CN117454048B - New energy automobile endurance test system and method for test driving - Google Patents

New energy automobile endurance test system and method for test driving Download PDF

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CN117454048B
CN117454048B CN202310732458.5A CN202310732458A CN117454048B CN 117454048 B CN117454048 B CN 117454048B CN 202310732458 A CN202310732458 A CN 202310732458A CN 117454048 B CN117454048 B CN 117454048B
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CN117454048A (en
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徐照明
邓晰文
尚永强
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Kunming University of Science and Technology
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    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
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Abstract

The invention relates to the technical field of new energy automobiles, and particularly discloses a new energy automobile endurance test system and method for test driving, wherein the method comprises the following steps: s1, determining the positions and the current experience mode of service personnel and consumers in a vehicle; s2, associating the positions of service personnel and consumers with the acquisition subareas; s3, acquiring the current position of the vehicle during trial riding, and acquiring the traffic condition of each road section within a preset distance according to the current position of the vehicle; s4, identifying voice contents of service personnel and consumers, and extracting car scene information of the consumers from the voice contents; s5, after the trial taking is finished, generating a trial driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the customer vehicle scene; and S6, acquiring power consumption data in the test driving process, and calculating estimated endurance mileage according to the power consumption data in the test driving process. By adopting the technical scheme of the invention, the driving state of the consumer can be effectively simulated when the consumer performs test driving, so that the vehicle endurance can be calculated in a targeted manner.

Description

New energy automobile endurance test system and method for test driving
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a new energy automobile endurance test system and method for test driving.
Background
The new energy vehicle adopts unconventional vehicle fuel as a power source, integrates the advanced technology in the aspects of power control and driving of the vehicle, and forms the automobile with advanced technical principle, new technology and new structure. The new energy vehicles comprise hybrid electric vehicles, pure electric vehicles, fuel cell electric vehicles, other new energy vehicles and the like.
The actual endurance mileage of the new energy automobile is one of the most concerned problems before the consumer purchases. In the prior art, in order to evaluate the endurance mileage of a new energy automobile, a cycle condition method is generally adopted for testing. In the testing process, the driving mileage and electric quantity changes corresponding to different working condition speed sections of the vehicle are obtained, so that the fine analysis of the driving mileage of the new energy automobile is realized, or the whole driving mileage of the vehicle is estimated according to the mileage and electric quantity changes of different working condition speed sections. Because driving habits of different users are different, the use scenes are different, the climate environments are different, and the actual driving range and the nominal driving range of the vehicle are easy to have larger access.
At present, consumers often know that a channel for actually cruising a vehicle is introduction of sales personnel or sharing of existing vehicle owners, and the channel has great uncertainty. Therefore, a new energy automobile endurance test system and a new energy automobile endurance test method for testing driving are needed, wherein the new energy automobile endurance test system can effectively simulate the driving state of a consumer when the consumer tests driving, so that the vehicle endurance can be calculated in a targeted manner.
Disclosure of Invention
The invention aims to provide a new energy automobile endurance test method for test driving, which can effectively simulate the driving state of a consumer when the consumer performs test driving so as to calculate the vehicle endurance in a targeted manner.
In order to solve the technical problems, the application provides the following technical scheme:
The new energy automobile endurance test method for test driving comprises the following steps:
s1, acquiring an image in a vehicle, determining the positions of service personnel and consumers according to the image in the vehicle, and determining a current experience mode according to the positions of the service personnel, wherein the experience module comprises trial riding and trial driving;
s2, associating the positions of service personnel and consumers with the acquisition subareas;
s3, acquiring the current position of the vehicle during trial riding, and acquiring the traffic condition of each road section within a preset distance according to the current position of the vehicle;
S4, respectively acquiring voices of corresponding acquisition subareas of service personnel and consumers, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to the use scene of the vehicle, if so, extracting vehicle scene information of the consumers from the voice contents, and transferring to the step S5, wherein the vehicle scene information comprises a driving route and driving time; if not, repeating the step S4;
S5, after the trial taking is finished, generating a trial driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the customer vehicle scene;
and S6, after the test driving is finished, acquiring power consumption data in the test driving process, and calculating estimated endurance mileage according to the power consumption data in the test driving process.
The basic scheme principle and the beneficial effects are as follows:
The traditional fuel oil automobile does not generally support voice partition recognition, a camera is not arranged in the automobile, the automobile has limited calculation power, and complex data processing is not supported. With the development of new energy automobiles, the configuration of sensors in the automobiles is complete, and the computational power of the automobiles is greatly improved. In the case of hardware support, the inventors propose this solution. According to the scheme, firstly, when a person in a car is tried on, the person in the car is distinguished, and the positions of consumers and service personnel are determined, so that the current experience mode is determined. For example, the service personnel is located on the main driver seat, and the experience mode is trial-and-error. During trial riding, service personnel can inquire about problems related to the use scene of the vehicle of the consumer, and through collecting conversations, namely voices, of the consumer and the service personnel, recognition analysis is carried out, so that information related to the use scene information of the consumer can be obtained. The test driving route can be planned in combination with the traffic condition of the customer vehicle scene and the current traffic condition.
In conclusion, in the scheme, when a consumer performs test driving, the driving state of the consumer can be effectively simulated, and the test driving route is close to the real driving scene of the consumer, so that the vehicle endurance can be calculated in a targeted manner, and the accuracy of estimating the endurance mileage is improved.
Further, in the step S5, after the test taking is finished, a test driving route for the vehicle experience is generated; generating a continuous navigation test driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the traffic scene of a consumer; the terminal point of the vehicle experience test driving route is the starting point of the continuous driving test driving route.
One of the purposes of the test driving of the vehicle by the consumer is to know each performance of the vehicle, the test driving is carried out with the purpose, the control mode is different from the control mode of the real scene to a certain extent, for example, 100% of electric switches are used for testing the acceleration performance of the vehicle, the electricity consumption under the condition can be different from that of the real scene to a certain extent, in the preferred scheme, the test driving route is divided into a vehicle experience test driving route and a continuous test driving route, the vehicle experience test driving route is used for the user to carry out the experience of each performance, the continuous test driving route is used for the user to drive according to the daily mode, and the accuracy of estimated continuous mileage can be improved.
Further, step S0 is included, a test driving mode control instruction is received, and a test driving mode is entered;
In step S1, in the driving test mode, an image in the vehicle is obtained, the positions of the service personnel and the consumer are determined according to the image in the vehicle, and the current experience mode is determined according to the position of the service personnel.
The optimal scheme can be independently used on trial driving, and is convenient to independently maintain and update.
Further, in the step S6, the electricity consumption data in the test driving process includes the driving mileage and the electricity consumption percentage of the continuous test driving route, and the calculation formula of the estimated continuous mileage S 2 is as follows:
S2＝S1/(S0×T)×S0
Wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance.
Further, in the step S4, it is also analyzed whether the voice content is related to the driving style, and if so, the driving style information of the consumer is extracted;
Further comprises:
s7, acquiring identity information of a consumer and weather information of a test drive, and creating a test drive file according to the information of the driving style, the estimated overall endurance, the identity information of the consumer and the weather information of the test drive;
s8, after the consumer purchases the vehicle, acquiring the actual endurance of the consumer vehicle and the corresponding road section and weather information; calculating the same meteorological information of the same consumer and the deviation percentage of actual cruising and estimated cruising mileage under the same road section as the driving route acquired by trial riding;
s9, calculating a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style;
S10, calculating estimated endurance mileage S 2 of a new consumer according to the correction value, wherein the calculation formula is as follows:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
And calculating the deviation percentage of the estimated endurance mileage of the consumers in the test driving and the actual endurance after the vehicle purchase so as to know the difference between the actual endurance mileage and the estimated endurance mileage of each consumer. And then calculating a correction value according to the deviation percentages of all consumers under the same driving style in the same weather information in a big data mode, and introducing the correction value into a calculation formula of the estimated range, so that the accuracy of the calculation of the follow-up estimated range can be improved.
The second purpose of the invention is to provide a new energy automobile endurance test system for test driving, which comprises an image acquisition module, a voice acquisition module, a navigation module, a processing module and an electric control module;
The image acquisition module is used for acquiring an image in the vehicle; the voice acquisition module is used for acquiring voices of all acquisition partitions in the vehicle; the electric control module is used for collecting electricity consumption data of the vehicle;
The processing module is used for determining the positions of service personnel and consumers according to the images in the vehicle, determining the current experience mode according to the positions of the service personnel, and the experience module comprises trial taking and trial driving; and also for associating the locations of service personnel and consumers with the acquisition partition;
The processing module is also used for respectively acquiring voices of corresponding acquisition subareas of service personnel and consumers during trial riding, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to the use scene of the vehicle, and extracting vehicle scene information of the consumers from the voice contents if the voice contents are related to the use scene information of the vehicle, wherein the vehicle scene information comprises a driving route and driving time;
the navigation module is used for acquiring traffic conditions of corresponding vehicle scenes according to the vehicle scene information;
The processing module is also used for acquiring the traffic condition of each road section within the preset distance through the navigation module after the trial taking is finished, and generating a trial driving route according to the traffic condition of each road section within the preset distance and the traffic condition of the traffic scene of the consumer;
and the processing module is also used for acquiring power consumption data of the test driving process from the electronic control module after the test driving is finished, and calculating estimated endurance mileage according to the power consumption data of the test driving process.
Further, the processing module is further used for generating a vehicle experience test driving route after the test taking is finished, acquiring the traffic condition of each road section within the preset distance through the navigation module, and generating a continuous navigation test driving route according to the traffic condition of each road section within the preset distance and the traffic condition of a customer vehicle scene; the terminal point of the vehicle experience test driving route is the starting point of the continuous driving test driving route.
Further, the processing module is further used for analyzing whether the voice content is related to the driving style, and if so, extracting information of the driving style of the consumer, and recording according to the information of the driving style of the consumer.
Further, the electricity consumption data in the test driving process includes the driving mileage and the electricity consumption percentage of the continuous navigation test driving route, and the calculation formula of the estimated continuous navigation mileage S 2 is as follows:
S2＝S1/(S0×T)×S0
Wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance.
The system further comprises a server and a communication module, wherein the processing module is further used for sending the information of the driving style of the consumer and the estimated endurance mileage to the server through the communication module;
The server is also used for acquiring the identity information of the consumer and the weather information of the test driving, and creating a test driving file according to the driving style information, the estimated overall endurance, the identity information of the consumer and the weather information of the test driving;
The server is also used for acquiring the actual endurance of the consumer vehicle and the corresponding road section and weather information after the consumer purchases the vehicle; calculating the deviation percentage of actual endurance and estimated endurance mileage under the same section of the same driving route acquired by trial and taking the same meteorological information of the same consumer;
The server is also used for calculating a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style, and sending the correction value to the communication module;
The processing module is further configured to calculate an estimated endurance mileage S 2 according to the correction value, where a calculation formula is:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
Drawings
Fig. 1 is a flowchart of a first embodiment of a new energy automobile endurance test method for test driving;
fig. 2 is a logic block diagram of a fifth embodiment of a new energy automobile endurance test system for test driving.
Detailed Description
The following is a further detailed description of the embodiments:
Example 1
The new energy automobile endurance test system for test driving comprises an image acquisition module, a voice acquisition module, a navigation module, a processing module and an electric control module.
The image acquisition module is used for acquiring an image in the vehicle; in this embodiment, the image acquisition module adopts the camera, in order to reduce the collection blind area, can all set up the camera at the front and back row in the car.
The voice acquisition module is used for acquiring voices of all acquisition partitions in the vehicle; in this embodiment, the voice acquisition module adopts a microphone, and each seat corresponds to one acquisition zone, so that in order to realize voice acquisition of the zones, a microphone needs to be arranged near each seat in the vehicle.
The electric control module is used for collecting electricity consumption data of the vehicle; the electricity consumption data comprises the driving mileage and the electricity consumption percentage of the continuous navigation test driving route.
The processing module is used for determining the positions of service personnel and consumers according to the images in the vehicle, determining the current experience mode according to the positions of the service personnel, and the experience module comprises trial taking and trial driving; and also for associating the locations of service personnel and consumers with the acquisition partition; specifically, the processing module is further used for detecting a human face from the image in the vehicle, comparing the detected human face with the human face of a prestored service person, and determining the positions of the service person and the consumer according to the comparison result; the position comprises a main driver seat, a co-driver seat, a rear right seat, a rear left seat and a rear right seat, if the main driver seat is a service person, the experience mode is a test riding mode, and if the main driver seat is a non-service person, namely a consumer, the experience mode is a test driving mode.
The processing module is also used for respectively acquiring voices of corresponding acquisition subareas of service personnel and consumers during trial riding, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to the use scene of the vehicle, and extracting vehicle scene information of the consumers from the voice contents if the voice contents are related to the use scene information of the vehicle, wherein the vehicle scene information comprises a driving route and driving time. In this embodiment, firstly, voice contents of service personnel and consumers are converted into text through voice recognition, and then the contents in the text are analyzed through natural language processing to judge whether the text is used with vehicles or not. For example, the dialog is as follows: service personnel: "do you start commuting at ordinary times? ", consumer: "the trolley is for commuting", the attendant "what you commute route is", the consumer: "from xx cell to xxx industry park", service personnel: "this road is blocked", consumer: "yes", attendant: "you go out from the office at ordinary times, go out from the ban on the office" consumers: "normally 8 points out of the door, 6 points out of the office at night". The extracted vehicle scene information includes: driving time: 8:00, driving route: xx cell to xxx industry park; driving time: 18:00, driving route: xxx industry park to xx cell.
The processing module is also used for analyzing whether the voice content is related to the driving style, and if so, extracting and recording the information of the driving style of the consumer. In this embodiment, driving style includes aggressive and smooth, and in other embodiments may be categorized by energy consumption type, such as energy conservation, comfort, and sport.
The navigation module is used for acquiring the current position of the vehicle, and acquiring the traffic condition of the corresponding vehicle scene according to the vehicle scene information, wherein the traffic condition of the vehicle scene refers to the traffic condition of each road section in the corresponding driving route in the driving time. In this embodiment, the corresponding traffic condition is obtained from the API call history data provided by the third-party navigation service provider. Traffic conditions include clear, slow running and congestion. Road segments are divided according to the length of the road, for example, 1 km is a road segment 1, and in order to provide accuracy, the divided length can be reduced according to practical situations, for example, 500m is a road segment 1.
The processing module is also used for planning a vehicle experience test driving route after the test taking is finished, acquiring the traffic condition of each road section within a preset distance through the navigation module, and generating a continuous navigation test driving route according to the traffic condition of each road section within the preset distance and the traffic condition of a customer vehicle scene; in other words, the complete test drive route includes the vehicle experience test drive route and the endurance test drive route. The terminal point of the vehicle experience test driving route is the starting point of the continuous driving test driving route.
When generating a continuous navigation test driving route, the embodiment firstly screens out road sections which are the same as traffic conditions of a vehicle scene from a preset distance, and then connects the road sections into a complete route. The traffic condition of each road section in the continuous navigation test driving route is the same as the traffic condition of the scene of the vehicle, and the sequence is not required to be consistent, for example, the road section is firstly driven to be congested, the road section is secondly driven to be smooth, the road section is firstly driven to be smooth, and the road section is firstly driven to be congested. The success rate of matching can be improved without requiring the sequence of road sections.
And the processing module is also used for acquiring power consumption data of the test driving process from the electronic control module after the test driving is finished, and calculating estimated endurance mileage according to the power consumption data of the test driving process.
The calculation formula of the estimated endurance mileage S 2 is:
S2＝S1/(S0×T)×S0
Wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance.
As shown in fig. 1, the embodiment also provides a new energy automobile endurance test method for test driving, which includes the following contents:
s1, acquiring an image in a vehicle, determining the positions of service personnel and consumers according to the image in the vehicle, and determining a current experience mode according to the positions of the service personnel, wherein the experience module comprises trial riding and trial driving;
s2, associating the positions of service personnel and consumers with the acquisition subareas;
s3, acquiring the current position of the vehicle during trial riding, and acquiring the traffic condition of each road section within a preset distance according to the current position of the vehicle;
S4, respectively acquiring voices of corresponding acquisition subareas of service personnel and consumers, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to the use scene of the vehicle, if so, extracting vehicle scene information of the consumers from the voice contents, wherein the vehicle scene information comprises a driving route and driving time, and transferring to the step S5; if not, repeating the step S4; also analyzing whether the voice content is related to the driving style, and if so, extracting the information of the driving style of the consumer;
S5, after trial riding is finished, planning a vehicle experience trial driving route according to the information of the driving style of the consumer and the current position of the vehicle; generating a continuous navigation test driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the traffic scene of a consumer; the terminal point of the vehicle experience test driving route is the starting point of the continuous driving test driving route.
And S6, after the test driving is finished, acquiring power consumption data in the test driving process, wherein the power consumption data comprise the driving mileage and the power consumption percentage of the continuous navigation test driving route. And calculating estimated endurance mileage according to the electricity consumption data in the test driving process. The calculation formula of the estimated endurance mileage S 2 is:
S2＝S1/(S0×T)×S0
Wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance.
Example two
The difference between the present embodiment and the first embodiment is that the system of the present embodiment further includes a server and a communication module.
The processing module is also used for sending the driving style information of the consumer and the estimated endurance mileage to the server through the communication module;
The server is also used for acquiring the identity information of the consumer and the weather information of the test driving, and creating a test driving file according to the driving style information, the estimated overall endurance, the identity information of the consumer and the weather information of the test driving. The weather information includes temperature and altitude.
The server is also used for acquiring the actual endurance of the consumer vehicle and the corresponding road section and weather information after the consumer purchases the vehicle; calculating the deviation percentage G of actual endurance and estimated endurance mileage under the same weather information of the same consumer and the same road section as the driving route acquired by trial riding, wherein the calculation is as follows:
G＝(S2-S3)/S2×100％
wherein S 3 is the actual endurance. In this embodiment, the actual cruising is an average value of cruising of the consumer collected for multiple times in the actual driving scene.
The server is also used for calculating a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style, and sending the correction value to the communication module; in this embodiment, the average value of the deviation percentages is used as the correction value.
The processing module is further configured to calculate an estimated endurance mileage S 2 according to the correction value, where a calculation formula is:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
The method of the embodiment further comprises the following steps:
s7, acquiring identity information of a consumer and weather information of a test drive, and creating a test drive file according to the information of the driving style, the estimated overall endurance, the identity information of the consumer and the weather information of the test drive;
s8, after the consumer purchases the vehicle, acquiring the actual endurance of the consumer vehicle and the corresponding road section and weather information; calculating the same meteorological information of the same consumer and the deviation percentage of actual cruising and estimated cruising mileage under the same road section as the driving route acquired by trial riding;
s9, calculating a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style;
S10, calculating estimated endurance mileage S 2 of a new consumer according to the correction value, wherein the calculation formula is as follows:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
Example III
The difference between the present embodiment and the second embodiment is that, in the system of the present embodiment, the communication module is configured to receive a driving test mode control instruction, send the driving test mode control instruction to the processing module, and the processing module is configured to enter the driving test mode after receiving the driving test mode control instruction. Specifically, the server issues a corresponding instruction to perform mode adjustment, that is, performing mode adjustment in an OTA mode.
The method of the embodiment further comprises the step S0 of receiving a driving test mode control instruction and entering a driving test mode;
In step S1, in the driving test mode, an image in the vehicle is obtained, the positions of the service personnel and the consumer are determined according to the image in the vehicle, and the current experience mode is determined according to the position of the service personnel. And the related programs formed by using the endurance test method are convenient to independently maintain and update.
Example IV
The difference between the present embodiment and the first embodiment is that the system of the present embodiment further includes a driving data acquisition module, configured to acquire information such as traffic flow data, road surface flatness data, vehicle operating environment data, meteorological condition data, and the like, and aggregate and form big data of traffic conditions in the area where the big data is located. The method can provide powerful data support and basis for later extraction of vehicle operation key parameters, optimization of vehicle enterprise structure, improvement technology and new product design and research and development.
Example five
The difference between the present embodiment and the third embodiment is that the system of the present embodiment further includes a basic information acquisition module, a vehicle data acquisition module, and a vehicle machine data acquisition module.
The basic information acquisition module is used for acquiring basic information of a consumer, wherein the basic information comprises gender, age, expected price of purchasing a vehicle, vehicle environment, driving experience, personalized requirements and the like;
The vehicle data acquisition module is used for acquiring vehicle driving data. The running data includes vehicle speed, electric door pedal data, brake pedal data, and the like.
The vehicle-mounted data acquisition module is used for acquiring vehicle-mounted control data. The vehicle control data comprise a control mode and control content, wherein the control mode comprises voice control and entity control; the control content comprises navigation control, air conditioning control, multimedia control, setting control and the like.
The processing module is also used for planning a trial-taking route according to the basic information of the consumer during trial-taking. In the embodiment, firstly, a maximum distance of trial riding is preset by taking a departure point as a circle center, a trial riding range is defined by taking the maximum distance as a radius, experience contents are marked according to characteristics of road sections in the defined range, for example, the experience contents of urban rapid road sections are acceleration performance, the experience contents of construction road sections are NVH, the experience contents of hollow road sections or multi-deceleration strip road sections are chassis performance, and the experience contents of road sections mixed by people and vehicles are urban auxiliary driving; the experience content of the tunnel section with complicated light is HUD display effect, and the experience content of the turning-around section is turning radius and the like. And extracting the attention content of the consumer according to the gender and the age in the basic information of the consumer, wherein for example, the preset attention content of men in the age range of 20-30 years old is acceleration performance, and the preset attention content of men in the age range of 40-50 years old is chassis performance. And finally, planning a trial-taking route according to the attention content of the consumer and the experience content marked in advance. Specifically, the trial-taking route is guaranteed to have the same experience content section as the attention content, and then other sections are connected to form a complete route. The road sections can be divided according to the road connecting the two intersections, can be divided according to the length of the road, for example, the road sections are 1 road section with 2 kilometers, and can be divided by integrating the road sections and the road length, for example, the road length between the two intersections exceeds 2 kilometers, and the road sections are divided by referring to the length.
The processing module is also used for acquiring voice of the corresponding acquisition subarea of the service personnel, current vehicle driving data and current vehicle control data when the service personnel tries to take the service, and determining the characteristics of the vehicle currently displayed according to the voice, the vehicle driving data and the vehicle control data. In this embodiment, each vehicle feature has a corresponding voice description, vehicle-to-machine control data, or driving data. For example, from the service personnel's voice that our vehicle gives sound insulation well', the currently displayed vehicle characteristics are determined to be NVH, the electric door pedal data in the vehicle running data is determined to be 100% of the electric door, and the currently displayed vehicle characteristics are determined to be acceleration performance; the vehicle control data are music, and the characteristics of the vehicle currently displayed are determined to be sound performance and NVH performance.
The processing module is also used for identifying the expression of the consumer according to the image; acquiring the voice of the corresponding acquisition subarea of the consumer, and analyzing the satisfaction degree of the currently displayed vehicle characteristics according to the expression and the voice of the consumer;
In this embodiment, the satisfaction includes satisfaction, neutrality, and dissatisfaction. Positive expression is considered satisfactory, negative expression is considered unsatisfactory, no surface is considered neutral; expression in the forward direction such as smiling; negative expressions such as frowning; positive speech is considered satisfactory, negative speech is considered unsatisfactory, and no feedback is considered neutral. Forward speech is, for example, "good", "satisfactory", "stiff", etc. Negative speech is, for example, "no line", "somewhat bad", "to be improved", etc. In the embodiment, the satisfaction degree is preferentially judged through voice, and when voice judgment cannot be conducted, judgment is conducted through expression; wherein, the satisfaction degree of the acceleration performance is judged only by voice. For example, the attendant's voice mentions "our vehicle gives sound very well", the consumer responds "really very good", and the satisfaction of NVH performance is satisfactory.
The processing module is also configured to rank the vehicle characteristics according to satisfaction. In this embodiment, the ordering is in order of satisfaction, neutrality, and dissatisfaction.
The processing module is also used for extracting the vehicle characteristics mentioned by the consumer from the voice of the corresponding acquisition subarea of the consumer during trial riding, judging whether the mentioned vehicle characteristics are the vehicle characteristics displayed by service personnel or not, and if not, recording. For example, "how well this vehicle turns around," the vehicle characteristics of the turning around are recorded. The vehicle characteristics which are interesting to the consumers but not introduced or displayed by the service personnel can be added into the planning of the test driving route, so that the consumers can know the vehicle deeply.
The processing module is also used for extracting vehicle characteristics matched with preset selling points from the ordered vehicle characteristics and the recorded vehicle characteristics after the trial riding is finished, and planning a vehicle experience trial driving route according to the extracted vehicle characteristics. In this embodiment, only the vehicle characteristics satisfying in the ranking are selected for matching. For example, the new energy vehicle for test driving is a car running type, and the selling points are acceleration performance, chassis performance and NVH performance, and the selling points of the acceleration performance, the chassis performance and the NVH performance are matched with the ordered vehicle characteristics and the recorded vehicle characteristics. When the driving route is planned, firstly, the experience content road sections with the same characteristics as the matched characteristics are ensured in the vehicle experience test driving route, and then other road sections are connected to form the complete vehicle experience test driving route.
The processing module is also used for determining the road section of the test driving route of the current vehicle according to the current position of the vehicle when the test driving is performed, and generating experience prompts according to the vehicle characteristics corresponding to the road section. The customer is helped to know the meaning of the trial driving road section, the customer is prompted to carry out experience corresponding to the characteristics of the vehicle, meanwhile, prompt is given to service personnel, and the service personnel can carry out auxiliary explanation.
The method of the present embodiment uses the above-described system.
According to the scheme of the embodiment, in the trial riding mode, the vehicle characteristics which are currently introduced by the service personnel and the vehicle characteristics which are displayed in the running process of the vehicle can be obtained through the voice of the service personnel, the current vehicle running data and the vehicle machine control data; through analyzing the expression and the voice of the consumer, the satisfaction degree of the consumer on the displayed vehicle characteristics or the introduced vehicle characteristics can be known, the vehicle characteristics are ranked according to the satisfaction degree, after trial taking is finished, the vehicle experience trial driving route is planned according to the ranked vehicle characteristics, the consumer can verify the satisfied vehicle characteristics again in the trial driving process, the consumer can be helped to know the vehicle deeply, necessary information is provided for the purchase decision of the consumer, the propaganda effect of manufacturers can be improved, and the trade is facilitated.
Example six
The difference between the present embodiment and the fifth embodiment is that in the present embodiment, the processing module is further configured to analyze the number of overlapping of each road segment in the history-planned vehicle experience test driving route, and plan the test driving route according to the road segment with the number of overlapping greater than the threshold value and the basic information of the consumer. In this embodiment, the threshold is 10. The higher the overlapping number is, the more attention is paid to experience content corresponding to a certain road section by a consumer, namely the vehicle characteristics. Specifically, firstly, the trial-taking route is guaranteed to have experience content sections which are the same as the attention content, and the sections with the overlapping number larger than a threshold value are connected by other sections to form a complete route.
The method of the present embodiment uses the above-described system.
Example seven
The difference between the present embodiment and the fifth embodiment is that, in the system of the present embodiment, the processing module is further configured to determine the number of consumers according to the image in the vehicle, determine whether the number of consumers is greater than 1 person, if so, mark the consumers on the passenger seat as waiting drivers, and mark the consumers on the rear row as passengers; the basic information of the consumer is bound with the waiting driver.
The processing module is also used for acquiring voices of the corresponding partitions of the driver and the passenger during trial riding, recognizing voice content, judging decision makers in the driver and the passenger according to the recognized voice content, marking the corresponding decision maker if the recognition is successful, and marking the driver to be the decision maker if the recognition is failed. In this embodiment, the occupant is marked as a decision maker when the driver asks the occupant for more than 3 experiences of the vehicle, otherwise the recognition fails. Decision makers refer to consumers who are able to decide on purchasing a vehicle.
The processing module is also used for judging whether a vehicle control instruction exists according to the voice content of the identified decision maker, if so, calculating the quantity of the control instruction, judging whether the quantity exceeds a threshold value, and if so, marking familiarity labels for the decision maker; vehicle control commands such as closing windows, opening music, opening air conditioning, opening seat ventilation, etc. Compared with the traditional vehicle, the new energy vehicle is more mature in voice control, and is familiar with using voice control, so that a decision maker is familiar with the new energy vehicle. If the threshold value is not exceeded, the image acquisition module is also used for acquiring an image outside the vehicle; the processing module is also used for analyzing the image outside the vehicle, judging whether a new energy vehicle exists according to the image outside the vehicle, if the new energy vehicle exists, identifying the vehicle model of the new energy vehicle, and acquiring the characteristic information of the corresponding vehicle from a preset vehicle database according to the vehicle model; for example, the characteristic information corresponding to a certain vehicle model is that the space is large, and the seat is comfortable.
The processing module is also used for identifying the voice content of the decision maker and judging whether the description of the corresponding vehicle characteristics is included, if so, the description of the decision maker is matched with the acquired characteristic information, if any item of matching is successful, the decision maker is marked with a familiar label, and if not, the decision maker is marked with an unfamiliar label. Through the description of the decision maker on the new energy vehicles randomly appearing in the road, the familiarity degree of the decision maker on the new energy vehicles can be known, and the judgment accuracy is high.
When the driver is a decision maker and is familiar with the labels, the processing module is also used for extracting vehicle characteristics matched with preset selling points from the ordered vehicle characteristics and the recorded vehicle characteristics and planning a vehicle experience test driving route according to the extracted vehicle characteristics; the method can enable a decision maker with high familiarity to the new energy automobile to feel interesting vehicle selling points in a limited test driving time.
When the driver is a decision maker and is unfamiliar with the labels, the processing module is also used for planning a vehicle experience test driving route according to the ordered vehicle characteristics and the recorded vehicle characteristics; the method can enable a decision maker unfamiliar with the new energy automobile to feel all concerned vehicle characteristics except the selling point in a limited test driving time.
When the driver is a non-decision maker and is familiar with the labels, the processing module is also used for extracting vehicle characteristics matched with preset selling points from the ordered vehicle characteristics and the recorded vehicle characteristics and planning a vehicle experience test driving route according to the extracted vehicle characteristics;
When the driver is not a decision maker and is unfamiliar with the labels, the processing module is also used for planning the vehicle experience test driving route according to the ordered vehicle characteristics, the recorded vehicle characteristics and the selling points. That is, the selling points are used as supplements outside the ordered vehicle characteristics and the recorded vehicle characteristics, and if the ordered vehicle characteristics and the recorded vehicle characteristics do not comprise all the selling points, the non-included selling points are supplemented. The decision maker can fully feel the characteristics of the vehicle, and the probability of the success is improved. In this embodiment, the decision maker in the consumer can be effectively identified, the decision maker is classified, and different test driving routes are individually planned in a limited test driving time according to the familiarity degree of the decision maker to the new energy vehicle, so that the consumer effectively experiences the characteristics of the vehicle. The high-quality test driving experience is realized, necessary information is provided for purchasing decisions of consumers, the propaganda effect of manufacturers can be improved, the trade is facilitated, and win-win is realized.
The method of the present embodiment uses the above-described system.
The foregoing is merely an embodiment of the present application, the present application is not limited to the field of this embodiment, and the specific structures and features well known in the schemes are not described in any way herein, so that those skilled in the art will know all the prior art in the field before the application date or priority date of the present application, and will have the capability of applying the conventional experimental means before the date, and those skilled in the art may, in light of the present application, complete and implement the present scheme in combination with their own capabilities, and some typical known structures or known methods should not be an obstacle for those skilled in the art to practice the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.

Claims (5)

1. The new energy automobile endurance test method for test driving is characterized by comprising the following steps of:
s1, acquiring an image in a vehicle, determining the positions of service personnel and consumers according to the image in the vehicle, and determining a current experience mode according to the positions of the service personnel, wherein the experience module comprises trial riding and trial driving;
s2, associating the positions of service personnel and consumers with the acquisition subareas;
s3, acquiring the current position of the vehicle during trial riding, and acquiring the traffic condition of each road section within a preset distance according to the current position of the vehicle;
S4, respectively acquiring voices of corresponding acquisition partitions of service personnel and consumers, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to driving styles, and if so, extracting driving style information of the consumers; analyzing whether the voice content is related to the use scene of the vehicle, if so, extracting the vehicle scene information of the consumer from the voice content, and jumping to the step S5, wherein the vehicle scene information comprises a driving route and driving time; if not, repeating the step S4;
S5, after the trial taking is finished, generating a trial driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the customer vehicle scene;
S6, after the test driving is finished, acquiring power consumption data in the test driving process, and calculating estimated endurance mileage according to the power consumption data in the test driving process; the power consumption data in the test driving process comprises the driving mileage and the power consumption percentage of the continuous navigation test driving route, and the calculation formula of the estimated continuous navigation mileage S 2 is as follows:
S2＝S1/(S0×T)×S0
wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance;
s7, acquiring identity information of a consumer and weather information of a test drive, and creating a test drive file according to the information of the driving style, the estimated overall endurance, the identity information of the consumer and the weather information of the test drive;
s8, after the consumer purchases the vehicle, acquiring the actual endurance of the consumer vehicle and the corresponding road section and weather information; calculating the same meteorological information of the same consumer and the deviation percentage of actual cruising and estimated cruising mileage under the same road section as the driving route acquired by trial riding;
s9, calculating a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style;
S10, calculating estimated endurance mileage S 2 of a new consumer according to the correction value, wherein the calculation formula is as follows:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
2. The new energy automobile endurance test method for test driving according to claim 1, wherein the method is characterized in that: in the step S5, after the test taking is finished, a test driving route for the vehicle experience is generated; generating a continuous navigation test driving route according to the traffic conditions of all road sections within a preset distance and the traffic conditions of the traffic scene of a consumer; the terminal point of the vehicle experience test driving route is the starting point of the continuous driving test driving route.
3. The new energy automobile endurance test method for test driving according to claim 1, wherein the method is characterized in that: s0, receiving a test driving mode control instruction and entering a test driving mode;
In step S1, in the driving test mode, an image in the vehicle is obtained, the positions of the service personnel and the consumer are determined according to the image in the vehicle, and the current experience mode is determined according to the position of the service personnel.
4. The new energy automobile endurance test system for test driving is characterized by comprising an image acquisition module, a voice acquisition module, a navigation module, a processing module and an electric control module;
The image acquisition module is used for acquiring an image in the vehicle; the voice acquisition module is used for acquiring voices of all acquisition partitions in the vehicle; the electric control module is used for collecting electricity consumption data of the vehicle;
The processing module is used for determining the positions of service personnel and consumers according to the images in the vehicle, determining the current experience mode according to the positions of the service personnel, and the experience module comprises trial taking and trial driving; and also for associating the locations of service personnel and consumers with the acquisition partition;
The processing module is also used for respectively acquiring voices of corresponding acquisition subareas of service personnel and consumers during trial riding, identifying voice contents of the service personnel and the consumers, analyzing whether the voice contents are related to the use scene of the vehicle, and extracting vehicle scene information of the consumers from the voice contents if the voice contents are related to the use scene information of the vehicle, wherein the vehicle scene information comprises a driving route and driving time; also analyzing whether the voice content is related to the driving style, and if so, extracting driving style information of the consumer;
the navigation module is used for acquiring traffic conditions of corresponding vehicle scenes according to the vehicle scene information;
The processing module is also used for acquiring the traffic condition of each road section within the preset distance through the navigation module after the trial taking is finished, and generating a trial driving route according to the traffic condition of each road section within the preset distance and the traffic condition of the traffic scene of the consumer;
The processing module is also used for acquiring power consumption data of the test driving process from the electronic control module after the test driving is finished, and calculating estimated endurance mileage according to the power consumption data of the test driving process; the power consumption data in the test driving process comprises the driving mileage and the power consumption percentage of the continuous navigation test driving route, and the calculation formula of the estimated continuous navigation mileage S 2 is as follows:
S2＝S1/(S0×T)×S0
wherein S 1 is the driving mileage, T is the power consumption percentage, and S 0 is the nominal endurance;
The processing module acquires the identity information of the consumer and the weather information of the test driving, and creates a test driving file according to the driving style information, the estimated overall endurance, the identity information of the consumer and the weather information of the test driving;
After the consumer purchases the vehicle, the processing module acquires the actual cruising of the consumer vehicle and the corresponding road section and weather information; calculating the same meteorological information of the same consumer and the deviation percentage of actual cruising and estimated cruising mileage under the same road section as the driving route acquired by trial riding;
the processing module calculates a correction value according to the same weather information, the same road section as the driving route acquired by trial riding and the deviation percentages of all consumers under the same driving style;
Calculating the estimated endurance mileage S 2 of the new consumer according to the correction value, wherein the calculation formula is as follows:
S2＝S1/(S0×T)×S0×(1-F)
wherein F is a correction value.
5. The new energy automobile endurance test system for test driving according to claim 4, wherein: the processing module is also used for generating a vehicle experience test driving route after the test taking is finished, acquiring the traffic condition of each road section within the preset distance through the navigation module, and generating continuous navigation measurement according to the traffic condition of each road section within the preset distance and the traffic condition of a customer vehicle scene.
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