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

Abstract

The invention discloses a vehicle active lightning protection early warning and protection system and method based on electric field induction, and relates to the technical field of vehicle safety. According to the invention, multi-source data related to vehicle thunder and lightning risks are obtained and subjected to time synchronization through a multi-source sensing acquisition module, and a standardized data stream is output; the threatening recognition module is used for carrying out separation and mode recognition and outputting threatening event information and credibility indexes; the air breakdown risk assessment module quantifies the breakdown risk near the vehicle; the climate prior fusion module carries out drift correction on prior climate data of month and hour indexes and outputs a corrected risk index; and the fusion decision and execution module takes multiple types of indexes as input, generates a comprehensive risk index, performs grading judgment, outputs a corresponding protection instruction, and controls an execution mechanism to implement a corresponding grading protection action. According to the invention, active early warning and graded protection of the vehicle in the thunder and lightning environment are realized, and the safety of the whole vehicle and passengers and the reliability of an electronic system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle safety technology, in particular to a vehicle active lightning strike early warning and protection system and method based on electric field induction. BACKGROUND

[0002] Lightning is a typical strong pulse electromagnetic phenomenon, and its transient high current and high voltage will not only cause serious damage to the vehicle body and vehicle-mounted electronic equipment, but also endanger the personal safety of passengers. The existing vehicle lightning protection technology mainly relies on the "Faraday cage effect" of the vehicle body metal shell to achieve passive protection, but it generally lacks real-time monitoring of electric field and identification of corona discharge signals, cannot actively perceive lightning precursors, cannot obtain early warning information before lightning strikes, and leads to lagging protection measures; due to the insulating properties of tires, static charge accumulation is easy to occur in vehicles in strong electric fields, increasing the probability of forming an upward leader, and the existing scheme lacks real-time monitoring and active release mechanism of the charge state of the vehicle body, and the risk of charge accumulation on the vehicle body has not been effectively controlled; it also fails to effectively integrate climate and environmental data, resulting in a lack of correction and verification of external environmental conditions in risk assessment. Traditional schemes are mostly "all or nothing" protection logic, i.e., relying only on the shielding effect of the vehicle body when lightning occurs, lacking a graded response and progressive intervention mechanism, and unable to take targeted protective measures under different risk levels.

[0003] Therefore, there is still an urgent need for an integrated technical solution that can actively perceive lightning precursors, risk grading assessment, and multi-level vehicle / passenger protection to improve the safety of vehicles in lightning environments. SUMMARY

[0004] In view of the problems of lack of precursor identification, insufficient control of vehicle body charge, low risk assessment accuracy, and single protection response in the existing vehicle lightning protection system, the present application proposes a vehicle active lightning strike early warning and protection system and method based on electric field induction.

[0005] The present application achieves the above-mentioned purposes through the following technical solutions:

[0006] The vehicle active lightning strike early warning and protection system based on electric field induction comprises:

[0007] A multi-source perception acquisition module for acquiring and time synchronizing multi-source data related to vehicle lightning risk, outputting standardized data streams, the data streams at least including electric field measurement data, corona pulse data, vehicle body charge data, millimeter wave radar data, and climate prior data;

[0008] A premonition recognition module for signal separation and pattern recognition based on the electric field measurement data and corona pulse data to distinguish corona discharge pulses from vehicle self or environmental background noise, identify potential lightning premonition events, and output time-stamped premonition event information and premonition credibility indicators;

[0009] An air breakdown risk assessment module for assessing the possibility of lightning conduction channel formation near the vehicle based on the electric field measurement data and millimeter wave radar data, combining the premonition event information and premonition credibility indicators, and outputting a breakdown risk indicator;

[0010] A climate prior fusion module for drift correction based on the climate prior data, combining global statistical results of electric field measurement data, and outputting a climate prior risk indicator;

[0011] A fusion decision and execution module for taking the premonition credibility indicators, breakdown risk indicators, and climate prior risk indicators as main inputs, incorporating the vehicle body charge data, generating a comprehensive risk index, comparing the comprehensive risk index with a grading threshold, outputting protection instructions, and controlling the execution mechanism to implement corresponding grading protection actions.

[0012] As a preferred scheme of the present application, the multi-source perception acquisition module comprises:

[0013] An electric field sensor array arranged on the roof, rearview mirror, and bumper area for acquiring multi-channel electric field measurement data and outputting in an overlapped time window;

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

[0015] A vehicle body charge monitoring unit for acquiring vehicle body static charge and indicating whether the vehicle body static charge reaches a dangerous threshold in the output, forming vehicle body charge data, and the dangerous threshold is set to be not less than 500 ;

[0016] A vehicle-mounted millimeter wave radar for obtaining cumulonimbus cloud motion vector and reflectivity parameters, forming millimeter wave radar data;

[0017] A data interface unit for receiving meteorological observations and lightning location information, and forming climate prior data based on "month x hour" index;

[0018] A time synchronization and standardization processing unit for unified clock synchronization and standardized formatting processing of the above-mentioned multi-source data, and outputting standardized data stream.

[0019] As a preferred scheme of the present application, the premonition recognition module comprises:

[0020] a signal separation unit for receiving the electric field measurement data and the corona pulse data, encoding the input time series into a corona discharge pulse component factor and an ambient background noise component factor

[0021] a dual-decoder structure comprising a corona discharge pulse decoder and an ambient background noise decoder based on the corona discharge pulse component factor and the ambient background noise component factor respectively, reconstructing a corona discharge pulse signal and an ambient background noise signal

[0022] a decoupling validation mechanism for exchanging the ambient background noise component factor between input data of different time segments and calculating an exchange validation error

[0023] a precursor event discrimination unit for extracting amplitude, energy density and time continuity features based on the reconstructed corona discharge pulse signal, identifying potential lightning precursor events in combination with the exchange validation error, and outputting time-tagged precursor event information and a precursor confidence index.

[0024] As a preferred scheme of the present application, the exchange validation error is calculated according to the following formula:

[0025]

[0026] wherein, denotes the exchange validation error between the input data of the i-th time segment and the j-th time segment , are the corona discharge pulse component factors of the i-th time segment and the j-th time segment respectively , are the ambient background noise component factors of the i-th time segment and the j-th time segment respectively , is the calibrated reference corona discharge pulse signal of the i-th time segment , is the calibrated reference ambient background noise signal of the i-th time segment .

[0027] As a preferred scheme of the present application, the air breakdown risk assessment module comprises:

[0028] ​​​​​​​an electric field feature extraction unit configured to calculate an electric field intensity gradient and a rate of change of the electric field intensity gradient over time based on the electric field measurement data;

[0029] a cumulonimbus parameter extraction unit configured to extract a cumulonimbus motion vector and a reflectivity parameter based on the millimeter wave radar data;

[0030] a risk trigger association unit configured to trigger an air breakdown risk assessment when the precursor reliability index exceeds a preset threshold, and to locate a hazard source and establish a spatiotemporal correspondence between the hazard source and the electric field intensity distribution and the cumulonimbus motion vector after triggering, and to form a breakdown risk judgment input in combination with the electric field intensity gradient and the cumulonimbus motion vector;

[0031] a breakdown probability calculation unit configured to calculate a breakdown risk index of a lightning strike conduction channel formed near a vehicle, with a calculation formula being:

[0032]

[0033] wherein, is a breakdown risk index at a time t; is a Sigmoid mapping function; is an electric field intensity gradient at a time t, is a rate of change of the electric field intensity gradient over time; is a cumulonimbus motion intensity measurement function, wherein represents a cumulonimbus motion vector component, corresponding to a speed in X, Y, and Z directions respectively; is a maximum value of a reflectivity parameter, is a precursor reliability index; is a weight parameter.

[0034] As a preferred scheme of the present application, the climate prior fusion module comprises:

[0035] a global statistical analysis unit configured to obtain a drift reference value of the electric field intensity distribution based on a time series statistical result of the electric field measurement data;

[0036] a drift correction unit configured to compare the climate prior data with the drift reference value, and to dynamically adjust the climate prior data by using a correction function to obtain a climate prior risk index.

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

[0038]

[0039] wherein, is a time stamp under a unified clock;​​​ express A priori climate risk indicators at the moment; Indicates the month With hours The corresponding climate prior data, Indicates The drift reference value obtained from the electric field measurement data at any moment; is the fusion weight parameter, and its value range is [0,1].

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

[0041] Risk index generation unit, used to receive the precursor credibility index , breakdown risk indicator , climate prior risk indicators And body charge data , combined with the time smoothing factor , calculate the comprehensive risk index:

[0042] ;

[0043] Where, 、 Respectively time, Comprehensive risk index at the moment; is the weight parameter; is the dangerous threshold value of the electrostatic charge on the vehicle body;

[0044] A grading determination unit is used to convert the comprehensive risk index and the set classification threshold Perform interval comparison and generate corresponding risk levels;

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

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

[0047] As a preferred solution of the present invention, the generation of corresponding risk levels is specifically as follows:

[0048] when When the risk level is determined to be zero;

[0049] when When the risk level is determined to be level one;

[0050] when When the risk level is determined to be level 2;

[0051] When the risk level is determined to be level three;

[0052] The corresponding protection instructions are generated under different risk levels, specifically:

[0053] When the risk level is level one, an audible and visual warning instruction is generated, and prompt information is displayed on the central control screen;

[0054] When the risk level is level two, instructions to close the sunroof, retract the antenna, and start the surge protector are generated;

[0055] When the risk level is level three, instructions to start the vehicle bottom conductive belt to release charge, enable electromagnetic shielding device, and trigger occupant risk avoidance posture guidance are generated.

[0056] The vehicle active lightning strike warning and protection method based on electric field induction comprises:

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

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

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

[0060] Based on the climate priori data, drift correction is performed based on the global statistical results of the electric field measurement data, and a climate priori risk index is output;

[0061] The precursor credibility index, breakdown risk index, and climate priori risk index are used as main inputs, and the vehicle body charge data is combined to generate a comprehensive risk index. The comprehensive risk index is compared with the grading threshold value, and the protection instruction is output, and the corresponding grading protection action is controlled to be implemented by the execution mechanism.

[0062] The beneficial effects of the present application are: through the cooperation of the electric field sensor and the high-frequency pulse sensor in collecting data, combined with signal separation and pattern recognition methods, the electric field discharge pulse and background noise can be effectively distinguished, thereby identifying the lightning precursor event in advance, and outputting a credibility quantitative index, providing a trigger condition for subsequent risk assessment; the electric field intensity gradient, the electric field intensity change rate, the cumulonimbus motion vector and the reflectivity parameter are jointly analyzed, and the spatiotemporal correspondence relationship of the precursor event information is introduced, the breakdown probability is calculated, and the quantitative assessment of the risk of the formation of the conducting channel near the vehicle is realized; the drift correction is performed on the meteorological statistical data based on the "month x hour" index, so that the risk assessment result can reflect the external climate trend and the local electric field change at the same time, thereby improving the prediction accuracy; the precursor credibility index, the breakdown risk index, the climate prior risk index and the vehicle body charge data are comprehensively calculated to generate a comprehensive risk index, and compared with the multi-level threshold, the risk level division of zero level, first level, second level and third level is realized. Different levels correspond to sound and light warning, vehicle protection measures and passenger safety intervention, forming a progressive active protection system; under the high risk level, the system not only controls the sunroof, the antenna, the conductive belt and the electromagnetic shielding device, but also prompts the passengers to take a safe posture through the man-machine interface and the seat vibration mode, thereby reducing the damage risk of the passengers and the vehicle-mounted electronic equipment caused by the side flash and the electromagnetic pulse. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them: Figure 1 It is a schematic diagram of the modular structure of the system of the present application; Figure 2 It is a schematic diagram of the multi-source perception acquisition module of the embodiment of the present application; Figure 3 It is a schematic diagram of the precursor identification module of the embodiment of the present application; Figure 4 It is a schematic diagram of the air breakdown risk assessment module of the embodiment of the present application; Figure 5 It is a schematic diagram of the climate prior fusion module of the embodiment of the present application; Figure 6 It is a schematic diagram of the fusion decision and execution module of the embodiment of the present application; Figure 7 It is a flow chart of the method of the present application. DETAILED DESCRIPTION

[0064] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0065] As shown in FIG. 1, an embodiment of the present application provides an electric field induction-based active lightning strike early warning and protection system for a vehicle, which comprises a multi-source perception acquisition module, a precursor identification module, an air breakdown risk assessment module, a climate prior fusion module, and a fusion decision and execution module. Figure 1

[0066] The multi-source perception acquisition module is configured to acquire and time-synchronize multi-source data related to lightning risk of the vehicle, and output standardized data streams, at least including electric field measurement data, corona pulse data, vehicle body charge data, millimeter wave radar data, and climate prior data.

[0067] As shown in FIG. 2, the multi-source perception acquisition module of the embodiment comprises: Figure 2 An electric field sensor array is fixedly arranged on the roof of the vehicle, the housings of the left and right rearview mirrors, and the front and rear bumpers, and is configured to collect electric field intensity at multiple spatial positions. The electric field sensor array forms a multi-channel input signal, each channel signal is time-synchronized during collection, and is processed by an overlapping time window for slicing, so that electric field measurement data with time continuity can be obtained during continuous driving. The electric field measurement data is uniformly arranged as an electric field measurement data stream, which is used as input for subsequent precursor identification and air breakdown risk assessment.

[0068] A high-frequency pulse sensor is arranged at the top of the vehicle, and is configured to collect transient high-frequency electromagnetic signals in a frequency band of 0.5-10 MHz. The sampling bandwidth of the high-frequency pulse sensor covers the pulse frequency interval commonly seen in corona discharge and lightning leader discharge, and can distinguish pulse signals from external lightning precursors. The collected signals are output as corona pulse data.

[0069] A vehicle body charge monitoring unit is arranged between the metal body of the vehicle and the chassis structure, and is configured to collect the static charge of the vehicle body by using a capacitive or electrostatic charge sensor structure. The vehicle body charge monitoring unit compares the real-time measured static charge value of the vehicle body with a set dangerous threshold (not less than 500 V), and outputs a dangerous state indication signal in the output data when the monitoring result reaches or exceeds the dangerous threshold. The output of the vehicle body charge monitoring unit constitutes the vehicle body charge data, which is called by the fusion decision and execution module.

[0070] ​​Vehicle-mounted millimeter wave radar: installed on the front of the vehicle, used to obtain target cumulonimbus motion vector and reflectivity parameters, which can reflect the moving direction, speed and internal water vapor and ice crystal content characteristics of the cloud. The processed information forms millimeter wave radar data, providing dynamic meteorological input for the air breakdown risk assessment module.

[0071] Data interface unit: through the wireless communication module, data connection is established with external meteorological observation station and lightning positioning system, used to receive meteorological observation information and lightning positioning information. The received data is indexed to form climate prior data corresponding to "month x hour". This data includes lightning occurrence probability level information based on long-term observation statistics, which is used for subsequent risk fusion.

[0072] Time synchronization and standardization processing unit: connected with each of the above acquisition sub-modules, used for unified clock synchronization of electric field measurement data, corona pulse data, vehicle body charge data, millimeter wave radar data and climate prior data, to ensure that multi-source data is consistent in time dimension. At the same time, this processing unit standardizes the data of different formats and dimensions, and unifies them into a standardized data stream, so as to be processed and called in real time in the subsequent precursory identification, air breakdown risk assessment and risk fusion modules.

[0073] The precursory identification module is used for signal separation and pattern recognition based on electric field measurement data and corona pulse data, to distinguish corona discharge pulse from vehicle itself or environmental background noise, identify potential lightning precursory events, and output time-labeled precursory event information and precursory credibility index.

[0074] As shown in Figure 3 , the precursory identification module of the embodiment includes: A signal separation unit is used to receive electric field measurement data and corona pulse data, pre-process the input time series data by band-pass filtering, amplitude normalization, time alignment, etc., and map the input sequence to two types of potential representation factors by using a deep representation encoder:

[0075] A corona discharge pulse component factor , used to represent potential lightning precursory signals;

[0076] An environmental background noise component factor , used to represent non-precursory noise in the vehicle itself or environment;

[0077] This separation method can ensure that the corona pulse and background noise are decoupled in the feature space.

[0078] A dual-decoder structure includes: A corona discharge pulse decoder : input is the corona discharge pulse component factor and environmental background noise component factors , the output is the reconstructed corona discharge pulse signal;

[0079] Ambient background noise decoder : The input is the same factor pair, and the output is the reconstructed environmental background noise signal;

[0080] Through the above dual decoder structure, the corona pulse signal and the environmental background noise signal can be reconstructed separately to form a signal pair corresponding to the original input for subsequent verification and discrimination.

[0081] Represents a decoupled verification mechanism for exchanging ambient background noise component factors between input data of different time segments , to test the decoupling effect. Specifically, for the The first and The exchange verification error is calculated after exchanging the background noise component factors of the input time segments. , the formula is:

[0082] ;

[0083] Where, Number two different time segments; 、 Respectively Time segment, Corona discharge pulse component factor for each time segment; 、 Respectively Time segment, The environmental background noise component factor of each time segment; For the The calibration reference corona discharge pulse signal of each time segment is For the The calibration of each time segment is based on the background noise signal of the environment;

[0084] By exchanging the environmental background noise factors of different time segments and calculating the cross-validation error, the independence and decoupling effect of factor separation can be verified, thereby improving the credibility of recognition.

[0085] The precursor event discrimination unit is used to extract the amplitude (the maximum instantaneous amplitude of the corona discharge pulse signal in the detection frequency band), energy density (the integrated energy value in the 0.5-10MHz frequency band) and time continuity characteristics (whether the pulse event occurs continuously, based on the statistical pulse interval) of the reconstructed corona discharge pulse signal output by the dual decoder, and combines it with the exchange verification error calculated by the decoupling verification mechanism to represent the identify potential lightning pre-stroke events, output time-stamped pre-stroke event information and pre-stroke credibility indicators;

[0086] Exemplarily, the pre-stroke event information is represented as:

[0087] The pre-stroke credibility indicator is represented as:

[0088] wherein, represents the pre-stroke event information; is a time stamp, representing the absolute or relative time (e.g. millisecond-level UTC time, or vehicle system time) when the pre-stroke event is identified; is a sensor channel number, corresponding to a specific collection position in the electric field sensor array (e.g. roof, left rearview mirror, right rearview mirror, front bumper, rear bumper); is the peak amplitude of the corona pulse, in V / m or , corresponding to the transient pulse amplitude detected within the 0.5-10 MHz frequency band; is the energy integral value within the corona pulse detection frequency band (obtained through band-pass filtering energy norm), in J; is the pulse duration (full width half maximum FWHM or envelope duration), in ; is a flag indicating whether the event belongs to a continuous pulse sequence, which can be a Boolean value (single / continuous) or an integer (number of continuous pulses);

[0089] is the pre-stroke credibility indicator; represents a Sigmoid function, is a calibration weight; in this way, the pre-stroke credibility indicator is explicitly in the 0-1 interval and is quantified.

[0090] The air breakdown risk assessment module is used to assess the possibility of lightning conduction channel formation near the vehicle based on electric field measurement data and millimeter wave radar data, combined with pre-stroke event information and pre-stroke credibility indicators, and output a breakdown risk indicator.

[0091] As shown in Figure 4 , in a preferred embodiment of the present application, the air breakdown risk assessment module comprises:

[0092] ​​The electric field feature extraction unit is configured to calculate an electric field intensity gradient and a rate of change of the electric field intensity gradient over time based on the electric field measurement data. The electric field intensity gradient represents a differential result of electric field intensity in a space around the vehicle between different sampling points, and the rate of change of the electric field intensity gradient over time represents a rate of change of the electric field intensity gradient on a time axis, for characterizing local electric field instability. These features can reflect the trend of electric field concentration before the lightning conduction channel is formed.

[0093] The cumulonimbus parameter extraction unit is configured to extract a cumulonimbus motion vector (respectively representing velocity components of the cumulonimbus in X, Y, and Z directions, with a unit of meters per second) and a reflectivity parameter (obtained from echo intensity of a space grid point by the millimeter wave radar, reflecting density distribution of water droplets or ice crystals) based on the millimeter wave radar data.

[0094] The risk trigger association unit is configured to determine that there is a high reliability of lightning precursor when the precursor credibility index exceeds a preset threshold (such as 0.7), so as to trigger an air breakdown risk assessment process. After triggering, the dangerous source direction or area is located through the channel number in the precursor event information and the corresponding sensor arrangement position (for example, when a high-credibility precursor signal appears in the electric field sensor channel at the position of the rearview mirror of the vehicle roof, the system marks this direction as a potential dangerous source position). Further, the spatial position of the dangerous source is matched with the electric field intensity distribution at the same time and the cumulonimbus motion vector extracted by the millimeter wave radar by aligning the time stamp in the precursor event information with the unified clock provided by the time synchronization and standardization processing unit. The time and space correspondence between the dangerous source and the electric field intensity distribution and the cumulonimbus motion vector is established through the time stamp alignment and the spatial channel positioning. The spatial positioning result of the dangerous source, the electric field intensity gradient, and the cumulonimbus motion vector are combined to generate input parameters for air breakdown risk determination. The determination input is then transmitted to the breakdown probability calculation unit for quantitatively calculating a breakdown risk index.

[0095] The breakdown probability calculation unit is configured to calculate a breakdown risk index of a lightning conduction channel formed near the vehicle. The calculation formula is as follows:

[0096] ;

[0097] In the formula, P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. P(t) is the breakdown risk index at the time t. is the maximum value of the reflectivity parameter, , represents the strongest reflection region inside the cumulonimbus cloud, and is used to quantify the potential charge accumulation center; is the precursor reliability index; is the weight parameter.

[0098] The climate prior fusion module is used to correct the drift based on the global statistical results of the climate prior data combined with the electric field measurement data, and output the climate prior risk index.

[0099] As shown in Figure 5 , the climate prior fusion module of the embodiment can include:

[0100] A 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 the electric field measurement data; the electric field measurement data is collected by the vehicle-mounted electric field sensor array, and the system samples the electric field intensity within a time window (for example, several minutes to several hours) and calculates the statistical characteristic quantities of the window, such as the mean, variance, and extreme value, which are comprehensively used as the drift reference value, denoted as , which is used to represent the overall level of the local electric field in which the vehicle is located;

[0101] A drift correction unit is used to compare the climate prior data with the drift reference value, and to obtain the climate prior risk index by weighted adjustment based on a correction function;

[0102] The climate prior data is derived from meteorological observations and lightning location information, which has been organized in the data interface unit of the multi-source perception collection module according to the "month x hour" index. For a given month and hour , the corresponding climate prior data can be extracted from the prior database, denoted as ;

[0103] The external prior data and the local drift reference value are weighted and fused through the correction function:

[0104]

[0105] The climate prior risk index at time is obtained after correction;

[0106] In the formula, is the timestamp under the unified clock; represents the climate prior risk index at time ; represents the drift reference value obtained by statistical analysis of the electric field measurement data at time ; is the fusion weight parameter, with a value range of [0, 1].

[0107] The fusion decision and execution module is used for taking the premonition credibility index, the breakdown risk index and the climate prior risk index as main inputs, integrating the vehicle body charge data, generating a comprehensive risk index, comparing the risk index with a grading threshold, outputting a protection instruction, and controlling an execution mechanism to implement a corresponding grading protection action.

[0108] As shown in Figure 6 , in one of the specific embodiments of the present application, the fusion decision and execution module comprises:

[0109] a risk index generation unit for receiving the premonition credibility index output by the premonition identification module , the breakdown risk index output by the air breakdown risk assessment module , the climate prior risk index output by the climate prior fusion module , and the vehicle body charge data output by the vehicle body charge monitoring unit , a time smoothing factor is introduced to improve the robustness of the system to transient signal fluctuations , and a comprehensive risk index is calculated:

[0110] ;

[0111] In the formula, , respectively represent the comprehensive risk index at time , the comprehensive risk index at time ; is a weight parameter corresponding to the four types of inputs respectively; is a dangerous threshold of the vehicle body static charge; When is larger, the risk index is smoother, which is suitable for avoiding false triggering; when is smaller, the system response is more agile;

[0112] a grading determination unit for performing interval comparison between the comprehensive risk index and a set grading threshold to generate a corresponding risk level, and this grading determination method ensures progressive protection under different risk levels and avoids the single response mode of "all or nothing", specifically:

[0113] When , it is determined that the risk level is zero;

[0114] When , it is determined that the risk level is one;

[0115] When , it is determined that the risk level is two;

[0116] When When the risk level is determined to be level three;

[0117] The instruction generation unit is used to generate corresponding protection instructions at different risk levels, specifically:

[0118] When the risk level reaches level one, an audible and visual warning command is generated, including a buzzer and a warning light on the instrument panel, and a prompt message is displayed on the central control screen to remind passengers to avoid contact with metal parts;

[0119] When the risk level reaches level two, commands are generated to close the sunroof (to prevent lightning from entering the vehicle), retract the antenna (to reduce the probability of protrusions attracting lightning), and activate the surge protector (to release some energy in advance to prevent electromagnetic pulses from entering critical electronic systems).

[0120] When the risk level reaches level three, commands are generated to activate the conductive strips under the vehicle to release charge (to maintain the same potential as the vehicle body and the ground), activate the electromagnetic shielding device (to provide partial shielding protection for the ECU and onboard control bus), and trigger occupant avoidance posture guidance (displaying an avoidance posture icon on the central control screen and accompanied by seat vibration prompts to remind occupants to adopt a curled-up posture).

[0121] The execution control unit is used to send protection instructions to the corresponding on-board actuators (such as the sunroof controller, antenna controller, conductive belt driver, shielding circuit switch, etc.) and receive real-time feedback information on the execution status, such as: the sunroof closing status, whether the conductive belt is fully extended; if the execution status is inconsistent with the instruction, the system will issue a secondary warning prompt to ensure the implementation of the protection action.

[0122] like Figure 7 FIG. 1 is another embodiment of the present invention, which provides a vehicle active lightning strike warning and protection method based on electric field induction, comprising the following steps:

[0123] S1: Acquire and time-synchronize multi-source data related to vehicle lightning risks and output standardized data streams, including at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter wave radar data, and climate prior data;

[0124] S2: Performs signal separation and pattern recognition based on electric field measurement data and corona pulse data to distinguish corona discharge pulses from vehicle noise or environmental background noise, identifies potential lightning precursor events, and outputs time-stamped precursor event information and precursor credibility indicators.

[0125] S3: Based on electric field measurement data and millimeter-wave radar data, combined with precursor event information and precursor credibility indicators, it assesses the possibility of a lightning conduction channel forming near the vehicle and outputs a breakdown risk indicator;

[0126] S4: Based on the climate prior data, drift correction is carried out in combination with the global statistical result of the electric field measurement data, and a climate prior risk index is output;

[0127] S5: The premonitory credibility index, the breakdown risk index and the climate prior risk index are taken as main inputs, and the body charge data is combined to generate a comprehensive risk index, the comprehensive risk index is compared with a grading threshold, a protection instruction is output, and a corresponding grading protection action is implemented by controlling the execution mechanism.

[0128] In summary, through the cooperative design of multi-source data acquisition, premonitory identification, air breakdown risk assessment, climate prior fusion and grading decision execution, the deficiencies of the existing vehicle lightning protection technology in premonitory identification, body charge management, environmental data fusion and single protection response are overcome, active early warning and multi-level protection of the vehicle in the lightning environment are realized, and the overall safety and reliability of the vehicle are significantly improved.

[0129] In the above embodiments, all or part of them can be realized by software, hardware, firmware or any other combination. When realized by software, all or part of them can be realized in the form of 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 the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another.

[0130] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically, or two or more units can be integrated in one module. The above integrated module can be realized in the form of hardware or software function module. When the above integrated module is realized in the form of software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk.

[0131] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. The vehicle active lightning warning and protection system based on electric field induction is characterized by: The system comprises: A multi-source sensing and acquisition module is used to acquire and time-synchronize multi-source data related to vehicle lightning risks 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; a precursor recognition module for performing signal separation and pattern recognition based on the electric field measurement data and the corona pulse data to distinguish the corona discharge pulse from the vehicle itself or the environmental background noise, identify potential lightning precursor events, and output precursor event information with a time stamp and a precursor credibility indicator; an air breakdown risk assessment module, configured to assess the likelihood of a lightning conduction channel being formed near the vehicle based on the electric field measurement data and millimeter-wave radar data, in combination with the precursor event information and the precursor credibility index, and output a breakdown risk index; A climate prior fusion module is used to perform drift correction based on the climate prior data and combined with global statistical results of electric field measurement data to output a climate prior risk index; A fusion decision-making and execution module is used to take the precursor credibility index, breakdown risk index, and climate prior risk index as main inputs, and incorporate the vehicle body charge data to generate a comprehensive risk index, compare the comprehensive risk index with a classification threshold, output a protection instruction, and control the actuator to implement the corresponding graded protection action.

2. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 1 is characterized in that: The multi-source perception acquisition module includes: The electric field sensor array is placed on the roof, rearview mirrors, and bumper areas to collect multi-channel electric field measurement data and output it in slices using overlapping time windows; High-frequency pulse sensor, used to collect corona pulse data in the 0.5–10 MHz 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 of the vehicle body has reached the dangerous threshold, forming the vehicle body charge data. The dangerous threshold is set to not less than 500 ; Vehicle-mounted millimeter-wave radar, used to obtain cumulonimbus cloud motion vectors and reflectivity parameters to generate millimeter-wave radar data; Data interface unit, used to receive meteorological observation and lightning location information, and form 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 on the multi-source data collected above, and output a standardized data stream.

3. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 2 is characterized in that: The precursor identification module includes: Signal separation unit, used to receive electric field measurement data and corona pulse data, and encode 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 , respectively based on the corona discharge pulse component factors and environmental background noise component factors Reconstruct the corona discharge pulse signal and the environmental background noise signal; Represents a decoupling verification mechanism for exchanging the environmental background noise component factor between input data of different time segments , and calculate the exchange verification error; The precursor event identification unit is used to extract the amplitude, energy density and time continuity characteristics based on the reconstructed corona discharge pulse signal, and identify potential lightning precursor events in combination with the exchange verification error, and output precursor event information with time stamp and precursor credibility index.

4. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 3 is characterized in that: The calculation formula of the exchange verification error is: ； Where, Indicates the The first and Exchange verification error between time segment input data; 、 Respectively Time segment, Corona discharge pulse component factor for each time segment; 、 Respectively Time segment, The environmental background noise component factor of each time segment; For the The calibration reference corona discharge pulse signal of each time segment is For the The calibration of each time segment is based on the background noise signal of the environment.

5. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 2 is characterized in that: The air breakdown risk assessment module includes: an electric field feature extraction unit, configured 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; Cumulonimbus cloud parameter extraction unit, used to extract cumulonimbus cloud motion vector and reflectivity parameters based on millimeter wave radar data; a risk trigger association unit, configured to trigger an air breakdown risk assessment when the precursor credibility index exceeds a preset threshold, locate the hazard source through the precursor event information, establish a spatiotemporal correspondence between the hazard source and the electric field intensity distribution and the cumulonimbus cloud motion vector, and form a breakdown risk determination input by combining the electric field intensity gradient and the cumulonimbus cloud motion vector; The breakdown probability calculation unit is used to calculate the breakdown risk index of the lightning conduction channel formed near the vehicle. The calculation formula is: ； Where, for Breakdown risk indicator at each moment; is the Sigmoid mapping function; for The electric field intensity gradient at time , is the rate of change of the electric field intensity gradient with time; is the cumulonimbus cloud motion intensity measurement function, where Represents the cumulonimbus cloud motion vector components, corresponding to the speed in the X, Y, and Z directions respectively; is the maximum value of the reflectivity parameter, It is an indicator of the credibility of the precursor; is the weight parameter.

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

7. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 6 is characterized in that: The correction function is expressed as: ； Where, It is the timestamp under the unified clock; express A priori climate risk indicators at the moment; Indicates the month With hours The corresponding climate prior data, Indicates The drift reference value obtained from the electric field measurement data at any moment; is the fusion weight parameter, and its value range is [0,1].

8. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 7 is characterized in that: The fusion decision and execution module includes: Risk index generation unit, used to receive the precursor credibility index , breakdown risk indicator , climate prior risk indicators And body charge data , combined with the time smoothing factor , calculate the comprehensive risk index: ； Where, 、 Respectively time, Comprehensive risk index at the moment; is the weight parameter; is the dangerous threshold value of the electrostatic charge on the vehicle body; A grading determination unit is used to convert the comprehensive risk index and the set classification threshold Perform interval comparison and generate corresponding risk levels; An instruction generation unit, used to generate corresponding protection instructions under different risk levels; The execution control unit is used to send the protection instruction to the corresponding vehicle-mounted actuator and receive feedback information on the execution status in real time.

9. The vehicle active lightning strike warning and protection system based on electric field induction according to claim 8 is characterized in that: The risk level corresponding to the generation is specifically: when When the risk level is determined to be zero; when When the risk level is determined to be level one; when When the risk level is determined to be level 2; when When the risk level is determined to be level three; The corresponding protection instructions are generated at different risk levels as follows: When the risk level is level one, 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, instructions are generated to close the sunroof, retract the antenna, and activate the surge protector; When the risk level is level three, instructions are generated to start the conductive belt under the vehicle to release the charge, enable the electromagnetic shielding device, and trigger the occupant's risk avoidance posture guidance.

10. A vehicle active lightning strike warning and protection method based on electric field induction, applied to a vehicle active lightning strike warning and protection system based on electric field induction as claimed in any one of claims 1 to 9, characterized in that: The method comprises: Acquire and time-synchronize multi-source data related to vehicle lightning risk and output a standardized data stream, the data stream including at least: electric field measurement data, corona pulse data, vehicle body charge data, millimeter wave radar data, and climate prior data; Performing signal separation and pattern recognition based on the electric field measurement data and the corona pulse data to distinguish the corona discharge pulse from the vehicle itself or the environmental background noise, identifying potential lightning precursor events, and outputting precursor event information with time stamps and precursor credibility indicators; Based on the electric field measurement data and millimeter-wave radar data, combined with the precursor event information and the precursor credibility index, the possibility of a lightning conduction channel being formed near the vehicle is assessed, and a breakdown risk index is output; Based on the climate prior data, combined with the global statistical results of the electric field measurement data, drift correction is performed to output a climate prior risk index; The precursor credibility index, breakdown risk index, and climate prior risk index are used as main inputs, and the vehicle body charge data is incorporated to generate a comprehensive risk index. The comprehensive risk index is compared with the classification threshold, a protection instruction is output, and the actuator is controlled to implement the corresponding graded protection action.

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