A safety passage guidance management system and method for highways in severe weather

Abstract

This invention discloses a safety lane guidance management system and method for highways in adverse weather conditions, relating to the field of big data analytics. The invention utilizes a multi-sensor array to collect and analyze data, achieving precise acquisition and structured integration of real-time vehicle speed and relative vehicle distance data. Simultaneously, it relies on real-time meteorological data to match and limit following distance, dynamically determining the time threshold for guidance control commands. By retrieving historical command change timestamps of guidance lights and using a response attenuation sliding window to calculate real-time change duration, extracting historical communication timestamps to analyze and predict communication delay duration, and integrating delay datasets to calculate command issuance advance time, the invention accurately determines the command recipient. Combined with directional broadcasting from same-lane nodes and multi-node mutual verification distributed storage, it achieves dynamic calibration of guidance commands and reliable data storage, providing stable following distance guidance support for vehicles and improving highway driving safety and lane traffic order during adverse weather conditions.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to a safety passage guidance and management system and method for highways in adverse weather conditions. Background Technology

[0002] In severe weather, highways are prone to accidents such as rear-end collisions and chain-reaction collisions due to reduced visibility and decreased road surface adhesion. Traditional guidance methods are difficult to accurately adapt to real-time vehicle conditions and dynamic weather changes, and cannot effectively ensure safe following and orderly passage of vehicles. Therefore, safe passage guidance in severe weather is of key significance for avoiding driving risks, maintaining traffic order, and improving road traffic safety.

[0003] When vehicles are traveling on highways in adverse weather conditions, maintaining a safe following distance is a core element for ensuring driving safety. As a key device for guiding following distance, the guidance lights are susceptible to fluctuations in communication transmission and performance degradation due to hardware aging. These fluctuations directly lead to delays in guidance control response, making it difficult to accurately match the timing of guidance commands with actual road conditions. Current technologies lack a management mechanism that can dynamically adapt to communication fluctuations and hardware response delays and accurately calibrate the execution sequence of guidance commands, thus failing to provide stable and reliable following distance guidance support for vehicles. Therefore, there is an urgent need for a safety lane guidance management system and method for highways in adverse weather conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a safety passage guidance management system and method for highways in severe weather, in order to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for guiding and managing safe passages in severe weather on highways, the method comprising the following steps: Step S1: Obtain the real-time vehicle speed of each lane of the highway based on the multi-sensor array deployed by the guidance lights, and store the real-time vehicle speed data in association with the timestamp data of the acquisition, thereby constructing a real-time vehicle speed guidance analysis set. Step S1-1: The multi-sensor array includes a vehicle speed acquisition sensor and a vehicle distance recognition sensor. Each sensor in the multi-sensor array is bound to a corresponding guide light, and a data interaction link is established with the corresponding guide light through a unique communication protocol. After the multi-sensor array is activated, the vehicle speed acquisition sensor is used to collect the real-time speed of vehicles traveling in the corresponding lane, and simultaneously records the unique identifier of the vehicle speed acquisition sensor, the acquisition timestamp, and the lane ID. The vehicle distance recognition sensor is used to collect the relative distance between vehicles traveling in the corresponding lane and its bound guide light in real time, and determines the lane the vehicle belongs to based on the relative distance, simultaneously recording the unique identifier of the vehicle distance recognition sensor, the acquisition timestamp, and the relative distance data between the vehicle and the guide light. Steps S1-2: Construct a master data table with lane ID as the primary key and collection timestamp as the foreign key. The master data table stores the unique mapping relationship between lane ID and collection timestamp. Using the primary key and foreign key of this master data table as the association basis, establish a related data table, using real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor as the core fields of the related data table to achieve bidirectional association between the master data table and the related data table. Associate the related data with the corresponding bound unique identifier of the guidance light, and arrange the bound data in ascending order of collection timestamp. Use a standardized data storage format to structurally integrate the sorted data to form a real-time vehicle speed guidance analysis set. The real-time vehicle speed guidance analysis set includes the unique identifier of the guidance light, lane ID as the primary key, collection timestamp, real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor. Store the corresponding guidance light according to the unique identifier of the guidance light. By binding and deploying vehicle speed sensors, vehicle distance recognition sensors, and guidance lights one by one, and establishing a data interaction link based on a unique communication protocol, the system accurately collects real-time vehicle speeds, relative distances between vehicles and guidance lights, related unique identifiers, and collection timestamp data for each lane. Then, by constructing a master data table and related data tables, the system achieves bidirectional data association. The data is arranged in an orderly manner according to the collection timestamp and structured and integrated to form a real-time vehicle speed guidance analysis set, which effectively solves the problems of scattered vehicle condition data collection and low correlation in traditional guidance methods.

[0006] Step S2: Based on the real-time vehicle speed guidance analysis set and the limited following distance corresponding to the real-time weather on the highway, obtain the time threshold of the guidance control command for the limited following distance of the guidance light; Step S2-1: Call the highway meteorological monitoring database to obtain the real-time meteorological data of the current highway. Based on the preset weather-corresponding following distance mapping standard library, match the limited following distance corresponding to the real-time meteorological data. Classify and divide the real-time vehicle speed guidance analysis set according to lane ID to generate a lane-level vehicle speed dataset exclusive to each lane ID. Retrieve the preset guidance light structured deployment file and extract the guidance light deployment spacing data. The guidance light deployment spacing data represents the fixed physical distance data between two adjacent guidance lights under the same lane ID. Associate and bind the guidance light deployment spacing data with the corresponding lane-level vehicle speed dataset according to the unique identifier of the guidance light. Step S2-2: For the lane-level vehicle speed dataset corresponding to a single lane ID, based on the relative distance data between the vehicle and the guide light, filter out the nearest traveling vehicle located downstream of the currently monitored guide light in that lane, defining it as the target following vehicle; extract the real-time vehicle speed of the currently traveling vehicle associated with the currently monitored guide light, and the real-time vehicle speed corresponding to the target following vehicle; combine the guide light deployment spacing data and the relative distance data between the vehicle and the guide light, and calculate the actual distance between the currently traveling vehicle and the target following vehicle through geometric relationships. The specific process is as follows: Step S2-2-1: Draw a perpendicular line from the guide light to the road surface of the corresponding lane, and determine the foot of the perpendicular as the lane projection reference point of the guide light; retrieve the preset guide light installation height data, which is the vertical distance from the center of the guide light body to the road surface of the corresponding lane; establish a longitudinal coordinate system with the lane projection reference point of the currently monitored guide light as the origin and the driving direction of the lane as the positive direction; clarify the lane projection reference point of the currently monitored guide light and the lane projection reference point of the adjacent guide light downstream, and the distance between the two lane projection reference points along the longitudinal direction of the lane is equal to the guide light layout spacing data; Step S2-2-2: Construct a right triangle with the lane projection reference point of the currently monitored guide light as the right-angle vertex, the guide light installation height data as one right-angle side, and the relative distance data between the currently traveling vehicle and the guide light as the hypotenuse. Calculate the specific position of the currently traveling vehicle in the longitudinal coordinate system using the Pythagorean theorem. Retrieve the installation height data of the downstream adjacent guide light bound to the target following vehicle. Construct a right triangle with the lane projection reference point of the downstream adjacent guide light as the right-angle vertex, its installation height data as one right-angle side, and the relative distance data between the target following vehicle and the guide light as the hypotenuse. Calculate the target following vehicle's position in the longitudinal coordinate system using the Pythagorean theorem. The longitudinal distance along the lane from the vehicle to the lane projection reference point is used to determine the positional relationship between the target vehicle and the lane projection reference point based on the vehicle's direction of travel: If the vehicle has not yet passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by subtracting the longitudinal distance along the lane from the target vehicle to the lane projection reference point from the spacing data of the guide lights between the two lane projection reference points; if the vehicle has already passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by adding the longitudinal distance along the lane from the target vehicle to the lane projection reference point to the spacing data of the guide lights between the two lane projection reference points. Step S2-2-3: Compare the relative positions of the currently driving vehicle and the target following vehicle in the longitudinal coordinate system, calculate the difference between their relative positions, and obtain the actual distance between the currently driving vehicle and the target following vehicle. Step S2-3: If the real-time speed of the target vehicle is less than or equal to the real-time speed of the currently traveling vehicle, set the guidance control command time threshold to the minimum response trigger duration of the guidance light; if the real-time speed of the target vehicle is greater than the real-time speed of the currently traveling vehicle, first obtain the speed difference between the two vehicles by the difference between the real-time speed of the target vehicle and the real-time speed of the currently traveling vehicle, then obtain the distance to be converged by the difference between the actual distance and the limited following distance; obtain the convergence time required for the distance to converge to the limited following distance by the ratio of the distance to be converged to the speed difference; use the convergence time as the guidance control command time threshold. The guidance control command time threshold is expressed as the convergence time required for the vehicle distance to converge to the limit following distance or the minimum response trigger time of the guidance light, based on the relationship between the limited following distance corresponding to real-time weather, the actual distance between the current vehicle and the target following vehicle, and the vehicle speed. By calling real-time weather data to match the corresponding limited following distance, and combining the spacing data of the guide lights with the lane-level vehicle speed dataset, the actual vehicle distance is accurately calculated using a longitudinal coordinate system and the Pythagorean theorem. Then, the time threshold of the guidance control command is determined based on the speed relationship between the current vehicle and the target vehicle. This not only achieves dynamic matching between the limited following distance and real-time weather, but also solves the defects of inaccurate vehicle distance calculation and static guidance threshold in traditional technologies, allowing the time threshold of the guidance control command to adapt to weather changes and vehicle driving status in real time.

[0007] Step S3: Select any one of the guiding lights as the research object, and denote it as the target guiding light; collect and analyze the change timestamp data of the historical guiding control commands of the target guiding light to obtain the change time interval of the historical guiding control commands, and set the response decay sliding window analysis to obtain the real-time change duration of the target guiding light; Step S3-1: Retrieve the historical guidance control command execution records of the target guidance light, extract the two timestamps corresponding to each historical command, including the timestamp of receiving the guidance control command of the target guidance light and the timestamp of confirming the completion of the guidance light state change; calculate the difference between the two timestamps corresponding to each historical command to obtain the change time interval of each historical guidance control command, and construct a set of historical change time intervals. Step S3-2: Set and initialize the response decay sliding window. Extract data from the historical transformation time interval set through the response decay sliding window. Calculate the arithmetic mean of the extracted transformation time intervals to obtain the real-time transformation duration of the target guide light. By retrieving the receiving timestamps and state transition confirmation timestamps of the target guide lights' historical guidance control commands, calculating the historical transition time intervals and constructing a set, and then extracting data through a response attenuation sliding window and calculating the arithmetic mean to obtain the real-time transition duration, the impact of the performance degradation of the guide lights due to the accumulation of hardware service time on the response speed is accurately captured. The response delay data at the hardware level is quantified, filling the gap in traditional technology that does not consider the response delay caused by hardware attenuation.

[0008] Step S4: Collect the timestamps of the target guide lights’ historical data communication, and construct a communication time analysis set based on the collected data communication timestamps; use a time series prediction model to process the communication time analysis set and predict the delay time of the next data communication of the target guide lights. Step S4-1: Retrieve the historical data communication interaction records of the target guidance light, and extract the communication timestamps corresponding to each data communication interaction record, including the communication request initiation timestamp, the three-way handshake connection establishment confirmation timestamp, and the data transmission completion feedback timestamp. Step S4-2: Calculate the communication delay duration for each historical data communication record. Specifically, this is the difference between the data transmission completion feedback timestamp and the communication request initiation timestamp. This difference includes the three-way handshake link establishment delay and the data transmission delay. Sort the effective historical communication delay durations in ascending order of the communication request initiation timestamp to construct a communication time analysis set. Step S4-3: Use the communication time analysis set as input data for the time series prediction model; use the time series prediction model to fit and analyze the historical communication delay variation pattern of the target guide light, and output the delay duration of the next data communication of the target guide light; The time series forecasting model is specifically the ARMA model, and the calculation formula is as follows: ; In the formula, N T+1 denoted as the predicted data communication delay duration for the next target-guided light, i.e., the predicted data communication delay value output by the time series prediction model; c represents the constant term of the time series prediction model, which is the mean term, representing the time series baseline level of the data communication delay; p represents the autoregressive order, indicating the number of historical communication delay observations that need to be referenced; A i Represented as the i-th autoregressive coefficient, quantifying the weight of the influence of the i-th historical communication delay observation on the current predicted value; N T-i+1 Represented as historical data communication observations at time T-i+1; q represents the moving average order, indicating the number of historical error terms to be referenced; B j The z-th moving average coefficient quantifies the correction weight of the j-th historical error term to the current predicted value; T-j+1 This is represented by the random error term at time T-j+1, which is the deviation value in the predicted value of historical data communication delay; z T+1 The random error term, expressed as time T+1, represents the random predictive fluctuations in the current forecast. By extracting the request initiation, connection establishment confirmation, and transmission completion feedback timestamps from the historical data communication of the target guidance light, the historical communication delay duration, including link establishment and data transmission, is calculated and a communication time analysis set is constructed. By using a time series prediction model to fit the historical delay change pattern, the next communication delay duration is accurately predicted. This dynamically adapts to the fluctuation phenomenon in the communication transmission process, solves the problem of unpredictable communication delay in traditional technologies, and provides reliable communication-level data support for the timing calibration of guidance command issuance.

[0009] Step S5 synchronizes the data processing from steps S3 to S4 to obtain the real-time change duration and data communication duration of each guide light. It is associated and stored according to the unique identifier of the guide light to obtain the delay dataset of each guide light. The guide light is issued guidance control commands in combination with the guidance control command time threshold. Each guide light is selected as a data broadcast node. Based on the guidance control command time threshold analysis, the delay dataset of each guide light is broadcast and stored for management. Step S5-1: Retrieve the real-time change duration and predicted next data communication delay duration for all guide lights; using the unique identifier of the guide light as the core association key, bind the real-time change duration and data communication delay duration of each guide light to construct a delay dataset exclusive to each guide light; the delay dataset includes the unique identifier of the guide light, the lane projection reference point ID, the real-time change duration and the data communication delay duration fields, and is stored in the system's distributed database according to the unique identifier of the guide light. Step S5-2: Read the time threshold of the guidance control command corresponding to each guidance light, and match the guidance control command time threshold with the delay dataset of the corresponding guidance light; calculate the advance time of the actual issuance of the guidance control command, specifically: based on the guidance control command time threshold, subtract the sum of the real-time change time and data communication delay time of the corresponding guidance light; if the calculation result is non-negative, the command is issued according to the advance time; if the calculation result is negative, the actual advance time is set as the minimum response trigger time of the guidance light. Step S5-3: Using the lane's driving direction as a reference, the area in front of the vehicle is downstream, and the area behind it is upstream; based on the relative position of the target following vehicle in the longitudinal coordinate system, combined with the lane projection reference point positions of the currently monitored guide light and the adjacent guide light downstream, determine the positional relationship between the target following vehicle and the adjacent guide light downstream. The specific determination process is as follows: If the target vehicle has not passed the lane projection reference point of the downstream adjacent guide light, then the downstream adjacent guide light is identified as the instruction receiving subject; if the target vehicle has passed the lane projection reference point of the downstream adjacent guide light, then the next downstream adjacent guide light is identified as the instruction receiving subject, i.e., the currently monitored guide light; the guidance control instruction is issued to the instruction receiving subject according to the calculated actual advance issuance time. Step S5-4: After receiving the confirmation signal, the instruction receiving entity initiates the directional broadcast process for adjacent nodes in the same lane. It broadcasts its complete delay dataset only to its upstream and downstream adjacent guide lights. The adjacent guide lights receiving the broadcast data use the unique identifier of the guide light and the lane ID as verification to compare the received delay dataset with the corresponding historical data stored in their own storage. After the comparison is successful, the broadcast data is written to the local dedicated storage partition and the broadcast data is forwarded to its non-initiating adjacent guide lights. The data reception, comparison and storage process of all guide light nodes in the same lane is completed in this iterative manner, forming a distributed data storage system with multi-node mutual verification. By integrating the real-time change duration of the guidance lights with the predicted communication delay duration to form a dedicated delay dataset, the actual advance timing of the guidance control command is calculated based on the time threshold. Combined with the position of the target following vehicle, the command receiving subject is accurately determined. Then, through directional broadcasting from adjacent nodes in the same lane, data consistency comparison, and distributed storage with multi-node mutual verification, the timing of guidance command issuance is dynamically adapted to hardware response attenuation and communication fluctuations, and the command execution sequence is accurately calibrated. At the same time, the reliability of data storage is ensured, which effectively solves the problem of the lack of dynamic adaptation to communication and hardware delays and timing calibration mechanisms in existing technologies, and provides stable and reliable following distance guidance support for vehicles.

[0010] Furthermore, a severe weather safety passage guidance and management system for highways includes a vehicle speed acquisition and integration module, a vehicle distance analysis threshold module, a guidance light change duration module, a communication delay prediction module, and a command issuance and data broadcasting module. The vehicle speed acquisition and integration module is used to acquire real-time vehicle speeds and related data for each lane of the highway, and construct a real-time vehicle speed guidance analysis set; the vehicle distance analysis threshold module is used to match and limit following distances, calculate the actual vehicle distance, and determine the time threshold for guidance control commands; the guidance light change duration module is used to collect historical change time intervals of the target guidance lights and calculate their real-time change duration; the communication delay prediction module is used to collect historical communication delay durations of the target guidance lights and predict the next data communication delay duration; the command issuance and data broadcasting module is used to integrate the guidance light delay dataset, issue guidance control commands, and broadcast and store the dataset. The vehicle speed acquisition and integration module includes a sensor data acquisition unit and a data structuring and integration unit. The sensor data acquisition unit is used to acquire the real-time vehicle speed, relative distance between vehicles and related signs, and acquisition timestamps for the corresponding lane. The data structuring and integration unit is used to construct an associated data table and arrange the data in an orderly manner, and the structured integration forms a real-time vehicle speed guidance analysis set. The vehicle distance analysis threshold module includes a meteorological lane data association unit and a vehicle distance calculation threshold determination unit; the meteorological lane data association unit is used to call real-time meteorological data to match and limit the following distance, and associate the spacing of the guidance lights with the lane-level vehicle speed dataset; the vehicle distance calculation threshold determination unit is used to calculate the actual distance between the currently driving vehicle and the target following vehicle, and determine the time threshold of the guidance control command. The guide light switching duration module includes a historical switching interval acquisition unit and a sliding window duration calculation unit; the historical switching interval acquisition unit is used to retrieve the historical guidance control command execution record of the target guide light and acquire the historical switching time interval; the sliding window duration calculation unit is used to extract data through the response attenuation sliding window and calculate the real-time switching duration of the target guide light. The communication delay prediction module includes a historical communication delay acquisition unit and a time-series delay prediction unit; the historical communication delay acquisition unit is used to retrieve historical data communication interaction records of the target guidance light, collect historical communication delay durations and construct a set; the time-series delay prediction unit is used to input the communication time analysis set into a time series prediction model to predict the next data communication delay duration; The instruction issuance data broadcasting module includes a delayed data integration and issuance unit and an instruction receiving data broadcasting unit. The delayed data integration and issuance unit is used to integrate the real-time change duration of the guidance lights with the predicted communication delay duration, calculate the advance duration, and issue guidance control instructions. The instruction receiving data broadcasting unit is used to receive guidance control instructions and then broadcast the delayed dataset in a directional manner to complete the distributed storage of mutual verification among multiple nodes in the same lane.

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves accurate collection and structured integration of real-time lane speed and relative vehicle distance data by binding vehicle speed sensors, distance recognition sensors, and guidance lights one-to-one. Combined with a bidirectional association design of master data table and related data table, the invention constructs a real-time vehicle speed guidance analysis set that provides orderly and comprehensive data support for subsequent guidance control. At the same time, it relies on real-time weather data to match and limit following distance, accurately calculates the actual vehicle distance using the longitudinal coordinate system and the Pythagorean theorem, and dynamically determines the guidance control command time threshold based on the relationship between vehicle speed and distance, thus achieving real-time adaptation of guidance threshold to weather and vehicle conditions.

[0012] 2. This invention retrieves the historical guidance control command change timestamps of the guidance lights and uses a response decay sliding window to calculate the real-time change duration, accurately quantifying the impact of performance degradation caused by the cumulative service time of hardware on the response speed; and by extracting historical communication timestamps to construct a communication time analysis set, it uses a time series prediction model to accurately predict the next communication delay duration, dynamically adapting to communication transmission fluctuations, and providing accurate data support at both the hardware and communication levels for guidance command execution timing calibration.

[0013] 3. This invention integrates the real-time change duration of the guidance lights with the predicted communication delay duration to form a dedicated delay dataset. Using the guidance control command time threshold as a benchmark, it calculates the actual advance timing of command issuance and accurately determines the command recipient based on the target vehicle's position, thus achieving dynamic calibration of the guidance command issuance timing. Simultaneously, through directional broadcasting from adjacent nodes in the same lane, data consistency comparison, and distributed storage with multi-node mutual verification, it ensures the reliability of data storage, ultimately achieving stable and reliable following distance guidance support, improving the safety of highway driving and the order of traffic flow in adverse weather conditions. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a method for guiding and managing safe passages on highways during severe weather, according to the present invention. Figure 2 This is a schematic diagram of the structure of a safety passage guidance and management system for highways in severe weather according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1: As Figure 1 As shown, the present invention provides a technical solution: a method for guiding and managing safe passages in severe weather on highways. The method includes the following steps: Step S1: Obtain the real-time vehicle speed of each lane of the highway based on the multi-sensor array deployed by the guidance lights, and store the real-time vehicle speed data in association with the timestamp data of the acquisition, thereby constructing a real-time vehicle speed guidance analysis set. Step S1-1: The multi-sensor array includes a vehicle speed acquisition sensor and a vehicle distance recognition sensor. Each sensor in the multi-sensor array is bound to a corresponding guide light, and a data interaction link is established with the corresponding guide light through a unique communication protocol. After the multi-sensor array is activated, the vehicle speed acquisition sensor is used to collect the real-time speed of vehicles traveling in the corresponding lane, and simultaneously records the unique identifier of the vehicle speed acquisition sensor, the acquisition timestamp, and the lane ID. The vehicle distance recognition sensor is used to collect the relative distance between vehicles traveling in the corresponding lane and its bound guide light in real time, and determines the lane the vehicle belongs to based on the relative distance, simultaneously recording the unique identifier of the vehicle distance recognition sensor, the acquisition timestamp, and the relative distance data between the vehicle and the guide light. Steps S1-2: Construct a master data table with lane ID as the primary key and collection timestamp as the foreign key. The master data table stores the unique mapping relationship between lane ID and collection timestamp. Using the primary key and foreign key of this master data table as the association basis, establish a related data table, using real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor as the core fields of the related data table to achieve bidirectional association between the master data table and the related data table. Associate the related data with the corresponding bound unique identifier of the guidance light, and arrange the bound data in ascending order of collection timestamp. Use a standardized data storage format to structurally integrate the sorted data to form a real-time vehicle speed guidance analysis set. The real-time vehicle speed guidance analysis set includes the unique identifier of the guidance light, lane ID as the primary key, collection timestamp, real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor. Store the corresponding guidance light according to the unique identifier of the guidance light. In practical implementation, the one-to-one binding of sensor arrays and guidance lights requires a dedicated link for data transmission based on a unique communication protocol to avoid data confusion between different lanes or guidance lights. The acquisition actions of vehicle speed acquisition sensors and vehicle distance recognition sensors must be strictly synchronized to ensure accurate correlation between vehicle speed and relative distance data at the same timestamp. During the data structure integration process, the primary key and foreign key mapping rules of the master data table and related data tables must be strictly followed to ensure the orderliness and integrity of the real-time vehicle speed guidance analysis set. Through sensor collaborative acquisition and data association storage, a precise data source is provided for guidance control.

[0017] Step S2: Based on the real-time vehicle speed guidance analysis set and the limited following distance corresponding to the real-time weather on the highway, obtain the time threshold of the guidance control command for the limited following distance of the guidance light; Step S2-1: Call the highway meteorological monitoring database to obtain the real-time meteorological data of the current highway. Based on the preset weather-corresponding following distance mapping standard library, match the limited following distance corresponding to the real-time meteorological data. Classify and divide the real-time vehicle speed guidance analysis set according to lane ID to generate a lane-level vehicle speed dataset exclusive to each lane ID. Retrieve the preset guidance light structured deployment file and extract the guidance light deployment spacing data. The guidance light deployment spacing data represents the fixed physical distance data between two adjacent guidance lights under the same lane ID. Associate and bind the guidance light deployment spacing data with the corresponding lane-level vehicle speed dataset according to the unique identifier of the guidance light. Step S2-2: For the lane-level vehicle speed dataset corresponding to a single lane ID, based on the relative distance data between the vehicle and the guide light, filter out the nearest traveling vehicle located downstream of the currently monitored guide light in that lane, defining it as the target following vehicle; extract the real-time vehicle speed of the currently traveling vehicle associated with the currently monitored guide light, and the real-time vehicle speed corresponding to the target following vehicle; combine the guide light deployment spacing data and the relative distance data between the vehicle and the guide light, and calculate the actual distance between the currently traveling vehicle and the target following vehicle through geometric relationships. The specific process is as follows: Step S2-2-1: Draw a perpendicular line from the guide light to the road surface of the corresponding lane, and determine the foot of the perpendicular as the lane projection reference point of the guide light; retrieve the preset guide light installation height data, which is the vertical distance from the center of the guide light body to the road surface of the corresponding lane; establish a longitudinal coordinate system with the lane projection reference point of the currently monitored guide light as the origin and the driving direction of the lane as the positive direction; clarify the lane projection reference point of the currently monitored guide light and the lane projection reference point of the adjacent guide light downstream, and the distance between the two lane projection reference points along the longitudinal direction of the lane is equal to the guide light layout spacing data; Step S2-2-2: Construct a right triangle with the lane projection reference point of the currently monitored guide light as the right-angle vertex, the guide light installation height data as one right-angle side, and the relative distance data between the currently traveling vehicle and the guide light as the hypotenuse. Calculate the specific position of the currently traveling vehicle in the longitudinal coordinate system using the Pythagorean theorem. Retrieve the installation height data of the downstream adjacent guide light bound to the target following vehicle. Construct a right triangle with the lane projection reference point of the downstream adjacent guide light as the right-angle vertex, its installation height data as one right-angle side, and the relative distance data between the target following vehicle and the guide light as the hypotenuse. Calculate the target following vehicle's position in the longitudinal coordinate system using the Pythagorean theorem. The longitudinal distance along the lane from the vehicle to the lane projection reference point is used to determine the positional relationship between the target vehicle and the lane projection reference point based on the vehicle's direction of travel: If the vehicle has not yet passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by subtracting the longitudinal distance along the lane from the target vehicle to the lane projection reference point from the spacing data of the guide lights between the two lane projection reference points; if the vehicle has already passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by adding the longitudinal distance along the lane from the target vehicle to the lane projection reference point to the spacing data of the guide lights between the two lane projection reference points. Step S2-2-3: Compare the relative positions of the currently driving vehicle and the target following vehicle in the longitudinal coordinate system, calculate the difference between their relative positions, and obtain the actual distance between the currently driving vehicle and the target following vehicle. Step S2-3: If the real-time speed of the target vehicle is less than or equal to the real-time speed of the currently traveling vehicle, set the guidance control command time threshold to the minimum response trigger duration of the guidance light; if the real-time speed of the target vehicle is greater than the real-time speed of the currently traveling vehicle, first obtain the speed difference between the two vehicles by the difference between the real-time speed of the target vehicle and the real-time speed of the currently traveling vehicle, then obtain the distance to be converged by the difference between the actual distance and the limited following distance; obtain the convergence time required for the distance to converge to the limited following distance by the ratio of the distance to be converged to the speed difference; use the convergence time as the guidance control command time threshold. The guidance control command time threshold is expressed as the convergence time required for the vehicle distance to converge to the limit following distance or the minimum response trigger time of the guidance light, based on the relationship between the limited following distance corresponding to real-time weather, the actual distance between the current vehicle and the target following vehicle, and the vehicle speed. In practical implementation, the call to real-time meteorological data must ensure accurate matching with highway sections. The preset weather-corresponding following distance mapping standard library must cover common severe weather types. The extraction of guide light deployment spacing data must be strictly bound to lane IDs to avoid cross-lane confusion. The establishment of the longitudinal coordinate system during the vehicle distance conversion process must be based on the lane driving direction as a unified benchmark. The determination of the target following vehicle's position must be combined with the driving direction to ensure accurate relative position calculation. Combining weather and vehicle condition dynamic parameters, the time threshold of the guidance control command adapted to the real-time scenario is obtained through geometric conversion and logical judgment.

[0018] Step S3: Select any one of the guiding lights as the research object, and denote it as the target guiding light; collect and analyze the change timestamp data of the historical guiding control commands of the target guiding light to obtain the change time interval of the historical guiding control commands, and set the response decay sliding window analysis to obtain the real-time change duration of the target guiding light; Step S3-1: Retrieve the historical guidance control command execution records of the target guidance light, extract the two timestamps corresponding to each historical command, including the timestamp of receiving the guidance control command of the target guidance light and the timestamp of confirming the completion of the guidance light state change; calculate the difference between the two timestamps corresponding to each historical command to obtain the change time interval of each historical guidance control command, and construct a set of historical change time intervals. Step S3-2: Set and initialize the response decay sliding window. Extract data from the historical transformation time interval set through the response decay sliding window. Calculate the arithmetic mean of the extracted transformation time intervals to obtain the real-time transformation duration of the target guide light. In practical implementation, the historical guidance control command execution records of the target guidance light need to be completely extracted, including both the timestamps for receiving and the timestamps for completing the state transition. This is to avoid deviations in the calculation of the transition time interval due to missing timestamps. The initialization of the response attenuation sliding window needs to be adapted to the actual service life of the guidance light and the amount of historical data. The data extraction process should prioritize retaining recent valid data to improve the timeliness of real-time transition duration. Based on the historical response data of the guidance light, the response delay caused by hardware performance degradation is analyzed and quantified through the sliding window.

[0019] Step S4: Collect the timestamps of the target guide lights’ historical data communication, and construct a communication time analysis set based on the collected data communication timestamps; use a time series prediction model to process the communication time analysis set and predict the delay time of the next data communication of the target guide lights. Step S4-1: Retrieve the historical data communication interaction records of the target guidance light, and extract the communication timestamps corresponding to each data communication interaction record, including the communication request initiation timestamp, the three-way handshake connection establishment confirmation timestamp, and the data transmission completion feedback timestamp. Step S4-2: Calculate the communication delay duration for each historical data communication record. Specifically, this is the difference between the data transmission completion feedback timestamp and the communication request initiation timestamp. This difference includes the three-way handshake link establishment delay and the data transmission delay. Sort the effective historical communication delay durations in ascending order of the communication request initiation timestamp to construct a communication time analysis set. Step S4-3: Use the communication time analysis set as input data for the time series prediction model; use the time series prediction model to fit and analyze the historical communication delay variation pattern of the target guide light, and output the delay duration of the next data communication of the target guide light; In practical implementation, the extraction of historical data communication interaction records of target guidance lights must cover the entire process of request initiation, connection establishment confirmation, and transmission completion feedback timestamps to ensure that the calculation of communication delay duration includes both link establishment and data transmission. The arrangement of the communication time analysis set must strictly follow the chronological order to meet the input requirements of the time series prediction model. The model fitting process must fully adapt to the fluctuation pattern of communication delay to avoid prediction deviation. By mining the delay change characteristics through historical communication data, accurate prediction of the next communication delay can be achieved.

[0020] Step S5 synchronizes the data processing from steps S3 to S4 to obtain the real-time change duration and data communication duration of each guide light. It is associated and stored according to the unique identifier of the guide light to obtain the delay dataset of each guide light. The guide light is issued guidance control commands in combination with the guidance control command time threshold. Each guide light is selected as a data broadcast node. Based on the guidance control command time threshold analysis, the delay dataset of each guide light is broadcast and stored for management. Step S5-1: Retrieve the real-time change duration and predicted next data communication delay duration for all guide lights; using the unique identifier of the guide light as the core association key, bind the real-time change duration and data communication delay duration of each guide light to construct a delay dataset exclusive to each guide light; the delay dataset includes the unique identifier of the guide light, the lane projection reference point ID, the real-time change duration and the data communication delay duration fields, and is stored in the system's distributed database according to the unique identifier of the guide light. Step S5-2: Read the time threshold of the guidance control command corresponding to each guidance light, and match the guidance control command time threshold with the delay dataset of the corresponding guidance light; calculate the advance time of the actual issuance of the guidance control command, specifically: based on the guidance control command time threshold, subtract the sum of the real-time change time and data communication delay time of the corresponding guidance light; if the calculation result is non-negative, the command is issued according to the advance time; if the calculation result is negative, the actual advance time is set as the minimum response trigger time of the guidance light. Step S5-3: Using the lane's driving direction as a reference, the area in front of the vehicle is downstream, and the area behind it is upstream; based on the relative position of the target following vehicle in the longitudinal coordinate system, combined with the lane projection reference point positions of the currently monitored guide light and the adjacent guide light downstream, determine the positional relationship between the target following vehicle and the adjacent guide light downstream. The specific determination process is as follows: If the target vehicle has not passed the lane projection reference point of the downstream adjacent guide light, then the downstream adjacent guide light is identified as the instruction receiving subject; if the target vehicle has passed the lane projection reference point of the downstream adjacent guide light, then the next downstream adjacent guide light is identified as the instruction receiving subject, i.e., the currently monitored guide light; the guidance control instruction is issued to the instruction receiving subject according to the calculated actual advance issuance time. Step S5-4: After receiving the confirmation signal, the instruction receiving entity initiates the directional broadcast process for adjacent nodes in the same lane. It broadcasts its complete delay dataset only to its upstream and downstream adjacent guide lights. The adjacent guide lights receiving the broadcast data use the unique identifier of the guide light and the lane ID as verification to compare the received delay dataset with the corresponding historical data stored in their own storage. After the comparison is successful, the broadcast data is written to the local dedicated storage partition and the broadcast data is forwarded to its non-initiating adjacent guide lights. The data reception, comparison and storage process of all guide light nodes in the same lane is completed in this iterative manner, forming a distributed data storage system with multi-node mutual verification. In practical implementation, the construction of the delay dataset must be based on the unique identifier of the guide light to ensure exclusive data binding. The calculation of the advance time of instruction issuance must accurately superimpose the real-time change time and the communication delay time. The determination of the instruction receiving subject must be strictly based on the positional relationship between the target following vehicle and the lane projection reference point. Data broadcasting must be limited to adjacent nodes in the same lane to avoid redundant transmission across lanes. Consistency comparison must use the unique identifier of the guide light and the lane ID as dual verification standards. Hardware and communication delay data are integrated to calibrate the instruction issuance timing. Directional broadcasting and distributed storage are used to ensure data reliability and instruction execution effectiveness.

[0021] Example 2, as Figure 2 As shown, the present invention provides a severe weather safety passage guidance and management system for highways. The severe weather safety passage guidance and management system includes a vehicle speed acquisition and integration module, a vehicle distance analysis threshold module, a guidance light change duration module, a communication delay prediction module, and a command issuance and data broadcasting module. The vehicle speed acquisition and integration module is used to acquire real-time vehicle speeds and related data for each lane of the highway, and construct a real-time vehicle speed guidance analysis set; the vehicle distance analysis threshold module is used to match and limit following distances, calculate the actual vehicle distance, and determine the time threshold for guidance control commands; the guidance light change duration module is used to collect historical change time intervals of the target guidance lights and calculate their real-time change duration; the communication delay prediction module is used to collect historical communication delay durations of the target guidance lights and predict the next data communication delay duration; the command issuance and data broadcasting module is used to integrate the guidance light delay dataset, issue guidance control commands, and broadcast and store the dataset. The vehicle speed acquisition and integration module includes a sensor data acquisition unit and a data structuring and integration unit. The sensor data acquisition unit is used to acquire the real-time vehicle speed, relative distance between vehicles and related signs, and acquisition timestamps for the corresponding lane. The data structuring and integration unit is used to construct an associated data table and arrange the data in an orderly manner, and the structured integration forms a real-time vehicle speed guidance analysis set. The vehicle distance analysis threshold module includes a meteorological lane data association unit and a vehicle distance calculation threshold determination unit; the meteorological lane data association unit is used to call real-time meteorological data to match and limit the following distance, and associate the spacing of the guidance lights with the lane-level vehicle speed dataset; the vehicle distance calculation threshold determination unit is used to calculate the actual distance between the currently driving vehicle and the target following vehicle, and determine the time threshold of the guidance control command. The guide light switching duration module includes a historical switching interval acquisition unit and a sliding window duration calculation unit; the historical switching interval acquisition unit is used to retrieve the historical guidance control command execution record of the target guide light and acquire the historical switching time interval; the sliding window duration calculation unit is used to extract data through the response attenuation sliding window and calculate the real-time switching duration of the target guide light. The communication delay prediction module includes a historical communication delay acquisition unit and a time-series delay prediction unit; the historical communication delay acquisition unit is used to retrieve historical data communication interaction records of the target guidance light, collect historical communication delay durations and construct a set; the time-series delay prediction unit is used to input the communication time analysis set into a time series prediction model to predict the next data communication delay duration; The instruction issuance data broadcasting module includes a delayed data integration and issuance unit and an instruction receiving data broadcasting unit. The delayed data integration and issuance unit is used to integrate the real-time change duration of the guidance lights with the predicted communication delay duration, calculate the advance duration, and issue guidance control instructions. The instruction receiving data broadcasting unit is used to receive guidance control instructions and then broadcast the delayed dataset in a directional manner to complete the distributed storage of mutual verification among multiple nodes in the same lane.

[0022] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A method for guiding and managing safe passages in severe weather on highways, characterized in that: The method for guiding and managing safe passages during severe weather includes the following steps: Step S1: Obtain the real-time vehicle speed of each lane of the highway based on the multi-sensor array deployed by the guidance lights, and store the real-time vehicle speed data in association with the timestamp data of the acquisition, thereby constructing a real-time vehicle speed guidance analysis set. Step S2: Based on the real-time vehicle speed guidance analysis set and the limited following distance corresponding to the real-time weather on the highway, obtain the time threshold of the guidance control command for the limited following distance of the guidance light. Step S3: Select any one of the guiding lights as the research object, and denote it as the target guiding light; collect and analyze the change timestamp data of the historical guiding control commands of the target guiding light to obtain the change time interval of the historical guiding control commands, and set the response decay sliding window analysis to obtain the real-time change duration of the target guiding light; Step S4: Collect the timestamps of the target guide lights’ historical data communication, and construct a communication time analysis set based on the collected data communication timestamps; use a time series prediction model to process the communication time analysis set and predict the delay time of the next data communication of the target guide lights. Step S5 synchronizes the data processing from steps S3 to S4 to obtain the real-time change duration and data communication duration of each guide light. It associates and stores the data according to the unique identifier of each guide light to obtain the delay dataset of each guide light. It then issues guidance control commands to the guide lights based on the guidance control command time threshold. Finally, it selects each guide light as a data broadcast node and performs broadcast storage management on the delay dataset of each guide light based on the guidance control command time threshold analysis.

2. The method for guiding and managing safe passages in severe weather on highways according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1: The multi-sensor array includes a vehicle speed acquisition sensor and a vehicle distance recognition sensor. The sensors in the multi-sensor array are bound to the guide lights one by one and establish a data interaction link with the corresponding guide lights through a unique communication protocol. After the multi-sensor array is started, the vehicle speed acquisition sensor is used to collect the real-time vehicle speed of vehicles traveling in the corresponding lane in real time, and synchronously record the unique identifier of the vehicle speed acquisition sensor, the collection timestamp, and the lane ID to which it belongs. The vehicle distance recognition sensor is used to collect the relative distance between the vehicle and its own bound guide light in real time. The vehicle's lane is determined by the relative distance. The sensor also records the unique identifier of the vehicle distance recognition sensor, the collection timestamp, and the relative distance data between the vehicle and the guide light. Step S1-2: Construct a master data table with lane ID as the primary key and collection timestamp as the foreign key. The master data table stores the unique mapping relationship between lane ID and collection timestamp. Using the primary key and foreign key of the master data table as the association basis, a related data table is established. Real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor are used as the core fields of the related data table to achieve bidirectional association between the master data table and the related data table. The associated data is then bound to the corresponding unique identifier of the guidance light, and the bound data is arranged in ascending order according to the acquisition timestamp. The sorted data is then structured and integrated using a standardized data storage format to form a real-time vehicle speed guidance analysis set. This set includes the unique identifier of the guidance light, lane ID as the primary key, acquisition timestamp, real-time vehicle speed, unique identifier of the vehicle speed acquisition sensor, and unique identifier of the vehicle distance recognition sensor. The corresponding guidance lights are stored according to their unique identifiers.

3. The method for guiding and managing safe passages in severe weather on highways according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: Call the highway meteorological monitoring database to obtain the real-time meteorological data of the current highway, and match the limited following distance corresponding to the real-time meteorological data based on the preset weather-corresponding following distance mapping standard library. The real-time vehicle speed guidance analysis set is classified and divided according to lane ID, generating lane-level vehicle speed datasets exclusive to each lane ID; the preset structured deployment file of guidance lights is retrieved, and the guidance light deployment spacing data is extracted from it. The guidance light deployment spacing data represents the fixed physical distance data between two adjacent guidance lights under the same lane ID. The guidance light deployment spacing data is associated and bound with the corresponding lane-level vehicle speed dataset according to the unique identifier of the guidance light. Step S2-2: For the lane-level vehicle speed dataset corresponding to a single lane ID, based on the relative distance data between the vehicle and the guide light, filter out the nearest traveling vehicle located downstream of the currently monitored guide light in that lane, defining it as the target following vehicle; extract the real-time vehicle speed of the currently traveling vehicle associated with the currently monitored guide light, and the real-time vehicle speed corresponding to the target following vehicle; combine the guide light deployment spacing data and the relative distance data between the vehicle and the guide light, and calculate the actual distance between the currently traveling vehicle and the target following vehicle through geometric relationships. The specific process is as follows: Step S2-2-1: Draw a perpendicular line from the guide light to the road surface of the corresponding lane, and determine the foot of the perpendicular as the lane projection reference point of the guide light; retrieve the preset guide light installation height data, which is the vertical distance from the center of the guide light body to the road surface of the corresponding lane; establish a longitudinal coordinate system with the lane projection reference point of the currently monitored guide light as the origin and the driving direction of the lane as the positive direction; clarify the lane projection reference point of the currently monitored guide light and the lane projection reference point of the adjacent guide light downstream, and the distance between the two lane projection reference points along the longitudinal direction of the lane is equal to the guide light layout spacing data; Step S2-2-2: Construct a right triangle with the lane projection reference point of the currently monitored guide light as the right-angle vertex, the guide light installation height data as one right-angle side, and the relative distance data between the currently traveling vehicle and the guide light as the hypotenuse. Calculate the specific position of the currently traveling vehicle in the longitudinal coordinate system using the Pythagorean theorem. Retrieve the installation height data of the downstream adjacent guide light bound to the target following vehicle. Construct a right triangle with the lane projection reference point of the downstream adjacent guide light as the right-angle vertex, its installation height data as one right-angle side, and the relative distance data between the target following vehicle and the guide light as the hypotenuse. Calculate the target following vehicle's position in the longitudinal coordinate system using the Pythagorean theorem. The longitudinal distance along the lane from the vehicle to the lane projection reference point is used to determine the positional relationship between the target vehicle and the lane projection reference point based on the vehicle's direction of travel: If the vehicle has not yet passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by subtracting the longitudinal distance along the lane from the target vehicle to the lane projection reference point from the spacing data of the guide lights between the two lane projection reference points; if the vehicle has already passed the lane projection reference point, the relative position of the target vehicle in the longitudinal coordinate system is obtained by adding the longitudinal distance along the lane from the target vehicle to the lane projection reference point to the spacing data of the guide lights between the two lane projection reference points. Step S2-2-3: Compare the relative positions of the currently driving vehicle and the target following vehicle in the longitudinal coordinate system, calculate the difference between their relative positions, and obtain the actual distance between the currently driving vehicle and the target following vehicle. Step S2-3: If the real-time speed of the target vehicle is less than or equal to the real-time speed of the currently traveling vehicle, set the guidance control command time threshold to the minimum response trigger duration of the guidance light; if the real-time speed of the target vehicle is greater than the real-time speed of the currently traveling vehicle, first obtain the speed difference between the two vehicles by the difference between the real-time speed of the target vehicle and the real-time speed of the currently traveling vehicle, and then obtain the distance to be converged by the difference between the actual distance and the limited following distance; obtain the convergence time required for the distance to converge to the limited following distance by the ratio of the distance to be converged to the speed difference; use the convergence time as the guidance control command time threshold.

4. A method for guiding and managing safe passages in severe weather on highways according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1: Retrieve the historical guidance control command execution records of the target guidance light, extract the two timestamps corresponding to each historical command, including the timestamp of receiving the guidance control command of the target guidance light and the timestamp of confirming the completion of the guidance light state change; calculate the difference between the two timestamps corresponding to each historical command to obtain the change time interval of each historical guidance control command, and construct a set of historical change time intervals. Step S3-2: Set and initialize the response decay sliding window. Extract data from the historical transformation time interval set through the response decay sliding window. Calculate the arithmetic mean of the extracted transformation time intervals to obtain the real-time transformation duration of the target guide light.

5. A method for guiding and managing safe passages in severe weather on highways according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: Retrieve the historical data communication interaction records of the target guidance light, and extract the communication timestamps corresponding to each data communication interaction record, including the communication request initiation timestamp, the three-way handshake connection establishment confirmation timestamp, and the data transmission completion feedback timestamp. Step S4-2: Calculate the communication delay duration for each historical data communication record. Specifically, this is the difference between the data transmission completion feedback timestamp and the communication request initiation timestamp. This difference includes the three-way handshake link establishment delay and the data transmission delay. Sort the effective historical communication delay durations in ascending order of the communication request initiation timestamp to construct a communication time analysis set. Step S4-3: Use the communication time analysis set as input data for the time series prediction model; use the time series prediction model to fit and analyze the historical communication delay variation of the target guide light, and output the delay duration of the next data communication of the target guide light.

6. A method for guiding and managing safe passages in severe weather on highways according to claim 5, characterized in that: The specific steps of step S5 are as follows: Step S5-1: Retrieve the real-time change duration and predicted next data communication delay duration for all guide lights; using the unique identifier of the guide light as the core association key, bind the real-time change duration and data communication delay duration of each guide light to construct a delay dataset exclusive to each guide light; the delay dataset includes the unique identifier of the guide light, the lane projection reference point ID, the real-time change duration and the data communication delay duration fields, and is stored in the system's distributed database according to the unique identifier of the guide light. Step S5-2: Read the time threshold of the guidance control command corresponding to each guidance light, and match the guidance control command time threshold with the delay dataset of the corresponding guidance light; calculate the advance time of the actual issuance of the guidance control command, specifically: based on the guidance control command time threshold, subtract the sum of the real-time change time and data communication delay time of the corresponding guidance light; if the calculation result is non-negative, the command is issued according to the advance time; if the calculation result is negative, the actual advance time is set as the minimum response trigger time of the guidance light.

7. A method for guiding and managing safe passages in severe weather on highways according to claim 6, characterized in that: Step S5 also includes: Step S5-3: Using the lane's driving direction as a reference, the area in front of the vehicle is downstream, and the area behind it is upstream; based on the relative position of the target following vehicle in the longitudinal coordinate system, combined with the lane projection reference point positions of the currently monitored guide light and the adjacent guide light downstream, determine the positional relationship between the target following vehicle and the adjacent guide light downstream. The specific determination process is as follows: If the target vehicle has not passed the lane projection reference point of the downstream adjacent guide light, then the downstream adjacent guide light is identified as the instruction receiving subject; if the target vehicle has passed the lane projection reference point of the downstream adjacent guide light, then the next downstream adjacent guide light is identified as the instruction receiving subject, i.e., the currently monitored guide light; the guidance control instruction is issued to the instruction receiving subject according to the calculated actual advance issuance time. Step S5-4: After receiving the confirmation signal, the instruction receiving entity initiates the directional broadcast process for adjacent nodes in the same lane. It broadcasts its complete delay dataset only to its upstream and downstream adjacent guide lights. The adjacent guide lights receiving the broadcast data use the unique identifier of the guide light and the lane ID as verification to compare the received delay dataset with the corresponding historical data stored in their own storage. After the comparison is successful, the broadcast data is written to the local dedicated storage partition and simultaneously forwarded to its non-initiating adjacent guide lights. The data reception, comparison, and storage process for all guide light nodes in the same lane is completed iteratively, forming a distributed data storage system with multi-node mutual verification.

8. A severe weather safety passage guidance and management system for highways, which applies the severe weather safety passage guidance and management method for highways as described in any one of claims 1-7, characterized in that: The severe weather safety passage guidance and management system includes a vehicle speed acquisition and integration module, a vehicle distance analysis threshold module, a guidance light change duration module, a communication delay prediction module, and a command issuance and data broadcasting module. The vehicle speed acquisition and integration module is used to acquire real-time vehicle speeds and related data for each lane of the highway, and construct a real-time vehicle speed guidance analysis set; the vehicle distance analysis threshold module is used to match and limit following distances, calculate the actual vehicle distance, and determine the time threshold for guidance control commands; the guidance light change duration module is used to collect historical change time intervals of the target guidance lights and calculate their real-time change duration; the communication delay prediction module is used to collect historical communication delay durations of the target guidance lights and predict the next data communication delay duration; the command issuance and data broadcasting module is used to integrate the guidance light delay dataset, issue guidance control commands, and broadcast and store the dataset.

9. A severe weather safety passage guidance and management system for highways according to claim 8, characterized in that: The vehicle speed acquisition and integration module includes a sensor data acquisition unit and a data structuring and integration unit. The sensor data acquisition unit is used to acquire the real-time vehicle speed, relative distance between vehicles and related signs, and acquisition timestamps for the corresponding lane. The data structuring and integration unit is used to construct an associated data table and arrange the data in an orderly manner, and the structured integration forms a real-time vehicle speed guidance analysis set. The vehicle distance analysis threshold module includes a meteorological lane data association unit and a vehicle distance calculation threshold determination unit; the meteorological lane data association unit is used to call real-time meteorological data to match and limit the following distance, and associate the spacing of the guidance lights with the lane-level vehicle speed dataset; the vehicle distance calculation threshold determination unit is used to calculate the actual distance between the currently driving vehicle and the target following vehicle, and determine the time threshold of the guidance control command. The guide light switching duration module includes a historical switching interval acquisition unit and a sliding window duration calculation unit; the historical switching interval acquisition unit is used to retrieve the historical guidance control command execution record of the target guide light and acquire the historical switching time interval; the sliding window duration calculation unit is used to extract data through the response attenuation sliding window and calculate the real-time switching duration of the target guide light.

10. A severe weather safety passage guidance and management system for highways according to claim 8, characterized in that: The communication delay prediction module includes a historical communication delay acquisition unit and a time-series delay prediction unit; the historical communication delay acquisition unit is used to retrieve historical data communication interaction records of the target guidance light, collect historical communication delay durations and construct a set; the time-series delay prediction unit is used to input the communication time analysis set into a time series prediction model to predict the next data communication delay duration; The instruction issuance data broadcasting module includes a delayed data integration and issuance unit and an instruction receiving data broadcasting unit. The delayed data integration and issuance unit is used to integrate the real-time change duration of the guidance lights with the predicted communication delay duration, calculate the advance duration, and issue guidance control instructions. The instruction receiving data broadcasting unit is used to receive guidance control instructions and then broadcast the delayed dataset in a directional manner to complete the distributed storage of mutual verification among multiple nodes in the same lane.

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