Second-hand vehicle auction packaging processing method and device, computer equipment and medium

Abstract

The invention provides a packaging processing method and device for second-hand vehicle auction, computer equipment and a medium, and the method comprises the steps: obtaining an auction vehicle database which comprises historical transaction information corresponding to a plurality of first auction vehicles; based on the historical transaction information corresponding to each first auction vehicle, determining an auction transaction estimation probability corresponding to each first auction vehicle; based on the auction transaction pre-estimated probability corresponding to each first auction vehicle, performing probability grouping on the plurality of first auction vehicles to obtain a target vehicle group and a candidate vehicle group; and packaging and combining each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group to obtain a packaging processing result of the second-hand vehicle auction. Therefore, the second-hand vehicles to be auctioned are combined and packaged, so that the auction duration of the second-hand vehicles is effectively optimized, multiple vehicles can be auctioned at a time, and the auction efficiency of the second-hand vehicles is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the technical field of used car auctions, and in particular, to a packaging processing method, apparatus, computer equipment, and medium applicable to a used car auction. Background Art

[0002] In the used car industry, there are many ways to auction vehicles, such as simultaneous auctions, online auctions, offline auctions, fixed-price auctions, etc. Whether it is determining the auction start time, auction duration, auction venue or other auction information, it is all for the purpose of displaying vehicle information one by one in a designated way for car dealers to bid for the auction.

[0003] No matter which vehicle auction model is used in the existing technology, vehicles are presented to dealers one by one, and dealers can only choose one by one. With the rapid development of society, people's time is becoming more and more precious, and the pace of life is getting faster and faster. The existing vehicle auction model of one by one will have the problem of long vehicle auction time, which affects the efficiency of second-hand vehicle auction. Summary of the invention

[0004] The embodiments described herein provide a packaging processing method, apparatus, computer device, and medium for a used vehicle auction, which overcome the above-mentioned problems.

[0005] In a first aspect, according to the present disclosure, a packaging method for a used vehicle auction is provided, comprising:

[0006] Acquire an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a second-hand vehicle;

[0007] Determining an estimated auction success probability corresponding to each of the first auction vehicles based on historical transaction information corresponding to each of the first auction vehicles;

[0008] Based on the estimated auction success probability corresponding to each of the first auction vehicles, a plurality of the first auction vehicles are probability grouped to obtain a target vehicle group and a candidate vehicle group, wherein the target vehicle group includes: at least one second auction vehicle, the estimated auction success probability corresponding to the second auction vehicle is greater than or equal to a preset probability, and the second auction vehicle corresponds to one of the first auction vehicles; and the candidate vehicle group includes: at least one third auction vehicle, the estimated auction success probability corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another of the first auction vehicles;

[0009] Packing and combining each of the second auction vehicles included in the target vehicle group and each of the third auction vehicles included in the candidate vehicle group to obtain a packing processing result of the second-hand vehicle auction;

[0010] The packaging processing result includes: multiple vehicle auction packages, each of which includes: at least one of the second auction vehicles and at least one of the third auction vehicles, or at least one of the second auction vehicles, or at least one of the third auction vehicles.

[0011] In a second aspect, according to the present disclosure, a packaging and processing device for a second-hand vehicle auction is provided, comprising:

[0012] An acquisition module, used to acquire an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a second-hand vehicle;

[0013] A determination module, configured to determine an estimated auction transaction probability corresponding to each of the first auction vehicles based on historical transaction information corresponding to each of the first auction vehicles;

[0014] a grouping module, configured to perform probability grouping of a plurality of the first auction vehicles based on the estimated auction success probability corresponding to each of the first auction vehicles, to obtain a target vehicle group and a candidate vehicle group, wherein the target vehicle group includes: at least one second auction vehicle, the estimated auction success probability corresponding to the second auction vehicle is greater than or equal to a preset probability, and the second auction vehicle corresponds to one of the first auction vehicles; and the candidate vehicle group includes: at least one third auction vehicle, the estimated auction success probability corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another of the first auction vehicles;

[0015] a combining module, used for packaging and combining each of the second auction vehicles included in the target vehicle group and each of the third auction vehicles included in the candidate vehicle group, to obtain a packaging processing result of the second-hand vehicle auction;

[0016] The packaging processing result includes: multiple vehicle auction packages, each of which includes: at least one of the second auction vehicles and at least one of the third auction vehicles, or at least one of the second auction vehicles, or at least one of the third auction vehicles.

[0017] In a third aspect, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the packaging and processing method for used vehicle auctions in any of the above embodiments are implemented.

[0018] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the packaging and processing method for used vehicle auctions in any of the above embodiments are implemented.

[0019] The packaging processing method for a used vehicle auction provided in an embodiment of the present application obtains an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a used vehicle; based on the historical transaction information corresponding to each first auction vehicle, an estimated probability of auction transaction corresponding to each first auction vehicle is determined; based on the estimated probability of auction transaction corresponding to each first auction vehicle, a plurality of first auction vehicles are probability grouped to obtain a target vehicle group and a candidate vehicle group, wherein the target vehicle group includes: at least one second auction vehicle, wherein the estimated probability of auction transaction corresponding to the second auction vehicle is greater than or equal to a preset probability, and the second auction vehicle corresponds to a first auction vehicle; and the candidate vehicle group includes: at least one third auction vehicle, wherein the estimated probability of auction transaction corresponding to the third auction vehicle is less than a preset probability, and the third auction vehicle corresponds to another first auction vehicle; and each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group are packaged and combined to obtain a packaging processing result of the used vehicle auction; wherein the packaging processing result includes: a plurality of vehicle auction packages, wherein each vehicle auction package includes: at least one second auction vehicle and at least one third auction vehicle, or at least one second auction vehicle, or at least one third auction vehicle. In this way, by combining and packaging the used vehicles to be auctioned, the auction time of used vehicles can be effectively optimized, so that multiple vehicles can be auctioned at a time, thereby improving the efficiency of used vehicle auctions.

[0020] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be noted that the drawings described below only relate to some embodiments of the present disclosure, but are not intended to limit the present disclosure, wherein:

[0022] Figure 1 It is a flow chart of a packaging processing method for a used vehicle auction provided by the present invention.

[0023] Figure 2It is a structural schematic diagram of a packaging and processing device for second-hand vehicle auction provided by the present invention.

[0024] Figure 3 It is a structural schematic diagram of a computer device provided by the present disclosure.

[0025] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work also fall within the scope of protection of the present disclosure.

[0027] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by a person skilled in the art to which the subject matter of the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant art, and will not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. As used herein, a statement that two or more parts are "connected" or "coupled" together shall mean that the parts are joined together directly or through one or more intermediate components.

[0028] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase "embodiments" in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists, A and B exist at the same time, and B exists. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. Terms such as "first" and "second" are only used to distinguish one component (or a part of a component) from another component (or another part of a component).

[0030] In the description of the present application, unless otherwise specified, "plurality" means more than two (including two), and similarly, "multiple groups" means more than two groups (including two).

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0032] Figure 1 is a flow chart of a packaging processing method for a second-hand vehicle auction provided by an embodiment of the present disclosure, such as Figure 1 As shown, the specific process of the packaging processing method for second-hand vehicle auctions includes:

[0033] S110 , obtaining an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles.

[0034] Among them, the first auction vehicle is used to describe a second-hand vehicle.

[0035] Each first auction vehicle will have its unique corresponding historical transaction information. The historical transaction information corresponding to the first auction vehicle can be used to describe the multi-category attribute data of the first auction vehicle when it is sold for the first time, such as vehicle brand and series, vehicle condition, total mileage, new car guide price, actual selling price, vehicle engine type, etc.

[0036] S120: Determine an estimated auction success probability corresponding to each first auction vehicle based on historical transaction information corresponding to each first auction vehicle.

[0037] The estimated probability of successful auction of the first auction vehicle is the estimated probability of successful auction of the first auction vehicle in a used car auction.

[0038] In some embodiments, the historical transaction information corresponding to each first auction vehicle includes: two categories of transaction data. The two categories of transaction data may include any two of the following data: vehicle brand and series, vehicle condition, vehicle total mileage, vehicle new car guide price, vehicle actual selling price, and vehicle engine type.

[0039] Specifically, when determining the historical transaction information corresponding to a first auction vehicle, two of the multiple category attribute data may be selected as the corresponding category transaction data. It is understandable that the two category transaction data corresponding to different first auction vehicles may not be the same category of data, that is, the two category transaction data corresponding to a first auction vehicle are: vehicle brand and series, vehicle condition, and the two category transaction data corresponding to another first auction vehicle are: vehicle brand and series, vehicle total mileage.

[0040] When determining the two categories of transaction data corresponding to the first auction vehicle, it can be determined by the priority ranking level coefficient of each category attribute data in the same brand, such as selecting the two category attribute data with the highest priority ranking level coefficient as the corresponding two category transaction data.

[0041] For example, if the priority ranking coefficients of each category of attribute data corresponding to a first auction vehicle in the same brand are: vehicle brand and series is 0.8, vehicle condition is 0.7, vehicle total mileage is 0.5, vehicle new car suggested retail price is 0.4, vehicle actual selling price is 0.6, and vehicle engine type is 0.8, then the two categories of transaction data corresponding to this first auction vehicle are: vehicle brand and series, and vehicle engine type.

[0042] Based on the historical transaction information corresponding to each first auction vehicle, determining the estimated probability of auction transaction corresponding to each first auction vehicle includes:

[0043] The two categories of transaction data corresponding to each first auction vehicle are represented by feature vectors respectively to obtain two category feature vectors corresponding to each first auction vehicle; based on the vector similarity between the two category feature vectors corresponding to each first auction vehicle, the auction transaction estimated probability corresponding to each first auction vehicle is determined.

[0044] The two categories of transaction data corresponding to each first auction vehicle can be represented by feature vectors through a natural language processing model, and the text words can be mapped to a low-dimensional vector space to obtain two category feature vectors corresponding to each first auction vehicle. The natural language processing model may include but is not limited to: Word Embedding model, TF-IDF (Term Frequency-Inverse Document Frequency) model, and N-gram model.

[0045] The vector similarity between the two category feature vectors corresponding to each first auction vehicle can be expressed by cosine similarity, that is, the similarity is 1 when the angle between the two vectors is 0, and the similarity is 0 when the angle between the two vectors is 90°. The value range of the vector similarity between the two category feature vectors corresponding to each first auction vehicle is [-1, 1]. The specific formula is shown in the following formula (1).

[0046]

[0047] In formula (1), Si is the vector similarity between the two category feature vectors corresponding to the first auction vehicle; A is one of the two category feature vectors corresponding to the first auction vehicle; and B is the other of the two category feature vectors corresponding to the first auction vehicle.

[0048] Based on the vector similarity between the two category feature vectors corresponding to each first auction vehicle, determining the estimated probability of auction success corresponding to each first auction vehicle may include: if the vector similarity between the two category feature vectors corresponding to a first auction vehicle is less than or equal to a preset threshold (such as 0.1), then the estimated probability of auction success corresponding to the first auction vehicle is the preset threshold; if the vector similarity between the two category feature vectors corresponding to a first auction vehicle is greater than the preset threshold, then the estimated probability of auction success corresponding to the first auction vehicle is the corresponding vector similarity.

[0049] The estimated probability P of auction success corresponding to the first auction vehicle is specifically shown in the following formula (2).

[0050]

[0051] Therefore, by representing the two categories of transaction data corresponding to each first auction vehicle with feature vectors, the auction transaction estimated probability corresponding to each first auction vehicle can be effectively determined according to vector similarity.

[0052] S130 . Probabilistically grouping the plurality of first auction vehicles based on the estimated auction success probability corresponding to each first auction vehicle to obtain a target vehicle group and a candidate vehicle group.

[0053] Among them, the target vehicle group includes: at least one second auction vehicle, the estimated probability of auction success corresponding to the second auction vehicle is greater than or equal to the preset probability, and the second auction vehicle corresponds to a first auction vehicle; the candidate vehicle group includes: at least one third auction vehicle, the estimated probability of auction success corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another first auction vehicle.

[0054] Based on the estimated auction success probability corresponding to each first auction vehicle, a plurality of first auction vehicles are probability grouped to obtain a target vehicle group and a candidate vehicle group, which may include: if the estimated auction success probability corresponding to a first auction vehicle is greater than or equal to a preset probability, then the first auction vehicle is grouped into the target vehicle group; if the estimated auction success probability corresponding to a first auction vehicle is less than a preset probability, then the first auction vehicle is grouped into the candidate vehicle group. The preset probability may be 0.6.

[0055] S140: Pack and combine each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group to obtain a packing result of the second-hand vehicle auction.

[0056] The packaging processing result includes: a plurality of vehicle auction packages, each vehicle auction package includes: at least one second auction vehicle and at least one third auction vehicle, or at least one second auction vehicle, or at least one third auction vehicle.

[0057] In some embodiments, the multiple second auction vehicles included in the target vehicle group are arranged in descending order according to the estimated probability of auction success, and the multiple third auction vehicles included in the candidate vehicle group are arranged in descending order according to the estimated probability of auction success.

[0058] Each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group are packaged and combined to obtain a packaged processing result of the second-hand vehicle auction, including:

[0059] A second auction vehicle with the highest estimated probability of auction success in the current state is selected from the target vehicle group in turn, and at least one third auction vehicle with the highest estimated probability of auction success in the current state is selected from the candidate vehicle group to form multiple vehicle auction packages, and the estimated probability of auction success corresponding to the vehicle auction packages is less than or equal to the preset probability.

[0060] Among them, when determining a vehicle auction package, it is obtained by combining a second auction vehicle and at least one third auction vehicle, that is, in this process, a vehicle auction package includes a second auction vehicle and one or two or more third auction vehicles.

[0061] If the target vehicle group no longer has a second auction vehicle in the current state, and there is still at least one third auction vehicle in the candidate vehicle group in the current state, a vehicle auction package is formed based on the at least one third auction vehicle; if the candidate vehicle group no longer has a third auction vehicle in the current state, and there is still at least one second auction vehicle in the target vehicle group in the current state, a vehicle auction package is formed based on the at least one second auction vehicle.

[0062] Correspondingly, if the target vehicle group no longer has a second auction vehicle in the current state, and the candidate vehicle group no longer has a third auction vehicle in the current state, it is determined that the second-hand vehicle auction result packaging is completed.

[0063] In some embodiments, a second auction vehicle with the highest estimated probability of auction success in the current state is selected from the target vehicle group, and at least one third auction vehicle with the highest estimated probability of auction success in the current state is selected from the candidate vehicle group to form multiple vehicle auction packages, including:

[0064] A second auction vehicle with the highest estimated probability of auction success in the current state is selected from the target vehicle group, and a third auction vehicle with the highest estimated probability of auction success in the current state is selected from the candidate vehicle group to form a vehicle combination package; based on the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected third auction vehicle, and the packing coefficient associated with the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected second auction vehicle is determined; if the estimated probability of auction success corresponding to the vehicle combination package is greater than the preset probability, another third auction vehicle with the highest estimated probability of auction success in the current state is continuously selected from the candidate vehicle group, and the vehicle combination package is updated until the estimated probability of auction success corresponding to the vehicle combination package is less than or equal to the preset probability; continue to perform the following operations: select a vehicle in the current state from the target vehicle group A second auction vehicle with the highest estimated probability of auction success in the next auction, and a third auction vehicle with the highest estimated probability of auction success in the current state are selected from the candidate vehicle group to form a vehicle combination package; the estimated probability of auction success corresponding to the vehicle combination package is determined based on the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected third auction vehicle, and the packing coefficient associated with the estimated probability of auction success corresponding to the selected second auction vehicle; if the estimated probability of auction success corresponding to the vehicle combination package is greater than the preset probability, another third auction vehicle with the highest estimated probability of auction success in the current state is continuously selected from the candidate vehicle group, and the vehicle combination package is updated until the estimated probability of auction success corresponding to the vehicle combination package is less than or equal to the preset probability; until there is no second auction vehicle in the target vehicle group, or there is no third auction vehicle in the candidate vehicle group.

[0065] For example, the target vehicle group is group X and the candidate vehicle group is group Y. The execution steps are as follows:

[0066] Step 1: Take the first second auction vehicle X1 from group X, and then take the first third auction vehicle Y1 from group Y, combine X1 and Y1 into a vehicle package, and calculate the auction success estimated probability P(X1Y1) corresponding to the vehicle package composed of X1 and Y1 by using the auction success estimated probability P1 of X1, the auction success estimated probability P2 of X2, and the packing coefficient R1 associated with P1, as shown in the following formula (3).

[0067] P(X1Y1)=P(X1)*P(Y1)*R (3)

[0068] If P(X1Y1)>0.6, proceed to step 2.

[0069] Step 2: Continue to take the third auction vehicle Y2 from group Y to update the vehicle combination package, and calculate the estimated auction success probability P(X1Y1Y2) corresponding to the updated vehicle combination package; if P(X1Y1Y2)≤0.6, execute step 3; if P(X1Y1Y2)>0.6, continue to execute step 2 until P(X1Y1Y2...)≤0.6.

[0070] Step 3: Take a second auction vehicle X2 from group X and a third auction vehicle Y3 from group Y to form a vehicle package. Continue to execute according to the principles of step 1 and step 2 until there is no corresponding auction vehicle in group X or group Y.

[0071] The packing factor is shown in Table 1 below.

[0072] Table 1 Packing coefficient table

[0073] Estimated probability of auction success (P) Packing factor (R) P<0.1 8 0.1≤P<0.2 4.5 0.2≤P<0.3 3 0.3≤P<0.4 2.5 0.5≤P<0.6 1.2 P≥0.6 1

[0074] Therefore, a binary search algorithm is used to realize the number of vehicles and specific vehicles in a combined auction package, effectively improving the efficiency of vehicle matching and reducing invalid matching.

[0075] In this embodiment, an auction vehicle database is obtained, the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, the first auction vehicle is used to describe a second-hand vehicle; based on the historical transaction information corresponding to each first auction vehicle, the auction transaction estimated probability corresponding to each first auction vehicle is determined; based on the auction transaction estimated probability corresponding to each first auction vehicle, the plurality of first auction vehicles are probability grouped to obtain a target vehicle group and a candidate vehicle group, the target vehicle group includes: at least one second auction vehicle, the auction transaction estimated probability corresponding to the second auction vehicle is greater than or equal to a preset probability, the second auction vehicle corresponds to a first auction vehicle, the candidate vehicle group includes: at least one third auction vehicle, the auction transaction estimated probability corresponding to the third auction vehicle is less than the preset probability, the third auction vehicle corresponds to another first auction vehicle; each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group are packaged and combined to obtain a packaged processing result of the second-hand vehicle auction; wherein the packaged processing result includes: a plurality of vehicle auction packages, each vehicle auction package includes: at least one second auction vehicle and at least one third auction vehicle, or, at least one second auction vehicle, or, at least one third auction vehicle. In this way, by combining and packaging the used vehicles to be auctioned, the auction time of used vehicles can be effectively optimized, so that multiple vehicles can be auctioned at a time, thereby improving the efficiency of used vehicle auctions.

[0076] In some embodiments, the method of this embodiment further includes:

[0077] If it is detected that the category transaction data corresponding to the first auction vehicle changes, the category change data corresponding to the first auction vehicle is obtained; based on the category change data corresponding to the first auction vehicle, the category transaction data corresponding to the first auction vehicle is updated.

[0078] Among them, due to the temporary adjustment of the latest market fluctuations, the second-hand car industry directly affects the transaction of the auction items with the change of vehicle market conditions. For example, the impact of the price reduction of new energy vehicles on fuel vehicles, the sample data itself has a lag, and the market fluctuations will also affect the transaction probability of the auction. At this time, by updating the category transaction data corresponding to the first auction vehicle in real time, it is convenient to effectively avoid the impact on the accuracy of the data caused by the packaging efficiency of second-hand cars.

[0079] In addition, when it is detected that the category transaction data corresponding to the first auction vehicle changes, the used car combination packaging operation can also be carried out by manual packaging.

[0080] In some embodiments, the method of this embodiment further includes:

[0081] If it is detected that the category transaction data corresponding to the first auction vehicle changes, the category change data corresponding to the first auction vehicle is obtained; based on the category change data corresponding to the first auction vehicle, the packaging processing result of the second-hand vehicle auction is adjusted.

[0082] Among them, based on the category change data corresponding to the first auction vehicle, the packaging processing results of the second-hand vehicle auction are adjusted, which may include: if the analog change data corresponding to a first auction vehicle indicates a data decrease, the first auction vehicle is deleted from the vehicle auction package corresponding to the first auction vehicle, and a new vehicle auction package is assembled based on the first auction vehicle; if the analog change data corresponding to a first auction vehicle indicates an increase in data, the first auction vehicle is deleted from the vehicle auction package corresponding to the first auction vehicle, and the first auction vehicle is added to a vehicle auction package with the lowest estimated probability of auction success, so as to balance the estimated probability of auction success of this vehicle auction package and further improve the efficiency of vehicle auctions.

[0083] In some embodiments, the method of this embodiment further includes:

[0084] Acquire additional information of the vehicle auctioneer at the second-hand vehicle auction, where the additional information is used to describe the packaging requirements for multiple first auction vehicles at the auction; and adjust the packaging processing results of the second-hand vehicle auction based on the additional information of the vehicle auctioneer at the second-hand vehicle auction.

[0085] Among them, the additional information of the vehicle auctioneer may be: one package auction for the entire session, designated vehicles included in the package auction, etc.

[0086] Based on the additional information of the vehicle auctioneer at the second-hand vehicle auction, the packaging processing results of the second-hand vehicle auction are adjusted, which may include: if the additional information of the vehicle auctioneer is one package auction for the entire session, all auction vehicles are merged into one vehicle auction package; if the additional information of the vehicle auctioneer is to auction designated vehicles in a package, the designated vehicles are taken out from their corresponding vehicle auction packages respectively, and combined into a new vehicle auction package, so as to quickly adjust the packaging processing results of the second-hand vehicle auction according to the auction needs of the vehicle auctioneer.

[0087] This embodiment estimates the probability of the vehicle being sold based on the vehicle condition, historical transaction price, valuation, reserve price and other information of the vehicle in this session. If the probability is greater than a set threshold, the vehicle is predicted to be sold, thereby estimating the number of vehicles sold in this session.

[0088] If vehicles with high transaction probability and vehicles with low transaction probability are packaged into one package, the overall transaction rate will be reduced. If the transaction probability of the entire package is greater than the set threshold, the entire package is determined to be sold, and all the vehicles used are sold. According to the rule of maximizing the transaction volume of the session, vehicles with good quality and vehicles with poor quality are automatically packaged into one lot to meet the requirement of the maximum transaction volume of the session. The packaging coefficient determines the number of packaged vehicles and the number of packaged packages, as well as the probability that each package can meet a higher transaction. In this way, the diversity of packaging modes is effectively improved.

[0089] Figure 2 This is a schematic diagram of the structure of a packaging and processing device for a used vehicle auction provided in this embodiment. The packaging and processing device for a used vehicle auction may include: an acquisition module 210, a determination module 220, a grouping module 230 and a combination module 240.

[0090] The acquisition module 210 is used to acquire an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a second-hand vehicle.

[0091] The determination module 220 is used to determine the estimated auction transaction probability corresponding to each first auction vehicle based on the historical transaction information corresponding to each first auction vehicle.

[0092] The grouping module 230 is used to probability group multiple first auction vehicles based on the estimated auction success probability corresponding to each first auction vehicle to obtain a target vehicle group and a candidate vehicle group. The target vehicle group includes: at least one second auction vehicle, the estimated auction success probability corresponding to the second auction vehicle is greater than or equal to the preset probability, and the second auction vehicle corresponds to one first auction vehicle; the candidate vehicle group includes: at least one third auction vehicle, the estimated auction success probability corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another first auction vehicle.

[0093] The combining module 240 is used to package and combine each second auction vehicle included in the target vehicle group and each third auction vehicle included in the candidate vehicle group to obtain a package processing result of the second-hand vehicle auction.

[0094] The packaging processing result includes: a plurality of vehicle auction packages, each vehicle auction package includes: at least one second auction vehicle and at least one third auction vehicle, or at least one second auction vehicle, or at least one third auction vehicle.

[0095] In this embodiment, optionally, the historical transaction information corresponding to each first auction vehicle includes: two categories of transaction data.

[0096] The determination module 220 is specifically configured to:

[0097] The two categories of transaction data corresponding to each first auction vehicle are represented by feature vectors respectively to obtain two category feature vectors corresponding to each first auction vehicle; based on the vector similarity between the two category feature vectors corresponding to each first auction vehicle, the auction transaction estimated probability corresponding to each first auction vehicle is determined.

[0098] In this embodiment, optionally, the multiple second auction vehicles included in the target vehicle group are arranged in descending order according to the estimated probability of auction success, and the multiple third auction vehicles included in the candidate vehicle group are arranged in descending order according to the estimated probability of auction success.

[0099] The combination module 240 is specifically used for:

[0100] A second auction vehicle with the highest estimated probability of auction success in the current state is selected from the target vehicle group in turn, and at least one third auction vehicle with the highest estimated probability of auction success in the current state is selected from the candidate vehicle group to form multiple vehicle auction packages, and the estimated probability of auction success corresponding to the vehicle auction package is less than or equal to the preset probability; if the target vehicle group no longer has the second auction vehicle in the current state, and the candidate vehicle group still has at least one third auction vehicle in the current state, a vehicle auction package is formed based on the at least one third auction vehicle; if the candidate vehicle group no longer has the third auction vehicle in the current state, and the target vehicle group still has at least one second auction vehicle in the current state, a vehicle auction package is formed based on the at least one second auction vehicle.

[0101] In this embodiment, optionally, the combination module 240 is specifically used for:

[0102] A second auction vehicle with the highest estimated probability of auction success in the current state is selected from the target vehicle group, and a third auction vehicle with the highest estimated probability of auction success in the current state is selected from the candidate vehicle group to form a vehicle combination package; based on the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected third auction vehicle, and the packing coefficient associated with the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected second auction vehicle is determined; if the estimated probability of auction success corresponding to the vehicle combination package is greater than the preset probability, another third auction vehicle with the highest estimated probability of auction success in the current state is continuously selected from the candidate vehicle group, and the vehicle combination package is updated until the estimated probability of auction success corresponding to the vehicle combination package is less than or equal to the preset probability; continue to perform the following operations: select a vehicle in the current state from the target vehicle group A second auction vehicle with the highest estimated probability of auction success in the next auction, and a third auction vehicle with the highest estimated probability of auction success in the current state are selected from the candidate vehicle group to form a vehicle combination package; the estimated probability of auction success corresponding to the vehicle combination package is determined based on the estimated probability of auction success corresponding to the selected second auction vehicle, the estimated probability of auction success corresponding to the selected third auction vehicle, and the packing coefficient associated with the estimated probability of auction success corresponding to the selected second auction vehicle; if the estimated probability of auction success corresponding to the vehicle combination package is greater than the preset probability, another third auction vehicle with the highest estimated probability of auction success in the current state is continuously selected from the candidate vehicle group, and the vehicle combination package is updated until the estimated probability of auction success corresponding to the vehicle combination package is less than or equal to the preset probability; until there is no second auction vehicle in the target vehicle group, or there is no third auction vehicle in the candidate vehicle group.

[0103] In this embodiment, optionally, it also includes: a first processing module.

[0104] The first processing module is used to obtain the category change data corresponding to the first auction vehicle if it is detected that the category transaction data corresponding to the first auction vehicle has changed; based on the category change data corresponding to the first auction vehicle, update the category transaction data corresponding to the first auction vehicle.

[0105] In this embodiment, optionally, it also includes: a second processing module.

[0106] The second processing module is used to obtain the category change data corresponding to the first auction vehicle if it is detected that the category transaction data corresponding to the first auction vehicle has changed; based on the category change data corresponding to the first auction vehicle, adjust the packaging processing result of the second-hand vehicle auction.

[0107] In this embodiment, optionally, it also includes: a third processing module.

[0108] The third processing module is used to obtain additional information of the vehicle auctioneer during the second-hand vehicle auction, where the additional information is used to describe the packaging requirements when auctioning multiple first auction vehicles; based on the additional information of the vehicle auctioneer during the second-hand vehicle auction, the packaging processing results of the second-hand vehicle auction are adjusted.

[0109] The packaging and processing device for second-hand vehicle auctions provided in the present disclosure can execute the above method embodiments. Its specific implementation principles and technical effects can be found in the above method embodiments, and the present disclosure will not repeat them here.

[0110] The present application also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0111] The computer device includes a memory 310 and a processor 320 that are connected to each other through a system bus. It should be noted that the figure only shows a computer device with a memory 310 and a processor 320, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0112] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0113] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes a non-volatile memory or a volatile memory, for example, a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc., and the RAM may include a static RAM or a dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, for example, a hard disk or a memory of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, or a flash card (FlashCard) equipped on the computer device. Of course, the memory 310 may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory 310 is generally used to store the operating system and various application software installed on the computer device, such as the program code of the above method. In addition, the memory 310 may also be used to temporarily store various data that have been output or are to be output.

[0114] The processor 320 is generally used to perform the overall operation of the computer device. In this embodiment, the memory 310 is used to store program codes or instructions, the program code includes computer operation instructions, and the processor 320 is used to execute the program codes or instructions stored in the memory 310 or process data, such as running the program code of the above method.

[0115] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus system can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0116] Another embodiment of the present application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads a computer-readable program code stored in the computer-readable medium, so that the processor can execute the functional actions specified in each step or a combination of steps in the above method; and generate a device for implementing the functional actions specified in each block or a combination of blocks in the block diagram.

[0117] Computer-readable media include but are not limited to electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any appropriate combination of the foregoing, the memory is used to store program codes or instructions, the program codes include computer operating instructions, and the processor is used to execute the program codes or instructions of the above methods stored in the memory.

[0118] For the definitions of memory and processor, please refer to the description of the aforementioned computer device embodiment and will not be repeated here.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] Each functional unit or module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0121] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0122] In the claims, any reference symbols placed between brackets shall not be construed as limiting the claims. The word "comprising" described in the present application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented with the aid of hardware comprising several different elements and with the aid of a suitably programmed computer. In a unit claim that lists a number of devices, several units of these devices may be embodied by the same hardware item. The use of first, second, and third, etc. does not indicate any order, and these words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

[0123] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A packaging processing method for second-hand vehicle auction, characterized in that: include: Acquire an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a second-hand vehicle; Determining an estimated auction success probability corresponding to each of the first auction vehicles based on historical transaction information corresponding to each of the first auction vehicles; Based on the estimated auction success probability corresponding to each of the first auction vehicles, a plurality of the first auction vehicles are probability grouped to obtain a target vehicle group and a candidate vehicle group, wherein the target vehicle group includes: at least one second auction vehicle, the estimated auction success probability corresponding to the second auction vehicle is greater than or equal to a preset probability, and the second auction vehicle corresponds to one of the first auction vehicles; and the candidate vehicle group includes: at least one third auction vehicle, the estimated auction success probability corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another of the first auction vehicles; Packing and combining each of the second auction vehicles included in the target vehicle group and each of the third auction vehicles included in the candidate vehicle group to obtain a packing processing result of the second-hand vehicle auction; The packaging processing result includes: multiple vehicle auction packages, each of which includes: at least one of the second auction vehicles and at least one of the third auction vehicles, or at least one of the second auction vehicles, or at least one of the third auction vehicles.

2. The method according to claim 1, characterized in that The historical transaction information corresponding to each of the first auction vehicles includes: transaction data of two categories; The determining, based on the historical transaction information corresponding to each of the first auction vehicles, the estimated probability of auction transaction corresponding to each of the first auction vehicles comprises: Respectively representing the two categories of transaction data corresponding to each of the first auction vehicles with feature vectors to obtain the two category feature vectors corresponding to each of the first auction vehicles; Based on the vector similarity between the two category feature vectors corresponding to each of the first auction vehicles, the auction transaction estimated probability corresponding to each of the first auction vehicles is determined.

3. The method according to claim 1, characterized in that The plurality of second auction vehicles included in the target vehicle group are arranged in descending order according to the estimated probability of auction success, and the plurality of third auction vehicles included in the candidate vehicle group are arranged in descending order according to the estimated probability of auction success; The step of packaging and combining each of the second auction vehicles included in the target vehicle group and each of the third auction vehicles included in the candidate vehicle group to obtain a packaging result of the second-hand vehicle auction includes: Selecting one of the second auction vehicles with the highest estimated probability of auction success in the current state from the target vehicle group, and selecting at least one of the third auction vehicles with the highest estimated probability of auction success in the current state from the candidate vehicle group, to form a plurality of vehicle auction packages, wherein the estimated probability of auction success corresponding to the vehicle auction packages is less than or equal to the preset probability; If the target vehicle group no longer has the second auction vehicle in the current state, and the candidate vehicle group still has at least one third auction vehicle in the current state, forming a vehicle auction package based on the at least one third auction vehicle; If the candidate vehicle group currently does not have the third auction vehicle, and the target vehicle group currently still has at least one of the second auction vehicles, a vehicle auction package is formed based on the at least one of the second auction vehicles.

4. The method according to claim 3, characterized in that The step of sequentially selecting one of the second auction vehicles with the highest estimated probability of auction success in the current state from the target vehicle group, and selecting at least one of the third auction vehicles with the highest estimated probability of auction success in the current state from the candidate vehicle group, to form a plurality of vehicle auction packages, includes: Selecting the second auction vehicle with the highest estimated probability of successful auction in the current state from the target vehicle group, and selecting the third auction vehicle with the highest estimated probability of successful auction in the current state from the candidate vehicle group, to form a vehicle combination package; Determine the estimated auction success probability corresponding to the vehicle combination package based on the estimated auction success probability corresponding to the selected second auction vehicle, the estimated auction success probability corresponding to the selected third auction vehicle, and the packing coefficient associated with the estimated auction success probability corresponding to the selected second auction vehicle; If the auction success estimated probability corresponding to the vehicle combination package is greater than the preset probability, then continue to select another third auction vehicle with the highest auction success estimated probability in the current state from the candidate vehicle group, and update the vehicle combination package until the auction success estimated probability corresponding to the vehicle combination package is less than or equal to the preset probability; Continue as follows: Selecting one of the second auction vehicles with the highest estimated probability of auction success in the current state from the target vehicle group, and selecting one of the third auction vehicles with the highest estimated probability of auction success in the current state from the candidate vehicle group, to form a vehicle combination package; determining the estimated probability of auction success corresponding to the vehicle combination package based on the estimated probability of auction success corresponding to the selected one of the second auction vehicles, the estimated probability of auction success corresponding to the selected one of the third auction vehicles, and the packing coefficient associated with the estimated probability of auction success corresponding to the selected one of the second auction vehicles; if the estimated probability of auction success corresponding to the vehicle combination package is greater than the preset probability, continuing to select another one of the third auction vehicles with the highest estimated probability of auction success in the current state from the candidate vehicle group, and updating the vehicle combination package until the estimated probability of auction success corresponding to the vehicle combination package is less than or equal to the preset probability; Until the second auction vehicle does not exist in the target vehicle group, or the third auction vehicle does not exist in the candidate vehicle group.

5. The method according to claim 2, characterized in that: Also includes: If it is detected that the category transaction data corresponding to the first auction vehicle changes, obtaining the category change data corresponding to the first auction vehicle; Based on the category change data corresponding to the first auction vehicle, the category transaction data corresponding to the first auction vehicle is updated.

6. The method according to claim 2, characterized in that Also includes: If it is detected that the category transaction data corresponding to the first auction vehicle changes, obtaining the category change data corresponding to the first auction vehicle; Based on the category change data corresponding to the first auction vehicle, the packaging processing result of the second-hand vehicle auction is adjusted.

7. The method according to claim 1, characterized in that Also includes: Acquire additional information of a vehicle auctioneer during a second-hand vehicle auction, wherein the additional information is used to describe packaging requirements when auctioning a plurality of the first auction vehicles; Based on the additional information of the vehicle auctioneer at the second-hand vehicle auction, the packaging processing result of the second-hand vehicle auction is adjusted.

8. A packaging and processing device for a second-hand vehicle auction, characterized in that: include: An acquisition module, used to acquire an auction vehicle database, wherein the auction vehicle database includes: historical transaction information corresponding to a plurality of first auction vehicles, wherein the first auction vehicle is used to describe a second-hand vehicle; A determination module, configured to determine an estimated auction transaction probability corresponding to each of the first auction vehicles based on historical transaction information corresponding to each of the first auction vehicles; a grouping module, configured to perform probability grouping of a plurality of the first auction vehicles based on the estimated auction success probability corresponding to each of the first auction vehicles, to obtain a target vehicle group and a candidate vehicle group, wherein the target vehicle group includes: at least one second auction vehicle, the estimated auction success probability corresponding to the second auction vehicle is greater than or equal to a preset probability, and the second auction vehicle corresponds to one of the first auction vehicles; and the candidate vehicle group includes: at least one third auction vehicle, the estimated auction success probability corresponding to the third auction vehicle is less than the preset probability, and the third auction vehicle corresponds to another of the first auction vehicles; a combining module, used for packaging and combining each of the second auction vehicles included in the target vehicle group and each of the third auction vehicles included in the candidate vehicle group, to obtain a packaging processing result of the second-hand vehicle auction; The packaging processing result includes: multiple vehicle auction packages, each of which includes: at least one of the second auction vehicles and at least one of the third auction vehicles, or at least one of the second auction vehicles, or at least one of the third auction vehicles.

9. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a packaging and processing method for second-hand vehicle auction as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the packaging and processing method for second-hand vehicle auction as claimed in any one of claims 1 to 7 is implemented.

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