Load calibration method and device of road vehicle, electronic equipment and storage medium

Abstract

The invention provides a load calibration method and device for a road vehicle, electronic equipment and a storage medium, and relates to the technical field of load calibration, and the method comprises the steps: obtaining a displacement signal and a vehicle type of the road vehicle; determining whether the road vehicle meets a load calibration condition based on the displacement signal; if the road vehicle meets the load calibration working condition, the displacement signal is converted into an electric signal; determining an actual load value based on the electric signal, the vehicle type and a load calibration model; the load calibration model comprises a calibration model and an error correction model. According to the mode, the load values of different vehicles of the same vehicle type are closer to the actual load value, the convenience of load determination is improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of load calibration, and in particular to a load calibration method, device, electronic equipment and storage medium for a road vehicle. Background Art

[0002] With the rapid development of the transportation industry, the problem of overloaded vehicles on roads is becoming increasingly serious, which not only causes great damage to road infrastructure, but also seriously threatens road traffic safety. At present, there are some systems with vehicle overload warning on the market, but most of these systems have problems such as complex calibration, low accuracy and poor compatibility.

[0003] Due to production process reasons, different vehicles of the same model have different displacements of the body to the axle under the same load. This causes a certain weighing error when the on-board dynamic weighing system trained on the prototype vehicle is used on mass-produced vehicles of the same model. The current calibration technology is too dependent on the vehicle offline calibration procedure, which greatly affects the vehicle production efficiency. It also needs to be adapted as the vehicle model expands, which makes it extremely inconvenient to use and has high costs. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a load calibration method, device, electronic device and storage medium for road vehicles, so that the load values ​​of different vehicles of the same model are closer to the actual load values, improve the convenience of load determination and reduce costs.

[0005] In a first aspect, an embodiment of the present invention provides a load calibration method for a road vehicle, comprising: obtaining a displacement signal and a vehicle model of a road vehicle; determining whether the road vehicle meets the load calibration condition based on the displacement signal; if the road vehicle meets the load calibration condition, converting the displacement signal into an electrical signal; determining an actual load value based on the electrical signal, the vehicle model and a load calibration model; the load calibration model comprises: a calibration model and an error correction model.

[0006] In a preferred embodiment of the present invention, the above-mentioned determination of whether the road vehicle meets the load calibration condition based on the displacement signal includes: determining the driving state of the road vehicle based on the displacement signal; if the driving state is a stationary state of the vehicle, it meets the load calibration condition.

[0007] In a preferred embodiment of the present invention, the above-mentioned determination of the actual load value based on the electrical signal, vehicle type and load calibration model includes: calibrating the load value of the road vehicle through the calibration model based on the vehicle type and the electrical signal to obtain an initial load value; and performing error correction on the initial load value through the error correction model to obtain the actual load value.

[0008] In a preferred embodiment of the present invention, the above-mentioned error correction model is used to perform error correction on the initial load values ​​of different vehicles of the same model; the steps for establishing the error correction model are: classifying the weighing errors of different vehicles of the same model to obtain error types; the error types include: load displacement error and zero point error; determining the mathematical model of the load displacement error as the first model, and determining the mathematical model of the zero point error as the second model; establishing the relationship between the actual value of the front and rear axle displacement of the vehicle and the theoretical value of the front and rear axle displacement; and obtaining the error correction model based on the first model, the second model, the relationship and the predetermined error parameters.

[0009] In a preferred embodiment of the present invention, the above-mentioned error parameters are determined in the following manner: determining a first function of the static load of the vehicle to be calibrated and the body to the axle and a second function of the static load of the vehicle to be calibrated and the body to the axle; and determining the error parameters based on the relationship between the first function, the second function and the pre-measured parameters.

[0010] In a preferred embodiment of the present invention, the first model is: Among them, X a1 and X b1 They are the displacement of the front axle and the displacement of the rear axle with load displacement error respectively; and are the theoretical displacements of the front and rear axles respectively; k1 is the error compensation of the front axle displacement; k2 is the error compensation of the rear axle displacement; the second model is: in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; X 0a is the zero bias of the front axle sensor; X 0b is the zero bias of the rear axle sensor; the relationship is: X m =KX e +X0; where X m is the relationship; K is the error coefficient matrix: X e e is a marking symbol used to distinguish variables with different meanings; X0 is the zero bias of the front and rear axle sensors, X0 = (X 0a X 0b ) T , T is the transpose of the matrix; the first function is: in, and are the theoretical displacements of the front and rear axles respectively, coefficients a and b are coefficients multiplied by variables, coefficient c is a constant term, and has a fixed offset effect on the value of function G; the second function is: in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; coefficient d and coefficient e are coefficients multiplied by variables, and coefficient f is a constant term, which has a fixed offset effect on the value of function G; the parameter relationship is: when G = 60, in, and are the theoretical displacements of the front and rear axle sensors respectively; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; the error parameter is: Wherein, k1 is the error compensation of the front axle displacement; coefficient a and coefficient d are coefficients multiplied by the variable; k2 is the error compensation of the rear axle displacement; coefficient b and coefficient e are coefficients multiplied by the variable; X 0a and X 0b They are the zero bias of the front and rear axle sensors; is the zero offset displacement of the front axle sensor with load displacement error and zero point error; is the theoretical displacement of the front axle sensor zero bias; is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; Theoretical zero bias of the rear axle sensor

[0011] X e =K -1 (X m -X0)

[0012] Displacement; the error correction model is: Among them, X e Where e is a marker symbol used to distinguish variables with different meanings; K is the error coefficient matrix, and X m is the relationship; X0 is the zero bias of the front and rear axle sensors; and are the theoretical displacements of the front and rear axles respectively; coefficient a and coefficient d are coefficients multiplied by the variables; coefficient b and coefficient e are coefficients multiplied by the variables; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, It is the zero offset displacement of the rear axle sensor with load displacement error and zero point error.

[0013] In a preferred embodiment of the present invention, after determining the actual load value based on the electrical signal and the load calibration model, the method also includes: determining the overload condition of the road vehicle based on the actual load value and a preset overload threshold; and reminding and / or protecting the road vehicle based on the overload condition.

[0014] In a second aspect, an embodiment of the present invention further provides a load calibration device for a road vehicle, comprising: a displacement signal acquisition module, used to acquire a displacement signal of a road vehicle; a load calibration condition judgment module, used to determine whether the road vehicle meets the load calibration condition based on the displacement signal; a displacement signal conversion module, used to convert the displacement signal into an electrical signal if the road vehicle meets the load calibration condition; an actual load value determination module, used to determine the actual load value based on the electrical signal and a load calibration model; the load calibration model includes: a calibration model and an error correction model.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the load calibration method for road vehicles of the first aspect.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the load calibration method for road vehicles of the first aspect.

[0017] The embodiments of the present invention bring the following beneficial effects:

[0018] The embodiment of the present invention provides a load calibration method, device, electronic device and storage medium for a road vehicle, which obtains the displacement signal and vehicle model of the road vehicle, determines whether the road vehicle meets the load calibration condition based on the displacement signal, and if the road vehicle meets the load calibration condition, converts the displacement signal into an electrical signal, and determines the actual load value based on the electrical signal, vehicle model and load calibration model. This method makes the load values ​​of different vehicles of the same vehicle model closer to the actual load value, improves the convenience of load determination, and reduces costs.

[0019] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0020] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flow chart of a load calibration method for a road vehicle provided by an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a load sensing system provided by an embodiment of the present invention;

[0024] Figure 3 A flow chart of another method for calibrating a load of a road vehicle provided by an embodiment of the present invention;

[0025] Figure 4 A schematic structural diagram of a load calibration device for a road vehicle provided by an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not 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.

[0028] With the rapid development of the transportation industry, the problem of overloaded vehicles on roads is becoming increasingly serious, which not only causes great damage to road infrastructure, but also seriously threatens road traffic safety. At present, there are some systems with vehicle overload warning on the market, but most of these systems have problems such as complex calibration, low accuracy and poor compatibility.

[0029] Due to production process reasons, different vehicles of the same model have different displacements of the body to the axle under the same load. This causes a certain weighing error when the on-board dynamic weighing system trained on the prototype vehicle is used on mass-produced vehicles of the same model. The current calibration technology is too dependent on the vehicle offline calibration procedure, which greatly affects the vehicle production efficiency. It also needs to be adapted as the vehicle model expands, which makes it extremely inconvenient to use and has high costs.

[0030] Specifically, the existing technology mainly includes the following defects and shortcomings:

[0031] 1. The calibration process is cumbersome: The calibration of existing systems usually requires professionals to use professional equipment to operate, which is time-consuming and labor-intensive.

[0032] 2. Insufficient accuracy: Due to the limitations of sensors and data processing technology, the existing system has errors in measuring vehicle load.

[0033] 3. Poor compatibility: It cannot be applied to multiple models and different vehicle platforms at the same time, and cannot be better integrated with other systems.

[0034] Based on this, the load calibration method, device, electronic device and storage medium of a road vehicle provided by the embodiment of the present invention can obtain the displacement signal and vehicle model of the road vehicle, determine whether the road vehicle meets the load calibration condition based on the displacement signal, and if the road vehicle meets the load calibration condition, convert the displacement signal into an electrical signal, and determine the actual load value based on the electrical signal, vehicle model and load calibration model. This method makes the load values ​​of different vehicles of the same vehicle model closer to the actual load value, improves the convenience of load determination, and reduces costs.

[0035] To facilitate understanding of this embodiment, a load calibration method for a road vehicle disclosed in an embodiment of the present invention is first introduced in detail.

[0036] Example 1

[0037] The embodiment of the present invention provides a load calibration method for a road vehicle. Figure 1 The present invention provides a flow chart of a method for calibrating a load of a road vehicle. Figure 1 As shown, the load calibration method of the road vehicle may include the following steps:

[0038] Step S101, obtaining the displacement signal and vehicle type of the road vehicle.

[0039] The displacement signal is the height change value of the rear drive axle of the road vehicle. The driving state of the road vehicle can be determined through the displacement signal to determine whether the road vehicle meets the load calibration condition.

[0040] Among them, the calibration algorithms used for load calibration of different vehicle models may be different. The vehicle model and the calibration algorithm have a corresponding relationship, and the vehicle model can be obtained for subsequent load calibration.

[0041] Step S102: determining whether the road vehicle meets the load calibration condition based on the displacement signal.

[0042] Specifically, determining whether the road vehicle meets the load calibration condition based on the displacement signal may include: determining the driving state of the road vehicle based on the displacement signal; if the driving state is a stationary state of the vehicle, the load calibration condition is met.

[0043] The driving state may include a vehicle stationary state and a vehicle moving state. In order to minimize the error effect of load transfer on the calculation of the load sensing system, the load calibration condition is usually limited to the state where the road vehicle is stationary.

[0044] Step S103: if the road vehicle meets the load calibration condition, the displacement signal is converted into an electrical signal.

[0045] Step S104, determining the actual load value based on the electrical signal, vehicle type and load calibration model.

[0046] The load calibration model includes: a calibration model and an error correction model.

[0047] Among them, the error correction model is used to correct the error of the load values ​​of different vehicles of the same model.

[0048] Among them, the calibration model can determine the corresponding calibration algorithm according to the vehicle model, perform preliminary calibration, and obtain the initial load value. The initial load value does not take into account the situation that different vehicles of the same model also have weighing errors. The error correction model is further used to perform error correction to determine the actual load value, which improves simplicity and measurement accuracy.

[0049] Furthermore, after determining the actual load value based on the electrical signal and the load calibration model, the method also includes: determining the overload condition of the road vehicle based on the actual load value and a preset overload threshold; and reminding and / or protecting the road vehicle based on the overload condition.

[0050] Among them, the overload threshold includes a first overload threshold and a second overload threshold. The first overload threshold is 100%. When the actual load value exceeds 100%, the overload situation is overload; the second overload threshold is 130%. When the actual load value exceeds 130%, the overload situation is severe overload.

[0051] Among them, when a road vehicle is overloaded, an alarm will be issued; when a road vehicle is seriously overloaded, an alarm will be issued and the safety control mode will be entered at the same time.

[0052] Furthermore, the load calibration method for a road vehicle provided in an embodiment of the present invention can be applied to a load sensing system of a road vehicle, which load sensing system may include a load sensor, a VCU (Vehicle Control Unit) controller, an IHU (Infotainment Head Unit) controller and an MCU (Motor Control Uni) controller.

[0053] Among them, the displacement signal is collected by the load sensor and converted into an electrical signal, the load calibration condition is judged by the MCU controller, the actual load value is calculated and the overload condition is judged by the VCU controller, and information interaction is realized by the IHU controller.

[0054] Among them, the VCU controller, IHU controller and MCU controller are connected to the CAN line, and information exchange can be realized on the CAN network. The load sensor and the VCU controller are hard-wired through the wiring harness.

[0055] Among them, the VCU controller is the vehicle controller, the IHU controller is the human-computer interaction system controller, and the MCU controller is the motor controller. The VCU controller integrates the load calibration model, and the IHU controller integrates the large-screen display and early warning prompt system.

[0056] Furthermore, the load sensing system can be divided into modules for easy understanding. Figure 2 A schematic diagram of a load sensing system provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the load sensing system may specifically include a signal module, a control module, a processing module and an interaction module. The signal module includes a load sensor, the control module includes an MCU controller, the processing module includes a VCU sensor, and error compensation control is performed through the VCU sensor. The interaction module may include an IHU controller. Early warning prompts are performed through the IHU sensor.

[0057] The load calibration method for a road vehicle provided in an embodiment of the present invention can obtain the displacement signal and vehicle model of the road vehicle, determine whether the road vehicle meets the load calibration condition based on the displacement signal, and if the road vehicle meets the load calibration condition, convert the displacement signal into an electrical signal, and determine the actual load value based on the electrical signal, vehicle model and load calibration model. This method makes the load values ​​of different vehicles of the same vehicle model closer to the actual load value, improves the convenience of load determination, and reduces costs.

[0058] Example 2

[0059] An embodiment of the present invention also provides another method for load calibration of road vehicles; this method is implemented on the basis of the method in the above embodiment; this method focuses on describing the specific implementation method of determining the actual load value based on electrical signals, vehicle models and load calibration models.

[0060] Figure 3 A flow chart of another method for calibrating a load of a road vehicle provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the actual load value is determined based on the electrical signal, vehicle type and load calibration model, and the following steps may be included:

[0061] Step S201 , calibrating the load value of the road vehicle through a calibration model based on the vehicle type and the electrical signal to obtain an initial load value.

[0062] Among them, the calibration model can determine the corresponding calibration algorithm according to the vehicle model, perform preliminary calibration, and obtain the initial load value. The initial load value does not take into account the situation that different vehicles of the same model also have weighing errors.

[0063] Step S202: performing error correction on the initial load value by using an error correction model to obtain an actual load value.

[0064] Specifically, an improved Kalman filter and RBF (Radial Basis Function) neural network can be used as the weighing algorithm for vehicle-mounted dynamic weighing, and the error of the weighing algorithm can be compensated for the vehicles to be calibrated of the same model to improve the weighing accuracy.

[0065] The steps for establishing the error correction model are as follows: Step A1 to Step A4:

[0066] Step A1, classifying the weighing errors of different vehicles of the same model to obtain error types.

[0067] Among them, the error types include: load displacement error and zero point error.

[0068] Step A2, determining the mathematical model of the load displacement error as the first model, and determining the mathematical model of the zero point error as the second model.

[0069] Among them, different vehicles of the same model have different load-bearing components, so under the same load, the displacement distance of the body to the axle is different, and the corresponding first model is:

[0070] Among them, X a1 and X b1 They are the displacement of the front axle and the displacement of the rear axle with load displacement error respectively; and They are the theoretical displacements of the front and rear axles respectively; k1 is the error compensation for the front axle displacement; k2 is the error compensation for the rear axle displacement.

[0071] Among them, after the weighing system is installed on different vehicles of the same model, a zero point offset error is generated due to installation error and the zero point of the components is not zero. The corresponding second model is: The second model is:

[0072] in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; X 0a is the zero bias of the front axle sensor; X 0b is the zero bias of the rear axle sensor;

[0073] Step A3, establishing a relationship between the actual value of the front and rear axle displacement of the vehicle and the theoretical value of the front and rear axle displacement.

[0074] Among them, the relationship between the actual value of the front and rear axle displacement and the theoretical value of the front and rear axle displacement is: m =KX e +X0.

[0075] Among them, X m is the relationship; K is the error coefficient matrix: X e e is a marking symbol used to distinguish variables with different meanings; X0 is the zero bias of the front and rear axle sensors, X0 = (X 0a X 0b ) T , T is the transpose of the matrix.

[0076] Step A4, obtaining an error correction model based on the first model, the second model, the relationship and a predetermined error parameter.

[0077] The error parameters are determined by: determining a first function of the static load of the vehicle to be calibrated and the body to the axle and a second function of the static load of the vehicle to be calibrated and the body to the axle; and determining the error parameters based on the relationship between the first function, the second function and pre-measured parameters.

[0078] Wherein, the first function is:

[0079] in, and They are the theoretical displacements of the front and rear axles respectively. Coefficient a and coefficient b are coefficients multiplied by the variables. Coefficient c is a constant term, which has a fixed offset effect on the value of function G.

[0080] The second function is:

[0081] in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; coefficient d and coefficient e are coefficients multiplied by the variables, and coefficient f is a constant term, which has a fixed offset effect on the value of function G.

[0082] The parameter relationship is: when G = 60,

[0083] in, and are the theoretical displacements of the front and rear axle sensors respectively; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, It is the zero offset displacement of the rear axle sensor with load displacement error and zero point error.

[0084] The error parameters are:

[0085] Wherein, k1 is the error compensation of the front axle displacement; coefficient a and coefficient d are coefficients multiplied by the variable; k2 is the error compensation of the rear axle displacement; coefficient b and coefficient e are coefficients multiplied by the variable; X 0a and X 0b They are the zero bias of the front and rear axle sensors; is the zero offset displacement of the front axle sensor with load displacement error and zero point error; is the theoretical displacement of the front axle sensor zero bias; is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; is the theoretical displacement of the rear axle sensor zero bias.

[0086] X e =K -1 (X m -X0)

[0087] The error correction model is: Among them, X e Where e is a marker symbol used to distinguish variables with different meanings; K is the error coefficient matrix, and X m is the relationship; X0 is the zero bias of the front and rear axle sensors; and are the theoretical displacements of the front and rear axles respectively; coefficient a and coefficient d are coefficients multiplied by the variables; coefficient b and coefficient e are coefficients multiplied by the variables; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, It is the zero offset displacement of the rear axle sensor with load displacement error and zero point error.

[0088] Example 3

[0089] Corresponding to the above method embodiment, an embodiment of the present invention provides a load calibration device for a road vehicle, Figure 4 A schematic diagram of a load calibration device for a road vehicle provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the load calibration device of the road vehicle may include:

[0090] The displacement signal acquisition module 301 is used to acquire the displacement signal of the road vehicle.

[0091] The load calibration working condition judgment module 302 is used to determine whether the road vehicle meets the load calibration working condition based on the displacement signal.

[0092] The displacement signal conversion module 303 is used to convert the displacement signal into an electrical signal if the road vehicle meets the load calibration condition.

[0093] The actual load value determination module 304 is used to determine the actual load value based on the electrical signal and the load calibration model; the load calibration model includes: a calibration model and an error correction model.

[0094] The load calibration device for road vehicles provided in the embodiment of the present invention can obtain the displacement signal and vehicle model of the road vehicle, determine whether the road vehicle meets the load calibration condition based on the displacement signal, and if the road vehicle meets the load calibration condition, convert the displacement signal into an electrical signal, and determine the actual load value based on the electrical signal, vehicle model and load calibration model. This method makes the load values ​​of different vehicles of the same vehicle model closer to the actual load value, improves the convenience of load determination, and reduces costs.

[0095] In some embodiments, the load calibration working condition judgment module is also used to determine the driving state of the road vehicle based on the displacement signal; if the driving state is a stationary state of the vehicle, it meets the load calibration working condition.

[0096] In some embodiments, the actual load value determination module is further used to calibrate the load value of the road vehicle through a calibration model based on the vehicle model and the electrical signal to obtain an initial load value; and to perform error correction on the initial load value through an error correction model to obtain an actual load value.

[0097] In some embodiments, the error correction model is used to perform error correction on the initial load values ​​of different vehicles of the same model; the actual load value determination module is also used to classify the weighing errors of different vehicles of the same model to obtain error types; the error types include: load displacement error and zero point error; the mathematical model for determining the load displacement error is the first model, and the mathematical model for determining the zero point error is the second model; the relationship between the actual value of the front and rear axle displacement of the vehicle and the theoretical value of the front and rear axle displacement is established; the error correction model is obtained based on the first model, the second model, the relationship and the predetermined error parameters.

[0098] In some embodiments, the actual load value determination module is also used to determine a first function of the static load of the vehicle to be calibrated and the body to the axle and a second function of the static load of the vehicle to be calibrated and the body to the axle; and determine an error parameter based on the first function, the second function and the pre-measured parameter relationship.

[0099] In some embodiments, the first model is: Among them, X a1 and X b1 They are the displacement of the front axle and the displacement of the rear axle with load displacement error respectively; and are the theoretical displacements of the front and rear axles respectively; k1 is the error compensation of the front axle displacement; k2 is the error compensation of the rear axle displacement; the second model is: in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; X 0a is the zero bias of the front axle sensor; X 0b is the zero bias of the rear axle sensor; the relationship is: X m =KX e +X0; where X m is the relationship; K is the error coefficient matrix: X e e is a marking symbol used to distinguish variables with different meanings; X0 is the zero bias of the front and rear axle sensors, X0 = (X 0a X 0b ) T , T is the transpose of the matrix; the first function is: in, and are the theoretical displacements of the front and rear axles respectively, coefficients a and b are coefficients multiplied by variables, coefficient c is a constant term, and has a fixed offset effect on the value of function G; the second function is: in, and are the displacements of the front and rear axes with load displacement error and zero point error respectively; coefficient d and coefficient e are coefficients multiplied by variables, and coefficient f is a constant term, which has a fixed offset effect on the value of function G; the parameter relationship is: when G = 60, in, and are the theoretical displacements of the front and rear axle sensors respectively; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; the error parameter is: Wherein, k1 is the error compensation of the front axle displacement; coefficient a and coefficient d are coefficients multiplied by the variable; k2 is the error compensation of the rear axle displacement; coefficient b and coefficient e are coefficients multiplied by the variable; X 0a and X 0b They are the zero bias of the front and rear axle sensors; is the zero offset displacement of the front axle sensor with load displacement error and zero point error; is the theoretical displacement of the front axle sensor zero bias; is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; is the theoretical displacement of the rear axle sensor zero bias;

[0100] X e =K -1 (X m -X0)

[0101] The error correction model is: Among them, X e Where e is a marker symbol used to distinguish variables with different meanings; K is the error coefficient matrix, and X m is the relationship; X0 is the zero bias of the front and rear axle sensors; and are the theoretical displacements of the front and rear axles respectively; coefficient a and coefficient d are coefficients multiplied by the variables; coefficient b and coefficient e are coefficients multiplied by the variables; and are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, It is the zero offset displacement of the rear axle sensor with load displacement error and zero point error.

[0102] In some embodiments, the actual load value determination module is further used to determine the overload condition of the road vehicle based on the actual load value and a preset overload threshold; and to remind and / or protect the road vehicle based on the overload condition.

[0103] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0104] Example 4

[0105] The embodiment of the present invention also provides an electronic device for running the load calibration method of the road vehicle; see Figure 5 A structural schematic diagram of an electronic device is shown, the electronic device includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned road vehicle load calibration method.

[0106] Further, Figure 5 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0107] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0108] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0109] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned road vehicle load calibration method. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0110] The computer program product for the method for load calibration of road vehicles provided in the embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0115] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0116] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for calibrating a load of a road vehicle, characterized in that: The method comprises: Obtain the displacement signal and model of road vehicles; determining whether the road vehicle complies with a load calibration condition based on the displacement signal; If the road vehicle meets the load calibration condition, converting the displacement signal into an electrical signal; The actual load value is determined based on the electrical signal, the vehicle type and a load calibration model; the load calibration model includes: a calibration model and an error correction model.

2. The method according to claim 1, characterized in that The determining whether the road vehicle meets the load calibration condition based on the displacement signal comprises: determining a driving state of the road vehicle based on the displacement signal; If the driving state is a stationary state of the vehicle, the load calibration condition is met.

3. The method according to claim 1, characterized in that The determining of the actual load value based on the electrical signal, the vehicle type and the load calibration model comprises: Calibrate the load value of the road vehicle through the calibration model based on the vehicle type and the electrical signal to obtain an initial load value; The initial load value is error corrected by the error correction model to obtain an actual load value.

4. The method according to claim 3, characterized in that The error correction model is used to perform error correction on initial load values ​​of different vehicles of the same model; The steps for establishing the error correction model are: Classify the weighing errors of different vehicles of the same model to obtain the error types; The error types include: load displacement error and zero point error; Determine the mathematical model of the load displacement error as the first model, and determine the mathematical model of the zero point error as the second model; Establishing the relationship between the actual displacement value of the front and rear axles of the vehicle and the theoretical displacement value of the front and rear axles; The error correction model is obtained based on the first model, the second model, the relationship and a predetermined error parameter.

5. The method according to claim 4, characterized in that The error parameter is determined as follows: Determine a first function of the static load of the vehicle to be calibrated and the body to the axle and a second function of the static load of the vehicle to be calibrated and the body to the axle; The error parameter is determined based on the first function, the second function and a pre-measured parameter relationship.

6. The method according to claim 5, characterized in that The first model is: Among them, X a1 and X b1 They are the displacement of the front axle and the displacement of the rear axle with load displacement error respectively; are the theoretical displacements of the front and rear axles respectively; k1 is the error compensation for the front axle displacement; k2 is the error compensation for the rear axle displacement; The second model is: in, are the displacements of the front and rear axes with load displacement error and zero point error respectively; X 0a is the zero bias of the front axle sensor; X 0b is the zero bias of the rear axle sensor; The relationship is: X m =KX e +X0; where X m is the relationship; K is the error coefficient matrix: X e e is a marking symbol used to distinguish variables with different meanings; X0 is the zero bias of the front and rear axle sensors, X0 = (X 0a X 0b ) T , T is the transpose of the matrix; The first function is: in, are the theoretical displacements of the front and rear axles respectively. Coefficients a and b are coefficients multiplied by variables. Coefficient c is a constant term, which has a fixed offset effect on the value of function G. The second function is: in, are the displacements of the front and rear axes with load displacement error and zero point error respectively; coefficient d and coefficient e are coefficients multiplied by variables, and coefficient f is a constant term, which has a fixed offset effect on the value of function G; The parameter relationship is: when G = 60, in, are the theoretical displacements of the front and rear axle sensors respectively; are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; The error parameters are: Wherein, k1 is the error compensation of the front axle displacement; coefficient a and coefficient d are coefficients multiplied by the variable; k2 is the error compensation of the rear axle displacement; coefficient b and coefficient e are coefficients multiplied by the variable; X 0a and X 0b They are the zero bias of the front and rear axle sensors; is the zero offset displacement of the front axle sensor with load displacement error and zero point error; is the theoretical displacement of the front axle sensor zero bias; is the zero offset displacement of the rear axle sensor with load displacement error and zero point error; is the theoretical displacement of the rear axle sensor zero bias; X e =K -1 (X m -X0) The error correction model is: Among them, X e Where e is a marker symbol used to distinguish variables with different meanings; K is the error coefficient matrix, and X m is the relationship; X0 is the zero bias of the front and rear axle sensors; are the theoretical displacements of the front and rear axles respectively; coefficient a and coefficient d are coefficients multiplied by the variables; coefficient b and coefficient e are coefficients multiplied by the variables; are the displacements of the front and rear axle sensors with load displacement error and zero point error respectively; is the theoretical displacement of the front axle sensor zero bias, is the theoretical displacement of the rear axle sensor zero bias, is the zero offset displacement of the front axle sensor with load displacement error and zero point error, It is the zero offset displacement of the rear axle sensor with load displacement error and zero point error.

7. The method according to claim 1, characterized in that After determining the actual load value based on the electrical signal and the load calibration model, the method further includes: determining an overload condition of the road vehicle based on the actual load value and a preset overload threshold; The road vehicle is warned and / or protected based on the overloading condition.

8. A load calibration device for a road vehicle, characterized in that: The device comprises: A displacement signal acquisition module, used to acquire a displacement signal of a road vehicle; A load calibration working condition judgment module, used to determine whether the road vehicle meets the load calibration working condition based on the displacement signal; a displacement signal conversion module, configured to convert the displacement signal into an electrical signal if the road vehicle meets the load calibration condition; The actual load value determination module is used to determine the actual load value based on the electrical signal and the load calibration model; the load calibration model includes: a calibration model and an error correction model.

9. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the load calibration method for a road vehicle as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the load calibration method for a road vehicle as described in any one of claims 1 to 7.

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