CN112016964A - Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium - Google Patents
Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112016964A CN112016964A CN202010878368.3A CN202010878368A CN112016964A CN 112016964 A CN112016964 A CN 112016964A CN 202010878368 A CN202010878368 A CN 202010878368A CN 112016964 A CN112016964 A CN 112016964A
- Authority
- CN
- China
- Prior art keywords
- data
- price
- public praise
- word
- sample set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012549 training Methods 0.000 claims abstract description 50
- 230000008451 emotion Effects 0.000 claims description 30
- 238000004458 analytical method Methods 0.000 claims description 19
- 238000005516 engineering process Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 3
- 230000006399 behavior Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 6
- 230000002996 emotional effect Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009193 crawling Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 241000353135 Psenopsis anomala Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0278—Product appraisal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Finance (AREA)
- Human Resources & Organizations (AREA)
- Accounting & Taxation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application discloses a second-hand vehicle dynamic pricing method, a second-hand vehicle dynamic pricing device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring public praise data related to the second-hand vehicles and inherent attribute data of the second-hand vehicles; reconstructing a training sample set based on the public praise data and the inherent attribute data; and training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model. According to the embodiment of the application, when the price of the second-hand car is predicted, public praise information of the second-hand car is referred to on the basis of the inherent attribute of the second-hand car, so that the accuracy of price prediction is improved, and meanwhile, due to the fact that public praise behaviors are different in time, when the price is predicted by referring to public praise data, the purpose of dynamically adjusting the price based on the public praise data is achieved.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a second-hand vehicle dynamic pricing method and device, electronic equipment and a storage medium.
Background
Currently, the market for used cars is prosperous, the trading volume of the market for used cars far exceeds the trading volume of new cars, and the price of the used cars can significantly affect the trading of the used cars, so how to predict the price of the used cars is necessary.
At present, the price prediction of second-hand vehicles is based on the inherent attributes of the vehicles, wherein the inherent attributes can be roughly divided into brands, vehicle types, configurations, years of delivery, vehicle mileage, fuel types, years of purchase and the like. Although the mapping relation between the price and the inherent attribute of the automobile can be found through various machine learning algorithms, the price does not change linearly along with the inherent attribute, namely, the dynamic adjustment of pricing cannot be realized.
Disclosure of Invention
The embodiment of the application provides a used vehicle dynamic pricing method, a used vehicle dynamic pricing device, electronic equipment and a storage medium, so that the purpose of dynamically adjusting the used vehicle pricing is achieved.
In a first aspect, an embodiment of the present application provides a used vehicle dynamic pricing method, where the method includes:
acquiring public praise data related to the second-hand vehicles and inherent attribute data of the second-hand vehicles;
reconstructing a training sample set based on the public praise data and the inherent attribute data;
and training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
In a second aspect, an embodiment of the present application provides a used vehicle dynamic pricing device, where the device includes:
the data acquisition module is used for acquiring public praise data related to the used cars and inherent attribute data of the used cars;
the sample construction module is used for reconstructing a training sample set based on the public praise data and the inherent attribute data;
and the training and predicting module is used for training a pre-constructed price predicting model based on the training sample set and dynamically pricing by using the trained price predicting model.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, the one or more programs cause the one or more processors to implement a used vehicle dynamic pricing method according to any embodiment of the present application.
In a fourth aspect, the present application further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the used vehicle dynamic pricing method according to any embodiment of the present application.
In the embodiment of the application, a training sample set is reconstructed based on the acquired second-hand car public praise data and the inherent attribute data, a pre-constructed price prediction model is trained based on the training sample set, and dynamic pricing is performed by using the trained price prediction model. Therefore, when the price of the second-hand car is predicted, the public praise information of the second-hand car is referred on the basis of the inherent attribute of the second-hand car, so that the accuracy of price prediction is improved, and meanwhile, due to different public praise behavior time, the purpose of dynamically adjusting the price based on the public praise data is achieved when the price is predicted by referring to the public praise data.
Drawings
Fig. 1 is a schematic flow chart of a used vehicle dynamic pricing method according to a first embodiment of the present application;
fig. 2a is a schematic flow chart of a used vehicle dynamic pricing method according to a second embodiment of the present application;
FIG. 2b is a block diagram of a pre-constructed dictionary in accordance with a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a used vehicle dynamic pricing device according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device for implementing a used vehicle dynamic pricing method according to the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Fig. 1 is a flow chart of a used vehicle dynamic pricing method according to a first embodiment of the present application, which is applicable to a situation of predicting a price of a used vehicle, and the method may be executed by a used vehicle dynamic pricing device, which may be implemented in a software and/or hardware manner, and may be integrated on an electronic device, for example, on a computer or a server.
As shown in fig. 1, the second-hand vehicle dynamic pricing method specifically includes the following processes:
s101, word-of-mouth data related to the used cars and inherent attribute data of the used cars are obtained.
In the embodiment of the application, the inventor finds that public praise information has a certain influence on price setting of the used cars, so that the public praise information is creatively introduced into a price setting scene of the used cars in the application to improve the accuracy of price prediction of the used cars, wherein the public praise information refers to qualitative information provided by a consumer to other consumers, for example, the public praise information can be a short text 'i buy a car in 8 months, and no problem exists in four days'. Since qualitative data cannot be used as a sample for model training, it is necessary to quantitatively extract the word-of-mouth information in advance to obtain quantitative word-of-mouth data.
The intrinsic attribute data of the used vehicle comprises basic data such as the number of engine cylinders, the service time, the vehicle type, the vehicle grade or the fuel type, and optionally crawls the used vehicle from a used vehicle transaction website (such as a melon seed used vehicle transaction website) through a crawler technology when the intrinsic attribute data of the used vehicle is acquired.
And S102, reconstructing a training sample set based on the public praise data and the inherent attribute data.
When the training sample set is constructed, transaction data of a used vehicle (including inherent attribute data and transaction price of the vehicle) and related public praise data of the vehicle are used as a sample, and therefore the training sample set comprising a plurality of samples is obtained.
S103, training the pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
The price prediction model trained in advance can be a random forest model or a linear regression model, and each sample in the training sample set is input into the price prediction model for training. It should be noted that, when a price prediction model constructed in advance is trained based on a training sample set, the model prediction accuracy may also be evaluated according to a prediction result output by the model.
In an alternative embodiment, the evaluation operation is as follows: obtaining the price predicted value output by the price prediction model
And the actual value of the price y in the sample label; calculating a price forecast
And the average absolute error, mean square error and/or root mean square error between the price actual value y; wherein the mean absolute error
Mean square error
Root mean square error
And evaluating the accuracy of the price prediction model according to the average absolute error, the mean square error and/or the root mean square error. In an alternative embodiment, a threshold of the mean absolute error, the mean square error and/or the root mean square error may be preset, and when the mean absolute error, the mean square error and/or the root mean square error is smaller than the preset threshold, the price prediction model is considered to meet the demand.
It should be noted here that, because the action time of the public praise information is different, two used cars that are identical may have different public praises, and thus pricing of the two cars is different. Therefore, when the price prediction model is used for pricing the second-hand vehicles, dynamic pricing can be achieved.
In the embodiment of the application, a training sample set is reconstructed based on the acquired second-hand car public praise data and the inherent attribute data, a pre-constructed price prediction model is trained based on the training sample set, and dynamic pricing is performed by using the trained price prediction model. Therefore, when the price of the second-hand car is predicted, the public praise information of the second-hand car is referred on the basis of the inherent attribute of the second-hand car, so that the accuracy of price prediction is improved, and meanwhile, due to different public praise behavior time, the purpose of dynamically adjusting the price based on the public praise data is achieved when the price is predicted by referring to the public praise data.
Fig. 2a is a schematic flow chart of a second embodiment of a dynamic pricing method for a used vehicle according to the present application, where the present embodiment is optimized based on the foregoing embodiments, and referring to fig. 2a, the method includes: .
S201, acquiring original text containing tombstone information of the used cars and inherent attribute data of the used cars based on a crawler technology.
In the embodiment of the application, before the original text and the inherent attribute data are acquired based on the crawler technology, a crawler tool is constructed, and illustratively, the constructed crawler tool is an API (application program interface) or an OCR recognition tool of a website. The OCR recognition tool is used for grabbing the whole webpage into an image and then extracting pure text materials as original texts by applying an OCR layout analysis technology. In an alternative embodiment, the original text of the public praise information is extracted from the client comments on the transaction website, the comment articles on the web forum, the social media short text, and the news reports based on crawler technology. The public praise information related to the used cars is hidden in client comments, comment articles on the internet forum, social media short texts and news reports.
Further, after the original text is obtained, for the convenience of identification, the original text data may be preprocessed, for example, data cleaning, missing value filling, abnormal value detection and deletion, and the like.
S202, aiming at any original text, identifying the original document according to a pre-constructed dictionary to obtain a target word included in the original text.
In the embodiment of the application, in order to quickly extract quantitative word-of-mouth data from an original text, the original text can be divided into attributes which can be described according to a fixed standard. In an alternative implementation manner, the original document is recognized according to a pre-constructed dictionary, and a target word included in the original text is obtained. The pre-constructed dictionary comprises subject words with different dimensions and corresponding target words under each subject word, for example, refer to fig. 2b, which shows a schematic diagram of a composition structure of the dictionary, wherein the subject words comprise Safety, Driving, Comfort for Comfort, internal part of Interior, Utility and Technology; each subject term includes at least one target term behind, for example, the target term corresponding to the subject term Safety includes airbag, accident, damage, and the like.
In the embodiment of the application, the pre-constructed dictionary is essentially a network model, and when the original text is identified, the original text can be input into the dictionary model, and the target word is determined according to the output of the dictionary model. Illustratively, an original text is "my tesla X is very strong and comfortable to accelerate, but endurance is worried that i do not drive too far on vacation", a subject word corresponding to the original text is "driving" after being recognized by a dictionary, and target words included in the original text are determined as "acceleration" and "endurance" by comparing the original text with the target words corresponding to the subject word.
S203, calculating the emotion score of each target word based on aspect-oriented emotion analysis, and taking the target word and the corresponding emotion score as word-of-speech data.
In an alternative implementation mode, the original text and the target words are input into a pre-trained emotion analysis model, and emotion scores of the target words are determined according to the output of the emotion analysis model. The emotion Analysis model is an ABSA (Aspect-Based Sentiment Analysis) model, and the ABSA model is an LCF-BERT model or an AEN-BERT model.
Illustratively, the original text and the target word input into the emotion analysis model are respectively: "My Tesla X is very powerful, acceleration feeling is comfortable, but endurance is worried that I does not drive too far on vacation", "acceleration", the emotion analysis model outputs the results: < endurance, driving, positive >, wherein the emotional polarity "positive" may also be a score. On the basis, the target words and the corresponding emotion scores can be used as word-of-mouth data.
Further, since the influence of the word-of-mouth information is weakened in a long term, in the embodiment of the present application, the short-term influence of the word-of-mouth is mainly considered, that is, the extracted and quantized word-of-mouth data needs to be divided according to a preset time window, and the emotional score or emotional polarity of the word-of-mouth data in each time window is determined, where the preset time window may be one month. Illustratively, for a certain car model, if the word-of-mouth of 3 months is found to be positive through emotional analysis, a dummy variable 0 is added to the word-of-mouth data of 3 months; if month 4 causes some problems with the model because of frequent explosions, making the word of mouth of month 4 negative, a dummy variable 1 is added to the word of mouth data of month 4. Therefore, the public praise of the same vehicle type may be different in different time, so that the public praise needs to be divided according to the time window. And aiming at the divided public praise data, constructing a training sample according to S204 so as to train a price prediction model.
And S204, reconstructing a training sample set based on the public praise data and the inherent attribute data.
S205, training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
In the embodiment of the application, the target words can be accurately obtained through the pre-constructed dictionary, emotion analysis is carried out on the found target words and the original text, the target words and corresponding emotion scores are used as public praise data, so that public praise data is extracted and quantized, and guarantee is provided for predicting the price of the second-hand vehicle based on the public praise data.
Fig. 3 is a schematic structural diagram of a used vehicle dynamic pricing device according to a third embodiment of the present application, the device is used for predicting the price of used vehicles, and referring to fig. 3, the device includes:
the data acquisition module 301 is used for acquiring public praise data related to the used cars and inherent attribute data of the used cars;
a sample construction module 302 for reconstructing a training sample set based on the public praise data and the inherent attribute data;
and the training and predicting module 303 is used for training a pre-constructed price predicting model based on the training sample set and dynamically pricing by using the trained price predicting model.
In the embodiment of the application, a training sample set is reconstructed based on the acquired second-hand car public praise data and the inherent attribute data, a pre-constructed price prediction model is trained based on the training sample set, and dynamic pricing is performed by using the trained price prediction model. Therefore, when the price of the second-hand car is predicted, the public praise information of the second-hand car is referred on the basis of the inherent attribute of the second-hand car, so that the accuracy of price prediction is improved, and meanwhile, due to different public praise behavior time, the purpose of dynamically adjusting the price based on the public praise data is achieved when the price is predicted by referring to the public praise data.
On the basis of the foregoing embodiment, optionally, the data obtaining module includes:
the data crawling unit is used for acquiring an original text containing second-hand car public praise information based on a crawler technology;
the target word recognition unit is used for recognizing the original document according to a pre-constructed dictionary aiming at any original text to obtain a target word included in the original text;
and the emotion analysis unit is used for calculating the emotion score of each target word based on the emotion analysis of the aspect and taking the target word and the corresponding emotion score as word-of-speech data.
On the basis of the foregoing embodiment, optionally, the data crawling unit is specifically configured to:
original text of public praise information is extracted from client comments on transaction websites, comment articles on web forums, short social media text and news reports based on crawler technology.
On the basis of the above embodiment, optionally, the pre-constructed dictionary includes subject words with different dimensions and corresponding target words under each subject word.
On the basis of the foregoing embodiment, optionally, the emotion analysis unit is specifically configured to:
and inputting the original text and the target word into a pre-trained emotion analysis model, and determining the emotion score of the target word according to the output of the emotion analysis model.
On the basis of the foregoing embodiment, optionally, the apparatus further includes:
the price value acquisition module is used for acquiring a price predicted value output by the price prediction model and a price actual value in the sample label;
the error calculation module is used for calculating the average absolute error, the mean square error and/or the root mean square error between the price predicted value and the price actual value;
and the evaluation module is used for evaluating the accuracy of the price prediction model according to the average absolute error, the mean square error and/or the root mean square error.
On the basis of the foregoing embodiment, optionally, the apparatus further includes:
and the dividing module is used for dividing the word-of-mouth data according to a preset time window and determining the emotion score of the word-of-mouth data in each time window before reconstructing the training sample set based on the word-of-mouth data and the inherent attribute data.
The used vehicle dynamic pricing device provided by the embodiment of the application can execute the used vehicle dynamic pricing method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As shown in fig. 4, the electronic device provided in the embodiment of the present application includes: one or more processors 402 and memory 401; the processor 402 in the electronic device may be one or more, and one processor 402 is taken as an example in fig. 4; the memory 401 is used to store one or more programs; the one or more programs are executed by the one or more processors 402, such that the one or more processors 402 implement a used vehicle dynamic pricing method as described in any of the embodiments of the present application.
The electronic device may further include: an input device 403 and an output device 404.
The processor 402, the memory 401, the input device 403 and the output device 404 in the electronic apparatus may be connected by a bus or other means, and fig. 4 illustrates an example of connection by a bus.
The storage device 401 in the electronic device is used as a computer-readable storage medium for storing one or more programs, which may be software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the application control method provided in the embodiments of the present application. The processor 402 executes various functional applications and data processing of the electronic device by running software programs, instructions and modules stored in the storage device 401, that is, the method for dynamically pricing the used vehicle in the above method embodiment is implemented.
The storage device 401 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 401 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 401 may further include memory located remotely from the processor 402, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 403 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus. The output device 404 may include a display device such as a display screen.
And, when one or more programs included in the above-mentioned electronic device are executed by the one or more processors 402, the programs perform the following operations:
acquiring public praise data related to the second-hand vehicles and inherent attribute data of the second-hand vehicles;
reconstructing a training sample set based on the public praise data and the inherent attribute data;
and training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
Of course, it can be understood by those skilled in the art that when one or more programs included in the electronic device are executed by the one or more processors 402, the programs may also perform related operations in the application control method provided in any embodiment of the present application.
One embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program for executing a method for dynamic pricing of used cars, the method comprising:
acquiring public praise data related to the second-hand vehicles and inherent attribute data of the second-hand vehicles;
reconstructing a training sample set based on the public praise data and the inherent attribute data;
and training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
Optionally, the program, when executed by a processor, may be further configured to perform the method provided in any of the embodiments of the present application.
The computer storage media of the embodiments of the present application may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. A computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take a variety of forms, including, but not limited to: an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including, for example, a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means 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 application. 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.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (10)
1. A method for dynamically pricing used cars, the method comprising:
acquiring public praise data related to the second-hand vehicles and inherent attribute data of the second-hand vehicles;
reconstructing a training sample set based on the public praise data and the inherent attribute data;
and training a pre-constructed price prediction model based on the training sample set, and dynamically pricing by using the trained price prediction model.
2. The method of claim 1, wherein obtaining word-of-mouth data related to used cars comprises:
acquiring an original text containing second-hand car public praise information based on a crawler technology;
aiming at any original text, recognizing the original document according to a pre-constructed dictionary to obtain a target word included in the original text;
and calculating the emotion score of each target word based on the aspect emotion analysis, and taking the target word and the corresponding emotion score as word-of-speech data.
3. The method of claim 2, wherein obtaining the original text containing the public praise information based on a crawler technology comprises:
original text of public praise information is extracted from client comments on transaction websites, comment articles on web forums, short social media text and news reports based on crawler technology.
4. The method of claim 2, wherein the pre-constructed dictionary comprises subject words with different dimensions and corresponding target words under each subject word.
5. The method of claim 2, wherein computing an emotion score for each target word based on aspect-oriented emotion analysis comprises:
and inputting the original text and the target word into a pre-trained emotion analysis model, and determining the emotion score of the target word according to the output of the emotion analysis model.
6. The method of claim 1, wherein when training a pre-constructed price prediction model based on the training sample set, the method further comprises:
obtaining a price predicted value output by the price prediction model and a price actual value in the sample label;
calculating the average absolute error, mean square error and/or root mean square error between the price predicted value and the price actual value;
and evaluating the accuracy of the price prediction model according to the average absolute error, the mean square error and/or the root mean square error.
7. The method of claim 1, wherein prior to reconstructing a training sample set based on the public praise data and intrinsic property data, the method further comprises:
dividing the word-of-mouth data according to a preset time window, and determining the emotion score of the word-of-mouth data in each time window.
8. A used vehicle dynamic pricing device, the device comprising:
the data acquisition module is used for acquiring public praise data related to the used cars and inherent attribute data of the used cars;
the sample construction module is used for reconstructing a training sample set based on the public praise data and the inherent attribute data;
and the training and predicting module is used for training a pre-constructed price predicting model based on the training sample set and dynamically pricing by using the trained price predicting model.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a used vehicle dynamic pricing method as claimed in any of claims 1-7.
10. A storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method for dynamic pricing of used cars according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010878368.3A CN112016964A (en) | 2020-08-27 | 2020-08-27 | Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010878368.3A CN112016964A (en) | 2020-08-27 | 2020-08-27 | Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112016964A true CN112016964A (en) | 2020-12-01 |
Family
ID=73502623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010878368.3A Pending CN112016964A (en) | 2020-08-27 | 2020-08-27 | Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112016964A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837802A (en) * | 2021-09-24 | 2021-12-24 | 深圳闪回科技有限公司 | Second-hand mobile phone price prediction algorithm with time sequence process and mobile phone defect feature depth fusion |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844403A (en) * | 2015-12-07 | 2017-06-13 | 北京国双科技有限公司 | Information processing method and device |
| CN107944060A (en) * | 2018-01-02 | 2018-04-20 | 天津大学 | A kind of product information search method towards automotive vertical website |
| CN110442728A (en) * | 2019-06-28 | 2019-11-12 | 天津大学 | Sentiment dictionary construction method based on word2vec automobile product field |
| CN110706039A (en) * | 2019-10-16 | 2020-01-17 | 爱驰汽车有限公司 | Electric vehicle residual value rate evaluation system, method, equipment and medium |
-
2020
- 2020-08-27 CN CN202010878368.3A patent/CN112016964A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844403A (en) * | 2015-12-07 | 2017-06-13 | 北京国双科技有限公司 | Information processing method and device |
| CN107944060A (en) * | 2018-01-02 | 2018-04-20 | 天津大学 | A kind of product information search method towards automotive vertical website |
| CN110442728A (en) * | 2019-06-28 | 2019-11-12 | 天津大学 | Sentiment dictionary construction method based on word2vec automobile product field |
| CN110706039A (en) * | 2019-10-16 | 2020-01-17 | 爱驰汽车有限公司 | Electric vehicle residual value rate evaluation system, method, equipment and medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837802A (en) * | 2021-09-24 | 2021-12-24 | 深圳闪回科技有限公司 | Second-hand mobile phone price prediction algorithm with time sequence process and mobile phone defect feature depth fusion |
| CN113837802B (en) * | 2021-09-24 | 2024-05-28 | 深圳闪回科技有限公司 | Secondhand mobile phone price prediction method integrating time sequence process and mobile phone defect feature depth |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10872296B2 (en) | Training neural networks using a variational information bottleneck | |
| CN109858010B (en) | Method and device for recognizing new words in field, computer equipment and storage medium | |
| US20180196800A1 (en) | Learning document embeddings with convolutional neural network architectures | |
| US10489510B2 (en) | Sentiment analysis of product reviews from social media | |
| CN111324739B (en) | Text emotion analysis method and system | |
| CN111210335A (en) | User risk identification method and device and electronic equipment | |
| CN111046952A (en) | Method and device for establishing label mining model, storage medium and terminal | |
| CN111581945B (en) | Public opinion analysis-based data analysis method, device and system | |
| CN111210336A (en) | User risk model generation method and device and electronic equipment | |
| US20220391598A1 (en) | Text checking method based on knowledge graph, electronic device, and medium | |
| CN116151233A (en) | Data labeling and generating method, model training method, device and medium | |
| CN111598122A (en) | Data verification method and device, electronic equipment and storage medium | |
| CN111191677A (en) | User characteristic data generation method and device and electronic equipment | |
| CN112016964A (en) | Second-hand vehicle dynamic pricing method and device, electronic equipment and storage medium | |
| US20190259104A1 (en) | Computer-implemented methods, computer-readable media, and systems for identifying causes of loss | |
| CN113051911A (en) | Method, apparatus, device, medium, and program product for extracting sensitive word | |
| CN112749238A (en) | Search ranking method and device, electronic equipment and computer-readable storage medium | |
| CN109933704A (en) | Vehicle consultation information processing method and processing device | |
| US9922352B2 (en) | Multidimensional synopsis generation | |
| CN114626370A (en) | Training method, risk early warning method, apparatus, device, medium, and program product | |
| CN113191137A (en) | Operation risk obtaining method and system, electronic equipment and storage medium | |
| CN112069807A (en) | Text data theme extraction method and device, computer equipment and storage medium | |
| US20230038645A1 (en) | Method, electronic device and storage medium for remote damage assessment of vehicle | |
| CN114647733B (en) | Question and answer corpus evaluation method and device, computer equipment and storage medium | |
| CN117172632B (en) | Enterprise abnormal behavior detection method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |