Directed electromagnetic pulse vehicle stoppage control method and system

Abstract

The application discloses a directional electromagnetic pulse vehicle stopping control method and system, relates to the technical field of directional electromagnetic pulse stopping, and comprises the following steps: acquiring the speed of a target vehicle, the transmission distance between the target vehicle and a transmission source, and the slope information of a road ahead, calculating a minimum pulse energy benchmark required for the target vehicle to lose power based on the slope information and the transmission distance, and determining an initial intensity value of the directional electromagnetic pulse. The application integrates sliding dynamics trajectory prediction, dynamic risk space demarcation, quantitative risk assessment and risk-over-standard dynamic intensity correction into a closed loop process, realizes the triple balance of stopping effectiveness, safety controllability and scene adaptability through the combination of physical principles and quantitative models.

Description

Technical Field

[0001] This invention relates to the field of directional electromagnetic pulse (EMIP) stopping technology, specifically to a directional EMIP vehicle stopping control method and system. Background Technology

[0002] With the development of technology, vehicle-mounted electromagnetic interference equipment can achieve non-contact interception by intervening in the vehicle's control system through directional electromagnetic pulses, causing the vehicle to shut down automatically.

[0003] Existing technologies typically employ a fixed energy pattern of detection, targeting, and firing for interception, neglecting the collateral effects of pulse energy emission on the surrounding environment and constantly moving vehicles around the target vehicle. Even when environmental risks are considered and attempts are made to adjust the interception energy, the adjustment strategy is often passive, inefficient, and lacks precise guidance. When the current risk is deemed too high, the pulse intensity level is simply increased in fixed steps, and then a full-process, complex risk assessment is conducted again.

[0004] However, in dynamic scenarios of high-speed pursuit, iterative cycles of multiple assessments and adjustments consume valuable time, potentially leading to missed interception opportunities. Furthermore, the intensity increases are often arbitrary, failing to identify the extent of risk offset by a single increase or to anticipate new risks arising from the expanded impact area. Risk adjustments focus solely on the total risk value, neglecting crucial situational information regarding the spatial distribution of risk. Energy compensation strategies should differ for different risk spatial patterns, but current technologies cannot differentiate between them. The lack of intelligent decision-making methods that can directly, quickly, and accurately determine the optimal interception energy parameters based on real-time risk assessment results results in a significant challenge in achieving an effective balance between decision-making speed, interception accuracy, and security when dealing with complex dynamic environments. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a directional electromagnetic pulse vehicle stopping control method and system.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for controlling the stopping of a vehicle using a directional electromagnetic pulse includes the following steps:

[0008] The system acquires the target vehicle's speed, the transmission distance between it and the source, and the slope of the road ahead. Based on the slope information and transmission distance, it calculates the minimum pulse energy reference required for the target vehicle to lose power and determines the initial intensity value of the directional electromagnetic pulse.

[0009] The speed and gradient information of the target vehicle are input into the skidding dynamics model to calculate the predicted skidding trajectory of the target vehicle. Based on the predicted skidding trajectory and the estimated electromagnetic influence range corresponding to the initial intensity value, the initial risk assessment space range is dynamically delineated.

[0010] The probability of interaction between social vehicles and the target vehicle within the initial risk assessment space is quantitatively calculated and fused to obtain an initial comprehensive safety risk value.

[0011] Determine whether the overall safety risk value is lower than the safety threshold;

[0012] If so, a launch command corresponding to the initial intensity level will be generated;

[0013] Otherwise, the difference between the comprehensive safety risk value and the safety threshold is calculated as the risk excess.

[0014] Spatial situation parameters are obtained by quantitatively analyzing the spatial scope of risk assessment.

[0015] Based on space situation parameters and launch distance, a dynamic effectiveness model for risk offsetting per unit intensity is constructed.

[0016] By inputting the risk excess into the performance model, the strength compensation amount that reduces the comprehensive safety risk value to below the safety threshold is calculated.

[0017] The corrected intensity value is calculated based on the intensity compensation amount and the initial intensity value, and a launch command corresponding to the corrected intensity value is generated.

[0018] Preferably, after calculating the corrected strength value based on the strength compensation amount and the initial strength value, the method further includes:

[0019] The estimated comprehensive safety risk value is calculated based on the corrected strength value;

[0020] Determine whether the estimated comprehensive safety risk value is lower than the safety threshold;

[0021] If so, execute the launch command corresponding to the corrected intensity value;

[0022] Otherwise, cancel the launch command corresponding to the corrected strength value and output an interception failure signal.

[0023] Preferably, the safety threshold is a dynamic value that is adjusted in real time based on the environmental sensitivity information of the current road environment;

[0024] The environmental sensitivity information includes at least one or more of the following: geographical proximity of preset sensitive points around the road, real-time traffic flow density, and weather visibility.

[0025] Preferably, the risk probability includes:

[0026] Type I collision risk probability: Based on the predicted skidding trajectory and the predicted trajectory of other vehicles, calculate the conditional probability of a spatial collision between the target vehicle and other vehicles.

[0027] Type II electromagnetic interference risk probability: Based on the initial intensity value, transmission distance, and the position of the other vehicle relative to the target vehicle, calculate the probability that the electromagnetic pulse will cause collateral interference to the electronic system of the other vehicle.

[0028] Preferably, the spatial situation parameters include at least the following two parameters:

[0029] Risk surface density parameter: the ratio of the initial comprehensive safety risk value to the area of ​​the initial risk assessment space;

[0030] Electromagnetic risk proportion parameter: that is, the proportion of the risk probability contributed by the second type of electromagnetic interference risk among all the risk probabilities that constitute the initial comprehensive safety risk value.

[0031] Preferably, the unit intensity risk offsetting effectiveness model is a prediction model trained by a machine learning algorithm, and its training sample data comes from historical scenario data containing different launch distances, different space risk situation parameters and corresponding risk changes.

[0032] Preferably, the effectiveness model for risk offsetting per unit intensity follows the following functional relationship:

[0033] ;

[0034] in, Risk mitigation effectiveness per unit intensity;

[0035] The baseline performance coefficient is a normal number calibrated through experiments or simulations.

[0036] This refers to the launch distance;

[0037] For risk surface density parameters;

[0038] This is a parameter representing the proportion of electromagnetic risk.

[0039] As an adjustment function, its output value is positively correlated with both the input risk surface density parameter and the electromagnetic risk proportion parameter.

[0040] Preferably, the initial risk assessment spatial range is dynamically delineated based on the estimated electromagnetic influence range corresponding to the initial intensity value, specifically including:

[0041] The reference electromagnetic influence radius is obtained by querying the preset mapping relationship based on the initial intensity value;

[0042] The attenuation compensation calculation of the reference electromagnetic influence radius is performed based on the transmission distance to obtain the current effective influence radius;

[0043] Using the predicted gliding trajectory as the center line and the current effective influence radius as the buffer distance, a polygonal region is generated through geographic information system buffer analysis, which serves as the initial risk assessment spatial range.

[0044] Preferably, the minimum pulse energy benchmark required to calculate the loss of power of the target vehicle based on slope information and transmission distance specifically includes:

[0045] The slope information is converted into a component of gravity along the road direction, and the basic energy value required to overcome the target vehicle's current kinetic energy and this component force is calculated.

[0046] Based on the transmission distance, the basic energy value is corrected using an electromagnetic wave spatial propagation attenuation model to obtain the minimum pulse energy benchmark.

[0047] A directional electromagnetic pulse vehicle stopping control system, comprising:

[0048] The perception module is used to acquire data on the target vehicle's speed, launch distance, road gradient, and surrounding vehicles.

[0049] The processing and decision-making module is used to execute the above methods;

[0050] The execution module is used to control the directional electromagnetic pulse transmitter based on the transmission command and intensity value output by the processing and decision module.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention employs a full-chain correction process, encompassing risk overshoot calculation, spatial situation parameter extraction, performance model construction, and intensity compensation calculation. This allows for precise intensity enhancement to offset redundant risks when risks exceed limits, without abandoning the interception mission. It is particularly suitable for urgent interception scenarios. The performance model, built based on spatial situation parameters and launch distance, dynamically adapts to different risk distributions and launch distances. Compared to existing technologies using fixed compensation coefficients, this invention improves intensity compensation accuracy, effectively avoiding additional interference caused by overcompensation. It overcomes the technical bottlenecks of existing directional electromagnetic pulse interception methods, which rely on coarse intensity control and qualitative risk assessment. The invention integrates gliding dynamics trajectory prediction, dynamic risk space delineation, quantitative risk assessment, and dynamic intensity correction for risk overshoot into a closed-loop process. Through the combination of physical principles and quantitative models, it achieves a triple balance between interception effectiveness, safety controllability, and scenario adaptability. Attached Figure Description

[0053] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0054] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0055] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0056] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0057] like Figure 1-2 As shown, a directional electromagnetic pulse vehicle stopping control method includes the following steps:

[0058] The system acquires the target vehicle's speed, the transmission distance between it and the source, and the slope of the road ahead. Based on the slope information and transmission distance, it calculates the minimum pulse energy reference required for the target vehicle to lose power and determines the initial intensity value of the directional electromagnetic pulse.

[0059] Specifically, traditional electromagnetic pulse interception uses a fixed intensity emission, which either results in insufficient energy for effective interception or excessive energy that could damage the electronic systems of surrounding vehicles. By collecting parameters and performing quantitative calculations, the initial intensity can be precisely matched to the interception requirements of the current scenario.

[0060] First, obtain the following data:

[0061] Target vehicle speed: The speed of the target vehicle is collected through radar, cameras and vehicle network data. The higher the speed, the greater the kinetic energy of the vehicle and the more pulse energy is required.

[0062] Transmission distance: The transmission distance between the transmitter and the target vehicle is collected by a laser rangefinder and GPS positioning. The farther the transmission distance, the more severe the spatial attenuation of electromagnetic waves, and the higher the initial energy required.

[0063] Road slope ahead: The slope of the road ahead is collected by slope sensors or high-precision maps. When going uphill, the component of gravity acts as resistance, and when going downhill, the component of gravity acts as power. Therefore, the energy demand when going downhill is higher than the energy demand when going uphill.

[0064] Furthermore, since the electromagnetic pulse's function is to disrupt the vehicle's electronic control system, causing it to lose power, the pulse energy needs to at least offset the vehicle's inertia and the additional force from the slope. Therefore, the base energy value needs to overcome the vehicle's kinetic energy and the slope component force.

[0065] Calculate the current kinetic energy of the target vehicle based on its speed:

[0066] ;

[0067] in, Estimate the mass of the target vehicle. The speed of the target vehicle;

[0068] Calculate the work done by the component of gravity along the road direction using the slope information of the road ahead:

[0069] The component of gravity along the road direction is: ;

[0070] in, The slope angle is when going uphill. As resistance, when going downhill As a driving force;

[0071] The work done by the component of gravity along the road is: ;

[0072] in, The predicted gliding distance;

[0073] Calculate the base energy value:

[0074] Calculate the base energy value when going uphill. As resistance helps the vehicle slow down, it can reduce energy demand: ;

[0075] Calculate the base energy value during downhill driving. To provide power, the gliding distance will be extended, requiring increased energy consumption. ;

[0076] Calculate the base energy value for a level road surface; at this point, there is no... It can be simplified to: .

[0077] Furthermore, electromagnetic waves attenuate according to the inverse square law when propagating in space, meaning that the degree of energy attenuation is proportional to the square of the transmission distance. Therefore, the basic energy value needs to be compensated and corrected according to the transmission distance, as follows:

[0078] Invoke the preset electromagnetic wave spatial propagation attenuation model:

[0079] ;

[0080] in, The attenuation coefficient is determined by the performance of the transmitting device and the environmental medium.

[0081] This refers to the launch distance;

[0082] The corrected energy value is the minimum pulse energy reference. :

[0083] ;

[0084] in, This is the minimum energy threshold required to just cause the target vehicle to lose power at the current distance.

[0085] Furthermore, there is a one-to-one quantitative mapping relationship between pulse energy and pulse intensity. This mapping relationship is pre-calibrated by experiments or simulations and stored in the system, as follows:

[0086] Based on the calculated minimum pulse energy benchmark Query the system's preset energy and intensity mapping table;

[0087] The initial intensity value is obtained by matching the corresponding electromagnetic pulse intensity value. .

[0088] The system's pre-configured energy and intensity mapping table is generated and stored in the database based on experimental calibration and simulation verification. The table below is compatible with small and medium-sized directional electromagnetic pulse transmitters, covering both conventional and extreme road interception conditions. The data accuracy is controlled within ±5%. It can be dynamically called upon in conjunction with electromagnetic wave spatial propagation attenuation models and target vehicle parameters to adapt to small and medium-sized directional electromagnetic pulse transmitters, covering conventional road interception scenarios from small cars to mid-size SUVs, with a transmission distance of 10-200m:

[0089] Minimum pulse energy benchmark Match the energy range of the mapping table:

[0090] like If it falls within the energy range corresponding to a certain intensity, then that level is directly taken as the initial intensity value;

[0091] like If the energy level is at the critical point between two energy ranges, linear interpolation is used to calculate the corresponding intensity level. The interpolation formula is as follows:

[0092] ;

[0093] in, The initial strength value to be determined;

[0094] , They are respectively The lower limit intensity level and lower limit energy value of the interval;

[0095] , They are respectively The upper limit intensity level and upper limit energy value of the range.

[0096]

[0097] The energy range of the above mapping table has taken into account the effect of electromagnetic wave propagation attenuation, so there is no need to correct it repeatedly when calling it. The calibration environment of the above mapping table is a scenario with no strong electromagnetic interference at a temperature of 25℃ and humidity of 50%. If the actual application environment is significantly different, such as extreme low temperature, high humidity, or strong electromagnetic interference, the corresponding mapping table can be directly called from the database.

[0098] In the aforementioned technologies, by calculating the minimum pulse energy benchmark, it is ensured that the electromagnetic pulse still has enough energy to damage the electronic system when it propagates to the target vehicle, effectively avoiding blindly increasing the intensity and reducing the risk of electromagnetic interference to the surrounding environment from the source. By introducing the slope parameter, the problem of energy demand variation caused by the difference in vehicle gliding inertia under different scenarios such as uphill / downhill / flat road is solved.

[0099] The speed and gradient information of the target vehicle are input into the skidding dynamics model to calculate the predicted skidding trajectory of the target vehicle. Based on the predicted skidding trajectory and the estimated electromagnetic influence range corresponding to the initial intensity value, the initial risk assessment space range is dynamically delineated.

[0100] Specifically, traditional electromagnetic pulse (EMP) interception schemes often delineate the risk zone by drawing a circle centered on the target vehicle's current position. However, this approach fails to consider the vehicle's sliding trajectory after losing power, neglects the risk of collisions with other vehicles at the end of the sliding path, and includes areas without electromagnetic influence in the assessment scope, increasing the system's computational burden and reducing risk assessment efficiency. By using trajectory prediction and dynamic buffering, the risk space is ensured to cover the entire range of motion of the target vehicle by using the sliding trajectory as the center, and the effective electromagnetic radius is used as a buffer to ensure that the risk space only includes the area affected by the pulse. The area where these two factors overlap constitutes the core range of collision risk and electromagnetic interference risk that needs to be assessed, thus achieving precise anchoring of the risk space.

[0101] First, input the gliding dynamics model to calculate the predicted gliding trajectory of the target vehicle;

[0102] Simply input the two core parameters to be collected, and simultaneously call the system's preset constants; no additional data collection is required.

[0103] Real-time collected data: speed of the target vehicle and the slope of the road ahead ;

[0104] System preset constant: Estimated mass of the target vehicle Extracted from the system's pre-set vehicle model database;

[0105] Road surface friction coefficient Dry asphalt usually Take 0.6, for wet and slippery roads usually Take 0.3;

[0106] air drag coefficient Extracted from the system's pre-set vehicle model database, the value is typically 0.25-0.35 for sedans and 0.35-0.45 for SUVs;

[0107] Through force analysis and kinematic equations, the entire process of the target vehicle from losing power to coming to a complete stop is simulated. The core formulas are as follows:

[0108] Calculate the total external forces on the vehicle after power loss:

[0109] ;

[0110] in, The component of gravity along the road direction;

[0111] Road surface friction, which is opposite to the direction of motion;

[0112] Air resistance is proportional to the square of the vehicle speed. For air density, take at room temperature , The frontal area of ​​a vehicle is extracted from a pre-set vehicle model database; for sedans, it is typically... ;

[0113] When going uphill, the component of gravity acts as resistance, and the net external force is the superposition of three forces in opposite directions:

[0114] ;

[0115] When going downhill, the component of gravity is the driving force, and the net external force is the driving force minus the resistance:

[0116] ;

[0117] On flat ground, , The net external force can be simplified as follows:

[0118] ;

[0119] Calculate deceleration based on Newton's second law:

[0120] ;

[0121] When going uphill A negative value indicates faster deceleration, especially downhill. As the absolute value decreases, the deceleration becomes slower.

[0122] When going uphill: ;

[0123] When going downhill: ;

[0124] acceleration It is the vehicle speed As the vehicle speed decreases, the absolute value of the acceleration decreases, and the trend of coasting deceleration gradually slows down.

[0125] Furthermore, the formula for calculating the gliding distance is:

[0126] Because the acceleration changes dynamically with the vehicle speed, calculus is required to calculate the maximum sliding distance from the initial velocity to a standstill. :

[0127] Using kinematic relationships:

[0128] ;

[0129] Transformed to: ;

[0130] Integral upper and lower limits: initial velocity initial position Termination speed End position ;

[0131] ;

[0132] By substituting the acceleration formula and solving it numerically, the maximum gliding distance can be obtained. The model can be embedded using a preset numerical integration algorithm, such as MATLABode45, which automatically outputs the results after inputting parameters.

[0133] Furthermore, the predicted skidding trajectory of the target vehicle is calculated:

[0134] The gliding distance is a one-dimensional parameter and needs to be converted into two-dimensional / three-dimensional spatial trajectory coordinates based on the road direction, using a Cartesian coordinate system.

[0135] Real-time vehicle coasting distance: (over time) (cumulative distance of change);

[0136] Real-time coordinates:

[0137] ;

[0138] ;

[0139] in, The initial position coordinates of the vehicle when it loses power are acquired by GPS / radar. By discretizing the time t, a continuous sequence of trajectory coordinates can be obtained, ultimately yielding the predicted skidding trajectory, which includes the following information:

[0140] Trajectory path: A coordinate sequence that conforms to the direction of the road;

[0141] Gliding distance The total length from the point of power loss to the point of complete rest;

[0142] Track duration t: The time the vehicle coasts on each segment of the track.

[0143] Furthermore, based on the predicted gliding trajectory, the initial risk assessment spatial range is dynamically defined, as follows:

[0144] Determine the estimated electromagnetic influence range corresponding to the initial intensity:

[0145] The radius of influence of an electromagnetic pulse is positively correlated with its intensity and is affected by attenuation over distance.

[0146] Based on the initial strength value The reference radius is obtained by querying the system's preset electromagnetic influence radius mapping table. Then, by substituting the electromagnetic wave propagation attenuation model into the reference radius, the current effective electromagnetic influence radius is obtained. :

[0147] ;

[0148] in, This refers to the launch distance; the greater the distance, the higher the launch distance. The smaller.

[0149] Geographic Information System (GIS) buffer analysis technology is employed to ensure the accuracy and practicality of the spatial scope. The predicted skidding trajectory is first used as the centerline, because the target vehicle will skid along the trajectory after losing power; the risk distribution is a linear band rather than a circular point. Then, the effective electromagnetic influence radius is used... As a buffer distance on both sides, it extends evenly to both sides of the trajectory line and is trimmed according to the actual road boundaries, such as green belts, guardrails and lane dividers, to finally generate an initial risk assessment space range of polygons that fit the road shape.

[0150] For example, if the predicted skidding trajectory is a straight section 50m long and the effective electromagnetic influence radius is 10m, then the risk space is a rectangular area 50m long and 20m wide; if the trajectory is a curve, then the space is a strip-shaped area conforming to the curvature of the curve.

[0151] In the aforementioned technologies, trajectory prediction and dynamic buffering are used to achieve precise anchoring of the risk space. Centered on the gliding trajectory, the risk space is ensured to cover the entire range of motion of the target vehicle. The effective electromagnetic radius is used as a buffer to ensure that the risk space only includes the area affected by the pulse. This ensures that the risk assessment is comprehensive and only targets the overlapping areas that the target vehicle may touch during gliding and that may be affected by the electromagnetic pulse, eliminating invalid assessment areas outside the road. This reduces the system's computing power consumption while improving the accuracy of risk assessment, precisely anchoring the risk boundary. The trajectory prediction is dynamically adjusted according to the slope, vehicle speed, and road curvature, and the electromagnetic influence range is dynamically corrected according to the initial intensity and transmission distance, effectively improving the adaptability to different road conditions.

[0152] The probability of interaction between social vehicles and the target vehicle within the initial risk assessment space is quantitatively calculated and fused to obtain an initial comprehensive safety risk value.

[0153] Specifically, the probability of risk includes:

[0154] Type I collision risk probability Based on the predicted skidding trajectory and the predicted trajectory of other vehicles, the conditional probability of a spatial collision between the target vehicle and other vehicles is calculated, including vehicle scraping, rear-end collision and side collision. The influencing factors of this type of risk are the skidding trajectory of the target vehicle, the real-time position / speed of the other vehicles and the degree of intersection of their trajectories.

[0155] Type II electromagnetic interference risk probability Based on the initial intensity value, transmission distance, and the position of the social vehicle relative to the target vehicle, the probability of the electromagnetic pulse causing collateral interference to the electronic system of the social vehicle is calculated, including navigation failure and braking electronic system failure. The influencing factors of this type of risk are the initial intensity of the pulse, the distance between the social vehicle and the target vehicle, and the electronic anti-interference threshold of the social vehicle.

[0156] Example scenario: a mid-size SUV going downhill

[0157] The risk space is a rectangular area with a length of 45m and a width of 2.26m. The coordinates of the four vertices are: (98.87,200), (101.13,200), (98.87,245), (101.13,245).

[0158] Using radar, cameras, vehicle-to-everything (V2X) technology, the system collects core parameters and real-time location coordinates of all vehicles within the risk zone. Driving speed , driving direction Vehicle model.

[0159] Type I collision risk probability calculate:

[0160] Compare the target vehicle's sliding trajectory (straight line) The driving trajectory of social vehicles:

[0161] If the trajectories of other vehicles and the target vehicle do not intersect, then ;

[0162] If an intersection point exists, calculate its coordinates. And determine whether the intersection point is within the risk space, that is:

[0163] ;

[0164] Calculate the time it takes for the target vehicle to reach the intersection. Time of arrival of social vehicles at the intersection :

[0165] Target vehicle coasting time: Calculated from the coasting dynamics model, the target vehicle's coasting time from the starting point (100, 200) to the intersection... Gliding time ;

[0166] in, The target vehicle's real-time coasting speed decreases over time;

[0167] Travel time for private vehicles:

[0168] ;

[0169] Time difference threshold: Set a safe time difference ,like There is no risk of collision.

[0170] Quantization is performed using a Gaussian probability model, as shown in the following formula:

[0171] ;

[0172] in This is the time deviation coefficient, determined experimentally.

[0173] Example calculation:

[0174] Assume there is one civilian vehicle within the risk space, with coordinates (100, 210) and speed... The direction of travel is due east. The intersection (100, 210) is within the risk space. The time it takes for the target vehicle to arrive at this point... Arrival time of social vehicles Time difference :

[0175] Substituting into the formula, we get: .

[0176] Type II electromagnetic interference risk probability :

[0177] ;

[0178] in, This represents the real-time straight-line distance between social vehicles and the target vehicle. Effective electromagnetic influence radius;

[0179] At that time, when social vehicles are within the effective influence range of the electromagnetic pulse, the closer the distance, the higher the probability of interference;

[0180] At that point, the electromagnetic pulse energy has decayed to the point where it can no longer interfere with the electronic system, and the probability is 0.

[0181] Example calculation: Using the above social vehicle parameters, ,so It is 0.

[0182] For a single private vehicle, the probabilities of the two types of risks are independent events, and the total risk probability is calculated using the addition principle. ( For the first (a number of private vehicles)

[0183] ;

[0184] The above example calculation: .

[0185] Furthermore, the initial comprehensive safety risk value is obtained through fusion. The comprehensive safety risk value is the sum of the risk contributions of all social vehicles within the risk space, and the specific calculation is as follows:

[0186] Determine the risk weight coefficient: The two types of risks have different degrees of harm and need to be assigned different weights;

[0187] Collision risk weights: It poses a great threat and has a high priority.

[0188] Electromagnetic interference risk weights: The harm is relatively small;

[0189] The weighting coefficients were determined experimentally and satisfy the following conditions: ;

[0190] The formula for calculating the comprehensive safety risk value is as follows:

[0191] ;

[0192] in, The total number of social vehicles within the risk space;

[0193] For the first The probability of a vehicle colliding with another vehicle;

[0194] For the first The probability of electromagnetic interference risk to the vehicle;

[0195] Initial comprehensive security risk value, range of values ;

[0196] Example calculation: Assuming there is only one such private vehicle within the risk space, substituting into the formula yields:

[0197] .

[0198] Determine whether the overall safety risk value is lower than the safety threshold;

[0199] If so, a launch command corresponding to the initial intensity level will be generated;

[0200] The calculated initial comprehensive security risk value is numerically compared with the matched security threshold:

[0201] like This indicates that the interception scheme is safe, the initial intensity meets the requirements of effective interception and low risk, and a launch command corresponding to the initial intensity level is directly generated.

[0202] Based on the calculation results for the mid-size SUV scenario:

[0203] Initial overall security risk value: ;

[0204] Matching security threshold: ;

[0205] The current initial intensity scheme is safe and requires no modification, allowing for launch operations. It effectively avoids the drawbacks of traditional blind launches. Through quantitative comparison, it ensures that the launch operation will not harm social vehicles or sensitive equipment in the risk space, while also guaranteeing the effectiveness of intercepting the target vehicle.

[0206] Furthermore, the core parameters of the launch command specifically include:

[0207] Pulse intensity level: Initial intensity value matched through an energy intensity mapping table. ;

[0208] Pulse duration: determined by the target vehicle's coasting time calculated by the coasting dynamics model;

[0209] Launch direction: determined by the relative orientation of the target vehicle's initial position and the launch source;

[0210] Launch trigger timing: Matching the time when the target vehicle slides to the optimal electromagnetic influence zone.

[0211] Additional security verification instructions: The transmitter is required to reconfirm that there are no new civilian vehicles in the vicinity.

[0212] The control system generates standardized digital commands based on the above parameters. The system automatically verifies the consistency between the command parameters and the real-time scenario, such as whether the target vehicle is still on the trajectory and whether there are no new vehicles in the risk space. Then, the command is transmitted to the directional electromagnetic pulse transmitter via wired / wireless communication. After the transmitter executes the command, it sends a successful transmission signal back to the control system, while continuously monitoring the power status of the target vehicle.

[0213] Example of launch command: Activate the directional electromagnetic pulse transmitter, output pulses at level 3 medium-low intensity for 0.5s, launch direction is 90° due east, trigger after 2s delay, and perform a second scan of the risk assessment space (X: 98.87-101.13m, Y: 200-245m) 0.1s before triggering. Execute launch after confirming that there are no new social vehicles.

[0214] Otherwise, the difference between the comprehensive safety risk value and the safety threshold is calculated as the risk excess.

[0215] Spatial situation parameters are obtained by quantitatively analyzing the spatial scope of risk assessment.

[0216] Based on space situation parameters and launch distance, a dynamic effectiveness model for risk offsetting per unit intensity is constructed.

[0217] By inputting the risk excess into the performance model, the strength compensation amount that reduces the comprehensive safety risk value to below the safety threshold is calculated.

[0218] The corrected intensity value is calculated based on the intensity compensation amount and the initial intensity value, and a launch command corresponding to the corrected intensity value is generated.

[0219] Specifically, when First, the degree of risk exceeding the limit is quantified. The difference between the comprehensive safety risk value and the safety threshold represents the amount of risk redundancy that needs to be offset through intensity adjustment.

[0220] Calculate the risk excess:

[0221] ;

[0222] Furthermore, a quantitative analysis of the spatial scope of the risk assessment yielded spatial situation parameters, as follows:

[0223] Risk surface density parameter This refers to the ratio of the initial overall safety risk value to the area of ​​the initial risk assessment spatial range.

[0224] ;

[0225] in, The area of ​​the initial risk assessment space.

[0226] Electromagnetic risk percentage parameter That is, the proportion of the risk probability contributed by the second type of electromagnetic interference risk among all the risk probabilities that constitute the initial comprehensive safety risk value:

[0227] ;

[0228] in, This is a weighting coefficient for electromagnetic interference risk;

[0229] It is the sum of the electromagnetic interference risk probabilities of all social vehicles within the risk space.

[0230] The effectiveness model for risk offsetting per unit intensity is a predictive model trained by machine learning algorithms. Its training sample data comes from historical scenario data containing different launch distances, different space risk situation parameters, and corresponding risk changes.

[0231] The effectiveness model for risk mitigation per unit intensity follows the following functional relationship:

[0232] ;

[0233] in, Risk mitigation effectiveness per unit intensity;

[0234] The baseline performance coefficient is a normal number calibrated through experiments or simulations.

[0235] This refers to the launch distance;

[0236] For risk surface density parameters;

[0237] This is a parameter representing the proportion of electromagnetic risk.

[0238] As an adjustment function, its output value is positively correlated with both the input risk surface density parameter and the electromagnetic risk proportion parameter.

[0239] Further, calculate the strength compensation amount:

[0240] The amount of strength compensation is directly proportional to the amount of risk excess and inversely proportional to the risk mitigation effectiveness per unit strength.

[0241] ;

[0242] Calculate the corrected strength value:

[0243] ;

[0244] Generate corresponding to The launch command.

[0245] Example calculation:

[0246] , ; ;

[0247] Risk space area ;

[0248] ;

[0249] ; ;

[0250] The electromagnetic interference risk probabilities of the three vehicles are as follows:

[0251] ;

[0252] ;

[0253] This indicates that the overall safety risk value per square meter within this risky space is approximately 0.0177;

[0254] Electromagnetic risk weighted sum: ;

[0255] Electromagnetic risk percentage: This indicates that electromagnetic interference risk accounts for 5% of the overall safety risks, while collision risk is the main source of risk.

[0256] ;

[0257] Substitute into the model formula: ;

[0258] ;

[0259] ;

[0260] In the example The value is extremely small, resulting in a very large compensation amount. This is because the current scenario is mainly about collision risk, while the pulse intensity has a weak effect on offsetting collision risk. The pulse mainly stops the target vehicle by interfering with the electronic system, and cannot directly reduce the probability of physical collision.

[0261] ;

[0262] judge Is it within the device's range? If yes, then issue the command:

[0263] Activate the directional electromagnetic pulse transmitter with an intensity value of 6670, a duration adjusted to 0.1s, a transmission direction of 90° due east, and a trigger delay of 0.5s. 0.1s before triggering, rescan the risk space, update parameters, and verify them. The system continuously monitors the target vehicle's power status and the electronic system status of other vehicles after launch, until the target vehicle comes to a stop.

[0264] The aforementioned technology employs a full-chain correction process involving risk over-limit calculation, spatial situation parameter extraction, effectiveness model construction, and intensity compensation calculation. This process can precisely enhance intensity to offset redundant risks when risks exceed limits, without abandoning the interception mission. It is particularly suitable for urgent scenarios such as emergency interception. The effectiveness model, built based on spatial situation parameters and launch distance, can dynamically adapt to different risk distributions and launch distances. Compared with the fixed compensation coefficient correction method of existing technologies, the intensity compensation accuracy is improved, effectively avoiding additional interference caused by over-compensation. This technology overcomes the technical bottlenecks of the existing directional electromagnetic pulse interception's coarse intensity control and qualitative risk assessment. It integrates gliding dynamic trajectory prediction, dynamic risk space delineation, quantitative risk assessment, and dynamic intensity correction for risk over-limit into a closed-loop process. Through the combination of physical principles and quantitative models, it achieves a triple balance of interception effectiveness, safety controllability, and scenario adaptability.

[0265] After calculating the corrected strength value based on the strength compensation amount and the initial strength value, the following is also included:

[0266] The estimated comprehensive safety risk value is calculated based on the corrected strength value;

[0267] Determine whether the estimated comprehensive safety risk value is lower than the safety threshold;

[0268] If so, execute the launch command corresponding to the corrected intensity value;

[0269] Otherwise, cancel the launch command corresponding to the corrected strength value and output an interception failure signal.

[0270] Specifically, by simulating and verifying the actual risk control effect of the modified scheme, the absolute safety of the final launch command is ensured, forming a dual safety closed loop of modification → simulation → verification → execution. This further compensates for the shortcomings of existing technologies that allow direct launch after modification without secondary verification, as illustrated in the above example. Continued calculation:

[0271] Based on the corrected strength values, the corresponding estimated comprehensive safety risk value is calculated through simulation:

[0272] Attenuation compensation formula: ;

[0273] in. Depend on The electromagnetic influence radius mapping table for strength reference is obtained from this. Exceeding ,according to Query, ;

[0274] Substitution ;

[0275] ;

[0276] The high-intensity pulse caused the target vehicle's electronic system to fail more quickly. Based on the skidding dynamics model, the skidding distance of 20m was recalculated, and the trajectory coordinate range was updated as follows:

[0277] ;

[0278] ;

[0279] Risk assessment space area ;

[0280] Expanding to 3.54m, the comprehensive calculation yields: ;

[0281] ;so If the revised plan is ineffective and the risk level is still not met, immediately terminate the issuance of the revised strength command, clear the launch device's readiness status, and avoid accidental launch.

[0282] The following is another example calculation:

[0283] This scenario involves a mid-size SUV being stopped on a regular road in the suburbs of a city.

[0284] Target vehicle and road parameters: , , , ;

[0285] Core constraint parameters of electromagnetic pulse device: , , ;

[0286] Risk assessment parameters: , , ;

[0287] Social vehicle risk parameters: ;

[0288] Weights and calibration parameters: =0.3, =0.7, =0.15;

[0289] ;

[0290] ;

[0291] ;

[0292] ;

[0293] ;

[0294] ;

[0295] Risk exceeding the limit:

[0296] ;

[0297] Strength compensation amount:

[0298] ;

[0299] Rounding ;

[0300] Strength verification after correction:

[0301] ;

[0302] Actual risk offset amount:

[0303] ;

[0304] Revised estimated risk value:

[0305] ;

[0306] The corrected scheme is effective, the risk meets the standard, and a non-maximum intensity launch command is output with the following parameters: intensity value 17, duration 0.12s, launch direction 90° due east, and trigger delay 0.3s.

[0307] The safety threshold is a dynamic value that is adjusted in real time based on the environmental sensitivity information of the current road environment.

[0308] Environmental sensitivity information includes at least one or more of the following: geographical proximity of preset sensitive points around the road, real-time traffic flow density, and weather visibility.

[0309] Specifically, safety threshold It is the critical value for judging whether the stop operation is safe. It is the upper limit of the maximum allowable comprehensive safety risk value within the risk assessment space. If it exceeds this value, there is a risk of collision or electromagnetic interference accidents. If it is below this value, the stop operation is safe and controllable.

[0310] The safety threshold is a dynamic value and needs to be calibrated through experimental statistics and scenario adaptation. Key reference factors include:

[0311]

[0312] The system will automatically match the corresponding safety threshold based on real-time collected road type, traffic density, and surrounding environment information.

[0313] Risk probabilities include:

[0314] Type I collision risk probability: Based on the predicted skidding trajectory and the predicted trajectory of other vehicles, calculate the conditional probability of a spatial collision between the target vehicle and other vehicles.

[0315] Type II electromagnetic interference risk probability: Based on the initial intensity value, transmission distance, and the position of the other vehicle relative to the target vehicle, calculate the probability that the electromagnetic pulse will cause collateral interference to the electronic system of the other vehicle.

[0316] Spatial situation parameters include at least the following two parameters:

[0317] Risk surface density parameter: the ratio of the initial comprehensive safety risk value to the area of ​​the initial risk assessment space;

[0318] Electromagnetic risk proportion parameter: that is, the proportion of the risk probability contributed by the second type of electromagnetic interference risk among all the risk probabilities that constitute the initial comprehensive safety risk value.

[0319] The effectiveness model for risk offsetting per unit intensity is a predictive model trained by machine learning algorithms. Its training sample data comes from historical scenario data containing different launch distances, different space risk situation parameters, and corresponding risk changes.

[0320] The effectiveness model for risk mitigation per unit intensity follows the following functional relationship:

[0321] ;

[0322] in, Risk mitigation effectiveness per unit intensity;

[0323] The baseline performance coefficient is a normal number calibrated through experiments or simulations.

[0324] This refers to the launch distance;

[0325] For risk surface density parameters;

[0326] This is a parameter representing the proportion of electromagnetic risk.

[0327] As an adjustment function, its output value is positively correlated with both the input risk surface density parameter and the electromagnetic risk proportion parameter.

[0328] Specifically, the effectiveness model for risk offsetting per unit intensity follows the functional relationship described in the above scenario:

[0329] ;

[0330] in, The risk mitigation effectiveness per unit intensity is the comprehensive safety risk that can be mitigated for every 1 unit increase in intensity.

[0331] The baseline performance coefficient is a normal number calibrated through experiments or simulations and is positively correlated with the performance of the launching device.

[0332] This refers to the launch distance;

[0333] For risk surface density parameters;

[0334] This is the distance attenuation factor, which follows the inverse square law of electromagnetic wave attenuation; the greater the transmission distance, the smaller this factor becomes. The lower the value, the weaker the risk mitigation effect per unit intensity at long distances;

[0335] This is a parameter representing the proportion of electromagnetic risk.

[0336] As an adjustment function, its output value is positively correlated with the input risk surface density parameter and electromagnetic risk proportion parameter. That is, the denser the risk and the higher the electromagnetic risk proportion, the better the risk mitigation effect per unit intensity.

[0337] This is the core of the model, and the specific formula is as follows:

[0338] ;

[0339] in, The scene calibration factor, a core scaling parameter, is used to match targets in different scenes. Values ​​that eliminate differences in parameter magnitude;

[0340] The specific calculations are as follows:

[0341] The instance parameters are: , , =0.7, =0.3, =0.0913;

[0342] ;

[0343] .

[0344] The initial risk assessment spatial range is dynamically delineated based on the estimated electromagnetic influence range corresponding to the initial intensity value, specifically including:

[0345] The reference electromagnetic influence radius is obtained by querying the preset mapping relationship based on the initial intensity value;

[0346] The attenuation compensation calculation of the reference electromagnetic influence radius is performed based on the transmission distance to obtain the current effective influence radius;

[0347] Using the predicted skidding trajectory as the center line and the current effective influence radius as the buffer distance, a polygonal region is generated through the buffer analysis of the geographic information system, which serves as the initial spatial range for risk assessment.

[0348] Specifically, the following calculation uses a mid-size SUV going downhill as an example:

[0349] Parameter values: ;

[0350] ;

[0351] ;

[0352] ;

[0353] ;

[0354] ;

[0355] ;

[0356] Due east;

[0357] Calculate the net external force in a downhill scenario:

[0358] ;

[0359] Calculate the initial acceleration:

[0360] ;

[0361] Numerical integration to calculate maximum gliding distance ;

[0362] Generate trajectory coordinates:

[0363] initial position , The trajectory coordinate sequence is:

[0364] ;

[0365] The final resting position is The trajectory is a straight line 45m long in the due east direction.

[0366] Furthermore, by consulting the energy-intensity mapping table, the initial intensity level of the directional electromagnetic pulse is determined. ;

[0367] Launch range ;

[0368] Query the strong reference electromagnetic influence radius mapping table:

[0369]

[0370] Query results: Corresponding ;

[0371] Substitute into the attenuation compensation formula to calculate:

[0372] ;

[0373] Conclusion: At a transmission distance of 50m, the effective radius of influence of this intensity pulse is approximately 1.13m.

[0374] Furthermore, the initial risk assessment spatial scope is generated:

[0375] Trajectory type: Straight line trajectory;

[0376] Starting coordinates of the trajectory: ;

[0377] coordinates of the trajectory endpoint: ;

[0378] Track length: 45m;

[0379] Perform GIS buffer analysis:

[0380] Buffer distance: Single-sided buffer Total width on both sides of the trajectory ;

[0381] With trajectory line → With the center line as the axis, extend 1.13m to the north and south sides respectively. The coordinates of the four vertices are: (98.87,200), (101.13,200), (98.87,245), (101.13,245).

[0382] The minimum pulse energy benchmark required for the target vehicle to lose power is calculated based on slope information and launch distance, specifically including:

[0383] The slope information is converted into a component of gravity along the road direction, and the basic energy value required to overcome the target vehicle's current kinetic energy and this component force is calculated.

[0384] Based on the transmission distance, the basic energy value is corrected using an electromagnetic wave spatial propagation attenuation model to obtain the minimum pulse energy benchmark.

[0385] Specifically, let's take a mid-size SUV as an example for calculation:

[0386] Estimated quality ;

[0387] speed ;

[0388] Launch range ;

[0389] Driving on a road with a 3° downhill slope, at a temperature of 25°C and humidity of 50%, the attenuation coefficient is... ;

[0390] Predicted gliding distance is ;

[0391] Calculate the vehicle's kinetic energy: ;

[0392] Calculate the work done by the component of the force due to the slope:

[0393] ;

[0394] ;

[0395] Base energy value when going downhill: ;

[0396] Calculate distance attenuation:

[0397] ;

[0398] Minimum pulse energy reference: ;

[0399] Query the mapping table: The value far exceeds the normal range in the table, indicating an extreme operating condition. Therefore, it is necessary to call the mapping table for the range corresponding to strength 10 (400~450kJ) and simultaneously trigger the emergency approval process.

[0400] Example of linear interpolation method, assuming the calculation is after adjusting the operating conditions. For values ​​within the intensity range of 3 (50-100kJ), the interpolation formula is:

[0401] ;

[0402] Substitute parameters :

[0403] ;

[0404] The initial strength value obtained from the matching is 3.5.

[0405] A directional electromagnetic pulse vehicle stopping control system, comprising:

[0406] The perception module is used to acquire data on the target vehicle's speed, launch distance, road gradient, and surrounding vehicles.

[0407] The processing and decision-making module is used to execute the steps of a directional electromagnetic pulse vehicle stop control method.

[0408] The execution module is used to control the directional electromagnetic pulse transmitter based on the transmission command and intensity value output by the processing and decision module.

[0409] Specifically, the sensing module is used to acquire all the input parameters required by the real-time, high-precision control method:

[0410] Millimeter-wave radar unit: Collects target vehicle speed, transmission distance, real-time position coordinates of the target vehicle, and position, speed, and direction of travel of other vehicles.

[0411] Slope sensor unit: Collects the slope angle of the road ahead.

[0412] Visual acquisition unit: Acquires information on the number of vehicles, vehicle types, road boundaries, and the distribution of sensitive electronic devices.

[0413] Vehicle-to-everything (V2X) communication unit: collects information on the types of onboard electronic systems of vehicles and stop instructions from remote command centers.

[0414] Environmental sensor unit: collects ambient temperature, humidity and surrounding electromagnetic interference intensity.

[0415] The processing and decision-making module is used to embed the entire algorithm of the interception control method. It receives input data from the sensing module, completes all logical operations of energy benchmark calculation, trajectory prediction, risk delineation, risk quantification, safety assessment, intensity correction, and simulation review, and finally outputs clear decision instructions.

[0416] Main control computing unit: used to perform numerical calculations such as gliding dynamics model, risk probability quantification, and strength compensation calculation.

[0417] Model Deployment Unit: Used to deploy unit-strength risk-offset efficiency models for machine learning training.

[0418] Storage unit: Used to store preset mapping tables, historical scene data, and security threshold calibration parameters.

[0419] Decision output unit: Outputs launch command, cancellation command and failure signal, and synchronizes them to the execution module and command center.

[0420] The execution module receives instructions from the processing and decision-making module, drives the directional electromagnetic pulse transmitter to perform the launch operation according to parameters, and provides real-time feedback on the launch status and target vehicle response.

[0421] Command parsing unit: Used to receive commands from the processing and decision-making module and parse parameters such as intensity level, duration, and transmission direction.

[0422] Power drive unit: Used to output the corresponding pulse power and energy based on the analyzed intensity value.

[0423] Directional emission unit: Used to focus electromagnetic pulses towards the target vehicle and control the range of electromagnetic influence.

[0424] Status monitoring unit: used to monitor the pulse electric field intensity during the launch process and monitor the dynamic status of the target vehicle.

[0425] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for controlling the stopping of a vehicle using a directional electromagnetic pulse, characterized in that, Includes the following steps: The system acquires the target vehicle's speed, the transmission distance between it and the source, and the slope of the road ahead. Based on the slope information and transmission distance, it calculates the minimum pulse energy reference required for the target vehicle to lose power and determines the initial intensity value of the directional electromagnetic pulse. The speed and gradient information of the target vehicle are input into the skidding dynamics model to calculate the predicted skidding trajectory of the target vehicle. Based on the predicted skidding trajectory and the estimated electromagnetic influence range corresponding to the initial intensity value, the initial risk assessment space range is dynamically delineated. The time difference between the target vehicle and other vehicles arriving at the preset position is obtained, and the probability of the first type of collision risk is quantitatively calculated based on the time difference using a Gaussian probability model. Calculate the probability of Type II electromagnetic interference risk based on the initial intensity value, the transmission distance, and the position of other vehicles relative to the target vehicle; The comprehensive safety risk value is obtained by weighted fusion of the probability of Type I collision risk and the probability of Type II electromagnetic interference risk. Determine whether the overall safety risk value is lower than the safety threshold; If so, a launch command corresponding to the initial intensity level will be generated; Otherwise, the difference between the comprehensive safety risk value and the safety threshold is calculated as the risk excess. The ratio of the initial comprehensive safety risk value to the area of ​​the initial risk assessment space is used as the risk surface density parameter; The proportion of the probability of Category II electromagnetic interference risk in the initial comprehensive safety risk value is used as the electromagnetic risk proportion parameter; The risk surface density parameter and the electromagnetic risk proportion parameter are used as spatial situation parameters, and a dynamic unit intensity risk offset effectiveness model is constructed by combining the launch distance. The effectiveness model is a prediction model trained by machine learning algorithm. By inputting the risk excess into the performance model, the strength compensation amount that reduces the comprehensive safety risk value to below the safety threshold is calculated. The corrected intensity value is calculated based on the intensity compensation amount and the initial intensity value, and a launch command corresponding to the corrected intensity value is generated.

2. The directional electromagnetic pulse vehicle stopping control method according to claim 1, characterized in that: After calculating the corrected strength value based on the strength compensation amount and the initial strength value, the following is also included: The estimated comprehensive safety risk value is calculated based on the corrected strength value; Determine whether the estimated comprehensive safety risk value is lower than the safety threshold; If so, execute the launch command corresponding to the corrected intensity value; Otherwise, cancel the launch command corresponding to the corrected strength value and output an interception failure signal.

3. The directional electromagnetic pulse vehicle stopping control method according to claim 2, characterized in that: The safety threshold is a dynamic value that is adjusted in real time based on the environmental sensitivity information of the current road environment. The environmental sensitivity information includes at least one or more of the following: geographical proximity of preset sensitive points around the road, real-time traffic flow density, and weather visibility.

4. The directional electromagnetic pulse vehicle stopping control method according to claim 1, characterized in that: The effectiveness model for risk offsetting per unit intensity follows the following functional relationship: ； in, Risk mitigation effectiveness per unit intensity; The baseline performance coefficient is a normal number calibrated through experiments or simulations. This refers to the launch distance; For risk surface density parameters; This is a parameter representing the proportion of electromagnetic risk. As an adjustment function, its output value is positively correlated with both the input risk surface density parameter and the electromagnetic risk proportion parameter.

5. The directional electromagnetic pulse vehicle stopping control method according to claim 1, characterized in that: The initial risk assessment spatial range is dynamically delineated based on the estimated electromagnetic influence range corresponding to the initial intensity value, specifically including: The reference electromagnetic influence radius is obtained by querying the preset mapping relationship based on the initial intensity value; The attenuation compensation calculation of the reference electromagnetic influence radius is performed based on the transmission distance to obtain the current effective influence radius; Using the predicted gliding trajectory as the center line and the current effective influence radius as the buffer distance, a polygonal region is generated through geographic information system buffer analysis, which serves as the initial risk assessment spatial range.

6. The directional electromagnetic pulse vehicle stopping control method according to claim 1, characterized in that: The minimum pulse energy benchmark required for the target vehicle to lose power is calculated based on slope information and launch distance, specifically including: The slope information is converted into a component of gravity along the road direction, and the basic energy value required to overcome the target vehicle's current kinetic energy and this component force is calculated. Based on the transmission distance, the basic energy value is corrected using an electromagnetic wave spatial propagation attenuation model to obtain the minimum pulse energy benchmark.

7. A directional electromagnetic pulse vehicle stopping control system, characterized in that, include: The perception module is used to acquire data on the target vehicle's speed, launch distance, road gradient, and surrounding vehicles. The processing and decision-making module is used to perform the method as described in any one of claims 1-6; The execution module is used to control the directional electromagnetic pulse transmitter based on the transmission command and intensity value output by the processing and decision module.

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