CN113642832A - Method and system for evaluating driving behavior of commercial vehicle - Google Patents
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Abstract
The invention discloses a method and a system for evaluating driving behaviors of a commercial vehicle, which comprises the steps of collecting driving data in a vehicle network, screening and calculating index data required for evaluating the driving behavior safety of a user; taking the extracted index data as a characteristic variable, and establishing a driving behavior safety evaluation system and a grading factor set of the commercial vehicle according to the characteristic variable; determining the weight vector of each evaluation index by using a method combining an analytic hierarchy process and an entropy weight method; analyzing the characteristics of each characteristic variable, and adopting a proper membership function; obtaining single-factor membership degree vectors corresponding to the characteristic variables by applying a fuzzy mathematical theory; obtaining a fuzzy comprehensive evaluation matrix by using a fuzzy evaluation algorithm and the weight vector and the single-factor membership vector; and analyzing the driving behavior and the driving score of the user according to the obtained fuzzy comprehensive evaluation matrix. The method improves the utilization rate of vehicle data and the evaluation accuracy, and has instructive significance for the next safe driving of the driver.
Description
Technical Field
The invention relates to the technical field of Internet of vehicles data application, in particular to a method and a system for evaluating driving behaviors of a commercial vehicle.
Background
The rapid development of the logistics industry and the maturity of the related technologies of the internet of vehicles lead to more data analysis and application in the field of traffic safety. Aiming at the problem that the accident rate of the commercial vehicle is rapidly increased to cause huge casualties and property loss, the driving behavior safety evaluation research of the commercial vehicle based on the vehicle networking data is selected as an entry point to strive for improving the traffic safety problem.
At present, in the commercial vehicle networking field, there are low and extravagant vehicle driving data utilization ratio to the research of driving action security evaluation, and the index dimension that most evaluation methods considered is single, and the index is fuzzy, and the analysis ability is not strong. In order to standardize driving behaviors and improve driving safety, a more sufficient and effective method for evaluating the driving behaviors by using the internet of vehicles data is needed.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the method solves the problems that the vehicle running data is not fully utilized and the evaluation method has single consideration index.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of collecting driving data in the internet of vehicles, screening and calculating index data required for evaluating the driving behavior safety of a user; taking the extracted index data as a characteristic variable, and establishing a driving behavior safety evaluation system and a grading factor set of the commercial vehicle according to the characteristic variable; determining the weight vector of each evaluation index by using a method combining an analytic hierarchy process and an entropy weight method; analyzing the characteristics of each characteristic variable and adopting a proper membership function; obtaining a single-factor membership vector corresponding to the characteristic variable by applying a fuzzy mathematical theory; obtaining a fuzzy comprehensive evaluation matrix by using a fuzzy evaluation algorithm according to the weight vector and the single-factor membership degree vector; and analyzing the driving behavior and the driving score of the user according to the obtained fuzzy comprehensive evaluation matrix.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: setting the sampling frequency of data acquisition equipment as a required frequency; vehicle driving data of a plurality of users are collected through the Internet of vehicles system.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: the running data comprises a vehicle identification number, driving time, GPS longitude of a vehicle, GPS latitude of the vehicle, GPS altitude of the vehicle, ECU total oil consumption of the vehicle, accumulated total oil consumption of the vehicle, instantaneous oil consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine speed of the vehicle, acceleration of the vehicle, engine torque of the vehicle, engine load of the vehicle, total engine idle time of the vehicle and total running time of the vehicle.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: dividing the running data into a plurality of journey section data, and calculating the index data of each journey according to the vehicle running data in each journey; the index data comprises a travel distance, a vehicle speed mean value, a vehicle speed standard deviation, a maximum vehicle speed, a rapid acceleration frequency, a rapid braking frequency, a large throttle frequency, an overspeed duration ratio, a neutral gear sliding frequency, a low gear high speed frequency and a fatigue alarm frequency.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: analyzing the relation between each characteristic variable and driving behavior; establishing a commercial vehicle driving behavior safety evaluation system based on the screened characteristic variables, and solving a weight vector of each characteristic variable in the evaluation system; and establishing a corresponding relation between the comment sets and the evaluation scores, wherein the comment sets are divided into five comments which correspond to 0-100-point uniform sections.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: the method comprises the steps of constructing a comparison scale for a driving behavior evaluation index; constructing judgment matrixes of all layers of a driving behavior evaluation system; carrying out consistency check on the constructed judgment matrix, and if the constructed judgment matrix does not meet the requirement, correcting the judgment matrix; and (4) calculating a weight vector of the judgment matrix passing the consistency test by using a characteristic value method.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: comprising K-th order parabolic membership functions; ridge-type membership functions; class gaussian membership functions.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: determining characteristic variables based on screened Internet of vehicles index data, and establishing a driving behavior safety evaluation system and a factor set for commercial vehicle users; establishing a corresponding relation between a commercial vehicle user driving behavior safety evaluation comment set and an evaluation score; on the basis of a fuzzy mathematical theory, solving a single-factor membership vector corresponding to the characteristic variable by adopting a proper membership function, and carrying out fuzzy transformation on the obtained single-factor membership vector and the weight vector to obtain a fuzzy comprehensive evaluation matrix; and analyzing according to the obtained fuzzy comprehensive evaluation matrix to obtain the user score.
As a preferable aspect of the driving behavior evaluation method of a commercial vehicle according to the present invention, wherein: the method comprises the steps of solving a single-factor membership vector by adopting a weighted average fuzzy operator according to the characteristics of a weighted operator in a fuzzy mathematical theory; and (4) solving a final fuzzy evaluation matrix by using a fuzzy evaluation algorithm, analyzing the driving behavior characteristics by using the evaluation matrix, and solving a driving behavior score.
As a preferable aspect of the driving behavior evaluation system of a commercial vehicle according to the present invention, wherein: the system comprises a vehicle networking data acquisition module, a data preprocessing module, an evaluation feature screening module, an evaluation model establishing module and an evaluation model using module, wherein the vehicle networking data acquisition module acquires vehicle driving data of a plurality of commercial vehicles at required sampling frequency through vehicle sensors and a can data network carried by the commercial vehicles; the data preprocessing module comprises a data cleaning submodule and a stroke dividing submodule, wherein the data cleaning submodule is used for cleaning the collected driving data of the commercial vehicle, eliminating invalid data and filling missing data, and the stroke dividing submodule is used for dividing the cleaned data into effective driving strokes; the evaluation characteristic screening module is used for screening and calculating index items capable of evaluating the driving behaviors of the user; the evaluation model building module comprises an evaluation system submodule and an evaluation model building submodule, wherein the evaluation system submodule is used for building a driving behavior evaluation system with a step hierarchical structure for the extracted characteristic variables; the evaluation model establishing submodule establishes a driving behavior evaluation safety model of the commercial vehicle based on a fuzzy comprehensive evaluation algorithm; and the evaluation model using module is used for calculating the score in the journey changing section of the driver based on the calculated fuzzy comprehensive evaluation matrix and giving an analysis evaluation.
The invention has the beneficial effects that: the method fully considers the vehicle driving data, takes all a plurality of indexes which possibly influence the driving behavior of the user into consideration, establishes an evaluation system with a step hierarchy structure, establishes a user driving behavior evaluation model based on a fuzzy comprehensive evaluation algorithm, improves the utilization rate of the vehicle data and the evaluation accuracy, and has guiding significance for the next safe driving of a driver.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a driving behavior evaluation method of a commercial vehicle according to a first embodiment of the invention;
fig. 2 is a schematic view of a hierarchy of safety index evaluation according to the method for evaluating driving behavior of a commercial vehicle according to the first embodiment of the present invention;
fig. 3 is a schematic diagram of a composition framework of a driving behavior evaluation system of a commercial vehicle according to a second embodiment of the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 and 2, a driving behavior evaluation method for a commercial vehicle is provided as a first embodiment of the present invention, which specifically includes:
s1: the driving data in the internet of vehicles is collected, and index data required for evaluating the driving behavior safety of the user is screened and calculated.
S2: and establishing a driving behavior safety evaluation system and a grading factor set of the commercial vehicle according to the characteristic variables by taking the extracted index data as the characteristic variables.
S3: and determining the weight vector of each evaluation index by using a method combining an analytic hierarchy process and an entropy weight method.
S4: and analyzing the characteristics of each characteristic variable, and adopting a proper membership function.
S5: and (4) obtaining the single-factor membership degree vector corresponding to the characteristic variable by applying a fuzzy mathematical theory.
S6: and obtaining a fuzzy comprehensive evaluation matrix by using a fuzzy evaluation algorithm according to the weight vector and the single-factor membership degree vector.
S7: and analyzing the driving behavior and the driving score of the user according to the obtained fuzzy comprehensive evaluation matrix.
Preferably, the present embodiment further needs to be described as follows:
setting the sampling frequency of the data acquisition equipment as a required frequency;
the method comprises the steps that vehicle driving data of a plurality of users are collected through a vehicle networking system;
the running data comprises a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the vehicle position, GPS altitude of the vehicle position, ECU total oil consumption of the vehicle, accumulated total oil consumption of the vehicle, instantaneous oil consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine rotating speed of the vehicle, acceleration of the vehicle, engine torque of the vehicle, engine load of the vehicle, total engine idle time of the vehicle and total running time of the vehicle.
It is understood that the method also comprises the following steps:
dividing the driving data into a plurality of travel segment data, and calculating index data of each travel according to the vehicle driving data in each travel;
the index data comprises travel distance, vehicle speed mean value, vehicle speed standard deviation, maximum vehicle speed, emergency acceleration times, emergency braking times, large throttle times, overspeed duration ratio, neutral gear sliding times, low gear high speed times and fatigue alarm times.
Preferably, the method comprises the following steps:
analyzing the relation between each characteristic variable and the driving behavior;
establishing a commercial vehicle driving behavior safety evaluation system based on the screened characteristic variables, and solving a weight vector of each characteristic variable in the evaluation system;
establishing a corresponding relation between the comment sets and the evaluation scores, wherein the comment sets are divided into five comments which correspond to 0-100-point uniform sections;
constructing a comparison scale for the driving behavior evaluation index;
constructing judgment matrixes of all layers of a driving behavior evaluation system;
carrying out consistency check on the constructed judgment matrix, and if the constructed judgment matrix does not meet the requirement, correcting the judgment matrix;
solving a weight vector of the judgment matrix passing the consistency test by using a characteristic value method;
a K-th order parabolic membership function;
ridge-type membership functions;
class gaussian membership functions.
Further, the method also comprises the following steps:
determining characteristic variables based on the screened Internet of vehicles index data, and establishing a driving behavior safety evaluation system and a factor set for commercial vehicle users;
establishing a corresponding relation between a commercial vehicle user driving behavior safety evaluation comment set and an evaluation score;
on the basis of a fuzzy mathematical theory, solving a single-factor membership vector corresponding to the characteristic variable by adopting a proper membership function, and carrying out fuzzy transformation on the obtained single-factor membership vector and the weight vector to obtain a fuzzy comprehensive evaluation matrix;
and analyzing according to the obtained fuzzy comprehensive evaluation matrix to obtain the user score.
Preferably, the method further comprises the following steps:
according to the characteristics of a weighting operator in a fuzzy mathematical theory, solving a single-factor membership vector by adopting a weighted average fuzzy operator;
and (4) solving a final fuzzy evaluation matrix by using a fuzzy evaluation algorithm, analyzing the driving behavior characteristics by using the evaluation matrix, and solving a driving behavior score.
Example 2
Referring to fig. 3, a second embodiment of the present invention is different from the first embodiment in that a driving behavior evaluation system of a commercial vehicle is provided, which includes a vehicle networking data collection module, a data preprocessing module, an evaluation feature screening module, an evaluation model establishing module and an evaluation model using module.
Specifically, the vehicle networking data acquisition module acquires vehicle driving data of a plurality of commercial vehicles at a required sampling frequency through vehicle sensors and a can data network of the commercial vehicles; the data preprocessing module comprises a data cleaning submodule and a stroke dividing submodule, the data cleaning submodule is used for cleaning the collected driving data of the commercial vehicle, eliminating invalid data and filling missing data, and the stroke dividing submodule is used for dividing the cleaned data into effective driving strokes.
Furthermore, the evaluation characteristic screening module is used for screening and calculating index items capable of evaluating the driving behaviors of the user; the evaluation model building module comprises an evaluation system submodule and an evaluation model building submodule, wherein the evaluation system submodule is used for building a driving behavior evaluation system with a step hierarchical structure for the extracted characteristic variables; the evaluation model establishing submodule establishes a driving behavior evaluation safety model of the commercial vehicle based on a fuzzy comprehensive evaluation algorithm; and the evaluation model using module is used for calculating the score in the journey changing section of the driver based on the calculated fuzzy comprehensive evaluation matrix and giving an analysis evaluation.
Still further, the data collected by the vehicle networking data collection module includes a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the vehicle, GPS altitude of the vehicle, total oil consumption of the ECU of the vehicle, total accumulated oil consumption of the vehicle, instantaneous oil consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine speed of the vehicle, load of the vehicle, acceleration of the vehicle, engine torque of the vehicle, brake pedal travel value of the vehicle, engine load of the vehicle, total engine idle time of the vehicle, and total driving time of the vehicle.
Preferably, the journey division submodule divides the vehicle networking driving data into a plurality of journeys, and calculates an index item of each journey according to the vehicle driving data in each journey, wherein the index item comprises at least one of the following indexes: the system comprises a travel starting time, a travel ending time, a total driving mileage, a travel starting longitude, a travel starting latitude, a travel ending longitude, a travel ending latitude, a travel duration, a travel total oil consumption, a total engine rotating speed, a maximum driving speed, a rapid acceleration frequency, a rapid braking frequency, a large throttle frequency, a neutral sliding frequency, a low-gear high-speed frequency, a fatigue alarm frequency, an overspeed time ratio, a non-economic rotating speed ratio and an idle time ratio.
It is easy to understand that the characteristic screening module analyzes the relation between each characteristic variable and the driving behavior based on the index items and screens indexes required by driving behavior evaluation; the evaluation model establishing module is used for establishing a commercial vehicle driving behavior evaluation system based on the screened characteristic variables; obtaining the weight vector of each characteristic variable in the system by a characteristic value method; establishing a step-level evaluation system based on the screened features and an analytic hierarchy process, and calculating a weight vector of each evaluation index in the evaluation system:
(1) constructing a comparison scale for the driving behavior evaluation index;
(2) constructing judgment matrixes of all layers of a driving behavior evaluation system;
(3) carrying out consistency check on the constructed judgment matrix, and if the constructed judgment matrix does not meet the requirement, correcting the judgment matrix;
(4) calculating subjective weight vectors of the judgment matrix passing the consistency test by using a characteristic value method;
(5) calculating objective weight vectors based on index characteristics and an entropy weight method;
(6) and comprehensively considering the subjective weight and the objective weight to determine a final weight vector.
Preferably, the evaluation model establishing submodule establishes the driving behavior evaluation model of the commercial vehicle based on a fuzzy comprehensive evaluation algorithm, and comprises:
(1) establishing a commercial vehicle user driving behavior evaluation comment set;
(2) constructing a corresponding relation between a comment set of the comment set and the evaluation score;
(3) and solving the single-factor membership vector by adopting a proper membership function.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A driving behavior evaluation method of a commercial vehicle is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting driving data in the internet of vehicles, screening and calculating index data required for evaluating the driving behavior safety of a user;
taking the extracted index data as a characteristic variable, and establishing a driving behavior safety evaluation system and a grading factor set of the commercial vehicle according to the characteristic variable;
determining the weight vector of each evaluation index by using a method combining an analytic hierarchy process and an entropy weight method;
analyzing the characteristics of each characteristic variable and adopting a proper membership function;
obtaining a single-factor membership vector corresponding to the characteristic variable by applying a fuzzy mathematical theory;
obtaining a fuzzy comprehensive evaluation matrix by using a fuzzy evaluation algorithm according to the weight vector and the single-factor membership degree vector;
and analyzing the driving behavior and the driving score of the user according to the obtained fuzzy comprehensive evaluation matrix.
2. The driving behavior evaluation method of a commercial vehicle according to claim 1, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
setting the sampling frequency of the data acquisition equipment as a required frequency;
vehicle driving data of a plurality of users are collected through the Internet of vehicles system.
3. The driving behavior evaluation method of a commercial vehicle according to claim 1 or 2, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the running data comprises a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the vehicle position, GPS altitude of the vehicle position, ECU total oil consumption of the vehicle, accumulated total oil consumption of the vehicle, instantaneous oil consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine rotating speed of the vehicle, acceleration of the vehicle, engine torque of the vehicle, engine load of the vehicle, total engine idle time of the vehicle and total running time of the vehicle.
4. The driving behavior evaluation method of a commercial vehicle according to claim 3, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
dividing the driving data into a plurality of travel segment data, and calculating the index data of each travel according to the vehicle driving data in each travel;
the index data comprises a travel distance, a vehicle speed mean value, a vehicle speed standard deviation, a maximum vehicle speed, a rapid acceleration frequency, a rapid braking frequency, a large throttle frequency, an overspeed duration ratio, a neutral gear sliding frequency, a low gear high speed frequency and a fatigue alarm frequency.
5. The driving behavior evaluation method of a commercial vehicle according to claim 4, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
analyzing the relation between each characteristic variable and driving behavior;
establishing a commercial vehicle driving behavior safety evaluation system based on the screened characteristic variables, and solving a weight vector of each characteristic variable in the evaluation system;
and establishing a corresponding relation between the comment sets and the evaluation scores, wherein the comment sets are divided into five comments which correspond to 0-100-point uniform sections.
6. The driving behavior evaluation method of a commercial vehicle according to claim 5, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing a comparison scale for the driving behavior evaluation index;
constructing judgment matrixes of all layers of a driving behavior evaluation system;
carrying out consistency check on the constructed judgment matrix, and if the constructed judgment matrix does not meet the requirement, correcting the judgment matrix;
and (4) calculating a weight vector of the judgment matrix passing the consistency test by using a characteristic value method.
7. The driving behavior evaluation method of a commercial vehicle according to claim 6, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
a K-th order parabolic membership function;
ridge-type membership functions;
class gaussian membership functions.
8. The driving behavior evaluation method of a commercial vehicle according to claim 7, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
determining characteristic variables based on the screened Internet of vehicles index data, and establishing a driving behavior safety evaluation system and a factor set for commercial vehicle users;
establishing a corresponding relation between a commercial vehicle user driving behavior safety evaluation comment set and an evaluation score;
on the basis of a fuzzy mathematical theory, solving a single-factor membership vector corresponding to the characteristic variable by adopting a proper membership function, and carrying out fuzzy transformation on the obtained single-factor membership vector and the weight vector to obtain a fuzzy comprehensive evaluation matrix;
and analyzing according to the obtained fuzzy comprehensive evaluation matrix to obtain the user score.
9. The driving behavior evaluation method for commercial vehicles according to claim 8, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
according to the characteristics of a weighting operator in a fuzzy mathematical theory, solving a single-factor membership vector by adopting a weighted average fuzzy operator;
and (4) solving a final fuzzy evaluation matrix by using a fuzzy evaluation algorithm, analyzing the driving behavior characteristics by using the evaluation matrix, and solving a driving behavior score.
10. A commercial vehicle driving behavior evaluation system is characterized in that: comprises a vehicle networking data acquisition module, a data preprocessing module, an evaluation characteristic screening module, an evaluation model establishing module and an evaluation model using module,
the vehicle networking data acquisition module acquires vehicle driving data of a plurality of commercial vehicles at required sampling frequency through vehicle sensors and a can data network of the commercial vehicles;
the data preprocessing module comprises a data cleaning submodule and a stroke dividing submodule, wherein the data cleaning submodule is used for cleaning the collected driving data of the commercial vehicle, eliminating invalid data and filling missing data, and the stroke dividing submodule is used for dividing the cleaned data into effective driving strokes;
the evaluation characteristic screening module is used for screening and calculating index items capable of evaluating the driving behaviors of the user;
the evaluation model building module comprises an evaluation system submodule and an evaluation model building submodule, wherein the evaluation system submodule is used for building a driving behavior evaluation system with a step hierarchical structure for the extracted characteristic variables; the evaluation model establishing submodule establishes a driving behavior evaluation safety model of the commercial vehicle based on a fuzzy comprehensive evaluation algorithm;
and the evaluation model using module is used for calculating the score in the journey changing section of the driver based on the calculated fuzzy comprehensive evaluation matrix and giving an analysis evaluation.
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