CN110544135A - Data processing method and device - Google Patents
Data processing method and device Download PDFInfo
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- CN110544135A CN110544135A CN201910855442.7A CN201910855442A CN110544135A CN 110544135 A CN110544135 A CN 110544135A CN 201910855442 A CN201910855442 A CN 201910855442A CN 110544135 A CN110544135 A CN 110544135A
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- G06Q30/0283—Price estimation or determination
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- 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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0611—Request for offers or quotes
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Abstract
The invention provides a data processing method and a data processing device, wherein the method comprises the following steps: obtaining pricing request information of the vehicle selling source, wherein the pricing request information comprises: vehicle source information on a vehicle source and a selling price in a current selling period; determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range; according to the vehicle source information, a candidate price of the selling price of the vehicle source in the next selling period as the current selling period is determined from the plurality of candidate prices of the vehicle source. By the data processing method and the data processing device, the whole pricing process does not need manual participation, and the accuracy of pricing the vehicle-selling source is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data processing method and device.
background
Currently, after a second-hand vehicle collection operation, a dealer of a second-hand vehicle gives a selling price of a vehicle source to be purchased before the vehicle source is put on a shelf.
the selling price of the vehicle source is determined by the salesman according to the smoothness of the city where the vehicle type of the vehicle source is located, the vehicle receiving price of the vehicle source and the judgment result of the salesman on the vehicle condition of the sold vehicle source.
The salesperson subjectively determines the selling price of the vehicle, which easily causes the selling price of the vehicle source to be inaccurate.
Disclosure of Invention
In order to solve the above problem, embodiments of the present invention provide a data processing method and apparatus.
in a first aspect, an embodiment of the present invention provides a data processing method, including:
obtaining pricing request information of a vehicle source, wherein the pricing request information of the vehicle source comprises: the vehicle source information of the vehicle source and the selling price in the current selling period;
Determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range;
According to the vehicle source information, a candidate price of the selling price of the vehicle source in the next selling period which is the current selling period is determined from the plurality of candidate prices of the vehicle source.
in a second aspect, an embodiment of the present invention further provides a data processing apparatus, including:
The obtaining module is used for obtaining pricing request information of a vehicle selling source, and the pricing request information of the vehicle source comprises: the vehicle source information of the vehicle source and the selling price in the current selling period;
The determining module is used for determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range;
and the processing module is used for determining a candidate price which is the selling price of the vehicle selling source in the next selling period of the current selling period from the plurality of candidate prices of the vehicle selling source according to the vehicle source information.
in a third aspect, the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first aspect.
In a fourth aspect, embodiments of the present invention also provide a data processing apparatus, which includes a memory, a processor, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor to perform the steps of the method according to the first aspect.
In the solutions provided in the foregoing first to fourth aspects of the embodiments of the present invention, a plurality of candidate prices of the vehicle source in the next selling period of the current selling period are determined according to the vehicle source information of the vehicle source and the selling price in the current selling period, and the selling price of the vehicle source in the next selling period of the current selling period is determined from the plurality of candidate prices of the vehicle source, so that compared with a manner in which a service person subjectively determines the selling price of a vehicle in the related art, the selling price of the vehicle source in the next selling period of the current selling period can be obtained according to the vehicle source information of the vehicle source and the selling price in the current selling period, and the whole pricing process does not need manual participation, thereby improving the accuracy of pricing the vehicle source; in addition, the selling price of the vehicle source in different selling periods can be adjusted, the aim of dynamically adjusting the selling price of the vehicle source is fulfilled, and the pricing accuracy is further improved.
in order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram showing the structure of the server applicable to the embodiment of the present invention;
fig. 2 is a flowchart illustrating a data processing method according to embodiment 1 of the present invention;
Fig. 3 is a flowchart illustrating the establishment of a vehicle source pricing model in a server in a data processing method provided in embodiment 1 of the present invention;
Fig. 4 is a schematic structural diagram of a data processing apparatus according to embodiment 2 of the present invention;
fig. 5 is a schematic structural diagram of another data processing apparatus provided in embodiment 3 of the present invention.
Detailed Description
Currently, pricing mechanisms for selling prices of vehicle sources in the used vehicle industry are completely dependent on a salesperson who is familiar and experienced in the industry. And the service staff determines according to the smoothness of the city where the vehicle type of the vehicle source is located, the vehicle receiving price of the vehicle source and the judgment result of the service staff on the vehicle condition of the vehicle source sold. Although the manual pricing method is simple, the following defects exist: firstly, in the pricing process, the salesman is greatly limited by personal previous subjective experience and cognitive deviation,' one person is one price is serious, and a comprehensive, objective and uniform pricing standard cannot be provided for the price of a second-hand vehicle; secondly, the used-vehicle can be seriously influenced by various complex market factors such as new vehicle price, stock, season, market supply and demand and the like, and even a very experienced operator can not dynamically adjust the price of the used-vehicle by tracking the change of the market factors in real time. Therefore, the selling price of the vehicle source is easily inaccurate through the selling price of the vehicle subjectively determined by the service staff. Based on this, the present embodiment provides a data processing method and apparatus, where a plurality of candidate prices of a vehicle source in a next selling period of a current selling period are determined through vehicle source information of the vehicle source and a selling price in the current selling period, and a selling price of the vehicle source in the next selling period of the current selling period is determined from the plurality of candidate prices of the vehicle source, so that the whole pricing process does not need manual participation, and the accuracy of pricing the vehicle source is improved; in addition, the selling price of the vehicle source in different selling periods can be adjusted, the aim of dynamically adjusting the selling price of the vehicle source is fulfilled, and the pricing accuracy is further improved.
Fig. 1 shows a block diagram of the server applicable to the embodiment of the present invention. As shown in fig. 1, the server 200 includes: memory 201, processor 202, and network module 203.
the memory 201 may be used to store software programs and modules, such as program instructions/modules corresponding to the data processing method and apparatus in the embodiment of the present invention, and the processor 202 executes various functional applications and data processing by running the software programs and modules stored in the memory 201, so as to implement the data processing method in the embodiment of the present invention. Memory 201 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. Further, the software programs and modules may further include: an operating system 221 and a service module 222. The operating system 221, which may be LINUX, UNIX, WINDOWS, for example, may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The service module 222 runs on the basis of the operating system 221, and monitors a request from the network through the network service of the operating system 221, completes corresponding data processing according to the request, and returns a processing result to the client. That is, the service module 222 is used to provide network services to clients.
The network module 203 is used for receiving and transmitting network signals. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 1 is merely illustrative and that server 200 may include more or fewer components than shown in fig. 1 or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof. In addition, the server in the embodiment of the present invention may further include a plurality of servers with different specific functions.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
Example 1
In the data processing method provided by this embodiment, the execution main body is the server described above.
In order to price the on-vehicle source, a vehicle source pricing model needs to be established in the server, so that the on-vehicle source is priced through the established vehicle source pricing model, and therefore, before describing the flow of pricing the on-vehicle source proposed in this embodiment, a description needs to be given to how to establish the vehicle source pricing model in the server.
Referring to a flow chart of establishing a vehicle source pricing model in a server in a data processing method proposed by the embodiment shown in fig. 3, a flow of establishing a vehicle source pricing model in a server may perform the following steps:
Step 300, obtaining all vehicle source information and vehicle source selling behavior information, wherein all vehicle source information comprises: the vehicle source information of the vehicle selling source and the vehicle source information of the vehicle already made.
The vehicle source information is stored in the server, and includes but is not limited to: the brand, the model, the train system, the color, the travel mileage, the year of the license plate, the price of the new vehicle, the date of the new vehicle on the shelf and the date of the transaction; and the valuation data, the maintenance record and the insurance record of the vehicle source on a third-party vehicle source platform.
the brand, model, train system, color, mileage and year of the vehicle source are input into the server by the service person through reading the running certificate of the vehicle on sale and the dashboard of the vehicle source.
The new vehicle transaction price is recorded in an invoice issued by a salesperson during the new vehicle transaction according to the vehicle source, and after the salesperson obtains the invoice from the vehicle source, the salesperson inputs the new vehicle transaction price recorded on the invoice and the vehicle source information into the server together.
The third-party vehicle source platform can be, but is not limited to: the second-hand vehicle dealer can use the data such as the evaluation data, the maintenance records and the insurance records on the third-party vehicle source platform under the authorization of the third-party vehicle source platform, and input the data such as the evaluation data, the maintenance records and the insurance records on the third-party vehicle source platform into the server.
the listing date is automatically generated by the server after the vehicle source is listed, and is used for indicating the selling starting date of the vehicle source in the second-hand vehicle merchant platform.
The bargaining date is automatically generated by the server after the bargaining of the vehicle source and is used for indicating the selling completion date of the vehicle source in the second-hand vehicle merchant platform.
After the vehicle source is put on the second-hand vehicle merchant platform, the vehicle source information of the vehicle source can be browsed by a user logging in the second-hand vehicle merchant platform, and therefore vehicle source selling behavior information is generated. Then, the server stores the generated car source selling behavior information.
the vehicle source selling behavior information is stored in the server, and includes but is not limited to: the number of times the vehicle source is viewed by the user, clicked on by the user, and the number of times the user is intended to purchase a build lead, build a work order, and take a test drive.
Step 302, determining the transaction duration of the transaction source according to the shelf date and the transaction date of the transaction source recorded in the vehicle source information of the transaction source.
in the step 302, the transaction duration of the transaction source is a time length obtained by subtracting the date of the transaction from the date of the transaction source, and the server may determine how long the transaction source is on the shelf for transaction according to the transaction duration of the transaction source, so as to label the transaction source.
And 304, preprocessing all the vehicle source information and the vehicle source selling behavior information, and training the transaction duration of the transaction vehicle source and the preprocessed all the vehicle source information and the preprocessed vehicle source selling behavior information by using an artificial intelligence algorithm to obtain a vehicle source pricing model.
In step 304, the artificial intelligence algorithm may adopt a supervised machine learning binary algorithm, which may be but is not limited to: logistic Regression (LR), Support Vector Machine (SVM), extreme gradient boost (XGBOOST), light gradient boost (LightGBM), and Deep Neural Network (DNN).
the above-mentioned process of preprocessing the vehicle source information and the vehicle source selling behavior information includes, but is not limited to: and performing abnormal data elimination, data duplication elimination, data missing item filling, numerical data normalization and one-hot coding expansion on the category data on all the vehicle source information and the vehicle source selling behavior information. The specific processes of the above preprocessing are all realized by the prior art, and are not described in detail here.
The artificial intelligence algorithm is used for training the transaction probability of the vehicle source on the transaction duration of the transaction vehicle source, the preprocessed vehicle source information, the preprocessed vehicle source selling behavior information and the preprocessed label, and based on the transaction probability, the maximum selling price expected value is used as a target to obtain a pricing model of the vehicle source.
After the car source pricing model is established in the server through the process described in the above steps 300 to 304, the following steps 100 to 104 may be executed to adjust the selling price of the car source.
Referring to a flowchart of a data processing method shown in fig. 2, the present embodiment provides a data processing method, including the following specific steps:
Step 100, obtaining pricing request information of a vehicle selling source, wherein the pricing request information comprises: the vehicle source information of the vehicle source and the selling price in the current selling period.
In step 100, the server monitors the selling periods of all the in-service vehicle sources, and when it is determined that the current selling period of the in-service vehicle sources is about to end, the server acquires the vehicle source information of the in-service vehicle sources and the selling price in the current selling period from the vehicle source information stored in the server itself to generate pricing request information of the in-service vehicle sources, thereby triggering the data processing flow proposed in this embodiment.
the server stores the corresponding relation between the vehicle source identification of the vehicle source and the remaining selling time of the current selling period, and when the remaining selling time of the vehicle source in the current selling period is smaller than the selling time threshold value, the current selling period of the vehicle source is determined to be about to end.
In order to facilitate management of the selling process of the vehicles on sale, the time range from the time of putting on the shelf at the vehicle source to the maximum selling days can be divided into a plurality of selling periods; and a sale period (i.e., a sale period length) may be set to any one of 1 day to 10 days as long as the time is obtained in units of days.
the maximum selling days are stored in the server and are used for indicating the maximum selling time of the vehicle source on the second-hand vehicle merchant platform.
In one implementation, if the maximum number of sales days at the vehicle source is 45 days and the sales cycle is set to 5 days, the vehicle source has 9 sales cycles. The server may then adjust the selling price of the source of the vehicle 8 times in addition to the selling price given in the initial selling period to dynamically adjust the selling price of the vehicle being sold.
Step 102, according to the vehicle source information and the selling price in the current selling period of the vehicle source, determining a pricing range of the vehicle source in the next selling period of the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range.
Specifically, in order to determine the pricing range of the vehicle source in the next selling period of the current selling period, the above step 102 may perform the following steps (1) to (2):
(1) Determining the selling price of the vehicle source in the current selling period of the vehicle source as the highest pricing of the vehicle source in the pricing range of the vehicle source in the next selling period of the vehicle source in the current selling period;
(2) And obtaining the lowest transaction price of the transaction vehicle source with the same vehicle source information as the on-sale vehicle source from the vehicle source information of the transaction vehicle source in the current selling period, and determining the lowest transaction price as the vehicle source lowest price of the pricing range of the on-sale vehicle source in the next selling period of the on-sale vehicle source in the current selling period, thereby determining the pricing range of the on-sale vehicle source in the next selling period of the current selling period.
In the step (1), if the vehicle source is a newly-shelved vehicle source, the server determines the new vehicle transaction price of the vehicle source as the highest pricing value of the vehicle sources in the pricing range in the initial selling period of the newly-shelved vehicle source.
in the step (2), the already-delivered vehicle source having the same vehicle source information as the on-sale vehicle source is: the brand, model, vehicle series, color, and year of branding are the same as those of the vehicle source on sale; and the difference between the mileage of travel and the mileage of travel of the vehicle selling source is less than or equal to the mileage threshold value.
the transaction source in the current selling period means that the transaction date of the transaction source is in the current selling period.
if the consignment vehicle source with the same vehicle source information as the current vehicle source is not inquired in the current selling period, the server multiplies the highest vehicle source pricing of the pricing range of the vehicle source in the next selling period of the current selling period by the price adjusting coefficient stored in the server to obtain the lowest vehicle source pricing.
The price adjustment coefficient may be any value between 0.8 and 1.
and 104, according to the vehicle source information, determining a candidate price which is the selling price of the vehicle source in the next selling period of the current selling period from the plurality of candidate prices of the vehicle source.
Specifically, in the step 104, the following steps (1) to (3) may be performed:
(1) Obtaining a selling period length, and inputting the selling period length, the vehicle source information of the vehicle source and the candidate prices into a vehicle source pricing model to obtain a transaction probability of each candidate price in the candidate prices of the vehicle source in the next selling period of the current selling period;
(2) Respectively calculating the selling price expectation value of each candidate price of the vehicle source in the next selling period of the current selling period according to each candidate price of the vehicle source in the next selling period of the current selling period, the bargaining probability of each candidate price and the vehicle source lowest price of the pricing range of the vehicle source;
(3) and determining the candidate price corresponding to the maximum selling price expectation value in the calculated selling price expectation values of the candidate prices as the selling price of the vehicle source in the next selling period of the current selling period.
In the step (1), the selling period length, the vehicle source information of the vehicle source and the plurality of candidate prices are input into a vehicle source pricing model, and a transaction probability of each candidate price in the plurality of candidate prices of the vehicle source in the next selling period of the current selling period is obtained.
In the above step (2), the selling price expectation value of each of the candidate prices is calculated by the following formula:
E＝Pr(m,n)*m+(1-Pr(m,n))*m_b
Wherein E represents a selling price expectation value; m represents a candidate price; pr (m, n) represents a probability of a deal of the candidate price in a next sale period of the current sale period; m _ b represents the vehicle source minimum pricing of the pricing range of the vehicle source in the next selling period of the current selling period of the vehicle source.
the selling price expectation value is used for indicating the probability mathematical expectation of the price sold according to the candidate price in the next selling period of the current selling period.
Therefore, the greater the expected selling price expectation value is calculated, the greater the possible revenue of the vehicle source sold according to the candidate price is, and the greater the profit is.
therefore, the expected selling price value of each candidate price can be obtained through the formula.
in the step (3), the candidate prices are ranked according to the calculated expected selling price value from large to small or from small to large, so that the candidate price corresponding to the maximum expected selling price value is determined, and the candidate price corresponding to the maximum expected selling price value is determined as the selling price of the vehicle source in the next selling period of the current selling period.
in summary, according to the data processing method provided by this embodiment, a plurality of candidate prices of the vehicle source in the next selling period of the current selling period are determined through the vehicle source information of the vehicle source and the selling price in the current selling period, and the selling price of the vehicle source in the next selling period of the current selling period is determined from the plurality of candidate prices of the vehicle source, compared with a manner in which a service person subjectively determines the selling price of a vehicle in the related art, the selling price of the vehicle source in the next selling period of the current selling period can be obtained through the vehicle source information of the vehicle source and the selling price in the current selling period, and the entire pricing process does not need manual participation, thereby improving the accuracy of pricing the vehicle source; in addition, the selling price of the vehicle source in different selling periods can be adjusted, the aim of dynamically adjusting the selling price of the vehicle source is fulfilled, and the pricing accuracy is further improved.
Based on the same inventive concept, embodiments of the present application further provide a data processing method and a corresponding data processing apparatus, and as the principle of solving the problem of the apparatus in the embodiments of the present application is similar to the data processing method described in embodiment 1 of the present application, the implementation of the apparatus may refer to the implementation of the foregoing data processing method, and repeated details are omitted.
Example 2
Referring to a schematic structural diagram of a data processing apparatus shown in fig. 4, the data processing apparatus proposed in this embodiment includes:
An obtaining module 400, configured to obtain pricing request information of a vehicle selling source, where the pricing request information includes: the vehicle source information of the vehicle source and the selling price in the current selling period;
A determining module 402, configured to determine, according to vehicle source information and a selling price in a current selling cycle of the vehicle source, a pricing range of the vehicle source in a next selling cycle of the current selling cycle, and obtain, from the pricing range, a plurality of candidate prices of the vehicle source in the next selling cycle of the current selling cycle;
A processing module 404, configured to determine, according to the vehicle source information, a candidate price that is a selling price of the in-vehicle source in a next selling period of the current selling period from the plurality of candidate prices of the in-vehicle source.
The determining module 402 is specifically configured to: determining the selling price of the vehicle source in the current selling period of the vehicle source as the highest pricing of the vehicle source in the pricing range of the vehicle source in the next selling period of the vehicle source in the current selling period;
and obtaining the lowest transaction price of the transaction vehicle source with the same vehicle source information as the on-sale vehicle source from the vehicle source information of the transaction vehicle source in the current selling period, and determining the lowest transaction price as the vehicle source lowest price of the pricing range of the on-sale vehicle source in the next selling period of the on-sale vehicle source in the current selling period, thereby determining the pricing range of the on-sale vehicle source in the next selling period of the current selling period.
the processing module 404 is specifically configured to: obtaining a selling period length, and inputting the selling period length, the vehicle source information of the vehicle source and the candidate prices into a vehicle source pricing model to obtain a transaction probability of each candidate price in the candidate prices of the vehicle source in the next selling period of the current selling period;
Respectively calculating the selling price expectation value of each candidate price of the vehicle source in the next selling period of the current selling period according to each candidate price of the vehicle source in the next selling period of the current selling period, the bargaining probability of each candidate price and the vehicle source lowest price of the pricing range of the vehicle source;
and determining the candidate price corresponding to the maximum selling price expectation value in the calculated selling price expectation values of the candidate prices as the selling price of the vehicle source in the next selling period of the current selling period.
In summary, according to the data processing apparatus provided by this embodiment, a plurality of candidate prices of the vehicle source in the next selling period of the current selling period are determined through the vehicle source information of the vehicle source and the selling price in the current selling period, and the selling price of the vehicle source in the next selling period of the current selling period is determined from the plurality of candidate prices of the vehicle source, compared with a manner in which a service person subjectively determines the selling price of a vehicle in the related art, the selling price of the vehicle source in the next selling period of the current selling period can be obtained through the vehicle source information of the vehicle source and the selling price in the current selling period, and the entire pricing process does not need manual participation, thereby improving the accuracy of pricing the vehicle source; in addition, the selling price of the vehicle source in different selling periods can be adjusted, the aim of dynamically adjusting the selling price of the vehicle source is fulfilled, and the pricing accuracy is further improved.
based on the same inventive concept, embodiments of the present application further provide a data processing method and a corresponding computer storage medium and a corresponding data processing apparatus, and as the principles of solving the problems of the computer storage medium and the apparatus in the embodiments of the present application are similar to those of the data processing method described in embodiment 1 of the present application, the apparatus may be implemented by referring to the implementation of the data processing method, and repeated details are omitted.
Example 3
The present embodiment proposes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the data processing method described in embodiment 1 above. For specific implementation, refer to method embodiment 1, which is not described herein again.
In addition, referring to another schematic structural diagram of the data processing apparatus shown in fig. 5, the present embodiment further provides a data processing apparatus, which includes a bus 51, a processor 52, a transceiver 53, a bus interface 54, a memory 55, and a user interface 56. The data processing means comprise a memory 55.
In this embodiment, the data processing apparatus further includes: one or more programs stored on the memory 55 and executable on the processor 52, configured to be executed by the processor for performing the following steps (1) to (3):
(1) obtaining pricing request information of a vehicle selling source, wherein the pricing request information comprises: the vehicle source information of the vehicle source and the selling price in the current selling period;
(2) determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range;
(3) According to the vehicle source information, a candidate price of the selling price of the vehicle source in the next selling period which is the current selling period is determined from the plurality of candidate prices of the vehicle source.
a transceiver 53 for receiving and transmitting data under the control of the processor 52.
In fig. 5, a bus architecture (represented by bus 51), bus 51 may include any number of interconnected buses and bridges, with bus 51 linking together various circuits including one or more processors, represented by general purpose processor 52, and memory, represented by memory 55. The bus 51 may also link various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further in this embodiment. A bus interface 54 provides an interface between the bus 51 and the transceiver 53. The transceiver 53 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 53 receives external data from other devices. The transceiver 53 is used for transmitting data processed by the processor 52 to other devices. Depending on the nature of the computing system, a user interface 56, such as a keypad, display, speaker, microphone, joystick, may also be provided.
The processor 52 is responsible for managing the bus 51 and the usual processing, running a general-purpose operating system as described above. And memory 55 may be used to store data used by processor 52 in performing operations.
Alternatively, processor 52 may be, but is not limited to: a central processing unit, a singlechip, a microprocessor or a programmable logic device.
It will be appreciated that the memory 55 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 55 of the systems and methods described in this embodiment is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 55 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof: an operating system 551 and application programs 552.
the operating system 551 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 552 includes various applications, such as a Media Player (Media Player), a Browser (Browser), and the like, for implementing various application services. A program implementing the method of an embodiment of the present invention may be included in the application 552.
In summary, the computer-readable storage medium and the data processing apparatus provided in this embodiment determine, through the vehicle source information of the vehicle source and the selling price in the current selling period, a plurality of candidate prices of the vehicle source in the next selling period of the current selling period, and determine, from the plurality of candidate prices of the vehicle source, the selling price of the vehicle source in the next selling period of the current selling period, compared with a manner in which a service person subjectively determines the selling price of a vehicle in the related art, the selling price of the vehicle source in the next selling period of the current selling period can be obtained through the vehicle source information of the vehicle source and the selling price in the current selling period, and the entire pricing process does not need human intervention, thereby improving the accuracy of pricing the vehicle source; in addition, the selling price of the vehicle source in different selling periods can be adjusted, the aim of dynamically adjusting the selling price of the vehicle source is fulfilled, and the pricing accuracy is further improved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a data processing method, comprising:
obtaining pricing request information of a vehicle selling source, wherein the pricing request information comprises: the vehicle source information of the vehicle source and the selling price in the current selling period;
Determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range;
According to the vehicle source information, a candidate price of the selling price of the vehicle source in the next selling period which is the current selling period is determined from the plurality of candidate prices of the vehicle source.
2. The method of claim 1, wherein determining a pricing range of the vehicle source in a next selling period of the current selling period according to the vehicle source information and a selling price of the vehicle source in the current selling period, and obtaining a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range comprises:
Determining the selling price of the vehicle source in the current selling period of the vehicle source as the highest pricing of the vehicle source in the pricing range of the vehicle source in the next selling period of the vehicle source in the current selling period;
And obtaining the lowest transaction price of the transaction vehicle source with the same vehicle source information as the on-sale vehicle source from the vehicle source information of the transaction vehicle source in the current selling period, and determining the lowest transaction price as the vehicle source lowest price of the pricing range of the on-sale vehicle source in the next selling period of the on-sale vehicle source in the current selling period, thereby determining the pricing range of the on-sale vehicle source in the next selling period of the current selling period.
3. The method of claim 1, wherein determining a candidate price for the vehicle source in a next sale period of the current sale period from the plurality of candidate prices for the vehicle source based on the vehicle source information comprises:
Obtaining a selling period length, and inputting the selling period length, the vehicle source information of the vehicle source and the candidate prices into a vehicle source pricing model to obtain a transaction probability of each candidate price in the candidate prices of the vehicle source in the next selling period of the current selling period;
Respectively calculating the selling price expectation value of each candidate price of the vehicle source in the next selling period of the current selling period according to each candidate price of the vehicle source in the next selling period of the current selling period, the bargaining probability of each candidate price and the vehicle source lowest price of the pricing range of the vehicle source;
And determining the candidate price corresponding to the maximum selling price expectation value in the calculated selling price expectation values of the candidate prices as the selling price of the vehicle source in the next selling period of the current selling period.
4. The method of claim 3, wherein calculating the expected selling price value of each candidate price of the on-vehicle source in the next selling period of the current selling period according to each candidate price of the on-vehicle source in the next selling period of the current selling period, the bargaining probability of each candidate price, and the vehicle source minimum pricing of the pricing range of the on-vehicle source respectively comprises:
calculating a selling price expectation value of each candidate price by the following formula:
E＝Pr(m,n)*m+(1-Pr(m,n))*m_b
wherein E represents a selling price expectation value; m represents a candidate price; pr (m, n) represents a probability of a deal of the candidate price in a next sale period of the current sale period; m _ b represents the vehicle source minimum pricing of the pricing range of the vehicle source in the next selling period of the current selling period of the vehicle source.
5. the method of claim 1, wherein the vehicle source information comprises: shelf dates and deal dates;
The method further comprises the following steps:
Obtain all car source information and car source and sell the action information, all car source information includes: the vehicle source information of the vehicle selling source and the vehicle source information of the vehicle already made traffic source;
Determining the transaction duration of the transaction source according to the listing date and the transaction date of the transaction source recorded in the vehicle source information of the transaction source;
And preprocessing all the vehicle source information and the vehicle source selling behavior information, and training the transaction duration of the transaction vehicle source and the preprocessed all the vehicle source information and the vehicle source selling behavior information by using an artificial intelligence algorithm to obtain a vehicle source pricing model.
6. A data processing apparatus, comprising:
An obtaining module, configured to obtain pricing request information of a vehicle selling source, where the pricing request information includes: the vehicle source information of the vehicle source and the selling price in the current selling period;
the determining module is used for determining a pricing range of the vehicle source in the next selling period of the current selling period according to the vehicle source information and the selling price of the vehicle source in the current selling period, and acquiring a plurality of candidate prices of the vehicle source in the next selling period of the current selling period from the pricing range;
And the processing module is used for determining a candidate price which is the selling price of the vehicle selling source in the next selling period of the current selling period from the plurality of candidate prices of the vehicle selling source according to the vehicle source information.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
determining the selling price of the vehicle source in the current selling period of the vehicle source as the highest pricing of the vehicle source in the pricing range of the vehicle source in the next selling period of the vehicle source in the current selling period;
And obtaining the lowest transaction price of the transaction vehicle source with the same vehicle source information as the on-sale vehicle source from the vehicle source information of the transaction vehicle source in the current selling period, and determining the lowest transaction price as the vehicle source lowest price of the pricing range of the on-sale vehicle source in the next selling period of the on-sale vehicle source in the current selling period, thereby determining the pricing range of the on-sale vehicle source in the next selling period of the current selling period.
8. The apparatus of claim 6, wherein the processing module is specifically configured to:
Obtaining a selling period length, and inputting the selling period length, the vehicle source information of the vehicle source and the candidate prices into a vehicle source pricing model to obtain a transaction probability of each candidate price in the candidate prices of the vehicle source in the next selling period of the current selling period;
respectively calculating the selling price expectation value of each candidate price of the vehicle source in the next selling period of the current selling period according to each candidate price of the vehicle source in the next selling period of the current selling period, the bargaining probability of each candidate price and the vehicle source lowest price of the pricing range of the vehicle source;
and determining the candidate price corresponding to the maximum selling price expectation value in the calculated selling price expectation values of the candidate prices as the selling price of the vehicle source in the next selling period of the current selling period.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 5.
10. a data processing apparatus comprising a memory, a processor and one or more programs, wherein the one or more programs are stored in the memory and configured to cause the processor to perform the steps of the method of any of claims 1-5.
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