Vehicle load determination method, control device, and storage medium

Abstract

The application provides a vehicle load discrimination method, a control device and a storage medium, comprising: acquiring the speed and acceleration of the vehicle at multiple time points in a braking or acceleration process; inputting the speed and acceleration at any time point among the multiple time points into a preset first discrimination model to obtain a first discrimination result; determining the value of a first parameter and the value of a second parameter according to the speed and acceleration at the multiple time points, inputting the value of the first parameter and the value of the second parameter into a preset second discrimination model to obtain a second discrimination result, wherein the first parameter is the slope of a straight line equation, the second parameter is the intercept of the straight line equation, the straight line equation takes the acceleration as the dependent variable and the speed as the independent variable, and the load state of the vehicle includes an empty load state and a full load state; and determining the load state of the vehicle according to the first discrimination result and the second discrimination result. The application can reduce the cost of vehicle load discrimination.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method, control device, and storage medium for determining vehicle load. Background Technology

[0002] In commercial vehicle transportation, the vehicle's load condition is one of the most critical indicators. Different vehicle loads not only affect fuel consumption but also significantly impact driving safety.

[0003] Existing dynamic weighing methods and systems for vehicle load weights are basically based on sensor technology. They collect and process weight signals from sensors, analyze the data through a microcontroller or host computer, and then display the weight or upload the data to a regulatory agency. Examples include strain gauge sensors installed on the vehicle frame or ground that utilize the relationship between resistance and stress, or capacitive sensors installed in the vehicle's suspension spring system or frame system that utilize the principle of capacitance and deformation under stress.

[0004] However, regardless of the type of sensor used, installing these sensors incurs significant costs. How to reduce costs while dynamically acquiring the vehicle's load status is a technical problem that urgently needs to be solved by existing technologies. Summary of the Invention

[0005] In view of this, the present invention provides a vehicle load determination method, control device and storage medium, which can solve the problem of high cost of the prior art in determining the vehicle load status by installing sensors.

[0006] In a first aspect, embodiments of the present invention provide a method for determining vehicle load, including:

[0007] Acquire the vehicle's speed and acceleration at multiple points during a single braking or acceleration process;

[0008] Input the velocity and acceleration at any one of the multiple moments into a preset first discrimination model to obtain a first discrimination result;

[0009] Based on the velocity and acceleration at multiple moments, the values ​​of the first parameter and the second parameter are determined. The values ​​of the first parameter and the second parameter are input into a preset second discrimination model to obtain a second discrimination result. The first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation. The linear equation has acceleration as the dependent variable and velocity as the independent variable. The first discrimination result and the second discrimination result are used to represent the load state of the vehicle. The load state of the vehicle includes an empty state and a fully loaded state.

[0010] Based on the first discrimination result and the second discrimination result, the load state of the vehicle is determined in one possible implementation. The construction process of the first discrimination model includes:

[0011] Multiple sets of first sample data are obtained based on the vehicle's historical driving data. Each set of first sample data includes the vehicle's speed value, acceleration value, and the value corresponding to the vehicle's load state at the same time.

[0012] The first discriminant model is obtained by training the support vector machine model using the multiple sets of first sample data.

[0013] In one possible implementation, the construction process of the second discriminant model includes:

[0014] Multiple sets of second sample data are obtained based on the vehicle's historical driving data. Each set of second sample data includes the value of the first parameter, the value of the second parameter corresponding to the first parameter, and the value corresponding to the vehicle's load status.

[0015] The second discriminant model is obtained by training the preset model using the multiple sets of second sample data.

[0016] In one possible implementation, the process of obtaining the multiple sets of second sample data includes:

[0017] The historical driving data of the vehicle under no-load conditions is obtained. Based on the historical driving data of the vehicle under no-load conditions, a linear regression algorithm is used to construct n linear equations with acceleration as the dependent variable and velocity as the independent variable to obtain n sets of second sample data, where n is a positive integer greater than or equal to 2.

[0018] The historical driving data of the vehicle under full load is obtained. Based on the historical driving data of the vehicle under full load, a linear regression algorithm is used to construct a linear equation with m accelerations as dependent variables and velocity as independent variables to obtain m sets of second sample data, where m is a positive integer greater than or equal to 2.

[0019] The historical driving data corresponds to multiple historical driving segments, each segment corresponding to a braking process or an acceleration process of the vehicle during the historical driving process. The historical driving data includes speed values ​​and acceleration values ​​at multiple moments within each segment.

[0020] In one possible implementation, determining the vehicle's load status based on the first discrimination result and the second discrimination result includes:

[0021] If the first determination result and the second determination result are consistent, then the first determination result and / or the second determination result shall be taken as the load status of the vehicle;

[0022] If the first determination result and the second determination result are inconsistent, multiple determinations are made during one trip of the vehicle. Each determination includes one first determination result and one second determination result, until the first determination result and the second determination result are consistent. The first determination result and / or the second determination result are then taken as the load status of the vehicle.

[0023] In one possible implementation, after determining the load status of the vehicle based on the first discrimination result and the second discrimination result, the method further includes:

[0024] If the vehicle is fully loaded, and during the vehicle's operation, if the absolute value of the change in the vehicle's heading angle exceeds a preset threshold in a second preset time period, and the vehicle's speed is greater than a first preset speed, then an alarm signal is sent to prompt the driver to reduce the vehicle's speed to a second preset speed, where the second preset speed is less than the first preset speed.

[0025] And / or, if the vehicle is fully loaded, and during the vehicle's operation, if the gradient of the road where the vehicle is located is greater than a preset gradient value, and the vehicle's speed is greater than a third preset speed, then an alarm signal is sent to prompt the driver to reduce the vehicle's speed to a fourth preset speed, which is less than the third preset speed.

[0026] Secondly, embodiments of the present invention provide a vehicle load determination device, comprising: an acquisition module, a first determination module, a second determination module, and a determination module;

[0027] The acquisition module is used to acquire the speed and acceleration of the vehicle at multiple moments during a single braking or acceleration process;

[0028] The first discrimination module is used to input the velocity and acceleration at any one of the plurality of moments into a preset first discrimination model to obtain a first discrimination result;

[0029] The second discrimination module is used to determine the values ​​of a first parameter and a second parameter based on the velocity and acceleration at the multiple moments, and input the values ​​of the first parameter and the second parameter into a preset second discrimination model to obtain a second discrimination result. The first parameter is the slope of the linear equation, the second parameter is the intercept of the linear equation, the linear equation has acceleration as the dependent variable and velocity as the independent variable, and the first discrimination result and the second discrimination result are used to represent the load state of the vehicle, the load state of the vehicle includes an empty state and a fully loaded state.

[0030] The determining module is used to determine the load status of the vehicle based on the first discrimination result and the second discrimination result.

[0031] In one possible implementation, the first discrimination module is further configured to:

[0032] Multiple sets of first sample data are obtained based on the vehicle's historical driving data. Each set of first sample data includes the vehicle's speed value, acceleration value, and the value corresponding to the vehicle's load state at the same time.

[0033] The first discriminant model is obtained by training the support vector machine model using the multiple sets of first sample data.

[0034] Thirdly, embodiments of the present invention provide a control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0035] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0036] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0037] This invention pre-trains two discrimination models to acquire the vehicle's speed and acceleration at multiple moments during a single braking or acceleration process. The speed and acceleration at any one moment are input into a pre-set first discrimination model to obtain a first discrimination result. Based on the speed and acceleration at multiple moments, the values ​​of a first parameter and a second parameter are determined, and these values ​​are input into a pre-set second discrimination model to obtain a second discrimination result. Here, the first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation, with acceleration as the dependent variable and speed as the independent variable. The first and second discrimination results represent the vehicle's load state, including both empty and fully loaded states. The vehicle's load state is determined based on the first and second discrimination results. The method provided by this invention enables online determination of vehicle load state without the need for pre-installing sensors on the vehicle or road, reducing the cost of determining vehicle load state. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the implementation of a vehicle load determination method provided in an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the vehicle load determination device provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the control device provided in an embodiment of the present invention. Detailed Implementation

[0042] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0044] See Figure 1 The flowchart illustrating the vehicle load determination method provided in this embodiment of the invention is shown below:

[0045] In step 101, the speed and acceleration of the vehicle at multiple moments during a single braking or acceleration process are acquired.

[0046] In this embodiment of the invention, when a vehicle is driven once, it will include multiple acceleration processes, braking processes, and constant speed driving processes. In this step, the speed and acceleration values ​​of the vehicle at multiple moments during a braking or acceleration process are obtained. That is, a driving segment in which the acceleration of the vehicle is not zero is obtained. Sampling is performed according to a preset rule to determine multiple moments, such as sampling the speed and acceleration data at a moment every 5 seconds to obtain the speed and acceleration values ​​at multiple moments in this step. This embodiment of the invention does not limit the method of determining multiple moments.

[0047] It should be noted that the method provided in the embodiments of the present invention can be applied to vehicles of the same or similar models. For vehicles of the same or similar models, the same first discriminant model and second discriminant model can be used to reduce the time cost and computational cost of model training.

[0048] In step 102, the velocity and acceleration at any one of the multiple moments are input into a preset first discrimination model to obtain the first discrimination result.

[0049] In this embodiment of the invention, speed box curves under unloaded and fully loaded conditions are obtained based on historical vehicle driving data, as well as acceleration box curves under unloaded and fully loaded conditions. The following conclusions are drawn: compared with unloaded conditions, the speed and acceleration of a fully loaded vehicle both show a decreasing trend and the difference is significant. Therefore, vehicle speed and acceleration can be used as important criteria for judging the load status of a vehicle.

[0050] It should be noted that, in the embodiments of the present invention, the acceleration can be positive or negative, and the embodiments of the present invention do not limit it.

[0051] In this embodiment of the invention, the vehicle's historical driving data can be obtained through actual measurement or through simulation, and this embodiment of the invention does not limit this.

[0052] In one possible implementation, the construction process of the first discriminant model includes: obtaining multiple sets of first sample data based on the vehicle's historical driving data, wherein each set of first sample data includes the vehicle's speed value, acceleration value, and the value corresponding to the vehicle's load state at the same time; and training the support vector machine model with multiple sets of first sample data to obtain the first discriminant model.

[0053] In this embodiment of the invention, the discrimination performance of different models is evaluated by classification accuracy and Kappa value, and it is confirmed that the support vector machine (SVM) model has the highest discrimination accuracy.

[0054] In step 103, based on the velocity and acceleration at multiple moments, the values ​​of the first parameter and the second parameter are determined. The values ​​of the first parameter and the second parameter are input into a preset second discrimination model to obtain a second discrimination result. The first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation. The linear equation has acceleration as the dependent variable and velocity as the independent variable. The first discrimination result and the second discrimination result are used to represent the load state of the vehicle. The load state of the vehicle includes an empty state and a fully loaded state.

[0055] For either the first discriminant model or the second discriminant model, in one possible implementation, if the model outputs 0, the vehicle is empty; if the model outputs 1, the vehicle is fully loaded. In another possible implementation, if the model outputs 1, the vehicle is empty; if the model outputs 0, the vehicle is fully loaded. This embodiment of the invention does not limit the output format of the discriminant result.

[0056] In one possible implementation, the construction process of the second discriminant model includes: obtaining multiple sets of second sample data based on the vehicle's historical driving data, wherein each set of second sample data includes the value of the first parameter, the value of the second parameter corresponding to the first parameter, and the value corresponding to the vehicle's load state; and training the preset model with multiple sets of second sample data to obtain the second discriminant model.

[0057] In one possible implementation, the process of obtaining multiple sets of second sample data includes: obtaining historical driving data of the vehicle under no-load conditions; based on the historical driving data of the vehicle under no-load conditions, constructing n linear equations with acceleration as the dependent variable and velocity as the independent variable using a linear regression algorithm to obtain n sets of second sample data, where n is a positive integer greater than or equal to 2; obtaining historical driving data of the vehicle under fully loaded conditions; based on the historical driving data of the vehicle under fully loaded conditions, constructing m linear equations with acceleration as the dependent variable and velocity as the independent variable using a linear regression algorithm to obtain m sets of second sample data, where m is a positive integer greater than or equal to 2; wherein, the historical driving data corresponds to multiple historical driving segments, each segment corresponding to a braking process or an acceleration process of the vehicle during the historical driving process, and the historical driving data includes velocity values ​​and acceleration values ​​at multiple moments within each segment.

[0058] The equation of the line can be expressed as a = kv + b, where k is the value of the first parameter and b is the value of the second parameter.

[0059] In step 104, the load status of the vehicle is determined based on the first discrimination result and the second discrimination result.

[0060] In one possible implementation, if the first discrimination result and the second discrimination result are consistent, then the first discrimination result and / or the second discrimination result are taken as the load state of the vehicle; if the first discrimination result and the second discrimination result are inconsistent, then multiple discriminations are performed during one trip of the vehicle, each discrimination including a first discrimination result and a second discrimination result, until the first discrimination result and the second discrimination result are consistent, and then the first discrimination result and / or the second discrimination result are taken as the load state of the vehicle.

[0061] The load status of a vehicle is related to safe driving. Furthermore, after obtaining the load status of the vehicle, the method provided in this embodiment of the invention further includes:

[0062] If the vehicle is fully loaded, and during the vehicle's operation, if the absolute value of the change in the vehicle's heading angle exceeds a preset threshold in the second preset time period, and the vehicle's speed is greater than the first preset speed, then an alarm signal will be sent to prompt the driver to reduce the vehicle's speed to the second preset speed, which is less than the first preset speed.

[0063] And / or, if the vehicle is fully loaded, and during the vehicle's operation, if the gradient of the road where the vehicle is located is greater than a preset gradient value, and the vehicle's speed is greater than a third preset speed, then an alarm signal will be sent to prompt the driver to reduce the vehicle's speed to a fourth preset speed, which is less than the third preset speed.

[0064] This invention pre-trains two discrimination models to acquire the vehicle's speed and acceleration at multiple moments during a single braking or acceleration process. The speed and acceleration at any one moment are input into a pre-set first discrimination model to obtain a first discrimination result. Based on the speed and acceleration at multiple moments, the values ​​of a first parameter and a second parameter are determined, and these values ​​are input into a pre-set second discrimination model to obtain a second discrimination result. Here, the first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation, with acceleration as the dependent variable and speed as the independent variable. The first and second discrimination results represent the vehicle's load state, including both empty and fully loaded states. The vehicle's load state is determined based on the first and second discrimination results. The method provided by this invention enables online determination of vehicle load state without the need for pre-installing sensors on the vehicle or road, reducing the cost of determining vehicle load state.

[0065] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0067] Figure 2 A schematic diagram of the vehicle load determination device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0068] like Figure 2As shown, the vehicle load determination device 2 includes: an acquisition module 21, a first determination module 22, a second determination module 23, and a determination module 24;

[0069] The acquisition module 21 is used to acquire the speed and acceleration of the vehicle at multiple moments during a single braking or acceleration process;

[0070] The first discrimination module 22 is used to input the velocity and acceleration at any one of multiple moments into a preset first discrimination model to obtain a first discrimination result;

[0071] The second discrimination module 23 is used to determine the values ​​of the first parameter and the second parameter based on the velocity and acceleration at multiple moments, and input the values ​​of the first parameter and the second parameter into a preset second discrimination model to obtain the second discrimination result. The first parameter is the slope of the linear equation, the second parameter is the intercept of the linear equation, the linear equation takes acceleration as the dependent variable and velocity as the independent variable, and the first discrimination result and the second discrimination result are used to represent the load state of the vehicle. The load state of the vehicle includes the empty state and the fully loaded state.

[0072] The determination module 24 is used to determine the load status of the vehicle based on the first discrimination result and the second discrimination result.

[0073] In one possible implementation, the first discrimination module 22 is used for:

[0074] Multiple sets of first sample data are obtained based on the vehicle's historical driving data. Each set of first sample data includes the vehicle's speed, acceleration, and load status at the same time.

[0075] The first discriminant model is obtained by training the support vector machine model with multiple sets of first sample data.

[0076] In one possible implementation, the second discrimination module 23 is used for:

[0077] Multiple sets of second sample data are obtained based on the vehicle's historical driving data. Each set of second sample data includes the value of the first parameter, the value of the second parameter corresponding to the first parameter, and the value corresponding to the vehicle's load status.

[0078] The second discriminant model is obtained by training the preset model with multiple sets of second sample data.

[0079] In one possible implementation, the second discriminant model 23 is used for:

[0080] Obtain historical driving data of the vehicle under no-load conditions. Based on the historical driving data of the vehicle under no-load conditions, construct n linear equations with acceleration as the dependent variable and velocity as the independent variable through a linear regression algorithm to obtain n sets of second sample data, where n is a positive integer greater than or equal to 2.

[0081] Obtain historical driving data of the vehicle under full load. Based on the historical driving data of the vehicle under full load, construct a linear equation with m accelerations as dependent variables and velocity as independent variables through a linear regression algorithm to obtain m sets of second sample data, where m is a positive integer greater than or equal to 2.

[0082] Among them, the historical driving data corresponds to multiple historical driving segments. Each segment corresponds to a braking process or an acceleration process of the vehicle during the historical driving process. The historical driving data includes the speed and acceleration values ​​at multiple moments within each segment.

[0083] In one possible implementation, the determining module 24 is used for:

[0084] If the first and second judgment results are consistent, then the first and / or second judgment results shall be taken as the vehicle's load status.

[0085] If the first and second judgment results are inconsistent, multiple judgments will be made during one trip of the vehicle. Each judgment includes a first judgment result and a second judgment result, until the first and second judgment results are consistent. The first judgment result and / or the second judgment result will be taken as the vehicle's load status.

[0086] In one possible implementation, the determining module 24 is also used for:

[0087] If the vehicle is fully loaded, and during the vehicle's operation, if the absolute value of the change in the vehicle's heading angle exceeds a preset threshold in the second preset time period, and the vehicle's speed is greater than the first preset speed, then an alarm signal will be sent to prompt the driver to reduce the vehicle's speed to the second preset speed, which is less than the first preset speed.

[0088] And / or, if the vehicle is fully loaded, and during the vehicle's operation, if the gradient of the road where the vehicle is located is greater than a preset gradient value, and the vehicle's speed is greater than a third preset speed, then an alarm signal will be sent to prompt the driver to reduce the vehicle's speed to a fourth preset speed, which is less than the third preset speed.

[0089] This invention pre-trains two discrimination models to acquire the vehicle's speed and acceleration at multiple moments during a single braking or acceleration process. The speed and acceleration at any one moment are input into a pre-set first discrimination model to obtain a first discrimination result. Based on the speed and acceleration at multiple moments, the values ​​of a first parameter and a second parameter are determined, and these values ​​are input into a pre-set second discrimination model to obtain a second discrimination result. Here, the first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation, with acceleration as the dependent variable and speed as the independent variable. The first and second discrimination results represent the vehicle's load state, including both empty and fully loaded states. The vehicle's load state is determined based on the first and second discrimination results. The method provided by this invention enables online determination of vehicle load state without the need for pre-installing sensors on the vehicle or road, reducing the cost of determining vehicle load state.

[0090] The vehicle load determination device provided in this embodiment can be used to execute the above-described vehicle load determination method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0091] Figure 3 This is a schematic diagram of a control device provided in an embodiment of the present invention. Figure 3 As shown, the control device 3 in this embodiment includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the various vehicle load determination method embodiments described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of units 21 to 24 shown.

[0092] For example, the computer program 32 can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 32 in the control device 3.

[0093] The control device 3 may be a control module / control device / control chip installed on the vehicle, or a control submodule in an existing control system of the vehicle. The control device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of control device 3 and does not constitute a limitation on control device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the control device may also include input / output devices, network access devices, buses, etc.

[0094] The processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0095] The memory 31 can be an internal storage unit of the control device 3, such as a hard disk or memory of the control device 3. The memory 31 can also be an external storage device of the control device 3, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the control device 3. Furthermore, the memory 31 can include both internal storage units and external storage devices of the control device 3. The memory 31 is used to store the computer program and other programs and data required by the control device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0097] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0099] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / control device and method can be implemented in other ways. For example, the apparatus / control device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various vehicle load determination method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0103] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for determining vehicle load, characterized in that, include: Acquire the vehicle's speed and acceleration at multiple points during a single braking or acceleration process; Input the velocity and acceleration at any one of the multiple moments into a preset first discrimination model to obtain a first discrimination result; Based on the velocity and acceleration at multiple moments, the values ​​of a first parameter and a second parameter are determined. The values ​​of the first parameter and the second parameter are then input into a preset second discrimination model to obtain a second discrimination result. Here, the first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation. The linear equation uses acceleration as the dependent variable and velocity as the independent variable. The first discrimination result and the second discrimination result are used to represent the vehicle's load state, which includes an empty load state and a fully loaded load state. The linear equation uses... It means that, among them, For acceleration, The value of the first parameter, For speed, The value of the second parameter; Based on the first discrimination result and the second discrimination result, the load status of the vehicle is determined; Determining the load status of the vehicle based on the first discrimination result and the second discrimination result includes: If the first determination result and the second determination result are consistent, then the first determination result and / or the second determination result shall be taken as the load status of the vehicle; If the first determination result and the second determination result are inconsistent, multiple determinations are made during one trip of the vehicle. Each determination includes one first determination result and one second determination result, until the first determination result and the second determination result are consistent. The first determination result and / or the second determination result are then taken as the load status of the vehicle.

2. The method according to claim 1, characterized in that, The construction process of the first discriminant model includes: Multiple sets of first sample data are obtained based on the vehicle's historical driving data. Each set of first sample data includes the vehicle's speed value, acceleration value, and the value corresponding to the vehicle's load state at the same time. The first discriminant model is obtained by training the support vector machine model using the multiple sets of first sample data.

3. The method according to claim 1, characterized in that, The construction process of the second discriminant model includes: Multiple sets of second sample data are obtained based on the vehicle's historical driving data. Each set of second sample data includes the value of the first parameter, the value of the second parameter corresponding to the first parameter, and the value corresponding to the vehicle's load status. The second discriminant model is obtained by training the preset model using the multiple sets of second sample data.

4. The method according to claim 3, characterized in that, The process of obtaining the multiple sets of second sample data includes: The historical driving data of the vehicle under no-load conditions is obtained. Based on the historical driving data of the vehicle under no-load conditions, a linear regression algorithm is used to construct n linear equations with acceleration as the dependent variable and velocity as the independent variable to obtain n sets of second sample data, where n is a positive integer greater than or equal to 2. The historical driving data of the vehicle under full load is obtained. Based on the historical driving data of the vehicle under full load, a linear equation is constructed using a linear regression algorithm with m accelerations as dependent variables and velocity as independent variables to obtain m sets of second sample data, where m is a positive integer greater than or equal to 2. The historical driving data corresponds to multiple historical driving segments, each segment corresponding to a braking process or an acceleration process of the vehicle during the historical driving process. The historical driving data includes speed values ​​and acceleration values ​​at multiple moments within each segment.

5. The method according to any one of claims 1 to 4, characterized in that, After determining the load status of the vehicle based on the first discrimination result and the second discrimination result, the method further includes: If the vehicle is fully loaded, and during the vehicle's operation, if the absolute value of the change in the vehicle's heading angle exceeds a preset threshold in a second preset time period, and the vehicle's speed is greater than a first preset speed, then an alarm signal is sent to prompt the driver to reduce the vehicle's speed to a second preset speed, where the second preset speed is less than the first preset speed. And / or, if the vehicle is fully loaded, and during the vehicle's operation, if the gradient of the road where the vehicle is located is greater than a preset gradient value, and the vehicle's speed is greater than a third preset speed, then an alarm signal is sent to prompt the driver to reduce the vehicle's speed to a fourth preset speed, which is less than the third preset speed.

6. A vehicle load determination device, characterized in that, include: The system comprises an acquisition module, a first discrimination module, a second discrimination module, and a determination module. The acquisition module is used to acquire the speed and acceleration of the vehicle at multiple moments during a single braking or acceleration process; The first discrimination module is used to input the velocity and acceleration at any one of the plurality of moments into a preset first discrimination model to obtain a first discrimination result; The second discrimination module is used to determine the values ​​of a first parameter and a second parameter based on the velocity and acceleration at the multiple moments, and input the values ​​of the first parameter and the second parameter into a preset second discrimination model to obtain a second discrimination result. The first parameter is the slope of the linear equation, and the second parameter is the intercept of the linear equation. The linear equation uses acceleration as the dependent variable and velocity as the independent variable. The first discrimination result and the second discrimination result are used to represent the load state of the vehicle, which includes an empty state and a fully loaded state. The linear equation uses... It means that, among them, For acceleration, The value of the first parameter, For speed, The value of the second parameter; The determining module is used to determine the load status of the vehicle based on the first discrimination result and the second discrimination result; The determining module is specifically used for: If the first determination result and the second determination result are consistent, then the first determination result and / or the second determination result shall be taken as the load status of the vehicle; If the first determination result and the second determination result are inconsistent, multiple determinations are made during one trip of the vehicle. Each determination includes one first determination result and one second determination result, until the first determination result and the second determination result are consistent. The first determination result and / or the second determination result are then taken as the load status of the vehicle.

7. The apparatus according to claim 6, characterized in that, The first discrimination module is also used for: Multiple sets of first sample data are obtained based on the vehicle's historical driving data. Each set of first sample data includes the vehicle's speed value, acceleration value, and the value corresponding to the vehicle's load state at the same time. The first discriminant model is obtained by training the support vector machine model using the multiple sets of first sample data.

8. A control device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5 above.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5 above.

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