Control method, device and system for vehicle suspension system, vehicle

Abstract

The application relates to the technical field of vehicle control, and discloses a control method, device and system for a vehicle suspension system and a vehicle, wherein the control method comprises the following steps: acquiring risk environment data affecting vehicle driving safety; acquiring vehicle operation data related to vehicle driving safety; determining a current risk level of the vehicle according to the risk environment data and the vehicle operation data; and generating a target suspension adjustment instruction according to the current risk level, so as to adjust the vehicle suspension system according to the target suspension adjustment instruction. The risk environment data and the vehicle operation data are fused and analyzed, the current environment and the current operation information of the vehicle are comprehensively considered, the comprehensiveness during data analysis is improved, a more accurate current risk level is obtained, the target suspension adjustment instruction generated according to the current risk level can realize intelligent adjustment of the suspension system, the comprehensive performance of the vehicle is improved, and the safety and stability of the vehicle in a complex environment are improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, such as a control method, device and system for a vehicle suspension system, and a vehicle. Background Technology

[0002] With the increasingly sophisticated layout of vehicle sensors and the diversification of vehicle actuators, the level of automotive intelligence and electrification has improved, and vehicle functions have become more complete and powerful. Currently, vehicle suspension systems have evolved from passive suspensions to active suspensions, which can actively adjust suspension height and ride comfort according to user needs and vehicle conditions. However, suspension functions are mostly internal to the suspension system, and their functional integration with other vehicle systems still needs to be improved.

[0003] In related technologies, a control method for air suspension has been proposed, which includes: collecting wheel spring height signals based on the height sensor of the air suspension, and determining whether the vehicle is in a water-wading state based on the current state of the vehicle; when it is determined that the vehicle is in a water-wading state, adjusting the air suspension height.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] The relevant technologies only consider water wading conditions and do not comprehensively consider other factors that may affect the vehicle's safety status, resulting in lower vehicle safety and stability in complex environments.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a control method, device, system, and vehicle for a vehicle suspension system, improving the safety and stability of the vehicle in complex environments.

[0009] In some embodiments, a control method for a vehicle suspension system includes: acquiring risk environment data affecting vehicle driving safety; acquiring vehicle operation data related to vehicle driving safety; determining the current risk level of the vehicle based on the risk environment data and the vehicle operation data; and generating a target suspension adjustment command based on the current risk level to adjust the vehicle suspension system according to the target suspension adjustment command.

[0010] Optionally, when the risk environment data includes vehicle wading data, the current risk level of the vehicle is determined based on the risk environment data and the vehicle operation data, including: determining the current water depth data in the vehicle wading data, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; quantizing the current water depth data, lateral and longitudinal acceleration and turning rate using feature values, and calculating the current wading risk level of the vehicle based on the quantified data.

[0011] Optionally, when the risk environment data includes wind speed data around the vehicle, the current risk level of the vehicle is determined based on the risk environment data and the vehicle operation data, including: determining the current wind speed data in the wind speed data around the vehicle, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; quantifying the current wind speed data, lateral and longitudinal acceleration and turning rate using feature values, and calculating the current gale risk level of the vehicle based on the quantified data.

[0012] Optionally, when the risk environment data includes vehicle wading data and wind speed data around the vehicle, the current risk level of the vehicle is determined based on the risk environment data and vehicle operation data. This includes: determining the current water depth data in the vehicle wading data and the current wind speed data in the vehicle's surrounding wind speed data, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; performing eigenvalue quantification and comprehensive risk assessment on the current water depth data and current wind speed data to obtain comprehensive risk data; performing eigenvalue quantification on the lateral and longitudinal acceleration and turning rate, and calculating the current comprehensive risk level of the vehicle based on the quantified data and comprehensive risk data.

[0013] Optionally, feature value quantification and comprehensive risk assessment are performed on the current water depth data and current wind speed data to obtain comprehensive risk data, including: assigning initial weights to the current water depth data and current wind speed data according to the vehicle design and actual usage scenario; adjusting the initial weights according to the real-time operating status of the vehicle to obtain target weights; and performing feature value quantification and comprehensive risk assessment on the current water depth data and current wind speed data according to the target weights to obtain comprehensive risk data.

[0014] Optionally, generating a target suspension adjustment instruction based on the current risk level includes: determining a suspension adjustment strategy based on the current risk level; generating a target suspension adjustment instruction based on the suspension adjustment strategy; the target suspension adjustment instruction includes adjustment height and adjustment direction.

[0015] Optionally, the control method further includes: after generating the target suspension adjustment command, sending the target suspension adjustment command to the user for confirmation; and determining whether to execute the target suspension adjustment command based on the user's confirmation result.

[0016] In some embodiments, the control device for a vehicle suspension system includes a processor and a memory storing program instructions, the processor being configured to execute the control method for a vehicle suspension system as described above when the program instructions are executed.

[0017] In some embodiments, the control system for a vehicle suspension system includes: an environmental sensor module configured to acquire risk environment data affecting vehicle driving safety; a vehicle sensor module configured to acquire vehicle operation data related to vehicle driving safety; a suspension height adjustment controller configured to determine the current risk level of the vehicle based on the risk environment data and the vehicle operation data, and generate a target suspension adjustment command based on the current risk level; and a suspension adjustment actuator configured to adjust the vehicle suspension system according to the target suspension adjustment command.

[0018] In some embodiments, the vehicle includes: a vehicle body; a vehicle suspension system disposed on the vehicle body; and a control system for the vehicle suspension system as described above, disposed on the vehicle body and connected to the vehicle suspension system.

[0019] The control method, device, system, and vehicle for a vehicle suspension system provided in this disclosure can achieve the following technical effects:

[0020] In this embodiment of the disclosure, based on risk environment data surrounding the vehicle, factors in the current environment that may affect vehicle driving safety can be identified; based on vehicle operation data related to vehicle driving safety, the current driving status of the vehicle can be confirmed. By fusing risk environment data and vehicle operation data, the current environment and current operating information of the vehicle can be comprehensively considered, improving the comprehensiveness of data analysis and thus obtaining a more accurate current risk level. Based on the target suspension adjustment command generated according to the current risk level, intelligent adjustment of the suspension system can be achieved, improving the overall performance of the vehicle and thereby enhancing the vehicle's safety and stability in complex environments.

[0021] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0022] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0023] Figure 1 This is a schematic diagram of a control method for a vehicle suspension system provided in an embodiment of this disclosure;

[0024] Figure 2This is a schematic diagram of a vehicle entering a flooded area according to an embodiment of this disclosure;

[0025] Figure 3 This is a schematic diagram illustrating risk warnings via an interactive terminal in a water-related scenario, provided by an embodiment of this disclosure.

[0026] Figure 4 This is a schematic diagram illustrating the display of vehicle adjustment status via an interactive terminal in a water-wading scenario, as provided in this embodiment of the disclosure.

[0027] Figure 5 This is a schematic diagram illustrating a risk warning provided via an interactive terminal in a high-wind scenario, according to an embodiment of this disclosure.

[0028] Figure 6 This is a schematic diagram illustrating the display of vehicle adjustment status via an interactive terminal in a windy scenario, as provided in this embodiment of the disclosure.

[0029] Figure 7 This is a schematic diagram of a control device for a vehicle suspension system provided in an embodiment of the present disclosure;

[0030] Figure 8 This is a schematic diagram of a control system for a vehicle suspension system provided in an embodiment of this disclosure;

[0031] Figure 9 This is a simplified signal topology diagram of a control system for a vehicle suspension system provided in an embodiment of this disclosure. Detailed Implementation

[0032] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0033] The terms "first," "second," etc., used in the technical solutions described in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0034] Unless otherwise stated, the term "multiple" means two or more.

[0035] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0036] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0037] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0038] Combination Figure 1 As shown, this disclosure provides a control method for a vehicle suspension system. The execution subject of the control method may be a processor, and the control method includes:

[0039] S101, the processor acquires risk environmental data that affects vehicle driving safety.

[0040] S102, the processor acquires vehicle operation data related to vehicle driving safety.

[0041] S103, the processor determines the current risk level of the vehicle based on risk environment data and vehicle operation data.

[0042] S104, the processor generates a target suspension adjustment command based on the current risk level, so as to adjust the vehicle suspension system according to the target suspension adjustment command.

[0043] In this embodiment of the disclosure, based on risk environment data surrounding the vehicle, factors in the current environment that may affect vehicle driving safety can be identified; based on vehicle operation data related to vehicle driving safety, the current driving status of the vehicle can be confirmed. By fusing risk environment data and vehicle operation data, the current environment and current operating information of the vehicle can be comprehensively considered, improving the comprehensiveness of data analysis and thus obtaining a more accurate current risk level. Based on the target suspension adjustment command generated according to the current risk level, intelligent adjustment of the suspension system can be achieved, improving the overall performance of the vehicle and thereby enhancing the vehicle's safety and stability in complex environments.

[0044] Optionally, the risk environment data includes vehicle wading data and / or wind speed data around the vehicle.

[0045] In this embodiment, to achieve height adjustment of the vehicle suspension system in wading and high-wind scenarios, wading data around the vehicle can be acquired using a wading sensor, and wind speed data of the vehicle's environment can be acquired using a wind speed sensor. Through real-time monitoring and comprehensive analysis of the wading and wind speed data, the risk level of the vehicle can be accurately assessed, and corresponding target suspension adjustment commands can be generated, thereby improving the vehicle's safety and stability in complex environments.

[0046] Optionally, acquiring vehicle wading data includes: acquiring water depth data through ultrasonic sensors, lidar, or cameras installed at the front, side, or bottom of the vehicle; calculating the water level change rate by continuously monitoring the rate of change of water depth data; and determining the wading area range by scanning the wading area range around the vehicle using cameras or lidar.

[0047] In this embodiment, by monitoring various water-related data, it is possible to determine whether a vehicle is about to enter a water-crossing area or is currently in a water-crossing state, thereby assessing whether the vehicle can safely pass through the water-crossing area or whether it needs to detour.

[0048] Optionally, wind speed data around the vehicle can be acquired by: measuring horizontal wind speed using a wind speed sensor mounted on the top or side of the vehicle; measuring vertical wind speed, such as gusts or eddies, using a wind speed sensor; and acquiring wind direction information using a wind direction sensor.

[0049] In this embodiment, horizontal wind speed can be used to assess the impact of lateral winds on a vehicle during operation, especially at high speeds. Vertical wind speed can be used to assess the impact of wind on the vehicle in the vertical direction, particularly on bridges or elevated roads. Combining horizontal and vertical wind speed data allows for a more comprehensive assessment of the wind's influence on the vehicle. For example, crosswinds can significantly affect vehicle stability, while tailwinds or headwinds have relatively smaller effects.

[0050] Optionally, vehicle operation data related to vehicle driving safety includes: the vehicle's lateral and longitudinal acceleration and steering rate. Furthermore, in different embodiments, the vehicle operation data may also include one or more of the following: vehicle speed, wheel speed signals, steering rate, power status, yaw rate, left front vehicle height signal, right front vehicle height signal, left rear vehicle height signal, and right rear vehicle height signal.

[0051] In this embodiment, vehicle operation data provides comprehensive information on the vehicle's operating status. For example, vehicle speed can be used to determine vehicle stability; wheel speed signals can be used for slippage detection and road condition assessment; power status can ensure system functionality; lateral and longitudinal acceleration can be used to determine vehicle balance and stability; and vehicle height signals can be used for suspension status monitoring.

[0052] Optionally, when the risk environment data includes vehicle wading data, the current risk level of the vehicle is determined based on the risk environment data and the vehicle operation data, including: determining the current water depth data in the vehicle wading data, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; quantizing the current water depth data, lateral and longitudinal acceleration and turning rate using feature values, and calculating the current wading risk level of the vehicle based on the quantified data.

[0053] In this embodiment, by quantifying characteristic values ​​such as water depth, acceleration, and turning rate, and combining them with algorithms such as weighted summation, the risk level of a vehicle in wading situations can be accurately assessed. This not only considers the influence of a single factor but also integrates the interaction of multiple factors, improving the accuracy and reliability of risk assessment. Figure 2 As shown, when a vehicle enters a wading area, the predicted wading depth h2 after traveling a distance l is calculated based on the wading depth h1 of the front wheels in the direction of travel, the road slope α displayed on the navigation system, and the vehicle's real-time tilt angle signal, thus determining whether the vehicle is about to enter a deep water area. Combining this with the vehicle's current operating status signals, such as lateral and longitudinal acceleration and turning rate, the current wading risk level is determined. For example, assuming the currently collected data is: water depth 25cm, lateral and longitudinal acceleration 0.6g, and turning rate 9° / s. The above data is quantized using feature values. If the water depth data considers a range of 0 to 50cm, the quantized data for water depth is 5; if the lateral and longitudinal acceleration considers a range of 0 to 1.5g, the quantized data for lateral and longitudinal acceleration is 4; and if the turning rate considers a range of 0 to 15° / s, the quantized data for turning rate is 6. Then, weights are assigned to each feature value for weighted calculation. For example, water depth is weighted at 0.6, lateral and longitudinal acceleration at 0.2, and turning rate at 0.2. Based on this, the current risk level is calculated as 5 × 0.6 + 4 × 0.2 + 6 × 0.2 = 5. Assuming risk levels are divided into low risk (levels 1-3), medium risk (levels 4-6), and high risk (levels 7-10), then the current water wading risk level of 5 can be determined as medium risk.

[0054] Optionally, when the risk environment data includes wind speed data around the vehicle, the current risk level of the vehicle is determined based on the risk environment data and the vehicle operation data, including: determining the current wind speed data in the wind speed data around the vehicle, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; quantifying the current wind speed data, lateral and longitudinal acceleration and turning rate using feature values, and calculating the current gale risk level of the vehicle based on the quantified data.

[0055] In this embodiment, by quantifying feature values ​​such as wind speed, acceleration, and turning rate, and combining them with algorithms such as weighted summation, the risk level of a vehicle under strong wind conditions can be accurately assessed. This not only considers the influence of a single factor but also integrates the interaction of multiple factors, improving the accuracy and reliability of risk assessment. When a vehicle is traveling at high speed and a high wind speed is detected, the current wind risk level is determined based on the current wind speed data and the vehicle's current lateral and longitudinal acceleration, turning rate, and other signals. For example, assuming the currently collected data is: wind speed 21 m / s, lateral and longitudinal acceleration 0.9 g, and turning rate 3° / s, feature value quantification is performed on the above data. If the considered range of wind speed data is 0 to 30 m / s, the quantized data for wind speed is 7; if the considered range of lateral and longitudinal acceleration is 0 to 1.5 g, the quantized data for lateral and longitudinal acceleration is 6; and if the considered range of turning rate is 0 to 15° / s, the quantized data for turning rate is 2. Then, weights are assigned to each feature value for weighted calculation. For example, wind speed is weighted at 0.6, lateral and longitudinal acceleration at 0.2, and angular rate at 0.2. Based on this, the current risk level is calculated as 7 × 0.6 + 6 × 0.2 + 2 × 0.2 = 5.8. Assuming risk levels are divided into low risk (levels 1-3), medium risk (levels 4-6), and high risk (levels 7-10), then the current gale risk level of 5.8 can be determined as medium risk.

[0056] Optionally, when the risk environment data includes vehicle wading data and wind speed data around the vehicle, the current risk level of the vehicle is determined based on the risk environment data and vehicle operation data. This includes: determining the current water depth data in the vehicle wading data and the current wind speed data in the vehicle's surrounding wind speed data, and determining the lateral and longitudinal acceleration and turning rate in the vehicle operation data; performing eigenvalue quantification and comprehensive risk assessment on the current water depth data and current wind speed data to obtain comprehensive risk data; performing eigenvalue quantification on the lateral and longitudinal acceleration and turning rate, and calculating the current comprehensive risk level of the vehicle based on the quantified data and comprehensive risk data.

[0057] In this embodiment, by comprehensively considering water wading data, wind speed data, and the vehicle's operating status, the risk level of the vehicle in a complex environment can be fully assessed.

[0058] Optionally, feature value quantification and comprehensive risk assessment are performed on the current water depth data and current wind speed data to obtain comprehensive risk data, including: assigning initial weights to the current water depth data and current wind speed data according to the vehicle design and actual usage scenario; adjusting the initial weights according to the real-time operating status of the vehicle to obtain target weights; and performing feature value quantification and comprehensive risk assessment on the current water depth data and current wind speed data according to the target weights to obtain comprehensive risk data.

[0059] In this embodiment, initial weights can be assigned to the current water depth and wind speed data based on vehicle design and actual usage scenarios. For example, considering the vehicle's wading depth and wind resistance design standards, as well as the vehicle's primary operating environment (e.g., urban roads, mountain roads, coastal areas), the safety priority of wading risk and wind risk can be determined based on the vehicle's safety design. Furthermore, the initial weights can be reallocated according to actual usage scenarios, such as temporarily increasing the wading weight during rainy weather. Assuming the vehicle has a high wading risk and strong wind resistance, the initial weights can be assigned, with the initial weight for current water depth data at 0.7 and the initial weight for current wind speed data at 0.3. Then, the initial weights are adjusted based on the vehicle's real-time operating status to obtain the target weights. For example, wind speed may have a greater impact at high speeds, while wading may have a greater impact at low speeds; high acceleration may increase vehicle instability in strong winds; and high turning rates may increase the risk of rollover in wading or strong winds. Therefore, the rules for adjusting the weights can be preset as follows: if the vehicle speed is high, increase the weight of wind speed data and decrease the weight of water depth data; if the lateral and longitudinal accelerations are high, increase the weight of wind speed data; if the turning rate is high, increase the weight of water depth data. Finally, a comprehensive risk assessment is performed on the current water depth data and current wind speed data based on the target weights to obtain comprehensive risk data. Assume the currently collected data is: water depth 25cm, wind speed 21m / s, vehicle speed 70km / h, lateral and longitudinal acceleration 0.6g, and turning rate 9° / s. Based on the vehicle design and actual usage scenario, the initial weight of water depth data is determined to be 0.7, and the initial weight of current wind speed data is 0.3. Due to the high current vehicle speed, the weight of wind speed data needs to be increased to 0.4, and the weight of water depth data needs to be decreased to 0.6. The water depth and wind speed data are quantized using eigenvalues. If the water depth range is 0 to 50 cm, the quantized value is 5; if the wind speed range is 0 to 30 m / s, the quantized value is 7. Therefore, the overall risk level can be calculated as 5 × 0.6 + 7 × 0.4 = 5.8. Next, the lateral and longitudinal acceleration and turning rate are quantized using eigenvalues. If the lateral and longitudinal acceleration range is 0 to 1.5 g, the quantized value is 4; if the turning rate range is 0 to 15° / s, the quantized value is 6. Then, weights are assigned to each eigenvalue for a weighted calculation. For example, the overall risk level is weighted at 0.8, the lateral and longitudinal acceleration at 0.1, and the turning rate at 0.1. Based on this, the current overall risk level is calculated as 5.8 × 0.8 + 4 × 0.1 + 6 × 0.1 = 5.64.

[0060] Optionally, feature value quantification and comprehensive risk assessment are performed on the current water depth data and current wind speed data according to the target weights, including: obtaining a water wading risk index based on the quantified data of the current water depth data and its corresponding target weights; obtaining a strong wind risk index based on the quantified data of the current wind speed data and its corresponding target weights; and comparing the water wading risk index and the strong wind risk index to obtain a comprehensive risk assessment result.

[0061] In this embodiment, the wading risk index is the product of the quantified data of the current water depth and its corresponding target weight, and the wind risk index is the product of the quantified data of the current wind speed and its corresponding target weight. By comparing the magnitudes of the wading risk index and the wind risk index, it can be determined which risk has a greater impact on the vehicle. Specifically, if the wading risk index is greater than the wind risk index, then the wading risk is the primary risk; if the wind risk index is greater than the wading risk index, then the wind risk is the primary risk; if the wading risk index and the wind risk index are equal, then the two risks have a comparable impact on the vehicle. Based on the above comparison results, a comprehensive risk assessment result can be obtained, thereby determining the suspension height adjustment strategy. Specifically, if the wading risk is the primary risk, then an instruction to increase the suspension height is generated; if the wind risk is the primary risk, then an instruction to decrease the suspension height is generated; if the two risks have a comparable impact on the vehicle, then further judgment is made based on the risk level, selecting the suspension adjustment strategy corresponding to the higher risk level, or prompting the user to make a selection. For example, if the current quantified value of water depth is 5 and the target weight of water depth is 0.6, then the water wading risk index is 3; if the current quantified value of wind speed is 7 and the target weight of wind speed is 0.4, then the strong wind risk index is 2.8. Therefore, water wading risk is the main risk in this comprehensive risk assessment.

[0062] Optionally, generating a target suspension adjustment instruction based on the current risk level includes: determining a suspension adjustment strategy based on the current risk level; generating a target suspension adjustment instruction based on the suspension adjustment strategy; the target suspension adjustment instruction includes adjustment height and adjustment direction.

[0063] In this embodiment, different risk levels correspond to differentiated suspension adjustment strategies, such as height adjustment range and adjustment speed. This ensures safety while reducing unnecessary suspension movements, optimizing energy consumption and comfort. For example, the wading risk level is correlated with the suspension height increase: at wading risk level 2, the suspension height increase is 20mm; at wading risk level 8, the suspension height increase is 80mm. This avoids situations where a fixed height increase leads to insufficient or excessive height, ensuring wading safety and preventing water from entering the air intake. Similarly, the wind risk level is correlated with the suspension height decrease: for example, at wind risk level 1, the suspension height decreases by 5mm; at wind risk level 7, the suspension height decreases by 35mm. This reduces the vehicle's drag coefficient in strong winds, lowers the center of gravity to reduce the probability of rollover, and improves stability at high speeds. For combined risk levels, the conflict between wading and strong wind scenarios needs to be considered to prevent repeated height adjustments triggered by a single risk. For example, if the vehicle encounters both gusts of wind and fluctuating water depth simultaneously, the action with the higher safety level should be prioritized. Based on the primary risks identified in the comprehensive risk assessment, specific suspension adjustment strategies can be determined. For example, if the primary risk is water wading, the suspension needs to be raised; if the primary risk is strong winds, the suspension needs to be lowered.

[0064] Optionally, the control method further includes: after generating the suspension adjustment command, sending the suspension adjustment command to the user for confirmation; and determining whether to execute the suspension adjustment command based on the user's confirmation result.

[0065] In this embodiment, combined with Figure 3 and Figure 4 As shown, based on different water wading risk levels, different colors are used to provide risk warnings to users via the interactive interface, prompting them to adjust the vehicle height. Users then choose whether to adjust as needed. When the user selects "yes," the suspension will rise to adjust the vehicle height, with the maximum adjustment range based on the vehicle's design. Simultaneously, the vehicle's adjustment status is displayed to the user, and the wading depth level and color are dynamically displayed based on the real-time vehicle status. When the vehicle is stationary, if water-related sensors detect a water wading risk, signals including the wading risk level, color, and a request to adjust the vehicle height are transmitted to the user's mobile app and displayed. Users can then choose whether to activate vehicle height adjustment via the mobile app, which similarly displays the adjustment status and the current wading level and color. Figure 5 and Figure 6As shown, depending on the different wind risk levels, the system can also send wind risk level and adjustment request signals to the user via the interactive terminal for risk warning. The user can then choose whether to adjust as needed. When the user selects "yes," the suspension will adjust the vehicle height in real time, with the maximum adjustment range based on the vehicle's design. Simultaneously, the vehicle's adjustment status will be displayed to the user. When the vehicle is stationary, if a strong wind warning signal is generated in an environment such as a hurricane, a suspension system adjustment request signal will be actively sent to the user's app. After the user confirms, the suspension will actively adjust to lower the vehicle height.

[0066] Optionally, the control method further includes: after adjusting the height of the suspension system according to the suspension adjustment command, obtaining feedback on the stability and safety of the vehicle; and adjusting the height of the suspension system again according to the feedback.

[0067] In this embodiment, through real-time feedback and secondary adjustment mechanisms, dynamic optimization and enhanced fault tolerance of suspension control can be achieved to cope with dynamic changes in complex environments, such as sudden rises in water levels or increased wind gusts, identifying and correcting deficiencies or failures in the initial adjustment. After the initial adjustment, the stability and safety of the vehicle are monitored, such as by measuring IMU yaw rate, suspension displacement sensor readings, continuous water depth readings from the wading sensor, and wind speed changes, to verify the adjustment effect and make fine adjustments. For example, after the initial suspension increase, if the lateral sway of the vehicle body still exceeds the threshold, the height is increased by 5% again; after the suspension wind resistance is lowered, if the wind speed sensor shows a sudden decrease in wind force, the height is automatically adjusted back to a comfortable position.

[0068] Combination Figure 7 As shown, this disclosure provides a control device 600 for a vehicle suspension system, including a processor 700 and a memory 701, and may also include a communication interface 702 and a bus 703. The processor 700, communication interface 702, and memory 701 can communicate with each other via the bus 703. The communication interface 702 can be used for information transmission. The processor 700 can call logical instructions in the memory 701 to execute the control method for the vehicle suspension system described in the above embodiment.

[0069] Furthermore, the logic instructions in the aforementioned memory 701 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0070] The memory 701, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 700 executes functional applications and data processing by running the program instructions / modules stored in the memory 701, thereby implementing the control method for the vehicle suspension system in the above method embodiments.

[0071] The memory 701 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 701 may include high-speed random access memory and may also include non-volatile memory.

[0072] Combination Figure 8 As shown, this disclosure provides a control system 800 for a vehicle suspension system, including: an environmental sensor module 801, a vehicle sensor module 802, a suspension height adjustment controller 803, and a suspension adjustment actuator 804. The environmental sensor module 801 is configured to acquire risk environment data affecting vehicle driving safety. The vehicle sensor module 802 is configured to acquire vehicle operation data related to vehicle driving safety. The suspension height adjustment controller 803 is configured to determine the current risk level of the vehicle based on the risk environment data and vehicle operation data, and generate a target suspension adjustment command based on the current risk level. The suspension adjustment actuator 804 is configured to adjust the vehicle suspension system according to the target suspension adjustment command. The environmental sensor module 801 and the vehicle sensor module 802 are connected to the suspension height adjustment controller 803 via a gateway to achieve signal interaction. The suspension height adjustment controller 803 generates suspension adjustment commands, which are then implemented by the suspension adjustment actuator 804 to adjust the height of the suspension system, including left front suspension height adjustment, right front suspension height adjustment, left rear suspension height adjustment, and right rear suspension height adjustment. The suspension height adjustment controller 803 can also obtain suspension adjustment status feedback returned by the suspension adjustment actuator 804.

[0073] Optionally, the environmental sensor module 801 includes a wading sensor 811 and a wind speed sensor 821. The wading sensor 811 is configured to acquire wading data of the vehicle. The wind speed sensor 821 is configured to acquire wind speed data of the vehicle. When the vehicle is powered off, the wading sensor 811, the wind speed sensor 821, and related controllers remain in an active state to ensure that data about the vehicle's surroundings can be collected even after the vehicle is powered off, enabling height adjustment of the suspension system.

[0074] Optionally, the control system 800 also includes a human-machine interface module 805, which is connected to the suspension height adjustment controller 803 via a gateway. The human-machine interface module 805 is configured to send suspension adjustment commands to the user for confirmation. Figure 9 As shown, the suspension height adjustment controller 803 receives vehicle signal data such as vehicle speed, wheel speed, steering angle, steering rate, power status, longitudinal acceleration, lateral acceleration, and yaw rate, as well as water depth and wind speed level signals via CAN signals. It also receives vehicle height status signals (left front, right front, left rear, and right rear) via sensor signals. Based on APP-authorized control signals or vehicle-mounted system-authorized control signals, it adjusts the suspension height via the suspension adjustment actuator 804. The suspension height adjustment controller 803 then feeds back the left front, right front, left rear, and right rear vehicle height adjustment status, vehicle wading status, and external wind speed status to the human-machine interface module 805 for display on the instrument panel and APP interaction. Furthermore, in the event of a suspension system malfunction, the suspension height adjustment controller 803 will also feed back a suspension fault signal to the human-machine interface module 805.

[0075] Optionally, the human-machine interaction module 805 includes a vehicle infotainment system 815 and a mobile app 825.

[0076] This disclosure provides a vehicle, including: a vehicle body; a vehicle suspension system disposed on the vehicle body; and a control system for the vehicle suspension system as described above, disposed on the vehicle body and connected to the vehicle suspension system. The placement described herein is not limited to placement within the vehicle interior, but also includes installation and connection with other components of the vehicle, including but not limited to physical connections, electrical connections, or signal transmission connections.

[0077] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described control method for a vehicle suspension system.

[0078] This disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the aforementioned control method for a vehicle suspension system.

[0079] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0080] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more 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 method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0081] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the technical solutions described herein. As used in the technical solutions described herein, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used herein means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0083] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A control method for a vehicle suspension system, characterized by, The method comprises: acquiring risk environment data affecting vehicle driving safety; acquiring vehicle operation data related to vehicle driving safety; determining a current risk level of the vehicle according to the risk environment data and the vehicle operation data; generating a target suspension adjustment instruction according to the current risk level, so as to adjust the vehicle suspension system according to the target suspension adjustment instruction; wherein, when the risk environment data comprises vehicle water wading data and vehicle peripheral wind speed data, determining the current risk level of the vehicle according to the risk environment data and the vehicle operation data comprises: determining current water depth data in the vehicle water wading data, current wind speed data in the vehicle peripheral wind speed data, and lateral and longitudinal acceleration and cornering rate in the vehicle operation data; performing eigenvalue quantization and comprehensive risk assessment on the current water depth data and the current wind speed data to obtain comprehensive risk data; performing eigenvalue quantization on the lateral and longitudinal acceleration and the cornering rate, and calculating the current comprehensive risk level of the vehicle according to the quantized data and the comprehensive risk data; performing eigenvalue quantization and comprehensive risk assessment on the current water depth data and the current wind speed data to obtain comprehensive risk data comprises: assigning initial weights to the current water depth data and the current wind speed data according to the vehicle design and actual use scene; adjusting the initial weights according to the real-time running state of the vehicle to obtain target weights; performing eigenvalue quantization and comprehensive risk assessment on the current water depth data and the current wind speed data according to the target weights to obtain comprehensive risk data; generating the target suspension adjustment instruction according to the current risk level comprises: determining a suspension adjustment strategy according to the current risk level; generating the target suspension adjustment instruction according to the suspension adjustment strategy; the target suspension adjustment instruction comprises an adjustment height and an adjustment direction.

2. The control method according to claim 1, characterized by, when the risk environment data comprises vehicle water wading data, determining the current risk level of the vehicle according to the risk environment data and the vehicle operation data comprises: determining current water depth data in the vehicle water wading data, and determining lateral and longitudinal acceleration and cornering rate in the vehicle operation data; performing eigenvalue quantization on the current water depth data, the lateral and longitudinal acceleration, and the cornering rate, and calculating the current water wading risk level of the vehicle according to the quantized data.

3. The control method according to claim 1, characterized by, when the risk environment data comprises vehicle peripheral wind speed data, determining the current risk level of the vehicle according to the risk environment data and the vehicle operation data comprises: determining current wind speed data in the vehicle peripheral wind speed data, and determining lateral and longitudinal acceleration and cornering rate in the vehicle operation data; performing eigenvalue quantization on the current wind speed data, the lateral and longitudinal acceleration, and the cornering rate, and calculating the current high wind risk level of the vehicle according to the quantized data.

4. The control method according to any one of claims 1 to 3, characterized by, The method further comprises: after generating the target suspension adjustment instruction, sending the target suspension adjustment instruction to a user for confirmation; determining whether to execute the target suspension adjustment instruction according to the confirmation result of the user.

5. A control device for a vehicle suspension system comprising a processor and a memory having stored therein program instructions, characterised in that, The processor is configured to execute the control method for the vehicle suspension system according to any one of claims 1 to 4 when running the program instructions.

6. A control system for a vehicle suspension system, characterized by, The system comprises: an environment sensor module configured to acquire risk environment data affecting vehicle driving safety; a vehicle sensor module configured to acquire vehicle operation data related to vehicle driving safety; The suspension height adjustment controller is configured to determine a current risk level of the vehicle according to the risk environment data and the vehicle operation data, and generate a target suspension adjustment instruction according to the current risk level; The suspension adjustment executor is configured to adjust the vehicle suspension system according to the target suspension adjustment instruction; In the case that the risk environment data includes vehicle wading data and vehicle peripheral wind speed data, the determination of the current risk level of the vehicle according to the risk environment data and the vehicle operation data includes: determining current water depth data in the vehicle wading data, current wind speed data in the vehicle peripheral wind speed data, and determining lateral and longitudinal acceleration and cornering speed in the vehicle operation data; the current water depth data and the current wind speed data are subjected to eigenvalue quantification and comprehensive risk assessment to obtain comprehensive risk data; the lateral and longitudinal acceleration and the cornering speed are subjected to eigenvalue quantification, and the current comprehensive risk level of the vehicle is calculated according to the quantification data and the comprehensive risk data; The eigenvalue quantification and comprehensive risk assessment of the current water depth data and the current wind speed data to obtain the comprehensive risk data include: assigning initial weights to the current water depth data and the current wind speed data according to the vehicle design and actual use scene; adjusting the initial weights according to the real-time operation state of the vehicle to obtain target weights; the eigenvalue quantification and comprehensive risk assessment of the current water depth data and the current wind speed data according to the target weights to obtain the comprehensive risk data; The generation of the target suspension adjustment instruction according to the current risk level includes: determining a suspension adjustment strategy according to the current risk level; generating the target suspension adjustment instruction according to the suspension adjustment strategy; the target suspension adjustment instruction includes an adjustment height and an adjustment direction.

7. A vehicle characterized by comprising: It includes: A vehicle body; A vehicle suspension system arranged in the vehicle body; The control system for the vehicle suspension system according to claim 6 is arranged in the vehicle body and connected with the vehicle suspension system.

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