CN112365142A - Vehicle data analysis method and device and electronic equipment - Google Patents
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Abstract
The invention discloses a vehicle data analysis method, a vehicle data analysis device and electronic equipment, wherein the vehicle data analysis method comprises the following steps: acquiring vehicle data, wherein the vehicle data comprises evaluation data of a user on a vehicle; performing semantic analysis on the vehicle data to obtain the information of the vehicle demand of the user; standardizing the demand information by using a pre-established product characteristic catalog to obtain standard functional quality data; and feeding the standard function quality data back to the vehicle manufacturer so that the vehicle manufacturer can improve the vehicle. According to the vehicle data analysis method, a large amount of evaluation data of a user on the vehicle are analyzed by utilizing a semantic analysis technology, and standard function quality data are finally obtained, so that convenience is brought to docking of engineers of vehicle manufacturers, and improvement on the vehicle is facilitated.
Description
Technical Field
The present invention relates to the field of vehicle technologies, and in particular, to a method and an apparatus for analyzing vehicle data, and an electronic device.
Background
With the development of social economy, the Chinese vehicle market is also promoted by the economic market from 'three old' years to 'hundreds of flowers' at present in 20 years ago. The vehicle products are more and more diversified, and the users have more and more requirements on differentiation and individuation of the vehicle products. In order for a vehicle manufacturer to produce a vehicle product that meets market requirements, it is necessary to know the use feedback of the market users on historical products and the product characteristics.
In order to know the feedback information of the user on the vehicle use condition, in the related technology, an electronic questionnaire or a mode of counting internet media public opinion comments is adopted, the mode is high in labor cost and low in efficiency, and the counting result generally hardly meets the requirements of vehicle manufacturers.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, an object of the present invention is to provide a vehicle data analysis method, so that a vehicle manufacturer can accurately and quickly know feedback information of a user on a vehicle use condition, and further, the vehicle can be improved.
A second object of the present invention is to provide a vehicle data analysis device.
A third object of the invention is to propose an electronic device.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a vehicle data analysis method, including: acquiring vehicle data, wherein the vehicle data comprises evaluation data of a vehicle by a user; performing semantic analysis on the vehicle data to obtain the demand information of the user on the vehicle; standardizing the demand information by using a pre-established product feature catalog to obtain standard functional quality data; and feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
According to the vehicle data analysis method provided by the embodiment of the invention, vehicle data are firstly acquired, and then semantic analysis is carried out on the vehicle data to obtain the demand information of a user on the vehicle; then, standardizing the demand information by using a pre-established product special diagnosis catalog to obtain standard functional quality data; and feeding the standard function quality data back to the vehicle manufacturer so that the vehicle manufacturer can improve the vehicle. Therefore, the method is helpful for vehicle manufacturers to accurately and quickly know the feedback information of the user on the vehicle use condition, and further, the improvement on the vehicle is facilitated.
In addition, the vehicle data analysis method proposed by the above embodiment of the present invention may further have the following additional technical features:
according to an embodiment of the present invention, the semantically analyzing the vehicle data to obtain the information of the vehicle demand of the user includes: segmenting the vehicle data by utilizing a pre-established vehicle word bank to obtain a plurality of statement blocks; mining the statement block to obtain a vehicle keyword, wherein the vehicle keyword comprises a vehicle type subject term and an evaluation subject term of a vehicle type index; obtaining emotion information included in the vehicle data by using a pre-established emotion analysis model, wherein the emotion information is used for representing emotion tendency information represented by the vehicle data; and obtaining the demand information according to the vehicle keywords and the emotion information.
According to one embodiment of the invention, the product feature catalog includes a first level indicator, a second level indicator, a third level indicator and a fourth level indicator, wherein the second level indicator is a refinement of the first level indicator, the third level indicator is a refinement of the second level indicator, and the fourth level indicator is a refinement of the third level indicator.
According to one embodiment of the invention, the primary indicator comprises: the system comprises at least one of appearance design, interior decoration design, quality, space, human-computer interaction and infotainment system of a real vehicle, comfortableness, dynamic property, controllability, convenience in use, safety, economy, off-road performance, brand consideration, price consideration, configuration consideration and service experience.
According to an embodiment of the present invention, the appearance design corresponding to the secondary indexes in the primary indexes include: at least one of a vehicle body form, a vehicle key shape, a vehicle body color, a vehicle body line style, a vehicle body proportion, a right rear angle, a right front angle, a right side angle, a familiarization expression, an appearance decoration, an appearance detail, an appearance overall feeling, a front face and a logo design.
According to one embodiment of the invention, the second-level indicators corresponding to the space in the indicators comprise driver spaces, and the third-level indicators corresponding to the driver spaces comprise: at least one of a driver knee space, a driver leg space, a driver shoulder space, a driver head space, and a seat space.
According to an embodiment of the present invention, the first-level index includes 17 indexes, the second-level index includes 76 indexes, the third-level index includes 315 indexes, and the fourth-level index includes 881 indexes.
According to one embodiment of the invention, the words included in the vehicle thesaurus include at least one of vehicle-class index subject words, attribute words, similar meaning words and adjectives.
In order to achieve the above object, a second aspect of the present invention provides a vehicle data analysis device, including: the vehicle data acquisition module is used for acquiring vehicle data, wherein the vehicle data comprises evaluation data of a user on a vehicle; the analysis module is used for performing semantic analysis on the vehicle data to obtain the demand information of the user on the vehicle; the processor module is used for carrying out standardized processing on the demand information by utilizing a pre-established product feature catalog to obtain standard functional quality data; and the feedback module is used for feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
According to the vehicle data analysis device provided by the embodiment of the invention, vehicle data are obtained through the obtaining module, wherein the vehicle data comprise evaluation data of a user on a vehicle; semantic analysis is carried out on the vehicle data through an analysis module to obtain the information of the vehicle demand of a user; standardizing the demand information by using a pre-established product feature catalog through a processing module to obtain standard functional quality data; and the standard function quality data is fed back to the vehicle manufacturer through a feedback module, so that the vehicle manufacturer can improve the measurement. Therefore, the device is beneficial to vehicle manufacturers to accurately and quickly know the feedback information of the user on the use condition of the vehicle, and is further convenient for improving the vehicle.
In order to achieve the above object, a third aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, the vehicle data analysis method of the above embodiment is implemented.
According to the electronic equipment provided by the embodiment of the invention, when the computer program stored on the memory runs in the processor, a vehicle manufacturer can accurately and quickly know the feedback information of the user on the use condition of the vehicle, so that the improvement on the vehicle is facilitated.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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Fig. 1 is a flowchart of a vehicle data analysis method according to an embodiment of the present invention.
FIG. 2 is a flow chart of a vehicle data analysis method in accordance with an embodiment of the present invention.
Fig. 3 is a block diagram showing the configuration of a vehicle data analysis device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A vehicle data analysis method, a device, and an electronic apparatus according to an embodiment of the present invention are described below with reference to the drawings.
Fig. 1 is a flowchart of a vehicle data analysis method according to an embodiment of the present invention.
As shown in fig. 1, the vehicle data analysis method includes the steps of:
and S100, acquiring vehicle data.
The vehicle data comprises evaluation data of the vehicle by the user, and can comprise a large amount of text data from different users for various vehicle types of various brands.
In some possible examples, the approach to obtaining the evaluation data of the vehicle by the user may be: the sample questionnaire survey, group seating, etc. may be conducted by offline quantitative and qualitative user research, crawling based on product public opinion feedback data, etc., or may be obtained through online internet (e.g., from user's spontaneous discussion in public praise tiles of internet forums such as automobile homes, etc.). The evaluation data of the vehicle by the user may be communication by the user to existing satisfaction points, complaint points, occurred troubles, and the like of the vehicle. The evaluation data obtained by the above-described questionnaire or user survey method needs to be used for analysis in the form of electronic text data.
In some possible examples, the user's evaluation data of the vehicle may be obtained from a user's spontaneous discussion in a public praise block of an internet forum such as a car house through big data crawler technology. Therefore, data islands are broken through the collection of the internet full-network data sources, the evaluation data of the vehicles are not limited to user groups of certain vehicle products any more, but are internet users all over the country, the collectable sample user data sources are wider, and the improvement of vehicle products meeting the requirements of most users is facilitated.
S200, performing semantic analysis on the vehicle data to obtain the demand information of the user on the vehicle.
And S300, standardizing the demand information by using a pre-established product feature catalog to obtain standard functional quality data.
The pre-established product feature catalog can comprise a first-level index, a second-level index, a third-level index and a fourth-level index, wherein the second-level index is used for refining the first-level index, the third-level index is used for refining the second-level index, and the fourth-level index is used for refining the third-level index. Wherein, the first grade index includes 17 indexes, the second grade index includes 76 indexes, tertiary index includes 315 indexes, the fourth grade index includes 881 indexes.
Further, the primary indicators include: the system comprises at least one of appearance design, interior decoration design, quality, space, human-computer interaction and infotainment system of a real vehicle, comfortableness, dynamic property, controllability, convenience in use, safety, economy, off-road performance, brand consideration, price consideration, configuration consideration and service experience.
Further, the appearance design in the primary indexes corresponds to secondary indexes including: at least one of a vehicle body form, a vehicle key shape, a vehicle body color, a vehicle body line style, a vehicle body proportion, a right rear angle, a right front angle, a right side angle, a familiarization expression, an appearance decoration, an appearance detail, an appearance overall feeling, a front face and a logo design. The second-level index corresponding to the space in the first-level index comprises a driver space, and the third-level index corresponding to the driver space comprises: at least one of a driver knee space, a driver leg space, a driver shoulder space, a driver head space, and a seat space.
Specifically, after the user evaluation and mining in the vehicle data are finished, the mined data (i.e., the demand information) can be stored in a database (such as a cloud database), then the data stored in the database can be subjected to user sound standardized conversion according to a product feature catalog established in advance, and the satisfaction points, complaint points and fault points related to vehicle interior, appearance, space, controllability, dynamic property and the like mentioned by the user are mined and classified into the corresponding product feature catalog, so that functional quality data taking the product system indexes as the standards are finally formed and can be stored.
And S400, feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
It can be understood that the standard functional quality data obtained by standardizing the demand information can present the subjective evaluation of the user to the vehicle manufacturer and/or corresponding technical personnel in a more objective way, which is beneficial for the technical personnel to understand the demand of the user, present the demand degree of the user to the corresponding demand in a way of marking the weight of the demand information, and determine the priority of product improvement.
Therefore, the vehicle data analysis method provided by the embodiment of the invention can obtain the standard functional quality data which is convenient to interface with the vehicle engineer of a vehicle manufacturer by processing a large amount of vehicle data, and further can be convenient for the vehicle engineer to improve the vehicle.
In an embodiment of the present invention, as shown in fig. 2, step S200 of the above embodiment may include:
and S210, segmenting the vehicle data by utilizing a pre-established vehicle word stock to obtain a plurality of statement blocks.
The words included in the vehicle thesaurus may include feature subject words and part keywords of all vehicle bodies in the automobile market, and may include at least one of vehicle index subject words, attribute words, similar meaning words and adjectives. Because the spoken language of internet data is more serious, the user is strange in problem description, and vehicle evaluation information can be more comprehensively and accurately identified based on the feedback of a pre-established vehicle word bank to the user.
In some possible examples, the segmentation process on the vehicle data may include: the whole post is segmented, then sentence segmentation is carried out on the segmented post data, and then the sentence is segmented to obtain a sentence block.
And S220, mining the sentence blocks to obtain the vehicle keywords.
The vehicle keywords comprise vehicle type subject terms and evaluation subject terms of vehicle type indexes.
In some possible examples, mining the sentence block to obtain the vehicle keyword may include: mining vehicle type keywords and subjective evaluation mining of consumers on vehicle type index words in vehicle data (such as posts) with sentences divided to obtain vehicle type subject words and evaluation subject words of vehicle type indexes.
And S230, obtaining emotion information included in the vehicle data by using a pre-established emotion analysis model.
The pre-established emotion analysis model can be used for predicting emotion tendencies (such as positive direction, negative direction, neutral direction and the like) contained in the vehicle data, and can be realized through a neural network model. The emotional information is used for representing emotional tendency information represented by the vehicle data.
In some possible examples, taking the text "the fresh breath in smell inside the X-model vehicle" in the vehicle data as an example, the vehicle keywords in the text include "X-model vehicle", "in-vehicle smell", "fresh"; the emotion tendency analysis can be carried out on the text through a pre-established emotion analysis model, the obtained 'freshness' evaluation is negative, and the 'freshness' in the keywords needs to be corrected at the moment to obtain 'freshness unclear'. Taking the text "this Y-model vehicle is not strong in-car taste" in the vehicle data as an example, the vehicle keywords in the text include "Y-model vehicle", "in-car taste", "not large"; the emotion tendency analysis can be carried out on the text through the emotion analysis model which is established in advance, the obtained 'small' evaluation is 'neutral', and the 'small' in the keywords can not be corrected. Taking the text "this kind of Z-model vehicle is very good in smell in car" in the vehicle data as an example, the vehicle keywords in the text include "Z-model vehicle", "in-car smell", "good smell"; the emotion tendency analysis can be carried out on the text through a pre-established emotion analysis model, the evaluation of the 'good smell' is obtained in a 'forward' mode, the 'good smell' in the key words can be corrected, and the description of the 'good smell' degree, such as 'good smell', is obtained.
It is particularly noted that in some examples, the user's evaluation of the vehicle may be verbally contradictory, and may also be expressed in terms of politeness without express speculation, or in terms of express expression of look-ahead. At this time, before or after or simultaneously with word mining on the sentence block, the evaluation of the user can be judged from the perspective of the whole vehicle data through the emotion analysis model, and the emotion mood of the whole post is compared and calibrated, that is, the emotion information included in the vehicle data is acquired, so that the real emotion expression of the user can be determined.
And S240, obtaining the demand information according to the vehicle keywords and the emotion information.
Specifically, the obtained keywords are corrected according to the emotion information to obtain keywords capable of accurately expressing the meaning of the user, that is, the demand information. Therefore, more accurate standard function quality data can be obtained, and vehicle manufacturers can improve the vehicles conveniently.
Fig. 3 is a block diagram of a vehicle data analysis device according to an embodiment of the present invention.
As shown in fig. 3, the vehicle data analysis device 100 includes: the system comprises an acquisition module 101, an analysis module 102, a processing module 103 and a feedback module 104.
Specifically, the obtaining module 101 is configured to obtain vehicle data, where the vehicle data includes an evaluation of a vehicle by a user; the analysis module 102 is configured to perform semantic analysis on the vehicle data to obtain information about a vehicle demand of a user; the processing module 103 is configured to perform standardized processing on the demand information by using a pre-established product feature catalog to obtain standard functional quality data; and the feedback module 104 is used for feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
In some possible examples, the obtaining module 101 may obtain the vehicle data from the internet (e.g., in a vehicle-related forum) through big data crawler technology.
In some possible examples, the pre-established product feature catalog may include a first level indicator, a second level indicator, a third level indicator, and a fourth level indicator, wherein the second level indicator is a refinement of the first level indicator, the third level indicator is a refinement of the second level indicator, and the fourth level indicator is a refinement of the third level indicator. Wherein, the first grade index includes 17 indexes, the second grade index includes 76 indexes, tertiary index includes 315 indexes, the fourth grade index includes 881 indexes.
In some possible examples, the primary metrics may include: the system comprises at least one of appearance design, interior decoration design, quality, space, human-computer interaction and infotainment system of a real vehicle, comfortableness, dynamic property, controllability, convenience in use, safety, economy, off-road performance, brand consideration, price consideration, configuration consideration and service experience.
Wherein, the second level index corresponding to the appearance design in the first level index may include: at least one of a vehicle body form, a vehicle key shape, a vehicle body color, a vehicle body line style, a vehicle body proportion, a right rear angle, a right front angle, a right side angle, a familiarization expression, an appearance decoration, an appearance detail, an appearance overall feeling, a front face and a logo design. The spatially corresponding secondary indicators in the primary indicators may include a driver space, and the spatially corresponding tertiary indicators may include: at least one of a driver knee space, a driver leg space, a driver shoulder space, a driver head space, and a seat space.
In some possible examples, analysis module 102 is specifically configured to: segmenting the vehicle data by utilizing a pre-established vehicle word bank to obtain a plurality of statement blocks, wherein words recorded in the vehicle word bank comprise at least one of vehicle index subject words, attribute words, similar meaning words and adjectives; mining the statement block to obtain a vehicle keyword, wherein the vehicle keyword comprises a vehicle type subject term and an evaluation subject term of a vehicle type index; obtaining emotion information included in the vehicle data by using a pre-established emotion analysis model, wherein the emotion information is used for representing emotion tendency information represented by the vehicle data; and obtaining the demand information according to the vehicle keywords and the emotion information.
In some possible examples, the words included in the vehicle thesaurus may include at least one of a vehicle class index subject word, an attribute word, a near word, and an adjective.
For another specific implementation of the vehicle data analysis device according to the embodiment of the present invention, reference may be made to the vehicle data analysis method according to the above-described embodiment of the present invention.
According to the vehicle data analysis device provided by the embodiment of the invention, through processing of a large amount of vehicle data, standard function quality data which is convenient to interface with a vehicle engineer of a vehicle manufacturer can be obtained, and further the vehicle engineer can improve the vehicle conveniently.
The invention also proposes an electronic device comprising a memory, a processor and a computer program stored on said memory.
In this embodiment, the computer program, when executed by the processor, may implement the vehicle data analysis method described in the above-described embodiment.
According to the electronic equipment provided by the embodiment of the invention, a vehicle manufacturer can accurately and quickly know the feedback information of the user on the vehicle use condition, so that the improvement on the vehicle is facilitated.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A vehicle data analysis method, characterized by comprising the steps of:
acquiring vehicle data, wherein the vehicle data comprises evaluation data of a vehicle by a user;
performing semantic analysis on the vehicle data to obtain the demand information of the user on the vehicle;
standardizing the demand information by using a pre-established product feature catalog to obtain standard functional quality data;
and feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
2. The vehicle data analysis method according to claim 1, wherein the semantic analysis of the vehicle data to obtain the information about the demand of the user on the vehicle comprises:
segmenting the vehicle data by utilizing a pre-established vehicle word bank to obtain a plurality of statement blocks;
mining the statement block to obtain a vehicle keyword, wherein the vehicle keyword comprises a vehicle type subject term and an evaluation subject term of a vehicle type index;
obtaining emotion information included in the vehicle data by using a pre-established emotion analysis model, wherein the emotion information is used for representing emotion tendency information represented by the vehicle data;
and obtaining the demand information according to the vehicle keywords and the emotion information.
3. The vehicle data analysis method according to claim 1, wherein the product characteristics catalog includes a first-level index, a second-level index, a third-level index, and a fourth-level index, wherein the second-level index is a refinement of the first-level index, the third-level index is a refinement of the second-level index, and the fourth-level index is a refinement of the third-level index.
4. The vehicle data analysis method according to claim 3, wherein the primary index includes: the system comprises at least one of appearance design, interior decoration design, quality, space, human-computer interaction and infotainment system of a real vehicle, comfortableness, dynamic property, controllability, convenience in use, safety, economy, off-road performance, brand consideration, price consideration, configuration consideration and service experience.
5. The vehicle data analysis method according to claim 4, wherein the design-corresponding secondary index in the primary index includes: at least one of a vehicle body form, a vehicle key shape, a vehicle body color, a vehicle body line style, a vehicle body proportion, a right rear angle, a right front angle, a right side angle, a familiarization expression, an appearance decoration, an appearance detail, an appearance overall feeling, a front face and a logo design.
6. The vehicle data analysis method according to claim 4, wherein the spatially corresponding secondary index of the primary indexes includes a driver space, and the spatially corresponding tertiary index of the driver space includes: at least one of a driver knee space, a driver leg space, a driver shoulder space, a driver head space, and a seat space.
7. The vehicle data analysis method according to claim 3, wherein the primary index includes 17 indices, the secondary index includes 76 indices, the tertiary index includes 315 indices, and the quaternary index includes 881 indices.
8. The vehicle data analysis method according to claim 2, wherein the words included in the vehicle thesaurus include at least one of vehicle-type index subject words, attribute words, near-meaning words, and adjectives.
9. A vehicle data analysis device characterized by comprising:
the vehicle data acquisition module is used for acquiring vehicle data, wherein the vehicle data comprises evaluation data of a user on a vehicle;
the analysis module is used for performing semantic analysis on the vehicle data to obtain the demand information of the user on the vehicle;
the processing module is used for carrying out standardized processing on the demand information by utilizing a pre-established product feature catalog to obtain standard functional quality data;
and the feedback module is used for feeding the standard function quality data back to a vehicle manufacturer so that the vehicle manufacturer can improve the vehicle.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory, wherein the computer program, when executed by the processor, implements a vehicle data analysis method as claimed in any one of claims 1-8.
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