in V / m or This corresponds to the transient pulse amplitude detected within the 0.5–10MHz frequency band; The energy integral value within the corona pulse detection frequency band (obtainable from the energy norm after bandpass filtering), in J; Pulse duration (full width at half height FWHM or envelope duration), in units of ; This flag indicates whether the event belongs to a continuous pulse sequence and can be a Boolean value (single / continuous) or an integer (number of continuous pulses).

[0099] As an indicator of the reliability of early warning signs; This represents the Sigmoid function. To assign weights, the confidence index of the precursor is clearly defined in the 0–1 range and can be quantified.

[0100] The air breakdown risk assessment module is used to assess the possibility of a lightning strike conduction channel forming near a vehicle based on electric field measurement data and millimeter-wave radar data, combined with precursor event information and precursor confidence index, and outputs a breakdown risk index.

[0101] like Figure 4 As shown, in a preferred embodiment of the present invention, the air breakdown risk assessment module includes:

[0102] The electric field feature extraction unit is used to calculate the electric field intensity gradient and the rate of change of the electric field intensity gradient over time based on the electric field measurement data. The electric field intensity gradient represents the difference between the electric field intensity of the space around the vehicle at different sampling points, and the rate of change of the electric field intensity gradient over time represents the rate of change of the electric field intensity gradient on the time axis, which is used to characterize the local electric field instability. These features can reflect the electric field concentration trend before the possible formation of the lightning strike conduction channel.

[0103] The cumulonimbus cloud parameter extraction unit is used to extract the motion vector of cumulonimbus clouds based on millimeter-wave radar data. (These represent the velocity components of the cumulonimbus cloud in the X, Y, and Z directions, respectively, in meters per second) and reflectivity parameters. (Obtained from the echo intensity of millimeter-wave radar at spatial grid points, reflecting the density distribution of water droplets or ice crystals);

[0104] The risk triggering and correlation unit is used to determine that there is a high reliability of lightning strike precursors when the precursor confidence index exceeds a preset threshold (e.g., 0.7), thereby triggering the air breakdown risk assessment process. After triggering, the system locates the direction or area of ​​the hazard source by using the channel number and corresponding sensor deployment location in the precursor event information (e.g., when a high-confidence precursor signal appears in the electric field sensor channel at the rearview mirror position on the roof, the system marks that direction as the location of a potential hazard source). The system further uses the timestamp in the precursor event information to align with the unified clock provided by the time synchronization and standardization processing unit, matching the spatial location of the hazard source with the electric field intensity distribution and the cumulonimbus cloud motion vector extracted by the millimeter-wave radar at the same time. Through timestamp alignment and spatial channel positioning, the spatiotemporal correspondence between the hazard source and the electric field intensity distribution and the cumulonimbus cloud motion vector is established. The spatial positioning result of the hazard source, the electric field intensity gradient, and the cumulonimbus cloud motion vector are combined to generate the input parameters for air breakdown risk determination. This determination input is then transmitted to the breakdown probability calculation unit for quantitative calculation of the breakdown risk index.

[0105] The breakdown probability calculation unit is used to calculate the breakdown risk index of a lightning strike conduction channel forming near the vehicle. The calculation formula is as follows:

[0106] ;

[0107] In the formula, for The risk indicators are constantly being breached; For Sigmoid mapping functions; for The electric field intensity gradient at time t. This represents the rate of change of the electric field intensity gradient with time. Let be the function that measures the intensity of cumulonimbus cloud motion. ; This represents the maximum value of the reflectivity parameter. This represents the strongest reflective region inside a cumulonimbus cloud, used to quantify potential charge accumulation centers; As an indicator of the reliability of early warning signs; These are the weight parameters.

[0108] The climate prior fusion module is used to perform drift correction based on climate prior data and combined with global statistical results of electric field measurement data, and output climate prior risk indicators.

[0109] like Figure 5 As shown, the climate prior fusion module in this embodiment may include:

[0110] The global statistical analysis unit is used to obtain the drift baseline value of the electric field intensity distribution based on the time series statistical results of the electric field measurement data. The electric field measurement data is collected by an on-board electric field sensor array. The system samples the electric field intensity within a time window (e.g., several minutes to several hours) and calculates the statistical characteristics of that window, such as mean, variance, and extreme values. These statistical characteristics are then combined to form the drift baseline value, denoted as [missing value]. It is used to characterize the overall level of the local electric field where the vehicle is located;

[0111] The drift correction unit is used to compare the climate prior data with the drift baseline value and perform weighted adjustment based on the correction function to obtain the climate prior risk index.

[0112] Climate prior data, derived from meteorological observations and lightning location information, has been organized by "month × hour" index in the data interface unit of the multi-source sensing acquisition module. For a given month... and hours The corresponding climate prior data can be extracted from the prior database and denoted as... ;

[0113] Through the correction function:

[0114]

[0115] By weighted and fused with the local drift baseline, the external prior data is obtained. Time-corrected climate prior risk indicators;

[0116] In the formula, For timestamps under a unified clock; express A priori climate risk indicators at any given moment; Indicates in The drift reference value is obtained from statistical analysis of electric field measurement data at any given moment; The fusion weight parameter has a value range of [0,1].

[0117] The integrated decision-making and execution module takes the precursor confidence index, breakdown risk index, and climate prior risk index as the main inputs, and incorporates vehicle body charge data to generate a comprehensive risk index. It compares the risk index with the classification threshold, outputs protection commands, and controls the execution mechanism to implement the corresponding classification protection actions.

[0118] like Figure 6 As shown, in one specific embodiment of the present invention, the fusion decision-making and execution module includes:

[0119] The risk index generation unit is used to receive the precursor confidence index output by the precursor identification module. Breakdown risk indicators output by the air breakdown risk assessment module Climate prior risk indicators output by the climate prior fusion module and the vehicle charge data output by the vehicle charge monitoring unit To improve the system's robustness to transient signal fluctuations, a time smoothing factor is introduced. Calculate the comprehensive risk index:

[0120] ;

[0121] In the formula, , They represent time, The overall risk index at any given moment; These are the weight parameters, corresponding to the four types of inputs; The dangerous threshold for the static charge on the vehicle body; ,when When the value is larger, the risk index is smoother, which is suitable for avoiding false triggers; when... When the size is smaller, the system responds more quickly;

[0122] The classification and determination unit is used to classify the comprehensive risk index. With the set grading threshold By comparing risk levels across different risk ranges, this tiered approach ensures progressive protection at varying levels of danger, avoiding a single "all or nothing" response. Specifically:

[0123] when At that time, the risk level was determined to be level zero;

[0124] when At that time, the risk level was determined to be Level 1;

[0125] when At that time, the risk level was determined to be Level 2;

[0126] when At that time, the risk level was determined to be Level 3;

[0127] The instruction generation unit is used to generate corresponding protection instructions under different risk levels, specifically:

[0128] When the risk level is Level 1, an audible and visual warning command is generated, including a buzzer and instrument panel indicator lights, and a warning message is displayed on the central control screen to remind occupants to avoid contact with metal parts;

[0129] When the risk level is level 2, commands are generated to close the sunroof (to prevent lightning from entering the vehicle), retract the antenna (to reduce the probability of protrusions absorbing lightning), and activate the surge protector (to release some energy in advance to prevent electromagnetic pulses from entering critical electronic systems).

[0130] When the risk level is level three, the system generates commands to release charge from the conductive strip under the vehicle (to keep the vehicle body and the ground at the same potential), activate the electromagnetic shielding device (to provide partial shielding protection for the ECU and the vehicle control bus), and trigger occupant evasive posture guidance (to display an evasive posture diagram on the central control screen, and to remind occupants to adopt a curled-up posture in conjunction with seat vibration prompts).

[0131] The execution control unit is used to send protection commands to the corresponding vehicle actuators (such as sunroof controller, antenna controller, conductive strip driver, shielded circuit switch, etc.) and receive feedback information on the execution status in real time, such as the sunroof closing status and whether the conductive strip is fully extended. If the execution status is inconsistent with the command, the system issues a secondary warning to ensure that the protection action is implemented.

[0132] like Figure 7 As shown, another embodiment of the present invention provides a vehicle active lightning strike warning and protection method based on electric field induction, comprising the following steps:

[0133] S1: Acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, including at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data;

[0134] S2: Based on electric field measurement data and corona pulse data, perform signal separation and pattern recognition to distinguish corona discharge pulses from vehicle or environmental background noise, identify potential lightning strike precursor events, and output time-stamped precursor event information and precursor confidence index.

[0135] S3: Based on electric field measurement data and millimeter-wave radar data, combined with precursor event information and precursor confidence index, assess the possibility of lightning strike conduction channel forming near the vehicle and output breakdown risk index.

[0136] S4: Based on climate prior data, and combined with the global statistical results of electric field measurement data, drift correction is performed, and climate prior risk indicators are output.

[0137] S5: It takes the precursor confidence index, breakdown risk index and climate prior risk index as the main inputs, and incorporates the vehicle body charge data to generate a comprehensive risk index. It compares the comprehensive risk index with the classification threshold, outputs protection commands, and controls the actuators to implement the corresponding classification protection actions.

[0138] In summary, this invention overcomes the shortcomings of existing vehicle lightning protection technologies in terms of precursor identification, vehicle body charge management, environmental data fusion, and single protection response by using a collaborative design that integrates multi-source data acquisition, precursor identification, air breakdown risk assessment, climate prior fusion, and hierarchical decision execution. It achieves proactive early warning and multi-level protection for vehicles in lightning environments, significantly improving the overall safety and reliability of vehicles.

[0139] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle active lightning strike warning and protection system based on electric field induction, characterized in that, The system includes: The multi-source sensing and acquisition module is used to acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, which includes at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data. The precursor identification module is used to perform signal separation and pattern recognition based on the electric field measurement data and corona pulse data, so as to distinguish the corona discharge pulse from the vehicle itself or environmental background noise, identify potential lightning strike precursor events, and output precursor event information with time stamps and precursor confidence index. The air breakdown risk assessment module is used to assess the possibility of a lightning strike conduction channel forming near the vehicle based on the electric field measurement data and millimeter-wave radar data, combined with the precursor event information and precursor confidence index, and output the breakdown risk index. The climate prior fusion module is used to perform drift correction based on the climate prior data and the global statistical results of the electric field measurement data, and output the climate prior risk index. The integrated decision-making and execution module is used to generate a comprehensive risk index by taking the precursor confidence index, breakdown risk index, and climate prior risk index as the main inputs and incorporating the vehicle body charge data. The comprehensive risk index is compared with the classification threshold, and protection instructions are output. The module also controls the execution mechanism to implement the corresponding classification protection actions.

2. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 1, characterized in that, The multi-source sensing and acquisition module includes: An electric field sensor array is deployed on the roof, rearview mirror, and bumper areas to collect multi-channel electric field measurement data and output it in slices with a coverage time window. A high-frequency pulse sensor for acquiring corona pulse data in the 0.5–10MHz frequency band; The vehicle body charge monitoring unit is used to collect the static charge of the vehicle body and indicate in the output whether the static charge has reached a danger threshold, thus generating vehicle body charge data. The danger threshold is set to be no less than 500. ; Vehicle-mounted millimeter-wave radar is used to acquire the motion vector and reflectivity parameters of cumulonimbus clouds to form millimeter-wave radar data. The data interface unit is used to receive meteorological observations and lightning location information, and to generate climate prior data based on the "month × hour" index; The time synchronization and standardization processing unit is used to perform unified clock synchronization and standardized formatting processing on the multi-source data collected above, and output a standardized data stream.

3. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 2, characterized in that, The precursor identification module includes: The signal separation unit receives electric field measurement data and corona pulse data, and encodes the input time series into corona discharge pulse component factors. and environmental background noise component factors ; Dual decoder structure, including corona discharge pulse decoder and ambient background noise decoder Based on the corona discharge pulse component factors respectively and environmental background noise component factors Reconstruct the corona discharge pulse signal and the environmental background noise signal; This represents a decoupling verification mechanism used to exchange the environmental background noise component factors between input data from different time segments. And calculate the exchange verification error; The precursor event discrimination unit is used to extract amplitude, energy density and temporal continuity features based on the reconstructed corona discharge pulse signal, and in combination with the exchange verification error, identify potential lightning precursor events, and output time-stamped precursor event information and precursor confidence index.

4. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 3, characterized in that, The formula for calculating the exchange verification error is as follows: ； In the formula, Indicates the first The and the first Verification error between input data of different time segments; , The first The first time segment, the first Corona discharge pulse component factors for a time segment; , The first The first time segment, the first Environmental background noise component factors for each time segment; For the first The calibration reference for each time segment is the corona discharge pulse signal. For the first The calibration reference for each time segment is the ambient background noise signal.

5. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 2, characterized in that, The air breakdown risk assessment module includes: An electric field feature extraction unit is used to calculate the electric field intensity gradient and the rate of change of the electric field intensity gradient over time based on the electric field measurement data. The cumulonimbus cloud parameter extraction unit is used to extract the motion vector and reflectivity parameters of cumulonimbus clouds based on millimeter-wave radar data. The risk triggering association unit is used to trigger an air breakdown risk assessment when the precursor confidence index exceeds a preset threshold. After triggering, the unit locates the hazard source through the precursor event information and establishes a spatiotemporal correspondence between the hazard source and the electric field intensity distribution and the cumulonimbus cloud motion vector. The unit combines the electric field intensity gradient and the cumulonimbus cloud motion vector to form a breakdown risk judgment input. The breakdown probability calculation unit is used to calculate the breakdown risk index of a lightning strike conduction channel forming near the vehicle. The calculation formula is as follows: ； In the formula, for The risk indicators are constantly being breached; For Sigmoid mapping functions; for The electric field intensity gradient at time t. This represents the rate of change of the electric field intensity gradient with time. Let be the metric function for the motion intensity of cumulonimbus clouds, where These represent the vector components of cumulonimbus cloud motion, corresponding to the velocities in the X, Y, and Z directions, respectively. This represents the maximum value of the reflectivity parameter. As an indicator of the reliability of early warning signs; These are the weight parameters.

6. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 5, characterized in that, The climate prior fusion module includes: The global statistical analysis unit is used to obtain the drift reference value of the electric field intensity distribution based on the time series statistical results of electric field measurement data; The drift correction unit is used to compare the climate prior data with the drift benchmark value, and dynamically adjust the climate prior data using a correction function to obtain the climate prior risk index.

7. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 6, characterized in that, The correction function is expressed as: ； In the formula, For timestamps under a unified clock; express A priori climate risk indicators at any given moment; Indicates month with hours The corresponding prior climate data, Indicates in The drift reference value is obtained from statistical analysis of electric field measurement data at any given moment; The fusion weight parameter has a value range of [0,1].

8. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 7, characterized in that, The fusion decision-making and execution module includes: The risk index generation unit is used to receive the early warning credibility index. Breaking through risk indicators Climate a priori risk indicators and vehicle body charge data And combined with time smoothing factor Calculate the comprehensive risk index: ； In the formula, , They represent time, The overall risk index at any given moment; These are weight parameters; The dangerous threshold for the static charge on the vehicle body; The classification and determination unit is used to classify the comprehensive risk index. With the set grading threshold Perform interval comparisons to generate corresponding risk levels; The instruction generation unit is used to generate corresponding protection instructions under different risk levels; The execution control unit is used to send the protection command to the corresponding vehicle-mounted actuator and receive feedback information on the execution status in real time.

9. The vehicle active lightning strike early warning and protection system based on electric field induction according to claim 8, characterized in that, The generation of the corresponding risk level is specifically as follows: when At that time, the risk level was determined to be level zero; when At that time, the risk level was determined to be Level 1; when At that time, the risk level was determined to be Level 2; when At that time, the risk level was determined to be Level 3; The generation of corresponding protection instructions under different risk levels is specifically as follows: When the risk level is Level 1, an audible and visual warning command is generated, and a prompt message is displayed on the central control screen; When the risk level is level 2, commands are generated to close the skylight, retract the antenna, and activate the surge protector. When the risk level is level three, instructions are generated to release the charge from the conductive strip under the vehicle, activate the electromagnetic shielding device, and trigger the occupant's hazard avoidance posture guidance.

10. A vehicle active lightning strike warning and protection method based on electric field induction, applied to the vehicle active lightning strike warning and protection system based on electric field induction as described in any one of claims 1-9, characterized in that, The method includes: Acquire and time-synchronize multi-source data related to vehicle lightning risk, and output a standardized data stream, which includes at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter-wave radar data, and climate prior data; Based on the electric field measurement data and corona pulse data, signal separation and pattern recognition are performed to distinguish corona discharge pulses from vehicle or environmental background noise, identify potential lightning strike precursor events, and output time-stamped precursor event information and precursor confidence index. Based on the electric field measurement data and millimeter-wave radar data, combined with the precursor event information and precursor confidence index, the possibility of a lightning strike conduction channel forming near the vehicle is assessed, and a breakdown risk index is output. Based on the aforementioned climate prior data, drift correction is performed using the global statistical results of electric field measurement data, and a climate prior risk index is output. Using the aforementioned precursor confidence index, breakdown risk index, and climate prior risk index as the main inputs, and incorporating the vehicle body charge data, a comprehensive risk index is generated. The comprehensive risk index is compared with the classification threshold, a protection command is output, and the actuator is controlled to implement the corresponding classification protection action.

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