A vehicle path planning method and system based on environmental changes

Abstract

The application provides a vehicle path planning method and system based on environmental changes, applied to the technical field of path planning, which identifies and quantifies local risk areas in the road network by monitoring vehicle operating environment data and cargo tolerance in real time, incorporates risk cost into the path evaluation model, and plans the driving path accordingly to avoid road sections with risk cost exceeding the preset threshold. The method has the advantages of being able to perceive and quantify local micro-environmental risks in real time, and incorporating them into path planning considerations, thereby effectively avoiding potential risk road sections and improving vehicle driving safety and cargo integrity.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a vehicle path planning method and system based on environmental changes. Background Technology

[0002] Traditional automated fleet routing systems primarily rely on macro-environmental information, such as traffic conditions, weather forecasts, and road construction data, with a central dispatch system planning the shortest or least energy-intensive routes for vehicles. These systems perform well in handling routine, predictable environmental changes, but they require real-time monitoring of ambient temperature, humidity, and road conditions, and predicting the impact of these environmental factors on vehicle operation—an impact that is often the most difficult to predict.

[0003] For example, a delivery vehicle carrying sophisticated electronic equipment travels along an optimal route planned by the system. This route avoids all known congestion, construction, and inclement weather areas. However, there is still a risk of skidding when passing under a bend of an overpass due to its perpetually shaded location and low road surface temperature, which can cause frost or slipperiness.

[0004] In addition, there were other incidents, such as a minor hydraulic oil leak from a construction vehicle the previous night, leaving a thin, barely visible oil film. This area was outside the coverage of traffic or weather monitoring, and the central dispatch system was unaware of it, still marking it as normal passage.

[0005] Unpredictable factors like these can make it difficult for existing systems to accurately correlate environmental risks with the potential impact on vehicles. Consequently, existing systems rely excessively on various sensors to monitor the environment and plan routes accordingly, resulting in blind spots in the route planning model. This exposes fleets to risks passively and repeatedly, increasing operating costs and safety hazards.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] In view of the shortcomings of the prior art, this application provides a vehicle route planning method and system based on environmental changes. Instead of relying on sensors to directly monitor environmental data, it directly senses the vehicle's operating environment data that changes in the environment in real time, quantifies local micro-environmental risks, and incorporates them into route planning considerations. This effectively avoids potentially risky road sections and improves vehicle driving safety and cargo integrity.

[0008] Firstly, a vehicle routing method based on environmental changes, the method comprising the steps of:

[0009] S1: Real-time monitoring of vehicle operating environment data during vehicle operation, and obtaining the tolerance of the transported goods to the vehicle operating environment data;

[0010] S2: Obtain the geographical location information of the transport vehicle, and identify local risk areas in the road network based on the vehicle operating environment data and the geographical location information;

[0011] S3: Quantify the risk of the local risk area based on the vehicle operating environment data, and generate the corresponding risk quantification value;

[0012] S4: Calculate the risk cost of the transport vehicle passing through the local risk area based on the tolerance level and the risk quantification value;

[0013] S5: Update the preset path evaluation model according to the risk cost, and plan a driving route for the vehicle based on the updated path evaluation model to avoid road sections where the risk cost exceeds a preset threshold.

[0014] Furthermore, step S1 includes:

[0015] S11: Obtain the wheel speed difference of the vehicle and the activation status of the traction control system and / or electronic stability program. The vehicle operating environment data includes at least the wheel speed difference and the activation status of the traction control system and / or electronic stability program.

[0016] S12: Obtain the type of the transported goods, and match and extract the corresponding cargo sensitivity description from the preset cargo sensitivity description database according to the type of the transported goods. The cargo sensitivity description includes at least the vibration tolerance threshold and the impact sensitivity level.

[0017] S13: The vibration tolerance threshold and impact sensitivity level of the transported goods are used as the tolerance level of the goods to the vehicle operating environment data; wherein, the vibration tolerance threshold is used to determine whether the impact intensity corresponding to the wheel speed difference exceeds the tolerance range of the transported goods, and the impact sensitivity level is used to determine whether the instability impact represented by the activation of the traction control system and / or electronic stability program exceeds the tolerance range of the transported goods.

[0018] Furthermore, step S2 includes:

[0019] S21: Receive the geographical location information of multiple vehicles and receive the vehicle operating environment data reported by each vehicle at its respective geographical location.

[0020] S22: When the vehicle operating environment data at the same geographical location contains the same type of abnormal events, and the number of reported abnormal events of the same type exceeds a preset number threshold, or the reporting density of abnormal events of the same type at the geographical location exceeds a preset density threshold, the geographical location is identified as the local risk area.

[0021] Furthermore, step S21 includes:

[0022] S211: When the wheel speed difference exceeds a preset difference threshold, or when the traction control system and / or electronic stability program is activated, receive the geographical location information of multiple vehicles.

[0023] S212: Receive vehicle operating environment data reported by multiple vehicles at their respective geographical locations. Each set of vehicle operating environment data includes at least one or more of the following information: wheel speed difference, traction control system activation status, electronic stability program activation status, vehicle lateral acceleration, vehicle longitudinal acceleration, and vehicle body angular velocity.

[0024] Furthermore, the vehicle operating environment data also includes the intervention strength of the traction control system and the intervention strength of the electronic stability program. Step S3 includes:

[0025] S31: Obtain all vehicle operating environment data reported within the local risk area;

[0026] S32: Calculate the normalized value of slippage intensity based on the wheel speed difference, calculate the normalized value of intervention intensity based on the intervention intensity of the traction control system and / or electronic stability program, and calculate the normalized value of vehicle attitude abnormality based on the vehicle lateral acceleration, vehicle longitudinal acceleration and vehicle body angular velocity.

[0027] S33: Multiply the normalized value of the slippage intensity, the normalized value of the intervention intensity, and the normalized value of the attitude anomaly by their respective preset weight coefficients, sum them up, and then multiply by the number of transport vehicles that reported the abnormal event in the local risk area to obtain the risk quantification value. The preset weight coefficient is a fixed value that is pre-calibrated and configured based on historical accident data.

[0028] Furthermore, step S32 includes:

[0029] S321: Divide the actual detected wheel speed difference by the preset maximum reference speed difference to obtain the normalized value of the slippage intensity;

[0030] S322: When only the traction control system is activated, the braking force of the traction control system on the engine is obtained and divided by the preset maximum reference braking force to obtain the first intervention intensity normalized value as the intervention intensity normalized value.

[0031] S323: When only the electronic stability program is activated, the braking pressure of the electronic stability program on the wheel is obtained and divided by the preset maximum reference braking pressure to obtain a second intervention intensity normalized value as the intervention intensity normalized value.

[0032] S324: When the traction control system and the electronic stability program are activated simultaneously, the maximum of the first intervention intensity normalized value and the second intervention intensity is selected as the intervention intensity normalized value.

[0033] S325: Divide the absolute values ​​of the vehicle's lateral acceleration, longitudinal acceleration, and angular velocity by their respective preset maximum reference values ​​to obtain three normalized components. Take the maximum value among the three normalized components as the attitude anomaly normalized value.

[0034] Furthermore, step S4 includes:

[0035] S41: Multiply the risk quantification value by a preset vehicle wear cost coefficient to obtain the vehicle component wear acceleration cost;

[0036] S42: Based on the vibration tolerance threshold in the tolerance level, determine the preset threshold range to which it belongs, and obtain the impact sensitivity level corresponding to the threshold range;

[0037] S43: Determine the cargo sensitivity multiplier based on the preset correspondence between the impact sensitivity level and the cargo sensitivity multiplier;

[0038] S44: Multiply the risk quantification value by the cargo sensitivity multiplier, and then multiply by the preset cargo damage cost coefficient to obtain the potential damage cost of the integrity of the transported cargo;

[0039] S45: The risk cost is obtained by adding the cost of accelerated wear of the vehicle components to the cost of potential damage to the integrity.

[0040] Furthermore, step S5 includes:

[0041] S51: Treat the risk cost as an independent cost item and add it to the preset path cost function to obtain the updated path cost function;

[0042] S52: Based on the updated path cost function, the shortest path search algorithm is used to plan a driving path for the vehicle, so as to minimize the total cost of the planned path.

[0043] S53: When planning a driving route, temporarily remove road segments whose risk cost exceeds a preset threshold from the road network to avoid those segments.

[0044] Furthermore, in step S51, the updated path cost function takes the following form:

[0045] C_total=W_time×T_path+W_dist×D_path+W_energy×E_path+W_risk×ΣC_risk;

[0046] Where T_path is the total travel time of the path, D_path is the total travel distance of the path, E_path is the total energy consumption of the path, W_time is the preset weight of the total travel time of the path, W_dist is the preset weight of the total travel distance of the path, W_energy is the preset weight of the total energy consumption of the path, ΣC_risk is the sum of the risk costs of all local risk areas traversed on the path, and W_risk is the preset weight of the risk cost.

[0047] Secondly, a vehicle routing system based on environmental changes, the system being used to implement the steps of any of the methods described above, the system comprising:

[0048] Acquisition module: Real-time monitoring of vehicle operating environment data during the operation of the transport vehicle, and acquisition of the tolerance of the transported goods to the vehicle operating environment data;

[0049] Identification module: acquires the geographical location information of the transport vehicle, and identifies local risk areas in the road network based on the vehicle's operating environment data and the geographical location information;

[0050] Quantification module: Quantifies the risk of the local risk area based on the vehicle operating environment data and generates the corresponding risk quantification value;

[0051] Calculation module: Calculates the risk cost of the transport vehicle passing through the local risk area based on the tolerance level and the risk quantification value;

[0052] Path planning module: Updates the preset path evaluation model according to the risk cost, and plans a driving path for the vehicle based on the updated path evaluation model to avoid road sections where the risk cost exceeds a preset threshold.

[0053] Beneficial effects: The vehicle route planning method and system proposed in this application, based on environmental changes, identifies and quantifies local risk areas in the road network by real-time monitoring of vehicle operating environment data and cargo tolerance, incorporates risk costs into the route evaluation model, and plans driving routes accordingly to avoid road sections where risk costs exceed a preset threshold. It has the advantages of being able to perceive and quantify local micro-environmental risks in real time and incorporate them into route planning considerations, thereby effectively avoiding potential risk road sections and improving vehicle driving safety and cargo integrity. Attached Figure Description

[0054] Figure 1 This is a flowchart of a vehicle routing method based on environmental changes proposed in this application.

[0055] Figure 2 This is a structural diagram of a vehicle routing system based on environmental changes proposed in this application.

[0056] Figure 3 This is a schematic diagram of a vehicle routing system based on environmental changes proposed in this application.

[0057] Labeling Explanation: 201. Acquisition Module; 202. Identification Module; 203. Quantization Module; 204. Calculation Module; 205. Path Planning Module. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0059] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0060] Please refer to Figure 1 A vehicle routing method based on environmental changes, comprising the following steps:

[0061] S1: Real-time monitoring of vehicle operating environment data during the operation of the transport vehicle, and obtaining the tolerance of the transported goods to the vehicle operating environment data;

[0062] S2: Obtain the geographical location information of the transport vehicle, and identify local risk areas in the road network based on the vehicle operating environment data and geographical location information;

[0063] S3: Quantify the risk of local risk areas based on vehicle operating environment data and generate corresponding risk quantification values;

[0064] S4: Calculate the risk cost of the transport vehicle passing through the local risk area based on the tolerance level and risk quantification value;

[0065] S5: Update the preset path evaluation model based on the risk cost, and plan the driving route for the vehicle based on the updated path evaluation model to avoid road sections where the risk cost exceeds the preset threshold.

[0066] This application obtains the most accurate interaction data between the vehicle and the road environment by actively extracting vehicle operating environment data from the vehicle's own dynamic response to the driving environment, thus avoiding the uncertainty of monitoring data from various external sensors or third-party platforms.

[0067] When a vehicle is in motion, its internal sensor network, such as wheel speed sensors, accelerometers, and gyroscopes, continuously measures the vehicle's operational data. This data constitutes the vehicle's operating environment data, which is the most direct and accurate data on the interaction between the vehicle and the road. Simultaneously, the cargo being transported is no longer treated as a load without attributes, but rather its tolerance level is assessed, assigning it specific physical characteristics.

[0068] For example, a batch of high-precision optical instruments and a truckload of ordinary textiles have drastically different sensitivities to vibration and impact during transportation. Determining the tolerance of the transported goods reveals their acceptability to road conditions. For instance, transporting a truckload of ordinary textiles is acceptable even if there are slippery sections in the planned route; however, transporting a batch of high-precision optical instruments requires eliminating all slippery sections during route planning, even if the planned route takes longer.

[0069] Then, identifying local risk areas in the road network is the process of transforming discrete vehicle events into geographically meaningful environmental information. If a vehicle skids at a certain location, there may be accidental factors, such as poor tire condition.

[0070] However, when multiple vehicles report similar skidding incidents at the same precise geographical location within a short period of time, this constitutes strong evidence that there are problems with the road surface conditions at that location. By aggregating geographical location information from different vehicles and corresponding vehicle operating environment data, and performing spatiotemporal correlation analysis, it is possible to uncover hidden local risk areas in the road network from massive amounts of driving data.

[0071] Quantifying the risk of a localized risk area involves transforming qualitative risk assessments into quantitative numerical indicators. Once an area is identified as risky, its severity needs to be evaluated. This process comprehensively analyzes all reported vehicle operating environment data within that localized risk area, such as the severity of skidding and the extent of vehicle instability. Through a series of calculations, a quantitative risk value that objectively reflects the hazard level of that road segment is obtained. The higher this value, the greater the control effort required for vehicles to pass through this segment, and the higher the potential danger.

[0072] It should be noted that the specific calculation steps for the quantified risk value, the parameters involved (such as the normalized value of slippage intensity, the normalized value of intervention intensity, and the normalized value of posture abnormality), and the setting method of the weight coefficients of each parameter are fully and specifically disclosed in the subsequent detailed description of step S3. They will not be repeated here. Please refer to the corresponding section below.

[0073] Calculating the risk cost of traversing a localized risk zone allows for the quantification of abstract risk values ​​and their integration with specific transportation tasks, transforming them into intuitive, comparable costs. This cost comprises two dimensions: first, the potential damage to the vehicle itself, such as the accelerated wear and tear on tires and suspension systems caused by frequent, aggressive maneuvers—this is calculated as the cost of accelerated wear on vehicle components; second, the potential damage to the transported cargo. This cost is calculated in close conjunction with the cargo's tolerance level; for highly sensitive cargo, even a moderate level of risk can significantly amplify the potential damage cost. Ultimately, these two costs are summed to constitute the total risk cost of traversing the risk zone.

[0074] Traditional path evaluation models primarily consider explicit costs such as time, distance, and energy consumption. This method, by incorporating the calculated risk cost as a new cost item into the evaluation model, alters the optimization objective of path planning.

[0075] At this point, the total cost of a route is no longer solely a reflection of driving efficiency, but a comprehensive assessment that integrates efficiency and safety. Based on this updated model, the route planning algorithm will re-search to find the route with the lowest total cost. This final planned route may not be the absolute shortest in terms of distance or time, but it is the optimal choice to deliver goods safely and intact to their destination with the lowest overall risk cost. For road sections with unacceptably high risk costs, they may even be temporarily removed from the road network to fundamentally prevent vehicles from being guided to extremely dangerous areas.

[0076] Among these, path planning algorithms are relatively mature in the existing technology. For example, Dijkstra's algorithm based on priority queues can be used. This algorithm starts from the starting point, gradually expands the search range, calculates the cumulative cost to reach each node, and always prioritizes exploring the node with the smallest current cumulative cost. During each expansion, the algorithm evaluates the cost from the current node to adjacent nodes according to the updated path cost function and adds this cost to the cumulative cost of the current node. When the algorithm reaches the target node, the cumulative cost it records is the minimum total cost from the starting point to the target point, and the corresponding path is the planned travel path. Alternatively, other path planning algorithms can also be used. Since this application does not focus on the path planning algorithm itself, existing general-purpose path planning algorithms can be applied to this application; therefore, this path planning algorithm will not be discussed in detail.

[0077] The innovation of this application lies in directly obtaining the most accurate feedback data of the vehicle based on road conditions and the vehicle operating environment data, and determining local risk areas based on the vehicle operating environment data. Then, the risk cost of the vehicle itself and the transported goods passing through the local risk areas is calculated, and this risk cost is added as a new cost item to the route evaluation model. This is different from traditional route planning methods and obtains the optimal transportation route for the transported vehicles and goods.

[0078] Through the steps described above, the method proposed in this application constructs a complete closed loop from data perception, risk identification, quantitative assessment to decision optimization. It effectively utilizes the fleet itself as a mobile sensor network to detect and update road environment information in real time and with precision, solving the perception blind spot problem of traditional route planning when facing local micro-risks. By directly linking risk to cargo sensitivity and vehicle health status, implicit costs are made explicit, enabling route planning to truly be tailored to local conditions and cargo specificity. This achieves significant technical results in ensuring the safety of high-value, sensitive cargo transportation and reducing long-term vehicle operating costs.

[0079] Furthermore, step S1 includes:

[0080] S11: Obtain the wheel speed difference of the vehicle and the activation status of the traction control system and / or electronic stability program. The vehicle operating environment data shall include at least the wheel speed difference and the activation status of the traction control system and / or electronic stability program.

[0081] S12: Obtain the type of cargo being transported, and match and extract the corresponding cargo sensitivity description from the preset cargo sensitivity description database according to the type of cargo being transported. The cargo sensitivity description shall at least include the vibration tolerance threshold and the shock sensitivity level.

[0082] S13: The vibration tolerance threshold and impact sensitivity level of the transported goods shall be used as the tolerance of the goods to the vehicle operating environment data; wherein, the vibration tolerance threshold is used to determine whether the impact intensity corresponding to the wheel speed difference exceeds the tolerance range of the transported goods, and the impact sensitivity level is used to determine whether the instability impact represented by the activation of the traction control system and / or electronic stability program exceeds the tolerance range of the transported goods.

[0083] In this specific implementation, the monitoring of vehicle operating environment data focuses on several core indicators that most directly reflect changes in road surface adhesion. The wheel speed difference, that is, the difference in rotational speed between the vehicle's driving wheels and driven wheels, or between the left and right wheels, is the most direct physical quantity for determining whether wheel slippage has occurred.

[0084] During normal driving, the speed difference between the wheels should fluctuate within a very small range. If the speed of a drive wheel is significantly higher than that of the others, it clearly indicates insufficient traction between that wheel and the ground, resulting in wheel spin and slippage. This refers to the absolute deviation of the linear velocity between the drive and driven wheels within the same sampling period, or the difference in rotational speed between the left and right wheels on the same axle. For vehicles equipped with wheel speed sensors, the real-time linear velocity is calculated by collecting the pulse signal frequency and combining it with the tire rolling radius. In a four-wheel drive configuration, this value is taken as the difference between the extreme and average rotational speeds of the four wheels. Specifically, the wheel speed measurement principle is as follows: For each rotation of the wheel, the wheel speed sensor generates a fixed number of pulse signals. The onboard control unit collects the pulse signal frequency per unit time and, combined with the known tire rolling radius, calculates the real-time linear velocity of the wheel using the following formula: v = 2π·r·f / N. Where v is the real-time linear velocity of the wheel (m / s), r is the tire rolling radius (m), f is the pulse signal frequency (Hz), and N is the number of pulses generated per wheel rotation, determined by the sensor specifications.

[0085] In a four-wheel drive configuration, since all four wheels can slip independently, it's not appropriate to simply use the difference between the drive wheels and non-drive wheels for judgment. Therefore, the wheel speed difference is calculated as follows: First, calculate the arithmetic mean of the linear velocities of the four wheels, vavg. Then, take the maximum value vmax among the four wheel linear velocities, and use vmax − vavg as the wheel speed difference. The physical meaning of this difference is that when one wheel spins due to insufficient traction, its linear velocity will be significantly higher than the other three wheels, thus increasing the difference.

[0086] When the wheel speed difference defined above exceeds a preset difference threshold, such as 0.5 m / s, and the duration of the deviation exceeds a preset time window length, such as 0.5 seconds, it is determined to be a valid road adhesion anomaly feedback, used to characterize the change in the friction coefficient of the road surface and tire contact interface.

[0087] Traction control systems and electronic stability programs are key components of modern vehicle active safety systems. These systems automatically activate when the vehicle's control unit detects wheel slippage or a tendency for the vehicle to sideslip, helping the driver regain control of the vehicle by reducing engine torque or applying braking to specific wheels.

[0088] Therefore, the activation state of these systems is itself a clear signal indicating that the vehicle is experiencing an unstable driving state, which is usually caused by sudden changes in the external road surface environment. Using this monitoring method, this application eliminates the need to use external sensors to monitor road surface conditions and weather factors to predict potential vehicle skidding; instead, it directly determines vehicle skidding through direct interaction data between the vehicle and the road surface.

[0089] Meanwhile, the process of obtaining cargo tolerance levels is designed as a structured, queryable workflow. A cargo sensitivity description database is pre-established in the central dispatch center's database. To better illustrate the cargo sensitivity description database, two examples are provided below. Of course, it is understandable that it is impossible to exhaustively list all the descriptive data in the cargo sensitivity description database within the limited space of this manual, but technical personnel can pre-write cargo sensitivity descriptions for each type of transported cargo based on actual conditions and add them to the database.

[0090] Example 1: Transporting high-precision semiconductor lithography equipment. Before the mission begins, the dispatcher identifies the type of goods as high-precision semiconductor lithography equipment based on the order information. Subsequently, the dispatcher queries and matches the corresponding entry in the goods sensitivity description database. This entry details the tolerance level of this type of equipment: the vibration tolerance threshold is set to an extremely low value, such as 0.1g, meaning that any sustained vibration exceeding this acceleration could cause the precision alignment of internal optical components to fail; the shock sensitivity level is rated at the highest level, such as level 5, which means that even a single, severe instability impact represented by the activation of the traction control system or electronic stabilization program is highly likely to cause permanent damage to the equipment.

[0091] Example 2: Transporting a whole truckload of clothing and textiles. The cargo sensitivity description matched by the dispatcher will show a very lenient tolerance level: the vibration tolerance threshold may be as high as 2.0g, and the shock sensitivity level is the lowest, level 1. This type of cargo is almost insensitive to dynamic changes during transportation.

[0092] In this way, abstract cargo characteristics are transformed into parameters that can be used for calculation. A direct correlation is established between the vibration tolerance threshold and wheel speed differences, as significant wheel speed differences are typically accompanied by high-frequency vibrations and impacts. Shock sensitivity levels are then correlated with activation events of traction control systems or electronic stability programs, as the activation of these systems signifies a significant instability event. This refined matching allows subsequent risk cost calculations to be truly cargo-specific, providing differentiated protection strategies for cargoes of varying value and characteristics.

[0093] Furthermore, step S2 includes:

[0094] S21: Receive the geographical location information of multiple transport vehicles and receive the vehicle operating environment data reported by each vehicle at its respective geographical location.

[0095] S22: When the vehicle operating environment data at the same geographic location contains the same type of abnormal events, and the number of reports of the same type of abnormal events exceeds a preset number threshold, or the reporting density of the same type of abnormal events at the geographic location exceeds a preset density threshold, the geographic location will be identified as a local risk area.

[0096] Receiving information from multiple transport vehicles significantly improves the accuracy and reliability of risk assessment. Its workflow is as follows: The central dispatch server continuously monitors data reports from all vehicles in the fleet. When any vehicle detects an abnormal event during its operation, such as a wheel speed difference exceeding a preset threshold or the traction control system being activated, the vehicle's onboard data acquisition unit immediately packages this event along with high-precision geographic coordinates and a timestamp into an abnormal event report. This report includes the transport vehicle's geographic location information and vehicle operating environment data, and is sent to the server via a wireless communication network.

[0097] The server's micro-risk inference module performs real-time spatiotemporal clustering analysis on all received reports. For example, a virtual geofence, such as a virtual circular area with a radius of 50 meters, and a time window, such as 10 minutes, can be set. When the server detects that within the same virtual geofence and time window, it has received a cumulative total of reports of the same type of abnormal events from different vehicles, such as traction control system activation, and the number of reports exceeds a preset threshold, such as 3 times, the server will make a high-confidence judgment: the location of this geographical information is a local risk area. Another judgment method is based on density, where reporting density refers to the number of reports per unit length per unit time. The density threshold can be adjusted according to road type (e.g., urban roads, highways). For example, if the reported skidding event density on a one-kilometer-long highway section exceeds 5 times per kilometer per hour, it can also be identified as a local risk area. The same type of abnormal event refers to reported vehicle operating environment data where the triggering conditions belong to the same category, such as all being triggered by wheel speed differences exceeding a threshold, or all being triggered by traction control system activation. Events triggered by different reasons are not classified into the same type.

[0098] This method of cross-validation using multiple vehicles effectively filters out false alarms caused by problems with individual vehicles, such as severe tire wear, ensuring that the identified risk areas are real problems caused by external environmental factors.

[0099] Furthermore, step S21 includes:

[0100] S211: When the wheel speed difference exceeds a preset difference threshold, or when the traction control system and / or electronic stability program is activated, receive the geographical location information of multiple vehicles.

[0101] S212: Receive vehicle operating environment data reported by multiple vehicles at their respective geographical locations. Each set of vehicle operating environment data includes at least one or more of the following information: wheel speed difference, traction control system activation status, electronic stability program activation status, vehicle lateral acceleration, vehicle longitudinal acceleration, and vehicle body angular velocity.

[0102] This event-triggered data reporting mechanism balances the timeliness of information acquisition with the economy of communication resources. The vehicle does not continuously send all its driving data to the server, but remains silent during normal driving. Only when preset trigger conditions are met—for example, the speed difference between the left and right drive wheels is greater than 0.5 meters per second, or the status flag of the traction control system and / or electronic stability program changes from 0 to 1—will the onboard data acquisition unit be activated to perform data packaging and reporting. The preset difference threshold is the preset trigger condition. In this embodiment, the preset difference threshold is set to 0.5 meters per second, suitable for heavy vehicles, such as fully loaded trucks and tractors. For light-duty trucks or empty vehicles, the difference threshold can be set to 0.3 meters per second. In practical applications, the preset difference threshold can be adjusted according to the vehicle type and load.

[0103] In addition to core data such as wheel speed differences and control system activation status, the reported information includes parameters that provide a more comprehensive description of vehicle dynamics. Lateral acceleration reflects the forces acting on the vehicle during cornering or skidding; abnormal increases in lateral acceleration are usually associated with lateral instability. Longitudinal acceleration reflects the vehicle's acceleration and deceleration; abnormally sharp fluctuations may be related to road bumps or emergency braking. Vehicle angular velocity, especially yaw rate around the vehicle's vertical axis, is a key indicator of whether the vehicle is spinning or fishtailing. Reporting this multi-dimensional data allows the central server to analyze the nature and severity of abnormal events in greater depth, providing a solid data foundation for subsequent risk quantification.

[0104] Furthermore, the vehicle operating environment data also includes the intervention intensity of the traction control system and the intervention intensity of the electronic stability program. Step S3 includes:

[0105] S31: Obtain all vehicle operating environment data reported within the local risk area;

[0106] S32: Calculate the normalized value of slippage intensity based on the wheel speed difference, calculate the normalized value of intervention intensity based on the intervention intensity of the traction control system and / or electronic stability program, and calculate the normalized value of vehicle attitude abnormality based on the vehicle lateral acceleration, vehicle longitudinal acceleration and vehicle body angular velocity.

[0107] S33: Multiply the normalized values ​​of slippage intensity, intervention intensity, and attitude anomaly by their respective preset weighting coefficients, sum them, and then multiply by the number of vehicles reporting abnormal events in the local risk area to obtain the risk quantification value. The preset weighting coefficients are fixed values ​​that are pre-calibrated and configured based on historical accident data.

[0108] This quantification process is a mathematical modeling process that transforms multi-source, heterogeneous raw sensor data into a single, standardized risk score. First, it requires acquiring complete operational environment data for all relevant vehicles reported within the geographical area of ​​the risk zone. Next, in order to comprehensively compare data from different physical units, normalization processing is necessary.

[0109] The specific normalization process includes:

[0110] S321: Divide the actual detected wheel speed difference by the preset maximum reference speed difference to obtain the normalized value of the slippage intensity;

[0111] S322: When only the traction control system is activated, obtain the braking force of the traction control system on the engine, divide it by the preset maximum reference braking force, and obtain the first intervention intensity normalized value as the intervention intensity normalized value.

[0112] S323: When only the electronic stability program is activated, obtain the braking pressure of the electronic stability program on the wheel, divide it by the preset maximum reference braking pressure, and obtain the second intervention intensity normalized value as the intervention intensity normalized value.

[0113] S324: When the traction control system and the electronic stability program are activated simultaneously, the larger of the first intervention intensity normalization value and the second intervention intensity is selected as the intervention intensity normalization value.

[0114] S325: Divide the absolute values ​​of the vehicle's lateral acceleration, longitudinal acceleration, and angular velocity by their respective preset maximum reference values ​​to obtain three normalized components. Take the maximum value among the three normalized components as the attitude anomaly normalization value.

[0115] The maximum reference speed difference can be preset according to vehicle type. For example, it can be set to 5 m / s for heavy trucks, 3.5 m / s for medium-sized vehicles, and 2.5 m / s for small passenger cars. This value can also be calibrated by rapid acceleration tests of the vehicle on a standard high-traction road surface.

[0116] For example, assuming the preset maximum reference speed difference is 5 meters per second, if a vehicle reports an actual wheel speed difference of 2 meters per second, then its normalized slippage intensity is 2 divided by 5, which is 0.4. Regarding intervention intensity, if the traction control system intervenes by reducing engine torque, its braking force can be equivalent to a single value. For example, if the maximum reference braking force is 500 N·m, and the actual braking force generated during intervention is 200 N·m, then the normalized value of the first intervention intensity is 0.4.

[0117] If the electronic stability program intervenes by applying braking to a single wheel, with a maximum reference braking pressure of 100 bar and an actual applied braking pressure of 60 bar, then the normalized value of the second intervention intensity is 0.6.

[0118] If both are activated simultaneously, the maximum value of 0.6 is taken as the final intervention intensity normalization value.

[0119] Similarly, for vehicle attitude, the absolute values ​​of lateral acceleration, longitudinal acceleration, and vehicle body angular velocity are divided by their respective maximum reference values, such as the vehicle design limit, to obtain three normalized components. The largest of these is taken as the attitude anomaly normalized value to reflect the degree of instability in the most severe direction.

[0120] These reference values ​​are calibrated based on physical limit data of vehicles performing fully loaded emergency acceleration or emergency braking on standard high-friction surfaces. The maximum reference speed difference is set as the theoretical speed deviation of the vehicle at a 20% slip rate.

[0121] The maximum reference braking force is taken from the peak pump pressure of the hydraulic braking module at the anti-lock braking system (ABS) trigger edge. These parameters are stored in a calibration lookup table in local memory, which is used to map the real-time physical quantities collected by the sensors to a dimensionless scale between 0 and 1, thereby achieving the same-scale fusion of data with different dimensions in the risk assessment model.

[0122] After obtaining these three core normalized values, the final risk quantification value is calculated using a weighted summation formula. For example, the risk quantification value = (Normalized slip intensity value × W_slip + Normalized intervention intensity value × W_tcs + Normalized posture anomaly value × W_yaw) × N_vehicles. Here, W_slip, W_tcs, and W_yaw are their respective weighting coefficients. These weighting coefficients are the preset weighting coefficients, which can be calibrated based on a large amount of historical data, especially historical accident data. The specific calibration method is as follows: Collect multiple historical accident data sets. For each accident, extract the normalized slip intensity value, normalized intervention intensity value, and normalized posture anomaly value reported by the vehicle before the accident. Calculate the average value of these three indicators across all accidents. Then, normalize the average value of each indicator so that their sum equals 1, thus obtaining the corresponding weighting coefficient. The preset weighting coefficients can be periodically recalibrated as accident data accumulates.

[0123] Finally, the weighted sum is multiplied by the total number of vehicles N_vehicles that reported the abnormal event in the region. This multiplier acts as a confidence enhancer, meaning that the more vehicles that confirm the risk, the higher its quantification value.

[0124] Furthermore, step S4 includes:

[0125] S41: Multiply the risk quantification value by the preset vehicle wear cost coefficient to obtain the vehicle component wear acceleration cost;

[0126] S42: Based on the vibration tolerance threshold in the tolerance level, determine the preset threshold range to which it belongs, and obtain the impact sensitivity level corresponding to the threshold range.

[0127] S43: Determine the cargo sensitivity multiplier based on the preset correspondence between the impact sensitivity level and the cargo sensitivity multiplier;

[0128] S44: Multiply the risk quantification value by the cargo sensitivity multiplier, and then multiply by the preset cargo damage cost coefficient to obtain the potential damage cost of the integrity of the transported cargo;

[0129] S45: The risk cost is obtained by adding the cost of accelerated wear and tear on vehicle parts to the cost of potential damage to their integrity.

[0130] This calculation process transforms dimensionless risk quantification values ​​into costs with practical economic significance. In one specific embodiment, the calculation of the cost of accelerated wear and tear on vehicle components is relatively straightforward: a vehicle wear cost coefficient is preset (e.g., 0.5 yuan per unit of risk quantification value), and the risk quantification value is multiplied by this coefficient. For example, if the risk quantification value of a certain road segment is 80, then the additional wear and tear cost incurred by the vehicle through that road segment is 80 × 0.5 = 40 yuan.

[0131] The calculation of potential damage costs related to cargo integrity needs to consider the cargo's sensitivity characteristics. First, according to steps S42 to S43, the shock sensitivity level (e.g., level 1 to 5) is determined based on the cargo's vibration tolerance threshold. Then, the cargo sensitivity multiplier is obtained according to a pre-set correspondence table. This multiplier is a dimensionless amplification factor used to reflect the difference in sensitivity of cargoes with different shock sensitivity levels to the same quantified risk value.

[0132] The pre-defined correspondence table is obtained based on two factors: the vibration tolerance threshold and the value of the goods. First, the shock sensitivity level is classified according to the vibration tolerance threshold (in grams). The lower the vibration tolerance threshold (e.g., <0.1g), the higher the shock sensitivity level (e.g., level 5); conversely, the higher the vibration tolerance threshold, the lower the shock sensitivity level. Then, based on the ratio of the average compensation amount or unit price of each level of goods in the event of transport damage to the benchmark goods (usually a level 1 ordinary package), the sensitivity multiplier for the corresponding level is determined. The benchmark goods multiplier is set to 1. If the average compensation amount for a certain level of goods is 10 times that of the benchmark goods, then its multiplier is set to 10. This correspondence typically exhibits non-linear growth to reflect the sharp amplification effect of highly sensitive goods on risk, ensuring that the route planning algorithm can proactively avoid high-risk sections rather than compromising between efficiency and safety.

[0133] Then, the risk quantification value, the cargo sensitivity multiplier, and the preset cargo damage cost coefficient are multiplied together. The cargo damage cost coefficient is a uniform economic conversion factor used to convert the dimensionless product result into monetary units. This coefficient does not change with the type of cargo, and all cargoes share the same coefficient.

[0134] Specifically, the expected economic loss per unit of risk-sensitivity product can be obtained by multiplying the average value of goods transported per trip by the average damage rate, and then dividing by a benchmark risk value corresponding to a typical reference scenario selected from historical operational data. For example, if statistics show that the average total compensation for damaged goods is 200,000 yuan for every 10,000 trips, the average cost of damaged goods per trip is 20 yuan; and if historical data shows that the average probability of damage is 0.1 when passing through road sections with a risk quantification value of 50 and a sensitivity multiplier of 1, then the damage cost coefficient can be calculated as 20 / (50 × 1 × 0.1) = 4 yuan. In practical applications, an empirical constant (such as 2 yuan or 5 yuan) can also be directly used, allowing operators to dynamically adjust it according to their own risk preferences. Once this coefficient is set, it is applied uniformly to all goods, and the value differences between goods are already reflected through the goods sensitivity multiplier.

[0135] For example, if the risk quantification value is 80, and the damage cost coefficient for transporting highly sensitive goods (sensitivity level 5, multiplier 10) is 2 yuan, then the potential damage cost for the goods under the same risk quantification value is 80 × 10 × 2 = 1600 yuan. If transporting ordinary goods (sensitivity level 1, multiplier 1), the damage cost under the same risk quantification value is only 80 × 1 × 2 = 160 yuan.

[0136] Finally, the cost of accelerated wear and tear on vehicle components is added to the cost of potential damage to cargo integrity to obtain the total risk cost of traversing this route. Continuing the previous example, the sum of 40 yuan (wear and tear cost) and 1600 yuan (cargo damage cost) is 1640 yuan. This risk cost will participate as an independent cost item in subsequent route evaluation models, thereby enabling proactive avoidance of high-risk routes.

[0137] It is understood that the example values ​​in the above embodiments are for reference only, and the various parameter values ​​for specific application scenarios should be determined according to the actual situation.

[0138] Furthermore, step S5 includes:

[0139] S51: Treat the risk cost as an independent cost item and add it to the preset path cost function to obtain the updated path cost function;

[0140] S52: Based on the updated path cost function, the shortest path search algorithm is used to plan the driving path for the vehicle so as to minimize the total cost of the planned path.

[0141] S53: When planning a driving route, temporarily remove road segments whose risk cost exceeds a preset threshold from the road network to avoid those segments.

[0142] In a preferred embodiment, in step S51, the updated path cost function takes the form of:

[0143] C_total=W_time*T_path+W_dist*D_path+W_energy*E_path+W_risk*ΣC_risk

[0144] Where T_path is the total travel time of the path, D_path is the total travel distance of the path, E_path is the total energy consumption of the path, W_time is the preset weight of the total travel time of the path, W_dist is the preset weight of the total travel distance of the path, W_energy is the preset weight of the total energy consumption of the path, ΣC_risk is the sum of the risk costs of all local risk areas traversed on the path, and W_risk is the preset weight of the risk cost.

[0145] Among them, W_time, W_dist, W_energy, and W_risk are the corresponding weights for each, and these weights can be dynamically adjusted by the operator according to different business strategies. For example, when performing emergency transportation tasks, the weight of W_risk can be appropriately lowered and the weight of W_time increased; while when transporting high-value and sensitive goods, W_risk should be set to a very high value to ensure that safety is the primary consideration.

[0146] Upon receiving a new transportation task, the route planning module runs a shortest path search algorithm on the road network map based on the updated route cost function, which includes risk costs. When evaluating each possible path, the algorithm accumulates a weighted sum of time, distance, energy consumption, and risk costs, ultimately selecting the path with the lowest total cost as the planning result. Furthermore, a safety barrier is in place: a preset risk cost threshold. This threshold is set by the transporter based on the cost they can bear, considering the goods being transported; for example, it could be set to 2000 yuan, but is not limited to this value. Any road segment whose risk cost exceeds this threshold will be temporarily removed from the road network map during the current route planning calculation, ensuring that vehicles are never guided to that segment under any circumstances.

[0147] It's important to understand that while this method uses real-time vehicle operating environment data for risk calculation and route planning, it is not without predictive capabilities. In practical applications, transportation operations teams often use more than one or even just a few vehicles. This application achieves the most suitable route planning for the same type of goods on the road network by sacrificing a few of the first few vehicles or the very first vehicle, thus ensuring the transportation benefits of subsequent vehicles. This kind of loss is unavoidable; neither traditional methods nor this application can achieve 100% accurate risk avoidance for unfamiliar road conditions in actual operation.

[0148] In traditional route planning, inaccurate predictions of road segments can also lead to losses in vehicle transportation costs. Moreover, such predictions are based on inaccurate external sensor monitoring or the inability to strongly correlate the data monitored by external sensors with the data during vehicle operation for accurate modeling and prediction. Therefore, after one instance, the remaining vehicles will often make the same route planning misjudgment.

[0149] This application addresses the problem of traditional technologies lacking intelligent correlation capabilities, which leads to all subsequent vehicles repeatedly encountering the same pitfalls, by sacrificing a few vehicles in the early stages. This application instantly transforms the real data of the first vehicle into quantifiable and disseminable road network risk information, enabling subsequent vehicles to avoid risks in advance and saving the overall fleet's long-term operating costs.

[0150] Please refer to Figure 2 , Figure 3A vehicle routing system based on environmental changes, the system being used to implement the steps of any of the above methods, the system comprising:

[0151] Module 201: Real-time monitoring of vehicle operating environment data during vehicle operation, and acquisition of the tolerance of transported goods to vehicle operating environment data;

[0152] Identification module 202: Acquires the geographical location information of the transport vehicle, and identifies local risk areas in the road network based on the vehicle operating environment data and geographical location information;

[0153] Quantification module 203: Quantifies the risk of local risk areas based on vehicle operating environment data and generates corresponding risk quantification values;

[0154] Calculation module 204: Calculates the risk cost of a transport vehicle passing through a local risk area based on the tolerance level and risk quantification value;

[0155] Path planning module 205: Updates the preset path evaluation model based on risk cost, and plans driving routes for vehicles based on the updated path evaluation model to avoid road sections where the risk cost exceeds the preset threshold.

[0156] Specifically, the acquisition module 201, the identification module 202, the quantization module 203, the calculation module 204, and the path planning module 205 can be configured as software modules and deployed in the central dispatch system or the vehicle-mounted computing unit.

[0157] For example, the acquisition module 201 can be a data acquisition and preprocessing program that communicates with the vehicle sensor network and cargo information database through an interface to receive and process vehicle operating environment data and cargo tolerance information in real time.

[0158] The identification module 202 can be a geographic information processing and event analysis program that identifies local risk areas by analyzing the data and vehicle geographic location information provided by the acquisition module 201.

[0159] The quantification module 203 can be a risk calculation program that calculates the risk quantification value based on the risk area information output by the identification module 202 and the environmental data provided by the acquisition module 201.

[0160] The calculation module 204 can be a cost accounting program that, in combination with the output of the quantification module 203 and the cargo's tolerance level, calculates the specific risk cost.

[0161] The path planning module 205 can be a program that integrates path search algorithm and path cost update logic. Based on the risk cost provided by the calculation module 204, it dynamically adjusts the path evaluation model and plans the optimal driving path.

[0162] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A vehicle routing method based on environmental changes, characterized in that, The method includes the following steps: S1: Real-time monitoring of vehicle operating environment data during vehicle operation, and obtaining the tolerance of the transported goods to the vehicle operating environment data; S2: Obtain the geographical location information of the transport vehicle, and identify local risk areas in the road network based on the vehicle operating environment data and the geographical location information; S3: Quantify the risk of the local risk area based on the vehicle operating environment data, and generate the corresponding risk quantification value; S4: Calculate the risk cost of the transport vehicle passing through the local risk area based on the tolerance level and the risk quantification value; S5: Update the preset path evaluation model according to the risk cost, and plan a driving route for the vehicle based on the updated path evaluation model to avoid road sections where the risk cost exceeds a preset threshold. Step S4 includes: S41: Multiply the risk quantification value by a preset vehicle wear cost coefficient to obtain the vehicle component wear acceleration cost; S42: Based on the vibration tolerance threshold in the tolerance level, determine the preset threshold range to which it belongs, and obtain the impact sensitivity level corresponding to the threshold range; S43: Determine the cargo sensitivity multiplier based on the preset correspondence between the impact sensitivity level and the cargo sensitivity multiplier; S44: Multiply the risk quantification value by the cargo sensitivity multiplier, and then multiply by the preset cargo damage cost coefficient to obtain the potential damage cost of the integrity of the transported cargo; S45: The risk cost is obtained by adding the cost of accelerated wear of the vehicle components to the cost of potential damage to the integrity.

2. The vehicle routing method based on environmental changes according to claim 1, characterized in that, Step S1 includes: S11: Obtain the wheel speed difference of the vehicle and the activation status of the traction control system and / or electronic stability program. The vehicle operating environment data includes at least the wheel speed difference and the activation status of the traction control system and / or electronic stability program. S12: Obtain the type of the transported goods, and match and extract the corresponding cargo sensitivity description from the preset cargo sensitivity description database according to the type of the transported goods. The cargo sensitivity description includes at least the vibration tolerance threshold and the impact sensitivity level. S13: The vibration tolerance threshold and impact sensitivity level of the transported goods are used as the tolerance level of the goods to the vehicle operating environment data; wherein, the vibration tolerance threshold is used to determine whether the impact intensity corresponding to the wheel speed difference exceeds the tolerance range of the transported goods, and the impact sensitivity level is used to determine whether the instability impact represented by the activation of the traction control system and / or electronic stability program exceeds the tolerance range of the transported goods.

3. The vehicle routing method based on environmental changes according to claim 1, characterized in that, Step S2 includes: S21: Receive the geographical location information of multiple vehicles and receive the vehicle operating environment data reported by each vehicle at its respective geographical location. S22: When the vehicle operating environment data at the same geographical location contains the same type of abnormal events, and the number of reported abnormal events of the same type exceeds a preset number threshold, or the reporting density of abnormal events of the same type at the geographical location exceeds a preset density threshold, the geographical location is identified as the local risk area.

4. The vehicle routing method based on environmental changes according to claim 3, characterized in that, Step S21 includes: S211: When the wheel speed difference exceeds a preset difference threshold, or when the traction control system and / or electronic stability program is activated, receive the geographical location information of multiple vehicles. S212: Receive vehicle operating environment data reported by multiple vehicles at their respective geographical locations. Each set of vehicle operating environment data includes at least one or more of the following information: wheel speed difference, traction control system activation status, electronic stability program activation status, vehicle lateral acceleration, vehicle longitudinal acceleration, and vehicle body angular velocity.

5. The vehicle routing method based on environmental changes according to claim 1, characterized in that, The vehicle operating environment data also includes the intervention strength of the traction control system and the intervention strength of the electronic stability program. Step S3 includes: S31: Obtain all vehicle operating environment data reported within the local risk area; S32: Calculate the normalized value of slippage intensity based on the wheel speed difference, calculate the normalized value of intervention intensity based on the intervention intensity of the traction control system and / or electronic stability program, and calculate the normalized value of vehicle attitude abnormality based on the vehicle lateral acceleration, vehicle longitudinal acceleration and vehicle body angular velocity. S33: Multiply the normalized value of the slippage intensity, the normalized value of the intervention intensity, and the normalized value of the attitude anomaly by their respective preset weight coefficients, sum them up, and then multiply by the number of transport vehicles that reported the abnormal event in the local risk area to obtain the risk quantification value. The preset weight coefficient is a fixed value that is pre-calibrated and configured based on historical accident data.

6. The vehicle routing method based on environmental changes according to claim 5, characterized in that, Step S32 includes: S321: Divide the actual detected wheel speed difference by the preset maximum reference speed difference to obtain the normalized value of the slippage intensity; S322: When only the traction control system is activated, the braking force of the traction control system on the engine is obtained and divided by the preset maximum reference braking force to obtain the first intervention intensity normalized value as the intervention intensity normalized value. S323: When only the electronic stability program is activated, the braking pressure of the electronic stability program on the wheel is obtained and divided by the preset maximum reference braking pressure to obtain a second intervention intensity normalized value as the intervention intensity normalized value. S324: When the traction control system and the electronic stability program are activated simultaneously, the maximum of the first intervention intensity normalized value and the second intervention intensity is selected as the intervention intensity normalized value. S325: Divide the absolute values ​​of the vehicle's lateral acceleration, longitudinal acceleration, and angular velocity by their respective preset maximum reference values ​​to obtain three normalized components. Take the maximum value among the three normalized components as the attitude anomaly normalized value.

7. The vehicle routing method based on environmental changes according to claim 1, characterized in that, Step S5 includes: S51: Treat the risk cost as an independent cost item and add it to the preset path cost function to obtain the updated path cost function; S52: Based on the updated path cost function, the shortest path search algorithm is used to plan a driving path for the vehicle, so as to minimize the total cost of the planned path. S53: When planning a driving route, temporarily remove road segments whose risk cost exceeds a preset threshold from the road network to avoid those segments.

8. The vehicle routing method based on environmental changes according to claim 7, characterized in that, In step S51, the updated path cost function takes the following form: C_total=W_time×T_path+W_dist×D_path+W_energy×E_path+W_risk×ΣC_risk Where T_path is the total travel time of the path, D_path is the total travel distance of the path, E_path is the total energy consumption of the path, W_time is the preset weight of the total travel time of the path, W_dist is the preset weight of the total travel distance of the path, W_energy is the preset weight of the total energy consumption of the path, ΣC_risk is the sum of the risk costs of all local risk areas traversed on the path, and W_risk is the preset weight of the risk cost.

9. A vehicle routing system based on environmental changes, characterized in that, The system is used to implement the steps of the method according to any one of claims 1-8, and the system includes: Acquisition module: Real-time monitoring of vehicle operating environment data during the operation of the transport vehicle, and acquisition of the tolerance of the transported goods to the vehicle operating environment data; Identification module: acquires the geographical location information of the transport vehicle, and identifies local risk areas in the road network based on the vehicle's operating environment data and the geographical location information; Quantification module: Quantifies the risk of the local risk area based on the vehicle operating environment data and generates the corresponding risk quantification value; Calculation module: Calculates the risk cost of the transport vehicle passing through the local risk area based on the tolerance level and the risk quantification value; Path planning module: Updates the preset path evaluation model according to the risk cost, and plans a driving path for the vehicle based on the updated path evaluation model to avoid road sections where the risk cost exceeds a preset threshold.

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