CN113181660A - Method and system for predicting number of active people in real time in game, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a method, a system, electronic equipment and a storage medium for predicting the number of active people in real time in a game, belonging to the field of internet, wherein the method comprises the following steps: acquiring the number information of active people in the game at historical moments within preset days before the current date, wherein each moment is obtained according to preset interval time; and (4) bringing the information of the number of active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of active people in the game at the current date. According to the method and the device, the information of the number of the active people in the game at the historical moment is acquired as the time sequence, the number of the active people in the game at the current date is automatically predicted by integrating the moving average autoregressive model, and the prediction efficiency and the accuracy of the prediction result are greatly improved.
Description
Technical Field
The application relates to the technical field of internet, in particular to a method, a system, electronic equipment and a storage medium for predicting the number of active people in real time in a game.
Background
With the development of the game industry, game developers want to know the number of active real-time players of the game predicted in future dates in advance so as to know the predicted operation condition of the game, and therefore, the game operation is adjusted in advance. Currently, generally, the actual data of the game service is observed manually, and the prediction result is given according to subjective judgment, so that the efficiency is low and the accuracy of the prediction result is low.
Disclosure of Invention
The embodiment of the application provides a method, a system, electronic equipment and a storage medium for predicting the number of active people in real time in a game, and aims to at least solve the problems that the efficiency is low and the accuracy of a prediction result is low in a mode of manually predicting the number of active people in real time in a future game in the related art.
In a first aspect, an embodiment of the present application provides a method for predicting a number of active people in a game in real time, including: acquiring the number information of active people in the game at historical moments within preset days before the current date, wherein each moment is obtained according to preset interval time; and substituting the information of the number of the active people in the game at the historical moment into a pre-constructed Integrated Moving Average Autoregressive (ARIMA) model, and predicting the number of the active people in the game at the current date.
In some of these embodiments, the integrated moving average autoregressive model is obtained by: whether the time sequence is stable is checked through a specified unit root checking method, and a target difference order is calculated; and determining parameter pairs of an autoregressive order and a moving regression order through a specified information criterion to obtain the integrated moving average autoregressive model, wherein the integrated moving average autoregressive model comprises the target difference order and the parameter pairs.
In some of these embodiments, the unit root test method includes at least one of an ADF (automated Dickey-filler test) test method, a PP (Philipps-Perron) test method, and a KPSS test method.
In some of these embodiments, the information criteria include bayesian information criteria.
In a second aspect, an embodiment of the application provides a system for predicting the number of active people in real time in a game, which includes an obtaining unit and a predicting unit, wherein the obtaining unit is used for obtaining information of the number of active people in the game at a historical moment within a preset number of days before a current date, and each moment is obtained according to a preset interval time; and the prediction unit is used for bringing the information of the number of the active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of the active people in the game at the current date.
In some of these embodiments, the integrated moving average autoregressive model is obtained by: whether the time sequence is stable is checked through a specified unit root checking method, and a target difference order is calculated; and determining parameter pairs of an autoregressive order and a moving regression order through a specified information criterion to obtain the integrated moving average autoregressive model, wherein the integrated moving average autoregressive model comprises the target difference order and the parameter pairs.
In some of these embodiments, the unit root test method comprises at least one of an ADF test method, a PP test method, and a KPSS test method.
In some of these embodiments, the information criteria include bayesian information criteria.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the computer program to perform any one of the methods described above.
In a fourth aspect, the present application provides a storage medium having a computer program stored therein, where the computer program is configured to execute any one of the above methods when the computer program runs.
According to the content, the method for predicting the number of active people in the game in real time provided by the embodiment of the application comprises the following steps: acquiring the number information of active people in the game at historical moments within preset days before the current date, wherein each moment is obtained according to preset interval time; and (4) bringing the information of the number of active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of active people in the game at the current date. Compared with the related technology, the method and the device for predicting the number of the active people of the game at the historical moment are used for acquiring the information of the number of the active people of the game at the historical moment as a time sequence, and the number of the active people of the game at the current date is automatically predicted by integrating the moving average autoregressive model, so that the prediction efficiency and the accuracy of the prediction result are greatly improved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for predicting the number of live persons in a game according to an embodiment of the present application;
FIG. 2 is a block diagram of a game real-time active people prediction system according to an embodiment of the present application;
fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
In order to solve the above problems, the inventors of the present application found that the ARIMA model is suitable for the above scenario, and that the ARIMA model can be implemented by a pmdarima packet in Python.
Therefore, an embodiment of the present application provides a method for predicting the number of active people in a game in real time, fig. 1 is a flowchart of the method for predicting the number of active people in a game in real time according to the embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s100: acquiring the number information of active people in the game at historical moments within preset days before the current date, wherein each moment is obtained according to preset interval time;
s200: and (4) bringing the information of the number of active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of active people in the game at the current date.
According to the content, the information of the number of the active people of the game at the historical moment is automatically acquired as the time sequence, and then the integrated moving average autoregressive model is input, so that the number of the active people of the game at the current date can be effectively and accurately predicted.
According to the idea of the embodiment of the application, the prediction of the number of the game real-time active persons at each time node in any period of time in the future can be realized, and a game developer can know the predicted number of the game real-time active persons in the future in advance so as to distinguish the number of the game real-time active persons in abnormal conditions from the number of the game real-time active persons in normal conditions. However, considering that the closer the predicted time is to the time when the real value is generated, the smaller the error of the prediction result is, therefore, the current date in the embodiment of the present application is the date of the next day relative to the generated data, and the error between the predicted real-time active number of the game and the real-time active number of the game at each time point can be kept small.
As an example, whether the time series is stable is checked through a specified unit root checking method, and a target difference order is calculated; and determining parameter pairs of the autoregressive orders and the moving regression orders through a specified information criterion so as to obtain an integrated moving average autoregressive model, wherein the integrated moving average autoregressive model comprises a target difference order and the parameter pairs.
As an example, the above-mentioned unit root test method includes an ADF test method, a PP test method, a KPSS test method, and the like; the information criterion includes a bayesian information criterion.
The embodiment of the application also provides a system for predicting the number of active people in game real time, and fig. 2 is a structural block diagram of the system for predicting the number of active people in game real time according to the embodiment of the application, as shown in fig. 2, the system comprises an acquisition unit 1 and a prediction unit 2, wherein the acquisition unit 1 is used for acquiring information of the number of active people in game at historical time within preset days before the current date, and each time is obtained according to preset interval time; the prediction unit 2 is used for substituting the information of the number of the active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of the active people in the game at the current date.
As an example, the obtaining unit 1 includes a database configuration module, a server timing scheduling module, and a database connection module, and the prediction unit 2 includes a calculation module and a prediction result writing module. The server timing scheduling module acquires the configuration information of a game server database (hereinafter referred to as the database) at regular time every day according to the database configuration module, and calls a truth table (hereinafter referred to as the truth table) of the number of the active people of the game, which is connected with the database by the database connection module, so that the information of the number of the active people of the game at the historical moment can be read. And then, starting a calculation module to calculate to obtain a prediction result. Then, the prediction result writing module writes the prediction result into a game real-time activity number prediction table (may be simply referred to as a prediction table) of the database.
For example, the database at the game server side comprises two tables, namely a real-time active number table and a real-time active number prediction table, wherein the real-time active number table comprises the real-time active number of the game at all times from the beginning of the game to the current time. For example, in the game real-time active people truth table, 288 times are provided at 24 hours per day with 5 minutes as time intervals, and each time has a corresponding game real-time active people. The real-time active number prediction table of the game is used for storing predicted data, and the time interval is consistent with the time and the truth table.
In the database configuration module, parameters of the database in the ini configuration file are read through a configparser packet of python, wherein the parameters comprise addresses and passwords of the connected database, read-write tables, predicted product IDs and the like.
In the database connection module, various operations such as reading and writing of mysql can be realized based on the pymysql packet in python, so that the information of the number of active people in the game at the historical moment in the truth table can be read from the database.
And the calculation module is used for bringing the information of the number of the active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model and predicting the number of the active people in the game at the current date. For example, the pmdarima package of python is called, and the real-time active number of the game on the current date is predicted based on the existing historical moment game active number information. The interval time is, for example, 5 minutes, then 288 game real-time active persons need to be predicted for the current day (including 24 hours).
As an example, the ARIMA model may be obtained by setting corresponding parameters to a predetermined function, for example, pmdatama.
Wherein m represents the number of time steps of a single seasonal period; p represents the autoregressive order of the trend; d represents a trend difference order; q represents the moving average order of the trend; p represents the seasonal autoregressive order; d represents a seasonal difference order; q represents the seasonal moving average order; n _ jobs represents the number of parallel threads; stepwise: false represents that the combination with the highest score is selected from all the parameter combinations about the information criterion corresponding to the information _ criterion parameter, True represents that the stepwise algorithm is used, the combination is quicker than the combination of all the parameters and the probability of overfitting is reduced; information _ Criterion represents Information Criterion, which is aic and bic, aic is Akaike Information Criterion, which represents akabane Information Criterion, bic is Bayesian Information Criterion, which represents Bayesian Information Criterion; seasingle: whether the True has seasonal regularity; test is used for designating a unit root checking method, such as an ADF (automatic document feeder) checking method, a PP (propene Polymer) checking method and a KPSS (Key Performance Standard) checking method which are commonly used, the unit root checking is used for checking whether a unit root exists in a time sequence, and if the unit root exists, the unit root is a non-stable time sequence; start _ p and max _ p represent the minimum and maximum values of search p; the same applies to start _ Q and max _ Q, start _ P and max _ P, and start _ Q and max _ Q, and will not be described again.
As an example, it is checked whether the time series after d-order difference is smooth according to a unit root check method (for example, test ═ ADF' indicates that the ADF check method is adopted) specified by the parameter test, and if so, the current difference order d is used as the target difference order; and if the time sequence is not stable, d-order difference operation is carried out on the non-stable time sequence in the interval of [0, max _ d ], the time sequence is checked through a unit root checking method, when the non-stable time sequence is converted into the stable time sequence, a target difference order d is obtained, and the max _ d represents the maximum difference order. Then, a parameter pair (p, q) of an auto-regression order p and a moving regression order q of the ARIMA model is determined in preset [ start _ p, max _ p ] and [ start _ q, max _ q ] intervals by an information criterion specified by information _ criterion (for example, information _ criterion is represented by 'bic' to adopt a bayesian information criterion), thereby obtaining the ARIMA model, and the ARIMA model comprises the parameters (p, d, q).
Therefore, the model arima _ model can be obtained by initializing the model based on the parameters of the predetermined function. Predict (number of predicted time instants) is then used again to predict the number of real-time active people of the game at a future time instant.
For example, when the current date is 6, the number of active persons in the game at half-hour intervals within 5 days before 6 needs to be obtained, the number 1 of 00:00 real-time active persons, the number 1 of 00:30 real-time active persons, and up to the number 5 of 23:30 real-time active persons can be obtained from the truth table, and then the model is initialized through the functions arima _ model ═ pmdatama.
Further, the prediction result writing module writes the predicted real-time active number of the game at each moment INTO the prediction table, and optionally, inserts the predicted result INTO the prediction table by adopting a REPLACE INTO insertion mode.
Based on the content, the embodiment of the invention can realize one-click prediction of the number of active people in the game in real time according to the current date of the system under the condition that the information of the number of active people in the game at the historical moment and the ARIMA model are obtained in advance, and is simple and convenient to operate.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, by combining the method for predicting the number of active people in real time in the game in the embodiment, the embodiment of the application can be realized by providing a storage medium. The storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements any of the above-described embodiments of the method for predicting a number of live players of a game.
An embodiment of the present application also provides an electronic device, which may be a terminal. The electronic device comprises a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method for predicting a number of active persons in a game in real time. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
In one embodiment, fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 3, there is provided an electronic device, which may be a server, and its internal structure diagram may be as shown in fig. 3. The electronic device comprises a processor, a network interface, an internal memory and a non-volatile memory connected by an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used for providing calculation and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing an environment for an operating system and the running of a computer program, the computer program is executed by the processor to realize a method for predicting the number of active people in real time in a game, and the database is used for storing data.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the electronic devices to which the subject application may be applied, and that a particular electronic device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for predicting the number of active people in real time in a game is characterized by comprising the following steps:
acquiring the number information of active people in the game at historical moments within preset days before the current date, wherein each moment is obtained according to preset interval time;
and bringing the information of the number of the active people of the game at the historical moment into a pre-constructed integrated moving average autoregressive model, and predicting the number of the active people of the game at the current date.
2. The method of claim 1, wherein the integrated moving average autoregressive model is obtained by:
whether the time sequence is stable is checked through a specified unit root checking method, and a target difference order is calculated;
and determining parameter pairs of an autoregressive order and a moving regression order through a specified information criterion to obtain the integrated moving average autoregressive model, wherein the integrated moving average autoregressive model comprises the target difference order and the parameter pairs.
3. The method of claim 2, wherein the unit root test method comprises at least one of an ADF test method, a PP test method, and a KPSS test method.
4. The method of claim 2, wherein the information criterion comprises a bayesian information criterion.
5. A system for predicting the number of active people in real time in a game, comprising:
the system comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring information of the number of active people in the game at the historical moment within preset days before the current date, and each moment is obtained according to preset interval time;
and the prediction unit is used for bringing the information of the number of the active people in the game at the historical moment into a pre-constructed integrated moving average autoregressive model and predicting the number of the active people in the game at the current date.
6. The system of claim 5, wherein the integrated moving average autoregressive model is obtained by:
whether the time sequence is stable is checked through a specified unit root checking method, and a target difference order is calculated;
and determining parameter pairs of an autoregressive order and a moving regression order through a specified information criterion to obtain the integrated moving average autoregressive model, wherein the integrated moving average autoregressive model comprises the target difference order and the parameter pairs.
7. The system of claim 6, wherein the unit root test method comprises at least one of an ADF test method, a PP test method, and a KPSS test method.
8. The system of claim 6, wherein the information criteria comprises Bayesian information criteria.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
10. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 4 when executed.
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| CN202110426085.XA CN113181660A (en) | 2021-04-20 | 2021-04-20 | Method and system for predicting number of active people in real time in game, electronic equipment and storage medium |
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| CN202110426085.XA CN113181660A (en) | 2021-04-20 | 2021-04-20 | Method and system for predicting number of active people in real time in game, electronic equipment and storage medium |
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| CN113633992A (en) * | 2021-08-09 | 2021-11-12 | 网易（杭州）网络有限公司 | Game operation data prediction method and device, electronic equipment and storage medium |
| CN115412923A (en) * | 2022-10-28 | 2022-11-29 | 河北省科学院应用数学研究所 | Multi-source sensor data credible fusion method, system, equipment and storage medium |
| CN116761185A (en) * | 2023-08-21 | 2023-09-15 | 北京融信数联科技有限公司 | Method, system and medium for predicting daily active users based on signaling |
| CN117851834A (en) * | 2024-01-12 | 2024-04-09 | 河南万得福仪器设备有限公司 | Intelligent agriculture big data optimal storage method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113633992A (en) * | 2021-08-09 | 2021-11-12 | 网易（杭州）网络有限公司 | Game operation data prediction method and device, electronic equipment and storage medium |
| CN115412923A (en) * | 2022-10-28 | 2022-11-29 | 河北省科学院应用数学研究所 | Multi-source sensor data credible fusion method, system, equipment and storage medium |
| CN116761185A (en) * | 2023-08-21 | 2023-09-15 | 北京融信数联科技有限公司 | Method, system and medium for predicting daily active users based on signaling |
| CN117851834A (en) * | 2024-01-12 | 2024-04-09 | 河南万得福仪器设备有限公司 | Intelligent agriculture big data optimal storage method and system |
| CN117851834B (en) * | 2024-01-12 | 2024-07-12 | 河南万得福仪器设备有限公司 | Intelligent agriculture big data optimal storage method and system |
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