Dynamic weighing method based on bumpy road surface

Abstract

The invention discloses a dynamic weighing method based on a bumpy road surface, relates to the technical field of vehicle dynamic weighing, and solves the technical problem that the universality is not high due to the fact that different vehicle models are not considered in the prior art. The method comprises the following steps: collecting historical elasticity data and historical vehicle data of different vehicles on different bumpy road surfaces, and collecting current road surface data and current elasticity data of a current vehicle when the current vehicle runs on the current bumpy road surface in real time; setting a corresponding model coefficient according to the model of the vehicle; calculating an elastic coefficient of the vehicle according to the historical vehicle data and the historical elastic data, and calculating the current weight of the current vehicle based on the elastic coefficient, the model of the current vehicle and the current elastic data; and calculating a danger coefficient of vehicle driving according to the current road surface data and the current vehicle data, comparing the danger coefficient with a preset danger threshold to obtain a danger level, and generating a corresponding vehicle driving suggestion according to the danger level.

Description

Technical Field

[0001] The invention belongs to the field of vehicle dynamic weighing, and in particular is a dynamic weighing method based on a bumpy road surface. Background Art

[0002] Dynamic weighing technology refers to the technology of weighing vehicles while they are moving. Dynamic weighing devices are widely used in applications such as weight-based charging and over-limit detection, and play an important role in traffic management, over-limit management, and import and export supervision; dynamic weighing technology not only improves the efficiency and safety of traffic management, but also reduces operating costs and reduces the impact on the environment. It is of great significance to the construction and maintenance of modern transportation systems.

[0003] The invention patent with application number CN112763038B discloses a dynamic weighing device and weighing method. The invention patent collects the vibration signal of the vehicle during driving through a vibration sensor, obtains the deformation through the vibration signal, and infers the weight of the vehicle based on the deformation. However, when the vehicle is driving on a bumpy road, the weight of the vehicle is not only closely related to the vibration signal, but also to the structure of the vehicle itself. Therefore, the units of the vibration signals generated are different for different vehicle models. Therefore, different models and different weights of vehicles may also generate the same vibration signal, which leads to large errors in the data obtained when the vehicle is dynamically weighed.

[0004] The present invention provides a dynamic weighing method based on a bumpy road surface to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a dynamic weighing method based on a bumpy road surface, which is used to solve the technical problem that the prior art does not take into account the differences in vehicle models and thus has low versatility.

[0006] To achieve the above object, the first aspect of the present invention provides a dynamic weighing method based on a bumpy road surface, comprising:

[0007] Step 1: Collect historical elasticity data and historical vehicle data of various vehicle models on different bumpy roads, and collect current road surface data and current elasticity data of the current vehicle on the bumpy road in real time;

[0008] Step 2: Calculate the elastic coefficient based on historical vehicle data and historical elastic data; calculate the current vehicle weight based on the elastic coefficient and current elastic data;

[0009] Step three: Set a danger threshold; combine the current vehicle weight and the current vehicle model to obtain the current vehicle data; calculate the danger coefficient based on the current road surface data and the current vehicle data, compare the danger coefficient with the danger threshold to obtain the danger level, and generate vehicle driving recommendations based on the danger level; wherein the danger threshold is set based on the vehicle being able to drive normally on different bumpy roads.

[0010] Preferably, the elastic coefficient is calculated based on historical vehicle data and historical elastic data, including:

[0011] Extracting historical vehicle data and historical elastic data; the historical elastic data includes the deformation amplitude and deformation coefficient of the spring system set in the vehicle suspension system; the historical vehicle data includes the weight and vehicle model of the vehicle;

[0012] The corresponding model coefficient is set according to the vehicle model; the current elastic coefficient TSX of the vehicle is calculated by the formula TSX=(α×XBR×XBF) / (β×XHR×ZTX); wherein XBR is the deformation amplitude of the spring system in the suspension system, XBF is the deformation coefficient, XHR is the model coefficient corresponding to the vehicle, and ZTX is the weight of the vehicle; α and β are proportional coefficients, which are set according to the differences in suspension systems of different types of vehicles of the same model.

[0013] Preferably, the setting of the corresponding model coefficient according to the vehicle model includes:

[0014] Extracting the model of the vehicle; the vehicle model includes a small vehicle, a medium vehicle and a large vehicle;

[0015] Three vehicles of different models are selected and marked as standard small car, standard medium car and standard large car respectively; the deformation coefficients of the spring systems of the standard small car, standard medium car and standard large car are extracted and marked as small car deformation coefficient, medium car deformation coefficient and large car deformation coefficient respectively;

[0016] Taking the standard CRRC as the reference vehicle, the ratio of the large car deformation coefficient to the medium car deformation coefficient is marked as the large car coefficient, the ratio of the small car deformation coefficient to the medium car deformation coefficient is marked as the small car coefficient, the medium car coefficient is 1, and the combination of the small car coefficient, the medium car coefficient and the large car coefficient is marked as the model coefficient.

[0017] Preferably, the current vehicle weight is calculated based on the elastic coefficient and the current elastic data, including:

[0018] Extract elastic coefficient and current elastic data;

[0019] Determine the model of the current vehicle, input the current elastic data collected in real time into the transformed formula XTX = (α×XBR×XBF) / (β×XHR×ZTX) of the formula TSX = (α×XBR×XBF) / (β×XHR×TSX), and obtain the current vehicle weight XTX.

[0020] Preferably, the calculating of the risk factor according to the current road surface data and the current vehicle data includes:

[0021] Extracting current road surface data and current vehicle data; the current road surface data includes the inclination and bump index of the current road surface;

[0022] Mark the model coefficient corresponding to the current vehicle model in the current vehicle data as the current model coefficient DXR;

[0023] The risk factor WXR of the current vehicle is calculated by the formula WXR=QXD×DPD×XTX / (δ×DXR); wherein QXD is the inclination of the current road surface, DPD is the bump index, and δ is the bump coefficient, which is set according to the degree of matching between the vehicle model and the current road surface.

[0024] Preferably, the step of comparing the risk coefficient with the risk threshold to obtain the risk level includes:

[0025] A1: extracting the risk factor and the risk threshold; the risk threshold includes a first risk threshold and a second risk threshold;

[0026] A2: Determine whether the risk factor is greater than the first risk threshold; if yes, mark the risk level of the vehicle as high risk; if no, jump to A3;

[0027] A3: Determine whether the risk factor is greater than the second risk threshold; if yes, mark the risk level of the vehicle as medium risk; if no, mark the risk level of the vehicle as low risk.

[0028] Preferably, generating and giving vehicle driving advice according to the danger level includes:

[0029] Extract the danger level of the vehicle;

[0030] When the danger level of the vehicle is high, the generated driving suggestion is that it is not recommended for the vehicle to drive on the current bumpy road;

[0031] When the danger level of the vehicle is medium danger, the generated vehicle driving advice is to drive carefully;

[0032] When the danger level of the vehicle is low, the generated vehicle driving recommendation is safe to drive.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention collects historical elastic data and historical vehicle data of different vehicles on different bumpy roads, and collects the current road surface data and current elastic data of the current vehicle when it is traveling on the current bumpy road surface in real time; sets a corresponding model coefficient according to the vehicle model, and by selecting a standard vehicle as a reference, the deformation coefficients of different vehicle models can be compared, which is convenient for standardization and normalization; calculates the elastic coefficient of the vehicle according to the historical vehicle data and the historical elastic data, and calculates the current weight of the current vehicle based on the elastic coefficient, the model of the current vehicle and the current elastic data, which can improve the accuracy of calculating the current vehicle weight and the convenience of dynamic weighing, and is applicable to a variety of vehicle models, and only needs to replace the model coefficient of the vehicle, which improves versatility; calculates the danger coefficient of vehicle driving according to the current road surface data and the current vehicle data, and compares the danger coefficient with the preset danger threshold to obtain the danger level, and generates corresponding vehicle driving suggestions according to the danger level, which can timely identify potential risk factors and give corresponding suggestions, which is helpful to prevent accidents, provide real-time road condition feedback to drivers, and help them make more informed driving decisions.

[0035] 2. The present invention selects standard vehicles from different models of vehicles and takes the standard vehicles as reference vehicles for vehicle models. The corresponding model coefficient is obtained by calculating the ratio of the deformation coefficients with other models of vehicles. By determining a standard vehicle as a benchmark, the evaluation of different models can be made comparable, avoiding evaluation deviations caused by vehicle differences, simplifying the vehicle evaluation process, and improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 The following is a flowchart of an embodiment of the present invention.

[0038] Figure 2 The figure is a complete flow chart for obtaining the danger level in one embodiment of the present invention.

[0039] Figure 3 A complete flow chart of generating vehicle driving suggestions according to risk levels in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1-Figure 3 The first aspect of the present invention provides a dynamic weighing method based on a bumpy road surface, comprising:

[0042] Step 1: Collect historical elasticity data and historical vehicle data of various vehicle models on different bumpy roads, and collect current road surface data of the current vehicle traveling on the bumpy road in real time.

[0043] Exemplarily, historical elastic data and historical vehicle data of different models of vehicles traveling on different bumpy roads are collected; wherein the vehicle models include small vehicles, medium-sized vehicles and large vehicles; the bumpy roads include bumpy road one, bumpy road two and bumpy road three, and the bump index and inclination of different bumpy roads are different.

[0044] Step 2: Calculate the elastic coefficient based on historical vehicle data and historical elastic data; calculate the current vehicle weight based on the elastic coefficient and current elastic data.

[0045] For example, three vehicles of different models are selected and marked as standard small car, standard medium car and standard large car respectively; the deformation coefficients of the standard small car, standard medium car and standard large car are marked as small car deformation coefficient, medium car deformation coefficient and large car deformation coefficient respectively; the standard small car, standard medium car and standard large car are driven on a flat road respectively; the deformation coefficient of the standard small car in this embodiment is 2×10 5 Newton per meter, the deformation coefficient of the standard medium-sized vehicle is 2.5×10 5 The deformation coefficient of a standard truck is 4×10 5 Newton per meter; take the standard CRRC as the reference vehicle, mark the ratio of the large vehicle deformation coefficient to the CRRC deformation coefficient as 1.6, mark the ratio of the small vehicle deformation coefficient to the CRRC deformation coefficient as 0.8, and set the CRRC coefficient to 1.

[0046] In this embodiment, the vehicle currently traveling is a medium-sized vehicle, the weight of a standard medium-sized vehicle is 2000 kg, and the deformation amplitude of the spring system in the suspension system is 0.05 m when traveling on a non-bumpy road. In this embodiment, the proportional coefficients α=0.4 and β=0.6 are set, and the elastic coefficient TSX=4.17 of the standard medium-sized vehicle is calculated by the formula TSX=(α×XBR×XBF) / (β×XHR×ZTX); the deformation coefficient of the currently traveling vehicle is 2.4×10 5, the deformation amplitude is 0.06 meters, and the current vehicle weight XTX = 2302 kilograms is obtained through the formula XTX = (α×XBR×XBF) / (β×XHR×TSX); there is no need to directly measure the vehicle weight, and the vehicle weight can be indirectly calculated by monitoring the deformation amplitude of the suspension system.

[0047] It should be noted that the criterion for distinguishing vehicle models is the net weight of the vehicle. The net weight of a small vehicle is between 1000kg and 1500kg, the net weight of a medium-sized vehicle is between 1500kg and 2500kg, and the net weight of a large vehicle is between 2500kg and 4000kg. In actual situations, even vehicles of the same model, such as small vehicles, have different deformation capacities of the spring systems in the suspension systems due to differences in brand and process. The standard vehicle in this application is divided according to the average value or mode of the model vehicles. In actual use, it can be calculated based on the actual values ​​of the vehicle.

[0048] It should be noted that when the weight of the vehicle changes, the deformation amplitude of the spring system in the suspension system will also change. If the weight of the vehicle increases or decreases during driving, it will be reflected through the deformation amplitude.

[0049] Step three: Set a danger threshold; combine the current vehicle weight and the current vehicle model to obtain the current vehicle data; calculate the danger coefficient based on the current road surface data and the current vehicle data, compare the danger coefficient with the danger threshold to obtain the danger level, and generate vehicle driving recommendations based on the danger level; wherein the danger threshold is set based on the vehicle being able to drive normally on different bumpy roads.

[0050] Exemplarily, in this embodiment, the first danger threshold is set to 90, and the second danger threshold is set to 60; in this embodiment, the inclination of the road on which the current vehicle is traveling is 15 degrees, the bumpiness index is 5 meters per kilometer, and the current vehicle is a medium-sized vehicle, then the current model coefficient is 1, and the bumpiness coefficient δ is set to 2000 in this embodiment; the danger coefficient of the current vehicle WXR=86.3 is calculated by the formula WXR=QXD×DPD×XTX / (δ×DXR).

[0051] Determine whether the risk factor is greater than the first risk threshold. If it is determined that the risk factor of the current vehicle is less than the first risk threshold, then continue to determine whether the risk factor is greater than the second risk threshold. If it is determined that the risk factor of the current vehicle is greater than the second risk threshold, the vehicle's risk level is medium risk, and the generated vehicle-connected driving advice is to recommend careful driving.

[0052] In other embodiments, if the danger level of the vehicle is high, the generated vehicle driving recommendation is that it is not recommended for the vehicle to drive on the current bumpy road. Due to the load or type of the vehicle, rollover or other dangerous behaviors may occur when driving on the current road.

[0053] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0054] Working principle of the present invention:

[0055] The present invention collects historical elasticity data and historical vehicle data of historical vehicles when traveling on different bumpy roads, and collects current road surface data and current elasticity data of vehicles currently traveling on bumpy roads in real time; sets a model coefficient based on the model of the vehicle, calculates the elasticity coefficient of the vehicle according to the model coefficient, historical elasticity data and historical vehicle data, and inputs the current road surface data and the current elasticity data into a variant of the elasticity coefficient calculation formula to obtain the current vehicle weight; combines the current vehicle weight with the model of the current vehicle to obtain the current vehicle data, calculates the vehicle's danger coefficient according to the current vehicle data and the current road surface data, compares the danger coefficient with a danger threshold, obtains the danger level of the vehicle traveling on the bumpy road, and generates a driving suggestion for the vehicle based on the danger level.

[0056] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A dynamic weighing method based on bumpy road surface, characterized in that: include: Step 1: Collect historical elasticity data and historical vehicle data of various vehicle models on different bumpy roads, and collect current road surface data and current elasticity data of the current vehicle on the bumpy road in real time; Step 2: Calculate the elastic coefficient based on historical vehicle data and historical elastic data; calculate the current vehicle weight based on the elastic coefficient and current elastic data; Step 3: Set the danger threshold; The current vehicle weight and the current vehicle model are combined to obtain the current vehicle data; the danger coefficient is calculated based on the current road surface data and the current vehicle data, the danger coefficient is compared with the danger threshold to obtain the danger level, and vehicle driving recommendations are generated based on the danger level; wherein the danger threshold is set according to the vehicle being able to drive normally on different bumpy roads.

2. A dynamic weighing method based on bumpy road surface according to claim 1, characterized in that: The elastic coefficient is calculated based on the historical vehicle data and the historical elastic data, including: Extracting historical vehicle data and historical elastic data; the historical elastic data includes the deformation amplitude and deformation coefficient of the spring system set in the vehicle suspension system; the historical vehicle data includes the weight and vehicle model of the vehicle; The corresponding model coefficient is set according to the vehicle model; the current elastic coefficient TSX of the vehicle is calculated by the formula TSX=(α×XBR×XBF) / (β×XHR×ZTX); wherein XBR is the deformation amplitude of the spring system in the suspension system, XBF is the deformation coefficient, XHR is the model coefficient corresponding to the vehicle, and ZTX is the weight of the vehicle; α and β are proportional coefficients.

3. A dynamic weighing method based on bumpy road surface according to claim 2, characterized in that: The method of setting a corresponding model coefficient according to the vehicle model includes: Extracting the model of the vehicle; the vehicle model includes a small vehicle, a medium vehicle and a large vehicle; Three vehicles of different models are selected and marked as standard small car, standard medium car and standard large car respectively; the deformation coefficients of the spring systems of the standard small car, standard medium car and standard large car are extracted and marked as small car deformation coefficient, medium car deformation coefficient and large car deformation coefficient respectively; Taking the standard CRRC as the reference vehicle, the ratio of the large car deformation coefficient to the medium car deformation coefficient is marked as the large car coefficient, the ratio of the small car deformation coefficient to the medium car deformation coefficient is marked as the small car coefficient, the medium car coefficient is 1, and the combination of the small car coefficient, the medium car coefficient and the large car coefficient is marked as the model coefficient.

4. A dynamic weighing method based on bumpy road surface according to claim 1, characterized in that: The elastic coefficient and the current elastic data are used to calculate the current vehicle weight, including: Extract elastic coefficient and current elastic data; Determine the model of the current vehicle, input the current elastic data collected in real time into the transformed formula XTX = (α×XBR×XBF) / (β×XHR×ZTX) of the formula TSX = (α×XBR×XBF) / (β×XHR×TSX), and obtain the current vehicle weight XTX.

5. The dynamic weighing method based on bumpy road surface according to claim 1 is characterized in that: The calculating of the risk factor according to the current road surface data and the current vehicle data includes: Extracting current road surface data and current vehicle data; the current road surface data includes the inclination and bump index of the current road surface; Mark the model coefficient corresponding to the current vehicle model in the current vehicle data as the current model coefficient DXR; The risk factor WXR of the current vehicle is calculated by the formula WXR=QXD×DPD×XTX / (δ×DXR); wherein QXD is the inclination of the current road surface, DPD is the bump index, and δ is the bump coefficient.

6. A dynamic weighing method based on bumpy road surface according to claim 1, characterized in that: The step of comparing the risk factor with the risk threshold to obtain the risk level includes: A1: extracting the risk factor and the risk threshold; the risk threshold includes a first risk threshold and a second risk threshold; A2: Determine whether the risk factor is greater than the first risk threshold; if yes, mark the risk level of the vehicle as high risk; if no, jump to A3; A3: Determine whether the risk factor is greater than the second risk threshold; if yes, mark the risk level of the vehicle as medium risk; if no, mark the risk level of the vehicle as low risk.

7. A dynamic weighing method based on bumpy road surface according to claim 1, characterized in that: The generating and giving of vehicle driving suggestions according to the danger level includes: Extract the danger level of the vehicle; When the danger level of the vehicle is high, the generated driving suggestion is that it is not recommended for the vehicle to drive on the current bumpy road; When the danger level of the vehicle is medium danger, the generated vehicle driving advice is to drive carefully; When the danger level of the vehicle is low, the generated vehicle driving recommendation is safe to drive.

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