Risk early warning method of risk potential energy fusion risk field model based on truck braking temperature rise

Abstract

The invention relates to a risk early warning method of a risk potential energy fusion risk field model based on truck braking temperature rise, which comprises the following steps: S1, acquiring a current vehicle state and an environment state, and calculating to obtain total risk potential energy; s2, judging whether the total risk potential energy is larger than a pre-configured first risk threshold value or not, and if yes, executing the step S3; s3, receiving one or more risk avoiding measures; s4, for any risk avoiding measure, the current vehicle state and the environment state are updated based on the risk avoiding measure, and the difference of the total risk potential energy before and after the risk avoiding measure is executed is calculated; s5, respectively calculating the execution cost of each risk avoiding measure; s6, the risk avoiding measures of which the difference between the total risk potential energy before and after the risk avoiding measures is negative are removed, and the income value of each risk avoiding measure is obtained through calculation; and S7, sorting the risk avoiding measures according to the profit values, and judging and displaying risk levels. Compared with the prior art, the method has the advantages that accurate early warning can be carried out on the brake failure risk caused by brake temperature rise of the truck on the long and large downhill road section, and the like.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation systems and vehicle safety technology, and in particular to a risk warning method based on a risk field model of risk potential energy fusion for truck braking temperature rise. Background Technology

[0002] On long downhill sections of mountain highways, trucks are prone to brake drum overheating due to prolonged braking, which can lead to brake failure and serious traffic accidents. Existing risk warning methods are mostly based on macroscopic factors such as road geometry and vehicle speed, lacking precise consideration and in-depth integration of the key risk factor of truck brake overheating, thus failing to meet practical needs. Furthermore, existing driving risk warning systems are insufficient in terms of timeliness and accuracy, often only able to warn of single risk factors, failing to comprehensively assess driving risks, resulting in incomplete warning information and affecting drivers' accurate risk judgment and timely response. Therefore, there is an urgent need for a risk warning method that can comprehensively consider multiple factors such as brake drum overheating to improve driving safety.

[0003] For example, Chinese patent CN120183194A discloses a cloud-based intelligent vehicle risk avoidance and prediction system. The system includes a data acquisition module, a driving data evaluation module, a driving risk prediction module, a risk warning module, and a risk avoidance push module. The data acquisition module collects multi-source data about the vehicle's surroundings. The driving data evaluation module uses a historical evaluation model to process the multi-source data and obtain the probability of an accident occurring on the current road segment. The driving risk prediction module obtains the driving threshold range corresponding to the vehicle's own state data for the predicted driving segment based on the probability of an accident occurring on the current road segment. The risk warning module then evaluates the vehicle's own state data to obtain the current driving risk level and generates an alarm signal. Finally, the risk avoidance push module takes evasive action based on the warning signal. However, existing models lack consideration for vehicle braking risk. Vehicles, especially freight vehicles, often experience excessive braking leading to brake failure or inability to stop on long downhill sections, posing a significant risk to the entire road segment. This model, by integrating a braking field model and a driving risk field model, more comprehensively addresses driving safety issues on mountainous roads, and the risk field model also reduces reliance on historical data.

[0004] Furthermore, existing risk warning models often lack cost-benefit analysis of evasive actions (such as slowing down, changing lanes, and activating retarders), leading to a disconnect between suggested solutions and actual driving needs. In emergency situations, drivers may delay evasive action due to information overload or inappropriate solutions. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology by providing a risk warning method based on a risk potential energy fusion risk field model for truck braking temperature rise.

[0006] The objective of this invention can be achieved through the following technical solutions: A risk early warning method based on a risk potential energy fusion risk field model of truck braking temperature rise includes: Step S1: Obtain the current vehicle status and environmental status, and calculate the braking temperature rise, road segment basic risk and vehicle risk potential energy based on the current vehicle status and environmental status, and further calculate the total risk potential energy. Step S2: Determine whether the total risk potential is greater than the pre-configured first risk threshold. If yes, proceed to step S3. Step S3: Send a first command signal to the outside world and receive one or more risk avoidance measures in response to the first command signal from the outside world; Step S4: For any avoidance measure, update the current vehicle state and environmental state based on the avoidance measure, and calculate the total risk potential energy after the avoidance measure is executed based on the updated vehicle state and environmental state, and further calculate the difference in total risk potential energy before and after the avoidance measure is executed. Step S5: Calculate the execution cost of each risk avoidance measure; Step S6: Eliminate hedging measures where the difference in total risk potential before and after the hedging measure is negative, and calculate the benefit value of each hedging measure based on the difference in total risk potential before and after the hedging measure and the execution cost. Step S7: Sort the hedging measures according to their return values, and judge and display the risk level based on the total risk potential after implementing the hedging measures.

[0007] The mathematical expression for the braking temperature rise is: in: For braking temperature rise, Q This is the heat generated during braking. For the mass of the brake drum, For the specific heat capacity of the brake drum, Thermal conductivity, For brake drum temperature, For ambient temperature, t Braking time, The efficiency of converting braking energy into heat energy. For braking force, For speed, For vehicle weight, It is the acceleration due to gravity. Braking distance, θ The slope of the road section.

[0008] The mathematical expression for the basic risk of the road segment is: in: For the basic risks of the road section, As the first weighted road segment, R For the curvature of the road section, As the second most important road segment, For real-time traffic flow density, As the third weighting of the road segment, It is the fourth weighted road segment. σ This refers to weather visibility.

[0009] The mathematical expression for the vehicle risk potential energy is: in: For vehicle risk potential energy. As a road impact factor, The equivalent mass of the vehicle. r This is the distance from a point on the road to the center of the vehicle. n 1 is the first correction factor. n 2 is the second correction factor. The angle between the line connecting a point on the road and the center of the vehicle and the direction of travel.

[0010] The mathematical expression for the equivalent mass of the vehicle is: The mathematical expression for the total risk potential energy is: in: For total risk potential energy, For nonlinear risk potential energy, As the first fusion weight, As the second fusion weight, As the third fusion weight, This is the fourth fusion weight.

[0011] The mathematical expression for the benefit value of the hedging measure is: in: The difference in total risk potential energy before and after implementing risk aversion measures. For cost weighting coefficients, For the cost of execution, The total risk potential energy before implementing risk aversion measures. The total risk potential energy after implementing risk avoidance measures.

[0012] The mathematical expression for the execution cost is: in: For the cost of execution, As a cost weight for execution time, For execution time, The cost weighting for energy loss, This refers to energy loss.

[0013] The method further includes: The dynamic evolution of the risk field is described by partial differential equations based on the calculated total risk potential energy: in: For total risk potential energy, The risk diffusion coefficient of the road section. As an environmental degradation factor, Traffic flow density, This represents the average speed of vehicles on the road segment.

[0014] A risk warning device based on a risk potential energy fusion risk field model of truck braking temperature rise includes a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements the method described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. An intelligent risk avoidance and early warning method and system based on the dynamic fusion of truck braking temperature rise and multi-dimensional risk field, through multi-dimensional data fusion and dynamic decision-making mechanism, realizes a complete closed loop from risk perception, assessment, early warning to risk avoidance, and provides a comprehensive, intelligent and efficient solution for truck driving safety on mountain highways, with significant social benefits and promotion and application value.

[0016] 2. To address the risk of brake failure in trucks on long downhill sections due to brake overheating, a physical model of brake temperature rise is introduced for quantitative assessment. This model considers parameters such as braking force, speed, vehicle weight, and gradient, directly simulating heat accumulation during braking. This allows the system to calculate the temperature change of the truck's brake drum in real time, thereby providing accurate early warning of brake failure risk.

[0017] 3. By adopting a risk potential energy fusion model, the braking temperature rise, basic road section risks (such as curvature and traffic flow density) and vehicle risk potential energy are integrated into a total risk potential energy, realizing the dynamic fusion of multi-dimensional data, describing the dynamic evolution of the risk field, and enabling the system to adapt to real-time environmental changes.

[0018] 4. A mechanism for calculating and ranking the returns of risk mitigation measures has been introduced. By quantifying the execution costs and risk reduction effects, the optimal risk mitigation plan is recommended.

[0019] 5. Real-time risk prediction is achieved through the dynamic evolution equation of the risk field, and it can be extended to multi-vehicle interaction scenarios. The diffusion coefficient and environmental attenuation factor are introduced to simulate the propagation and attenuation of risks on road segments, enabling the system to predict risk changes in a forward-looking manner (such as the cumulative effect of braking temperature rise). It is easy to integrate real-time traffic flow data and supports multi-vehicle risk interaction analysis.

[0020] 6. Specifically designed for long downhill sections for trucks, solving the practical pain point of brake failure. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the technical route of the present invention; Figure 2 A schematic diagram illustrating the comprehensive driving risks of a downhill section; Figure 3 This is a schematic diagram of the main steps of the method of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0023] A significant portion of truck accidents on mountain highways are caused by brake drum overheating and failure due to downhill braking. However, simply detecting or predicting brake drum temperature using sensors and other equipment does not directly reflect the driving risks of trucks and the safety hazards they pose to the road section. Therefore, the intelligent risk avoidance and early warning method and system proposed in this paper, which integrates the basic risk potential field of the road section, the driving risk field, and the risk potential energy of truck braking overheating, can achieve excellent results in truck risk avoidance scenarios on long downhill sections.

[0024] A risk early warning method based on a risk potential energy fusion risk field model of truck braking temperature rise, such as Figure 1 and Figure 3 As shown, it includes: Step S1: Obtain the current vehicle status and environmental status, and calculate the braking temperature rise, road segment basic risk and vehicle risk potential energy based on the current vehicle status and environmental status, and further calculate the total risk potential energy. Vehicle and environmental conditions include braking force, braking time, gradient, braking distance, speed, vehicle weight, ambient temperature, and road surface friction coefficient. These factors affect the heat generation and dissipation of the brake drum in different ways. For example, braking force and braking time are directly related to the energy conversion during braking; gradient and braking distance affect the conversion of the vehicle's potential energy; speed and vehicle weight are related to the vehicle's kinetic energy; ambient temperature affects the heat dissipation conditions of the brake drum; and the road surface friction coefficient is related to the vehicle's driving resistance, thus affecting the load on the braking system.

[0025] The mathematical expression for the braking temperature rise is: in: For braking temperature rise, Q This is the heat generated during braking. For the mass of the brake drum, For the specific heat capacity of the brake drum, Thermal conductivity, For brake drum temperature, For ambient temperature, t Braking time, The efficiency of converting braking energy into heat energy ranges from 0.8 to 0.9. For braking force, For speed, For vehicle weight, It is the acceleration due to gravity. Braking distance, θ The slope of the road section, α The heat dissipation coefficient of the brake drum reflects its ability to dissipate heat to the surrounding environment. κ This is a road surface friction heat correction factor, used to account for the impact of heat generated by road surface friction on brake drum temperature.

[0026] In this embodiment, the thermal parameters such as specific heat capacity and thermal conductivity can be obtained by experimental measurement or from the material property table. Multi-source sensors, including temperature sensors, pressure sensors, and ambient temperature sensors, are installed on the truck to transmit real-time data to the on-board computing unit. Using the established brake drum temperature rise model and real-time data, the brake drum temperature change is dynamically predicted to identify potential brake drum overheating risks in advance.

[0027] The mathematical expression for the basic risk of a road segment is: in: For the basic risks of the road section, As the first weighted road segment, R For the curvature of the road section, As the second most important road segment, For real-time traffic flow density, As the third weighting of the road segment, It is the fourth weighted road segment. σ For weather visibility, the first to fourth weights of road segments are determined using historical accident data to reflect the relative impact of different factors on risk.

[0028] The mathematical expression for the vehicle risk potential energy is: in: For vehicle risk potential energy. As a road impact factor, The equivalent mass of the vehicle. r This is the distance from a point on the road to the center of the vehicle. n 1 is the first correction factor. n 2 is the second correction factor. The angle between the line connecting a point on the road and the center of the vehicle and the direction of travel.

[0029] The mathematical expression for the equivalent mass of a vehicle is: The mathematical expression for total risk potential energy is: in: For total risk potential energy, For nonlinear risk potential energy, As the first fusion weight, As the second fusion weight, As the third fusion weight, As the fourth fusion weight, and The calibration is performed based on historical accident data to ensure that the risk potential function accurately reflects the actual risk.

[0030] Vehicle data is collected under different operating conditions, including brake drum temperature rise, temperature rise rate, and whether an accident has occurred. Machine learning algorithms are then used to analyze the data to determine the appropriate parameters under different conditions. The optimal values ​​for and are determined. These coefficients are dynamically adjusted according to changes in vehicle status and road conditions to improve the accuracy of risk assessment.

[0031] Step S2: Determine whether the total risk potential is greater than the pre-configured first risk threshold. If yes, proceed to step S3. Step S3: Send a first command signal to the outside world and receive one or more avoidance measures in response to the first command signal from the outside world. Avoidance measures include deceleration, lane change, emergency stop and activation of retarder, etc. Each avoidance measure has its specific implementation method and applicable scenario.

[0032] Step S4: For any avoidance measure, update the current vehicle state and environmental state based on the avoidance measure, and calculate the total risk potential energy after the avoidance measure is executed based on the updated vehicle state and environmental state, and further calculate the difference in total risk potential energy before and after the avoidance measure is executed. Step S5: Calculate the execution cost of each risk avoidance measure; Step S6: Eliminate hedging measures where the difference in total risk potential before and after the hedging measure is negative, and calculate the benefit value of each hedging measure based on the difference in total risk potential before and after the hedging measure and the execution cost. The mathematical expression for the payoff of hedging measures is: in: The difference in total risk potential energy before and after implementing risk aversion measures. For cost weighting coefficients, For the cost of execution, The total risk potential energy before implementing risk aversion measures. The total risk potential energy after implementing risk avoidance measures.

[0033] The mathematical expression for execution cost is: in: For the cost of execution, As a cost weight for execution time, For execution time, The cost weighting for energy loss, This refers to energy loss.

[0034] Step S7: Sort the hedging measures according to their return values, and judge and display the risk level based on the total risk potential after implementing the hedging measures.

[0035] Step S8: Based on the calculated total risk potential energy, the dynamic evolution of the risk field is described using partial differential equations. Specifically, in order to describe the dynamic changes of the risk field with time and space, a partial differential equation model is established, introducing the dynamic evolution of the risk field in the time dimension: in: For total risk potential energy, The risk diffusion coefficient of the road section. As an environmental degradation factor, Traffic flow density, This represents the average speed of vehicles on the road segment.

[0036] To establish the overall environmental field, this paper simulates a three-lane traffic scenario on a long downhill section in a mountainous area. The integrated risk field consists of moving vehicles, braking and heating potential energy, and the basic risk field strength of the road. In this setting, the parameters are calibrated as follows: η=0.8, k1=0.6, k2=0.6, w1=0.3, w2=0.2, w3=0.35, w4=0.15, ϵ=0.3, ε1=1.0 for heavy trucks, and ε1=0.6 and ε2=1 for light trucks. The vehicle speed is set to 20 m / s. The initial setting is λ=0.02. The resulting heat map of the integrated traffic risk field of the road section is shown below. Figure 2 As shown, due to the risk of brake failure, the driving risk in front of the truck is significantly increased, posing a safety hazard to the entire lane.

[0037] Based on the total risk potential U total The magnitude of the risk level determines its classification into different risk levels. These risk levels include low risk, medium risk, high risk, and extremely high risk. For example, the thresholds can be set as follows: Low risk: U total <0.3, medium risk: 0.3≤U total <0.5, high risk: 0.5≤U total <0.7, extremely high risk: U total ≥0.7; When the risk level reaches medium or higher, warning information will be sent to drivers through various means. The warning information includes the risk level, the distance from the current location to a safe area, and recommended actions.

[0038] Warning devices include in-vehicle terminals, AR-HUDs, and haptic seats. For example: The in-vehicle terminal displays risk levels and avoidance suggestions in a pop-up window, such as "Current risk level: High risk, it is recommended to reduce speed to 60 km / h." AR-HUD overlays warning information onto the driver's field of vision, using color gradients to mark high-risk areas, such as using red to mark sharp bends and high-risk road sections ahead. The haptic seat alerts the driver through vibration, with the vibration intensity adjusted according to the risk level.

[0039] Based on the optimal risk avoidance strategy, provide drivers with specific risk avoidance guidance. For example: Recommended deceleration: Based on risk potential and road conditions, the system recommends that drivers reduce their speed to a specific value, such as "reduce to 60 km / h". Lane change direction and safe distance: Instructs drivers which direction to change lanes and the safe distance to maintain, such as "change lanes to the right, maintain a 200 m distance". Escape lane location and remaining distance: Through real-time GIS matching, the system informs drivers of the location and distance of the escape lane, such as "There is an escape lane 500 m ahead". Brake cooling solution: The system recommends that drivers use intermittent braking to reduce brake drum temperature, such as "intermittent braking, every 30 seconds".

[0040] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A risk early warning method based on a risk potential energy fusion risk field model of truck braking temperature rise, characterized in that, include: Step S1: Obtain the current vehicle status and environmental status, and calculate the braking temperature rise, road segment basic risk and vehicle risk potential energy based on the current vehicle status and environmental status, and further calculate the total risk potential energy. Step S2: Determine whether the total risk potential is greater than the pre-configured first risk threshold. If yes, proceed to step S3. Step S3: Send a first command signal to the outside world and receive one or more risk avoidance measures in response to the first command signal from the outside world; Step S4: For any avoidance measure, update the current vehicle state and environmental state based on the avoidance measure, and calculate the total risk potential energy after the avoidance measure is executed based on the updated vehicle state and environmental state, and further calculate the difference in total risk potential energy before and after the avoidance measure is executed. Step S5: Calculate the execution cost of each risk avoidance measure; Step S6: Eliminate hedging measures where the difference in total risk potential before and after the hedging measure is negative, and calculate the benefit value of each hedging measure based on the difference in total risk potential before and after the hedging measure and the execution cost. Step S7: Sort each hedging measure according to its return value, and judge and display the risk level based on the total risk potential after implementing the hedging measure.

2. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 1, characterized in that, The mathematical expression for the braking temperature rise is: in: For braking temperature rise, Q This is the heat generated during braking. For the mass of the brake drum, For the specific heat capacity of the brake drum, Thermal conductivity, For brake drum temperature, For ambient temperature, t Braking time, The efficiency of converting braking energy into heat energy. For braking force, For speed, For vehicle weight, It is the acceleration due to gravity. Braking distance, θ The slope of the road section.

3. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 2, characterized in that, The mathematical expression for the basic risk of the road segment is: in: For the basic risks of the road section, As the first weighted road segment, R For the curvature of the road section, As the second most important road segment, For real-time traffic flow density, As the third weighting of the road segment, It is the fourth weighted road segment. σ This refers to weather visibility.

4. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 3, characterized in that, The mathematical expression for the vehicle risk potential energy is: in: For vehicle risk potential energy. As a road impact factor, The equivalent mass of the vehicle. r This is the distance from a point on the road to the center of the vehicle. n 1 is the first correction factor. n 2 is the second correction factor. The angle between the line connecting a point on the road and the center of the vehicle and the direction of travel.

5. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 4, characterized in that, The mathematical expression for the equivalent mass of the vehicle is: 。 6. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 4, characterized in that, The mathematical expression for the total risk potential energy is: in: For total risk potential energy, For nonlinear risk potential energy, As the first fusion weight, As the second fusion weight, As the third fusion weight, This is the fourth fusion weight.

7. The risk warning method based on a risk potential energy fusion risk field model of truck braking temperature rise according to claim 1, characterized in that, The mathematical expression for the benefit value of the hedging measure is: in: The difference in total risk potential energy before and after implementing risk aversion measures. For cost weighting coefficients, For the cost of execution, The total risk potential energy before implementing risk aversion measures. The total risk potential energy after implementing risk avoidance measures.

8. A risk warning method based on a risk potential energy fusion risk field model for truck braking temperature rise according to claim 7, characterized in that, The mathematical expression for the execution cost is: in: For the cost of execution, As a cost weight for execution time, For execution time, The cost weighting for energy loss, This refers to energy loss.

9. A risk warning method based on a risk potential energy fusion risk field model for truck braking temperature rise according to claim 1, characterized in that, The method further includes: The dynamic evolution of the risk field is described by partial differential equations based on the calculated total risk potential energy: in: For total risk potential energy, The risk diffusion coefficient of the road section. As an environmental degradation factor, Traffic flow density, This represents the average speed of vehicles on the road segment.

10. A risk warning device based on a risk potential energy fusion risk field model of truck braking temperature rise, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-9.

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