Intelligent suspension control system based on complex road condition recognition

Abstract

The invention provides an intelligent suspension control system based on complex road condition recognition, and relates to the technical field of suspension control. The system comprises a data acquisition module, a theoretical adjustment calculation module, an adjustment map generation module, a dynamic adjustment generation module and a real-time monitoring module. The data acquisition module generates structured road condition suspension matching data. The theoretical adjustment calculation module calculates a theoretical suspension adjustment reference value. The adjustment map generation module generates an initial suspension adjustment map. The dynamic adjustment generation module obtains a dynamic suspension adjustment map of the driving task. And the real-time monitoring module monitors the intelligent suspension control in real time. Various complex road conditions are recognized and classified through semantic analysis, an accurate road condition basis is provided, a theoretical adjustment reference value is calculated, meanwhile, a self-adaptive correction factor matrix is introduced, the adjustment reference and the fault-tolerant bandwidth are dynamically adjusted according to the real-time posture of the vehicle, vehicle body vibration and suspension air pressure, stable control is guaranteed, and the control accuracy is improved. And dynamic balance between comfort and controllability is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of suspension control, in particular to an intelligent suspension control system based on complex road condition recognition. BACKGROUND

[0002] With the rapid development of the automobile industry towards intelligence and comfort, users have higher requirements for the controllability, smoothness and safety during vehicle driving, and the adjustment performance of the suspension system as a core component connecting the vehicle body and the wheels directly determines the adaptability of the vehicle to different road conditions. The current traditional suspension system is designed with fixed parameters and can only provide basic support for regular flat road conditions, and has obvious limitations in the face of complex road conditions.

[0003] In actual driving scenarios, vehicles often face various and complex road conditions, and may also encounter unpredictable road conditions such as sudden obstacles and temporary construction areas. The traditional suspension system lacks the ability to accurately recognize complex road conditions and cannot dynamically adjust its parameters according to the changes in road conditions, resulting in excessive body vibration and decreased comfort on bumpy road sections, and difficulty in ensuring sufficient roll resistance in continuous curves or emergency avoidance scenarios, which affects the control stability and cannot meet the requirements of intelligent control of the suspension system in complex driving scenarios. SUMMARY

[0004] The application provides an intelligent suspension control system based on complex road condition recognition to solve the defects in the prior art that cannot meet the requirements of intelligent control of the suspension system in complex driving scenarios.

[0005] The application provides an intelligent suspension control system based on complex road condition recognition, comprising: A data acquisition module is configured to acquire multi-source road condition data during vehicle driving, identify complex road condition types in the multi-source road condition data through road condition semantic analysis, extract suspension adaptation characteristics under each road condition in combination with vehicle suspension parameters, and generate structured road condition suspension matching data.

[0006] A theoretical adjustment calculation module is configured to collect historical driving task data and extract road condition occurrence frequency, suspension response efficiency and driving smoothness indicators, and calculate a theoretical suspension adjustment reference value according to the road condition occurrence frequency and the suspension response efficiency.

[0007] An adjustment atlas generation module is configured to set a dynamic adjustment fault tolerance band, and generate an initial suspension adjustment atlas sorted by road condition priority in combination with the theoretical suspension adjustment reference value.

[0008] A dynamic adjustment generation module is configured to collect vehicle real-time attitude data, body vibration spectrum and suspension air pressure data to construct an adaptive correction factor matrix, and inject the adaptive correction factor matrix into the initial suspension adjustment atlas to obtain a dynamic suspension adjustment atlas for the driving task.

[0009] A real-time monitoring module is configured to collect a real-time suspension working data curve during driving, compare actual suspension adjustment averages in each road condition section, and monitor intelligent suspension control in real time by calculating a shape similarity between the real-time suspension working data curve and the dynamic suspension adjustment map in each road condition time window.

[0010] According to the intelligent suspension control system based on complex road condition recognition provided by the application, the multi-source road condition data includes road image data, road three-dimensional profile data, road obstacle data and road section road condition warning data. The vehicle suspension parameters include suspension spring stiffness coefficient, shock absorber damping adjustment range, stabilizer bar torsional stiffness, suspension travel limit value and spring pressure adjustment threshold value. The suspension adaptation characteristics include suspension stiffness target value, shock absorber damping coefficient set value, vehicle body height adjustment value, suspension response delay threshold value and vibration suppression rate target value.

[0011] According to the intelligent suspension control system based on complex road condition recognition provided by the application, the process of identifying complex road condition types in multi-source road condition data through road condition semantic analysis includes: The road image data is subjected to image segmentation and feature extraction to identify key road condition features, including road damage, water accumulation, snow accumulation and gravel.

[0012] Based on the road three-dimensional profile data, the road slope, undulation and jolt frequency are calculated, and the road condition grade is divided.

[0013] The road obstacle data and the road section road condition warning data are fused to mark the sudden road condition information, including the sudden road condition type and the danger level.

[0014] According to the key road condition features, the road condition grade and the sudden road condition information, the conventional flat road condition is filtered out, and the complex road condition type set is classified.

[0015] According to the intelligent suspension control system based on complex road condition recognition provided by the application, the process of generating structured road condition suspension matching data includes: According to the complex road condition type, the suspension spring stiffness coefficient and the shock absorber damping adjustment range are associated to determine the basic suspension adjustment parameter.

[0016] The vehicle body posture compensation amount is calculated in combination with the road slope and the undulation, and the suspension height adjustment demand value is derived.

[0017] The stabilizer bar torsional stiffness and the suspension travel limit value are associated to calibrate the suspension anti-roll and anti-jolt coefficients.

[0018] The road condition unique identifier, the suspension adjustment parameter set and the posture compensation amount are packaged as a structured data object to obtain the structured road condition suspension matching data.

[0019] According to the present invention, an intelligent suspension control system based on complex road condition recognition extracts road condition occurrence frequency, suspension response efficiency, and ride comfort indicators through the following process: Collect historical driving task data and filter historical driving records of the same vehicle model and driving area.

[0020] Analyze the road condition types in historical structured data objects and statistically analyze the frequency and duration of different complex road conditions.

[0021] By analyzing historical suspension sensor records, a mapping model of suspension adjustment time under road conditions is constructed to calculate suspension response efficiency.

[0022] Collect data on the vertical and lateral acceleration of the vehicle body during historical driving processes, and calculate the ride comfort index using the vibration comfort formula.

[0023] The intelligent suspension control system based on complex road condition recognition provided by the present invention includes the following process for calculating the theoretical suspension adjustment reference value according to the frequency of road condition occurrence and suspension response efficiency: Based on the frequency of road conditions, the basic suspension adjustment parameters under different road conditions are weighted and calculated to generate initial adjustment reference values.

[0024] By combining the suspension response efficiency, a response delay correction coefficient is introduced to adjust the initial adjustment reference value, thus obtaining the adjusted adjustment reference value.

[0025] Based on the ride comfort index, a ride comfort guarantee coefficient is introduced to optimize the adjustment reference value, thereby obtaining the theoretical suspension adjustment benchmark value.

[0026] The intelligent suspension control system based on complex road condition recognition provided by the present invention includes the following process for generating an initial suspension adjustment map sorted by road condition priority by combining theoretical suspension adjustment reference values: Create a three-dimensional coordinate system for timing, road conditions, and suspension parameters.

[0027] Based on the timing and priority of road conditions, the corresponding theoretical suspension adjustment benchmark values ​​are connected to form a suspension adjustment reference curve.

[0028] Differentiated adjustment of fault tolerance bandwidth is set based on road condition hazard level and driving speed.

[0029] Visualize and label the adjustment range boundaries and priority indicators under different road conditions to form an initial suspension adjustment map.

[0030] According to the present invention, an intelligent suspension control system based on complex road condition recognition includes the following process for constructing an adaptive correction factor matrix: Based on real-time vehicle attitude data, the roll angle and pitch angle change rates are calculated, and attitude correction coefficients are generated.

[0031] Based on the vehicle body vibration spectrum, calculate the vibration energy entropy value and calibrate the damper damping correction coefficient.

[0032] Analyze the fluctuation range of suspension air pressure data and generate pressure compensation correction coefficients.

[0033] By integrating attitude correction coefficients, damping correction coefficients, and pressure compensation correction coefficients, an adaptive correction factor matrix is ​​obtained.

[0034] According to the present invention, an intelligent suspension control system based on complex road condition recognition obtains a dynamic suspension adjustment map for a driving task through the following process: Based on the adaptive correction factor matrix, the suspension adjustment benchmark value in the initial map is adjusted for each road condition.

[0035] Based on the damping correction coefficient, the adjustable fault-tolerant bandwidth is dynamically expanded and contracted under different road conditions.

[0036] A correction factor numerical marker layer and a real-time road condition warning label are superimposed on the initial suspension adjustment map.

[0037] Establish a real-time data interface with the vehicle's CAN bus and update the adaptive correction factor matrix and dynamic suspension adjustment map according to a preset cycle.

[0038] According to the present invention, an intelligent suspension control system based on complex road condition recognition includes a process for calculating the morphological similarity between the real-time suspension operating data curve and the dynamic suspension adjustment map within each road condition time window. The duration of road conditions is extracted from the structured road condition suspension matching data, the time window boundary corresponding to each complex road condition is determined, and the reference curve of the map is obtained.

[0039] Data preprocessing is performed on the real-time suspension working data curves and the reference curves.

[0040] The dynamic time warping algorithm is used to calculate the morphological similarity between the preprocessed real-time suspension working data curve and the reference curve.

[0041] Pearson correlation coefficient was introduced as an auxiliary verification index to calculate the linear correlation between the real-time suspension working data curve and the reference curve.

[0042] The calculated morphological similarity results are stored together with the average actual suspension adjustment value for each road condition segment in the system database.

[0043] This invention provides an intelligent suspension control system based on complex road condition recognition. Through multi-source road condition data fusion and semantic parsing, it can comprehensively capture road surface images, three-dimensional contours, obstacles, and warning information, accurately identify and classify various complex road conditions, avoid recognition deviations caused by single sensor data, provide accurate road condition basis for subsequent suspension adjustment, and ensure that the suspension adjustment direction is highly matched with the actual road condition requirements.

[0044] By combining road condition frequency, response efficiency, and ride comfort indices extracted from historical driving data, theoretical adjustment benchmark values ​​are calculated. Simultaneously, an adaptive correction factor matrix is ​​introduced to dynamically adjust the adjustment benchmark and tolerance bandwidth based on real-time vehicle posture, body vibration, and suspension air pressure, forming a dynamic suspension adjustment map. This allows suspension parameters to adapt to changes in road conditions and vehicle status in real time. On bumpy roads, optimized damping and height parameters effectively suppress vibrations and improve comfort; in curves or sudden road conditions, it enhances anti-roll and support performance, ensuring handling stability and achieving a dynamic balance between comfort and handling. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating an intelligent suspension control system based on complex road condition recognition provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the process for identifying complex road condition types in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for generating structured road condition suspension matching data in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for calculating the theoretical suspension adjustment reference value in an embodiment of the present invention; Figure 5 This is a schematic diagram of the process for constructing the adaptive correction factor matrix in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] The following is combined Figures 1-5 This invention describes an intelligent suspension control system based on complex road condition recognition.

[0049] Figure 1 This is a schematic diagram of the structure of an intelligent suspension control system based on complex road condition recognition provided in an embodiment of the present invention.

[0050] like Figure 1 As shown in the figure, an intelligent suspension control system based on complex road condition recognition provided by an embodiment of the present invention includes a data acquisition module, a theoretical adjustment calculation module, an adjustment map generation module, a dynamic adjustment generation module, and a real-time monitoring module.

[0051] The data acquisition module is used to acquire multi-source road condition data during vehicle operation. It identifies complex road condition types in the multi-source road condition data through road condition semantic parsing, and extracts suspension adaptation features for each road condition by combining vehicle suspension parameters, generating structured road condition suspension matching data.

[0052] Multi-source road condition data includes road image data collected by onboard cameras, 3D road contour data scanned by LiDAR, road obstacle data detected by millimeter-wave radar, and road condition warning data issued by the vehicle-to-everything (V2X) network. Vehicle suspension parameters include suspension spring stiffness coefficient, shock absorber damping adjustment range, stabilizer bar torsional stiffness, suspension travel limits, and spring pressure adjustment threshold. Suspension adaptation features include target suspension stiffness value, shock absorber damping coefficient setting value, vehicle height adjustment value, suspension response delay threshold, and vibration suppression rate target value.

[0053] Figure 2 This is a schematic diagram of the process for identifying complex road condition types in an embodiment of the present invention.

[0054] like Figure 2 As shown, the process of identifying complex road condition types in multi-source road condition data through road condition semantic parsing includes: Image segmentation and feature extraction are performed on road surface image data to identify key road condition features, including road surface damage, water accumulation, snow accumulation, and gravel.

[0055] Based on the three-dimensional profile data of the road surface, the road surface slope, undulation and bump frequency are calculated to classify the road condition level.

[0056] By integrating road obstacle data and road condition early warning data, emergency road condition information is marked, including the type and hazard level of the emergency.

[0057] Based on key road condition characteristics, road condition levels, and information on sudden road conditions, regular smooth road conditions are filtered out, and a set of complex road condition types is obtained.

[0058] In this embodiment, the road image data collected by the vehicle-mounted camera is preprocessed, and the image segmentation is performed using the MaskR-CNN model based on deep learning. The road damage (cracks, potholes), water accumulation (reflective areas), snow accumulation (high brightness areas), and gravel (irregular small objects) features are extracted through a multi-scale feature fusion network. The non-maximum suppression algorithm is used to filter redundant detection boxes, and the feature location coordinates and confidence scores are output.

[0059] The system synchronously processes the 3D point cloud data of the lidar, separates the road surface point set through the ground point cloud segmentation algorithm, calculates the average height difference between adjacent frame point clouds to obtain the undulation, uses Fourier transform to convert the height sequence into the frequency domain to obtain the bump frequency, and combines the slope calculation results to divide the road condition into 5 levels according to the preset threshold. The higher the level, the worse the road condition.

[0060] For obstacle data from millimeter-wave radar, target distance, relative speed, and size information are extracted and spatiotemporally matched with early warning data (construction areas, accident points, etc.) issued by the vehicle network. The DS evidence theory is used to fuse multi-source information, mark the types of sudden road conditions (such as spilled objects and temporary obstacles), and quantify the risk level according to collision risk, which can include three levels: no risk, low risk, and high risk.

[0061] A semantic parsing decision tree is constructed, with key feature confidence, road condition level, and hazard level as input nodes. First, regular road conditions with feature confidence below a preset threshold and level ≤2 are filtered out. Then, the remaining data are classified into various complex road condition types, such as: severely damaged + high undulation + high risk combination, forming a structured set of complex road condition types.

[0062] Figure 3 This is a schematic diagram of the process for generating structured road condition suspension matching data in an embodiment of the present invention.

[0063] like Figure 3 As shown, the process of generating structured road condition suspension matching data includes: Based on the type of complex road conditions, the basic suspension adjustment parameters are determined by relating the suspension spring stiffness coefficient to the damper damping adjustment range.

[0064] By combining road slope and undulation, the vehicle body posture compensation is calculated, and the required value for suspension height adjustment is derived.

[0065] Correlate the torsional stiffness of the stabilizer bar with the suspension travel limit value to calibrate the suspension's anti-roll and anti-bump coefficients.

[0066] The unique road condition identifier, suspension adjustment parameter set, and attitude compensation amount are encapsulated into a structured data object to obtain structured road condition suspension matching data.

[0067] In this embodiment, a mapping relationship library between complex road condition types and suspension hardware parameters is established, and spring stiffness coefficient ranges and shock absorber damping adjustment ranges are preset for various complex road condition types. Using a fuzzy PID control algorithm, basic parameters are dynamically selected based on the real-time road condition level: for example, high stiffness coefficients and medium-high damping are matched for severely damaged road conditions with high undulations, forming a basic suspension adjustment parameter matrix.

[0068] Based on the road slope and undulation obtained by LiDAR, the vehicle body attitude compensation is calculated through a dynamic model, and a vehicle speed correction factor is introduced to generate a continuous suspension height adjustment curve.

[0069] By calling the stabilizer bar torsional stiffness database and combining the limit values ​​fed back by the suspension travel sensor, the anti-roll coefficient and anti-bump coefficient are calibrated through finite element analysis.

[0070] The data is encapsulated in JSON format: using road condition type code as a unique identifier, it includes basic parameter arrays (stiffness, damping), attitude compensation quantities (height, tilt angle), performance coefficient set (tilt, bump), and data collection timestamps, forming structured data.

[0071] The theoretical adjustment calculation module is used to collect historical driving task data and extract indicators such as road condition occurrence frequency, suspension response efficiency, and ride comfort. Based on the road condition occurrence frequency and suspension response efficiency, it calculates the theoretical suspension adjustment benchmark value.

[0072] The process of extracting indicators such as road condition frequency, suspension response efficiency, and ride comfort includes: Collect historical driving task data and filter historical driving records of the same vehicle model and driving area.

[0073] Analyze the road condition types in historical structured data objects and statistically analyze the frequency and duration of different complex road conditions.

[0074] By analyzing historical suspension sensor records, a mapping model of suspension adjustment time under road conditions is constructed to calculate suspension response efficiency.

[0075] Collect data on the vertical and lateral acceleration of the vehicle body during historical driving processes, and calculate the ride comfort index using the vibration comfort formula.

[0076] In this embodiment, historical driving data from the past year is collected. Data on the same vehicle model is filtered by vehicle identification number, and driving records within the same administrative region are extracted using GPS location information to form a basic dataset. Data cleaning algorithms are used to remove outliers, retaining complete records of road conditions and suspension interactions.

[0077] The road condition type field in the historical structured data object is analyzed, and the statistical period is divided into quarters. The frequency of occurrence of each type of complex road condition is calculated using frequency statistics, and the duration of each type of road condition is determined by combining the total driving time. A road condition frequency distribution table is established to mark the typical complex road condition types that occur frequently.

[0078] By retrieving historical suspension sensor records of adjustment commands and execution feedback data, and using time series analysis to determine the start and end times of each suspension adjustment, the adjustment process time is calculated. A mapping model between road conditions and adjustment time is constructed, and the ratio of adjustment time to road condition complexity is used as a quantitative indicator of suspension response efficiency, forming an efficiency evaluation matrix.

[0079] Continuous data from the vehicle's vertical and lateral acceleration sensors were collected during historical driving. Acceleration time-domain signals were extracted in segments according to road condition. The signals were weighted using a vibration comfort formula, converting the acceleration amplitude and frequency characteristics into a ride comfort index. A lower index indicates better ride comfort, thus establishing a ride comfort benchmark library for different road conditions.

[0080] The three types of indicators are integrated into a multi-dimensional feature vector, associated with the corresponding road condition type labels, to form a historical performance evaluation dataset.

[0081] Figure 4 This is a schematic diagram of the process for calculating the theoretical suspension adjustment reference value in an embodiment of the present invention.

[0082] like Figure 4 As shown, the process of calculating the theoretical suspension adjustment reference value based on the frequency of road conditions and suspension response efficiency includes: Based on the frequency of road conditions, the basic suspension adjustment parameters under different road conditions are weighted and calculated to generate initial adjustment reference values.

[0083] By combining the suspension response efficiency, a response delay correction coefficient is introduced to adjust the initial adjustment reference value, thus obtaining the adjusted adjustment reference value.

[0084] Based on the ride comfort index, a ride comfort guarantee coefficient is introduced to optimize the adjustment reference value, thereby obtaining the theoretical suspension adjustment benchmark value.

[0085] Weighting coefficients are determined based on the frequency and duration of various road conditions, with higher weights assigned to frequently occurring road conditions. The basic suspension adjustment parameters corresponding to different road conditions are then weighted and summed to generate initial adjustment reference values ​​covering all road condition scenarios.

[0086] The suspension response efficiency evaluation matrix is ​​analyzed, and a response delay correction coefficient system is constructed based on the adjustment time characteristics under different road conditions. When the suspension response efficiency is low under a certain road condition, the corresponding correction coefficient is increased to trigger the adjustment action earlier. The adjustment reference value after dynamic response optimization is obtained by multiplying the coefficient with the initial adjustment reference value.

[0087] A ride comfort index is introduced as a constraint, transforming the ride comfort index into a guarantee coefficient. For road conditions with high ride comfort requirements, the weight of the guarantee coefficient is increased to enhance vibration suppression. A multi-objective optimization algorithm is used to perform a secondary correction on the adjustment reference value, establishing a balance mechanism between response efficiency and ride comfort, ultimately generating a theoretical suspension adjustment benchmark value.

[0088] The suspension adjustment map generation module is used to set the dynamic adjustment tolerance zone and, in conjunction with the theoretical suspension adjustment benchmark value, generate an initial suspension adjustment map sorted by road condition priority. The process includes: Create a three-dimensional coordinate system for timing, road conditions, and suspension parameters.

[0089] Based on the timing and priority of road conditions, the corresponding theoretical suspension adjustment benchmark values ​​are connected to form a suspension adjustment reference curve.

[0090] Differentiated adjustment of fault tolerance bandwidth is set based on road condition hazard level and driving speed.

[0091] Visualize and label the adjustment range boundaries and priority indicators under different road conditions to form an initial suspension adjustment map.

[0092] A three-dimensional coordinate system was constructed, with the three dimensions corresponding to the driving time sequence, road condition type, and suspension adjustment parameters, respectively. The time axis was divided into intervals according to the actual driving progress, with each interval corresponding to the road condition changes of a specific road segment. The road condition axis was arranged by type, corresponding one-to-one with the actual detected road condition types. The parameter axis included all adjustable technical indicators of the suspension system.

[0093] Based on the order in which various road conditions occur during vehicle operation and their urgency, the theoretical suspension adjustment reference values ​​at each moment are marked on a coordinate system. These points are then connected sequentially using a smooth curve to form a continuous suspension adjustment reference curve.

[0094] Depending on the severity of the road conditions and the vehicle's current speed, different widths of adjustment tolerance strips are set for different road conditions. When the degree of danger is high or the driving speed is high, the tolerance strip is set narrower, requiring more precise suspension adjustment. When driving on normal roads or at low speeds, the tolerance strip is appropriately widened to retain some adjustment flexibility.

[0095] Different colors and line styles are used to mark the adjustment range boundaries for various road conditions in a coordinate system, and different priority road conditions are distinguished with eye-catching markers. These elements are integrated to form a complete initial suspension adjustment map, showing the adjustment standards and allowable fluctuation range that the suspension system should follow under different driving stages and road conditions.

[0096] The dynamic adjustment generation module is used to collect real-time vehicle attitude data, body vibration spectrum and suspension air pressure data to construct an adaptive correction factor matrix, and inject the adaptive correction factor matrix into the initial suspension adjustment map to obtain the dynamic suspension adjustment map for the driving task.

[0097] Figure 5 This is a schematic diagram of the process for constructing the adaptive correction factor matrix in an embodiment of the present invention.

[0098] like Figure 5 As shown, the process of constructing the adaptive correction factor matrix includes: Based on real-time vehicle attitude data, the roll angle and pitch angle change rates are calculated, and attitude correction coefficients are generated.

[0099] Based on the vehicle body vibration spectrum, calculate the vibration energy entropy value and calibrate the damper damping correction coefficient.

[0100] Analyze the fluctuation range of suspension air pressure data and generate pressure compensation correction coefficients.

[0101] By integrating attitude correction coefficients, damping correction coefficients, and pressure compensation correction coefficients, an adaptive correction factor matrix is ​​obtained.

[0102] The vehicle acquires three-dimensional attitude information of the vehicle body using an onboard gyroscope and tilt sensor, focusing on extracting real-time changes in the roll and pitch angles. A sliding window algorithm is used to smooth the continuously acquired angle data, eliminating transient jitter interference. The rate of change of the roll and pitch angles is then calculated using differential calculations. Based on the deviation of the rate of change from a preset attitude stability threshold, attitude correction coefficients are generated. When the rate of change exceeds the stability threshold, the coefficients are adjusted to enhance suspension stability. When the rate of change is within the stable range, the coefficients maintain a baseline value, ensuring precise matching between attitude correction and the vehicle's dynamic state.

[0103] Vibration sensors installed in key parts of the vehicle body (such as the chassis and seat rails) collect vibration signals at different frequency bands. The time-domain vibration data is converted into a frequency-domain spectrum using a Fast Fourier Transform (FFT). The energy percentage of each frequency component in the spectrum is calculated to obtain the vibration energy entropy value. A higher energy entropy value indicates a more disordered vibration distribution and poorer ride comfort. Based on the difference between the energy entropy value and the ideal ride comfort state, the damping correction coefficient of the shock absorber is calibrated: when the entropy value increases, the coefficient is adjusted to increase damping and suppress vibration; conversely, when the entropy value decreases, the damping is appropriately reduced to improve comfort.

[0104] The air pressure is collected in real time by pressure sensors in the suspension air circuit, and the fluctuations in air pressure during continuous driving are recorded to calculate the maximum fluctuation amplitude per unit time. Combined with the ideal operating range of the suspension air pressure, the impact of the fluctuation amplitude on the suspension support performance is determined. When the fluctuation amplitude exceeds the normal range, it indicates insufficient air pressure stability, and a pressure compensation correction coefficient needs to be generated. The operating state of the air pressure regulating valve is adjusted by the coefficient to reduce fluctuations and ensure that the suspension air pressure is maintained within the ideal range.

[0105] The attitude correction coefficient, shock absorber damping correction coefficient, and pressure compensation correction coefficient are organized according to preset dimensions and encapsulated in a structured matrix. The row dimensions of the matrix correspond to different vehicle dynamic states (such as steering, braking, and bumps), and the column dimensions correspond to the correction coefficient types, forming an adaptive correction factor matrix. After the matrix is ​​generated, it is synchronized to the suspension control unit in real time, providing a basis for subsequent updates to the dynamic adjustment map and realizing adaptive optimization of the suspension adjustment strategy.

[0106] The process of obtaining the dynamic suspension adjustment map for the driving task includes: Based on the adaptive correction factor matrix, the suspension adjustment benchmark value in the initial map is adjusted for each road condition.

[0107] Based on the damping correction coefficient, the adjustable fault-tolerant bandwidth is dynamically expanded and contracted under different road conditions.

[0108] A correction factor numerical marker layer and a real-time road condition warning label are superimposed on the initial suspension adjustment map.

[0109] Establish a real-time data interface with the vehicle's CAN bus and update the adaptive correction factor matrix and dynamic suspension adjustment map according to a preset cycle.

[0110] Upon obtaining the dynamic suspension adjustment map, the baseline values ​​in the initial suspension adjustment map are first adjusted for each road condition based on the constructed adaptive correction factor matrix. For each complex road condition type, the corresponding attitude correction coefficient and pressure compensation correction coefficient are extracted from the adaptive correction factor matrix and fused with the suspension adjustment baseline values ​​in the initial map. For example, in continuous curved road conditions, the suspension stiffness baseline value is adjusted in conjunction with the attitude correction coefficient to enhance the vehicle's anti-roll capability. In bumpy road conditions, the suspension height baseline value is optimized based on the pressure compensation correction coefficient to improve road adaptability, ensuring that the baseline value corresponding to each road condition matches the current dynamic state of the vehicle.

[0111] For road conditions with high vibration energy entropy, such as gravel roads and damaged roads, the damping correction coefficient tends to enhance the shock absorption effect. In this case, the adjustment tolerance bandwidth is narrowed simultaneously to strictly control the fluctuation range of the shock absorber damping and avoid affecting the vibration suppression effect due to damping adjustment deviation. On road conditions with gentle vibration, the damping correction coefficient tends to be more moderate, and the tolerance bandwidth is appropriately relaxed to retain a certain degree of adjustment flexibility and balance comfort and handling.

[0112] A numerical labeling layer for correction factors is added, visually annotating various coefficients in the adaptive correction factor matrix next to the adjustment baseline value for the corresponding road condition, facilitating intuitive viewing of the correction basis. Real-time road condition warning icons are integrated; based on temporary road condition information (such as sudden construction or road icing) issued by the vehicle network, warning symbols are marked in the map for the corresponding time period, reminding the system to pay close attention to the suspension adjustment accuracy of that road segment.

[0113] The system interfaces with the CAN bus via a dedicated communication protocol, acquiring the latest real-time data on vehicle attitude, vibration, and air pressure from the bus at preset intervals, and recalculating the adaptive correction factor matrix. Simultaneously, the updated matrix is ​​injected into the suspension adjustment map, completing the dynamic iteration of the map. This entire process ensures that the dynamic suspension adjustment map reflects the vehicle's operating status and road condition changes in real time, providing accurate and timely adjustment guidance for the suspension system.

[0114] The real-time monitoring module collects real-time suspension operating data curves during driving, compares them with the average actual suspension adjustment in each road condition segment, and performs real-time monitoring of intelligent suspension control by calculating the morphological similarity between the real-time suspension operating data curves and the dynamic suspension adjustment map within each road condition time window. The process includes: The duration of road conditions is extracted from the structured road condition suspension matching data, the time window boundary corresponding to each complex road condition is determined, and the reference curve of the map is obtained.

[0115] Data preprocessing is performed on the real-time suspension working data curves and the reference curves.

[0116] The dynamic time warping algorithm is used to calculate the morphological similarity between the preprocessed real-time suspension working data curve and the reference curve.

[0117] Pearson correlation coefficient was introduced as an auxiliary verification index to calculate the linear correlation between the real-time suspension working data curve and the reference curve.

[0118] The calculated morphological similarity results are stored together with the average actual suspension adjustment value for each road condition segment in the system database.

[0119] When the real-time monitoring module performs its monitoring work, it first extracts the duration information of various complex road conditions from the structured road condition suspension matching data. Combining this with the real-time road condition switching nodes during vehicle operation, it determines the exclusive time window boundary for each complex road condition, using the start and end times of the road condition as boundaries. Based on these boundaries, it extracts reference curve segments for the corresponding time periods from the dynamic suspension adjustment graph, ensuring that each reference curve accurately matches the currently monitored road condition time period, thus establishing a unified time dimension benchmark for subsequent curve comparisons.

[0120] For the real-time suspension operating data curves, a filtering algorithm is used to eliminate high-frequency noise mixed in during sensor acquisition, avoiding the impact of instantaneous interference data on the accuracy of the curve shape. Simultaneously, trend smoothing processing is applied to the curves to preserve the true changing trend of the suspension's operating state. For the reference curve, interpolation adjustment is performed based on the sampling frequency of the real-time data to ensure consistent sampling density between the two curves, eliminating comparison bias caused by differences in data acquisition frequency and laying a data foundation for subsequent similarity calculations.

[0121] First, the two curves are decomposed into multiple data feature points, and a distance matrix between the feature points is constructed. Then, dynamic programming is used to find the optimal matching path between the two curves, which best matches the morphological change trends of the two curves. The sum of the distances between the feature points on the optimal path is converted into an intuitive morphological similarity index. A higher index value indicates a closer similarity between the real-time suspension working data curve and the reference curve, and a higher degree of fit between the actual suspension working state and the ideal adjustment state.

[0122] The Pearson correlation coefficient is derived by calculating the covariance between the real-time suspension operating data curve and the reference curve, combined with the standard deviations of the two curves. This coefficient reflects the degree of linear correlation between the two curves. A high absolute value of the coefficient indicates a high degree of consistency in the overall trend of the two curves, thus verifying the reliability of the morphological similarity results from the perspective of linear correlation. If the coefficient deviates from the morphological similarity index, the system is triggered to conduct a preliminary investigation into the cause of the data anomaly, ensuring the rigor of the monitoring results.

[0123] Finally, the calculated morphological similarity results are correlated and stored with the actual average suspension adjustment value for each road condition segment. This includes the correspondence between road condition type, time window, similarity, and actual average adjustment value. The data is then organized into structured records and written to the system database in real time.

[0124] In summary, this embodiment provides an intelligent suspension control system based on complex road condition recognition. Through multi-source road condition data fusion and semantic parsing, it can comprehensively capture road surface images, three-dimensional contours, obstacles, and warning information, accurately identify and classify various complex road conditions, avoid recognition deviations caused by single sensor data, provide accurate road condition basis for subsequent suspension adjustment, and ensure that the suspension adjustment direction is highly matched with the actual road condition requirements.

[0125] By combining road condition frequency, response efficiency, and ride comfort indices extracted from historical driving data, theoretical adjustment benchmark values ​​are calculated. Simultaneously, an adaptive correction factor matrix is ​​introduced to dynamically adjust the adjustment benchmark and tolerance bandwidth based on real-time vehicle posture, body vibration, and suspension air pressure, forming a dynamic suspension adjustment map. This allows suspension parameters to adapt to changes in road conditions and vehicle status in real time. On bumpy roads, optimized damping and height parameters effectively suppress vibrations and improve comfort; in curves or sudden road conditions, it enhances anti-roll and support performance, ensuring handling stability and achieving a dynamic balance between comfort and handling.

[0126] By combining dynamic time warping algorithms with Pearson correlation coefficients, the morphological similarity between real-time suspension operating curves and dynamic graphs is calculated with precision. This not only quickly identifies obvious adjustment anomalies but also captures subtle morphological deviations, enabling accurate monitoring of the suspension's operating status. Simultaneously, the correlation and storage of monitoring data with actual adjustment mean values ​​provide comprehensive data support for subsequent suspension adjustment strategy iterations, fault diagnosis, and parameter optimization, forming a closed-loop system. This continuously improves the adaptability and reliability of the suspension system, further enhancing the vehicle's overall performance in complex driving scenarios and providing users with a superior driving experience.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent suspension control system based on complex road condition recognition, characterized in that, include: The data acquisition module is used to acquire multi-source road condition data during vehicle operation, identify complex road condition types in the multi-source road condition data through road condition semantic parsing, and extract suspension adaptation features under each road condition in combination with vehicle suspension parameters to generate structured road condition suspension matching data. The theoretical adjustment calculation module is used to collect historical driving task data and extract road condition occurrence frequency, suspension response efficiency and driving comfort index, and calculate the theoretical suspension adjustment benchmark value based on the road condition occurrence frequency and the suspension response efficiency; The adjustment map generation module is used to set the dynamic adjustment tolerance zone and generate an initial suspension adjustment map sorted by road condition priority in combination with the theoretical suspension adjustment benchmark value. The dynamic adjustment generation module is used to collect real-time vehicle attitude data, body vibration spectrum and suspension air pressure data to construct an adaptive correction factor matrix, and inject the adaptive correction factor matrix into the initial suspension adjustment map to obtain the dynamic suspension adjustment map for the driving task. The real-time monitoring module is used to collect real-time suspension working data curves during driving, compare the actual average suspension adjustment value in each road condition segment, and perform real-time monitoring of intelligent suspension control by calculating the morphological similarity between the real-time suspension working data curve in each road condition time window and the dynamic suspension adjustment map.

2. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The multi-source road condition data includes road surface image data, road surface three-dimensional contour data, road obstacle data, and road segment condition warning data; the vehicle suspension parameters include suspension spring stiffness coefficient, shock absorber damping adjustment range, stabilizer bar torsional stiffness, suspension travel limit value, and spring pressure adjustment threshold; the suspension adaptation features include suspension stiffness target value, shock absorber damping coefficient setting value, vehicle height adjustment value, suspension response delay threshold, and vibration suppression rate target value.

3. The intelligent suspension control system based on complex road condition recognition according to claim 2, characterized in that, The process of identifying complex road condition types in the multi-source road condition data through road condition semantic analysis includes: The road surface image data is segmented and feature extracted to identify key road condition features, including road surface damage, water accumulation, snow accumulation, and gravel. Based on the three-dimensional contour data of the road surface, the road surface slope, undulation and bump frequency are calculated, and the road condition level is classified. By integrating the road obstacle data and road condition early warning data, emergency road condition information is marked, including the type and hazard level of the emergency road condition. Based on the key road condition characteristics, road condition levels, and sudden road condition information, regular smooth road conditions are filtered out, and a set of complex road condition types is obtained.

4. The intelligent suspension control system based on complex road condition recognition according to claim 2, characterized in that, The process of generating structured road condition suspension matching data includes: Based on the complex road condition type, the suspension spring stiffness coefficient and the shock absorber damping adjustment range are correlated to determine the basic suspension adjustment parameters; By combining road slope and undulation, the vehicle body posture compensation amount is calculated, and the required value for suspension height adjustment is derived. By correlating the torsional stiffness of the stabilizer bar with the suspension travel limit value, the anti-roll and anti-bump coefficients of the suspension are calibrated. The unique road condition identifier, suspension adjustment parameter set, and attitude compensation amount are encapsulated into a structured data object to obtain structured road condition suspension matching data.

5. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The process of extracting indicators such as road condition frequency, suspension response efficiency, and ride comfort includes: Collect historical driving task data and filter historical driving records of the same vehicle type and driving area; Analyze the road condition types in historical structured data objects and count the frequency and duration of different complex road conditions. By analyzing historical suspension sensor records, a mapping model of suspension adjustment time under road conditions is constructed to calculate suspension response efficiency. Collect data on the vertical and lateral acceleration of the vehicle body during historical driving processes, and calculate the ride comfort index using the vibration comfort formula.

6. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The process of calculating the theoretical suspension adjustment benchmark value based on the frequency of road conditions and the suspension response efficiency includes: Based on the frequency of road conditions, the basic suspension adjustment parameters under different road conditions are weighted and calculated to generate initial adjustment reference values. Based on the suspension response efficiency, a response delay correction coefficient is introduced to adjust the initial adjustment reference value to obtain the adjustment reference value; Based on the aforementioned ride comfort index, a ride comfort guarantee coefficient is introduced to optimize the adjustment reference value, thereby obtaining the theoretical suspension adjustment benchmark value.

7. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The process of generating an initial suspension adjustment map sorted by road condition priority based on the theoretical suspension adjustment benchmark values ​​includes: Create a three-dimensional coordinate system for time series, road conditions, and suspension parameters; Based on the timing and priority of road conditions, the corresponding theoretical suspension adjustment benchmark values ​​are connected to form a suspension adjustment reference curve; Differentiated adjustment fault tolerance bandwidth is set based on road condition hazard level and driving speed; Visualize and label the adjustment range boundaries and priority indicators under different road conditions to form an initial suspension adjustment map.

8. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The process of constructing the adaptive correction factor matrix includes: Based on the real-time vehicle attitude data, calculate the rate of change of vehicle roll angle and pitch angle, and generate attitude correction coefficients. Based on the vehicle body vibration spectrum, calculate the vibration energy entropy value and calibrate the damper damping correction coefficient; Analyze the fluctuation range of the suspension air pressure data to generate a pressure compensation correction coefficient; By integrating attitude correction coefficients, damping correction coefficients, and pressure compensation correction coefficients, an adaptive correction factor matrix is ​​obtained.

9. The intelligent suspension control system based on complex road condition recognition according to claim 8, characterized in that, The process of obtaining the dynamic suspension adjustment map for the driving task includes: Based on the adaptive correction factor matrix, the suspension adjustment reference value in the initial map is adjusted for each road condition. Based on the aforementioned damping correction coefficient, the adjustable fault-tolerant bandwidth is dynamically expanded or contracted under different road conditions; A correction factor numerical marker layer and a real-time road condition warning label are superimposed on the initial suspension adjustment map; Establish a real-time data interface with the vehicle's CAN bus and update the adaptive correction factor matrix and dynamic suspension adjustment map according to a preset cycle.

10. The intelligent suspension control system based on complex road condition recognition according to claim 1, characterized in that, The process of calculating the morphological similarity between the real-time suspension operating data curve and the dynamic suspension adjustment map within each road condition time window includes: The duration of road conditions is extracted from the structured road condition suspension matching data, the time window boundary corresponding to each complex road condition is determined, and a map reference curve is obtained. Data preprocessing is performed on the real-time suspension working data curves and the reference curves. The dynamic time warping algorithm is used to calculate the morphological similarity between the preprocessed real-time suspension working data curve and the reference curve. Pearson correlation coefficient was introduced as an auxiliary verification index to calculate the linear correlation between the real-time suspension working data curve and the reference curve. The calculated morphological similarity results are stored together with the average actual suspension adjustment value for each road condition segment in the system database.

Images