Height sensor signal conversion weight calculation method and system based on clutch pack integration

Abstract

This invention proposes a method and system for weight calculation based on height sensor signals integrated into the gearshift assembly, belonging to the field of automotive electronics technology. The method includes: acquiring vehicle height signals in real time through height sensors deployed on each suspension, receiving and de-jittering the signals by a control module integrated into the gearshift assembly; determining the operating condition based on vehicle speed: in static or quasi-static conditions, calculating the total load mass and axle load distribution according to a calibrated vehicle height-load mapping model and providing visual prompts; during dynamic driving, calculating the dynamic load of each wheel at a preset frequency and determining the axle load transfer rate, identifying axle load transfer transient events when the dynamic threshold is exceeded, generating driving behavior data points that are synchronously uploaded to both local and cloud platforms. This invention can effectively monitor the vehicle's load capacity, prevent damage caused by overloading, and extend the vehicle's service life; it also achieves visual load reminders and routine monitoring of the entire vehicle load.

Description

Technical Field

[0001] This invention belongs to the field of automotive electronics technology, and in particular relates to a method and system for calculating weight based on the signal conversion of a height sensor integrated into a column shifter. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In existing vehicle weighing technologies, height sensor-based solutions are widely used. These solutions calculate the vehicle's load by collecting data on the relative height change between the vehicle body and the suspension, and by utilizing the physical relationship between the deformation of elastic elements and the load.

[0004] However, such methods generally suffer from the following technical problems in practical applications: First, existing solutions typically rely on independent electronic control units to collect, process, and calculate altitude signals and weight. This not only increases the overall vehicle hardware cost and wiring complexity but also makes it difficult to achieve effective data sharing and coordination with the vehicle's existing shifting, lighting, and other control systems.

[0005] Second, most existing weighing methods are only applicable to vehicles in static or quasi-static conditions, and cannot effectively monitor the real-time transfer of axle load during dynamic driving. This results in a lack of load change information under transient conditions such as rapid acceleration, emergency braking, and sharp turns, making it difficult to support the needs of derivative application scenarios such as driving behavior analysis, vehicle status warning, and insurance claims. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a weight calculation method and system based on the height sensor signal conversion integrated into the column shifter, which can effectively monitor the vehicle's load, prevent damage to the vehicle caused by overload, and extend the vehicle's service life; it also realizes visualized load reminders and routine monitoring of the vehicle's load.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for calculating weight based on the conversion of height sensor signals integrated into the side rail.

[0008] A weight calculation method based on the height sensor signal conversion integrated into the guardrail includes: The vehicle height signal is acquired in real time by multiple height sensors arranged on each suspension of the vehicle. The vehicle height signal is received by the control module integrated into the column shifter assembly, and then processed to reduce vibration. The vehicle's current operating condition is determined based on the vehicle speed signal, and corresponding operations are performed: When in a static or quasi-static condition, the total load mass of the vehicle and the axle load distribution of each axle are calculated based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model, and the results are displayed visually on the display terminal. When in dynamic driving conditions, the dynamic load of each wheel is calculated in real time at a preset frequency, and the real-time axle load transfer rate of the vehicle is determined. When the axle load transfer rate exceeds a preset dynamic threshold within a preset time window, an axle load transfer transient event is determined to have occurred. Based on the type and intensity of the axle load transfer transient event, driving behavior data points are generated and uploaded synchronously to the local machine and the cloud.

[0009] Furthermore, when performing jitter removal on the vehicle height signal, a software filtering window corresponding to the vehicle model in the preset jitter removal vehicle model database is used to perform jitter removal on the vehicle height signal within a preset time window, so as to eliminate measurement noise caused by vehicle body shaking.

[0010] Furthermore, the vehicle height-vehicle load mapping model incorporates compensation factors based on ramp angle and vehicle acceleration.

[0011] Furthermore, the calibration process of the vehicle height-vehicle load mapping model includes: When the vehicle is on a static level road surface, the calibrated counterweight mass applied to different positions of the vehicle is changed sequentially, and the signal values ​​output by each height sensor are recorded to establish a first mapping table. When the vehicle is on a slope with multiple preset slope angles, the calibration process is repeated to record the relationship between the slope angle, counterweight mass, and height signal value, forming a second mapping table. The compensation factor is a correction value calculated by querying the second mapping table based on the real-time slope angle signal and the vehicle longitudinal acceleration signal.

[0012] Furthermore, the vehicle height-vehicle load mapping model also embeds independent linear curves and nonlinear fitting curves for each height sensor. These are used to calculate the front axle load based on the signal from the front axle height sensor and the rear axle load based on the signal from the rear axle height sensor when calculating the total load mass of the vehicle and the axle load distribution of each axle based on the vehicle height-vehicle load mapping model, and to calculate the ratio between the two.

[0013] Furthermore, the axle load transfer rate includes the front-to-rear axle load transfer rate and the left-to-right axle load transfer rate.

[0014] Furthermore, the determination of the axle load transfer transient event includes: When the real-time dynamic load on the current axle decreases instantaneously and the real-time dynamic load on the rear axle increases instantaneously, and the rate of change exceeds a preset slope threshold, it is determined to be a rapid acceleration axle load transfer event. When the real-time dynamic load on the current axle increases instantaneously and the real-time dynamic load on the rear axle decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be an emergency braking axle load transfer event. When the real-time dynamic load on one side of the wheel increases instantaneously and the real-time dynamic load on the other side decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be a sharp turn axle load transfer event.

[0015] Furthermore, the driving behavior data is used to construct a driving style model, specifically including event occurrence time, event type, event intensity, vehicle speed at the time of the event, steering wheel angle, and load change curve segments for each wheel.

[0016] Furthermore, under the aforementioned dynamic driving conditions, a four-wheel impact warning procedure is also included: Real-time monitoring of the dynamic load change rate of individual wheels; When the rate of change of dynamic load of any wheel exceeds the impact threshold within a preset micro-time window, a road impact event is determined to have occurred. Based on the intensity and repetition frequency of impact events, abnormal warning signals for tires and suspension systems are generated.

[0017] A second aspect of the present invention provides a weight calculation system based on a height sensor signal conversion integrated with a side rail.

[0018] A weight calculation system based on a height sensor signal conversion integrated into the shift lever includes: Four height sensor assemblies are respectively arranged between the vehicle frame and the four suspension control arms to acquire vehicle height signals in real time; each height sensor assembly includes a link bracket, a ball pin, a link, a rocker arm assembly, a magnet, and a circuit board for outputting pulse width modulation signals; The column shifter assembly integrates a control module, which includes: The signal acquisition and preprocessing unit is used to receive the pulse width modulation signals output by the four height sensors and perform jitter removal processing; The operating condition discrimination unit is used to determine whether the vehicle is in a static operating condition, a quasi-static operating condition, or a dynamic driving condition based on the vehicle speed signal. The static weighing calculation unit is used to calculate the total load mass of the vehicle and the axle load distribution of each axle under static or quasi-static working conditions, based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model. The dynamic monitoring unit is used to calculate the dynamic load of each wheel in real time at a preset frequency under dynamic driving conditions, determine the real-time axle load transfer rate of the vehicle, and identify axle load transfer transient events; and generate driving behavior embedded data according to the type and intensity of the axle load transfer transient events. The display terminal is connected to the glove box assembly via the vehicle bus and is used to display the output signals of the static weighing calculation unit and the dynamic monitoring unit. The storage module is used to store the driving behavior data embedding points; The communication module is used to upload the driving behavior data to the cloud.

[0019] The above one or more technical solutions have the following beneficial effects: This invention integrates all functions such as signal acquisition, de-jitter processing, working condition judgment, and weight calculation into the control module of the column shifter assembly. There is no need to set up an additional independent weighing controller. It reuses the hardware resources and communication links of the vehicle's existing shifting, lighting, and other systems, which significantly reduces the overall vehicle material cost and wiring complexity. At the same time, it achieves seamless data sharing and collaboration between weighing data and other vehicle control systems.

[0020] This invention calculates the dynamic load and axle load transfer rate of each wheel in real time at a preset frequency under dynamic driving conditions. It accurately identifies transient axle load transfer events such as rapid acceleration, sudden braking, and sharp turning by preset dynamic thresholds, and generates driving behavior data points containing information such as event type, intensity, vehicle speed, and steering wheel angle. These data points are then uploaded to both local and cloud platforms, filling the gap in the collection of dynamic load change information and providing reliable data support for derivative applications such as driving behavior analysis, vehicle status warning, and insurance claims.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of the weight calculation method based on the height sensor signal conversion of the guardrail integration in Embodiment 1 of the present invention. Detailed Implementation

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0026] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0027] Example 1 This embodiment discloses a method for calculating weight based on the signal conversion of a height sensor integrated into a side rail.

[0028] like Figure 1 As shown, the weight calculation method based on the height sensor signal conversion integrated into the shifter includes: Step S1: Obtain vehicle height signals in real time by using multiple height sensors arranged on each suspension of the vehicle; Step S2: Receive the vehicle height signal through the control module integrated into the column shifter assembly and perform anti-shake processing; Step S3: Determine the vehicle's current operating condition based on the vehicle speed signal and perform the corresponding operation: When in a static or quasi-static condition, the total load mass of the vehicle and the axle load distribution of each axle are calculated based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model, and the results are displayed visually on the display terminal. When in dynamic driving conditions, the dynamic load of each wheel is calculated in real time at a preset frequency, and the real-time axle load transfer rate of the vehicle is determined. When the axle load transfer rate exceeds a preset dynamic threshold within a preset time window, an axle load transfer transient event is determined to have occurred. Based on the type and intensity of the axle load transfer transient event, driving behavior data points are generated and uploaded synchronously to the local machine and the cloud.

[0029] Based on the above process, this invention can effectively monitor the vehicle's load capacity, prevent damage caused by overload, and extend the vehicle's service life; it also achieves visualized load reminders and routine monitoring of the vehicle's load. To facilitate understanding of the technical solution of this invention, further explanation and description of the specific implementation methods are now provided.

[0030] In step S1, the vehicle height signal is acquired in real time by multiple height sensors arranged on each suspension of the vehicle.

[0031] A height sensor is installed on each of the vehicle's left front wheel, right front wheel, left rear wheel, and right rear wheel suspensions. Each height sensor is fixed between the vehicle frame and the suspension control arm. When the vehicle load changes, the suspension control arm swings relative to the vehicle body, causing the rotor inside the sensor to rotate, thereby changing the duty cycle of the output signal.

[0032] In this embodiment, the height sensor uses a pulse width modulation (PWM) signal output, with a linear relationship between the signal duty cycle and the suspension height. The sensor's power supply is provided by the shift lever assembly system, and the signal line is directly connected to the shift lever assembly's control module. During vehicle operation, the control module reads the high-level time and period of the PWM signal from each sensor at a fixed sampling period (e.g., every 5ms as a sampling period), calculates the real-time duty cycle, and then obtains the current vehicle height value at each suspension position based on the sensor's factory-calibrated duty cycle-height conversion relationship. To ensure the reliability of signal transmission, the wiring harness from the sensor to the control module uses shielded twisted-pair cable, and a hardware filtering circuit is set at the control module input to suppress electromagnetic interference. Through the above method, real-time and synchronous acquisition of vehicle height signals at all four wheels is achieved.

[0033] In step S2, the vehicle height signal is received by the control module integrated into the column shifter assembly, and the vehicle height signal within the preset time window is subjected to a de-shaking operation using the software filtering window of the corresponding vehicle model in the preset de-shaking vehicle database, so as to eliminate the measurement noise caused by vehicle body shaking.

[0034] A de-vibration vehicle database is pre-stored in the internal memory of the control module. This database stores the applicable filter window parameters (including filter time window width, filter algorithm type, etc.) for each vehicle model based on the natural suspension frequency and body vibration characteristics of different models. For the current vehicle, the control module automatically calls the corresponding filter window based on the vehicle model identifier.

[0035] For example, for a city SUV, the suspension bias frequency is approximately 1.2Hz, and body vibration is mainly concentrated in the low-frequency range. Therefore, a sliding time window with a width of 100ms is used, collecting multiple height samples within the window. An extreme value removal averaging filter (removing the maximum and minimum values ​​and taking the average) is used as the output. For a rugged off-road vehicle, the road excitation frequency of its non-independent suspension is higher, so a moving weighted average filter with a width of 50ms is used, assigning higher weights to recent samples. The control module receives the raw height signal output from step S1 in real time. Each new sample value is added to a circular queue, and then, based on the filtering algorithm selected for the current vehicle model, the statistical value of the effective samples within the time window is calculated as the de-jittered output height. This process is executed continuously, effectively suppressing high-frequency suspension vibrations caused by uneven road surfaces when the vehicle is traveling on bumpy roads. The output height signal smoothly reflects the vehicle's steady-state attitude, providing reliable input for subsequent weight calculations.

[0036] In step S3, the current operating condition of the vehicle is determined based on the vehicle speed signal, and the corresponding operations shown below are performed.

[0037] 1) Static or quasi-static operating conditions.

[0038] A vehicle height-vehicle load mapping model with embedded compensation factors based on ramp angle and vehicle acceleration is calibrated. This calibration is performed on a test track or bench during the vehicle development phase, and the specific calibration method is as follows: First, perform a level road calibration: Place the vehicle on a level platform, ensuring all four wheels are on the same horizontal plane. The vehicle is unloaded (fuel and coolant are filled to standard levels). Apply known weights to different locations on the vehicle (e.g., driver's seat, rear seats, trunk), gradually increasing the weight from unloaded to the maximum design load. After the suspension stabilizes under each load condition, record the output signal values ​​of the four height sensors (i.e., PWM duty cycle or converted height), and simultaneously record the total load mass. This establishes the first mapping table, which reflects the correspondence between the output values ​​of each suspension height sensor under different total loads and axle load distributions.

[0039] Next, slope calibration is performed: the vehicle is placed on an adjustable slope platform, and different slope angles (e.g., 0°, 3°, 6°, 9°, 12°, etc.) are set for both uphill and downhill sections. Tests are conducted at each slope under three load conditions: unloaded, half-loaded, and fully loaded. During the tests, the slope angle signal and longitudinal acceleration signal output from the vehicle's slope sensors are read via the vehicle's CAN bus. The actual slope, acceleration, output values ​​from the four height sensors, and the corresponding equivalent load (which can be measured using an axle load cell) are recorded for each condition. A second mapping table is then created. This second mapping table includes the influence of slope and acceleration on the height measurement values.

[0040] Based on this, during subsequent real-time operation, the control module calculates the height compensation factor in the second mapping table by looking up the currently read slope angle and longitudinal acceleration signal, which is the height measurement deviation caused by the slope inclination. Subtracting this deviation from the measured height yields the effective height caused only by the load, thus eliminating the influence of the slope on the weighing accuracy.

[0041] Furthermore, the vehicle height-vehicle load mapping model also embeds independent linear and nonlinear fitting curves for each height sensor. These curves are used to calculate the front axle load based on the signal from the front axle height sensor and the rear axle load based on the signal from the rear axle height sensor when calculating the total load mass and axle load distribution of the vehicle based on the vehicle height-vehicle load mapping model, and to calculate the ratio between the two.

[0042] In practical implementation, since the spring stiffness of the front and rear suspensions of a vehicle often differs, and there may also be slight differences between the left and right suspensions on the same axle, this model independently establishes a load-height conversion curve for each height sensor. For vehicles using linear coil springs, the load and height change are linearly related. Using the height data under different loads obtained during the aforementioned calibration process, the least squares method is used to fit the linear coefficient (i.e., stiffness) and zero-point offset of each sensor. For vehicles using air suspension or gradually increasing stiffness leaf springs, the load-height relationship is non-linear, so polynomial fitting is used to obtain quadratic or cubic curves. In real-time calculation, the control module first obtains the height values ​​of each wheel after de-jittering and slope compensation, subtracts them from the unloaded reference height to obtain the height change, and then calculates the load of each wheel based on the independent curve of each sensor. Subsequently, the loads of the left and right wheels of the front axle are added together to obtain the front axle load, and the loads of the left and right wheels of the rear axle are added together to obtain the rear axle load. The sum of the two is the total load mass. The front and rear axle loads are divided by the total load mass to obtain the front and rear axle load percentages. This ratio can be used to determine whether the vehicle load distribution is reasonable and to prevent driving safety hazards caused by uneven loading.

[0043] Based on this, when the vehicle is in a static or quasi-static condition, the total load mass of the vehicle and the axle load distribution of each axle are calculated based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model, and the results are displayed visually on the display terminal.

[0044] In practice, the control module acquires the vehicle speed signal via the CAN bus. When the vehicle speed remains below a certain low-speed threshold (e.g., 1 km / h) or the vehicle is in a creeping state with an extremely small rate of change in steering wheel angle, it is determined to be a static or quasi-static operating condition. Under this condition, the control module executes the following process: First, the stable height values ​​from the four height sensors are read after de-shaking and slope compensation processing. Then, based on the independent conversion curve of each height sensor, the load on each of the four wheels is calculated. Next, the front axle load, rear axle load, and total load mass are summed, and the front and rear axle load percentages are calculated. The control module encapsulates the calculation results into CAN messages or Ethernet signals and sends them to the central control screen or instrument cluster via the vehicle bus. After receiving the data, the display terminal displays the current load mass in real time in the form of numbers, progress bars, or pie charts, and warns the driver with different colors (e.g., green for reasonable load, yellow for near the limit, and red for overload).

[0045] Meanwhile, the front and rear axle load ratio is displayed as a bar chart or percentage. When the load ratio of a certain axle exceeds the safe range (e.g., the front axle load is below 30% or above 70%), the display terminal will give a text prompt: "Uneven load distribution, please adjust." The above display content is refreshed at regular intervals until the vehicle leaves the static or quasi-static working condition.

[0046] 2) Static or quasi-static operating conditions.

[0047] When in dynamic driving conditions, the dynamic load of each wheel is calculated in real time at a preset frequency, and the real-time axle load transfer rate of the vehicle is determined; the axle load transfer rate includes the front and rear axle load transfer rate and the left and right axle load transfer rate.

[0048] In practice, the control module monitors the vehicle speed in real time. When the speed consistently exceeds a certain threshold (e.g., 5 km / h), it switches to dynamic mode. In dynamic mode, the sampling frequency is increased to capture rapid load changes. The dynamic load calculation is still based on the real-time signal from each height sensor, but considering the lag of the slope compensation factor under dynamic conditions, to reduce latency, slope compensation can be temporarily ignored and the original height change can be used directly, coupled with high-frequency filtering. The control module calculates the dynamic load of the four wheels every 10 ms, then calculates the total front axle load (left front + right front) and the total rear axle load (left rear + right rear), thus obtaining the front-to-rear axle load transfer rate and the left-to-right axle load transfer rate, respectively: ; ; In the formula, Indicates the front and rear axle load transfer rate; This indicates the real-time dynamic load on the front axle. Indicates the static reference load on the front axle; This indicates the static reference gross vehicle weight. Indicates the load transfer rate between the left and right axles; This represents the sum of the dynamic loads on the left wheels. This represents the sum of the dynamic loads on the right-hand wheels. The static reference load and total mass are taken as the stable values ​​under the most recent static operating condition. By monitoring the changes in these two transfer rates in real time, the load redistribution during vehicle acceleration, deceleration, or cornering can be accurately reflected.

[0049] When the axle load transfer rate exceeds a preset dynamic threshold within a preset time window, an axle load transfer transient event is determined to have occurred, i.e.: When the real-time dynamic load on the current axle decreases instantaneously and the real-time dynamic load on the rear axle increases instantaneously, and the rate of change exceeds a preset slope threshold, it is determined to be a rapid acceleration axle load transfer event. When the real-time dynamic load on the current axle increases instantaneously and the real-time dynamic load on the rear axle decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be an emergency braking axle load transfer event. When the real-time dynamic load on one side of the wheel increases instantaneously and the real-time dynamic load on the other side decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be a sharp turn axle load transfer event.

[0050] In practice, the control module internally sets up three detectors to monitor rapid acceleration, rapid braking, and sharp turns, respectively. Each detector contains a sliding time window (e.g., a window length of 50ms), within which the load change rate is continuously calculated. Taking rapid acceleration as an example: the rate of change of the front axle load (i.e., the change in front axle load per millisecond or every 10ms) and the rate of change of the rear axle load are monitored in real time. If the front axle load decreases rapidly and the rear axle load increases rapidly, and the absolute value of both rates of change exceeds a set slope threshold, and this state continues for more than a short period of time (e.g., 30ms), it is determined to be a valid rapid acceleration axle load transfer event. The judgment logic for rapid braking is the opposite. For sharp turns, the change in the total load on both sides is monitored: when turning, the load on the outer wheel increases and the load on the inner wheel decreases. If the rate of change exceeds the threshold, and at the same time the steering wheel angular velocity signal indicates that the vehicle is turning, it is determined to be a sharp turn axle load transfer event.

[0051] To avoid false triggering by random road noise, all event determinations require a duration window condition, and a short blanking period must be set after an event is triggered to prevent the same event from being reported repeatedly.

[0052] Based on the type and intensity of the axle load transfer transient events, driving behavior tracking data is generated and synchronously uploaded to both local and cloud platforms. In specific implementation, whenever an axle load transfer transient event is confirmed, the control module immediately generates a tracking record. This record contains basic event information, such as event type (rapid acceleration, emergency braking, sharp turn), event occurrence time (system time accurate to milliseconds), and event intensity (divided into multiple levels based on the peak value of the axle load transfer rate). The control module first stores this tracking data in local non-volatile memory, forming a driving behavior log.

[0053] Simultaneously, the embedded data is uploaded to the cloud server in encrypted format via the vehicle's T-BOX (remote communication module). To save data usage, two strategies can be adopted: scheduled batch uploads or immediate uploads triggered by events. In the cloud, the backend system analyzes the embedded data to build driver style models and also provides data support for insurance companies' usage-based insurance business. Users can view driving behavior reports via mobile app or webpage to understand the frequency and trends of rapid acceleration, sudden braking, and sharp turns, thereby improving driving habits and reducing vehicle wear and tear and accident risks. The locally stored embedded data can also be accessed by 4S stores during maintenance to help determine whether the vehicle is frequently driven aggressively and to provide targeted maintenance recommendations.

[0054] Among them, the driving behavior data is used to build a driving style model, which includes the event time, event type, event intensity, vehicle speed at the time of the event, steering wheel angle, and load change curve segments of each wheel.

[0055] In practice, the data structure of the embedded data points is designed as follows: a fixed-length header plus a variable-length load segment. The header includes: an event timestamp (4 bytes), an event type code (1 byte), an intensity level (1 byte), the vehicle speed at the time of the event (2 bytes), the steering wheel angle (2 bytes), and the average longitudinal or lateral acceleration within the time window at the time of the event (1 byte). The load change curve segment records the dynamic load value sequence of each wheel within a certain time period (e.g., 100ms before and after) before and after the event trigger. These sequences are stored in relative or differential form to reduce the amount of data. The load change curve segment of each wheel can reflect the intensity and smoothness of the driver's operation (accelerator, brake, steering), which is a key feature for subsequent machine learning models to determine driving style.

[0056] By accumulating a large amount of vehicle data, a classifier that recognizes different driving styles can be trained in the cloud, thereby enabling driver labeling, differentiated insurance pricing, and personalized calibration of driver assistance systems.

[0057] Furthermore, under dynamic driving conditions, the following four-wheel impact warning steps are also included: Real-time monitoring of the dynamic load change rate of individual wheels; when the dynamic load change rate of any wheel exceeds the impact threshold within a preset micro-time window, a road impact event is determined to have occurred; based on the intensity and repetition frequency of the impact event, abnormal warning signals for the tire and suspension system are generated.

[0058] In practical implementation, the control module, in dynamic mode, not only calculates the axle load transfer rate but also independently monitors the instantaneous load change rate of each wheel. This monitoring uses a higher sampling rate (e.g., 1kHz) and a dedicated 5ms short-time window. When a wheel experiences a sharp increase or decrease in load within a very short time (e.g., 2-3 consecutive sampling cycles), and its rate of change exceeds a certain high threshold, it indicates that the wheel has encountered a strong impact from the road surface (such as passing over a pothole, a speed bump, or hitting a curb). The control module records the intensity and location of the impact event (which wheel). Subsequently, the system counts the number of high-intensity impact events occurring on the same wheel per unit time (e.g., every 10 minutes). If a wheel is frequently impacted, or if the intensity of a single impact is abnormally high, it is determined that the tire or suspension system may be damaged, posing a risk of tire blowout or shock absorber oil leakage. At this time, the control module sends a warning signal to the instrument panel and central control screen via the CAN bus, prompting the driver to "detect frequent and strong impacts to the left front wheel; please check the tires and suspension system." The warning signal is also sent to the cloud for proactive after-sales intervention. Through this step, the present invention not only realizes load measurement, but also provides road impact monitoring function based on dynamic load change rate, thereby improving vehicle safety.

[0059] Example 2 This embodiment discloses a weight calculation system based on the conversion of height sensor signals integrated into the guardrail.

[0060] A weight calculation system based on a height sensor signal conversion integrated into the shift lever includes: Four height sensor assemblies are respectively arranged between the vehicle frame and the four suspension control arms to acquire vehicle height signals in real time; each height sensor assembly includes a link bracket, a ball pin, a link, a rocker arm assembly, a magnet, and a circuit board for outputting pulse width modulation signals; The column shifter assembly integrates a control module, which includes: The signal acquisition and preprocessing unit is used to receive the pulse width modulation signals output by the four height sensors and perform jitter removal processing; The operating condition discrimination unit is used to determine whether the vehicle is in a static operating condition, a quasi-static operating condition, or a dynamic driving condition based on the vehicle speed signal. The static weighing calculation unit is used to calculate the total load mass of the vehicle and the axle load distribution of each axle under static or quasi-static working conditions, based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model. The dynamic monitoring unit is used to calculate the dynamic load of each wheel in real time at a preset frequency under dynamic driving conditions, determine the real-time axle load transfer rate of the vehicle, and identify axle load transfer transient events; and generate driving behavior embedded data according to the type and intensity of the axle load transfer transient events. The display terminal is connected to the glove box assembly via the vehicle bus and is used to display the output signals of the static weighing calculation unit and the dynamic monitoring unit. The storage module is used to store the driving behavior data embedding points; The communication module is used to upload the driving behavior data to the cloud.

[0061] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A weight calculation method based on the conversion of height sensor signals integrated into the side rail, characterized in that, include: The vehicle height signal is acquired in real time by multiple height sensors arranged on each suspension of the vehicle. The vehicle height signal is received by the control module integrated into the column shifter assembly, and then processed to reduce vibration. The vehicle's current operating condition is determined based on the vehicle speed signal, and corresponding operations are performed: When in a static or quasi-static condition, the total load mass of the vehicle and the axle load distribution of each axle are calculated based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model, and the results are displayed visually on the display terminal. When in dynamic driving conditions, the dynamic load of each wheel is calculated in real time at a preset frequency, and the real-time axle load transfer rate of the vehicle is determined. When the axle load transfer rate exceeds a preset dynamic threshold within a preset time window, an axle load transfer transient event is determined to have occurred. Based on the type and intensity of the axle load transfer transient events, driving behavior tracking data is generated and simultaneously uploaded to local and cloud platforms.

2. The weight calculation method based on the height sensor signal conversion of the pocket shifter integrated as described in claim 1, characterized in that, When performing jitter removal on the vehicle height signal, a software filtering window corresponding to the vehicle model in the preset jitter removal vehicle model database is used to perform jitter removal on the vehicle height signal within the preset time window to eliminate measurement noise caused by vehicle body shaking.

3. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 1, characterized in that, The vehicle height-vehicle load mapping model incorporates compensation factors based on ramp angle and vehicle acceleration.

4. The weight calculation method based on the height sensor signal conversion of the pocket shifter integrated as described in claim 3, characterized in that, The calibration process of the vehicle height-vehicle load mapping model includes: When the vehicle is on a static level road surface, the calibrated counterweight mass applied to different positions of the vehicle is changed sequentially, and the signal values ​​output by each height sensor are recorded to establish a first mapping table. When the vehicle is on a slope with multiple preset slope angles, the calibration process is repeated to record the relationship between the slope angle, counterweight mass, and height signal value, forming a second mapping table. The compensation factor is a correction value calculated by querying the second mapping table based on the real-time slope angle signal and the vehicle longitudinal acceleration signal.

5. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 3, characterized in that, The vehicle height-vehicle load mapping model also includes an independent linear curve and a nonlinear fitting curve for each height sensor. These curves are used to calculate the front axle load based on the signal from the front axle height sensor and the rear axle load based on the signal from the rear axle height sensor when calculating the total load mass and axle load distribution of the vehicle based on the vehicle height-vehicle load mapping model, and to calculate the ratio between the two.

6. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 1, characterized in that, The axle load transfer rate includes the front-to-rear axle load transfer rate and the left-to-right axle load transfer rate.

7. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 1, characterized in that, The determination of the shaft load transfer transient event includes: When the real-time dynamic load on the current axle decreases instantaneously and the real-time dynamic load on the rear axle increases instantaneously, and the rate of change exceeds a preset slope threshold, it is determined to be a rapid acceleration axle load transfer event. When the real-time dynamic load on the current axle increases instantaneously and the real-time dynamic load on the rear axle decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be an emergency braking axle load transfer event. When the real-time dynamic load on one side of the wheel increases instantaneously and the real-time dynamic load on the other side decreases instantaneously, and the rate of change exceeds the slope threshold, it is determined to be a sharp turn axle load transfer event.

8. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 1, characterized in that, The driving behavior data points are used to construct a driving style model, specifically including event occurrence time, event type, event intensity, vehicle speed at the time of the event, steering wheel angle, and load change curve segments for each wheel.

9. The weight calculation method based on the height sensor signal conversion of the guardrail integrated as described in claim 1, characterized in that, Under the aforementioned dynamic driving conditions, a four-wheel impact warning procedure is also included: Real-time monitoring of the dynamic load change rate of individual wheels; When the rate of change of dynamic load of any wheel exceeds the impact threshold within a preset micro-time window, a road impact event is determined to have occurred. Based on the intensity and repetition frequency of impact events, abnormal warning signals for tires and suspension systems are generated.

10. A weight calculation system based on a height sensor signal conversion integrated into a side rail, characterized in that, include: Four height sensor assemblies are respectively arranged between the vehicle frame and the four suspension control arms to acquire vehicle height signals in real time; each height sensor assembly includes a link bracket, a ball pin, a link, a rocker arm assembly, a magnet, and a circuit board for outputting pulse width modulation signals; The column shifter assembly integrates a control module, which includes: The signal acquisition and preprocessing unit is used to receive the pulse width modulation signals output by the four height sensors and perform jitter removal processing; The operating condition discrimination unit is used to determine whether the vehicle is in a static operating condition, a quasi-static operating condition, or a dynamic driving condition based on the vehicle speed signal. The static weighing calculation unit is used to calculate the total load mass of the vehicle and the axle load distribution of each axle under static or quasi-static working conditions, based on the de-shaking vehicle height signal and the pre-calibrated vehicle height-vehicle load mapping model. The dynamic monitoring unit is used to calculate the dynamic load of each wheel in real time at a preset frequency under dynamic driving conditions, determine the real-time axle load transfer rate of the vehicle, and identify axle load transfer transient events; and generate driving behavior embedded data according to the type and intensity of the axle load transfer transient events. The display terminal is connected to the glove box assembly via the vehicle bus and is used to display the output signals of the static weighing calculation unit and the dynamic monitoring unit. The storage module is used to store the driving behavior data embedding points; The communication module is used to upload the driving behavior data to the cloud.

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