A truck load dynamic metering sensor optimal installation method

Abstract

The application discloses a truck load dynamic metering sensor optimization installation method and relates to the technical field of truck load metering. The method comprises the following steps: S1, a sensor range selection model is established, an optimized matching relationship between a sensor range and a truck rated load is provided, and model support is provided for selection of a sensor installation position; and S2, the sensor range selection and the position installation are optimized. Firstly, the method uses a machine learning algorithm technology to obtain optimized matching parameters of truck rated load metering and a sensor range, and carries out sensor range selection work; secondly, a truck load beam deformation model is established to carry out optimization work of the sensor installation position, so that the rated load metering accuracy is finally within the allowable range.

Description

Technical Field

[0001] This invention relates to the field of truck load measurement technology, and in particular to an optimized installation method for a dynamic load measurement sensor for trucks. Background Technology

[0002] With the rapid development of the road transport industry, maximizing freight profits is the ultimate goal for both freight companies and individual freight operators. The closer a shipment is to its rated load capacity, the higher its marginal revenue. Therefore, freight drivers generally adopt a loading strategy that is as close to the rated load as possible. However, due to inconsistencies between the loading site and the weighbridge measurement site, this loading strategy easily leads to unintended overloading. On the one hand, overloading results in high legal costs or the cost of reloading and unloading (including unloading fees, round-trip time, and queuing time). On the other hand, insufficient load capacity leads to significant revenue loss, or even potential losses, for a single shipment. Even reloading wastes considerable round-trip time and transportation costs. Therefore, there is an urgent need to select and install a high-precision and accurate real-time truck load measurement sensor to reduce the number of times truck drivers need to queue at the weighbridge and save on transportation time and costs.

[0003] Currently, there are two main methods for installing truck load cells: pre-installation and retrofitting. Pre-installation refers to installing the load cell in the corresponding load-bearing structure of the truck before it leaves the factory; retrofitting refers to installing the load cell in the corresponding load-bearing structure after the truck leaves the factory without changing the truck's structure. Pre-installation provides more stable measurements, but it is time-consuming, costly, and cannot effectively adapt to the load cell measurement needs of different types of cargo loading environments. Retrofitting is more flexible and quicker to install, but the installation location is generally selected based on manual experience, which can easily lead to sensor overload damage and compromised accuracy. Summary of the Invention

[0004] This invention addresses the technical problem of inconsistencies between the loading site and the weighbridge measurement site, which can easily lead to passive overloading; and the technical problem that existing sensor installation methods cannot meet the loading strategies of truck drivers to load the load closest to the rated load. It provides an optimized installation method for dynamic load measurement sensors for trucks. This method uses machine learning algorithms to determine the sensor selection, improves the accuracy of truck load measurement data, and optimizes the sensor installation position to meet the loading strategies of truck drivers to load the load closest to the rated load.

[0005] Therefore, the technical solution of the present invention is an optimized installation method for a dynamic load measurement sensor for freight trucks, the method comprising the following steps:

[0006] S1. Establish a sensor range selection model to provide an optimized matching relationship between the sensor range and the rated load of the truck, providing model support for the selection of sensor installation location, including the following steps:

[0007] S1.1 Establish a model for the allowable error in truck load measurement;

[0008] S1.2. Obtain the load conversion coefficient of the truck deformation and the initial load sensing measurement bias according to the learning algorithm;

[0009] S1.3 Establish a longitudinal deformation sensing model for the crossbeam;

[0010] S1.4 Establish a sensor deformation sensing model;

[0011] S1.5 Establish a sensor minimum deformation sensing model;

[0012] S1.6 Establish a sensor load perception lower limit model;

[0013] S1.7 Establish a sensor range determination model;

[0014] S2. Optimize sensor range selection and installation location, including the following steps:

[0015] S2.1, Given parameters EI, L, l, W max , ε r , in i = 0, 1, L, m;

[0016] S2.2, Actual measurement (W,y), based on the learning algorithm, obtain the load conversion coefficient λ1 of the truck deformation and the initial load perception measurement bias;

[0017] S2.3 Determine the maximum allowable error ε based on the truck load measurement allowable error model;

[0018] S2.4 Calculate the lower limit of sensor deformation sensing based on the sensor's minimum deformation sensing model.

[0019] s(ε;x i ,EI,L,l),i=0,1,L,m;

[0020] S2.5. Determine the lower limit of the sensor's range based on the sensor's elastic modulus and the sensor's load sensing lower limit model.

[0021] w min (λ2,ε;x i ,EI,L,l),i=0,1,L,m;

[0022] S2.6, Given the lower limit w of the selectable sensor range. j, j=1,L,j * ;

[0023] S2.7 Calculation w * This is the lower limit of the range of the selected sensor;

[0024] S2.8, Calculation x * This means optimizing the installation location;

[0025] S2.9 Calculate the corresponding sensor range [w] based on λ3. min ,w max ], thereby determining w max Then, select the sensor's range based on redundancy.

[0026] Preferably, the allowable error model for truck load measurement is as follows:

[0027] ε=W max ε r (1)

[0028] Among them, W max Given the rated load capacity of the cargo, ε r This refers to the permissible relative error in truck load measurement.

[0029] Preferably, the method for obtaining the load conversion coefficient of truck deformation and the initial load sensing measurement bias is as follows: the cargo load is denoted as W, the length of the chassis deformation sensing beam is L, the width is b, the thickness is h, its bending stiffness is EI, x is the distance from the midpoint of the sensor to the left fixed end of the beam, y is the corresponding longitudinal deformation, l is the sensor length, and λ1 is the load conversion coefficient of truck deformation.

[0030] make

[0031]

[0032] Thus, a model is established to show the relationship between the truck's load and the vertical deformation of the crossbeam:

[0033]

[0034] Based on formula (3), the learning algorithm is used to obtain λ1 and the initial load sensing measurement bias.

[0035] Preferably, the longitudinal deformation sensing model of the beam is as follows:

[0036]

[0037] Preferably, the method for establishing the sensor deformation sensing model is to let

[0038]

[0039] A sensor deformation sensing model can be established from (4).

[0040]

[0041] Preferably, the method for establishing the sensor's minimum deformation sensing model is to obtain the sensor's minimum deformation sensing model according to formulas (1) and (6).

[0042]

[0043] Preferably, the lower limit model for sensor load sensing is:

[0044]

[0045] Where λ2 is the elastic modulus of the sensor semiconductor material.

[0046] Preferably, the method for establishing a sensor range determination model is as follows:

[0047] Based on the range ratio λ3 selected according to the target measurement range, the sensor sensing upper limit model is calculated as follows:

[0048] w max =λ3w min (9)

[0049] Therefore, the sensor range is:

[0050] [w min ,w max (10).

[0051] Preferably, the computation steps of the learning algorithm are as follows:

[0052] (1) Calculate k1(x) i ;L);

[0053] (2) Weighted y ij :

[0054] (3) Weighted average y ij :

[0055] (4) Calculate the coefficient λ1:

[0056] (5) Calculate the initial load sensing measurement bias:

[0057] Preferably, the sensor installation process is as follows: a pre-installation nut is first installed on the mounting plane of the crossbeam, the sensor is installed on the upper surface of the pre-installation nut, the sensor is fixedly connected to the pre-installation nut by a fixing screw, and a washer and a spring washer are installed between the sensor and the fixing screw in sequence.

[0058] The beneficial effects of this invention are as follows: First, it utilizes machine learning technology to obtain optimized matching parameters between the truck's rated load capacity measurement and the sensor's range, enabling the selection of the appropriate sensor range. Second, it establishes a deformation model of the truck's load-bearing beam to optimize the sensor's installation position, ultimately achieving the goal of keeping the rated load capacity measurement accuracy within the allowable range. This method breaks away from existing methods that rely solely on weighbridges for measurement after loading, enabling dynamic measurement of truck load capacity, on-site perception of the weight of the cargo loaded on the truck, and obtaining real-time load data, thus saving drivers transportation costs. Furthermore, this method uses machine learning technology to determine sensor selection, improving the accuracy of truck load capacity measurement data; and it determines scientific sensor installation positions, reducing the cost of blind manual inspections when replacing sensors. Attached Figure Description

[0059] Figure 1 This is a flowchart of an optimized installation method for a dynamic metering sensor for truck load according to the present invention.

[0060] Figure 2 This is a schematic diagram of the deformation of a stressed beam;

[0061] Figure 3 This is a schematic diagram of the sensor mounting structure.

[0062] Explanation of symbols in the diagram:

[0063] 1. Crossbeam; 2. Pre-installed nut; 3. Sensor; 4. Fixing screw; 41. Washer; 42. Spring washer. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] It should be noted that in the description of this invention, the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0066] Furthermore, it should be understood that, for ease of description, the dimensions of the various components shown in the accompanying drawings are not drawn to actual scale; for example, the thickness or width of some layers may be exaggerated relative to other layers.

[0067] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined or described in one figure, it will not need to be discussed or described in detail in the description of the subsequent figures.

[0068] Example 1

[0069] like Figure 1 First, machine learning technology is used to obtain optimized matching parameters between the rated load measurement of the truck and the sensor range, and sensor range selection is carried out. Second, a deformation model of the truck load beam is established to optimize the installation position of the sensor, so as to ultimately achieve the goal of keeping the rated load measurement accuracy within the allowable range.

[0070] An optimized installation method for a dynamic load measurement sensor for freight trucks, comprising the following steps:

[0071] S1. Establish a sensor range selection model

[0072] The main purpose of this step is to adapt to the measurement requirements of the rated load of trucks and to provide an optimized matching model between the sensor range and the rated load of trucks, so as to provide model support for the selection of sensor installation location.

[0073] S1.1 Establish a model for the allowable error in truck load measurement.

[0074] Given the rated load capacity W of the cargo max =10T, allowable relative error ε for truck load measurement r =1%, then the allowable error model for truck load measurement is:

[0075] ε=W max ε r =10T * 1% = 0.1T (1)

[0076] S1.2 Obtain the load conversion coefficient of the truck deformation.

[0077] The load capacity is denoted as W (0T≤W≤10T). Given the chassis deformation sensing beam with a length L=2040mm, width b=150mm, and thickness h=150mm, its bending stiffness is... x is the distance from the midpoint of the sensor to the left fixed end of the crossbeam, y is the corresponding longitudinal deformation, l is the sensor length (l = 80 mm), and λ1 is the load conversion coefficient of the truck deformation. Due to symmetry, the half position of the beam is considered as the analysis object.

[0078] make

[0079] For example, x∈[40mm,980mm](2)

[0080] Thus, a model is established to show the relationship between the truck's load and the vertical deformation of the crossbeam:

[0081]

[0082] Based on formula (3), the learning algorithm is used to obtain λ1 and the initial load sensing measurement bias.

[0083]

[0084] Where E is the elastic modulus and moment of inertia of the beam. and S1.3 Establish a longitudinal deformation sensing model for the beam.

[0085]

[0086] S1.4 Establish a sensor deformation sensing model

[0087] make

[0088]

[0089] A sensor deformation sensing model can be established from (4).

[0090]

[0091] S1.5 Establish a sensor minimum deformation sensing model

[0092] Based on formulas (1) and (6), the sensor's minimum deformation sensing model is obtained.

[0093]

[0094] S1.6 Establish a sensor load perception lower limit model

[0095]

[0096] Where λ2 is the elastic modulus of the sensor semiconductor material, such as 1.8*105Mpa.

[0097] S1.7 Establish a sensor range determination model

[0098] Based on the range ratio λ3 selected according to the target measurement range, the sensor sensing upper limit model is calculated as follows:

[0099] w max =λ3w min (9)

[0100] Therefore, the sensor range is:

[0101] [w min ,w max (10)

[0102] S2. Optimize sensor range selection and installation location, including the following steps:

[0103] S2.1, Given parameters EI, L, l, W max , ε r , in i = 0, 1, L, m;

[0104] S2.2, Actual measurement (W,y), according to the learning algorithm, obtain the load conversion coefficient λ1 of the truck deformation and the initial load perception measurement bias in formula (3);

[0105] S2.3 Determine the maximum permissible error ε according to formula (1);

[0106] S2.4 Calculate the lower limit of sensor deformation sensing s(ε; x) according to formula (7). i ,EI,L,l),i=0,1,L,m;

[0107] S2.5 Determine the lower limit of the sensor range based on the sensor's elastic modulus and formula (8).

[0108] w min (λ2,ε;x i ,EI,L,l),i=0,1,L,m;

[0109] S2.6, Given the lower limit w of the selectable sensor range. j , j=1,L,j * ;

[0110] S2.7 Calculation w * This is the lower limit of the range of the selected sensor.

[0111] S2.8, Calculation x * This means optimizing the installation location.

[0112] S2.9 Calculate the corresponding sensor range [w] based on λ3. min ,w max ], thereby determining w max Then, select the sensor's range based on redundancy.

[0113] An optimized installation method for dynamic load measurement sensors for trucks breaks away from the existing method of relying solely on weighbridges to measure loads after loading. This method can dynamically measure the load of trucks, sense the weight of the cargo loaded on-site, and obtain real-time load data, thus saving transportation costs for drivers. The method uses machine learning technology to determine the selection of sensors, improving the accuracy of truck load measurement data. It also determines scientific sensor installation locations, reducing the cost of blind manual inspections when replacing sensors.

[0114] Example 2

[0115] like Figure 2 As shown, based on the principle of symmetry, taking half of the crossbeam 1 as the analysis object, given the length of the chassis deformation sensing crossbeam 1 as L, x as the distance from the midpoint of sensor 3 to the left fixed end of crossbeam 1, y as the corresponding longitudinal deformation, and l as the sensor length, then...

[0116] like Figure 3 As shown, the installation process of sensor 3 is as follows: a pre-installation nut 2 is first installed on the mounting plane of beam 1, and sensor 3 is installed on the upper surface of the pre-installation nut 2. Sensor 3 is fixedly connected to the pre-installation nut 2 by fixing screw 4. A washer 41 and a spring washer 42 are installed between sensor 3 and fixing screw 4 in sequence. The pre-installation nut 2 simplifies the installation and positioning process, making it convenient for sensor installation and positioning.

[0117] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An optimized installation method for a dynamic load measurement sensor for freight trucks, characterized in that, The method includes the following steps: S1. Establish a sensor range selection model to provide an optimized matching relationship between the sensor range and the rated load of the truck, providing model support for the selection of sensor installation location, including the following steps: S1.1 Establish a model for the allowable error in truck load measurement; S1.

2. Obtain the load conversion coefficient of the truck deformation and the initial load sensing measurement bias according to the learning algorithm; S1.3 Establish a longitudinal deformation sensing model for the crossbeam; S1.4 Establish a sensor deformation sensing model; S1.5 Establish a sensor minimum deformation sensing model; S1.6 Establish a sensor load perception lower limit model; S1.7 Establish a sensor range determination model; S2. Optimize sensor range selection and installation location, including the following steps: S2.1, Given parameters , , , , , ,in , ; S2.2, Actual Measurement The load conversion coefficient of truck deformation is obtained based on the learning algorithm. and initial load sensing measurement bias ; S2.3 Determine the maximum permissible error based on the freight truck load measurement permissible error model. ; S2.4 Calculate the lower limit of sensor deformation sensing based on the sensor's minimum deformation sensing model. , ; S2.

5. Determine the lower limit of the sensor's range based on the sensor's elastic modulus and the sensor's load sensing lower limit model. , ; make , ; make ; S2.6, Provide the lower limit of the selectable sensor range , ; S2.7 Calculation , This is the lower limit of the range of the selected sensor; S2.8, Calculation ,x * This means optimizing the installation location; S2.9, according to Calculate the corresponding sensor range Thus determine Then, the sensor's range is selected based on redundancy; the calculation steps of the learning algorithm are as follows: (1) Calculation ; (2) Weighted update assignment : ; (3) Weighted average : : (4) Calculate the coefficients : : Calculate the initial load sensing metering bias : ; in, Cargo load capacity, For bending stiffness, The length of the beam. For the length of the sensor, This refers to the maximum rated load capacity of the cargo. The allowable relative error for truck load measurement is given by m and n, where m and n are natural numbers. For the corresponding vertical deformation, The elastic modulus of the sensor's semiconductor material. This is the lower limit of the rated load capacity of the cargo. To select the lower limit of the sensor's range, The number of selectable lower limits for sensor ranges. This represents the lower limit of the range of the selected sensor. Select the range ratio for the target measurement range. This is the load conversion factor for the deformation of the freight car.

2. The optimized installation method for a dynamic metering sensor for truck load according to claim 1, characterized in that, The permissible error model for truck load measurement is as follows: (1), where, This refers to the allowable error in measuring the load capacity of trucks.

3. The optimized installation method for a dynamic metering sensor for truck load according to claim 1, characterized in that, The method for obtaining the load conversion coefficient of truck deformation and the initial load sensing measurement bias is that the width is Thickness is , Let be the distance from the midpoint of the sensor to the left fixed end of the crossbeam. , (2) Thus, a model is established to show the relationship between the truck's load and the vertical deformation of the crossbeam: (3) Based on formula (3), the learning algorithm is used to obtain and initial load sensing measurement bias .

4. The optimized installation method for a dynamic metering sensor for truck load according to claim 3, characterized in that, The longitudinal deformation sensing model of the beam is (4).

5. The optimized installation method for a dynamic metering sensor for truck load according to claim 4, characterized in that, The method for establishing the sensor deformation sensing model is as follows: the sensor deformation sensing model can be established using formula (4): （6）。 6. The optimized installation method for a dynamic metering sensor for truck load according to claim 5, characterized in that, The method for establishing the sensor's minimum deformation sensing model is to obtain the sensor's minimum deformation sensing model according to formulas (1) and (6). (7).

7. The optimized installation method for a dynamic metering sensor for truck load according to claim 1, characterized in that, The method for establishing the sensor range determination model is to select the range ratio based on the target measurement range. The sensor perception upper limit model was calculated as follows: (9), thus the sensor range is: (10).

8. The optimized installation method for a dynamic metering sensor for truck load according to claim 1, characterized in that, The sensor installation process is as follows: a pre-installation nut is first installed on the mounting plane of the crossbeam, and the sensor is installed on the upper surface of the pre-installation nut. The sensor is fixedly connected to the pre-installation nut by a fixing screw, and a washer and a spring washer are installed between the sensor and the fixing screw in sequence.

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