Road condition data detection and damping adjustment system based on Internet of Things technology

Abstract

The invention discloses a road condition data detection and damping adjustment system based on the Internet of Things technology, and relates to the technical field of data processing, and the system comprises a vehicle system which comprises a plurality of modules for cooperation, a violent shake evaluation module which obtains an evaluation value by combining sudden brake and vehicle shake data, and a violent bump judgment module which judges the shake condition according to the evaluation value. And then, the sealing element related module evaluates and judges whether the sealing element is loosened or not, and an alarm is given for maintenance if a problem exists. According to the method, the sudden brake data and the vehicle shaking data are acquired, and are preprocessed and comprehensively evaluated to obtain the severely shaking evaluation value, so that the accuracy of whether the vehicle shakes severely or not is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to a road condition data detection and shock absorption adjustment system based on Internet of Things technology. Background Art

[0002] In the prior art, cameras are widely installed along roadsides and at the front of vehicles. These cameras continuously capture real-time road images. Obvious road condition problems such as potholes, bumps, and cracks on the road, as well as traffic signs, lane line conditions, and the dynamics of vehicles and pedestrians, can all be recorded. Through image recognition technology, the images captured by the cameras are analyzed and processed to extract useful road condition information, such as identifying whether there is a construction section ahead or whether the road is severely damaged.

[0003] After obtaining the road condition information, the vehicle in front transmits this information to the vehicle behind through a specific communication method. After the vehicle behind receives the information, the system adjusts the shock absorption according to this road condition feedback. If there are potholes on the road ahead and the vehicle behind is informed, the shock absorption system will adjust the parameters in advance to increase the shock absorption force to cope with the upcoming bumps, ensuring the comfort and stability of vehicle driving and reducing the adverse effects on the vehicle and passengers caused by poor road conditions.

[0004] For example, a method and system for evaluating and optimizing the shock absorption effect of a whole vehicle disclosed in the invention patent with the publication number of CN115659694B includes the following steps: matching multiple road scenarios according to the preset use of the vehicle. Extracting accessory parameters according to the original vehicle design drawing. Constructing vehicle simulation results according to the accessory parameters. Traversing multiple road scenarios for road simulation to construct multiple simulation test scenarios; obtaining shock absorption effect indicators. According to the vibration amplitude indicator and the vibration frequency indicator, inputting the vehicle simulation results into multiple simulation test scenarios in sequence for shock absorption testing to generate multiple shock absorption effect evaluation results. When multiple shock absorption effect evaluation results do not meet the expected shock absorption effect, optimizing the shock absorber model parameters and shock absorber installation parameters to generate an optimized result of the original vehicle design drawing. It solves the technical problem that the matching degree between the shock absorption installation parameters of the whole vehicle and the road scenarios in actual vehicle application in the prior art is not high, resulting in poor actual shock absorption effect of the shock absorption system.

[0005] For example, a shock absorber tuning method disclosed in the invention patent with the publication number of CN114739703B, which includes: first, conducting a road vibration test on the shock absorber and analyzing the relative movement speed of the shock absorber; establishing a multi-body dynamics model of the suspension and conducting a vibration simulation analysis of the suspension system; establishing a parametric model of the spatial installation position of the shock absorber and the damping curve; tuning the spatial installation position of the shock absorber; dynamically tuning the damping characteristics of the shock absorber; and finally verifying the effect of the comprehensive optimization scheme. The present invention comprehensively applies the dynamics theory, combines the shock absorber vibration test and the dynamics simulation technology, solves the problem that the existing methods for improving the transient roll stability performance and ride comfort of vehicles have poor effects. The analysis method of the present invention has high applicability and operability, and can significantly improve the transient roll stability performance and ride comfort of vehicles.

[0006] During vehicle driving, when an obstacle suddenly appears in front on the road, a vehicle making an emergency brake will instantaneously generate violent shaking under the action of inertia. This violent shaking will be transmitted to the vehicle suspension system, resulting in a sharp increase in the internal pressure of the shock absorber. Under such a high-intensity pressure impact, the sealing components inside the shock absorber cannot withstand it, thereby causing hydraulic oil leakage, and further weakening the buffering performance of the shock absorber. Summary of the Invention

[0007] Technical Problem to be Solved

[0008] Aiming at the deficiencies of the prior art, the present invention provides a road condition data detection and shock absorption adjustment system based on the Internet of Things technology, which solves the problem that when an obstacle suddenly appears in front during vehicle driving, the emergency brake causes the vehicle to violently shake due to inertia, and the shaking is transmitted to the suspension system, resulting in a sharp increase in the internal pressure of the shock absorber, damaging the sealing components and causing hydraulic oil leakage, weakening the buffering performance of the shock absorber.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: A road condition data detection and shock absorption adjustment system based on the Internet of Things technology, including the following specific steps: Severe shaking evaluation module: Used to obtain emergency braking data and vehicle shaking data in real time, preprocess the emergency braking data and vehicle shaking data, and comprehensively evaluate the preprocessed emergency braking data and vehicle shaking data to obtain a severe shaking evaluation value; Strong bump judgment module: Judge whether the vehicle shakes violently according to the severe shaking evaluation value; Send the judgment result of whether the vehicle shakes violently to the stability maintenance execution module; Stability maintenance execution module: Used to receive the judgment result of whether the vehicle shakes violently. If it is judged that the vehicle shakes violently, use the dynamic adaptive damping optimization algorithm to adjust the shock absorption to keep the vehicle stable, and return to the severe shaking evaluation module to continue obtaining emergency braking data and vehicle shaking data in real time until it is judged that the vehicle is stable. If it is judged that the vehicle is stable, execute the seal loosening evaluation module. Seal loosening evaluation module: Used to obtain seal loosening data in real time, preprocess the seal loosening data, and comprehensively evaluate the preprocessed seal loosening data to obtain a seal loosening evaluation value; Seal loosening judgment module: Judge whether the seal is loose according to the seal loosening evaluation value; Send the judgment result of whether the seal is loose to the seal loosening execution module; Seal loosening execution module: Receive the judgment result of whether the seal is loose. If it is judged that the seal is loose, immediately issue an alarm to prompt for maintenance and return to the seal loosening evaluation module to continue obtaining seal loosening data in real time until it is judged that the seal is normal; If it is judged that the seal is normal, continue to detect whether the seal is loose.

[0011] Further, in the severe shaking evaluation module, the emergency braking data includes first image data, second image data, vehicle speed, measured distance, bearing pressure of the tire, and road surface dynamic friction coefficient; The vehicle shaking data includes rolling angular velocity, pitch angular velocity, yaw angular velocity, and tire lift-off frequency; Perform filter noise reduction processing on the emergency braking data and vehicle shaking data; Perform normalization processing and comprehensive analysis on the emergency braking data and vehicle shaking data after filter noise reduction processing to obtain an emergency braking influence coefficient and a vehicle shaking anomaly coefficient; Perform standardization processing and comprehensive evaluation on the emergency braking influence coefficient and the vehicle shaking anomaly coefficient to obtain a severe shaking evaluation value; Among them, HP represents the severe shaking evaluation value; JX represents the emergency braking influence coefficient; CX represents the vehicle shaking anomaly coefficient.

[0012] Furthermore, the specific method for obtaining the emergency braking influence coefficient is as follows: the first image data and the second image data are calculated by an image contour detection algorithm and an image contour tracking algorithm to obtain contour image data, wherein the contour image data includes the pixel value of the obstacle, the pixel point coordinates of the obstacle, the actual color of the road surface, and the actual texture of the road surface; the vehicle sliding distance is obtained by comprehensive analysis of the vehicle speed, the measured distance, the tire pressure, and the road dynamic friction factor; when there are the pixel value of the obstacle and the pixel point coordinates of the obstacle, the vehicle sliding distance is compared with the measured distance in real time, and if If the vehicle's sliding distance is less than the measured distance, the comparison will continue; if the vehicle's sliding distance is greater than or equal to the measured distance, the vehicle will automatically brake suddenly; the difference between the pressure of the i-th tire and the pressure of the i-1-th tire is calculated by the number of tires; the actual texture of the road surface is converted into grayscale to obtain the grayscale value of the road surface texture, and the grayscale value of the road surface texture is calculated by the variance method to obtain the road surface texture influence value; the road surface texture influence value, the vehicle's sliding distance, the number of tires, the pressure of the i-th tire and the pressure of the i-1-th tire are comprehensively analyzed to obtain the emergency braking influence coefficient.

[0013] Furthermore, the specific method of obtaining the vehicle sliding distance is as follows: According to Newton's second law, we get where F 摩 It indicates the friction between the tire and the road surface; MY indicates the dynamic friction coefficient of the road surface; It represents the average pressure of the tire, n represents the number of tires, Represents the gravity of the vehicle; the vehicle's sliding distance is obtained through gravitational acceleration, the law of universal gravitation, Newton's second law and uniformly accelerated linear motion.

[0014] Furthermore, the specific method for obtaining the vehicle shake abnormality coefficient is as follows: the rolling angular velocity, the pitch angular velocity and the yaw angular velocity are respectively averaged to obtain the rolling angular velocity average value, the pitch angular velocity average value and the yaw angular velocity average value; the rolling angular velocity, the pitch angular velocity and the yaw angular velocity are calculated using the standard deviation method based on the rolling angular velocity average value, the pitch angular velocity average value and the yaw angular velocity average value to obtain the rolling angular velocity abnormal value, the pitch angular velocity abnormal value and the yaw angular velocity abnormal value respectively; the rolling angular velocity abnormal value, the pitch angular velocity abnormal value, the yaw angular velocity abnormal value and the frequency of the tire leaving the ground are comprehensively analyzed to obtain the vehicle shake abnormality coefficient.

[0015] Further, in the strong jolt judgment module, the violent jolt evaluation value is compared with the violent jolting threshold in real time. If the violent jolting evaluation value is greater than or equal to the violent jolting threshold, it is judged that the vehicle is jolting violently. If the violent jolting evaluation value is less than the violent jolting threshold, it is judged that the vehicle is stable. The judgment result of whether the vehicle is jolting violently is sent to the keep-stable execution module.

[0016] Further, the specific steps of the dynamic adaptive damping optimization algorithm are as follows: A three-dimensional coordinate system is established with the vehicle center point as the origin. The standard rolling angular velocity and the standard pitch angular velocity are respectively set through the pitch angular velocity and the roll angular velocity when the vehicle is driving smoothly. An interval from minus ninety degrees to plus ninety degrees is set for both the pitch angular velocity and the roll angular velocity to obtain the pitch interval and the roll interval. Shock-absorbing components are set. The shock-absorbing components include a first shock absorber, a second shock absorber, a third shock absorber, and a fourth shock absorber. If the center of gravity of the vehicle is offset front and back, the damping of the first shock absorber or the second shock absorber is increased. If the center of gravity of the vehicle is offset left and right, the damping of the third shock absorber or the fourth shock absorber is increased.

[0017] Further, in the seal loosening evaluation module, the seal loosening data includes the actual thrust, the moving distance, and the vibration amplitude. The seal loosening data is subjected to filtering and noise reduction processing. The filtered and noise-reduced seal loosening data is normalized and comprehensively analyzed to obtain the seal loosening anomaly coefficient. The seal loosening anomaly coefficient and the vehicle jolting anomaly coefficient are standardized and comprehensively evaluated to obtain the seal loosening evaluation value.

[0018] Further, the specific way to obtain the seal loosening anomaly coefficient is as follows: The normal thrust is obtained through the thrust detection of the seal under normal conditions. The normal thrust, the actual thrust, the moving distance, and the vibration amplitude are comprehensively analyzed to obtain the seal loosening anomaly coefficient.

[0019] Further, in the seal loosening judgment module, the seal loosening evaluation value is compared with the seal loosening threshold in real time. If the seal loosening evaluation value is greater than or equal to the seal loosening threshold, it is judged that the seal is loose. If the seal loosening evaluation value is less than the seal loosening threshold, it is judged that the seal is normal. The judgment result of whether the seal is loose is sent to the seal loosening execution module.

[0020] Beneficial effects

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0022] 1. By obtaining the hard brake data and the vehicle jolting data, and performing preprocessing and comprehensive evaluation on them, the violent jolting evaluation value is obtained, improving the accuracy for judging whether the vehicle is jolting violently.

[0023] 2. By obtaining the seal loosening data, preprocessing and comprehensively evaluating it, the seal loosening evaluation value is obtained, improving the accuracy rate for judging whether the seal is loose.

[0024] 3. By establishing a three-dimensional coordinate system with the gyroscope position, setting standard values and a variation range from -90° to 90° based on the pitch and roll angular velocities during the smooth driving of the vehicle, thereby judging the direction of the center-of-gravity offset, setting shock absorbers respectively located at the front, rear, left, and right of the vehicle, and relieving the vehicle shake by adjusting the damping. The greater the damping, the more stable the vehicle. If the center of gravity offsets front and back, increase the damping of the front or rear shock absorber; if the center of gravity offsets left and right, increase the damping of the left or right shock absorber.

[0025] 4. By detecting whether the seal is loose, when the seal is loose, a prompt for maintenance is issued.

[0026] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 FIG. is a structural diagram of a road condition data detection and shock absorption adjustment system based on the Internet of Things technology according to the present invention.

[0028] Figure 2 FIG. is a broken line graph showing the influence of vehicle shake on the pressure difference borne between two tires according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0031] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a road condition data detection and shock absorption adjustment system based on Internet of Things technology, including the following specific steps:

[0032] Severe shaking evaluation module: Real-time acquisition of emergency braking data through the first camera, the second camera, the wheel speed sensor, the lidar, and the first pressure sensor. The emergency braking data includes the first image data acquired by the first camera, the second image data acquired by the second camera, the vehicle speed acquired by the wheel speed sensor, the measured distance acquired by the lidar, the bearing pressure of the tire acquired by the first pressure sensor, and the road surface dynamic friction coefficient.

[0033] Among them, the first camera is set on the vehicle, the second camera is set on both sides of the road, and the second image data is transmitted to the vehicle through Internet of Things technology. The combination of the first image data and the second image data helps to improve the accuracy of comprehensive analysis. The measured distance is the distance between the vehicle itself and the obstacle on the road ahead.

[0034] Real-time acquisition of vehicle shaking data through the gyroscope sensor and the first pressure sensor. The vehicle shaking data includes the rolling angular velocity, pitch angular velocity, yaw angular velocity acquired by the gyroscope sensor, and the tire lift-off frequency acquired by the first pressure sensor.

[0035] Among them, the gyroscope sensor is used to detect whether the vehicle shakes. The tire lift-off frequency is the ratio of the number of times when the tire bearing pressure is zero to the vehicle shaking time. The vehicle shaking time is the time from when the vehicle starts to shake to when it finally stabilizes.

[0036] Filtering and noise reduction processing of the emergency braking data and the vehicle shaking data helps to improve the quality of the emergency braking data and the vehicle shaking data. Normalization processing and comprehensive analysis of the filtered and noise-reduced emergency braking data and vehicle shaking data help to eliminate the dimension, improve the calculation efficiency of the emergency braking data and the vehicle shaking data, and obtain the emergency braking influence coefficient and the vehicle shaking anomaly coefficient.

[0037] Standardization processing and comprehensive evaluation of the emergency braking influence coefficient and the vehicle shaking anomaly coefficient help to further eliminate different dimensions, improve the calculation efficiency of the emergency braking influence coefficient and the vehicle shaking anomaly coefficient, and obtain the severe shaking evaluation value.

[0038]

[0039] Among them, HP represents the severe shaking evaluation value, which is used to judge whether the vehicle shakes severely; JX represents the emergency braking influence coefficient, which reflects whether the emergency braking has an impact; CX represents the vehicle shaking anomaly coefficient, which reflects whether the vehicle shaking is abnormal.

[0040] The specific way to obtain the emergency braking influence coefficient is as follows:

[0041] The first image data and the second image data are calculated through an image contour detection algorithm and an image contour tracking algorithm to obtain contour image data. For example, the Sobel algorithm is used for the image contour detection algorithm. First, convolution calculations are respectively performed on the horizontal and vertical directions of the first image data and the second image data to obtain a first matrix and a second matrix of 3x3. Then, the first gradient value and the second gradient value are respectively calculated based on the first matrix and the second matrix. Finally, the contour image data is calculated through the first gradient value and the second gradient value. The contour image data includes the pixel values of the obstacles, the pixel coordinates of the obstacles, the actual color of the road surface, and the actual texture of the road surface. For the image contour tracking algorithm, for example, the optical flow method, in the continuous contour image data, the change of the pixel coordinates of the obstacles over time during the vehicle shaking time is calculated based on the principle that the pixel values of the obstacles remain unchanged, so as to obtain the movement trajectory of the obstacles, and prevent the accuracy of the measurement distance obtained by the lidar from being reduced in real time when the obstacles move.

[0042] The vehicle sliding distance is obtained through comprehensive analysis of the vehicle speed, the measurement distance, the tire bearing pressure, and the road surface dynamic friction coefficient. Among them, the road surface dynamic friction coefficient is obtained by comparing the template color and template texture of the road surface with the actual color and actual texture of the road surface by calling the China Highway Data Center database or the International Road Federation database.

[0043] When there are the pixel values of the obstacles and the pixel coordinates of the obstacles, the vehicle sliding distance and the measurement distance are compared in real time. If the vehicle sliding distance is less than the measurement distance, the comparison continues. If the vehicle sliding distance is greater than or equal to the measurement distance, the vehicle brakes automatically to avoid the vehicle hitting the obstacles due to the slow reaction speed of humans.

[0044] When braking automatically, the center of gravity of the vehicle transfers to the front of the vehicle under the action of inertia, the front of the vehicle sinks, and the rear of the vehicle tilts up, causing the vehicle to lose balance. In order to consume the kinetic energy of the vehicle continuing to move forward after automatic braking, the friction between the tires and the road surface does work to convert the kinetic energy into heat energy and sound energy.

[0045] The greater the vehicle sliding distance, the smaller the friction between the tires and the road surface, the slower the consumption of the vehicle's forward kinetic energy, the weaker the hindrance effect on the bottom of the vehicle, and the longer the time for the top of the vehicle to move forward under the action of inertia and the vehicle sliding distance. The difference in the motion states between the bottom and the top of the vehicle is greater, and the center of gravity of the vehicle shifts more significantly, resulting in more severe vehicle shaking. When the center of gravity of the vehicle is stable, the bearing pressure of each tire is the same. When the center of gravity of the vehicle shifts, the bearing pressure of each tire is different. Then, the difference between the bearing pressure of the i-th tire and the bearing pressure of the i-1-th tire is calculated through the number of tires, as Figure 2 As shown, the greater the difference, the more severe the vehicle's shaking;

[0046] Table 1 Influence of Vehicle Shaking on the Pressure Difference between Two Tires

[0047] Group Vehicle shaking (degrees) Pressure difference (Newton) 1 2-7 46-98 2 12-23 123-214 3 26-34 276-291

[0048] From the data in Table 1, it can be seen that there is a positive correlation between the degree of vehicle shaking and the pressure difference between the two tires. In Group 1, when the vehicle shaking angle ranges from 2 to 7 degrees, the pressure difference is between 46 and 98 Newtons; in Group 2, when the vehicle shaking angle increases to 12 - 23 degrees, the pressure difference significantly rises to 123 - 214 Newtons;

[0049] As the vehicle shaking angle further increases, such as in Group 3 where the shaking angle is between 26 and 34 degrees, the pressure difference climbs to 276 - 291 Newtons. This clearly shows that the greater the vehicle shaking angle, the greater the pressure difference between the two tires, and the more obvious the uneven force situation of the vehicle;

[0050] Perform gray - scale conversion on the actual texture of the road surface to obtain the gray - scale value of the road surface texture. Calculate the gray - scale value of the road surface texture by the variance method. According to the degree of dispersion of the gray - scale value of the road surface texture, determine the roughness of the road surface. The greater the degree of dispersion, the rougher the road surface, and thus the greater the frictional force on the tire, and obtain the influence value of the road surface texture;

[0051] Comprehensively analyze the influence value of the road surface texture, the vehicle sliding distance, the number of tires, the pressure borne by the i - th tire, and the pressure borne by the (i - 1) - th tire to obtain the emergency braking influence coefficient;

[0052]

[0053] Among them, JX represents the emergency braking influence coefficient, reflecting whether the emergency braking causes an impact; n represents the number of tires; represents the permutation and combination of selecting two tires from n tires; MW represents the influence value of the road surface texture. The greater the influence value of the road surface texture, the more severe the vehicle shaking; HJ represents the vehicle sliding distance; LY i represents the pressure borne by the i - th tire, LY i-1 represents the pressure borne by the (i - 1) - th tire. The greater the absolute value of the difference between the pressure borne by the i - th tire and the pressure borne by the (i - 1) - th tire, the more severe the vehicle shaking.

[0054] The specific method for obtaining the vehicle sliding distance is as follows:

[0055] According to Newton's second law F = ma, the magnitude of the acceleration of an object is directly proportional to the acting force and inversely proportional to the mass of the object. The direction of the acceleration is the same as the direction of the acting force, then Where F 摩 represents the frictional force between the tire and the road surface; MY represents the dynamic friction coefficient of the road surface; represents the average value of the pressure borne by the tire, n represents the number of tires, represents the gravity of the vehicle;

[0056] Through the acceleration due to gravity, the law of universal gravitation, and Newton's second law, it is obtained that the gravity of the vehicle is equal to mg. When F = F 摩 At this time, then ma = MY×mg. Since the acceleration during braking is opposite to the moving speed of the vehicle, then a = -MY×g; According to uniformly variable rectilinear motion, CZ 2 -CS 2 = 2×(-MY×g×HJ), where CZ represents the final speed of the vehicle, which is zero, CS represents the vehicle speed, MY represents the dynamic friction coefficient of the road surface, and g represents the acceleration due to gravity; Since the final speed of the vehicle is zero, the sliding distance of the vehicle is obtained;

[0057]

[0058] Where HJ represents the sliding distance of the vehicle, CS represents the vehicle speed, MY represents the dynamic friction coefficient of the road surface, and g represents the acceleration due to gravity.

[0059] The specific method for obtaining the vehicle shaking anomaly coefficient is as follows:

[0060] The average values of the rolling angular velocity, pitch angular velocity, and yaw angular velocity are calculated respectively. To determine the stability criteria for the discreteness of the subsequent rolling angular velocity, pitch angular velocity, and yaw angular velocity, the average rolling angular velocity, average pitch angular velocity, and average yaw angular velocity are obtained respectively. According to the average rolling angular velocity, average pitch angular velocity, and average yaw angular velocity, the standard deviation method is used to calculate the rolling angular velocity, pitch angular velocity, and yaw angular velocity respectively, which are used to measure the volatility of the rolling angular velocity, pitch angular velocity, and yaw angular velocity on the stability criteria. The greater the volatility, the more the vehicle shakes, and the rolling angular velocity anomaly value, pitch angular velocity anomaly value, and yaw angular velocity anomaly value are obtained respectively;

[0061] The rolling angular velocity anomaly value, pitch angular velocity anomaly value, yaw angular velocity anomaly value, and the frequency of the tire leaving the ground are comprehensively analyzed to obtain the vehicle shaking anomaly coefficient;

[0062]

[0063] Among them, CX represents the vehicle abnormal shaking coefficient, reflecting whether the vehicle shakes abnormally; LV represents the frequency of the tire leaving the ground. The greater the frequency of the tire leaving the ground, the greater the abnormal value of the rolling angular velocity or the abnormal value of the pitch angular velocity or the abnormal value of the yaw angular velocity, and the more abnormal the vehicle shaking; GY represents the abnormal value of the rolling angular velocity, reflecting whether the rolling angular velocity is abnormal; FY represents the abnormal value of the pitch angular velocity, reflecting whether the pitch angular velocity is abnormal; PY represents the abnormal value of the yaw angular velocity, reflecting whether the yaw angular velocity is abnormal.

[0064] When the frequency of the tire leaving the ground is zero, then e LV It reflects that the abnormal values of the rolling angular velocity, the pitch angular velocity, and the yaw angular velocity are small or also zero; when the abnormal value of the rolling angular velocity or the abnormal value of the pitch angular velocity or the abnormal value of the yaw angular velocity is zero, then the vehicle abnormal shaking coefficient is small or also zero.

[0065] Severe bump judgment module: Judge whether the vehicle shakes severely according to the severe shaking evaluation value; compare the severe shaking threshold value with the severe shaking evaluation value in real time. If the severe shaking evaluation value is greater than or equal to the severe shaking threshold value, it is judged that the vehicle shakes severely. If the severe shaking evaluation value is less than the severe shaking threshold value, it is judged that the vehicle is stable; send the judgment result of whether the vehicle shakes severely to the maintain stability execution module.

[0066] Maintain stability execution module: Receive the judgment result of whether the vehicle shakes severely. If it is judged that the vehicle shakes severely, use the dynamic adaptive damping optimization algorithm to adjust the shock absorption to keep the vehicle stable, and return to the severe shaking evaluation module to continue to obtain the emergency braking data and the vehicle shaking data in real time until it is judged that the vehicle is stable. If it is judged that the vehicle is stable, execute the seal loosening evaluation module.

[0067] The specific steps of the dynamic adaptive damping optimization algorithm are as follows:

[0068] A three-dimensional coordinate system is established with the origin based on the position distribution of the gyroscope sensor. By setting the standard roll angular velocity and the standard pitch angular velocity respectively according to the pitch angular velocity and the roll angular velocity when the vehicle is driving smoothly, they are used as the standards for detecting whether the center of gravity of the vehicle shifts forward and backward or left and right. An interval from -90 degrees to +90 degrees is set for both the pitch angular velocity and the roll angular velocity to obtain the pitch interval and the roll interval as the change ranges of the pitch angular velocity and the roll angular velocity. A shock-absorbing component is set. The shock-absorbing component relieves the shaking of the vehicle by adjusting the damping therein. The greater the adjusted damping, the more mechanical energy of the vehicle shaking is converted into heat energy, and the more stable the vehicle is. The shock-absorbing component includes a first shock absorber for being arranged at the front of the vehicle, a second shock absorber for being arranged at the rear of the vehicle, a third shock absorber for being arranged on the left side of the vehicle, and a fourth shock absorber for being arranged on the right side of the vehicle. If the center of gravity of the vehicle shifts forward and backward, the damping of the first shock absorber or the second shock absorber is increased. If the center of gravity of the vehicle shifts left and right, the damping of the third shock absorber or the fourth shock absorber is increased.

[0069] Seal loosening evaluation module: Real-time obtains seal loosening data through the second pressure sensor, the displacement sensor, and the vibration sensor. The seal loosening data includes the actual thrust obtained by the second pressure sensor, the moving distance obtained by the displacement sensor, and the vibration amplitude obtained by the vibration sensor.

[0070] Among them, the actual thrust is the force that pushes the seal to loosen. Severe shaking of the vehicle will cause the pressure in the shock-absorbing system to rise sharply, resulting in displacement of the originally stationary seal and thus loosening. When the vehicle shakes, the loose seal will vibrate along with the shaking of the vehicle.

[0071] Performing filtering and noise reduction processing on the seal loosening data helps to improve the quality of the seal loosening data; performing normalization processing and comprehensive analysis on the seal loosening data after filtering and noise reduction processing helps to eliminate the dimension and improve the calculation efficiency of the seal loosening data, and obtain the seal loosening anomaly coefficient.

[0072] Performing standardization processing and comprehensive evaluation on the seal loosening anomaly coefficient and the vehicle shaking anomaly coefficient helps to further eliminate different dimensions and improve the calculation efficiency of the seal loosening anomaly coefficient and the vehicle shaking anomaly coefficient, and obtain the seal loosening evaluation value.

[0073]

[0074] Among them, MP represents the seal loosening evaluation value, which is used to judge whether the seal is loose; MX represents the seal loosening anomaly coefficient, which reflects whether the seal loosening is abnormal; CX represents the vehicle shaking anomaly coefficient, which reflects whether the vehicle shaking is abnormal.

[0075] The specific method for obtaining the abnormal coefficient of seal loosening is as follows:

[0076] The second pressure sensor obtains the normal thrust in the thrust detection of the seal under normal conditions, and comprehensively analyzes the normal thrust, actual thrust, moving distance, and vibration amplitude to obtain the abnormal coefficient of seal loosening;

[0077]

[0078] Among them, MX represents the abnormal coefficient of seal loosening, reflecting whether the seal is abnormally loose; ST represents the actual thrust, ZT represents the normal thrust. If the actual thrust is greater than the normal thrust, it means the seal is abnormal. If the actual thrust is less than or equal to the normal thrust, it means the seal is normal; YJ represents the moving distance, reflecting the distance the seal moves after being pushed by the actual thrust. Under normal circumstances, the moving distance is zero; ZF represents the vibration amplitude, reflecting the amplitude of vibration of the seal as the vehicle shakes after the seal is loose. Under normal circumstances, the vibration amplitude is zero.

[0079] Seal loosening judgment module: Judge whether the seal is loose according to the seal loosening evaluation value; compare the seal loosening threshold value with the seal loosening evaluation value in real time. If the seal loosening evaluation value is greater than or equal to the seal loosening threshold value, it is judged that the seal is loose. If the seal loosening evaluation value is less than the seal loosening threshold value, it is judged that the seal is normal; send the judgment result of whether the seal is loose to the seal loosening execution module.

[0080] Seal loosening execution module: Receive the judgment result of whether the seal is loose. If it is judged that the seal is loose, immediately issue an alarm to prompt for maintenance, and return to the seal loosening evaluation module to continuously obtain the seal loosening data in real time until it is judged that the seal is normal; if it is judged that the seal is normal, continue to detect whether the seal is loose.

[0081] The above - disclosed preferred embodiments of the present invention are only used to help explain the present invention. The preferred embodiments do not elaborate on all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical field can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A road condition data detection and shock absorption adjustment system based on Internet of Things technology, characterized in that: Includes the following specific modules: Severe shaking assessment module: used to obtain emergency braking data and vehicle shaking data in real time, pre-process the emergency braking data and vehicle shaking data, and conduct comprehensive assessment on the pre-processed emergency braking data and vehicle shaking data to obtain a severe shaking assessment value; Severe turbulence judgment module: judges whether the vehicle is shaking violently according to the severe shaking evaluation value; sends the judgment result of whether the vehicle is shaking violently to the stability maintenance execution module; Maintaining stability execution module: used to receive the judgment result of whether the vehicle is shaking violently. If it is judged that the vehicle is shaking violently, the dynamic adaptive damping optimization algorithm is used to adjust the shock absorption to keep the vehicle stable, and return to the violent shaking assessment module to continue to obtain the emergency braking data and vehicle shaking data in real time until it is judged that the vehicle is stable. If it is judged that the vehicle is stable, the seal looseness assessment module is executed; Seal looseness assessment module: used to obtain seal looseness data in real time, perform preliminary processing on the seal looseness data, conduct comprehensive assessment on the seal looseness data after preliminary processing, and obtain the seal looseness assessment value; Seal loosening judgment module: judge whether the seal is loose according to the seal loosening assessment value; Sending the judgment result of whether the seal is loose to the seal loose execution module; Seal loosening execution module: receives the judgment result of whether the seal is loose. If the seal is judged to be loose, an alarm is immediately issued to prompt maintenance, and returns to the seal loosening assessment module to continue to obtain seal loosening data in real time until it is judged that the seal is normal; if the seal is judged to be normal, continue to detect whether the seal is loose.

2. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1 is characterized in that: In the severe shaking assessment module, the emergency braking data includes the first image data, the second image data, the vehicle speed, the measured distance, the tire bearing pressure and the road surface dynamic friction coefficient; the vehicle shaking data includes the rolling angular velocity, the pitching angular velocity, the yaw angular velocity and the tire ground lift frequency; Perform filtering and noise reduction on emergency braking data and vehicle shaking data; The emergency braking data and vehicle shaking data after filtering and noise reduction are normalized and comprehensively analyzed to obtain the emergency braking influence coefficient and vehicle shaking abnormality coefficient; The sudden braking influence coefficient and the vehicle shaking abnormal coefficient are standardized and comprehensively evaluated to obtain the severe shaking assessment value; Among them, HP represents the severe shaking assessment value; JX represents the sudden braking influence coefficient; CX represents the vehicle shaking abnormality coefficient.

3. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 2 is characterized in that: The specific method for obtaining the emergency brake influence coefficient is as follows: Calculating the first image data and the second image data by an image contour detection algorithm and an image contour tracking algorithm to obtain contour image data, wherein the contour image data includes a pixel value of an obstacle, a pixel point coordinate of the obstacle, an actual color of a road surface, and an actual texture of the road surface; The vehicle sliding distance is obtained by comprehensive analysis of vehicle speed, measurement distance, tire pressure and road dynamic friction factor; when there are pixel values ​​and pixel coordinates of obstacles, the vehicle sliding distance and the measured distance are compared in real time. If the vehicle sliding distance is less than the measured distance, the comparison is continued; if the vehicle sliding distance is greater than or equal to the measured distance, the vehicle automatically brakes; The difference between the bearing pressure of the i-th tire and the bearing pressure of the i-1-th tire is calculated by the number of tires; the actual texture of the road surface is converted into grayscale to obtain the grayscale value of the road surface texture, and the grayscale value of the road surface texture is calculated by the variance method to obtain the road surface texture influence value; A comprehensive analysis is performed on the impact value of road texture, vehicle sliding distance, the number of tires, the pressure borne by the i-th tire, and the pressure borne by the i-1-th tire to obtain the emergency braking influence coefficient.

4. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 3 is characterized in that: The specific method for obtaining the vehicle sliding distance is as follows: According to Newton's second law, we have where F 摩 It indicates the friction between the tire and the road surface; MY indicates the dynamic friction coefficient of the road surface; It represents the average pressure of the tire, n represents the number of tires, Indicates the weight of the vehicle; The vehicle's sliding distance is obtained through gravitational acceleration, the law of universal gravitation, Newton's second law and uniformly accelerated linear motion.

5. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 2 is characterized in that: The specific method for obtaining the vehicle shaking abnormal coefficient is as follows: The rolling angular velocity, the pitch angular velocity and the yaw angular velocity are averaged and calculated respectively to obtain the rolling angular velocity average value, the pitch angular velocity average value and the yaw angular velocity average value; the rolling angular velocity, the pitch angular velocity and the yaw angular velocity are calculated by the standard deviation method according to the rolling angular velocity average value, the pitch angular velocity average value and the yaw angular velocity average value to obtain the rolling angular velocity abnormal value, the pitch angular velocity abnormal value and the yaw angular velocity abnormal value; The abnormal values ​​of rolling angular velocity, pitching angular velocity, yaw angular velocity and the frequency of tire lifting off the ground are comprehensively analyzed to obtain the abnormal coefficient of vehicle shaking.

6. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1 is characterized in that: In the severe jolt judgment module, a real-time comparison is made between the severe shaking threshold and the severe shaking assessment value. If the severe shaking assessment value is greater than or equal to the severe shaking threshold, it is judged that the vehicle is shaking violently. If the severe shaking assessment value is less than the severe shaking threshold, it is judged that the vehicle is stable. The judgment result of whether the vehicle is shaking violently is sent to the stability maintenance execution module.

7. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1 is characterized in that: The specific steps of the dynamic adaptive damping optimization algorithm are as follows: The center point of the vehicle is transformed into the origin to establish a three-dimensional coordinate system. The standard rolling angular velocity and the standard pitching angular velocity are respectively set according to the pitching angular velocity and the rolling angular velocity when the vehicle is driving steadily. The pitching angular velocity and the rolling angular velocity are both set to a range from negative ninety degrees to positive ninety degrees to obtain a pitching range and a rolling range. A shock absorbing component is set, and the shock absorbing component includes a first shock absorber, a second shock absorber, a third shock absorber and a fourth shock absorber. If the center of gravity of the vehicle is offset frontward and rearward, the damping of the first shock absorber or the second shock absorber is increased. If the center of gravity of the vehicle is offset leftward and rightward, the damping of the third shock absorber or the fourth shock absorber is increased.

8. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1 is characterized in that: In the seal looseness assessment module, the seal looseness data includes actual thrust, moving distance and vibration amplitude; the seal looseness data is filtered and denoised; The seal loosening data after filtering and noise reduction are normalized and comprehensively analyzed to obtain the seal loosening anomaly coefficient; The seal loosening anomaly coefficient and the vehicle shaking anomaly coefficient are standardized and comprehensively evaluated to obtain the seal loosening assessment value.

9. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1 is characterized in that: The specific method for obtaining the seal loosening anomaly coefficient is as follows: By obtaining the normal thrust in the thrust detection of the seal under normal conditions, a comprehensive analysis is performed on the normal thrust, actual thrust, moving distance and vibration amplitude to obtain the seal loosening anomaly coefficient.

10. The road condition data detection and shock absorption adjustment system based on Internet of Things technology according to claim 1, characterized in that: In the seal loosening judgment module, a real-time comparison is performed between the seal loosening threshold and the seal loosening assessment value. If the seal loosening assessment value is greater than or equal to the seal loosening threshold, the seal is judged to be loose. If the seal loosening assessment value is less than the seal loosening threshold, the seal is judged to be normal. The judgment result of whether the seal is loose is sent to the seal loosening execution module.

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