Virtual resource information processing method and device, electronic equipment and storage medium

Abstract

The invention discloses an information processing method and device for virtual resources, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target attribute repeatability indication parameter of a target virtual resource to be analyzed; determining a target resource analysis model matched with the target attribute repeatability indication parameter from the plurality of resource analysis models according to a corresponding relationship between the trained resource analysis model and the attribute repeatability indication parameter; and according to the target resource analysis model, based on the resource association data of the target virtual resource, performing resource analysis on the target virtual resource to obtain a value evaluation result of the target virtual resource. On the basis, the proper resource analysis model is selected from the different resource analysis models by combining the characteristics of the virtual resources so as to carry out resource analysis on the virtual resources, so that the accuracy of resource analysis is improved, the problems of overlong selling time or loss and the like caused by self-pricing of game players are avoided, and the user experience is improved. And the transaction experience of game players is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, electronic device and storage medium for processing information of virtual resources. Background Art

[0002] With the development of smart devices and internet technology, more and more people are turning to gaming as a daily activity. Gamers can sell or purchase virtual resources on certain gaming virtual resource trading platforms. Typically, players set their own prices for selling virtual resources. However, these schemes can lead to overpricing, making it difficult to sell virtual resources, or underpricing, causing virtual resources to be sold out instantly, resulting in losses for gamers.

[0003] Therefore, the existing transaction processing method of virtual resources has the problem of inaccurate pricing, which affects the experience of game players. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, electronic device, and storage medium for processing information on virtual resources. By combining the characteristics of the virtual resources themselves, a suitable resource analysis model can be selected from different resource analysis models to perform resource analysis on the virtual resources, thereby improving the accuracy of resource analysis, avoiding problems such as long sales time or losses caused by game players' independent pricing, and improving the transaction experience of game players.

[0005] In a first aspect, an embodiment of the present application provides a method for processing information of a virtual resource, the method comprising:

[0006] Obtaining a target attribute repeatability indication parameter of a target virtual resource to be analyzed;

[0007] According to the correspondence between the trained resource analysis model and the attribute repeatability indication parameter, a target resource analysis model matching the target attribute repeatability indication parameter is determined from multiple resource analysis models;

[0008] According to the target resource analysis model and based on the resource association data of the target virtual resource, the target virtual resource is analyzed to obtain a value assessment result of the target virtual resource.

[0009] In a second aspect, an embodiment of the present application provides an information processing device for virtual resources, comprising:

[0010] A parameter acquisition module, used to obtain target attribute repeatability indication parameters of the target virtual resource to be analyzed;

[0011] a model determination module, configured to determine, from a plurality of resource analysis models, a target resource analysis model that matches the target attribute repeatability indication parameter based on a correspondence between the trained resource analysis models and the attribute repeatability indication parameter;

[0012] The analysis module is configured to perform resource analysis on the target virtual resource based on the target resource analysis model and the resource association data of the target virtual resource to obtain a value assessment result of the target virtual resource.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the information processing method of any virtual resource.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the information processing method of any virtual resource.

[0015] In the fifth aspect, an embodiment of the present application also provides a computer program product, including a computer program, which is stored in a computer-readable storage medium; when the processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device performs the steps of any one of the virtual resource information processing methods provided in the embodiments of the present application.

[0016] By adopting the solution of the embodiment of the present application, the target attribute repeatability indicator parameter of the target virtual resource to be analyzed can be obtained; based on the correspondence between the trained resource analysis model and the attribute repeatability indicator parameter, a target resource analysis model that matches the target attribute repeatability indicator parameter is determined from multiple resource analysis models; based on the target resource analysis model and the resource association data of the target virtual resource, a resource analysis is performed on the target virtual resource to obtain a value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, an appropriate resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long sales time or losses caused by game players' independent pricing, and improving the trading experience of game players. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a schematic diagram of an implementation environment scenario of the virtual resource information processing method provided in an embodiment of the present application;

[0019] Figure 2 This is a flow chart of an embodiment of a method for processing information of virtual resources provided in an embodiment of the present application;

[0020] Figure 3 This is a schematic diagram of a virtual resource transaction interface in the virtual resource information processing method provided in an embodiment of the present application;

[0021] Figure 4 This is a schematic diagram of the resource analysis model provided in the embodiments of the present application;

[0022] Figure 5 This is a schematic diagram of the use process of the resource analysis model provided in the embodiments of the present application;

[0023] Figure 6 It is a structural diagram of the information processing device of virtual resources provided in an embodiment of the present application;

[0024] Figure 7 It is a structural diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application. At the same time, in the description of the embodiments of the present application, the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0026] Research has found that in current gaming virtual resource trading scenarios, players typically set their own prices. However, this pricing can lead to the price being too high, resulting in virtual resource transactions being unsuccessful or taking too long. It can also lead to the price being too low, causing virtual resources to be sold out instantly, resulting in losses for players.

[0027] Therefore, existing virtual resource trading solutions have the problem of inaccurate pricing, which affects the experience of gamers.

[0028] To solve the above problems, the embodiments of the present application provide a method, device, electronic device, and computer-readable storage medium for processing information of virtual resources. Specifically, the present embodiment will be described from the perspective of the information processing device for virtual resources. The information processing device for virtual resources can be integrated into an electronic device, that is, the information processing method for virtual resources in the embodiments of the present application can be executed by an electronic device. Optionally, the electronic device can include: a terminal device. The terminal device can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, game console, or personal computer (PC) and other devices.

[0029] The virtual resource information processing method provided in the embodiments of the present application can be applied, for example, to a resource analysis system. The resource analysis system may include a player terminal device and a server. The terminal device may include both receiving and transmitting hardware, i.e., a device with receiving and transmitting hardware capable of performing bidirectional communication over a bidirectional communication link. The player terminal device and the server may communicate bidirectionally via a network.

[0030] Optionally, the server may be a standalone server, or a server network or server cluster consisting of servers, including but not limited to a computer, a network host, a single network server, a set of multiple network servers, or a cloud server consisting of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0031] In one embodiment of the present application, the method for processing virtual resource information can be run on a local terminal device or a server. When the method for processing virtual resource information is run on a server, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and a client device.

[0032] For example, when the information processing method of the virtual resource is run on a terminal, the terminal device stores a game application and is used to present a virtual scene in the game screen. The terminal device is used to interact with the user through a graphical user interface, for example, by downloading and installing a game application and running it through the terminal device. The terminal device may provide the graphical user interface to the user in a variety of ways, for example, it may be rendered and displayed on the display screen of the terminal device, or the graphical user interface may be presented through holographic projection. For example, the terminal device may include a touch screen and a processor, the touch screen being used to present the graphical user interface and receive operation instructions generated by the user acting on the graphical user interface, the graphical user interface including a game screen, and the processor being used to run the game, generate the graphical user interface, respond to operation instructions, and control the display of the graphical user interface on the touch screen.

[0033] For example, when the virtual resource information processing method runs on a server, it can be a cloud game. Cloud gaming refers to a gaming method based on cloud computing. In the cloud gaming mode, the game application runtime and the game screen presentation are separate. The storage and execution of the virtual resource information processing method are performed on the cloud gaming server. The game screen presentation is performed on the cloud gaming client. The cloud gaming client is primarily responsible for receiving and sending game data and presenting the game screen. For example, the cloud gaming client can be a display device with data transmission capabilities close to the user, such as a mobile terminal, television, computer, PDA, or personal digital assistant. However, the terminal device that processes the game data is the cloud gaming server in the cloud. When playing the game, the user operates the cloud gaming client to send operation instructions to the cloud gaming server. The cloud gaming server runs the game according to the operation instructions, encodes and compresses the game screen and other data, and returns it to the cloud gaming client via the network. Finally, the cloud gaming client decodes and outputs the game screen.

[0034] See also Figure 1 , taking the example of integrating the information processing device of virtual resources into electronic equipment, Figure 1This is a schematic diagram of an implementation scenario of the information processing method for virtual resources provided in an embodiment of the present application, wherein the electronic device can be a terminal device, which obtains the target attribute repeatability indication parameter of the target virtual resource to be analyzed; determines the target resource analysis model that matches the target attribute repeatability indication parameter from multiple resource analysis models based on the correspondence between the trained resource analysis model and the attribute repeatability indication parameter; and performs resource analysis on the target virtual resource based on the resource association data of the target virtual resource according to the target resource analysis model to obtain the value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, a suitable resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long selling time or losses caused by self-pricing by game players, and improving the transaction experience of game players.

[0035] It should be noted that Figure 1 The schematic diagram of the implementation environment scenario of the virtual resource information processing method shown is merely an example. The implementation environment scenario of the virtual resource information processing method described in the embodiments of this application is intended to more clearly illustrate the technical solutions of the embodiments of this application and does not constitute a limitation on the technical solutions provided by the embodiments of this application. Persons skilled in the art will appreciate that with the evolution of data processing and the emergence of new business scenarios, the technical solutions provided in this application are equally applicable to similar technical problems.

[0036] The solutions provided in the embodiments of the present application are specifically described by the following embodiments. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments.

[0037] This embodiment will be described from the perspective of an information processing device for virtual resources. The information processing device for virtual resources may be specifically integrated into an electronic device, which may be a terminal and / or a server, and this application does not impose any restrictions thereon.

[0038] See also Figure 2 , Figure 2 The information processing method of virtual resources provided in the embodiment of the present application may include the following steps 101 to 103:

[0039] Step 101: Obtain target attribute repeatability indication parameters of a target virtual resource to be analyzed;

[0040] Step 102: Determine a target resource analysis model that matches the target attribute repeatability indication parameter from a plurality of resource analysis models based on the correspondence between the trained resource analysis models and the attribute repeatability indication parameter;

[0041] Step 103: Perform resource analysis on the target virtual resource based on the target resource analysis model and the resource association data of the target virtual resource to obtain a value assessment result of the target virtual resource.

[0042] Among them, the virtual resources mentioned in the virtual resource information processing method provided in the embodiment of the present application can be displayed on the virtual resource transaction interface.

[0043] Specifically, a virtual resource transaction interface is displayed, wherein the virtual resource transaction interface includes multiple virtual resources. In response to a triggering operation of a target virtual resource among the multiple virtual resources, a value assessment result of the target virtual resource is displayed, wherein the value assessment result is obtained by performing a value assessment on the target virtual resource based on a target resource analysis model that matches the target virtual resource among the multiple trained resource analysis models.

[0044] Among them, the virtual resource trading interface refers to a game interface that can be used to conduct virtual resource trading.

[0045] The game interface refers to the interface corresponding to the application provided or displayed through the screen, which includes a UI interface and a game screen for players to interact. In an optional embodiment, the UI interface includes multiple interface elements. Interface elements may include game controls (such as skill controls, movement controls, function controls, etc.), indicators (such as direction indicators, character indicators, etc.), information display areas (such as the number of kills, match time, etc.), or game setting controls (such as system settings, stores, gold coins, etc.). In an optional embodiment, the game screen is a display screen corresponding to the virtual scene displayed by the terminal device. The virtual scene is a virtual environment displayed (or provided) when the application is running on the terminal. The virtual environment can be a simulation environment of the real world, a semi-simulated and semi-fictional three-dimensional environment, or a purely fictional three-dimensional environment. The game screen may include virtual objects such as game characters, NPC characters, AI characters, etc. that execute game logic in the virtual scene.

[0046] Among them, virtual resources refer to virtual game resources that game players can obtain, use, trade or sell in the game.

[0047] Virtual resources can be exchanged between different game players, and can include a variety of virtual resources. For example, virtual resources can be virtual equipment in Game A, virtual props in Game B, character skins owned by game players in Game C, virtual pets in Game D, or any other virtual resource that can be exchanged between game players. This embodiment of the present application does not specifically limit this.

[0048] When a game player wants to sell a virtual resource, they can publish it in the virtual resource trading interface. The target virtual resource corresponding to the virtual resource publishing operation is the virtual resource the game player wants to sell. Based on this, after publishing the virtual resource in the virtual resource trading interface, the target virtual resource to be traded can be uploaded to the virtual resource trading platform for trading.

[0049] The target virtual resources refer to the virtual resources that need to be valued.

[0050] The value assessment result refers to the result obtained by performing a value assessment on the target virtual resource based on the target resource analysis model, and is used to recommend a price for selling the target virtual resource.

[0051] The triggering operation may include a click operation, a long press operation, a long press and drag operation, a specific gesture, etc. For example, in response to a click operation on a target virtual resource among multiple virtual resources, the value evaluation result of the target virtual resource is displayed on the screen. For another example, in response to a long press operation on a target virtual resource among multiple virtual resources, the value evaluation result of the target virtual resource is displayed on the screen.

[0052] In some embodiments, the virtual resource transaction interface includes a resource area and a transaction area. Multiple virtual resources are displayed in the resource area. The transaction area includes a transaction value sub-area. After the target virtual resource is triggered, the transaction value sub-area is filled with the value assessment result.

[0053] The resource area refers to the area on the virtual resource transaction interface used to display virtual resources. The transaction area refers to the area on the virtual resource transaction interface used to display, fill in, or enter transaction-related information.

[0054] The transaction area may include a transaction value sub-area, which is used to display the transaction value of the selected target virtual resource. The transaction value sub-area is filled with the value evaluation result of the selected target virtual resource by default.

[0055] The transaction area may further include a transaction amount sub-area, which is used to display the transaction amount of the selected target virtual resource. The transaction amount sub-area may be used to fill in or input the transaction amount of the target virtual resource being traded.

[0056] For example, see Figure 3 , Figure 3 This is a schematic diagram of the virtual resource transaction interface provided in an embodiment of the present application. Figure 3The virtual resource transaction interface shown includes a resource area and a transaction area. Multiple virtual resources are displayed in the resource area. The transaction area includes a transaction value sub-area and a transaction quantity sub-area. After the target virtual resource is triggered, the value assessment result is filled in by default in the transaction value sub-area, and the transaction quantity of the target virtual resource to be traded is filled in or entered in the transaction quantity sub-area.

[0057] The target attribute repeatability indication parameter refers to the attribute repeatability indication parameter of the target virtual resource.

[0058] It should be noted that the virtual resource configuration has a specified number of resource attributes, wherein each resource attribute corresponds to an attribute type.

[0059] Based on this, the above process of obtaining the target attribute repeatability indication parameter of the target virtual resource can include: obtaining multiple resource attributes corresponding to the target virtual resource, where each resource attribute corresponds to an attribute type; determining the target attribute repeatability indication parameter of the target virtual resource based on the number of resource attributes with the same attribute type under the target virtual resource.

[0060] The target attribute repetitiveness indication parameter is used to indicate the number of resource attributes with the same attribute type under the target virtual resource.

[0061] Resource attributes refer to the attributes of virtual resources, which usually determine the function and value of virtual resources in the game.

[0062] The specified number of resource attributes can be determined based on the actual game or actual virtual resources, and the embodiments of the present application do not limit this. For example, the specified number of resource attributes is 4. For another example, the specified number of resource attributes is 6.

[0063] Attribute type refers to the type of resource attribute. The classification criteria for attribute types and their specific content can be determined based on actual conditions, actual gameplay, and the specific content of virtual resources, and are not limited by the embodiments of this application. For example, attribute types can be divided into basic attributes, enhanced attributes, growth attributes, etc. For another example, attribute types can be specific attribute types such as "root bone" attributes and "agility" attributes.

[0064] For example, let's use the game Westward Journey Online as an example. Westward Journey Online offers a variety of accessories (i.e., virtual resources) such as capes, pendants, rings, and belts. The value of different accessories is closely related to the attributes they contain. Westward Journey Online's accessory attribute pool contains a total of 100 resource attributes, and each resource attribute has a corresponding value range. It should be noted that each individual accessory (i.e., a virtual resource) can only have four (i.e., a specified number) resource attributes, and resource attributes for the same accessory can be repeated. For example, there is an accessory whose resource attributes include 1 mana, 1 dodge rate, and 2 health percentages. Based on this, the attribute repeatability indicator parameter for this accessory is 2, meaning that the accessory has two resource attributes of the same attribute type. For another example, there is an accessory whose resource attributes include 4 mana. Based on this, the attribute repeatability indicator parameter for this accessory is 4, meaning that the accessory has four resource attributes of the same attribute type.

[0065] The correspondence between the trained resource analysis model and the attribute repeatability indication parameter refers to a pre-established relationship for associating the resource analysis model with the attribute repeatability indication parameter. For example, a resource analysis model for a high-value virtual resource has an association relationship with the number of resource attributes of the same attribute type indicated by the attribute repeatability indication parameter being less than or equal to 2. For another example, a resource analysis model for a virtual resource with three attributes has an association relationship with the number of resource attributes of the same attribute type indicated by the attribute repeatability indication parameter being equal to 3.

[0066] The target resource analysis model refers to a model among multiple resource analysis models that is adapted to the characteristics of the target virtual resource itself and can be used to accurately and appropriately predict the price of the target virtual resource.

[0067] Specifically, based on the corresponding relationship, the resource analysis model associated with the attribute repeatability indication parameter that matches the target attribute repeatability indication parameter is determined, and the resource analysis model is used as the target resource analysis model to facilitate subsequent resource analysis of the target virtual resource based on the target resource analysis model.

[0068] The resource-related data refers to the related data used to perform resource analysis on the target virtual resource.

[0069] There can be multiple types of resource-related data, which can be adjusted according to actual conditions and are not limited in the embodiments of the present application. For example, resource-related data may include the historical transaction value contained in valid orders for the target virtual resource within a historical time period. For another example, resource-related data is player feature information (such as player level, player, credit score, total activity in the past month, maximum activity in the past month, maximum recharge amount in the past three months, total online time in the past month, and total recharge amount in the past three months).

[0070] It should be noted that, before determining the target resource analysis model that matches the target attribute repeatability indication parameters from multiple resource analysis models based on the correspondence between the trained resource analysis models and the attribute repeatability indication parameters, the information processing method of the virtual resource also involves a training process of the resource analysis model.

[0071] The training process of the resource analysis model can be adjusted according to the actual situation and the specific selection of the resource analysis model, and the embodiments of this application do not limit it.

[0072] It should be noted that resource analysis models can be trained according to the following universal process.

[0073] Specifically, the training process of the resource analysis model can refer to the following steps:

[0074] Acquire a number of virtual resource samples, where the virtual resource samples include attribute repeatability indication parameters corresponding to the virtual resources and historical resource association data;

[0075] Based on the attribute repeatability indication parameters corresponding to the multiple resource analysis models to be trained, selecting corresponding target virtual resource samples for each resource analysis model from the virtual resource samples;

[0076] For each resource analysis model, training is performed based on the target virtual resource sample corresponding to the resource analysis model and the historical resource association data corresponding to the target virtual resource sample to obtain a trained resource analysis model.

[0077] The virtual resource sample can be obtained by obtaining a virtual resource order and obtaining the virtual resource sample from the virtual resource order.

[0078] Among them, a virtual resource order refers to an order formed based on the sales behavior of a virtual resource sample. The virtual resource order includes one or more virtual resource samples, and the sales behavior can be a historical sales behavior within a certain time period. The certain time period can be set according to actual conditions, and the embodiments of this application are not limited. For example, a virtual resource order refers to an order formed by the sales behavior of a virtual resource sample in the past six months (i.e., the above-mentioned certain time period). For another example, a virtual resource order refers to an order formed by the sales behavior of a virtual resource sample in the past month (i.e., the above-mentioned certain time period).

[0079] A virtual resource sample refers to a virtual resource that can be used to train a resource analysis model. Historical resource-related data refers to data associated with the virtual resource sample during historical sales. This historical resource-related data includes, but is not limited to, historical transaction prices, historical transaction volumes, transacting players, and the server characteristics of the transacting players. The historical transaction price refers to the transaction price at which the virtual resource sample was sold during historical sales. The attribute repetitiveness indicator parameter for a virtual resource sample indicates the number of resource attributes with the same attribute type within the virtual resource sample.

[0080] The target virtual resource sample refers to the virtual resource sample used to train the resource analysis model.

[0081] There are many ways to construct a resource analysis model, which can be adjusted according to actual conditions, and the present application embodiment does not limit it. For example, the resource analysis model can be as follows: Figure 4 The model shown is constructed based on random forest, decision tree, XGBoot, etc. For another example, the resource analysis model can also be a model constructed based on multi-layer perceptron, convolutional neural network, etc.

[0082] Specifically, multiple resource analysis models to be trained that are bound to different attribute repeatability indication parameters are pre-built. From the virtual resource samples contained in the virtual resource order, target virtual resource samples are screened for each resource analysis model to be trained based on the attribute repeatability indication parameters corresponding to the multiple resource analysis models to be trained.

[0083] The number of resource analysis models can be determined according to the number of attribute repeatability indication parameters, and the embodiments of the present application do not impose any restrictions. For example, when the number of resource attributes of a virtual resource is 4, the number of resource attributes of the same attribute type under the virtual resource indicated by the attribute repeatability indication parameter can be (0, 2, 3, 4). Among them, the number indicated by the attribute repeatability indication parameter is 0, indicating that there are no resource attributes of the same attribute type. The number indicated by the attribute repeatability indication parameter is 2, indicating that there are two resource attributes of the same attribute type, the number indicated by the attribute repeatability indication parameter is 3, indicating that there are three resource attributes of the same attribute type, and the number indicated by the attribute repeatability indication parameter is 4, indicating that there are four resource attributes of the same attribute type. Other situations can be adjusted with reference to the above description, which will not be repeated here.

[0084] In some embodiments, the resource analysis model includes a first type of resource analysis model and a second type of resource analysis model, wherein different types of resource analysis models correspond to different attribute repeatability indication parameters, and the historical resource association data includes historical transaction value.

[0085] Based on this, the above process of screening corresponding target virtual resource samples for each resource analysis model may include:

[0086] For the first type of resource analysis model to be trained, based on the attribute repeatability indicator parameter corresponding to the first type of resource analysis model, value screening information corresponding to the first type of resource analysis model is obtained; for each virtual resource, based on the value screening information and the historical transaction value of the virtual resource sample, a target virtual resource sample of the first type of resource analysis model is screened out from multiple virtual resource samples corresponding to the virtual resource;

[0087] For the second type of resource analysis model to be trained, based on the attribute repeatability indication parameter corresponding to the second type of resource analysis model, a target virtual resource sample of the second type of resource analysis model is screened out from multiple virtual resource samples.

[0088] Among them, the first type of resource analysis model refers to a model that requires value screening information and attribute repeatability indication parameters to screen virtual resource samples, and the second type of resource analysis model refers to a model that only requires attribute repeatability indication parameters to screen target virtual resource samples.

[0089] The value screening information may refer to information used to screen target virtual resource samples according to the value of the virtual resource samples.

[0090] In some embodiments, the first type of resource analysis model is used to indicate a resource analysis model associated with an edge attribute repeatability indication parameter among multiple attribute repeatability indication parameters, and the second type of resource analysis model is used to indicate a resource analysis model associated with an attribute repeatability indication parameter located in the middle among multiple attribute repeatability indication parameters. For example, see Figure 4 , assuming that the number of resource attributes of the virtual resource is 4, the number indicated by the attribute repetitiveness indication parameter can be (0, 2, 3, 4). The first type of resource analysis model can be associated with the attribute repetitiveness indication parameter whose number is less than or equal to 2 (i.e. Figure 4 The first type of resource analysis model can also be associated with an attribute repeatability indicator parameter with an indicated number equal to 4 (i.e. Figure 4 The second type of resource analysis model is associated with the attribute repeatability indicator parameter with the indicated number equal to 3 (i.e. Figure 4 3-attribute model in ).

[0091] In some embodiments, the first type of resource analysis model includes a first resource analysis model and a second resource analysis model, the number indicated by the attribute repeatability indication parameter corresponding to the first resource analysis model is higher than the number indicated by the attribute repeatability indication parameter corresponding to the second resource analysis model, and the value screening information includes value percentiles.

[0092] Specifically, the attribute repeatability indication parameter corresponding to the second type of resource analysis model is between the attribute repeatability indication parameters corresponding to the first resource analysis model and the second resource analysis model respectively.

[0093] Among them, the target virtual resource sample of the first resource analysis model meets the screening condition set by the historical transaction value corresponding to the value percentile in the value range formed by the historical transaction value of multiple virtual resource samples.

[0094] Among them, the target virtual resource sample of the second resource analysis model meets the screening condition set by the historical transaction value corresponding to the value percentile in the value range formed by the historical transaction value of multiple virtual resource samples.

[0095] The value percentile refers to the value position of the target virtual resource sample within the value range formed by the historical transaction value of the virtual resource sample, which is used to screen the historical transaction prices that meet the value requirements. The historical transaction values within the value range are arranged in ascending order.

[0096] For example, the value percentile may include the 50th percentile, with the specific screening criteria being: the median historical transaction value ranking in the middle of the value range formed by the historical transaction value of the virtual resource sample. The target virtual resource samples for the first resource analysis model are those whose historical transaction value is no less than the median historical transaction value. The target virtual resource samples for the second resource analysis model are those whose historical transaction value is no greater than the median historical transaction value.

[0097] For example, the value percentile may include the 70th percentile, with the specific screening criteria being: the target historical transaction value ranking at the 70th percentile within the historical transaction value interval formed by the historical transaction values of multiple virtual resource orders. The target virtual resource samples for the first resource analysis model are those whose historical transaction values are no less than the target historical transaction value. The target virtual resource samples for the second resource analysis model are those whose historical transaction values are no greater than the target historical transaction value.

[0098] For example, following the above example, see Figure 4 For high-value data (i.e., the target virtual resource samples of the first resource analysis model), the target virtual resource samples with historical transaction values greater than or equal to the 70th percentile can be screened out from the virtual resource samples as the target virtual resource samples of the first resource analysis model. For low-value data (i.e., the target virtual resource samples of the second resource analysis model), the virtual resource samples with historical transaction values less than or equal to the 70th percentile can be screened out from the virtual resource samples as the target virtual resource samples of the second resource analysis model.

[0099] In some embodiments, the virtual resource sample further includes a server feature, wherein the server feature is used to indicate a server feature of a server corresponding to the game to which the virtual resource sample belongs.

[0100] Among them, server characteristics include but are not limited to the server opening time, the number of daily active users, the maximum number of players, etc., which can be adjusted according to actual conditions and are not limited in the embodiments of this application.

[0101] Since the target virtual resource sample can refer to all virtual resource samples that match the attribute repeatability indicator parameters corresponding to the second type of resource analysis model in the virtual resource sample. However, the virtual resource samples may be of different types, or the server characteristics of the server where the game player with the virtual resource sample is located may be different. This may result in different values of virtual resources. For example, the transaction value of virtual resources in different server segments is different. For example, the transaction value of the same virtual resource corresponding to "within 1 year of service launch" and "more than 5 years of service launch" is different. The transaction value of the virtual resource in "more than 5 years of service launch" is often higher than the transaction value of the virtual resource in "within 1 year of service launch".

[0102] Based on this, the above-mentioned process of training the target virtual resource samples corresponding to the resource analysis model to obtain a trained resource analysis model can also include: when the resource analysis model is a second-type resource analysis model, based on the server characteristics, determining the virtual resource samples to be trained that match the server characteristics from the target virtual resource samples of the second-type resource analysis model; training the second-type resource analysis model based on the virtual resource samples to be trained to obtain a second-type resource analysis model trained under the server characteristics.

[0103] The virtual resource samples to be trained refer to the virtual resource samples corresponding to the server characteristics in the target virtual resource samples of the second type of resource analysis model.

[0104] Based on this, the attribute repeatability indication parameter includes a target indication parameter, which is used to indicate that the number of resource attributes with the same attribute type under the virtual resource does not exceed a preset value. In the corresponding relationship, the number of resource analysis models corresponding to the target indication parameter is multiple. Among them, the preset value can refer to Figure 4 The attribute repetitiveness indicator parameter in the attribute indicates a quantity of 4.

[0105] Based on this, the above process of determining the target resource analysis model that matches the target attribute repeatability indication parameter from multiple resource analysis models based on the correspondence between the trained resource analysis model and the attribute repeatability indication parameter can also include: when the target attribute repeatability indication parameter is the target indication parameter, according to the correspondence, determining multiple resource analysis models that match the target indication parameter as candidate resource analysis models; based on the current server characteristics of the target server, determining the target resource analysis model that matches the server characteristics from multiple candidate resource analysis models, wherein the target server is the server corresponding to the game to which the target virtual resource belongs.

[0106] The candidate resource analysis model refers to the resource analysis model corresponding to the attribute repeatability indication parameter that matches the target attribute repeatability indication parameter. The number of candidate resource analysis models can be adjusted according to the type of virtual resources and server characteristics.

[0107] The following combination Figure 4 , the training process of the resource analysis model is explained with a specific embodiment.

[0108] Let's use the game Westward Journey Online as an example to explain this. Westward Journey Online offers a variety of accessories (i.e., virtual resources) such as cloaks, pendants, rings, and belts. The value of different accessories is closely related to the attributes they contain, with the following specific characteristics: The higher the value of a resource attribute, the higher its value. For example, the higher the value of the "agility" resource attribute, the higher its value. Specific resource attribute combinations are more valuable than non-specific resource attribute combinations. For example, if the accessory is a cloak, "Defense Penetration + Increased Health + Root Bones + Agility" (i.e., "Agility + Root Bones" is a specific resource attribute combination) is more valuable than "Defense Penetration + Increased Health + Root Bones + Dodge" (i.e., there is no specific resource attribute combination). Regardless of the resource attribute values, the more duplicated attributes a virtual resource contains, the higher its value. For example, a ring with four Agility resource attributes is more valuable than one with three Agility and one Spirit resource attribute.

[0109] Based on the above, the characteristics of the accessory can be determined, such as the number of unique attributes, attribute values, attribute upper limit ratio (i.e., attribute value / attribute upper limit), and specific fields (such as high_value_flag, which is used to indicate whether the virtual resource is a rare item). Among them, the attribute repeatability indicator parameter corresponding to the accessory can be determined based on the number of unique attributes.

[0110] For each accessory item, its value distribution characteristics (such as minimum value, 25th percentile value, 50th percentile value, 75th percentile value, maximum value) under each non-repeating attribute number (such as 0, 2, 3, 4) in a specific time period (such as within half a year, within a month) are counted.

[0111] Items with a unique attribute equal to N are named "items with an attribute repeatability indicator parameter of N." For example, after analyzing the value distribution of items, it was found that the value of accessories with a value of 2 indicated by the attribute repeatability indicator parameter is generally above the 80th percentile; while items with a value below the 50th percentile are generally concentrated in accessories with a value of 4 indicated by the attribute repeatability indicator parameter; and the value dispersion is relatively high for accessories with a value of 3 indicated by the attribute repeatability indicator parameter, which has a larger data volume. Based on these data characteristics, three models can be trained: a high-value model, a low-value model, and a three-attribute model (trained for accessories with a value of 3 indicated by the attribute repeatability indicator parameter).

[0112] It should be noted that sample preprocessing is required before training. Sample preprocessing can be adjusted according to actual conditions. For example, orders where virtual resources are sold out in seconds due to excessively low pricing should be filtered out. Another example is that orders determined by operations to be abnormal transactions should be filtered out. The reasons for abnormal transactions may be abnormalities in the player's game account, abnormal transaction value, etc. Another example is to remove all deducted orders. For another example, for orders where the number indicated by the attribute repeatability indicator parameter is greater than or equal to 3, limit them to non-second-sale orders to prevent them from being filtered out.

[0113] After the above preprocessing, we obtain virtual resource samples. We filter virtual resource samples whose historical transaction values are greater than the 70th percentile as training data to train a high-value model. For different virtual resources and in different server development periods, we filter virtual resource samples whose historical transaction values are less than or equal to the 70th percentile as training data to train a low-value model for the virtual resource in the same server development period. That is, there can be multiple low-value models. We filter virtual resources contained in virtual resource orders where the number indicated by the attribute repeatability indicator parameter is equal to 3 as training data to train a three-attribute model.

[0114] See below Figure 5 , combined with the Figure 4 The resource analysis model obtained from the training process of the resource analysis model shown in the figure describes the resource analysis application process. Figure 5In the actual application process of resource analysis, when there are virtual resources that require resource analysis, obtain the virtual resources and the attribute repeatability indication parameters of the virtual resources. The attribute repeatability indication parameters of the virtual resources are judged to select a suitable resource analysis model to perform resource analysis on the virtual resources. For example, when the number indicated by the attribute repeatability indication parameter of the virtual resource is less than or equal to 2, a high-value model is selected as the target resource analysis model to analyze the value of the virtual resource. For example, when the number indicated by the attribute repeatability indication parameter of the virtual resource is equal to 3, a 3-attribute model is selected as the target resource analysis model to analyze the value of the virtual resource. For example, when the number indicated by the attribute repeatability indication parameter of the virtual resource is equal to 4, a target resource analysis model is selected from multiple low-value models based on the server characteristics to analyze the value of the virtual resource.

[0115] In this way, a suitable resource analysis model is selected from different resource analysis models in combination with the characteristics of the virtual resources themselves to perform resource analysis on the virtual resources, thereby improving the accuracy of resource analysis, avoiding problems such as long selling time or losses caused by game players' independent pricing, and improving the trading experience of game players.

[0116] By adopting the solution of the embodiment of the present application, the target attribute repeatability indicator parameter of the target virtual resource to be analyzed can be obtained; based on the correspondence between the trained resource analysis model and the attribute repeatability indicator parameter, a target resource analysis model that matches the target attribute repeatability indicator parameter is determined from multiple resource analysis models; based on the target resource analysis model and the resource association data of the target virtual resource, a resource analysis is performed on the target virtual resource to obtain a value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, an appropriate resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long sales time or losses caused by game players' independent pricing, and improving the trading experience of game players.

[0117] This embodiment further provides an information processing device for virtual resources, which can be integrated into a terminal device.

[0118] For example, Figure 6 As shown, the information processing device of the virtual resource may include:

[0119] Parameter acquisition module 201, used to obtain target attribute repeatability indication parameters of the target virtual resource to be analyzed;

[0120] The model determination module 202 is configured to determine a target resource analysis model that matches the target attribute repeatability indication parameter from a plurality of resource analysis models based on the correspondence between the trained resource analysis models and the attribute repeatability indication parameter;

[0121] The analysis module 203 is configured to perform resource analysis on the target virtual resource based on the target resource analysis model and the resource association data of the target virtual resource to obtain a value assessment result of the target virtual resource.

[0122] In some embodiments, the parameter acquisition module 201 acquires the target attribute repeatability indication parameter of the target virtual resource to be analyzed, including:

[0123] The attribute acquisition submodule is used to acquire multiple resource attributes corresponding to the target virtual resource, wherein each resource attribute corresponds to an attribute type;

[0124] The parameter acquisition submodule is used to determine the target attribute repeatability indication parameter of the target virtual resource according to the number of resource attributes with the same attribute type under the target virtual resource.

[0125] In some embodiments, the above-mentioned attribute repeatability indication parameter includes a target indication parameter, which is used to indicate that the number of resource attributes with the same attribute type under the virtual resource does not exceed a preset value. In the corresponding relationship, there are multiple resource analysis models corresponding to the target indication parameter.

[0126] Based on this, the model determination module 202 determines a target resource analysis model that matches the target attribute repeatability indicator parameter from multiple resource analysis models based on the correspondence between the trained resource analysis models and the attribute repeatability indicator parameter, including:

[0127] A candidate model determination submodule is used to determine, when the target attribute repeatability indication parameter is the target indication parameter, a plurality of resource analysis models matching the target indication parameter according to the corresponding relationship as candidate resource analysis models;

[0128] The model determination submodule is used to determine a target resource analysis model that matches the server characteristics from multiple candidate resource analysis models based on the current server characteristics of the target server, wherein the target server is the server corresponding to the game to which the target virtual resource belongs.

[0129] In some embodiments, before determining the target resource analysis model that matches the target attribute repeatability indication parameter, the information processing device for the virtual resource further includes:

[0130] A sample acquisition unit, configured to acquire a number of virtual resource samples, wherein the virtual resource samples include attribute repeatability indication parameters corresponding to the virtual resources and historical resource association data;

[0131] A screening unit, configured to screen corresponding target virtual resource samples for each resource analysis model from the virtual resource samples based on attribute repeatability indication parameters corresponding to the plurality of resource analysis models to be trained;

[0132] The training unit is used to train each resource analysis model based on the target virtual resource sample corresponding to the resource analysis model and the historical resource association data corresponding to the target virtual resource sample to obtain a trained resource analysis model.

[0133] In some embodiments, the resource analysis model includes a first type of resource analysis model and a second type of resource analysis model, wherein different types of resource analysis models correspond to different attribute repeatability indication parameters, and the historical resource association data includes historical transaction value.

[0134] Based on this, the above screening of corresponding target virtual resource samples for each resource analysis model includes:

[0135] The first screening subunit is configured to obtain, for the first type of resource analysis model to be trained, value screening information corresponding to the first type of resource analysis model based on the attribute repeatability indicator parameter corresponding to the first type of resource analysis model; and for each virtual resource, screen out a target virtual resource sample for the first type of resource analysis model from a plurality of virtual resource samples corresponding to the virtual resource based on the value screening information and the historical transaction value of the virtual resource sample;

[0136] The second screening sub-unit is used to screen out target virtual resource samples of the second type of resource analysis model from multiple virtual resource samples based on the attribute repeatability indication parameters corresponding to the second type of resource analysis model to be trained.

[0137] In some embodiments, the above-mentioned first type of resource analysis model includes a first resource analysis model and a second resource analysis model, the number indicated by the attribute repeatability indication parameter corresponding to the first resource analysis model is higher than the number indicated by the attribute repeatability indication parameter corresponding to the second resource analysis model, and the value screening information includes value percentiles.

[0138] Based on this, the first screening sub-unit screens out target virtual resource samples for the first type of resource analysis model based on the value screening information and the historical transaction value of the virtual resource samples, including:

[0139] For the first resource analysis model, select from the virtual resource samples virtual resource samples whose value interval formed by the historical transaction value of the virtual resource samples is not less than the historical transaction value corresponding to the value percentile, and obtain the target virtual resource samples of the first resource analysis model;

[0140] For the second resource analysis model, from the virtual resource samples, the virtual resource samples whose value range formed by the historical transaction value of the virtual resource samples is not greater than the historical transaction value corresponding to the value percentile are screened out to obtain the target virtual resource samples of the second resource analysis model.

[0141] In some embodiments, the aforementioned virtual resource sample further includes a server feature, wherein the server feature is used to indicate a server feature of a server corresponding to a game to which the virtual resource sample belongs.

[0142] Based on this, the training unit performs training based on the target virtual resource samples corresponding to the resource analysis model to obtain a trained resource analysis model, including:

[0143] In the case where the resource analysis model is a second type of resource analysis model, based on the server characteristics, determining a to-be-trained virtual resource sample that matches the server characteristics from the target virtual resource samples of the second type of resource analysis model;

[0144] The second type of resource analysis model is trained based on the virtual resource samples to be trained to obtain the second type of resource analysis model trained under the server characteristics.

[0145] By adopting the solution of the embodiment of the present application, the target attribute repeatability indication parameter of the target virtual resource to be analyzed can be obtained through the parameter acquisition module 201; the model determination module 202 determines the target resource analysis model that matches the target attribute repeatability indication parameter from multiple resource analysis models based on the correspondence between the trained resource analysis model and the attribute repeatability indication parameter; the analysis module 203 performs resource analysis on the target virtual resource based on the resource association data of the target virtual resource according to the target resource analysis model, and obtains the value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, a suitable resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long selling time or losses caused by self-pricing by game players, and improving the transaction experience of game players.

[0146] Accordingly, an embodiment of the present application further provides an electronic device, which may be a terminal, such as a smartphone, a tablet computer, a laptop computer, a touch screen, a game console, a personal computer (PC), a personal digital assistant (PDA), or the like. Alternatively, the electronic device may be a server.

[0147] like Figure 7 As shown, Figure 7 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. It will be understood by those skilled in the art that the electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0148] The processor 301 is the control center of the electronic device 300. It connects the various parts of the entire electronic device 300 using various interfaces and lines. By running or loading software programs and / or units stored in the memory 302 and calling data stored in the memory 302, it executes various functions of the electronic device 300 and processes data, thereby monitoring the entire electronic device 300. The processor 301 can be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the various methods, steps, and logic blocks disclosed in the embodiments of this application.

[0149] In the embodiment of the present application, the processor 301 in the electronic device 300 loads instructions corresponding to one or more application processes into the memory 302 according to the following steps, and the processor 301 runs the application stored in the memory 302 to implement various functions, such as:

[0150] Displaying a virtual resource transaction interface, wherein the virtual resource transaction interface includes a plurality of virtual resources;

[0151] In response to a triggering operation of a target virtual resource among multiple virtual resources, a value assessment result of the target virtual resource is displayed, wherein the value assessment result is obtained by performing a value assessment on the target virtual resource based on a target resource analysis model that matches the target virtual resource among multiple trained resource analysis models.

[0152] By using the electronic device provided in the embodiment of the present application, the target attribute repeatability indication parameter of the target virtual resource to be analyzed can be obtained; based on the correspondence between the trained resource analysis model and the attribute repeatability indication parameter, a target resource analysis model that matches the target attribute repeatability indication parameter is determined from multiple resource analysis models; based on the target resource analysis model and the resource association data of the target virtual resource, a resource analysis is performed on the target virtual resource to obtain a value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, a suitable resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long sales time or losses caused by game players' independent pricing, and improving the trading experience of game players.

[0153] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0154] Optional, such as Figure 7 As shown, the electronic device 300 further includes: a touch screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. Among them, the processor 301 is electrically connected to the touch screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307 respectively. Those skilled in the art will understand that Figure 7 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0155] The touch display screen 303 can be used to display a graphical user interface and receive user operations generated by the graphical user interface. The touch display screen 303 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, a liquid crystal display (LCD), an organic light emitting diode (OLED) or the like can be used to configure the display panel. The touch panel can be used to collect user touch operations on or near it (such as operations performed by the user using any suitable object or accessory such as a finger, stylus or the like on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 301, and can receive the command sent by the processor 301 and execute it. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 301 to determine the type of touch event, and then the processor 301 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present application, the touch panel and the display panel can be integrated into the touch display screen 303 to realize the input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize the input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to realize the input function.

[0156] The radio frequency circuit 304 may be used to transmit and receive radio frequency signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and to transmit and receive signals with the network device or other electronic devices.

[0157] The audio circuit 305 can be used to provide an audio interface between the user and the electronic device through a speaker and microphone. The audio circuit 305 can convert the received audio data into an electrical signal and transmit it to the speaker, which then converts it into a sound signal for output. On the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 305 and converted into audio data. The audio data is then output to the processor 301 for processing, and then sent to another electronic device through the radio frequency circuit 304, or the audio data is output to the memory 302 for further processing. The audio circuit 305 may also include an earphone jack to provide communication between external headphones and the electronic device.

[0158] The input unit 306 may be configured to receive virtual resources to be analyzed, etc., selected or input by a user.

[0159] Power supply 307 is used to supply power to various components of electronic device 300. Optionally, power supply 307 can be logically connected to processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 307 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0160] although Figure 7 Not shown in the figure, the electronic device 300 may further include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail here.

[0161] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0163] To this end, an embodiment of the present application provides a computer-readable storage medium storing a plurality of computer programs, which can be loaded by a processor to execute any of the virtual resource information processing methods provided in the embodiments of the present application. The computer program can execute the following steps of the virtual resource information processing method:

[0164] Displaying a virtual resource transaction interface, wherein the virtual resource transaction interface includes a plurality of virtual resources;

[0165] In response to a triggering operation of a target virtual resource among multiple virtual resources, a value assessment result of the target virtual resource is displayed, wherein the value assessment result is obtained by performing a value assessment on the target virtual resource based on a target resource analysis model that matches the target virtual resource among multiple trained resource analysis models.

[0166] By using the computer-readable storage medium provided in the embodiment of the present application, the target attribute repeatability indication parameter of the target virtual resource to be analyzed can be obtained; based on the correspondence between the trained resource analysis model and the attribute repeatability indication parameter, a target resource analysis model that matches the target attribute repeatability indication parameter is determined from multiple resource analysis models; based on the target resource analysis model and the resource association data of the target virtual resource, a resource analysis is performed on the target virtual resource to obtain a value assessment result of the target virtual resource. Based on this, by combining the characteristics of the virtual resource itself, a suitable resource analysis model is selected from different resource analysis models to perform resource analysis on the virtual resource, thereby improving the accuracy of resource analysis, avoiding problems such as long sales time or losses caused by game players' independent pricing, and improving the trading experience of game players.

[0167] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0168] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0169] Since the computer program stored in the computer-readable storage medium can execute any virtual resource information processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any virtual resource information processing method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0170] According to one aspect of the present application, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in various optional implementations of the above embodiments.

[0171] In the above-described embodiments of the information processing device for virtual resources, computer-readable storage medium, electronic device, and computer program product, the descriptions of each embodiment have different focuses. For portions not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the above-described information processing device for virtual resources, computer-readable storage medium, computer program product, electronic device, and their corresponding units can be referred to in the description of the information processing method for virtual resources in the above embodiments, and the details will not be repeated here.

[0172] The above is a detailed introduction to the information processing method, device, electronic device, computer-readable storage medium and computer program product of a virtual resource provided by the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for processing virtual resource information, characterized in that: The method comprises: Obtaining a target attribute repeatability indication parameter of a target virtual resource to be analyzed; Determining a target resource analysis model that matches the target attribute repeatability indication parameter from a plurality of resource analysis models according to a correspondence between the trained resource analysis models and the attribute repeatability indication parameter; According to the target resource analysis model, based on the resource association data of the target virtual resource, a resource analysis is performed on the target virtual resource to obtain a value assessment result of the target virtual resource.

2. The method for processing virtual resource information according to claim 1, wherein: The step of obtaining the target attribute repeatability indication parameter of the target virtual resource to be analyzed includes: Acquire multiple resource attributes corresponding to the target virtual resource, wherein each resource attribute corresponds to an attribute type; A target attribute repeatability indication parameter of the target virtual resource is determined according to the number of resource attributes of the same attribute type under the target virtual resource.

3. The method for processing virtual resource information according to claim 2, wherein: The attribute repeatability indication parameter includes a target indication parameter, which is used to indicate that the number of resource attributes with the same attribute type under the virtual resource does not exceed a preset value, and in the corresponding relationship, the number of resource analysis models corresponding to the target indication parameter is multiple; The determining, based on the correspondence between the trained resource analysis models and the attribute repeatability indication parameters, a target resource analysis model that matches the target attribute repeatability indication parameters from a plurality of the resource analysis models includes: In a case where the target attribute repeatability indication parameter is the target indication parameter, determining, according to the corresponding relationship, a plurality of resource analysis models that match the target indication parameter as candidate resource analysis models; Based on the current server characteristics of the target server, a target resource analysis model that matches the server characteristics is determined from a plurality of candidate resource analysis models, wherein the target server is a server corresponding to the game to which the target virtual resource belongs.

4. The method for processing virtual resource information according to any one of claims 1 to 3, characterized in that: Before determining the target resource analysis model that matches the target attribute repeatability indication parameter, the method further includes: Acquire a number of virtual resource samples, wherein the virtual resource samples include attribute repeatability indication parameters corresponding to the virtual resources and historical resource association data; Based on attribute repeatability indication parameters corresponding to a plurality of resource analysis models to be trained, selecting corresponding target virtual resource samples for each resource analysis model from the virtual resource samples; For each of the resource analysis models, training is performed based on the target virtual resource samples corresponding to the resource analysis model and the historical resource association data corresponding to the target virtual resource samples to obtain the trained resource analysis model.

5. The method for processing virtual resource information according to claim 4, wherein: The resource analysis model includes a first type of resource analysis model and a second type of resource analysis model, wherein different types of resource analysis models correspond to different attribute repeatability indication parameters, and the historical resource association data includes historical transaction value; The step of screening corresponding target virtual resource samples for each resource analysis model includes: For the first type of resource analysis model to be trained, based on the attribute repeatability indication parameter corresponding to the first type of resource analysis model, value screening information corresponding to the first type of resource analysis model is obtained; for each virtual resource, based on the value screening information and the historical transaction value of the virtual resource sample, a target virtual resource sample for the first type of resource analysis model is screened from multiple virtual resource samples corresponding to the virtual resource; For the second type of resource analysis model to be trained, based on the attribute repeatability indication parameter corresponding to the second type of resource analysis model, a target virtual resource sample of the second type of resource analysis model is screened out from the plurality of virtual resource samples.

6. The method for processing virtual resource information according to claim 5, characterized in that: The first type of resource analysis model includes a first resource analysis model and a second resource analysis model, the quantity indicated by the attribute repeatability indication parameter corresponding to the first resource analysis model is higher than the quantity indicated by the attribute repeatability indication parameter corresponding to the second resource analysis model, and the value screening information includes a value percentile; The step of screening out target virtual resource samples for the first type of resource analysis model based on the value screening information and the historical transaction value of the virtual resource samples includes: For the first resource analysis model, screening out from the virtual resource samples virtual resource samples whose historical transaction values within a value range formed by the historical transaction values of the virtual resource samples are not less than the historical transaction values corresponding to the value percentile, thereby obtaining target virtual resource samples for the first resource analysis model; For the second resource analysis model, from the virtual resource samples, select the virtual resource samples whose value range formed by the historical transaction value of the virtual resource samples is not greater than the historical transaction value corresponding to the value percentile to obtain the target virtual resource samples of the second resource analysis model.

7. The method for processing virtual resource information according to claim 5, wherein: The virtual resource sample further includes a server feature, wherein the server feature is used to indicate a server feature of a server corresponding to the game to which the virtual resource sample belongs; The training is performed based on the target virtual resource sample corresponding to the resource analysis model to obtain the trained resource analysis model, including: In a case where the resource analysis model is the second type of resource analysis model, based on the server characteristics, determining a to-be-trained virtual resource sample that matches the server characteristics from target virtual resource samples of the second type of resource analysis model; The second-type resource analysis model is trained based on the virtual resource samples to be trained to obtain the second-type resource analysis model trained under the server characteristics.

8. An information processing device for virtual resources, characterized in that: The device comprises: A parameter acquisition module, used to obtain target attribute repeatability indication parameters of the target virtual resource to be analyzed; a model determination module, configured to determine, from a plurality of resource analysis models, a target resource analysis model that matches the target attribute repeatability indication parameter based on a correspondence between the trained resource analysis models and the attribute repeatability indication parameter; The analysis module is configured to perform resource analysis on the target virtual resource based on the target resource analysis model and the resource association data of the target virtual resource to obtain a value assessment result of the target virtual resource.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the virtual resource information processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The method comprises a computer program. When the computer program is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the virtual resource information processing method according to any one of claims 1 to 7.

Images