A game data analysis modeling method, device, equipment and medium

Abstract

The present invention discloses a game data analysis modeling method, device, equipment and medium, including: obtaining game parameters of several games at a preset frequency; classifying several game parameters, and storing each game parameter in an entity model, a relationship model or an event model in a preset data warehouse model according to the type of the game parameter; constructing a mapping relationship corresponding to the type of each game parameter; constructing a game indicator system; according to the needs of the target user, determining the demand indicators from the preset data warehouse model and displaying them. The present invention belongs to the field of electronic games. The present invention provides a point-of-sale specification, which is both universal and retains expansion capabilities, and can be compatible with data differences in different business scenarios; the present invention provides a preset data warehouse model, including an entity model, a relationship model or an event model, which can better balance the data calculation between business data and the indicator system, and build a set of suitable data bridges for different game business data and standard data indicator systems.

Description

Technical Field

[0001] The present invention relates to the field of electronic games, and in particular to a game data analysis modeling method, device, equipment and medium. Background Art

[0002] Video games are a medium of interactive entertainment through electronic devices. Players usually interact with the game through input devices such as controllers, touch screens, keyboards, etc. Video games can be played on a variety of platforms, including personal computers, game consoles, mobile devices, and web pages. Video games have a profound impact on society and culture, including promoting social interaction, creating career opportunities (such as professional gamers and game developers), and sparking discussions about violence and youth behavior.

[0003] In game operations, data-driven is crucial. For the game industry, core data indicators such as conversion rate, retention rate, payment rate, ARPU, ARPPU, CPC, CPA, CPR, CPL, LTV and ROI play an important role. The industry has provided many solutions for the design and calculation of core indicators. However, due to the differences in game-side data, there is currently a lack of a set of analytical modeling methods and tools for extracting core indicators from game-side data. At present, many game developers usually use empirical models and less data-driven when facing data-based operation needs. Even if a small amount of data-driven is introduced, it is usually a manual workshop style, and the game data indicators are gradually increased. This method is used to develop inefficient, imperfect indicators, low data reliability, and insufficient real-time performance. Some operators lack data thinking, and the decision-making method relies only on subjective experience and intuition, which directly leads to problems such as poor game experience and short life cycle, thereby seriously wasting productivity. In view of this, the present application provides a game data analysis modeling method to solve the above problems. Summary of the invention

[0004] The embodiments of the present application solve the technical problem of low automation level in the prior art when facing the demand for data-based operations by providing a game data analysis modeling method, device, equipment and medium, and achieve the technical problem of improving the automation level.

[0005] In a first aspect, the present application provides a game data analysis modeling method, the method comprising:

[0006] Obtain game parameters of several games at a preset frequency;

[0007] Classify a number of game parameters, and store each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter;

[0008] Construct the mapping relationship between the types of various game parameters;

[0009] Build a game indicator system;

[0010] According to the needs of target users, demand indicators are determined from the preset data warehouse model and displayed.

[0011] Furthermore, a number of game parameters are classified, and each game parameter is stored in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter, including:

[0012] Divide several game parameters into entity data, relationship data and event data, wherein each data includes historical values ​​and real-time values;

[0013] The entity data are stored in the entity model in the preset data warehouse model;

[0014] The plurality of relational data are stored in a relational model in a preset data warehouse model;

[0015] A number of event data are stored in an event model in a preset data warehouse model.

[0016] Furthermore, a mapping relationship corresponding to each type of game parameter is constructed, including:

[0017] According to the type of each game parameter, determining a mapping relationship corresponding to the type of the game parameter;

[0018] The real-time value of each game parameter is updated according to the associated event corresponding to each game parameter.

[0019] Further, including:

[0020] The game indicator system includes at least conversion rate, retention rate and payment rate.

[0021] Further, including:

[0022] Game parameters include at least the embedding business instructions, event occurrence time, event platform, network address, device representation, event source, user name and event content.

[0023] Furthermore, according to the needs of target users, demand indicators are determined from the preset data warehouse model and displayed, including:

[0024] According to the needs of target users, several target game parameters are determined from the preset data warehouse model;

[0025] Convert several target game parameters into required indicators in the game indicator system and display them.

[0026] Further, including

[0027] Entity data at least includes entity type, entity name, entity attributes, attribute latest time, and attribute extension field.

[0028] In a second aspect, the present application provides a game data analysis and modeling device, the device comprising:

[0029] An acquisition module, used to acquire game parameters of several games at a preset frequency;

[0030] A classification storage module is used to classify a number of game parameters and store each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter;

[0031] A mapping construction module is used to construct mapping relationships corresponding to the types of various game parameters;

[0032] System construction module, used to build the game indicator system;

[0033] The display module is used to determine and display demand indicators from the preset data warehouse model according to the needs of target users.

[0034] In a third aspect, the present application provides an electronic device, including:

[0035] processor;

[0036] a memory for storing processor-executable instructions;

[0037] Among them, the processor is configured to execute to implement a game data analysis modeling method as provided in the first aspect.

[0038] In a fourth aspect, the present application provides a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a game data analysis modeling method as provided in the first aspect.

[0039] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0040] The present application provides a game data analysis modeling method, the method includes: obtaining game parameters of several games at a preset frequency; classifying several game parameters, and storing each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter; constructing a mapping relationship corresponding to the type of each game parameter; constructing a game indicator system; according to the needs of the target user, determining the demand indicators from the preset data warehouse model and displaying them. The present application provides a point-of-sale specification, which is both universal and retains expansion capabilities, and can be compatible with data differences in different business scenarios; the present application provides a preset data warehouse model, including an entity model, a relational model or an event model, which can better balance the data calculation between business data and the indicator system, and build a set of suitable data bridges for different game business data and standard data indicator systems. The present application also provides a comprehensive and universal game data indicator system, which needs to ensure both universality and flexibility in the design of the indicator system; in the calculation of indicators, both reliability and timeliness must be guaranteed; the time dimension of indicators can be divided as needed; the present application can timely and effectively ensure that data is transformed from resources into assets through real-time calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flowchart of a game data analysis modeling method provided in this application;

[0043] Figure 2 A schematic diagram of the structure of a game data analysis and modeling device provided in this application;

[0044] Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0045] The embodiment of the present application solves the technical problem of low automation level in the prior art when facing the demand for data-based operations by providing a game data analysis modeling method.

[0046] The technical solution of the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0047] A game data analysis modeling method, the method comprising: obtaining game parameters of several games at a preset frequency; classifying the several game parameters, and storing each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter; constructing a mapping relationship corresponding to the type of each game parameter; constructing a game indicator system; and determining and displaying the demand indicators from the preset data warehouse model according to the needs of target users.

[0048] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0049] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0050] This application provides Figure 1 A game data analysis modeling method shown includes steps S11-S15:

[0051] Step S11, obtaining game parameters of several games at a preset frequency;

[0052] Step S12, classifying a plurality of game parameters, and storing each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter;

[0053] Step S13, constructing mapping relationships corresponding to the types of various game parameters;

[0054] Step S14, constructing a game indicator system;

[0055] Step S15: According to the needs of the target users, the demand indicators are determined from the preset data warehouse model and displayed.

[0056] It should be noted that the purpose of this application is to convert different game business data into common indicators in the game industry and to build a suitable conversion relationship between the two. Please refer to steps S11-S15 for details.

[0057] Regarding step S11, game parameters of several games are obtained at a preset frequency.

[0058] The preset frequency can be set according to actual conditions, for example, 10 minutes, 15 minutes or 20 minutes.

[0059] Game parameters include at least the embedding business instructions, event occurrence time, event platform, network address, device representation, event source, user name and event content.

[0060] In addition, this application also provides a tracking SDK tool for collecting game parameters. The tracking SDK tool is used for data collection and analysis, and is used in mobile applications and web pages. The SDK tool can monitor user behavior, activities, and interactions by adding code at specific locations (i.e., "tracking points"). The main functions of the tracking SDK tool include: event tracking, data analysis, user profiling, and performance monitoring.

[0061] The SDK compresses the game parameters of several games and reports them to the server at a preset frequency (for example, 10 minutes). After the server receives the information, step S12 is executed.

[0062] Regarding step S12, a number of game parameters are classified, and each game parameter is stored in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter.

[0063] According to the obtained game parameters, the game parameters are classified. This application divides the game parameters into three categories. In addition, the game parameters can also be classified according to the needs of the target users.

[0064] The present application divides several game parameters into entity data, relationship data and event data, wherein each data includes historical values ​​and real-time values.

[0065] Entity data includes: entity type, entity name, entity attributes, attribute latest time and attribute extension field, and may also include attribute time.

[0066] Relationship data includes: relationship type, relationship master name, relationship slave name, relationship latest time, and relationship extension field.

[0067] Event data includes: event type, event entity type, event entity, event time, and event extension fields.

[0068] After classifying several game parameters and determining the type of each game parameter, several entity data can be stored in the entity model in the preset data warehouse model; several relationship data can be stored in the relationship model in the preset data warehouse model; and several event data can be stored in the event model in the preset data warehouse model.

[0069] In addition, this application adopts the NoSQL method when storing data. NoSQL databases are generally suitable for storing unstructured or semi-structured data, have high scalability and flexibility, and can process large amounts of real-time data.

[0070] The preset data warehouse model provided in this application can establish different data model instances according to the type of game.

[0071] For example: user entity data includes the latest data of user attributes and daily status data of user attributes; user fleet entity data and user fleet ship entity data; user fleet relationship model and fleet ship relationship model.

[0072] Based on the preset data warehouse model provided by this application, it is compatible with data modeling in general games. Based on the existing nova1 and nova2 game scenarios, a set of model instances can be built as templates to facilitate reuse in other games by adjusting field configurations.

[0073] Regarding step S13, a mapping relationship corresponding to each type of each game parameter is constructed.

[0074] After storing each data parameter in each model in the preset data warehouse model, the mapping relationship corresponding to the type of each game parameter can be determined according to the type of the game parameter; the real-time value of each game parameter can be updated according to the associated events corresponding to the game parameters.

[0075] Specifically, the mapping relationship between each game parameter extension field and the entity data field in the preset data warehouse model can be marked according to business needs.

[0076] Tags are identifiers or labels related to business logic, which are used to focus on specific user behaviors or events. Extension fields are additional information fields that can be used to attach more context or detailed data. In event records, extension fields can provide a deeper analysis dimension. Mapping relationships refer to the correspondence between the extension fields of game parameters and entity data fields.

[0077] In the process of data analysis or event tracking, according to specific business needs, the extended information of each game parameter is mapped to the existing entity data to better perform data analysis and decision support. This application provides a mapping method, please refer to Table 1 for details.

[0078] Table 1

[0079]

[0080]

[0081] By establishing a mapping relationship, data from different sources can be integrated. The mapping relationship makes data analysis more intuitive and easy to operate. The mapping relationship can ensure consistency between different data sources, reduce data redundancy and errors, and thus improve data accuracy.

[0082] According to each game parameter and the associated event corresponding to the game parameter, the real-time value of each game parameter is updated, including: extracting attribute data from all associated event records of several game parameters of the preset data warehouse model, and determining to retain the latest generated attribute value according to the event time.

[0083] For example, the target game parameter is the remaining number of gold coins of user A. In order to obtain the latest remaining number of gold coins of user A, it is necessary to obtain all associated events related to user A's gold coins, and determine the real-time value of the remaining number of gold coins of user A based on the associated events related to user A's gold coins.

[0084] Regarding step S14, construct a game indicator system.

[0085] The game indicator system includes at least conversion rate, retention rate and payment rate. The game indicator system provided by this application also includes:

[0086] Conversion rate: number of impressions, clicks, downloads, installations, registrations, active users, and paid users;

[0087] Retention rate: number of registrations, next-day retention, 3-day retention, weekly retention, monthly retention, weighted retention;

[0088] Payment rate: number of registrations, number of active users, number of payments, payment amount, payment interval:

[0089] ARPU / ARPPU: payment amount, number of paying users, number of active users;

[0090] CPC / CPA / CPM / CVR / CTR: advertising fee, number of impressions, number of clicks, number of conversions, and number of payments;

[0091] PV / UV / GMV: number of visits, number of visitors, total payment;

[0092] LTV: Total lifetime value of a user;

[0093] ROI: return on investment, profit, investment amount;

[0094] RFM: payment interval, number of payments, and payment amount;

[0095] Activity effect: number of participants, number of participations, and activity payment;

[0096] Item effect: number of purchasers, purchase amount, purchase interval, item consumption:

[0097] Others: Custom indicators can be assembled through fields.

[0098] It can be understood that after constructing the indicators, the values ​​of each indicator in the game indicator system are determined respectively according to several game parameters in the preset data warehouse model provided in this application, and stored in a preset position in a certain time sequence. For example, the value of each indicator in the game indicator system can be determined once a day and stored.

[0099] Regarding step S15, according to the target user's needs, the demand indicators are determined from the preset data warehouse model and displayed.

[0100] According to the needs of target users, the value of the demand indicator can be determined from the preset data warehouse model and displayed on the client of the target user.

[0101] Specifically, according to the target user needs, several target game parameters can be determined from the preset data warehouse model; the several target game parameters can be converted into demand indicators in the game indicator system and displayed.

[0102] In summary, the present application provides a game data analysis modeling method, the method comprising: obtaining game parameters of several games at a preset frequency; classifying several game parameters, and storing each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter; constructing a mapping relationship corresponding to the type of each game parameter; constructing a game indicator system; according to the needs of the target user, determining the demand indicators from the preset data warehouse model and displaying them. The present application provides a point-of-sale specification, which is both universal and retains expansion capabilities, and can be compatible with data differences in different business scenarios; the present application provides a preset data warehouse model, including an entity model, a relational model or an event model, which can better balance the data calculation between business data and the indicator system, and build a set of suitable data bridges for different game business data and standard data indicator systems. The present application also provides a comprehensive and universal game data indicator system, which needs to ensure both universality and flexibility in the design of the indicator system; in the calculation of indicators, both reliability and timeliness must be guaranteed; the time dimension of indicators can be divided as needed; the present application can timely and effectively ensure that data is transformed from resources into assets through real-time calculation.

[0103] Based on the same inventive concept, the present application provides Figure 2 A game data analysis modeling method is shown, the device comprising:

[0104] An acquisition module 21 is used to acquire game parameters of several games at a preset frequency;

[0105] A classification storage module 22, used to classify a number of game parameters, and store each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter;

[0106] A mapping construction module 23, used to construct mapping relationships corresponding to the types of various game parameters;

[0107] A system construction module 24, used to construct a game indicator system;

[0108] The display module 25 is used to determine and display demand indicators from the preset data warehouse model according to the target user's needs.

[0109] Based on the same inventive concept, the present application also provides Figure 3 An electronic device as shown includes:

[0110] Processor 31;

[0111] A memory 32 for storing instructions executable by the processor 31;

[0112] The processor 31 is configured to execute to implement a game data analysis modeling method as provided above.

[0113] Based on the same inventive concept, the present application also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor 31 of the electronic device, the electronic device can execute a game data analysis modeling method as provided above.

[0114] Since the electronic device introduced in this embodiment is an electronic device used to implement the information processing method in the embodiment of the present application, based on the information processing method introduced in the embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements the electronic device used by the information processing method in the embodiment of the present application, it belongs to the scope of protection of this application.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0120] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A game data analysis modeling method, characterized in that: The method comprises: Obtain game parameters of several games at a preset frequency; Classify a number of game parameters, and store each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter; Construct the mapping relationship between the types of various game parameters; Build a game indicator system; According to the target user's needs, the demand indicators are determined from the preset data warehouse model and displayed.

2. A game data analysis modeling method as claimed in claim 1, characterized in that: The method of classifying a plurality of game parameters and storing each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter includes: Divide several game parameters into entity data, relationship data and event data, wherein each data includes historical values ​​and real-time values; Storing a plurality of entity data in the entity model in the preset data warehouse model; Storing a plurality of relational data in a relational model in the preset data warehouse model; A plurality of event data are stored in the event model in the preset data warehouse model.

3. A game data analysis modeling method as claimed in claim 1, characterized in that: The mapping relationships corresponding to the types of the game parameters are constructed, including: According to the type of each game parameter, determining a mapping relationship corresponding to the type of the game parameter; The real-time value of each game parameter is updated according to the associated event corresponding to each game parameter.

4. A game data analysis modeling method as claimed in claim 1, characterized in that: include: The game indicator system includes at least conversion rate, retention rate and payment rate.

5. A game data analysis modeling method as claimed in claim 1, characterized in that: include: The game parameters include at least the embedding business instructions, event occurrence time, event platform, network address, device representation, event source, user name and event content.

6. A game data analysis modeling method as claimed in claim 1, characterized in that: Determining and displaying demand indicators from the preset data warehouse model according to the target user's needs includes: According to the target user needs, determining a number of target game parameters from the preset data warehouse model; A number of target game parameters are converted into demand indicators in the game indicator system and displayed.

7. A game data analysis modeling method as claimed in claim 2, characterized in that: include The entity data at least includes entity type, entity name, entity attribute, attribute latest time and attribute extension field.

8. A game data analysis and modeling device, characterized in that: The device comprises: An acquisition module, used to acquire game parameters of several games at a preset frequency; A classification storage module is used to classify a number of game parameters and store each game parameter in an entity model, a relational model or an event model in a preset data warehouse model according to the type of the game parameter; A mapping construction module is used to construct mapping relationships corresponding to the types of various game parameters; System construction module, used to build the game indicator system; The display module is used to determine and display demand indicators from the preset data warehouse model according to the needs of target users.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute to implement a game data analysis and modeling method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to implement a game data analysis and modeling method as described in any one of claims 1 to 7.

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