CN109858956B - Game item pushing method and system based on big data - Google Patents
Game item pushing method and system based on big data Download PDFInfo
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Abstract
The technical scheme of the invention comprises a game item pushing method and a game item pushing system based on big data, which are used for realizing: according to the probability system, carrying out system design of the game according to the traditional mode; establishing a data model according to the system design of the game; according to the system design of the game, a feedback system model is established for dynamic modification; the game test collects user data, and the system dynamically generates self-adaptive parameters according to the game data; and releasing the corresponding parameters to a formal game system and performing online operation. The beneficial effects of the invention are as follows: the game reward system has dynamic coordination, and the balance of the game system is kept, so that players can find own targets and expectations in the game system, and the survival life cycle of the game is prolonged; adding more dimensions to the big data system; the big data system dynamically adjusts the generation probability of the rewarding articles according to the game data of all players, and has real-time variability.
Description
Technical Field
The invention relates to a game item pushing method and system based on big data, and belongs to the technical field of Internet.
Background
As games continue to be played, conventional game making methods are often based on the distribution of game rewards according to the probability of the whole game system.
Defects of the conventional mode:
1. generating rewarding articles according to the probability, thereby achieving the dynamic balance of the game system;
2. the bonus material cannot be dynamically adjusted according to the player's desire to make the game interesting and playable.
(often rewarding many items, but not own needs, economically called excess assets).
Disclosure of Invention
In order to solve the problems, the invention aims to provide a game item pushing method and a game item pushing system based on big data, and the system design of a game is performed according to a traditional mode according to a probability system; establishing a data model (mainly according to a normal distribution model, a uniform distribution model, an index distribution model and the like) according to the system design of the game; according to the system design of the game, a feedback system model (mainly adopting an exponential fit model, a deviation minimization model and the like) is established for dynamic modification; the game test collects user data, and the system dynamically generates self-adaptive parameters according to the game data; and releasing the corresponding parameters to a formal game system and performing online operation.
The invention solves the problems by adopting the technical scheme that: the game item pushing method based on big data is characterized by comprising the following steps: s100, collecting user information, wherein the user information comprises item information of current equipment of a user, item information held by the user and user grade information; s200, setting an initial expected value of a user on the game object according to the user information; s300, starting game operation, collecting user information in real time, and obtaining expected values of users on objects in the game by establishing a data model, wherein the data model comprises but is not limited to a normal distribution model, a uniform distribution model and an index distribution model; s400, carrying out secondary processing on the user expectation obtained in the S300 through a feedback system model to obtain a dynamically corrected user expectation value, wherein the feedback system model comprises but is not limited to an exponential fit model and a deviation minimization model; s500, repeatedly executing the steps S300 to S400, feeding back in real time according to the article information provided by the game system, obtaining the self-adaptive parameters, and obtaining the dynamic user expected value according to the self-adaptive parameters and the user expected value after secondary processing.
Further, the S100 includes: s101, obtaining user information through the Internet and/or user history data stored in a game server, wherein the user information comprises article equipment information currently worn on a person by a user, article equipment information stored in a user knapsack and/or warehouse, and grade information and role information of a user role.
Further, the S300 includes: s301, operating an open game, and simultaneously recording user information of a user in a game play in real time; s302, establishing a data model, wherein the model comprises a normal distribution model, a uniform distribution model and an index distribution model; s303, calculating expected values by taking user information as an input source and taking the user information as a basis by the data model to obtain the expected value of each game user after one-time processing, wherein the expected value is the expected value of the object provided by the user for the game.
Further, the S400 includes: s401, establishing a feedback system model, wherein the model comprises an exponential fit model and a deviation minimization model; s402, according to the expected value of the once processed user obtained in the step S300, the expected value of each game user after the secondary processing is obtained by using the feedback system model as an input source of the feedback system model on the basis of the expected value of the once processed user, wherein the expected value is the expected value of the user for the object provided by the game.
Further, the S500 includes: s501, pushing articles according to the secondarily processed user expected value obtained in the step S400, and monitoring the number of the articles and the change of information in the game in real time; s502, dynamically adjusting user expectations according to the quantity change of the types of the articles in the game and the expectations of various players on the articles, wherein the quantity of the articles provided by the game and the expectations of the players on the articles are in inverse proportion; s503, setting a dynamic self-adaptive parameter according to the processing principle of S502, wherein the input source of the self-adaptive parameter is the expected value of the user after secondary processing and the quantity and type of the articles provided by the game, and the output result is the expected value of the user.
The invention solves the problems by adopting the technical scheme that: a big data based game item pushing system, comprising: the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring user information, wherein the user information comprises the item information of the current equipment of a user, the item information held by the user and user grade information; the setting module is used for setting an initial expected value of a user on the game object according to the user information; the server module is used for starting game operation, collecting user information in real time and obtaining expected values of users on articles in the game by establishing a data model; and the dynamic adjustment module is used for feeding back in real time according to the article information provided by the game system to obtain the self-adaptive parameter, and obtaining the dynamic user expected value according to the self-adaptive parameter and the user expected value after secondary processing.
Further, the server module includes: the operation and information collection module is used for opening game operation and recording user information of a user in a game in real time; a first modeling module for building data models including, but not limited to, a normal distribution model, a uniform distribution model, and an exponential distribution model; the primary processing module is used for taking the user information as an input source, and the data model carries out expected value calculation based on the user information to obtain expected values of each game user after primary processing, wherein the expected values are expected values of objects provided by the users for games.
Further, the server module further includes: a second modeling module for building feedback system models including, but not limited to, an exponential fit model and a bias minimization model; the secondary processing module is used for taking the expected value of the once processed user obtained by the primary processing module as an input source of the feedback system model, and the feedback system model is based on the expected value of the once processed user to obtain the expected value of each game user after secondary processing, wherein the expected value is the expected value of the user for the object provided by the game.
Further, the dynamic adjustment module includes: the pushing module is used for pushing the articles according to the user expected value obtained by the secondary processing module after secondary processing and monitoring the number of the articles and the change of information in the game in real time; an adjustment module for dynamically adjusting user expectations in combination with the expectations of each player for the items according to the number changes of the types of the items in the game, wherein the number of the items provided by the game and the expectations of the players for the items are in inverse proportion; the parameter module is used for setting dynamic self-adaptive parameters according to the processing principle set by the adjustment module, the input source of the self-adaptive parameters is the expected value of the user after secondary processing and the quantity and the type of the articles provided by the game, and the output result is the dynamic expected value of the user.
The beneficial effects of the invention are as follows: the game reward system has dynamic coordination, and the balance of the game system is kept, so that players can find own targets and expectations in the game system, and the survival life cycle of the game is prolonged; in addition to the probability scheme reaching the balance of the game system, the dimensions required by the players are added, namely, the dimensions are changed from single reference factors to two-dimensional reference factors, and more dimensions can be added to a big data system; the reward system has variability, and the big data system dynamically adjusts the generation probability of the reward articles according to the game data of all players, so that the reward system has real-time variability.
Drawings
FIG. 1 is a flow chart of a method according to a preferred embodiment of the invention;
FIG. 2 is a schematic diagram of a system according to a preferred embodiment of the invention;
FIG. 3 is a system diagram of a conventional game bonus system;
fig. 4 shows a preferred embodiment according to the present invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly or indirectly fixed or connected to the other feature. Further, the descriptions of the upper, lower, left, right, etc. used in this disclosure are merely with respect to the mutual positional relationship of the various components of this disclosure in the drawings. As used in this disclosure, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description presented herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any combination of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could also be termed a second element, and, similarly, a second element could also be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
The normal distribution (Normal distribution), also known as the "normal distribution", also known as the gaussian distribution (Gaussian distribution), was originally obtained from the asymptotic formula for the bivariate distribution by a. C.f. gaussian derives it from another angle when studying measurement errors. The properties of the material were studied by p.s. laplace and gaussian. Is a probability distribution which is very important in the fields of mathematics, physics, engineering and the like, and has great influence on a plurality of aspects of statistics.
The normal curve is bell-shaped, the two ends are low, the middle is high, and the left and right symmetry is bell-shaped, so people are often called bell-shaped curves.
If the random variable X obeys a normal distribution with a mathematical expectation μ and variance σ 2, it is denoted as N (μ, σ 2). Its probability density function determines its position for the expected value μ of the normal distribution, and its standard deviation σ determines the magnitude of the distribution. The normal distribution when μ=0, σ=1 is a standard normal distribution.
In probability theory and statistics, an exponential distribution (also called negative exponential distribution) is a probability distribution describing the time between events in a poisson process, i.e. a process in which events occur continuously and independently at a constant average rate. This is a special case of gamma distribution. It is a continuous simulation of the geometrical distribution, it has the key property of memory-free. In addition to being used to analyze poisson processes, it can be found in various other environments.
An exponential distribution is distinguished from a classification of a family of distribution indices, which is a broad class of probability distributions that contain the exponential distribution as one of its members, as well as normal distributions, binomial distributions, gamma distributions, poisson distributions, and so forth.
An important feature of the exponential function is the memoryless (Memoryless Property, also known as lost memory). This means that if a random variable is exponentially distributed, there is P (T > t+s|t > T) =p (T > s) when s, T > 0. That is, if T is the life of a certain element, the known element uses T hours, and it uses a conditional probability of at least s+t hours in total, which is equal to the probability of it being used for at least s hours from the time of starting use.
Setting all possible values of the continuous random variable X to fill a finite interval [ a, b ] and the probability density of any point in the interval is the same, namely the density function f (X) is constant in the interval [ a, b ], and the distribution is uniformly distributed and is marked as U (a, b)
When X obeys the distribution U (a, b) on the [ a, b ], it is marked as X-U (a, b),
meaning of uniform distribution:
the probability that the random variable X, subject to uniform distribution over the interval a, b, falls within any equal length subinterval in the interval a, b is the same,
1. probability density
Probability density f (x) =c (constant) over interval [ a, b ], then
Define the integral operation as inte (a, b) [ ] a, b is the upper and lower limits
inte(a,b)[C]dx＝C*(b-a)＝1＝>C＝1/(b-a)
Also, since it is impossible to obtain values outside the interval [ a, b ] for the random variable X, the probability density is 0 outside the interval [ a, b ], and thus the probability density is
f(x)＝{1/(b-1),a<＝x<＝b；0,else}
Referring to figure 1 which is a schematic flow chart of a method according to a preferred embodiment of the invention,
the method comprises the following steps: s100, collecting user information, wherein the user information comprises item information of current equipment of a user, item information held by the user and user grade information; s200, setting an initial expected value of a user on the game object according to the user information; s300, starting game operation, collecting user information in real time, and obtaining expected values of users on objects in the game by establishing a data model, wherein the data model comprises but is not limited to a normal distribution model, a uniform distribution model and an index distribution model; s400, carrying out secondary processing on the user expectation obtained in the S300 through a feedback system model to obtain a dynamically corrected user expectation value, wherein the feedback system model comprises but is not limited to an exponential fit model and a deviation minimization model; s500, repeatedly executing the steps S300 to S400, feeding back in real time according to the article information provided by the game system, obtaining the self-adaptive parameters, and obtaining the dynamic user expected value according to the self-adaptive parameters and the user expected value after secondary processing.
S100 includes: s101, obtaining user information through the Internet and/or user history data stored in a game server, wherein the user information comprises article equipment information currently worn on a person by a user, article equipment information stored in a user knapsack and/or warehouse, and grade information and role information of a user role.
S300 includes: s301, operating an open game, and simultaneously recording user information of a user in a game play in real time; s302, establishing a data model, wherein the model comprises a normal distribution model, a uniform distribution model and an index distribution model; s303, calculating expected values by taking user information as an input source and taking the user information as a basis by the data model to obtain the expected value of each game user after one-time processing, wherein the expected value is the expected value of the object provided by the user for the game.
S400 includes: s401, establishing a feedback system model, wherein the model comprises an exponential fit model and a deviation minimization model; s402, according to the expected value of the once processed user obtained in the step S300, the expected value of each game user after the secondary processing is obtained by using the feedback system model as an input source of the feedback system model on the basis of the expected value of the once processed user, wherein the expected value is the expected value of the user for the object provided by the game.
S500 includes: s501, pushing articles according to the secondarily processed user expected value obtained in the step S400, and monitoring the number of the articles and the change of information in the game in real time; s502, dynamically adjusting user expectations according to the quantity change of the types of the articles in the game and the expectations of various players on the articles, wherein the quantity of the articles provided by the game and the expectations of the players on the articles are in inverse proportion; s503, setting a dynamic self-adaptive parameter according to the processing principle of S502, wherein the input source of the self-adaptive parameter is the expected value of the user after secondary processing and the quantity and type of the articles provided by the game, and the output result is the expected value of the user.
It should be appreciated that embodiments of the invention may be implemented or realized by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.
In summary, in the sense that,
first, according to probability system, system design of game according to traditional mode
Step two, according to the system design of the game, establishing a data model (mainly according to a normal distribution model and a uniform distribution model, an index distribution model and the like)
Third step, according to the system design of the game, a feedback system model (mainly adopting an exponential fit model, a deviation minimization model and the like) is established for dynamic modification
Fourthly, the game test carries out user data acquisition, and the system dynamically generates self-adaptive parameters according to the game data
And fifthly, releasing the corresponding parameters to a formal game system and performing online operation.
Referring to figure 2 which is a schematic diagram of a system architecture according to a preferred embodiment of the present invention,
comprising the following steps: the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring user information, wherein the user information comprises the item information of the current equipment of a user, the item information held by the user and user grade information; the setting module is used for setting an initial expected value of a user on the game object according to the user information; the server module is used for starting game operation, collecting user information in real time and obtaining expected values of users on articles in the game by establishing a data model; and the dynamic adjustment module is used for feeding back in real time according to the article information provided by the game system to obtain the self-adaptive parameter, and obtaining the dynamic user expected value according to the self-adaptive parameter and the user expected value after secondary processing.
The server module includes: the operation and information collection module is used for opening game operation and recording user information of a user in a game in real time; a first modeling module for building data models including, but not limited to, a normal distribution model, a uniform distribution model, and an exponential distribution model; the primary processing module is used for taking the user information as an input source, and the data model carries out expected value calculation based on the user information to obtain expected values of each game user after primary processing, wherein the expected values are expected values of objects provided by the users for games.
The server module further includes: a second modeling module for building feedback system models including, but not limited to, an exponential fit model and a bias minimization model; the secondary processing module is used for taking the expected value of the once processed user obtained by the primary processing module as an input source of the feedback system model, and the feedback system model is based on the expected value of the once processed user to obtain the expected value of each game user after secondary processing, wherein the expected value is the expected value of the user for the object provided by the game.
The dynamic adjustment module comprises: the pushing module is used for pushing the articles according to the user expected value obtained by the secondary processing module after secondary processing and monitoring the number of the articles and the change of information in the game in real time; an adjustment module for dynamically adjusting user expectations in combination with the expectations of each player for the items according to the number changes of the types of the items in the game, wherein the number of the items provided by the game and the expectations of the players for the items are in inverse proportion; the parameter module is used for setting dynamic self-adaptive parameters according to the processing principle set by the adjustment module, the input source of the self-adaptive parameters is the expected value of the user after secondary processing and the quantity and the type of the articles provided by the game, and the output result is the dynamic expected value of the user.
Referring to fig. 3, which is a conventional gaming bonus system, a single bonus item corresponds to a single bonus module.
Referring to fig. 4, in the game reward system of the present invention, a single reward item corresponds to a plurality of reward modules, the probability of dropping of the single reward item is linked with the desire of the user, and real-time correction is performed by combining with the big data dynamic correction system.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention.
The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
The present invention is not limited to the above embodiments, but can be modified, equivalent, improved, etc. by the same means to achieve the technical effects of the present invention, which are included in the spirit and principle of the present invention. Various modifications and variations are possible in the technical solution and/or in the embodiments within the scope of the invention.
Claims (5)
1. The game item pushing method based on big data is characterized by comprising the following steps:
s100, collecting user information, wherein the user information comprises item information of current equipment of a user, item information held by the user and user grade information;
s200, setting an initial expected value of a user on the game object according to the user information;
s300, starting game operation, collecting user information in real time, and obtaining expected values of users on objects in the game by establishing a data model, wherein the data model comprises but is not limited to a normal distribution model, a uniform distribution model and an index distribution model;
s400, carrying out secondary processing on the user expectation obtained in the S300 through a feedback system model to obtain a dynamically corrected user expectation value, wherein the feedback system model comprises but is not limited to an exponential fit model and a deviation minimization model;
specifically, the S400 includes: s401, establishing a feedback system model, wherein the model comprises an exponential fit model and a deviation minimization model; s402, according to the expected value of the once processed user obtained in the step S300, the expected value of each game user after secondary processing is obtained by using the expected value of the once processed user as an input source of a feedback system model, wherein the expected value is the expected value of the object provided by the user for the game;
s500, repeatedly executing the steps S300 to S400, feeding back in real time according to the article information provided by the game system to obtain self-adaptive parameters, and obtaining a dynamic user expected value according to the self-adaptive parameters and the user expected value after secondary processing;
specifically, the S500 includes: s501, pushing articles according to the secondarily processed user expected value obtained in the step S400, and monitoring the number of the articles and the change of information in the game in real time; s502, dynamically adjusting user expectations according to the quantity change of the types of the articles in the game and the expectations of various players on the articles, wherein the quantity of the articles provided by the game and the expectations of the players on the articles are in inverse proportion; s503, setting a dynamic self-adaptive parameter according to the processing principle of S502, wherein the input source of the self-adaptive parameter is a user expected value after secondary processing and the quantity and type of the articles provided by the game, and the output result is the dynamic expected value of the user, so that the generation probability of the rewarded articles is dynamically adjusted.
2. The big data based game item pushing method according to claim 1, wherein the S100 includes:
s101, obtaining user information through the Internet and/or user history data stored in a game server, wherein the user information comprises article equipment information currently worn on a person by a user, article equipment information stored in a user knapsack and/or warehouse, and grade information and role information of a user role.
3. The big data based game item pushing method according to claim 1, wherein the S300 includes:
s301, operating an open game, and simultaneously recording user information of a user in a game play in real time;
s302, establishing a data model, wherein the model comprises a normal distribution model, a uniform distribution model and an index distribution model;
s303, calculating expected values by taking user information as an input source and taking the user information as a basis by the data model to obtain the expected value of each game user after one-time processing, wherein the expected value is the expected value of the object provided by the user for the game.
4. A big data based game item pushing system, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring user information, wherein the user information comprises the item information of the current equipment of a user, the item information held by the user and user grade information;
the setting module is used for setting an initial expected value of a user on the game object according to the user information;
the server module is used for starting game operation, collecting user information in real time and obtaining expected values of users on articles in the game by establishing a data model;
wherein the server module further comprises: a second modeling module for building feedback system models including, but not limited to, an exponential fit model and a bias minimization model; the secondary processing module is used for taking the expected value of the once processed user obtained by the primary processing module as an input source of a feedback system model, and the feedback system model is based on the expected value of the once processed user to obtain the expected value of each game user after secondary processing, wherein the expected value is the expected value of the user for the object provided by the game;
the dynamic adjustment module is used for feeding back in real time according to the article information provided by the game system to obtain self-adaptive parameters, and obtaining a dynamic user expected value according to the self-adaptive parameters and the user expected value after secondary processing;
wherein, the dynamic adjustment module includes: the pushing module is used for pushing the articles according to the user expected value obtained by the secondary processing module after secondary processing and monitoring the number of the articles and the change of information in the game in real time; an adjustment module for dynamically adjusting user expectations in combination with the expectations of each player for the items according to the number changes of the types of the items in the game, wherein the number of the items provided by the game and the expectations of the players for the items are in inverse proportion; the parameter module is used for setting dynamic self-adaptive parameters according to the processing principle set by the adjustment module, the input sources of the self-adaptive parameters are the expected value of the user after secondary processing and the quantity and the type of the articles provided by the game, and the output result is the dynamic expected value of the user, so that the generation probability of the rewarded articles is dynamically adjusted.
5. The big data based game item pushing system of claim 4, wherein the server module comprises:
the operation and information collection module is used for opening game operation and recording user information of a user in a game in real time;
a first modeling module for building data models including, but not limited to, a normal distribution model, a uniform distribution model, and an exponential distribution model;
the primary processing module is used for taking the user information as an input source, and the data model carries out expected value calculation based on the user information to obtain expected values of each game user after primary processing, wherein the expected values are expected values of objects provided by the users for games.
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| CN110807150A (en) * | 2019-10-14 | 2020-02-18 | 腾讯科技（深圳）有限公司 | Information processing method and device, electronic equipment and computer readable storage medium |
| CN110975289B (en) * | 2019-11-14 | 2021-10-15 | 腾讯科技（深圳）有限公司 | Shooting mode switching control method and device, storage medium and electronic device |
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