Suspension and steering coupling self-adaptive safety adjusting method and system and vehicle

Abstract

The invention discloses a suspension and steering coupling self-adaptive safety adjusting method and system and a vehicle, and the self-adaptive safety adjusting method comprises the steps: collecting a first vehicle driving signal, and carrying out the preprocessing of the first vehicle driving signal through a cloud processing platform, and obtaining a first vehicle driving feature; constructing a self-adaptive fuzzy logic rule base, and obtaining vehicle abnormal data according to the first vehicle driving characteristics through the self-adaptive fuzzy logic rule base; and a control instruction is output according to the vehicle abnormal data, and safety adjustment is conducted on the suspension system and the steering system according to the control instruction. Intelligent safety adjustment of the suspension and the steering system is achieved, the fault recognition precision is improved, and the method can be widely applied to the technical field of vehicle safety adjustment.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle safety regulation, and in particular to a suspension and steering coupled adaptive safety regulation method, system, and vehicle. Background Art

[0002] With the rapid development of the automotive industry, consumers' demands for vehicle handling, stability, and comfort continue to rise. As core components of the vehicle chassis, the performance of the suspension and steering systems directly impacts the driving experience and safety. Currently, adjustments to automotive suspension and steering systems rely primarily on traditional mechanical and electronic control technologies. Traditional mechanical control relies primarily on pre-set mechanical designs and fixed parameter settings, while electronic control uses sensors to acquire vehicle driving status information and adjust suspension and steering system parameters accordingly.

[0003] However, these existing technologies still have some problems. First, traditional mechanical control cannot adapt to real-time road conditions and driving needs, making it difficult to meet the complex and changing driving environment. Second, while electronic control can achieve a certain degree of adaptive adjustment, its effectiveness is not ideal due to factors such as the number and accuracy of sensors and data processing capabilities. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a suspension and steering coupled adaptive safety adjustment method, system and vehicle to achieve intelligent coordinated adjustment of the suspension and steering systems.

[0005] To achieve the above objectives, one aspect of an embodiment of the present application provides a suspension and steering coupled adaptive safety adjustment method, which is applied to a suspension system and a steering system, and includes the following steps: Collecting a first vehicle driving signal, and preprocessing the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; Building an adaptive fuzzy logic rule base, and obtaining vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base; A control instruction is output according to the vehicle abnormality data, and the suspension system and the steering system are safely adjusted according to the control instruction.

[0006] In some embodiments, collecting the first vehicle driving signal and preprocessing the first vehicle driving signal through a cloud processing platform to obtain the first vehicle driving feature specifically includes: collecting the first vehicle driving signal through a vehicle sensor; The first vehicle driving signal is uploaded to the cloud processing platform, and the first vehicle driving signal is preprocessed by the cloud processing platform to obtain the first vehicle driving characteristics.

[0007] In some embodiments, preprocessing the first vehicle driving signal by the cloud processing platform to obtain the first vehicle driving characteristics specifically includes: performing filtering processing on the first vehicle driving signal to obtain a second vehicle driving signal; amplifying the second vehicle travel signal to obtain a third vehicle travel signal; performing sampling rate conversion on the third vehicle travel signal to obtain a fourth vehicle travel signal; performing feature extraction on the fourth vehicle driving signal to obtain a second vehicle driving feature; The second vehicle driving characteristics are normalized to obtain the first vehicle driving characteristics.

[0008] In some embodiments, the constructing of the adaptive fuzzy logic rule base specifically includes: Collect vehicle historical status data and obtain safety assessment standards; Marking the vehicle historical status data according to the safety assessment standard to obtain safe driving status data and unsafe driving status data; Training a preset deep neural network based on the safe driving state data and the unsafe driving state data to obtain fuzzy logic rules for determining the safe driving state of the vehicle; The fuzzy logic rules are stored in the cloud processing platform to obtain the adaptive fuzzy logic rule library.

[0009] In some embodiments, the adaptive fuzzy logic rule base includes multiple fault sub-bases, and obtaining vehicle abnormality data based on the first vehicle driving characteristics through the adaptive fuzzy logic rule base specifically includes: Inputting the first vehicle driving characteristics into the adaptive fuzzy logic rule base to obtain the vehicle abnormality data; Screening the abnormal vehicle data for abnormality types, classifying and storing the abnormal vehicle data into the corresponding fault sub-library according to the screening results, and generating an abnormality warning signal; The abnormal warning signal is sent to the corresponding vehicle through the cloud processing platform.

[0010] In some embodiments, the adaptive fuzzy logic rule base includes multiple fault sub-bases, including a high-speed sub-base, a low-speed sub-base, a medium-speed sub-base, and a functional failure sub-base. Outputting control instructions based on the vehicle abnormal data and safely adjusting the suspension system and the steering system according to the control instructions specifically include: Determine the fault sub-library storing the vehicle abnormal data; determining a first vehicle speed threshold and a second vehicle speed threshold; When the vehicle abnormality data is stored in the high-speed sub-library and the current vehicle speed is greater than the first vehicle speed threshold, a suspension priority control instruction is output to determine that the control priority of the suspension system is greater than that of the steering system; When the vehicle abnormality data is stored in the low speed sub-library and the current vehicle speed is less than the second vehicle speed threshold, a steering priority control instruction is output to determine that the control priority of the steering system is higher than that of the suspension system; When the vehicle abnormality data is stored in the medium speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity and outputting the control instruction according to the current yaw angular velocity; When the vehicle abnormality data is stored in the functional failure sub-library, the vehicle safety state is maintained through a redundant control strategy.

[0011] In some embodiments, when the vehicle abnormality data is stored in the medium speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity and outputting the control instruction according to the current yaw angular velocity specifically includes: When the vehicle abnormality data is stored in the medium vehicle speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity within a preset time domain; A yaw angle threshold is determined. If the current yaw angle velocity exceeds the yaw angle threshold, the suspension priority control instruction is output to determine that the control priority of the suspension system is higher than that of the steering system. If the current yaw angle does not exceed the yaw angle threshold, the steering priority control instruction is output to determine that the control priority of the steering system is higher than that of the suspension system.

[0012] To achieve the above objectives, another aspect of the present application provides a suspension and steering coupled adaptive safety adjustment system, comprising: A first module is configured to collect a first vehicle driving signal and pre-process the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; A second module is used to build an adaptive fuzzy logic rule base, and obtain vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base; The third module is configured to output a control instruction according to the abnormal vehicle data, and perform safety adjustments on the suspension system and the steering system according to the control instruction.

[0013] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a vehicle, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the program, when executed by the processor, implements the suspension and steering coupling adaptive safety adjustment method as described above.

[0014] To achieve the above objectives, another aspect of an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the aforementioned suspension and steering coupling adaptive safety adjustment method.

[0015] The beneficial effects of the present invention are as follows: the suspension and steering coupled adaptive safety adjustment method, system and vehicle of the present invention first collect a first vehicle driving signal, pre-process the first vehicle driving signal to obtain a first vehicle driving characteristic, then construct an adaptive fuzzy logic rule base, obtain vehicle abnormality data based on the first vehicle driving characteristic through the adaptive fuzzy logic rule base, and finally output a control instruction based on the vehicle abnormality data, and perform safety adjustment on the suspension system and steering system according to the control instruction. The present invention collects vehicle driving signals and monitors vehicle status, uploads data to a cloud processing platform through 5G communication for signal processing and data storage, and then establishes an adaptive fuzzy logic rule base through a machine learning algorithm, combined with historical data and expert experience, and filters data that does not conform to the vehicle safety status based on the adaptive fuzzy logic rule base, and sends instructions to the chassis domain to control the suspension and steering systems, thereby realizing intelligent safety adjustment of the suspension and steering systems and improving fault identification accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A flowchart of the steps of a suspension and steering coupling adaptive safety adjustment method provided by an embodiment of the present invention; Figure 2 A flowchart of step S101 provided in accordance with an embodiment of the present invention; Figure 3 A flowchart of step S1012 provided in accordance with an embodiment of the present invention; Figure 4 A flowchart of the steps for constructing an adaptive fuzzy logic rule base provided by one embodiment of the present invention; Figure 5 A flowchart of the steps for obtaining abnormal vehicle data provided by an embodiment of the present invention; Figure 6 A flowchart of step S103 provided in accordance with an embodiment of the present invention; Figure 7 A flowchart of step S1035 is provided for one embodiment of the present invention; Figure 8 A schematic flow chart of a suspension and steering coupling adaptive safety adjustment method provided by an embodiment of the present invention; Figure 9 A schematic diagram of the interaction process between a cloud processing platform and a vehicle provided in one embodiment of the present invention; Figure 10 A schematic structural diagram of a suspension and steering coupled adaptive safety adjustment system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0019] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0020] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0021] With the rapid development of the automotive industry, consumers' demands for vehicle handling, stability, and comfort continue to rise. As core components of the vehicle chassis, the performance of the suspension and steering systems directly impacts the driving experience and safety. Currently, adjustments to automotive suspension and steering systems rely primarily on traditional mechanical and electronic control technologies. Traditional mechanical control relies primarily on pre-set mechanical designs and fixed parameter settings, while electronic control uses sensors to acquire vehicle driving status information and adjust suspension and steering system parameters accordingly.

[0022] However, these previous technologies still have some problems. First, traditional mechanical control cannot adaptively adjust according to real-time road conditions and driving requirements, making it difficult to meet the needs of complex and changing driving environments. Second, although electronic control can achieve a certain degree of adaptive adjustment, its adjustment effect is not ideal due to factors such as the number and accuracy of sensors and data processing capabilities. In addition, the suspension and steering systems in previous technologies are often controlled independently and lack intelligent coordinated adjustment capabilities. This makes it difficult for vehicles to achieve optimal handling, stability, and comfort under different road conditions and driving requirements.

[0023] To achieve intelligent coordinated regulation, additional hardware devices such as sensors, controllers, and actuators, as well as more complex algorithms and software systems, are required. This not only increases system complexity and maintenance costs, but also potentially impacts system reliability and stability, leading to issues such as low microcontroller chip recognition accuracy, high hardware dependency, and high costs.

[0024] To this end, an embodiment of the present invention proposes a suspension and steering coupled adaptive safety adjustment method, which first collects a first vehicle driving signal, pre-processes the first vehicle driving signal, obtains a first vehicle driving characteristic, then constructs an adaptive fuzzy logic rule base, obtains vehicle abnormality data based on the first vehicle driving characteristic through the adaptive fuzzy logic rule base, and finally outputs a control instruction based on the vehicle abnormality data, and performs safety adjustment on the suspension system and steering system according to the control instruction. The present invention collects vehicle driving signals and monitors vehicle status, uploads data to a cloud processing platform through 5G communication for signal processing and data storage, and then establishes an adaptive fuzzy logic rule base through a machine learning algorithm combined with historical data and expert experience. Based on the adaptive fuzzy logic rule base, data that does not conform to the vehicle safety status is screened, and instructions are sent to the chassis domain to control the suspension and steering systems, thereby realizing intelligent safety adjustment of the suspension and steering systems and improving fault identification accuracy.

[0025] Reference Figure 1 , Figure 1 This is a flowchart of a suspension and steering coupling adaptive safety adjustment analysis method provided by an embodiment of the present invention. The embodiment of the present invention provides a suspension and steering coupling adaptive safety adjustment method, which is applied to a suspension system and a steering system. The method includes steps S101 to S103: S101: Collect a first vehicle driving signal, and pre-process the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; Reference Figure 2 , Figure 2 The flowchart of step S101 provided in one embodiment of the present invention is further provided as an optional implementation manner. Step S101 can be specifically divided into the following steps S1011 and S1012: S1011. Collect a first vehicle driving signal through a vehicle sensor; In some optional embodiments, a first vehicle driving signal is collected during vehicle driving by a vehicle sensor. The first vehicle driving signal may include but is not limited to a vehicle body posture signal, such as the vehicle's three-axis acceleration, three-axis angular velocity, etc., to calculate the vehicle body posture angle and stability; chassis dynamic signals, such as the steering wheel angle and steering wheel torque of the steering system, the suspension displacement of the suspension system, etc.; environmental perception signals, such as positioning data, environmental perception data, vehicle dynamics parameters, etc.; vehicle status signals: such as wheel speed, engine torque, brake pressure, etc. By collecting vehicle driving signals from multiple sources, the safety status of the vehicle can be accurately judged to provide data support for suspension-steering coupling control.

[0026] S1012: Upload the first vehicle driving signal to the cloud processing platform, and pre-process the first vehicle driving signal through the cloud processing platform to obtain the first vehicle driving characteristics.

[0027] Specifically, vehicle driving signals collected by vehicle sensors are uploaded to a cloud-based processing platform, where they are modularized to better reflect the vehicle's operating status and determine its driving characteristics. This modularized signal processing includes filtering the signal to remove interference, amplifying the signal to enhance signal strength, adjusting the sampling rate based on the amplification effect, extracting and analyzing signal features, standardizing the signal, and finally storing it in the cloud.

[0028] Reference Figure 3 , Figure 3The flowchart of step S1012 provided in one embodiment of the present invention is further divided into the following steps S10121 to S10125 as an optional implementation method, wherein the cloud processing platform pre-processes the first vehicle driving signal to obtain the first vehicle driving characteristics: S10121. Filter the first vehicle driving signal to obtain a second vehicle driving signal; S10122. Amplify the second vehicle driving signal to obtain a third vehicle driving signal; S10123. Perform sampling rate conversion on the third vehicle driving signal to obtain a fourth vehicle driving signal; S10124. Extract features from the fourth vehicle driving signal to obtain a second vehicle driving feature. S10125. Standardize the second vehicle driving characteristics to obtain the first vehicle driving characteristics.

[0029] In some optional embodiments, the collected vehicle driving signals are subjected to signal modularization processing through a cloud processing platform. First, the first vehicle driving signal is filtered to remove interference components in the original signal, including high-frequency vibration noise and high-frequency impact of the road surface. For example, different filtering parameters can be configured for different signal characteristics. For example, a Butterworth low-pass filter with a cutoff frequency of 20Hz is applied to the IMU three-axis acceleration signal to suppress the high-frequency vibration noise of the engine, and a 0.1-5Hz band-pass filter is used to filter out the high-frequency components of road impact and the slow drift of the vehicle body to obtain a second vehicle driving signal; then, the signal amplifier in the cloud processing platform is used to enhance the signal from which interference has been removed to improve the clarity of the signal characteristics and facilitate the subsequent extraction of signal characteristics to obtain a third vehicle driving signal; when the signal characteristics are not obvious, the sampling rate of the signal is automatically adjusted, and the signal is sampled more to obtain more detailed signal characteristics to obtain a fourth vehicle driving signal; then, for different signal types and expected states, the key feature vectors of the signal are extracted, such as steering characteristics, suspension travel change rate characteristics, tire slip characteristics, etc., to obtain a second vehicle driving characteristic; finally, the feature vectors of each type are standardized, and the first vehicle driving characteristics obtained after standardization are associated with the original data and stored in the feature database of the cloud processing platform.

[0030] S102: constructing an adaptive fuzzy logic rule base, and obtaining vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base; Specifically, an adaptive fuzzy logic rule base is established. This rule base is based on a large amount of existing vehicle status data and expert experience. This rule base uses machine learning algorithms to train vehicle status data and clearly identify the vehicle's safe driving status. When abnormalities in processed signal characteristics occur, they are identified and recorded in the corresponding fault sub-library.

[0031] Reference Figure 4 , Figure 4 The flowchart of the steps for constructing an adaptive fuzzy logic rule base provided in one embodiment of the present invention is further provided. As an optional implementation method, the step of constructing the adaptive fuzzy logic rule base can be specifically divided into the following steps S1021 to S1024: S1021. Collect vehicle historical status data and obtain safety assessment standards; Specifically, the vehicle's historical status data and expert experience are collected. This expert experience comes from professionals who have been engaged in vehicle safety research, driving training, and accident analysis for a long time. Through professionals, a comprehensive analysis of the key factors affecting vehicle driving safety is conducted, including but not limited to the corresponding vehicle body posture, body chassis, vehicle status, and environmental factors in the aforementioned vehicle driving signals. Based on the analysis and quantification results of key safety factors, corresponding safety assessment standards are formulated. Once formulated, the specified safety assessment standards can be verified through simulation experiments and actual road tests.

[0032] For example, in a simulation experiment, a scenario is set up where the vehicle suddenly makes a sharp turn while driving at high speed, and the corresponding vehicle body posture signals, chassis dynamic signals, etc. are input to test whether the safety assessment standard can issue a timely warning; in actual road tests, a closed test site is selected and different test scenarios are set up. For example, in a wet road test, the vehicle's wheel speed, brake pressure and other signals are recorded to verify the effectiveness of the safety assessment standard under low-adhesion road conditions.

[0033] S1022. Label the vehicle's historical status data according to the safety assessment standard to obtain safe driving status data and unsafe driving status data; Specifically, the collected data is labeled according to safety assessment criteria, categorizing it into safe driving state data and unsafe driving state data. For example, if the vehicle's three-axis acceleration exceeds a certain threshold and the steering wheel angle changes abnormally, the data is labeled as unsafe; otherwise, it is labeled as safe.

[0034] S1023. Training a preset deep neural network based on the safe driving state data and the unsafe driving state data to obtain fuzzy logic rules for determining the vehicle driving safety state; Specifically, the collected vehicle historical status data is input into the constructed deep neural network for training. The network determines whether the corresponding driving status of the vehicle is safe based on the vehicle historical status data and outputs the predicted status. The labeled safe driving status data and unsafe driving status data are then used as a test set. Through the back propagation algorithm, the parameters in the deep neural network are updated according to the test set and the predicted status output by the model to obtain fuzzy logic rules for judging the vehicle's driving safety status.

[0035] It should be noted that the deep neural network can be selected according to actual needs. For example, a convolutional neural network (CNN), a recurrent neural network (RNN) and its variants, such as a long short-term memory network (LSTM), can be used, or different types of networks can be combined for comprehensive processing, which is not limited here.

[0036] S1024. Storing the fuzzy logic rules in a cloud processing platform to obtain an adaptive fuzzy logic rule base.

[0037] Specifically, the trained fuzzy logic rules are stored in a cloud processing platform to form an adaptive fuzzy logic rule base. The adaptive fuzzy logic rule base stores vehicle status judgment rules trained based on a machine learning algorithm, which is used to judge the safety status of the vehicle based on the processed signal characteristics.

[0038] Reference Figure 5 , Figure 5 A flowchart of the steps for obtaining abnormal vehicle data is provided in an embodiment of the present invention. As an optional implementation method, the adaptive fuzzy logic rule base includes multiple fault sub-bases. The step of obtaining abnormal vehicle data based on the first vehicle driving characteristics through the adaptive fuzzy logic rule base can be specifically divided into the following steps S1025 to S1027: S1025, inputting the first vehicle driving characteristic into an adaptive fuzzy logic rule base to obtain vehicle abnormality data; S1026: Screen the vehicle abnormal data for abnormality types, classify and store the vehicle abnormal data into corresponding fault sub-libraries based on the screening results, and generate abnormality warning signals; Specifically, the processed first vehicle driving feature is input into the adaptive fuzzy logic rule library. When the rule library determines that the feature is abnormal, it is identified and recorded in the corresponding fault sub-library. The fault sub-library includes multiple sub-libraries, which are used to store vehicle abnormality data under high speed, low speed, medium speed and function failure conditions, and can be classified and recorded according to the type of abnormality.

[0039] S1027. Send the abnormal warning signal to the corresponding vehicle through the cloud processing platform.

[0040] Specifically, a real-time communication module is included between the cloud processing platform and the vehicle. When the rule library detects an abnormal vehicle status, it generates an abnormal warning signal. Through this real-time communication module, the abnormal warning signal is sent to the vehicle in a timely manner to remind the corresponding driver that the vehicle is in an unsafe state, and to take corresponding safety measures in combination with the fault sub-library.

[0041] S103: Outputting control instructions based on the vehicle abnormality data, and performing safety adjustments on the suspension system and steering system according to the control instructions; Specifically, based on the vehicle safety status determined by the rule base, control instructions are output to control the suspension and steering systems, and the vehicle body posture is adjusted to ensure safe driving of the vehicle.

[0042] Reference Figure 6 , Figure 6 The flowchart of step S103 provided in one embodiment of the present invention is further provided as an optional implementation manner. The adaptive fuzzy logic rule base includes multiple fault sub-bases, the fault sub-bases including a high speed sub-base, a low speed sub-base, a medium speed sub-base, and a functional failure sub-base. Step S103 can be specifically divided into the following steps S1031 to S1036: S1031. Determine a fault sub-library for storing abnormal vehicle data; S1032: Determine a first vehicle speed threshold and a second vehicle speed threshold; It should be noted that the first vehicle speed threshold (i.e., the high vehicle speed threshold Vh) and the second vehicle speed threshold (i.e., the low vehicle speed threshold Vl) can be automatically set based on the first vehicle driving signal collected above, according to the actual vehicle type, vehicle performance parameters, road type, road conditions, etc. For example, when the vehicle is a regular sedan traveling on a highway, the high vehicle speed threshold Vh can be determined to be 110 km / h or 120 km / h, and the low vehicle speed threshold Vl can be determined to be 15 km / h or 20 km / h. When the vehicle is a passenger car traveling on an urban road, the high vehicle speed threshold Vh can be determined to be 90 km / h or 100 km / h, and the low vehicle speed threshold Vl can be determined to be 10 km / h or 15 km / h.

[0043] S1033: When the vehicle abnormality data is stored in the high-speed sub-library and the current vehicle speed is greater than the first vehicle speed threshold, output a suspension priority control instruction to determine that the control priority of the suspension system is greater than that of the steering system; Specifically, at high speeds, the vehicle's driving stability is primarily affected by the suspension system. The suspension system can effectively cushion the impact of road surface irregularities, maintain vehicle stability, and reduce vehicle roll and pitch. If the steering system experiences an anomaly while the suspension system is functioning normally, prioritizing the suspension system can maximize vehicle stability and avoid serious safety incidents such as rollovers caused by excessive vehicle body sway. Therefore, when it is determined that vehicle anomaly data is stored in the high-speed sub-library and the current vehicle speed Vx is greater than the first speed threshold Vh, an embodiment of the present invention prioritizes suspension system control over the steering system. For example, when a vehicle is traveling at 120 km / h on a highway and suddenly encounters a pothole, if the suspension system can adjust in time to maintain vehicle stability, the risk of vehicle loss of control can be greatly reduced.

[0044] S1034: When the vehicle abnormality data is stored in the low-speed sub-library and the current vehicle speed is less than the second vehicle speed threshold, output a steering priority control instruction to determine that the control priority of the steering system is higher than that of the suspension system; Specifically, at low speeds, vehicle safety relies more heavily on the flexibility and accuracy of the steering system. For example, in narrow streets and parking lots, vehicles frequently make turns and U-turns. If a steering system anomaly occurs, while the suspension system may function normally, it cannot directly resolve the steering difficulty, potentially causing the vehicle to collide with obstacles or other vehicles. Therefore, when it is determined that vehicle anomaly data is stored in the low-speed sub-library and the current vehicle speed Vx is less than the second speed threshold V1, embodiments of the present invention prioritize steering system control over suspension control.

[0045] S1035: When the vehicle abnormality data is stored in the medium speed sub-library and the current vehicle speed is greater than the second speed threshold and less than the first speed threshold, calculate the current yaw angular velocity and output a control command based on the current yaw angular velocity; Reference Figure 7 , Figure 7 The flowchart of step S1035 provided in one embodiment of the present invention is further provided as an optional implementation manner. Step S1035 can be specifically divided into the following steps S10351 and S10352: S10351: When vehicle abnormality data is stored in the medium speed sub-library and the current vehicle speed is greater than the second speed threshold and less than the first speed threshold, calculate the current yaw angular velocity within the preset time domain; S10352. Determine the yaw angle threshold. If the current yaw angle velocity exceeds the yaw angle threshold, output a suspension priority control instruction to determine that the control priority of the suspension system is higher than that of the steering system. If the current yaw angle does not exceed the yaw angle threshold, output a steering priority control instruction to determine that the control priority of the steering system is higher than that of the suspension system.

[0046] Specifically, in a medium-speed scenario, where the vehicle's actual speed is between the second speed threshold Vl and the first speed threshold Vh, the yaw rate is calculated over a time period. Yaw rate is an important parameter that measures the vehicle's rotational speed around its vertical axis and reflects its steering stability and handling performance during driving. Therefore, this embodiment of the present invention dynamically selects corresponding control instructions based on the yaw rate calculation. If the current yaw rate does not exceed the yaw angle threshold, the steering system is prioritized over the suspension system. If the current yaw rate exceeds the yaw angle threshold, the suspension system is prioritized over the steering system.

[0047] S1036: When the vehicle abnormality data is stored in the functional failure sub-library, the vehicle safety state is maintained through a redundant control strategy.

[0048] Specifically, when vehicle anomaly data is stored in the functional failure sub-library, it indicates that a critical vehicle system (such as the suspension or steering system) has experienced a serious functional failure and is no longer functioning properly. In this case, a redundant control strategy is implemented, whereby a relatively functioning system is used to maintain the vehicle's safety. For example, if the suspension system fails while the steering system is relatively functioning, the system will adjust the steering system's parameters and control strategy to minimize the impact of the suspension failure. For example, this could include increasing steering assist to make it easier for the driver to control the vehicle's direction, or adjusting the steering ratio to provide greater vehicle stability. Conversely, if the steering system fails while the suspension system is relatively functioning, adjustments to suspension parameters such as stiffness and damping can be made to improve vehicle stability and reduce the risk of loss of control due to steering failure.

[0049] In summary, the process of the suspension and steering coupled adaptive safety adjustment method according to the embodiment of the present invention is as follows: Figure 8 As shown in the figure, the interaction process between the cloud processing platform and the vehicle is as follows Figure 9 As shown: D100, based on cloud signal acquisition, collects vehicle status signals and uploads them to the cloud processing platform for storage.

[0050] D200 modularly processes signals and pre-processes the collected signals, including filtering the signals to remove interference information; amplifying the signals to enhance the signal strength; determining the sampling rate conversion based on the signal amplification effect; extracting and analyzing the signal features; then standardizing the signals; and storing them in the cloud.

[0051] D300: Establish an adaptive fuzzy logic rule base and input processed signal features into the adaptive fuzzy logic rule base. When abnormalities occur in processed signal features, they are identified and recorded in the corresponding fault sub-library. The fault sub-library includes a high-speed sub-library, a low-speed sub-library, a medium-speed sub-library, and a functional failure sub-library, which are used to store vehicle abnormality data. D400 outputs control instructions, determines the vehicle's driving status based on the vehicle speed, controls the suspension and steering systems based on the speed, and adjusts the vehicle's body posture to ensure safe driving.

[0052] The above describes the adaptive safety adjustment method for suspension and steering coupling according to an embodiment of the present invention. It can be recognized that the embodiment of the present invention proposes an adaptive safety adjustment method for suspension and steering coupling based on a machine learning algorithm. The method realizes intelligent coordinated adjustment of the suspension and steering systems through 5G communication technology and large model training to improve the vehicle's handling, stability and comfort. On the one hand, the embodiment of the present invention collects vehicle driving signals and monitors vehicle status, and uploads data to a cloud processing platform through 5G communication for signal processing and data storage. Then, through a machine learning algorithm, an adaptive fuzzy logic rule base is established in combination with historical data and expert experience to learn vehicle safety status data and filter data that does not conform to the vehicle safety status, which is conducive to identifying complex or rare faults, thereby improving fault identification accuracy. On the other hand, based on the abnormal vehicle data judged by the adaptive fuzzy logic rule base, control instructions are sent to the chassis domain to adjust the control priority of the suspension and steering systems, thereby realizing intelligent safety adjustment of the suspension and steering systems.

[0053] Reference Figure 10 , an embodiment of the present invention further provides a suspension and steering coupled adaptive safety adjustment system, comprising: The first module is configured to collect a first vehicle driving signal and pre-process the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; The second module is used to build an adaptive fuzzy logic rule base, and obtain vehicle abnormal data according to the driving characteristics of the first vehicle through the adaptive fuzzy logic rule base; The third module is used to output control instructions according to vehicle abnormal data and perform safety adjustments to the suspension system and steering system according to the control instructions.

[0054] The contents of the above-mentioned suspension and steering coupling adaptive safety adjustment method embodiment are all applicable to the present suspension and steering coupling adaptive safety adjustment system embodiment. The functions specifically implemented by the present suspension and steering coupling adaptive safety adjustment system embodiment are the same as those of the above-mentioned suspension and steering coupling adaptive safety adjustment method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned suspension and steering coupling adaptive safety adjustment method embodiment.

[0055] An embodiment of the present invention further provides a vehicle comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the above-described suspension and steering coupling adaptive safety adjustment method is implemented. Specifically, the vehicle can be a private vehicle, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be an operating vehicle, such as a van, bus, small truck, or large trailer. The vehicle can be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0056] In addition, an embodiment of the present invention further provides a computer program product, including a computer program or computer instructions, the computer program or computer instructions being stored in a computer-readable storage medium, the processor of a computer device reading the computer program or computer instructions from the computer-readable storage medium, and the processor executing the computer program or computer instructions, so that the computer device executes the above-described suspension and steering coupling adaptive safety adjustment method. For example, the above-described Figures 1 to 7 The method steps in .

[0057] It is worth noting that since the computer program product of the embodiment of the present invention can execute the suspension and steering coupling adaptive safety adjustment method of any of the above-mentioned embodiments, the specific implementation methods and technical effects of the computer program product of the embodiment of the present invention can refer to the specific implementation methods and technical effects of the suspension and steering coupling adaptive safety adjustment method of any of the above-mentioned embodiments.

[0058] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0059] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0060] If the above 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 the present invention, or the portion that contributes to the prior art, or the 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above methods of various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0061] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0062] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0063] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0064] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0066] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A suspension and steering coupled adaptive safety adjustment method, applied to the suspension system and the steering system, characterized in that: The following steps are involved: Collecting a first vehicle driving signal, and preprocessing the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; Building an adaptive fuzzy logic rule base, and obtaining vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base; A control instruction is output according to the vehicle abnormality data, and the suspension system and the steering system are safely adjusted according to the control instruction.

2. The suspension and steering coupling adaptive safety adjustment method according to claim 1, characterized in that: The collecting of the first vehicle driving signal and pre-processing the first vehicle driving signal through a cloud processing platform to obtain the first vehicle driving feature specifically includes: collecting the first vehicle driving signal through a vehicle sensor; The first vehicle driving signal is uploaded to the cloud processing platform, and the first vehicle driving signal is preprocessed by the cloud processing platform to obtain the first vehicle driving characteristics.

3. The suspension and steering coupling adaptive safety adjustment method according to claim 2, characterized in that: The preprocessing of the first vehicle driving signal by the cloud processing platform to obtain the first vehicle driving feature specifically includes: performing filtering processing on the first vehicle driving signal to obtain a second vehicle driving signal; amplifying the second vehicle travel signal to obtain a third vehicle travel signal; performing sampling rate conversion on the third vehicle travel signal to obtain a fourth vehicle travel signal; performing feature extraction on the fourth vehicle driving signal to obtain a second vehicle driving feature; The second vehicle driving characteristics are normalized to obtain the first vehicle driving characteristics.

4. The suspension and steering coupling adaptive safety adjustment method according to claim 1, characterized in that: The construction of the adaptive fuzzy logic rule base specifically includes: Collect vehicle historical status data and obtain safety assessment standards; Marking the vehicle historical status data according to the safety assessment standard to obtain safe driving status data and unsafe driving status data; Training a preset deep neural network based on the safe driving state data and the unsafe driving state data to obtain fuzzy logic rules for determining the safe driving state of the vehicle; The fuzzy logic rules are stored in the cloud processing platform to obtain the adaptive fuzzy logic rule library.

5. The suspension and steering coupling adaptive safety adjustment method according to claim 1, characterized in that: The adaptive fuzzy logic rule base includes a plurality of fault sub-bases, and obtaining vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base specifically includes: Inputting the first vehicle driving characteristics into the adaptive fuzzy logic rule base to obtain the vehicle abnormality data; Screening the abnormal vehicle data for abnormality types, classifying and storing the abnormal vehicle data into the corresponding fault sub-library according to the screening results, and generating an abnormality warning signal; The abnormal warning signal is sent to the corresponding vehicle through the cloud processing platform.

6. The suspension and steering coupling adaptive safety adjustment method according to claim 1, characterized in that: The adaptive fuzzy logic rule base includes multiple fault sub-bases, including a high-speed sub-base, a low-speed sub-base, a medium-speed sub-base, and a functional failure sub-base. Outputting control instructions based on the vehicle abnormal data and performing safety adjustments on the suspension system and the steering system according to the control instructions specifically include: Determine the fault sub-library storing the vehicle abnormal data; determining a first vehicle speed threshold and a second vehicle speed threshold; When the vehicle abnormality data is stored in the high-speed sub-library and the current vehicle speed is greater than the first vehicle speed threshold, a suspension priority control instruction is output to determine that the control priority of the suspension system is greater than that of the steering system; When the vehicle abnormality data is stored in the low speed sub-library and the current vehicle speed is less than the second vehicle speed threshold, a steering priority control instruction is output to determine that the control priority of the steering system is higher than that of the suspension system; When the vehicle abnormality data is stored in the medium speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity and outputting the control instruction according to the current yaw angular velocity; When the vehicle abnormality data is stored in the functional failure sub-library, the vehicle safety state is maintained through a redundant control strategy.

7. The suspension and steering coupling adaptive safety adjustment method according to claim 6, characterized in that: When the vehicle abnormality data is stored in the medium vehicle speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity, and outputting the control instruction according to the current yaw angular velocity specifically includes: When the vehicle abnormality data is stored in the medium vehicle speed sub-library and the current vehicle speed is greater than the second vehicle speed threshold and less than the first vehicle speed threshold, calculating the current yaw angular velocity within a preset time domain; A yaw angle threshold is determined. If the current yaw angle velocity exceeds the yaw angle threshold, the suspension priority control instruction is output to determine that the control priority of the suspension system is higher than that of the steering system. If the current yaw angle does not exceed the yaw angle threshold, the steering priority control instruction is output to determine that the control priority of the steering system is higher than that of the suspension system.

8. A suspension and steering coupled adaptive safety adjustment system, characterized in that: include: A first module is configured to collect a first vehicle driving signal and pre-process the first vehicle driving signal through a cloud processing platform to obtain a first vehicle driving feature; A second module is used to build an adaptive fuzzy logic rule base, and obtain vehicle abnormality data according to the first vehicle driving characteristics through the adaptive fuzzy logic rule base; The third module is configured to output a control instruction according to the abnormal vehicle data, and perform safety adjustments on the suspension system and the steering system according to the control instruction.

9. A vehicle, characterized in that: The system comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the method for adaptive safety adjustment of the suspension and steering coupling according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the suspension and steering coupling adaptive safety adjustment method according to any one of claims 1 to 7 is implemented.

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