Highway traffic inspection method and system based on big data processing

Abstract

The application discloses a highway traffic inspection method and system based on big data processing, and particularly relates to the technical field of highway inspection. The application accesses floating car Beidou, vehicle detectors, accident platforms, structural sensors, weather and other multi-source data in real time, and unifies cleaning, constructs congestion, accident, pavement, structure, environment multi-dimensional index, and dynamically divides each kilometer road section into 1-4 risk levels. Then, according to the grading result, the risk change rate, the interval from the last inspection and the three factors of sudden events are introduced, and the dynamic inspection priority of each road section is calculated in real time. Finally, the improved VRPTW-PW algorithm is used to generate the shortest driving and highest priority inspection path under the constraints of vehicle endurance and real-time road conditions. The system includes five modules of multi-source data fusion, dynamic grading, frequency optimization, path planning and closed-loop feedback, which can significantly improve the hidden danger discovery rate, reduce the empty driving rate, and realize the precision and intelligence of highway inspection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of highway inspection, more particularly, the present application relates to a highway traffic inspection method and system based on big data processing. BACKGROUND

[0002] For a long time, highway inspection mainly relies on manual inspection or fixed period mechanical inspection. The staff carries simple instruments on the road, and visually inspects cracks, pits, and sign damage according to experience, and then manually enters the results into the system. This method not only consumes manpower, but is also greatly affected by weather and traffic conditions, and there are often missed inspections and incorrect inspections. More importantly, once the inspection plan is arranged, it is difficult to adjust, and it is not possible to flexibly change according to real-time road conditions or emergencies, resulting in that high-risk road sections cannot be paid attention to in time, and low-risk areas frequently occupy resources.

[0003] With the improvement of traffic informatization level, a large number of sensing devices have been laid on the road: Beidou floating car, geomagnetic vehicle detector, ETC gantry, bridge health monitor, weather station and various accident reporting platforms. Although the types of data seem to be rich, they are scattered in traffic police, operation, maintenance, weather and other departments, and the formats, frequencies and coordinate systems are different. There is a lack of unified data cleaning, space-time alignment and standardization process, resulting in the phenomenon of "data island", making it difficult to form a complete portrait of the road state, and it is even more difficult to make accurate decisions accordingly.

[0004] At present, there are automatic recognition based on video or simple early warning systems based on a single data source on the market, but there are generally the following deficiencies: first, only single-dimensional indicators such as traffic flow or structural safety are concerned, and there is a lack of a grading method that comprehensively considers real-time risk, infrastructure health and historical importance; second, the inspection frequency is fixed, and it cannot be dynamically adjusted according to the risk change, the interval between the last inspection or the emergency; third, the path planning stops at the "nearest principle" or simple scheduling, and does not consider the time efficiency constraints of vehicle endurance, real-time road conditions and high-priority road sections. Therefore, there is an urgent need for a complete technical solution that can integrate multi-source big data, dynamically evaluate road conditions, adaptively adjust the inspection rhythm and optimize the path, in order to truly improve the accuracy and timeliness of highway inspection. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a highway traffic inspection method and system based on big data processing.

[0006] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0007] The highway traffic inspection method based on big data processing comprises the following steps:

[0008] S1, multi-dimensional dynamic road condition classification: real-time data of each road is cleaned and aligned, congestion, accident, road surface, and structure indicators are calculated, and a 1-4 level risk grade of each kilometer road section is obtained;

[0009] S2, adaptive patrol frequency optimization: a basic patrol cycle is given according to the risk level, and the patrol priority of each road section at this moment is analyzed through dynamic weighting of risk change, time from last patrol, and sudden event;

[0010] S3, optimal patrol path planning: the priority is converted into a time window constraint, and an improved VRPTW-PW algorithm is used to generate the shortest time and highest priority driving route for all patrol vehicles under real-time road conditions and vehicle endurance restrictions.

[0011] Specifically, the specific process of S1 is as follows:

[0012] Multi-source heterogeneous data access and preprocessing:

[0013] Real-time access of traffic flow data, event and abnormal data, infrastructure state data, and environmental data;

[0014] The data is cleaned, time and space aligned, and standardized;

[0015] Multi-dimensional feature extraction and calculation:

[0016] Calculate the congestion risk index and accident risk index of the real-time risk dimension;

[0017] Calculate the pavement condition index PCI, structure safety index, and facility integrity rate of the infrastructure health dimension;

[0018] Extract the road function level weight and key node weight of the historical importance dimension;

[0019] Dynamic classification:

[0020] Based on the real-time risk dimension index, infrastructure health dimension index, and historical importance dimension index, through a classification rule engine or a lightweight classification model, a comprehensive road condition level RCL is dynamically assigned to each evaluation unit.

[0021] Specifically, in S1:

[0022] The traffic flow data includes Beidou data of floating cars, geomagnetic, microwave vehicle detector data, and ETC gantry data;

[0023] The event and abnormal data includes traffic police accident, event platform data, traffic control information, and social media or navigation App user reported data;

[0024] The infrastructure status data includes historical and real-time inspection reports, bridge or tunnel structure health monitoring sensor data, and road facility damage records;

[0025] The environmental data includes meteorological bureau API data and road environment sensor data;

[0026] The comprehensive road condition level includes:

[0027] Level 1 is triggered when any of the following conditions is met:

[0028] Extremely high accident risk, i.e., historical accident heat map peak plus real-time emergency braking frequency is greater than a threshold, or structural safety warning, i.e., bridge or tunnel monitoring data exceeds the limit;

[0029] Infrastructure status: PCI≤40 or visible damage area of road surface≥10%;

[0030] Level 2 is triggered when any of the following conditions is met and Level 1 is not met:

[0031] Real-time operation risk: persistent congestion, i.e., vehicle speed<40% of design value and lasts more than 30 minutes, or high accident risk, i.e., accident probability model output>0.7;

[0032] Infrastructure status: 40<PCI≤60 or facility damage density≥3 per kilometer;

[0033] Level 3 is triggered when any of the following conditions is met and a higher level is not met:

[0034] Potential risk: periodic congestion, i.e., peak vehicle speed is less than 60% of the design value, or 60<PCI≤80;

[0035] Environmental warning: meteorological yellow warning and impact on visibility or road adhesion coefficient;

[0036] Level 4 is the default level, which requires simultaneous satisfaction of:

[0037] Traffic operation: vehicle speed≥80% of design value and no persistent congestion;

[0038] Infrastructure: PCI>80 and no new damage reports;

[0039] Environmental conditions: no meteorological warning affecting driving;

[0040] The comprehensive road condition level is dynamically updated at a preset period, and reevaluation is triggered immediately when a major event or critical sensor data mutation occurs;

[0041] The major event includes traffic accidents or disaster weather warnings, and the critical sensor data includes bridge or tunnel structure health monitoring data and road surface environment sensor data.

[0042] Specifically, the specific process of S2 is as follows:

[0043] Establish the basic frequency mapping:

[0044] Pre-set the basic target inspection frequency BIF corresponding to different road condition levels RCL:

[0045] Level 1: target frequency = 1 time per 2 hours, BIF = 0.5 times / hour;

[0046] Level 2: target frequency = 1 time per 4 hours, BIF = 0.25 times / hour;

[0047] Level 3: target frequency = 1 time per 8 hours, BIF = 0.125 times / hour;

[0048] Level 4: target frequency = 1 time per 24 hours, BIF ≈ 0.042 times / hour;

[0049] Introduce a dynamic tuning factor:

[0050] Risk change trend factor Tf: calculated based on the change rate of real-time risk dimension index;

[0051] Time since last inspection factor Tl: calculate the interval from the last successful inspection to the current time t;

[0052] Event impact factor Ef: according to the type and severity of the event, the value is assigned, no event = 0, event > 0;

[0053] Calculate the dynamic priority weight DPW, calculate the priority of each road segment by weighted calculation At time The dynamic priority weight

[0054]

[0055] Where:

[0056] is the dynamic priority weight of road segment At time

[0057] is the basic target inspection frequency of road segment

[0058] is the normalized value of the risk change trend factor of road segment At time ​​​​​

[0059] For road segment At time Time since last inspection;

[0060] For Corresponding base period;

[0061] For road segment At time Event impact factor;

[0062] , , Adjustable weight coefficient, Controlling the importance of different factors;

[0063] Output priority weight: output all road segments ,The higher the value, the higher the inspection demand intensity.

[0064] Specifically, the specific process of S3 is as follows:

[0065] Problem modeling:

[0066] Define resources: available inspection vehicle set, each vehicle contains current position, state, endurance and working time attributes;

[0067] Define tasks: set of road segments to be inspected, each road segment Associated with dynamic priority weight Value and estimated inspection time ;

[0068] Set goals:

[0069] Maximize total coverage priority: ,=1 when the road segment Is planned to be covered, =0 indicates not covered; Minimize total travel time: generate road segment travel time matrix

[0070] Based on real-time traffic;

[0071] Meet timeliness constraints: set time window for high-priority road segments To achieve the preset standard value;

[0072] Set constraints:

[0073] Vehicle endurance and working time limit;

[0074] Path continuity;

[0075] Each road segment is covered by at most one vehicle;

[0076] Solving algorithm:

[0077] Improved VRPTW-PW algorithm with time window and priority weight:

[0078] Time window setting: according to road segment priority weight , set the expected service time window for road segment:

[0079] For The highest road segment, requires coverage within minutes in the future;

[0080] The second highest priority road segment, requires coverage within minutes in the future, and so on;

[0081] Objective function selects any of the following forms as needed:

[0082] Form 1: , where is a 0-1 variable, =1 indicates that the road segment is planned to be covered, =0 indicates not covered; is the priority weight coefficient, used to balance the coverage priority and travel cost; travel time is the total time spent by all inspection vehicles in the task;

[0083] Form 2: , where is the sum of the priority weights of the road segments planned to be covered; is the travel time budget, i.e. the maximum total travel time allowed.

[0084] Specifically, the solving algorithm in S3 further includes:

[0085] Pheromone initialization and update rules are integrated , release more pheromones for road segments greater than the threshold value;

[0086] Heuristic information includes and the remaining time window size;

[0087] Large-scale neighborhood search:

[0088] In the destruction phase, remove or timeout window road segments below the threshold value first;

[0089] In the repair phase, insert road segments greater than the threshold value and meeting the time window first;

[0090] Real-time traffic integration:

[0091] Calculate travel time When, real-time call map API traffic layer or based on traffic flow data to predict the travel time;

[0092] Output and feedback:

[0093] Output each vehicle detailed route sequence: vehicle K: [start->section A (priority weight ( , inspection time )->section B (priority weight ( , inspection time )->...->end / next start point];

[0094] Route instructions are issued to the vehicle terminal;

[0095] Receive the actual position, task state and new collected data of the vehicle feedback, trigger the dynamic grading and frequency optimization module to update.

[0096] The highway traffic inspection system based on big data processing comprises:

[0097] A multi-source data fusion module is used for real-time access and cleaning of Beidou, vehicle detector, weather, accident, structure sensing multi-source heterogeneous data, providing input for subsequent analysis;

[0098] A dynamic traffic grading module is used for automatically dividing each kilometer section into four risk levels according to three dimensions of real-time risk, infrastructure health and historical importance, and locking the area that needs to be focused on;

[0099] A patrol frequency optimization module is used for real-time calculation of the dynamic priority weight of each section based on the grading results and superimposed three factors of risk change, time from the last patrol and sudden event, to realize the adaptive patrol rhythm of high risk and high frequency, low risk and low frequency;

[0100] An intelligent path planning module is used for converting the priority weight into a VRPTW-PW model with a time window, calling an improved ant colony or large-scale neighborhood search algorithm, and generating an optimal inspection route under the constraints of vehicle endurance and real-time traffic;

[0101] A closed-loop execution and feedback module is used for issuing route instructions to the vehicle terminal, real-time returning vehicle position, task completion and new collected data, and refreshing the first four modules in reverse, forming a rolling optimization closed loop of perception, decision, execution and re-perception.

[0102] Technical effects and advantages of the present application:

[0103] The highway inspection is upgraded from the traditional mode of fixed time, fixed line and fixed person to an intelligent closed loop that changes with risks and data: by real-time collection and cleaning of multi-source heterogeneous data such as traffic flow, accidents, structure, weather and the like, 1-4 level risk classification of 1 km road section is automatically given every 5-15 minutes, and information silos are completely eliminated.

[0104] Based on the classification results, the inspection frequency changes from fixed period to dynamic rhythm of high risk twice a day and low risk once a day, and three factors of risk change, time from last inspection and sudden events are real-time weighted to ensure that resources are preferentially allocated to the most needed areas.

[0105] The path planning introduces a VRPTW-PW algorithm with time window, generates the optimal route with the shortest travel and the highest coverage under the constraints of vehicle endurance, real-time road conditions and priority, and issues and returns in real time through the vehicle terminal, realizing the rolling optimization of perception, decision, execution and re-perception.

[0106] Finally, the inspection coverage and hidden danger discovery rate are significantly improved, the missed detection rate and empty driving rate are simultaneously reduced, the maintenance funds and manpower investment are accurately configured, the road safety and traffic efficiency are simultaneously improved, and a replicable and generalizable engineering paradigm is provided for the sustainable operation of intelligent highway. BRIEF DESCRIPTION OF DRAWINGS

[0107] Figure 1 The method flowchart of the present application;

[0108] Figure 2 The system flowchart of the present application. DETAILED DESCRIPTION

[0109] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0110] As shown in Figure 1 The highway traffic inspection method based on big data processing includes the following steps:

[0111] Step 1, multi-dimensional dynamic road condition classification: aligning and cleaning real-time data of each road, calculating congestion, accident, road surface, structure and other indicators, and comprehensively obtaining 1-4 level risk grade of each kilometer road section;

[0112] Multi-source heterogeneous data access and preprocessing:

[0113] Traffic flow data: real-time access to Beidou floating cars (average speed, travel time, trajectory point density), geomagnetic / microwave vehicle detectors (flow, occupancy, vehicle speed), ETC gantry (section flow, vehicle type composition); event and anomaly data: access to traffic police accident / event platform (accident location, type, severity), traffic control information, social media / navigation App user reports (congestion, obstacles); infrastructure status data: access to historical and real-time inspection reports (pavement cracks, potholes, flatness index), bridge / tunnel structure health monitoring sensor data (vibration, strain), road property facilities (guardrails, markings) damage records; environmental data: access to meteorological bureau API (real-time and forecast rainfall, snow, fog, ice, strong wind), road environment sensors (road surface temperature, humidity, visibility);

[0114] Preprocessing: data cleaning (remove outliers, fill missing values), spatio-temporal alignment (unify timestamp, map geographic coordinates to specific road segments or grids), standardization / normalization.

[0115] Multi-dimensional feature extraction and calculation:

[0116] Real-time risk dimension: congestion risk index: based on real-time speed, flow / capacity ratio, speed variation coefficient; accident risk index: based on historical accident heat map, real-time hard braking / swerve frequency (from Beidou), abnormal parking detection (from video or event data), visibility / slippery index calculation; infrastructure health dimension: pavement condition index (PCI): based on historical damage data, recent inspection results, heavy vehicle flow (from ETC / weighting) to estimate the deterioration rate; structure safety index: based on bridge / tunnel monitoring data threshold alarm state, historical evaluation report; facility integrity rate: based on road property facility damage report frequency and density;

[0117] Historical importance dimension:

[0118] Road function level: expressway, national highway, provincial highway, county road, etc. (preset weight); key nodes: interchanges, long tunnels, large bridges, road sections around traffic hubs (preset weight).

[0119] Dynamic classification model: build a classification rule engine or train a lightweight classification model;

[0120] Input: each dimension index (real-time risk, infrastructure health, historical importance) calculated in step two;

[0121] Output: dynamically assign a comprehensive road condition level (RCL) to each evaluation unit (such as a 1-kilometer road segment). For example:

[0122] Level 1 (extremely high risk / deterioration), trigger if any of the following conditions are met:

[0123] Real-time safety risk: Very high accident risk (historic accident heat map peak + real-time hard braking frequency > threshold) or structural safety alert (bridge / tunnel monitoring data out of limit); Infrastructure status: PCI ≤ 40 (severe damage) or visible damage area ≥ 10%.

[0124] Level2 (High risk / concern), triggered when any of the following conditions are met and not reached Level1:

[0125] Real-time operation risk: Persistent congestion (vehicle speed < 40% of design value and lasts > 30 minutes) or high accident risk (accident probability model output > 0.7); Infrastructure status: 40 < PCI ≤ 60 (moderate damage) or facility damage density ≥ 3 per km.

[0126] Level3 (Medium risk / regular), triggered when any of the following conditions are met and not reached higher level:

[0127] Potential risk: Periodic congestion (peak hour vehicle speed < 60% of design value) or slight degradation of PCI (60 < PCI ≤ 80); Environmental warning: Meteorological yellow warning (moderate rain / fog / snow) affecting visibility or road adhesion coefficient.

[0128] Level4 (Low risk / good), default level, requires simultaneous satisfaction of:

[0129] Traffic operation: Vehicle speed ≥ 80% of design value and no persistent congestion; Infrastructure: PCI > 80 (good) and no new damage reports; Environmental conditions: No meteorological warning affecting driving.

[0130] Dynamic: The level assessment period is configurable (e.g., 5-15 minutes), and when a major event (accident, disaster weather warning) or critical sensor data mutation occurs, immediate re-evaluation is triggered.

[0131] Step two, adaptive patrol frequency optimization: Given the basic patrol period according to the risk level, then use risk change, time from last patrol, and sudden event three factors to dynamically weight, analyze the patrol priority of each road segment at this moment;

[0132] Establish a basic frequency-level mapping:

[0133] Preset different road condition levels (RCL) corresponding to basic target patrol frequencies (BIF):

[0134] Level 1: Target frequency = once every 2 hours (BIF = 0.5 times / hour); Level 2: Target frequency = once every 4 hours (BIF = 0.25 times / hour); Level 3: Target frequency = once every 8 hours (BIF = 0.125 times / hour); Level 4: Target frequency = once every 24 hours (BIF ≈ 0.042 times / hour);

[0135] Introducing dynamic tuning factors:

[0136] Risk Change Trend Factor (Tf): Calculates the rate of change (slope) of the real-time risk dimension index over a recent period (e.g., 1 hour); a sharp increase in risk increases the frequency weight, while a stable or declining trend decreases it appropriately; Time Since Last Inspection Factor (Tl): Calculates the time interval since the last successful inspection of this road segment. The longer the interval, the closer it is to or exceeds the BIF required cycle, the higher the current priority weight; Event Impact Factor (Ef): If a new event (accident, traffic control) occurs on this road segment or an adjacent road segment, the frequency requirement is significantly increased based on the event type and severity (potentially temporarily exceeding BIF);

[0137] Calculate the Dynamic Priority Weight (DPW) by weighting each road segment. In time Dynamic priority weights ( ):

[0138] ;

[0139] in:

[0140] For road section In time Dynamic priority weights; For road section The basic target inspection frequency; For road section In time Risk change trend factor (normalized value); For road section In time Time since the last inspection; for The corresponding basic cycle (e.g., Level 1 is 2 hours); For road section In time Event impact factor (0 indicates no event, >0 indicates an event, the larger the value, the greater the impact); , , Adjustable weighting coefficients ( ), control the importance of different factors;

[0141] Output: all road segments , The higher the value, the more the road segment needs to be prioritized and covered more frequently in the next round of inspection planning; Directly reflects the dynamic adjusted inspection demand intensity.

[0142] Step three, optimal inspection path planning: convert priority to time window constraint, use improved VRPTW-PW algorithm to generate the shortest time and highest priority driving route for all inspection vehicles under real-time traffic and vehicle endurance restrictions;

[0143] Problem modeling:

[0144] Resources: a group of available inspection vehicles (with sensors), each vehicle has current location, state (idle / busy), endurance / working time;

[0145] Task: all road segments to be inspected. Each road segment has a value (representing its priority weight) and an estimated inspection time ) (estimated according to road length, historical average inspection speed);

[0146] Objective: maximize total coverage priority: , =1 indicates that the road segment is planned to be covered, =0 indicates not covered;

[0147] Minimize total travel time: consider the travel time matrix between road segments ) caused by vehicle location, real-time traffic (from map API or traffic flow data);

[0148] Meet timeliness constraints: high-priority (high DPW) road segments should be covered as soon as possible (soft / hard time window can be set);

[0149] Constraints: vehicle working time and endurance limit; each road segment is inspected by at most one vehicle (or multiple times according to demand, but with intervals); path continuity.

[0150] Solving algorithm: use improved vehicle routing problem with time window and priority weight (VRPTW-PW) algorithm;

[0151] Convert road segment priority weight to "urgency requirement" for high-priority road segments, which can be reflected through time window constraints or objective function weights; specifically:

[0152] Time window setting: according to road segment priority weight Set the expected service time window for road segments:

[0153] For the highest priority road segment, require coverage within the next minutes; for the second highest priority road segment, require coverage within the next minutes (satisfy ); and so on, with the time window width being flexible and reflecting coverage flexibility;

[0154] Objective function:

[0155] Provide two forms for selection as needed:

[0156] Form 1: prioritize driving time as the optimization core, while considering priority coverage:

[0157] ;

[0158] Where: is a 0-1 variable, =1 indicates that the road segment is planned to be covered, =0 indicates that it is not covered; is the priority weight coefficient, used to balance coverage priority and driving cost; driving time is the total driving time of all inspection vehicles during the task;

[0159] Form 2: prioritize coverage priority as the optimization core, with driving time budget as a constraint:

[0160] ;

[0161] Where: is the sum of the priority weights of the road segments planned to be covered; is the driving time budget, i.e., the maximum total driving time allowed;

[0162] That is, prioritize high road segment coverage while limiting the total driving time to not exceed the budget .

[0163] Solver selection;

[0164] Small-scale scenarios (exact algorithm): use column generation algorithm;

[0165] Large-scale / real-time scenarios (heuristic / meta-heuristic): prefer improved ant colony optimization (ACO) or large-scale neighborhood search (LNS), with details as follows:

[0166] ACO improvement points: integrate pheromone initialization and update rules , to high segments release more pheromone; heuristic information contains both urgency and time window constraints.

[0167] LNS procedure:

[0168] Breakdown phase: preferentially remove segments with low or expired time windows; Repair phase: preferentially insert segments with high and satisfied time windows.

[0169] Real-time traffic integration: calculate travel time from current location to candidate segment start point, between segments: real-time call map API (e.g. traffic layer ETA function); or estimate current / predicted travel time based on accessed real-time traffic flow data.

[0170] Output and execution: algorithm output generates detailed route sequence for each inspection vehicle: vehicle K: [start -> segment A (priority weight , inspection time consumption ) -> segment B (priority weight , inspection time consumption ) ->... -> end / next start], including estimated arrival / start / end time for each task;

[0171] Route instructions are issued to vehicle terminal through vehicle networking / mobile network;

[0172] Closed-loop feedback: vehicle transmits actual position, task completion status, and newly collected traffic / infrastructure data in real time during task execution. These data:

[0173] are used to update real-time risk dimension and infrastructure health dimension data in step one; are used to update (reset to 0) and possible impact in step two; trigger or participate in the next round of dynamic grading, frequency tuning, and path planning (rolling optimization).

[0174] Highway traffic inspection system based on big data processing, including multi-source data fusion module, dynamic traffic grading module, inspection frequency tuning module, intelligent path planning module, and closed-loop execution and feedback module;

[0175] Multi-source data fusion module accesses and cleans Beidou, vehicle detector, weather, accident, structure sensor, and other multi-source heterogeneous data in real time, providing unified, clean, and spatiotemporally aligned input for subsequent analysis;

[0176] The dynamic road condition grading module automatically divides each kilometer road segment into four risk levels according to three dimensions of real-time risk, infrastructure health and historical importance, and quickly locks the area that needs to be focused on;

[0177] The inspection frequency optimization module calculates the dynamic priority weight of each road segment in real time based on the grading results and three factors of risk change, time since last inspection and emergency, to achieve adaptive inspection rhythm of high risk and high frequency, and low risk and low frequency.

[0178] The intelligent path planning module converts the priority weight into a VRPTW-PW model with a time window, calls an improved ant colony or large-scale neighborhood search algorithm, and generates an optimal inspection route under the constraints of vehicle endurance and real-time road conditions.

[0179] The closed-loop execution and feedback module sends route instructions to the vehicle terminal, and transmits vehicle position, task completion and newly collected data in real time, and reversely refreshes the first four modules to form a rolling optimization closed loop of perception, decision, execution and re-perception.

[0180] The above formulas are all dimensionless numerical calculations, and specific dimensionless methods can be standardized or other means, which are not described here. The formula is obtained by simulating the latest real situation through a large amount of data, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0181] The above embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, an ATA hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state ATA hard disk.

[0182] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0183] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0184] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0185] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0186] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0187] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile ATA hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0188] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A highway traffic inspection method based on big data processing, characterized in that, Comprising the following steps: S1, multi-dimensional dynamic road condition classification: clean and align each road real-time data, calculate congestion, accident, road surface, structure indicators, and comprehensively obtain the 1-4 level risk grade of each kilometer road section; S2, adaptive patrol frequency optimization: give the basic patrol cycle according to the risk level, dynamically weight through the risk change, the time from the last patrol and the sudden event, analyze the patrol priority of each road section at this moment; S3, optimal patrol path planning: convert the priority into a time window constraint, and use the improved VRPTW-PW algorithm to generate the shortest time and highest priority driving route for all patrol vehicles under real-time road conditions and vehicle endurance restrictions; The specific process of S3 is as follows: Problem modeling: Define resources: available patrol vehicle set, each vehicle contains current position, state, endurance and working time attributes; Define task: set of road segments to be inspected, each road segment Associated dynamic priority weight Value and estimated inspection time consumption ; Set the target: Maximize total coverage priority: , = 1 if the road segment is planned to be covered, = 0 if not covered; Minimizing total travel time: generating inter-segment travel time matrix based on real-time traffic conditions ; Meeting timeliness constraints: to achieve preset standards High priority section of values set time window; Set the constraints: Vehicle endurance and working time limit; Path continuity; Each road section is covered by at most one vehicle; Solving algorithm: Improved vehicle routing problem with time window and priority weight VRPTW-PW algorithm: Time window setting: based on link priority weight Set expected service time window for link: To The highest road segment, requiring future Covered within minutes; Next highest priority leg, required to be covered in the future minutes, and so on. The objective function is selected in the following form: Form 1: wherein is a 0-1 variable, = 1 indicates that the road segment is planned to be covered, = 0 indicates that it is not covered; is a priority weight coefficient for balancing the priority of coverage and the driving cost; The travel time is the sum of the travel time of all patrol vehicles in the task; Form 2: wherein is the sum of the priority weights of the planned covered segments; is the travel time budget, i.e. the maximum total travel time allowed upper limit.

2. The method for highway traffic inspection based on big data processing according to claim 1, characterized in that, The specific process of S1 is as follows: Multi-source heterogeneous data access and preprocessing: Access real-time traffic flow data, event and abnormal data, infrastructure state data and environmental data; Clean, space-time alignment and standardization processing of the data; Multi-dimensional feature extraction and calculation: Calculate the congestion risk index and accident risk index of real-time risk dimension; Calculate the pavement condition index PCI, structure safety index and facility integrity rate of infrastructure health dimension; Extract the road function level weight and key node weight of historical importance dimension; Dynamic classification: Based on the real-time risk dimension index, infrastructure health dimension index and historical importance dimension index, through the classification rule engine or lightweight classification model, dynamically assign the comprehensive road condition level RCL to each evaluation unit.

3. The method as claimed in claim 2, wherein, In S1: The traffic flow data includes floating car Beidou data, geomagnetic, microwave vehicle detector data and ETC gantry data; The event and abnormal data includes traffic police accident, event platform data, traffic control information and social media or navigation App user reported data; The infrastructure state data includes historical and real-time patrol report, bridge or tunnel structure health monitoring sensor data and road property facility damage record; The environmental data includes meteorological bureau API data and road environment sensor data; The comprehensive road condition level includes: Level 1, trigger when any of the following conditions is met: Very high accident risk, i.e. historical accident heat map peak plus real-time sudden brake frequency greater than threshold or structure safety alarm, i.e. bridge, tunnel monitoring data exceeds the limit; Infrastructure state: PCI≤40 or road visible damage area≥10%; Level 2, trigger when any of the following conditions is met and not Level 1: Real-time operation risk: sustained congestion, i.e. vehicle speed less than 40% of design value and sustained for more than 30 minutes, or high accident risk, i.e. accident probability model output>0.7; Infrastructure status: 40 < PCI ≤ 60 or facility damage density ≥ 3 per km; Level 3, triggered when any of the following conditions is met and no higher level is reached: Potential risk: Periodic congestion, i.e., peak traffic speed less than 60% of the design value or 60 < PCI ≤ 80; Environmental warning: Yellow weather warning affecting visibility or road adhesion coefficient; Level 4, default level, requires simultaneous satisfaction of: Traffic operation: Speed ≥ 80% of the design value and no persistent congestion; Infrastructure: PCI > 80 and no new damage reports; Environmental conditions: No weather warning affecting driving; The comprehensive road condition level is dynamically updated at a preset period, and re-evaluation is triggered immediately when a major event or critical sensor data mutation occurs; Wherein, the major event includes traffic accident or disaster weather warning, and the critical sensor data includes bridge or tunnel structure health monitoring data and road environment sensor data.

4. The method for highway traffic inspection based on big data processing according to claim 1, characterized in that, The specific process of S2 is as follows: Establish a basic frequency mapping: Pre-set different road condition levels RCL corresponding to the basic target patrol frequency BIF: Level 1: Target frequency = 1 time per 2 hours, BIF = 0.5 times / hour; Level 2: Target frequency = 1 time per 4 hours, BIF = 0.25 times / hour; Level 3: Target frequency = 1 time per 8 hours, BIF = 0.125 times / hour; Level 4: Target frequency = 1 time per 24 hours, BIF ≈ 0.042 times / hour; Introduce a dynamic tuning factor: Risk change trend factor Tf: calculated based on the rate of change of real-time risk dimension index; Time interval factor Tl from the last patrol time: calculated from the last successful patrol to the current time t; Event impact factor Ef: assigned according to the type and severity of the incident, no event = 0, event > 0; computing a dynamic priority weight DPW by weighted calculation of each road segment at a time of the dynamic priority weight : ； Wherein: for a road segment at a time dynamic priority weight; a basis target inspection frequency for a road segment of the road segment; for the road segment at the time of the risk change trend factor; for the road segment at the time time since last inspection; for corresponding base period; for a road segment at a time an event influence factor; , , are adjustable weight coefficients, , controlling the importance of different factors; Output priority weight: output all road segments' priority weight , The higher the value, the higher the inspection demand strength.

5. The method for highway traffic inspection based on big data processing according to claim 1, characterized in that, The solving algorithm in S3 further includes: Improved ant colony optimization: Pheromone initialization and update rules incorporated Pheromone release for segments greater than threshold Pheromone release for segments greater than threshold Heuristic information includes and a remaining time window size; Large-scale neighborhood search: The destruction phase preferentially removes below threshold or timeout window segments; Repair phase preferential insertion Segments greater than threshold and meeting time window Real-time road condition integration: Calculate travel time At this time, call the map API traffic layer or predict the travel time based on traffic flow data in real time. Output and feedback: Output detailed route sequence for each vehicle: Vehicle K: [Start -> Link A (priority weight ( , inspection time spent ) -> Link B (priority weight ( , inspection time spent ) ->... -> End / Next Start];​​ Route instructions are issued to the vehicle terminal; Receive actual position, task status and new collected data from vehicle feedback, trigger dynamic classification and frequency tuning module update.

6. The system for application in the big data processing based highway traffic inspection method according to any one of claims 1-5, characterized in that, Including the following modules: Multi-source data fusion module, used for real-time access and cleaning of Beidou, vehicle detector, weather, accident, structure sensor multi-source heterogeneous data, providing input for subsequent analysis; Dynamic road condition classification module, used to automatically divide each kilometer road segment into 4 risk levels according to real-time risk, infrastructure health and historical importance, and lock the area that needs to be paid attention to; Patrol frequency tuning module, based on the classification results and superimposed risk change, time interval from the last patrol and three major factors of sudden events, real-time calculation of dynamic priority weight of each road segment, realizing adaptive patrol rhythm of high risk high frequency and low risk low frequency; Intelligent path planning module, used to convert priority weight into VRPTW-PW model with time window, call improved ant colony or large-scale neighborhood search algorithm, generate optimal patrol route under the constraints of vehicle endurance and real-time road condition; A closed-loop execution and feedback module is configured to issue route instructions to the vehicle terminal, and to transmit the vehicle position, task completion and newly collected data in real time, so as to refresh the first four modules reversely, and form a rolling optimization closed loop of perception, decision, execution and re-perception.

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