Load safety monitoring method and system for rear-loading vehicle and vehicle

Abstract

The invention provides a loading safety monitoring method and system for a rear-loading vehicle and the vehicle. The method comprises the following steps that the arc height variable quantity of a plate spring of the vehicle in the current loading state is collected; based on the arc height variable quantity, a vehicle total weight estimated value is obtained through calculation; based on the axle load estimation values of the front axle and the rear axle and the axle distance of the vehicle, estimating the current gravity center height of the vehicle, and dynamically compensating the gravity center height according to the dynamic state parameters; and calculating a stability risk coefficient of the vehicle according to the total weight estimated value of the vehicle and the compensated gravity center height so as to carry out early warning. Displacement sensors are arranged on a front plate spring and a rear plate spring of a vehicle to collect arc height variation in real time, an axle load and a vehicle total weight estimated value are calculated based on a pre-calibrated plate spring rigidity coefficient, the gravity center height of the vehicle is estimated to evaluate the stability of the vehicle, and meanwhile the gravity center height is dynamically compensated based on dynamic parameters in the driving process. The evaluation precision under complex working conditions is improved, and the transportation safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, system, and vehicle for monitoring the load safety of aftermarket vehicles. Background Technology

[0002] Vehicle load safety inspection is a key aspect of vehicle management technology, especially for aftermarket vehicles such as trucks and lorries, whose load status directly affects driving safety and operational efficiency.

[0003] Rear-mounted vehicle load identification refers to monitoring the load weight of trucks, mining trucks, and other vehicles through retrofitted sensor devices. Current technologies primarily rely on weighbridges, calculating the weight of goods by measuring both empty and loaded states. However, this method has fixed application locations, impacting transportation efficiency. While retrofitting stress sensors or displacement sensors into the vehicle's suspension, such as leaf springs, to measure deformation and estimate weight can improve weighing efficiency, static or fixed-point detection methods struggle to achieve real-time, dynamic load monitoring. Furthermore, these methods cannot address rollover accidents caused by changes in center of gravity and load transfer during turns, braking, or emergency maneuvers, resulting in poor reliability and compromising transportation safety. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method, system and vehicle for monitoring the load safety of afterloading vehicles, in order to solve the technical problem that vehicles in the prior art cannot guarantee transportation safety.

[0005] To achieve the above objectives, in a first aspect, the present invention provides: a method for monitoring the load safety of aftermarket vehicles, comprising the following steps: The change in the arc height of the leaf springs under the current load condition is collected by displacement sensors installed on the front and rear leaf springs of the vehicle. Based on the arc height change and the pre-calibrated leaf spring stiffness coefficient, the estimated values ​​of the axle loads of the front axle and the rear axle are calculated respectively, and the estimated values ​​of the axle loads of each axle are superimposed to obtain the estimated value of the total vehicle weight. The vehicle's real-time dynamic status parameters are obtained through the vehicle's CAN bus, including lateral acceleration and longitudinal acceleration. Based on the estimated axle load values ​​of the front and rear axles and the vehicle wheelbase, the current center of gravity height of the vehicle is estimated, and the center of gravity height is dynamically compensated according to the dynamic state parameters. The vehicle's stability risk coefficient is calculated based on the estimated total weight of the vehicle and the compensated center of gravity height. The system outputs the estimated total vehicle weight, load information, and stability risk coefficient, and compares the results with the alarm threshold to trigger an early warning based on the risk level corresponding to the risk assessment results.

[0006] According to one aspect of the above technical solution, the calculation expression for the shaft load estimate is as follows: ; In the formula, Let be the estimated static load value for the a-th axis, and k be the leaf spring stiffness coefficient. This represents the change in arc height. The initial unloaded load is the axle load.

[0007] According to one aspect of the above technical solution, the method further includes: The leaf spring stiffness coefficient is estimated and updated online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ; In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height.

[0008] According to one aspect of the above technical solution, the formula for calculating the compensated center of gravity height is as follows: ; ; ; In the formula, To compensate for the height of the center of gravity, To statically estimate the height of the center of gravity, For dynamic compensation amount, The unloaded reference center of gravity height is L, where L is the vehicle wheelbase. For front axle load, For rear axle load, This is the longitudinal compensation coefficient. For longitudinal acceleration, This is the horizontal compensation coefficient. This is lateral acceleration.

[0009] According to one aspect of the above technical solution, the expression for calculating the stability risk coefficient is as follows: ; In the formula, S is the stability risk coefficient, T is the wheel track, and W is the estimated total weight of the vehicle.

[0010] According to one aspect of the above technical solution, the method further includes: The stability risk coefficient is dynamically compensated based on the vehicle's lateral acceleration according to the following formula: ; In the formula, The stability risk coefficient after compensation. For lateral acceleration, This is the acceleration due to gravity.

[0011] According to one aspect of the above technical solution, the method further includes: The vehicle's real-time speed is obtained via the vehicle's CAN bus, and the alarm threshold is dynamically compensated based on the vehicle speed and road surface adhesion coefficient according to the following calculation formula: ; ; In the formula, The alarm threshold is the compensated value, where v is the current vehicle speed and u is the road surface adhesion coefficient. This is the vehicle speed compensation coefficient.

[0012] Secondly, this solution also provides a rear-loading vehicle load safety monitoring system, including: The data acquisition module is used to collect the change in the arc height of the leaf springs under the current load condition of the vehicle by means of displacement sensors installed on the leaf springs of the front and rear axles of the vehicle. The calculation module is used to calculate the estimated values ​​of the axle loads of the front axle and the rear axle based on the arc height change and the pre-calibrated leaf spring stiffness coefficient, and to obtain the estimated value of the total vehicle weight by superimposing the estimated values ​​of the axle loads of each axle. The dynamic module is used to acquire real-time dynamic status parameters of the vehicle via the vehicle CAN bus, including lateral acceleration and longitudinal acceleration. The compensation module is used to estimate the current center of gravity height of the vehicle based on the estimated axle load values ​​of the front and rear axles and the vehicle wheelbase, and to dynamically compensate the center of gravity height according to the dynamic state parameters. The risk module is used to calculate the vehicle's stability risk coefficient based on the estimated total weight of the vehicle and the compensated center of gravity height. The early warning module is used to output the load information corresponding to the estimated total weight of the vehicle and the risk assessment results of comparing the stability risk coefficient with the alarm threshold, so as to trigger an early warning based on the risk level corresponding to the risk assessment results.

[0013] According to one aspect of the above technical solution, the system further includes: The update module is used to estimate and update the leaf spring stiffness coefficient online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ; In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height.

[0014] According to one aspect of the above technical solution, the system further includes: The risk compensation module is used to dynamically compensate the stability risk coefficient based on the vehicle's lateral acceleration according to the following formula: ; In the formula, The stability risk coefficient after compensation. For lateral acceleration, This is the acceleration due to gravity.

[0015] According to one aspect of the above technical solution, the system further includes: The alarm compensation module is used to obtain the vehicle's real-time speed via the vehicle's CAN bus and dynamically compensate the alarm threshold based on the vehicle speed and road surface adhesion coefficient according to the following calculation formula: ; ; In the formula, The alarm threshold is the compensated value, where v is the current vehicle speed and u is the road surface adhesion coefficient. This is the vehicle speed compensation coefficient.

[0016] Thirdly, this solution also provides a vehicle, including the aftermarket vehicle load safety monitoring system described in the above technical solution.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: by setting displacement sensors on the front and rear leaf springs of the vehicle to collect the change in arc height in real time, and calculating the axle load and the estimated value of the total vehicle weight based on the pre-calibrated leaf spring stiffness coefficient, the vehicle's center of gravity height is estimated to assess the vehicle's stability. At the same time, the center of gravity height is dynamically compensated based on the dynamic parameters during the driving process, which improves the assessment accuracy under complex working conditions and ensures transportation safety. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the method for monitoring the load safety of aftermarket vehicles in the first embodiment of the present invention. Figure 2 This is a structural block diagram of the aftermarket vehicle load safety monitoring system in the second embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0019] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0020] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0021] Unless otherwise defined, 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. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1 Please see Figure 1 The figure shows a flowchart of the rear-loading vehicle load safety monitoring method in the first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100: Displacement sensors installed on the front and rear axle leaf springs of the vehicle are used to collect the change in the leaf spring arc height under the current load condition. In this embodiment, the displacement sensor is a laser rangefinder sensor fixed to the vehicle frame by a bracket. The sensor sampling frequency is 100Hz, and the measurement accuracy is ±0.1mm, so as to accurately collect the change in the leaf spring arc height. Step S200: Based on the change in arc height and the pre-calibrated leaf spring stiffness coefficient, the estimated values ​​of the axle loads of the front axle and the rear axle are calculated respectively, and the estimated values ​​of the axle loads of each axle are superimposed to obtain the estimated value of the total vehicle weight.

[0023] Preferably, the calculation expression for the above-mentioned shaft load estimate is as follows: ; In the formula, Let be the estimated static load value for the a-th axis, and k be the leaf spring stiffness coefficient. This represents the change in arc height. The initial unloaded load is the axle load.

[0024] Specifically, the stiffness of the leaf springs was calibrated through loading experiments. The rear axle was gradually loaded to its rated load (15 tons), and the relationship between the change in arc height and axle load was recorded. The initial stiffness coefficient was obtained through fitting, and the stiffness coefficient of the front axle was also determined. The stiffness coefficient of the rear axle is 280 N / mm. It is 320 N / mm.

[0025] Preferably, in order to adapt to the instantaneous elastic effects of temperature changes, load frequency, and road impacts on the leaf spring, this method further includes: The leaf spring stiffness coefficient is estimated and updated online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ; In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height. By continuously collecting real-time changes in arc height and estimating the axle load using the current stiffness coefficient, the predicted value calculated using the current stiffness coefficient is compared with the actual measurement value from the sensor to obtain the prediction error. Based on the RLS algorithm, the stiffness coefficient value is automatically adjusted and updated according to the new error. This process is continuously iterated within different calculation cycles, making the predicted value constantly approach the actual measurement value, thus enabling the stiffness coefficient to continuously and adaptively adjust.

[0026] Step S300: Obtain the vehicle's real-time dynamic status parameters via the vehicle CAN bus. The dynamic status parameters include lateral acceleration and longitudinal acceleration.

[0027] Step S400: Based on the estimated axle load values ​​of the front and rear axles and the vehicle wheelbase, estimate the current center of gravity height of the vehicle, and dynamically compensate for the center of gravity height according to the dynamic state parameters.

[0028] Preferably, the formula for calculating the compensated center of gravity height is: ; ; ; In the formula, To compensate for the height of the center of gravity, To statically estimate the height of the center of gravity, For dynamic compensation amount, The unloaded reference center of gravity height is L, where L is the vehicle wheelbase. For front axle load, For rear axle load, This is the longitudinal compensation coefficient. For longitudinal acceleration, This is the horizontal compensation coefficient. This is lateral acceleration.

[0029] The purpose of statically estimating the center of gravity height is to estimate the longitudinal vertical height of the vehicle's center of gravity based on the principle of torque balance and the distribution of axle loads on the front and rear axles. This static estimation is highly accurate when the vehicle is traveling at a constant speed in a straight line on a level road. However, its accuracy decreases once the vehicle enters dynamic operating conditions. For example, the vehicle will "nose up" during acceleration and "nod" during braking, and these changes in vehicle posture will alter the height of the center of gravity relative to the chassis. When the vehicle is turning, the centrifugal force causes the body to tilt, resulting in the center of gravity shifting to the outside of the curve, and its effective height also changes accordingly. By compensating for longitudinal acceleration, the system can sense the risk changes caused by weight transfer during rapid acceleration or emergency braking. By compensating for lateral acceleration, the system can accurately detect the increased rollover risk caused by tilting during cornering, potentially issuing warnings earlier and improving the accuracy and reliability of monitoring and warning systems.

[0030] Step S500: Calculate the vehicle's stability risk coefficient based on the estimated total vehicle weight and the compensated center of gravity height. Specifically... Preferably, in this embodiment, the expression for calculating the stability risk coefficient is: ; In the formula, S is the stability risk coefficient, T is the wheel track, and W is the estimated total weight of the vehicle.

[0031] Furthermore, this method also includes: The stability risk coefficient is dynamically compensated based on the vehicle's lateral acceleration according to the following formula: ; In the formula, The stability risk coefficient after compensation. For lateral acceleration, This is the acceleration due to gravity.

[0032] The stability risk coefficient before compensation is based on the Static Stability Factor (SSF) principle. The higher the value of the stability risk coefficient, the higher the risk of rollover in the current state of the vehicle. The estimated total vehicle weight represents the total inertia of the vehicle. The stability risk coefficient is essentially a representation of the ratio of the overturning moment that causes the vehicle to roll over to the stabilizing moment that keeps the vehicle stable. The larger the value, the more difficult it is for the stabilizing moment to overcome the overturning moment, and the closer the vehicle is to the critical point of rollover.

[0033] Based on this, in this embodiment, the above method further includes: acquiring the vehicle's real-time speed via the vehicle CAN bus, and dynamically compensating the alarm threshold based on the vehicle speed and road surface adhesion coefficient according to the following calculation formula: ; ; In the formula, The alarm threshold is the compensated value, where v is the current vehicle speed and u is the road surface adhesion coefficient. This refers to the vehicle speed compensation coefficient. Specifically, the aforementioned road surface adhesion coefficient can be estimated based on the relationship between the acceleration of the inertial measurement unit and the vehicle's driving force, or directly based on weather data or camera identification of road surface type to assist in the judgment. In special weather conditions such as rain and snow, when the road surface adhesion coefficient decreases, the tire grip limit drops significantly, and the vehicle is prone to instability. In addition, the higher the vehicle speed, the greater the vehicle's kinetic energy and the shorter the driver's reaction time. Potential risks can be warned in advance by lowering the alarm threshold.

[0034] The system outputs the estimated vehicle gross weight, load information, and a risk assessment result comparing the stability risk coefficient with the alarm threshold. Based on the risk level corresponding to the risk assessment result, an early warning is triggered. Specifically, if the dynamically compensated stability risk coefficient exceeds the compensated alarm threshold, a risk warning is issued via the vehicle's loudspeaker.

[0035] Example 2 The second embodiment of this application also provides a rear-mounted vehicle load safety monitoring system, which is used to implement the embodiments and preferred embodiments described above, and will not be repeated hereafter. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0036] like Figure 2 As shown, the system includes: a data acquisition module 100, a calculation module 200, a dynamic module 300, a compensation module 400, a risk module 500, and an early warning module 600.

[0037] The data acquisition module 100 is used to acquire the change in the arc height of the leaf springs under the current load condition of the vehicle by means of displacement sensors installed on the leaf springs of the front and rear axles of the vehicle. The calculation module 200 is used to calculate the estimated values ​​of the axle loads of the front axle and the rear axle based on the arc height change and the pre-calibrated leaf spring stiffness coefficient, and to obtain the estimated value of the total vehicle weight by superimposing the estimated values ​​of the axle loads of each axle. The dynamic module 300 is used to acquire real-time dynamic status parameters of the vehicle via the vehicle CAN bus, the dynamic status parameters including lateral acceleration and longitudinal acceleration. The compensation module 400 is used to estimate the current center of gravity height of the vehicle based on the estimated values ​​of the axle loads of the front axle and the rear axle and the vehicle wheelbase, and to dynamically compensate the center of gravity height according to the dynamic state parameters. Risk module 500 is used to calculate the vehicle's stability risk coefficient based on the estimated total weight of the vehicle and the compensated center of gravity height. The early warning module 600 is used to output the load information corresponding to the estimated total weight of the vehicle and the risk assessment results of comparing the stability risk coefficient with the alarm threshold, so as to trigger an early warning based on the risk level corresponding to the risk assessment results.

[0038] Preferably, in this embodiment, the system further includes: The update module is used to estimate and update the leaf spring stiffness coefficient online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ; In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height.

[0039] Preferably, in this embodiment, the system further includes: The risk compensation module is used to dynamically compensate the stability risk coefficient based on the vehicle's lateral acceleration according to the following formula: ; In the formula, The stability risk coefficient after compensation. For lateral acceleration, This is the acceleration due to gravity.

[0040] Preferably, in this embodiment, the system further includes: The alarm compensation module is used to obtain the vehicle's real-time speed via the vehicle's CAN bus and dynamically compensate the alarm threshold based on the vehicle speed and road surface adhesion coefficient according to the following calculation formula: ; ; In the formula, The alarm threshold is the compensated value, where v is the current vehicle speed and u is the road surface adhesion coefficient. This is the vehicle speed compensation coefficient.

[0041] It should be noted that the modules can be functional modules or program modules, and can be implemented in software or hardware. For modules implemented in hardware, the modules can reside in the same processor; or the modules can be located in different processors in any combination.

[0042] The third embodiment of this application provides a vehicle including the aftermarket vehicle load safety monitoring system described in the above embodiments.

[0043] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for monitoring the load safety of afterloading vehicles, characterized in that, Includes the following steps: The change in the arc height of the leaf springs under the current load condition is collected by displacement sensors installed on the front and rear leaf springs of the vehicle. Based on the arc height change and the pre-calibrated leaf spring stiffness coefficient, the estimated values ​​of the axle loads of the front axle and the rear axle are calculated respectively, and the estimated values ​​of the axle loads of each axle are superimposed to obtain the estimated value of the total vehicle weight. The vehicle's real-time dynamic status parameters are obtained through the vehicle's CAN bus, including lateral acceleration and longitudinal acceleration. Based on the estimated axle load values ​​of the front and rear axles and the vehicle wheelbase, the current center of gravity height of the vehicle is estimated, and the center of gravity height is dynamically compensated according to the dynamic state parameters. The vehicle's stability risk coefficient is calculated based on the estimated total weight of the vehicle and the compensated center of gravity height. The system outputs the estimated total vehicle weight, load information, and stability risk coefficient, and compares the results with the alarm threshold to trigger an early warning based on the risk level corresponding to the risk assessment results.

2. The method for monitoring the load safety of afterloading vehicles according to claim 1, characterized in that, The formula for calculating the shaft load estimate is as follows: ； In the formula, Let be the estimated static load value for the a-th axis, and k be the leaf spring stiffness coefficient. This represents the change in arc height. The initial unloaded load is the axle load.

3. The method for monitoring the load safety of rear-loading vehicles according to claim 2, characterized in that, The method further includes: The leaf spring stiffness coefficient is estimated and updated online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ； In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height.

4. The method for monitoring the load safety of afterloading vehicles according to claim 1, characterized in that, The formula for calculating the height of the compensated center of gravity is: ； ； ； In the formula, To compensate for the height of the center of gravity, To statically estimate the height of the center of gravity, For dynamic compensation amount, The unloaded reference center of gravity height is L, where L is the vehicle wheelbase. For front axle load, For rear axle load, This is the longitudinal compensation coefficient. For longitudinal acceleration, This is the horizontal compensation coefficient. This is lateral acceleration.

5. The method for monitoring the load safety of rear-loading vehicles according to claim 4, characterized in that, The expression for calculating the stability risk coefficient is as follows: ； In the formula, S is the stability risk coefficient, T is the wheel track, and W is the estimated total weight of the vehicle.

6. The method for monitoring the load safety of rear-loading vehicles according to claim 1, characterized in that, The method further includes: The stability risk coefficient is dynamically compensated based on the vehicle's lateral acceleration according to the following formula: ； In the formula, The stability risk coefficient after compensation. For lateral acceleration, This is the acceleration due to gravity.

7. The method for monitoring the load safety of afterloading vehicles according to claim 1, characterized in that, The method further includes: The vehicle's real-time speed is obtained via the vehicle's CAN bus, and the alarm threshold is dynamically compensated based on the vehicle speed and road surface adhesion coefficient according to the following calculation formula: ； ； In the formula, The alarm threshold is the compensated value, where v is the current vehicle speed and u is the road surface adhesion coefficient. This is the vehicle speed compensation coefficient.

8. A load safety monitoring system for aftermarket vehicles, characterized in that, include: The data acquisition module is used to collect the change in the arc height of the leaf springs under the current load condition of the vehicle by means of displacement sensors installed on the leaf springs of the front and rear axles of the vehicle. The calculation module is used to calculate the estimated values ​​of the axle loads of the front axle and the rear axle based on the arc height change and the pre-calibrated leaf spring stiffness coefficient, and to obtain the estimated value of the total vehicle weight by superimposing the estimated values ​​of the axle loads of each axle. The dynamic module is used to acquire real-time dynamic status parameters of the vehicle via the vehicle CAN bus, including lateral acceleration and longitudinal acceleration. The compensation module is used to estimate the current center of gravity height of the vehicle based on the estimated axle load values ​​of the front and rear axles and the vehicle wheelbase, and to dynamically compensate the center of gravity height according to the dynamic state parameters. The risk module is used to calculate the vehicle's stability risk coefficient based on the estimated total weight of the vehicle and the compensated center of gravity height. The early warning module is used to output the load information corresponding to the estimated total weight of the vehicle and the risk assessment results of comparing the stability risk coefficient with the alarm threshold, so as to trigger an early warning based on the risk level corresponding to the risk assessment results.

9. The rear-loading vehicle load safety monitoring system according to claim 8, characterized in that, The system also includes: The update module is used to estimate and update the leaf spring stiffness coefficient online using the recursive least squares method. The calculation expression for the leaf spring stiffness coefficient update mechanism is as follows: ； In the formula, The spring stiffness coefficient at the current moment. The spring stiffness coefficient at the previous moment. For the regression vector, Forgetting factor, Let be the error covariance matrix of the previous time step. This is the transpose of the regression vector. This represents the change in arc height.

10. A vehicle, characterized in that, Includes the aftermarket vehicle load safety monitoring system as described in any one of claims 8-9.

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