Driving behavior evaluation method and device, equipment and storage medium

Abstract

The invention discloses a driving behavior evaluation method, device and equipment and a storage medium, and belongs to the technical field of vehicle driving, and the method comprises the steps: obtaining driving behavior data of a target vehicle within a preset mileage; inputting the driving behavior data into a target evaluation model to obtain a target evaluation result of the target driver; wherein the target driver refers to a vehicle driver of the target vehicle; the target evaluation model comprises a data analysis layer, a first evaluation result determination layer and a second evaluation result determination layer; the data analysis layer is connected with the first evaluation result determination layer; and the first evaluation result determination layer is connected with the second evaluation result determination layer. According to the method and the device, the target driver can timely correct the bad driving behavior according to the target evaluation result, so that the fuel consumption and the vehicle use cost caused by the bad driving behavior are reduced, the traffic accident rate is reduced, the service life of the target vehicle is prolonged, the fuel economy of the vehicle is improved, and the driving safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle driving, and particularly to a driving behavior evaluation method, device, equipment, and storage medium. Background Art

[0002] With the continuous development of technology, the competition in commercial vehicles is becoming increasingly fierce. The quality and performance of vehicles, such as durability, safety, and fuel economy, directly affect the user experience and are one of the core competitiveness of commercial vehicles. And the driving behavior of vehicle drivers is an important factor affecting aspects such as the service life of vehicles, vehicle driving safety, and vehicle fuel economy.

[0003] The bad driving behavior of vehicle drivers threatens traffic safety, easily forms traffic accidents, and is likely to cause unnecessary fuel consumption during the use of vehicles, increasing the vehicle use cost. If vehicle drivers have long-term bad driving, it will also reduce the service life of vehicles. Therefore, there is an urgent need for a driving behavior evaluation method based on safety and energy conservation to evaluate and guide the driving behavior of vehicle drivers. Summary of the Invention

[0004] The present invention provides a driving behavior evaluation method, device, equipment, and storage medium to reduce fuel consumption and vehicle use cost caused by bad driving behavior, reduce the incidence of traffic accidents, improve the service life of vehicles and vehicle fuel economy, and improve driving safety.

[0005] According to one aspect of the present invention, a driving behavior evaluation method is provided. The method includes:

[0006] Obtain the driving behavior data of the target vehicle within a preset mileage;

[0007] Input the driving behavior data into the target evaluation model to obtain the target evaluation result of the target driver; where the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

[0008] According to another aspect of the present invention, a driving behavior evaluation device is provided. The device includes:

[0009] A driving behavior data acquisition module, configured to obtain the driving behavior data of the target vehicle within a preset mileage;

[0010] A target evaluation result determination module, configured to input driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein, the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

[0011] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the driving behavior evaluation method of any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the driving behavior evaluation method of any embodiment of the present invention when executed.

[0016] According to another aspect of the present invention, there is provided a computer program product including a computer program that implements the driving behavior evaluation method of any embodiment of the present invention when executed by a processor.

[0017] The technical solution of the embodiment of the present invention obtains driving behavior data of a target vehicle within a preset mileage; inputs the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein, the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer. The above technical solution realizes the evaluation of the driving behavior of the target driver with the help of the target evaluation model, enabling the target driver to timely correct their bad driving behaviors according to the target evaluation result, thereby reducing fuel consumption and vehicle usage costs caused by bad driving behaviors, reducing the incidence of traffic accidents, and further improving the service life and vehicle fuel economy of the target vehicle, and improving driving safety.

[0018] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 is a flowchart of a driving behavior evaluation method provided in Embodiment 1 of the present invention;

[0021] Figure 2 is a flowchart of a driving behavior evaluation method provided in Embodiment 2 of the present invention;

[0022] Figure 3 is a schematic structural diagram of a driving behavior evaluation device provided in Embodiment 3 of the present invention;

[0023] Figure 4 is a schematic structural diagram of an electronic device for implementing the driving behavior evaluation method of the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] It should be noted that the terms "target", "initial", "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0026] Embodiment 1

[0027] Figure 1The following is a flowchart of a driving behavior evaluation method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of evaluating the driving behavior of vehicle drivers of commercial vehicles. This method can be executed by a driving behavior evaluation device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. As Figure 1 shown, the method includes:

[0028] S101. Obtain the driving behavior data of the target vehicle within a preset mileage.

[0029] Among them, the target vehicle refers to a commercial vehicle for which the driving behavior of its vehicle driver is to be evaluated. For example, the target vehicle can be a certain heavy-duty tractor. The preset mileage refers to the driving mileage of the target vehicle set in advance; optionally, the preset mileage can be determined according to actual business requirements or the expert experience in this field, and the embodiments of the present invention do not make specific limitations on it.

[0030] Among them, the driving behavior data refers to the data related to the driving behavior of the vehicle driver of the target vehicle; optionally, the driving behavior data includes bad driving behavior data; the bad driving behavior data includes the number of hard accelerations per 100 kilometers, the number of hard decelerations per 100 kilometers, the number of sharp turns per 100 kilometers, the number of speeding occurrences per 100 kilometers, the fatigue driving duration, and the long idling duration.

[0031] Among them, the bad driving behavior refers to the driving behavior that affects vehicle driving safety and vehicle fuel economy; optionally, the bad driving behavior includes, but is not limited to, illegal lane change, speeding, hard acceleration, hard deceleration, sharp turn, fatigue driving, and long idling. Among them, long idling refers to the state where the vehicle engine runs for a long time in neutral and the throttle is not fully released, being in a state of only consuming fuel but not generating effective power. Correspondingly, the bad driving behavior data refers to the data related to the bad driving behavior of the vehicle driver of the target vehicle.

[0032] Optionally, the driving behavior data can also include the maximum speed, average speed, maximum acceleration in the driving direction, maximum deceleration in the driving direction, and maximum lateral acceleration of the target vehicle within the preset mileage.

[0033] Specifically, the driving behavior data of the target vehicle within the preset mileage can be obtained through a vehicle intelligent terminal installed on the target vehicle.

[0034] S102. Input the driving behavior data into the target evaluation model to obtain the target evaluation result of the target driver.

[0035] Among them, the target driver refers to the vehicle driver of the target vehicle. The target evaluation result refers to the result obtained after the driving behavior data is processed by the target evaluation model, and can specifically be presented in the form of a score. The target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

[0036] It should be noted that the target evaluation model at least includes the following model parameters: target evaluation items and the corresponding target weight coefficients for each target evaluation item. Among them, the target evaluation item refers to the data item required to evaluate the driving behavior of the target driver in the target evaluation model. The target weight coefficient refers to the weight coefficient corresponding to the target evaluation item. It should be noted that one target evaluation item corresponds to one target weight coefficient. It should also be noted that the number of target evaluation items in the target evaluation model can be determined according to the expert experience in this field and combined with a large number of simulation experiments. For example, the target evaluation model can include the following target evaluation items: rapid acceleration, rapid deceleration, sharp turn, speeding, fatigue driving, long idling, economic speed, and throttle usage. Among them, the economic speed refers to the speed at which the target vehicle travels on a certain section of the road, with the maximum engine torque and the lowest fuel consumption. Optionally, the economic speed of the target vehicle can be determined according to the vehicle type of the target vehicle and the road section type of the road section where the target vehicle is located. Among them, the vehicle type of the target vehicle can be a tractor, a truck, or a dump truck; the road section type of the road section where the target vehicle is located can be an expressway, an urban expressway, an urban arterial road, an urban sub-arterial road, or an urban feeder road.

[0037] Specifically, the driving behavior data is input into the data analysis layer to obtain the target index data corresponding to each target evaluation item; the target index data corresponding to each target evaluation item is input into the first evaluation result determination layer to obtain the single-item evaluation result corresponding to each target evaluation item; the single-item evaluation results corresponding to each target evaluation item are input into the second evaluation result determination layer to obtain the target evaluation result of the target driver. Among them, the target index data refers to the index data corresponding to the target evaluation item. It should be noted that one target evaluation item corresponds to one target index data. The single-item evaluation result refers to the evaluation result of a single target evaluation item, and can specifically be presented in the form of a score.

[0038] More specifically, the driving behavior data is input into the data analysis layer of the target evaluation model. For each target evaluation item in the target evaluation model, the data analysis layer uses the field name of the target evaluation item as an index to obtain the target index data corresponding to the target evaluation item from the driving behavior data. For example, if there is a target evaluation item in the target evaluation model: hard acceleration, the data analysis layer searches in the driving behavior data with "hard acceleration" as the index, so as to obtain the target index data corresponding to this target evaluation item - the number of hard accelerations per 100 kilometers. Similarly, the target index data corresponding to each target evaluation item in the target evaluation model can be obtained.

[0039] After that, the target index data corresponding to each target evaluation item is input into the first evaluation result determination layer. For each target evaluation item, based on the correspondence between the evaluation item and the evaluation rule in the evaluation rule table in the first evaluation result determination layer, the target evaluation rule corresponding to this target evaluation item is determined from the evaluation rule table; according to the target index data corresponding to this target evaluation item, based on the target evaluation rule corresponding to this target evaluation item, the single-item evaluation result corresponding to this target evaluation item is obtained. Similarly, the single-item evaluation results corresponding to each target evaluation item can be obtained. Among them, the evaluation rule table can be set in advance according to the expert experience in this field, and the embodiments of the present invention do not make specific limitations on it.

[0040] After that, the single-item evaluation results corresponding to each target evaluation item are input into the second evaluation result determination layer, and based on the following comprehensive evaluation calculation formula in the second evaluation result determination layer, the target evaluation result of the target driver is obtained:

[0041]

[0042] Among them, Score represents the target evaluation result of the target driver; Score i represents the single-item evaluation result corresponding to the i-th target evaluation item; λ i represents the target weight coefficient corresponding to the i-th target evaluation item; n represents the number of target evaluation items in the target evaluation model; f(λ) represents the sum of the target weight coefficients corresponding to each target evaluation item.

[0043] In the technical solution of the embodiment of the present invention, driving behavior data of a target vehicle within a preset mileage is obtained; the driving behavior data is input into a target evaluation model to obtain a target evaluation result of a target driver, where the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer. Through the above technical solution, with the help of the target evaluation model, the evaluation of the driving behavior of the target driver is realized, so that the target driver can timely correct his own bad driving behavior according to the target evaluation result, thereby reducing the fuel consumption and vehicle use cost caused by bad driving behavior, reducing the incidence of traffic accidents, and further improving the service life and vehicle fuel economy of the target vehicle, and improving driving safety.

[0044] Embodiment 2

[0045] Figure 2 The flowchart of a driving behavior evaluation method provided by the second embodiment of the present invention. On the basis of the above embodiment, a preferred implementation scheme is provided. It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the relevant descriptions of other embodiments. As Figure 2 shown, the method includes:

[0046] S201. Obtain driving behavior data of a target vehicle within a preset mileage.

[0047] S202. Input the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver.

[0048] Wherein, the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

[0049] Optionally, after obtaining the target evaluation result of the target driver, the target evaluation result can be visually displayed to help the target driver quickly analyze his own bad driving behavior and correct it in time, thereby reducing the fuel consumption and vehicle use cost caused by bad driving behavior, reducing the incidence of traffic accidents, and improving driving safety.

[0050] Specifically, the target evaluation result can be visually displayed in the form of a chart on the vehicle intelligent instrument of the target vehicle. For example, the single-item evaluation result corresponding to each target evaluation item can be displayed in the form of a radar chart on the vehicle intelligent instrument of the target vehicle, and the single-item evaluation result corresponding to each target evaluation item and the target evaluation result can be displayed in the form of a data table.

[0051] S203. Generate driving prompt information according to the target evaluation result.

[0052] The driving prompt information refers to the information used to warn the target driver or guide the target driver during the driving process of the target vehicle.

[0053] Specifically, according to the target evaluation result and based on the corresponding relationship between the evaluation result and the driving type in the driving behavior classification rule table, the target driving type corresponding to the target evaluation result can be determined from the driving behavior classification rule table; and the driving prompt information can be generated according to the target driving type.

[0054] The driving behavior classification rule table can be preset according to the expert experience in this field, and the embodiments of the present invention do not make specific limitations on it. It should be noted that the driving types in the driving behavior classification rule table are divided according to the influence degree of the vehicle driver's driving behavior on the vehicle driving safety and the vehicle fuel economy. For example, the driving types in the driving behavior classification rule table can include steady driving, potential hazard driving, and dangerous driving. The target driving type refers to the driving type corresponding to the target evaluation result.

[0055] More specifically, the target evaluation result can be matched with the evaluation results in the driving behavior classification rule table to obtain a matching evaluation result; the matching evaluation result refers to the evaluation result that successfully matches the target evaluation result in the driving behavior classification rule table; based on the corresponding relationship between the evaluation result and the driving type in the driving behavior classification rule table, the driving type corresponding to the matching evaluation result in the driving behavior classification rule table is used as the target driving type corresponding to the target evaluation result; taking the target driving type as an index, the target generation rule corresponding to the target driving type is obtained from the prompt information generation rule library, and the driving prompt information is generated based on the target generation rule. The prompt information generation rule library pre-stores the prompt information generation rules corresponding to different driving types. The target generation rule refers to the prompt information generation rule corresponding to the target driving type.

[0056] Optionally, after generating the driving prompt information, the driving prompt information can also be visually displayed.

[0057] Specifically, in order to enable the target driver to more intuitively and clearly understand the driving prompt information, after generating the driving prompt information, the driving prompt information can also be visually displayed through the vehicle intelligent instrument on the target vehicle.

[0058] The technical solution of the embodiment of the present invention obtains the driving behavior data of the target vehicle within a preset mileage; inputs the driving behavior data into the target evaluation model to obtain the target evaluation result of the target driver, where the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer; according to the target evaluation result, driving prompt information is generated. In the above technical solution, after obtaining the target evaluation result, driving prompt information is generated according to the target evaluation result, so that the target driver can timely correct his own bad driving behavior according to the driving prompt information, thereby reducing the fuel consumption and vehicle use cost caused by bad driving behavior, reducing the incidence of traffic accidents, and further improving the service life and vehicle fuel economy of the target vehicle, and improving driving safety.

[0059] On the basis of the above embodiment, as an optional way of the embodiment of the present invention, a method for determining the target evaluation model is provided: determining the initial evaluation items of the initial evaluation model and the corresponding initial weight coefficients of each initial evaluation item; adjusting the initial evaluation model through simulation experiments to obtain the target evaluation model. Among them, the initial evaluation model refers to the driving behavior evaluation model constructed according to the vehicle type of the target vehicle and the expert experience in the field under initial conditions. The initial evaluation item refers to the data item required to evaluate the driving behavior of the vehicle driver in the initial evaluation model. The initial weight coefficient is the weight coefficient corresponding to the initial evaluation item. It should be noted that the initial evaluation model includes multiple initial evaluation items, and one initial evaluation item corresponds to one initial weight coefficient.

[0060] Specifically, a certain number of experts can be selected; for each expert, according to the expert's experience, a preset number of candidate driving behaviors that have a greater impact on the driving safety and vehicle fuel economy of the target vehicle are selected from the pre-statistical common vehicle driving behaviors; in the storage method of storing one candidate driving behavior selected by each expert in one line, the candidate driving behaviors selected by each expert are stored in an empty data table to obtain a driving behavior collection table; the number of times each candidate driving behavior is selected in the driving behavior collection table is counted; the number of times each candidate driving behavior is selected is sorted in descending order, and a preset number of candidate driving behaviors are selected from the front to back as the initial evaluation items of the initial evaluation model; then, for each initial evaluation item in the initial evaluation model, according to the weight coefficients assigned by each expert to this initial evaluation item, the corresponding initial weight coefficient of this initial evaluation item is determined. For example, for each initial evaluation item in the initial evaluation model, the weight coefficients assigned by each expert to this initial evaluation item are averaged to obtain the corresponding initial weight coefficient of this initial evaluation item. Similarly, the corresponding initial weight coefficients of each initial evaluation item in the initial evaluation model can be obtained. Then, in the simulation experiment, the vehicle driving mileage is preset, and the driving behaviors corresponding to each initial evaluation item in the initial evaluation model are simulated within the vehicle driving mileage to obtain simulation driving behavior data; the simulation driving behavior data is input into the initial evaluation model to obtain a simulation evaluation result; based on the negative correlation between the bad driving behavior data within the preset mileage and the driving behavior evaluation result, the rationality of the simulation evaluation result is verified; if it is detected that the simulation evaluation result is unreasonable, the initial evaluation items of the initial evaluation model or the corresponding initial weight coefficients of each initial evaluation item are adjusted. For example, a certain initial evaluation item in the initial evaluation model is replaced, or the corresponding initial weight coefficients of each initial evaluation item in the initial evaluation model are modified until the simulation evaluation result output by the initial evaluation model is reasonable, and the adjustment of the initial evaluation model is stopped to obtain the target evaluation model. Among them, the negative correlation between the bad driving behavior data within the preset mileage and the driving behavior evaluation result means that the more times or the longer the duration of the bad driving behavior within the preset mileage, the worse the driving behavior evaluation result.

[0061] It can be understood that by synthesizing the opinions of multiple experts, the initial evaluation items of the initial evaluation model and the corresponding initial weight coefficients of each initial evaluation item are determined, which improves the reliability of the initial evaluation items and the corresponding initial weight coefficients of each initial evaluation item in the initial evaluation model. Then, through simulation experiments, the initial evaluation model is continuously adjusted to obtain the target evaluation model, which improves the accuracy and reliability of the target evaluation model and makes the target evaluation result output by the target evaluation model more accurate.

[0062] Embodiment III

[0063] Figure 3The figure is a schematic structural diagram of a driving behavior evaluation device provided in Embodiment 3 of the present invention. This embodiment is applicable to the situation of evaluating the driving behavior of a vehicle driver of a commercial vehicle. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device. As Figure 3 shown, the device includes:

[0064] A driving behavior data acquisition module 301, configured to acquire driving behavior data of a target vehicle within a preset mileage;

[0065] A target evaluation result determination module 302, configured to input the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein, the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

[0066] The technical solution of the embodiment of the present invention is to acquire driving behavior data of a target vehicle within a preset mileage; input the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein, the target driver refers to the vehicle driver of the target vehicle; the target evaluation model includes a data analysis layer, a first evaluation result determination layer, and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer. With the above technical solution, by means of the target evaluation model, the evaluation of the driving behavior of the target driver is realized, so that the target driver can timely correct his own bad driving behavior according to the target evaluation result, thereby reducing the fuel consumption and vehicle use cost caused by bad driving behavior, reducing the incidence of traffic accidents, and further improving the service life and vehicle fuel economy of the target vehicle, and improving driving safety.

[0067] Optionally, the target evaluation result determination module 302 is specifically configured to:

[0068] Input the driving behavior data into the data analysis layer to obtain target index data corresponding to each target evaluation item;

[0069] Input the target index data corresponding to each target evaluation item into the first evaluation result determination layer to obtain a single evaluation result corresponding to each target evaluation item;

[0070] Input the single evaluation results corresponding to each target evaluation item into the second evaluation result determination layer to obtain the target evaluation result of the target driver.

[0071] Optionally, the device further includes:

[0072] A driving prompt information generation module, configured to generate driving prompt information according to a target evaluation result after obtaining the target evaluation result of a target driver.

[0073] Optionally, the driving prompt information generation module is specifically configured to:

[0074] According to the target evaluation result, based on the correspondence between the evaluation result and the driving type in the driving behavior classification rule table, determine the target driving type corresponding to the target evaluation result from the driving behavior classification rule table;

[0075] Generate driving prompt information according to the target driving type.

[0076] Optionally, the device further includes:

[0077] A driving prompt information display module, configured to visually display the driving prompt information after generating the driving prompt information.

[0078] Optionally, the device further includes a target evaluation model determination module, and the target evaluation model determination module is specifically configured to:

[0079] Determine the initial evaluation items of the initial evaluation model and the initial weight coefficients of each initial evaluation item;

[0080] Adjust the initial evaluation model through simulation experiments to obtain the target evaluation model.

[0081] Optionally, the driving behavior data includes bad driving behavior data; the bad driving behavior data includes the number of hard accelerations per 100 kilometers, the number of hard decelerations per 100 kilometers, the number of sharp turns per 100 kilometers, the number of speeding violations per 100 kilometers, the fatigue driving duration, and the long idling duration.

[0082] The driving behavior evaluation device provided by the embodiments of the present invention can execute the driving behavior evaluation method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each driving behavior evaluation method.

[0083] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0084] Embodiment 4

[0085] Figure 4FIG. 0 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0086] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the driving behavior evaluation method.

[0089] In some embodiments, the driving behavior evaluation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the driving behavior evaluation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the driving behavior evaluation method by any other suitable means (e.g., by means of firmware).

[0090] The various embodiments of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] The computer program for implementing the method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0096] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0097] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A driving behavior evaluation method, characterized in that: include: Obtain driving behavior data of the target vehicle within a preset mileage; Inputting the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein the target driver refers to the vehicle driver of the target vehicle; The target evaluation model includes a data analysis layer, a first evaluation result determination layer and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

2. The method according to claim 1, characterized in that The step of inputting the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver includes: Inputting the driving behavior data into the data analysis layer to obtain target indicator data corresponding to each target evaluation item; Inputting the target indicator data corresponding to each target evaluation item into the first evaluation result determination layer to obtain the single evaluation result corresponding to each target evaluation item; The single evaluation result corresponding to each target evaluation item is input into the second evaluation result determination layer to obtain the target evaluation result of the target driver.

3. The method according to claim 1, characterized in that After obtaining the target evaluation result of the target driver, it also includes: Driving prompt information is generated according to the target evaluation result.

4. The method according to claim 3, characterized in that Generating driving prompt information according to the target evaluation result includes: According to the target evaluation result, based on the correspondence between the evaluation result and the driving type in the driving behavior classification rule table, determining the target driving type corresponding to the target evaluation result from the driving behavior classification rule table; Driving prompt information is generated according to the target driving type.

5. The method according to claim 3 or 4, characterized in that: After generating driving prompt information, it also includes: The driving prompt information is displayed visually.

6. The method according to claim 1, characterized in that The determination process of the target evaluation model is as follows: Determine the initial evaluation items of the initial evaluation model and the initial weight coefficients corresponding to the initial evaluation items; The initial evaluation model is adjusted through simulation experiments to obtain a target evaluation model.

7. The method according to claim 1, characterized in that The driving behavior data includes bad driving behavior data; the bad driving behavior data includes the number of sudden accelerations per 100 kilometers, the number of sudden decelerations per 100 kilometers, the number of sharp turns per 100 kilometers, the number of speeding per 100 kilometers, the fatigue driving time and the long idling time.

8. A driving behavior evaluation device, characterized in that: include: A driving behavior data acquisition module is used to acquire driving behavior data of a target vehicle within a preset mileage; A target evaluation result determination module, used for inputting the driving behavior data into a target evaluation model to obtain a target evaluation result of a target driver; wherein the target driver refers to the vehicle driver of the target vehicle; The target evaluation model includes a data analysis layer, a first evaluation result determination layer and a second evaluation result determination layer; the data analysis layer is connected to the first evaluation result determination layer; the first evaluation result determination layer is connected to the second evaluation result determination layer.

9. The device according to claim 8, characterized in that The target evaluation result determination module is specifically used for: Inputting the driving behavior data into the data analysis layer to obtain target indicator data corresponding to each target evaluation item; Inputting the target indicator data corresponding to each target evaluation item into the first evaluation result determination layer to obtain the single evaluation result corresponding to each target evaluation item; The single evaluation result corresponding to each target evaluation item is input into the second evaluation result determination layer to obtain the target evaluation result of the target driver.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the driving behavior evaluation method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the driving behavior evaluation method according to any one of claims 1 to 7 when executed.

12. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the driving behavior evaluation method according to any one of claims 1 to 7 is implemented.

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